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Doc. No. 06-407-H1 Rev. 2 – February 2007

Italian Ministry for the Environment, Land and Sea Renewable Energy Resource Assessment

Wind, Solar, and Biomass Energy

Republic of Montenegro Assessment


All rights, including translation, reserved. No part of this document may be disclosed to any third party, for purposes other than the original, without written consent of CETMA.

Italian Ministry for the Environment, Land and Sea Renewable Energy Resource Assessment

Wind, Solar and Biomass Energy

Republic of Montenegro Assessment

Prepared by Signature Date

Carlo Barbieri

05/02/2007

Gianluca Cassulo

05/02/2007

Verified by Signature Date

Mario Lazzeri

05/02/2007

Approved by Signature Date

Marco G. Cremonini

05/02/2007

Rev. Description Prepared By Verified by Approved by Date 0 First Issue for comments CRB/GIC ML MGC 22/12/2006 1 Final Issue CRB/GIC ML MGC 23/01/2007 2 Final Issue CRB/GIC ML MGC 05/02/2007


Italian Ministry for the Environment, Land and Sea Renewable Energy Resource Assessment, Republic of Montenegro Wind, Solar and Biomass Energy Assessment

PREFACE In a rapidly changing global economy, energy self sufficiency plays an important role in a nation’s future. To cut their dependency on foreign energy sources and fossil fuels, many countries have established renewable energy research and development programs.

The combustion of fossil fuels, principally coal, oil and natural gas, releases large volumes of carbon dioxide (CO2) and other gases (greenhouse gases) to the atmosphere. This process has altered the composition of the atmosphere, leading to the so called greenhouse effect and to global warming.

Even though there is no simple solution to the challenge of climate change, it is widely recognized that a reduction in CO2 levels is a crucial prerequisite for reducing the harmful impacts of global warming. Since energy production represents one of the main sources of greenhouse gases, the renewable energy sources will play an important role for the generation of electricity and heat with little or no emissions of CO2.

The International Community started to deal with the climate change issue establishing the United Nations Framework Convention on Climate Change (UNFCCC), adopted in 1992 with the purpose of defining the overall framework for intergovernmental efforts to address climate change. The UNFCCC objectives were strengthened by the adoption of the Kyoto Protocol (3rd Conference of the Parties to the Convention, 1997), entered into force on 16 February 2005.

Within this general framework, the Montenegrin Ministry of Environmental Protection and Physical Planning (now renamed as Ministry of Tourism and Environmental Protection), the Montenegrin Ministry of Economy (now renamed as Ministry for Economic Development) and the Italian Ministry for Environment, Land and Sea (IMELS) on November 11th, 2004 signed a Memorandum of Understanding (MoU) on the “Cooperation for Environmental Protection”.

On this basis, the Italian and Montenegrin Ministries, during the Steering Committee held in Rome on June 28th, 2006, agreed on the implementation of an assessment of the renewable energy sources in Montenegro. IMELS then entrusted the CETMA Consortium (CETMA) to undertake the study, in close cooperation with the Montenegrin counterpart.

At this scope, on July and September 2006 the IMELS and CETMA experts performed dedicated meetings, with the Montenegrin Ministry of Agriculture, Forestry and Water Management, the Montenegrin Ministry of Economy and the Hydrometeorological Institute of Montenegro, in order to define the framework of the study and to collect the necessary data. The parties agreed to focus this renewable energy assessment on wind, solar and biomass sources.

CETMA is a consortium, based in Brindisi, Italy, composed of ENEA, the University of Lecce and several other private research organizations and small-medium entrerprises. CETMA aims to create a center of excellence in the South of Italy for research and design in the fields of energy, environment, and socioeconomics. One of the primary actors in the CETMA consortium is D’Appolonia S.p.A., a major Italian environmental and engineering consulting firm with headquarters located in Genoa, Italy.

The Renewable Energy Assessment in Montenegro would not have been possible without the efforts of the many Montenegrin and Italian technical and government bodies, above all, Mr. Miodrag Canovic, Deputy Minister – Ministry of Economy, and Mr. Radosav Nikcevic of the Ministry of Economy, Mr. Ranko Kankaras, Senior Advisor of the Ministry of Agriculture, Forestry and Water Management, and Mr. Corrado Clini, General Director of Italian Ministry for the Environment, Land and Sea.

CETMA is deeply grateful to Montenegrin Government bodies for their continuous assistance and collaboration provided in the project. A special thanks goes out to Mr. Radivoje Vuckovic and Ms. Mirjana Ivanov of the Hydrometeorological Institute of Montenegro. CETMA would like also to extend their gratitude to Mr. Robert Aleksic, head of the GIS project supported by UNDP, for the access to some of their valuable cartographic tools, and to Mr. Dragan Buzdovan, Technical Director


Italian Ministry for the Environment, Land and Sea Pag. ii Renewable Energy Resource Assessment, Republic of Montenegro Wind, Solar and Biomass Energy Assessment

of Montenegro Stars Hotel Group for the assistance and collaboration in providing the hot sanitary water and electricity consumption data for Montenegro and Blue Star hotels.

For this Project Mr. Christian Melis, Coordinator of IMELS office in Podgorica, and the whole IMELS team in Podgorica provided their experience in international initiatives and were also of invaluable support for the data collection of this project.

The wind energy modeling and assessment activities were carried out with the indispensable technical support of the DIFI, Department of Physics of the University of Genoa. The DIFI team was composed by Prof. Corrado Ratto, Dr. Massimiliano Burlando and Mr. Luca Villa.

The solar and biomass energy analysis was carried out with the essential support of Lahmeyer Intrernational GmbH team, composed by Mr. Steffen Gruber and Mr. Mathieu Sarran.

Within the CETMA team, Mr. Marco Cremonini provided coordination and oversight of project activities, while Mr. Gianluca Cassulo worked out the report review and the coordination with the local experts. Mr. Carlo Barbieri as project manager headed the project group during all phases of the project, Mr. Andrea Podestà conducted the renewable energy assessment with the support of Lahmeyer and DIFI, and Mr. Federico Breda and Ms. Roberta Piana provided GIS support for data analysis.

Contact information as follows:

Contracting Representative Dr. Corrado Clini – Director General Ministry for the Environment, Land and Sea Republic of Italy Via Cristoforo Colombo, 44 00147 Rome – Italy Tel.: +39 010 3628148 Fax +39 010 3621978

Point of Contact: Christian Melis Ministry for the Environment, Land and Sea Republic of Italy Task Force Central and Eastern Europe Rimski Trg 25 - Podgorica 81000 - Montenegro Tel: +381 81 205 891 Fax: +381 81 205 890 e-mail: [email protected]

Coordinator of Activities - Serbia and Montenegro: Mr. Gianluca Cassulo Via San Nazaro, 19 Tel.: +39 010 3628148 Fax +39 010 3621978 e-mail: [email protected]

Program Coordinator: Mr. Marco G. Cremonini Project Coordinator: Mr. Mario Lazzeri Project Manager: Mr. Carlo Barbieri


Italian Ministry for the Environment, Land and Sea Pag. i Renewable Energy Resource Assessment, Republic of Montenegro Wind, Solar and Biomass Energy Assessment

TABLE OF CONTENTS

Page

LIST OF TABLES IV

LIST OF FIGURES VI

1 INTRODUCTION 1 1.1 SCOPE OF THE STUDY 2

1.2 ASSESSMENT METHODOLOGY 2 1.2.1 Wind Energy Potential Mapping and Evaluation 3 1.2.2 Solar Energy Potential Mapping and Evaluation 3 1.2.3 Biomass to Energy Potential Mapping and Evaluation 4

1.3 STRUCTURE OF THE REPORT 4

1.4 LIMITATIONS OF THE STUDY 4 1.4.1 Wind Energy Assessment 5 1.4.2 Solar Energy Assessment 6 1.4.3 Biomass Energy Assessment 7

2 COUNTRY OVERVIEW 8 2.1 TERRITORY 9

2.2 INFRASTRUCTURES 10

2.3 POPULATION 10

2.4 CLIMATE 10

2.5 INDUSTRY 11

2.6 AGRICULTURE AND FORESTRY 13

2.7 TOURISM 14

2.8 ENERGY 15 2.8.1 Energy Regulatory Framework 15 2.8.2 Energy Production and Use 17

3 WIND POWER POTENTIAL ASSESSMENT 19 3.1 METHODOLOGICAL APPROACH 20

3.2 COMPUTATIONAL DOMAINS 20 3.2.1 Topographic Map 22 3.2.2 Land-sea Mask Map 23 3.2.3 Land Cover Map 24 3.2.4 Roughness Length Map 26 3.2.5 Displacement Level Map 27

3.3 SIMULATION CODE 29

3.4 WIND ALOFT DATA 30 3.4.1 “Re-analysis” of ECMWF 30 3.4.2 Statistical Analysis of the Wind Aloft 30

3.5 NUMERICAL SIMULATION OF THE WIND FIELDS 32

3.6 THEORICAL WIND POTENTIAL 34


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TABLE OF CONTENTS (Continuation)

Page

3.7 CORRECTION OF THE SIMULATED WIND POTENTIAL 36 3.7.1 Statistical Analysis of Onshore Anemometric Measurements 36 3.7.2 Statistical Analysis of Offshore Satellite Measurements 41 3.7.3 Correction factor 42

3.8 ACTUAL WIND POTENTIAL 44

3.9 EXPLOITABLE WIND POTENTIAL 47 3.9.1 Constraints to Wind Power Exploitation 47 3.9.2 Evaluation of the Exploitable Wind Potential 50

3.10 TYPICAL FEATURES OF WIND POWER PLANTS 51

3.11 PRELIMINARY ECONOMICAL ANALYSIS FOR WIND POWER SYSTEMS 54

4 SOLAR ENERGY ASSESSMENT 58 4.1 METHODOLOGICAL APPROACH 58

4.2 SOLAR POTENTIAL 59 4.2.1 Climate Overview 59 4.2.2 Solar Radiation Data 61 4.2.3 Solar Mapping 64 4.2.4 Analysis of the Solar Resource 65

4.3 SOLAR THERMAL ENERGY 68 4.3.1 Passive Solar Energy 68 4.3.2 Active Solar Heat 71

4.4 MARKET SECTORS IN MONTENEGRO 84 4.4.1 Residential Sector 84 4.4.2 Tourism Sector 85

4.5 CASE STUDY 1: SOLAR THERMAL ENERGY FOR HOUSEHOLDS 87 4.5.1 Location and Meteorological Data for Simulation 87 4.5.2 Basic Technical Case Study 87 4.5.3 Costs Assessment 89

4.6 CASE STUDY 2: SOLAR THERMAL ENERGY FOR THE TOURISM SECTOR 90 4.6.1 Meteorological Data for Simulation 90 4.6.2 Basic Technical Case Study 90 4.6.3 Cost Assessment 92

4.7 ECONOMIC PERFORMANCE OF SOLAR SYSTEMS 93 4.7.1 Methodology 93 4.7.2 Case Study 1: Residential Sector 94 4.7.3 Case Study 2: Tourism Sector 96

4.8 DEVELOPMENT PERSPECTIVES 99 4.8.1 Solar Thermal Technologies and Applications 99 4.8.2 Local Employment 99 4.8.3 Regulation, Incentives and Promotion 100 4.8.4 Market Conditions 101

5 BIOMASS TO ENERGY POTENTIAL ASSESSMENT 103 5.1 BIOMASS-TO-ENERGY CONVERSION TECHNOLOGIES 103


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TABLE OF CONTENTS (Continuation)

Page 5.1.1 Direct combustion 104 5.1.2 Biofuels 105 5.1.3 Energy Crops 106

5.2 BIOMASS POTENTIAL ESTIMATION 106 5.2.1 Definition of Biomass Potential 106 5.2.2 Methodology of Biomass Potential Estimation 107 5.2.3 Topographic Features 108 5.2.4 Global Land Cover Classification 109

5.3 BIOMASS FROM FOREST PRODUCTION 113 5.3.1 Forest Types 114 5.3.2 Environmental Advantages of Wood Biomass to Energy 115 5.3.3 Energy Potential from Forests 115

5.4 BIOMASS FROM AGRICULTURE 117 5.4.1 Main Agricultural Areas 118 5.4.2 Arable Land 118 5.4.3 Agricultural Land Use 122 5.4.4 Energy Potential from Agricultural Production 124

5.5 PRELIMINARY ECONOMICAL ANALYSIS 124

6 CONCLUSIONS AND RECOMMENDATION 129 6.1 WIND ENERGY POTENTIAL 130

6.2 SOLAR ENERGY POTENTIAL 131

6.3 BIOMASS ENERGY POTENTIAL 132

6.4 CONCLUDING REMARKS 134

REFERENCES

APPENDIX A: WIND MAPS

APPENDIX B: SOLAR MAPS


Italian Ministry for the Environment, Land and Sea Pag. iv Renewable Energy Resource Assessment, Republic of Montenegro Wind, Solar and Biomass Energy Assessment

LIST OF TABLES

Tables No. Title Page

Table 2.1 : Republic of Montenegro Key Statistical Data........................................................................ 8

Table 2.2 : Power Generation Plants in Montenegro (Year 2005) ........................................................ 17

Table 3.1 : Main Data of the Computational Domains .......................................................................... 22

Table 3.2 : Land Use/Land Cover levels ............................................................................................... 25

Table 3.3 : Roughness Length Classes for the Land Cover Types ...................................................... 27

Table 3.4 : Wind Speed and Direction Aloft .......................................................................................... 31

Table 3.5 : Main Data of the Anemometric Stations - DB1 Dataset ...................................................... 37

Table 3.6 : Main Data of the Anemometric Stations - DB2 Dataset ...................................................... 39

Table 3.7 : Examples of Offshore Wind Data from Satellite Measurements......................................... 42

Table 3.8 : Correction Factors for DB2 and QuikSCAT Datasets ......................................................... 43

Table 3.9 : Main Economic Parameters ................................................................................................ 55

Table 4.1 : Climate Data for the Coastal and Mountainous regions ..................................................... 60

Table 4.2 : Estimated Coordinates of the Meteorological Stations ....................................................... 61

Table 4.3 : Available Ground Based Solar Data ................................................................................... 62

Table 4.4 : Comparison between Satellite and Ground - based Data................................................... 63

Table 4.5 : Solar Radiation for the Major Cities in the Balkan Region .................................................. 65

Table 4.6 : Examples of Public Prices for Solar Collectors in Montenegro........................................... 73

Table 4.7 : Technical Specifications of Solar Collectors ....................................................................... 80

Table 4.8 : Typical Large Collective Systems ....................................................................................... 81

Table 4.9 : Cost Breakdown of a Standard Thermosyphon System in Greece .................................... 82

Table 4.10 : Cost Breakdown for a Typical Forced-Circulation System in Italy .................................... 82

Table 4.11 : Cost Breakdown for a Typical Thermosyphon System in Cyprus..................................... 83

Table 4.12 : Specific costs for Typical Solar Thermal Systems ............................................................ 83

Table 4.13 : Distribution of Households per Region.............................................................................. 85

Table 4.14 : Accommodation Capacities and Tourist Overnights (year 2005)...................................... 86

Table 4.15 : Input Values for the Residential Case Study..................................................................... 87

Table 4.16 : Assumed Technical Data for the Solar Thermal Systems ................................................ 88

Table 4.17 : Estimated Solar Energy for the Three Residential Case Studies ..................................... 89

Table 4.18 : Estimated Price for the Three Residential Case Studies .................................................. 90

Table 4.19 : Input Values for the Tourism Sector.................................................................................. 90

Table 4.20 : Rate of Occupancy of a "Standard" Hotel in the Coastal Region ..................................... 91

Table 4.21 : Technical Data for the Tourism Sector.............................................................................. 91

Table 4.22 : Estimated Solar Energy for the Tourism Sector................................................................ 92


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LIST OF TABLES (Continuation)

Tables No. Title Page

Table 4.23 : Estimated Price for the Tourism Sector Case Study......................................................... 92

Table 4.24 : Basic Data for the Residential Case Study ....................................................................... 95

Table 4.25 : Preliminary Cost Estimation - Residential Case Study ..................................................... 95

Table 4.26 : Basic Data for the Large Scale Hotel Case Study ............................................................ 98

Table 4.27 : Preliminary Cost Estimation – Large Scale Hotel Case Study.......................................... 98

Table 4.28 : Estimation of Potential Employment ............................................................................... 100

Table 4.29 : Strengths and Weaknesses of the Montenegrin Solar Market........................................ 102

Table 5.1 : Biofuel Applications ........................................................................................................... 105

Table 5.2 : Upper Heating Values for different Types of Biomass ...................................................... 108

Table 5.3 : Examples for Biomass Equivalents from Agricultural Production ..................................... 108

Table 5.4 : Vegetation Types .............................................................................................................. 111

Table 5.5 : Areas not usable for Biomass Exploitation........................................................................ 112

Table 5.6 : Forest Characterization Data ............................................................................................ 116

Table 5.7 : Sawmill Waste................................................................................................................... 116

Table 5.8 : Wood Waste available for Energetic Fuel ......................................................................... 117

Table 5.9 : Cereal Crop Production - year 2004.................................................................................. 119

Table 5.10 : Production of Fodder Crops ............................................................................................ 120

Table 5.11 : Production of Vegetable crops ........................................................................................ 120

Table 5.12 : Fruit Production ............................................................................................................... 121

Table 5.13 : Olive production - Year 2004........................................................................................... 122

Table 5.14 : Biomass Potential from Agricultural Production .............................................................. 124


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LIST OF FIGURES Figure No. Title Page

Figure 2.1 : Map of Montenegro .............................................................................................................. 9

Figure 2.2 : Population .......................................................................................................................... 11

Figure 2.3 : Active Population and Industry Sector ............................................................................... 12

Figure 2.4 : Agriculture Vocation ........................................................................................................... 13

Figure 2.5 : Forestry Utilization.............................................................................................................. 14

Figure 2.6 : Tourism Sector ................................................................................................................... 15

Figure 3.1 : Selected Computational Domains...................................................................................... 21

Figure 3.2 : Topographic Map ............................................................................................................... 23

Figure 3.3 : Land-Sea Mask .................................................................................................................. 24

Figure 3.4 : Land Cover Map................................................................................................................. 26

Figure 3.5 : Roughness Length Map ..................................................................................................... 28

Figure 3.6 : Displacement Level Map.................................................................................................... 28

Figure 3.7 : Wind Rose at 5,000 m a.s.l. ............................................................................................... 32

Figure 3.8 : Simulated Wind Field at 50 m a.g.l. ................................................................................... 33

Figure 3.9 : Simulated Wind Field at 10 m a.g.l. ................................................................................... 34

Figure 3.10 : Average wind speed at 50 m a.g.l.................................................................................... 35

Figure 3.11 : Theoretical Wind Potential at 50 m a.g.l. ......................................................................... 36

Figure 3.12 : Pljevlja Wind Rose based on the DB1 dataset ................................................................ 38

Figure 3.13 : Frequency Distribution of the Wind Speed at Pljevlja Station – DB1 Dataset ................. 39

Figure 3.14 : Pljevlja Wind Rose based on the DB2 Dataset................................................................ 40

Figure 3.15 : Frequency Distribution of the Wind Speed at Pljevlja Station – DB2 Dataset ................. 41

Figure 3.16 : Offshore Average Wind Speed (calculated by QuikSCAT dataset)................................. 42

Figure 3.17 : Correction Factor after Interpolation ................................................................................ 44

Figure 3.18 : Actual Average Wind Speed [m/s] at 50 m a.g.l. ............................................................. 45

Figure 3.19 : Actual Wind Potential [W/m2] at 50 m a.g.l. ..................................................................... 46

Figure 3.20 : Areas with Heights over 1800 m a.s.l. ............................................................................. 47

Figure 3.21 : Road network and Railway Lines..................................................................................... 48

Figure 3.22 : Electric Power Supply System ......................................................................................... 49

Figure 3.23 : Protected Natural Areas................................................................................................... 50

Figure 3.24 : Technical Layout of a Wind Turbine ................................................................................ 52

Figure 3.25 : Power Curve of a Typical Commercial Wind Turbine (Vestas V-52) ............................... 53

Figure 3.26 : Wind Speed - Capacity Factor Ranges (Vestas V-52 turbine) ........................................ 57

Figure 4.1 : Temperature Profile for the Coastal and Mountainous Regions........................................ 60

Figure 4.2 : Example of Trace Recorder for the Measured Solar Radiation ......................................... 61


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LIST OF FIGURES (Continuation)

Figure No. Title Page

Figure 4.3 : Average Deviation - Satellite and Ground Based Data...................................................... 64

Figure 4.4 : Map of Solar Radiation in Eastern Europe......................................................................... 66

Figure 4.5 : Annual Sunshine Hours ..................................................................................................... 67

Figure 4.6 : Average Daily Solar Radiation 1980 - 1986 – Ground-based Data ................................... 68

Figure 4.7 : Communist-era Buildings ................................................................................................... 69

Figure 4.8 : Typical houses ................................................................................................................... 69

Figure 4.9 : Typical Solar House ........................................................................................................... 70

Figure 4.10 : Solar Compact Systems................................................................................................... 74

Figure 4.11 : Pumped Solar Thermal System ....................................................................................... 76

Figure 4.12 : Flat-Plate Collector........................................................................................................... 77

Figure 4.13 : Absorber........................................................................................................................... 77

Figure 4.14 : Flat Plate Collector Scheme............................................................................................. 77

Figure 4.15 : Typical Scheme of an Evacuated Tube Collector ............................................................ 78

Figure 4.16 : Evacuated Tube Collector................................................................................................ 78

Figure 4.17 : Collecting Tube and the Heat Exchangers ...................................................................... 78

Figure 4.18 : Typical Collector Efficiencies for a Flat-Plate Collector ................................................... 80

Figure 4.19 : Solar Thermal Market in Europe since 1990.................................................................... 84

Figure 4.20 : Overnights Seasonality – Coastal Area (year 2004)........................................................ 86

Figure 4.21 : Estimated Costs Savings - Residential Sector................................................................. 94

Figure 4.22 : Estimated Costs Savings - Tourism Sector ..................................................................... 96

Figure 4.23 : Estimated Costs Savings vs. Electricity and Heating Oil - 60% Solar Fraction ............... 97

Figure 5.1 : Options for Biomass Energy Combustion ........................................................................ 104

Figure 5.2 : Lower Heating Value for different Types of Biomass....................................................... 107

Figure 5.3 : 3-D Topographic Map ...................................................................................................... 109

Figure 5.4 : 2-D land-use map............................................................................................................. 110

Figure 5.5 : Area Percentages considered for Biomass Potential Assessment.................................. 112

Figure 5.6 : Areas not considered for Biomass Potential Exploitation ................................................ 113

Figure 5.7 : Agricultural Land - Production Units................................................................................. 118

Figure 5.8 : Average Yield of the Main Cereal Crops.......................................................................... 119

Figure 5.9 : Agricultural Land Use by Municipalities ........................................................................... 123

Figure 5.10 : Comparison between forests and croplands.................................................................. 123


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GLOSSARY

AVHRR Advanced Very High Resolution Radiometer

CESI Centro Elettrotecnico Sperimentale Italiano (Italian Experimental Electrotechnical Centre)

CETMA CETMA Consortium

CHP Combined Heating Plant

CO2 Carbon Dioxide

CSE Centre for Solar Energy

DB Data Base

DEM Digital Elevation Model

DHW Domestic Hot Water

DIFI Department of Physics of the University of Genoa

DPRS Development and Poverty Reduction Strategy

EBRD European Bank for Reconstruction and Development

ECMWF European Centre for Medium-range Weather Forecast

ECSEE Energy Community of South Eastern Europe

EROS Earth Resources Observation and Science centre

ESTIF European Solar Thermal Industry Federation

EU European Union

FAO Food and Agriculture Organization of the United Nations

GB Ground Based Data

GEM Global Environment Monitoring Unit

GIS Geographic Information System

GLCC Global Land Cover Characterization

GTOPO30 Global 30 Arc-Second Elevation Data Set

HC-1 Helioclim-1 database

HIM Hydrometeorological Institute of Montenegro

IAPMS Industrial Air Pollution Management System

IEA International Energy Agency

IGBP International Geosphere Biosphere Programme

IMELS Italian Ministry for Environment, Land and Sea

IMF International Monetary Fund

IPA Ian Pope Associates Energy and Water Consulting

IRR Internal Rate of Return

IWA Italian Wind Atlas

JRC Joint Research Centre

LCCS Land Cover Classification System


Italian Ministry for the Environment, Land and Sea Pag. ix Renewable Energy Resource Assessment, Republic of Montenegro Wind, Solar and Biomass Energy Assessment

LI Lahmeyer International GmbH

MME Montenegrin Ministry of Economy

MMAFW Montenegrin Ministry of Agriculture, Forestry and Water management

NASA National Aeronautics and Space Administration

NES National Energy Strategy

NDVI Normalized Difference Vegetation Index

NGO Non Governmental Organization

NPV Net Present Value

OEM Original Equipment Manufacturer

ORC Organic Rankine Cycle

PBL Planetary Boundary Layer

PME Plant Methyl Ester

PV Photovoltaics

R&D Research and Development

RDF Refuse Derived Fuel

RMSE Root Mean Square Error

ROO Rate of Occupancy

SD Satellite Based Data

SHW Sanitary Hot Water

TPES Total Primary Energy Supply

UN United Nations

UNDP United Nations Development Programme

UNECE United Nations Economic Commission for Europe

UNEP United Nations Environment Programme

UNFCCC United Nations Framework Convention on Climate Change

UNL University of Nebraska-Lincoln

USGS United States Geological Survey’s

UTC Coordinated Universal Time

WGS84 World Geodetic Survey system (1984)

WINDS Wind-field Interpolation by Non-Divergent Schemes

WMO World Meteorological Organization


Consorzio CETMA

Cittadella della Ricerca S.S.7 Appia km 712+300 - 72100 BRINDISI

Tel. 0831/449111 Fax 0831/449120

RENEWABLE ENERGY ASSESSMENT REPUBLIC OF MONTENEGRO

WIND, SOLAR AND BIOMASS ENERGY RESOURCES ASSESSMENT

1 INTRODUCTION

Renewable energies, such as hydropower, bio-energy, wind, geothermal, solar, and ocean energy, play an increasingly important role within the energy policy framework in Europe and all over the world, as they are reaching the technical potential to meet large portions of the global energy demand. In general, renewable energies are essential contributors to the energy supply portfolio as they contribute to world energy supply security, reducing dependency of fossil fuel resources, and providing opportunities for reducing emissions of greenhouse gases and for environmental protection.

In “Renewables in Global Energy Supply” (2006), the International Energy Agency (IEA) states that in 2004 renewable energies accounted for 13.1% of the world total primary energy supply. Combustible renewables and waste represented 79.4% of total renewables, followed by hydro (16.7%), whereas wind energy accounted for 0.064% only. Nevertheless, the total renewables supply experienced an annual growth of 2.3% over the last 30 years, and the so-called new renewables including geothermal, solar, wind, tide etc. recorded a much higher annual growth of 8%.

In the reference scenario developed by IEA in the “World Energy Outlook 2005: Middle East and North Africa Insights”, which assumes continuation of present government policies and no major breakthrough in technologies, renewable energy consumption will increase by 1.8% per year from over 1400 million tons of oil equivalent (Mtoe) in 2003 to almost 2300 Mtoe in 2030, with a rise of more than 60%. In this scenario, the share of renewables in global energy consumption will remain largely unchanged at 14%. Traditional biomass currently accounts for 7% of world energy demand, but its share will fall as developing countries shift to modern forms of energy. World hydropower production will grow by 1.8% per year but its share will remain almost stable at around 2%. The shares of other renewables (including geothermal, solar and wind) will increase most rapidly at 6.2% per year but because they start from a very low base (0.5% share in 2003) they will still be the smallest component of renewable energy in 2030 with a share of only 1.7% of global energy demand. Non-hydro renewables in electricity generation will triple, however, from 2% in 2003 to 6% in 2030, and wind power will see the biggest increase in market share.

In Europe, in particular, the White Paper (1997) target of 40 GW of installed capacity by 2010 has already been reached in 2005, and wind power supplies more than 2% of gross electricity consumption in Europe. Since 2001, the EU has elaborated a new regulatory framework in order to accelerate the growth of European markets for renewable electricity. The most important instrument is the Directive 2001/77/EC on promotion of electricity produced from renewable energy sources in the internal electricity markets, which is notably also for the new Member States and Candidate States, as the Republic of Montenegro. Indeed, since the energy demand in transition economies is expected to increase steadily in the near future, renewable energy can play a role in future energy supply particularly for these countries by reducing import dependence, improving energy security, and increasing energy efficiency. Moreover, renewable energy projects may also be supported because of


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the region’s increased focus on environment, employment creation, and the need to modernize and upgrade obsolete production capacities.

The combustion of fossil fuels, principally coal, oil and natural gas, releases large volumes of carbon dioxide (CO2) and other gases (greenhouse gases) to the atmosphere. This process has altered the composition of the atmosphere, leading to the so called greenhouse effect and to global warming.

Even though there is no simple solution to the challenge of climate change, it is widely recognized that a reduction in CO2 levels is a crucial prerequisite for reducing the harmful impacts of global warming. Since energy production represents one of the main sources of greenhouse gases, the renewable energy sources will play an important role for the generation of electricity and heat with little or no emissions of CO2.

Within this general framework, the Montenegrin Ministry of Environmental Protection and Physical Planning, the Montenegrin Ministry of Economy and the Italian Ministry for Environment, Land and Sea (IMELS) in the framework of their “Cooperation for Environmental Protection” agreed on the implementation of an assessment of the renewable energy sources in Montenegro, focusing on wind, solar and biomass sources.

The study is conceived to provide a valid support for the implementation of energy production initiatives in the sector of renewable sources in the Montenegro territory.

1.1 SCOPE OF THE STUDY The main objective of this report is to provide a first Renewable Energy Resource Assessment for the Republic of Montenegro, with specific reference to the following three renewable energy sources: a) wind, b) solar, and c) biomass.

This study has been prepared by CETMA, with the support of Italian Ministry for Environment, Land and Sea (IMELS), Task Force for Central and Eastern Europe.

Specifically, the following activities, aimed at identifying the Renewable Energy Resource Assessment, have been performed in view of the development of this report:

• data collection;

• data analysis evaluation;

• assessment of potential for development for each specific energy source examined (wind, solar and biomass).

1.2 ASSESSMENT METHODOLOGY The availability of renewable energy resources is typically assessed in a hierarchical way, in which the maximum deployment of each technology is progressively reduced due to the application of physical, infrastructural, environmental, cost and other types of constraints. This approach varies according to the technology under consideration but generic resource definitions can be used to clarify the extent to which the renewable energy resources have been “limited” by the application of constraints.

For example, after the wind energy potential mapping has been performed, a more detailed technical potential evaluation has been conducted taking into account all the main restrictions that can reduce the potential of exploitation of the wind resource.

In particular, some factors such as, the site complexity and topography, site accessibility and roads, the presence of natural parks, railway lines, grid network, urban and environmental



context that could reduce significantly the possibility to exploit the wind energy has been considered.

The Renewable Energy Resources Assessment for Montenegro has been conducted on the basis of the activities as described in the following paragraphs.

1.2.1 Wind Energy Potential Mapping and Evaluation

The estimate of the countrywide wind energy potential was first performed and the results presented in the format of a countrywide wind map, showing the theoretical wind potential of the country over a horizontal grid spacing of around 1 kilometer and with a proper number of layers on a vertical axis.

More specifically, the potential mapping evaluation was based on a model combining statistical analyses of data relative to the wind speed aloft with numerical modeling of wind flows over complex terrain. Wind speed and direction data at the top of the atmospheric boundary layer over Montenegro, i.e. around 5000÷6000 meters above sea level, were used as initial conditions for the numerical simulation of atmospheric flow fields.

The calculation considered the surface roughness, dealing with the whole range from smooth surfaces such as water bodies, to rough surfaces such as heavily built up areas.

Simulation results were used to evaluate average wind potential maps accordingly with the statistical analyses performed on wind speed and direction at the boundary layer

As final result, a set of maps of the territory was prepared indicating average wind speed, average cubic wind speed and average wind power at the height of 50 meters above ground level.

Following the wind energy potential mapping, a more detailed technical potential evaluation was conducted in order to properly take into account all the main restrictions that can reduce the potential of exploitation of the wind resource.

Some factors such as the site complexity and topography, site accessibility and roads, the presence of natural parks, railway lines, grid network, urban and environmental context could reduce significantly the possibility to exploit the wind energy.

Wind measurements at the ground were used to validate and further refine the preliminary results above in order to obtain a final estimate of the annual wind energy production calibrated on actual wind data.

On the basis of these results, by defining a minimum energy potential, which is required for an economical operation of wind turbines, the most promising sites for further wind energy applications could be identified.

1.2.2 Solar Energy Potential Mapping and Evaluation

Digital maps of global solar radiation over the whole territory of Montenegro were created. The solar radiation maps show the theoretical solar potential of the country, i.e. the available global solar radiation on a site over a certain period of time (unaccounted for are all technical and economical restrictions). In particular, solar mapping is a means of showing the solar potential of buildings, in order to identify which ones are suitable for retrofitting to solar energy, particularly solar domestic hot water.



Within this study, the potential of use of solar energy was assessed in two of the most promising sectors: solar thermal energy for households and solar thermal energy for the tourism industry.

The evaluation was conducted on the basis of the mapping results and of the evaluation of the seasonal and yearly needs in terms of hot water for typical households in the coastal, central and mountainous areas. Specific aspects, such as investment and operational and maintenance costs, were also taken into account, with an evaluation of specific costs and profitability of solar thermal use in domestic hot water production for three specific case studies in each location: a) a single family house; b) a big building block; and c) a swimming pool.

Concerning the tourism sector, that is expected to grow rapidly in the next years, a similar approach was adopted, considering the different seasonal domestic hot water demand. The evaluation was mainly focus on the coastal area, i.e. the region where tourists mostly concentrate.

1.2.3 Biomass to Energy Potential Mapping and Evalu ation

The biomass energy potential assessment was focused on forestry resources, wood waste and agriculture.

For the forestry, the evaluation was based on the data provided by the competent authorities (forestry institutes, etc.). Concerning the wood waste, the available data on the wood industry sector in Montenegro were evaluated, with specific reference to the identification, description and evaluation of the local sector of activities (sawmills, other wood processing industries, etc), the assessment of production capabilities and capacities, the estimate of the amount of wood waste generated, etc.

Concerning agriculture, the potential was limited to the estimation of the possibility to produce bio-fuel.

1.3 STRUCTURE OF THE REPORT This Renewable Energy Resource Assessment study is organized in distinct sections, in order to provide an effective and comprehensive access to the results obtained for each of the renewable source evaluated. Specifically, this report presents the following information and results:

• the general information on Montenegro relevant to the study (Section 2);

• the wind power potential assessment (Section 3);

• the solar thermal potential assessment (Section 4);

• the biomass to energy potential assessment (Section 5);

• the main conclusions and recommendations (Section 6).

1.4 LIMITATIONS OF THE STUDY The extent of the study and the nature of the conclusions drawn is driven by, and indeed limited to, the range and accuracy of the data available. The principal limitations for wind, solar, and biomass assessment are highlighted in the following sections.



1.4.1 Wind Energy Assessment

This study, concerning the assessment of the wind potential of the Republic of Montenegro, is based almost entirely on the methodology already used to realize the Italian Wind Atlas (IWA), produced in 2002 through a collaboration between the Italian Experimental Electrotechnical Centre (CESI) and the Department of Physics of the University of Genoa (DIFI), in the framework of the Research on the Electric System, funded by decree of the Italian Government (CESI/DIFI, 2002). This methodology, well documented in a number of paper and communications (Botta et al. 2004; Burlando et al., 2002a; Burlando et al., 2002b; Podestà et al., 2002; Podestà et al., 2003; Burlando, 2004; Cassola et al., 2006) is based on the combination of three-dimensional numerical simulations of the wind field all over the territory under study, and their comparison with wind measurements at the ground level. Therefore, in principle two main sources of uncertainties have to be considered in order to evaluate the reliability of the final results:

• the uncertainty associated to the simulation procedure, which involves the skill of the simulation code with respect to its physical formulation and the way the simulation is performed;

• the representativeness of wind measurements due to the position of the anemometric masts with respect to surrounding obstacles.

As far as the simulation of the wind fields is concerned, all the numerical simulations were performed by means of the diagnostic flow model WINDS (Wind-field Interpolation by Non Divergent Schemes) (Ratto et al., 1990, Georgieva et al., 2003), which belongs to the class of mass-consistent codes. In general, mass-consistent models represent actual atmospheric states if the initialization data (i.e. meteorological observations) reflect the influence of all scales of motion and physical processes affecting the area under study. However, direct measurements are costly to obtain, may omit key variables, and often lack sufficient spatial or temporal density to describe the field adequately. In this respect, the most important limitation of these models is that they can represent only the original set of processes contained in the data and, therefore, cannot have greater detail than that resolvable, in space and time, by the observation set. For instance these models are unable to represent mountain-valley or sea-land breezes, when thermally driven circulations occur, and flow separation in the lee of topography, unless these features are already present in the initialization data. It is worth noting that only a very detailed experimental campaign can have ground and upper-level networks approaching a resolution sufficient to describe all phenomena of interest, so that it is almost impossible for applied-oriented purposes to satisfy the aforementioned requirements.

An alternative way of initializing mass-consistent codes, as adopted in the present study, consists in requiring that the wind field is in barotropic balance with the geostrophic or gradient wind aloft. Following this approach, vertical profiles of the wind velocity, based on similarity-theory formulations, can be used to initialize the first-guess wind field. This is the case that we have focused on in the present study as it does not require a great number of wind measurements but makes widely use of planetary boundary layer parameterizations. Moreover, given the climatological character of the present work, and being our interest focused on strong winds, the attention was limited to the case of neutral stability of the low atmospheric levels, even if this choice can cause too high wind intensities over broad flat terrain where the stability effects are determinant on the wind profile shape. Besides, the explicit consideration of stable and unstable conditions would have required not available (or scarcely available) information and much longer computing times.



Finally, it is worth noting that the range of applicability of the WINDS code is well known since during the last decade it has been extensively used for many geophysical and engineering applications like dispersion modeling on the local scale (Canepa and Builtjes, 2001), wind energy potential evaluations (Burlando et al., 2002a; 2002b; 2002c) and applied wind engineering problems (Castino et al., 2003), as well as it has been validated both against wind tunnel data (Trombetti et al., 1991) and data obtained from field campaigns in coastal mountainous terrains (Canepa et al., 1999).

As far as the wind measurements are concerned, some conditions relating to the surrounding and the measurement location as well as to the characteristics of the mast should be respected in order to achieve a useful representativeness of the observations. In general, the location of the wind mast should be such that an observation of the wind can be performed that is representative for an area with a radius of 30 kilometers around the measurement site. For wind measurements on the coast, the degree of representativeness is obviously partly dependent on the wind direction. This condition is based on statistical studies performed by Wieringa, who stated that, under appropriate measuring conditions, “for a separation of 30 kilometers between two observation points in a homogeneous landscape, the difference in wind speed is less than 5% for 90% of the time.” Therefore, to cover the whole territory under study, the density of the wind measurement network required follows from the level of representativeness of the single anemometers, while in the present case it should be particularly high due to the complexity of the territory of Montenegro.

Following WMO (1996), some conditions about the characteristics of the mast have to be respected in order to preserve the quality of wind data. At first, the sensors for measurement of wind speed and direction has to be mounted on a stable metal or plastic mast, and the sensor height should be 10 meters above flat terrain. Secondly, the surrounding roughness should be less than 0.5 meters in all directions and the distance from the wind mast to any obstacles in the vicinity must be at least ten times and preferably twenty times the height of the obstacle. Anyway, the terrain in the immediate vicinity of the wind mast, i.e. within a radius of 100 meters around the measurement site, has to be flat grassland or a water surface. Measures are likely not enough representatives of the actual wind conditions of the surrounding territory if any of the above constraints is missing, and in this case the direct comparison between simulated and measured data can not be performed.

Having considered the above limitations, the data mapping and the assessment presented in this report are consistent with the scope of the study and suitable to fully characterize the potential of wind as renewable energy source all over the Montenegro territory.

1.4.2 Solar Energy Assessment

Firstly, it must be observed that not all solar thermal energy applications were studied. Right from the beginning, it was decided to focus on the most promising options for Montenegro due to data constraints. Further resources could be dedicated to the assessment of high temperature or very low temperature applications for instance but the output is likely to be negative. Solar cooling was also let aside because it has not yet reached a state of industrial production and its use is still limited to show-case projects.

Secondly, the solar radiation assessment was limited to the study of ground based data and satellite based data without successful correlation between the two sets. Indeed, the ground based data provided were quite incomplete and of questionable reliability. After evaluation of these data and implementation of some preliminary correlation it became obvious that a complete correlation and the generation of a third set of data was impossible.



Thirdly, the assessment of the solar thermal technologies was indeed an assessment of their technical and economic viability in Montenegro. Therefore, the total technical potential of solar energy was not calculated. This evaluation of this viability was based on European standards. In the case of costs for instance, extremely cheap products, which can be found on other continents, were not taken into consideration because they do not meet the European quality standards or requirements and/or are barely distributed on the continent.

However, the data mapping and the assessment presented in this report are consistent with the scope of the study and suitable to fully characterize the potential of solar radiation as renewable energy source all over the Montenegro territory.

1.4.3 Biomass Energy Assessment

The limitations of the study are connected to the completeness of the data used in the assessment: two of the main sources were the statistical data available for the whole country (MONSTAT, 2005) and the global land cover data available in specialized databases.. The potentials in the forestry, as well in the agricultural systems, have been defined on four levels:

• theoretical;

• technical;

• economical;

• exploitable / sustainable.

Beginning with the spatial resolution of the available global land cover data, the rendering revealed the heterogeneous nature of the Montenegrin landscape. It is difficult to isolate large belts or expanses of a defined land cover. The limited resolution of 800×1000 m², means that a pixel (800×1000 m²) can be unspecific: i.e. for lands with a mosaic of croplands, forests, shrubland, and grasslands in which no one component comprises more than 60% of the landscape the pixel is classified as Cropland/Natural Vegetation Mosaics.

Using this data as a main source results in limitations to the resolution of evaluations, that is correlated to the results. For example the grids have a resolution limited to 800×1000 m². In the future, when more complete data could be available, a more detailed version should be used with more detailed information. The recommended resolution would be 100×100 m², at least 200×200 m².

Regarding the statistical yearbook, there is a limitation with respect to harvested biomass, for both forestry and the agricultural systems. It does not give relations in potentials.

Nevertheless, under those aspects, the data mapping and the assessment presented in this report are consistent with the scope of the study and suitable to fully characterize the potential of both the agricultural and forestry systems for biomass production.



2 COUNTRY OVERVIEW

In the following paragraphs, the main features of the Montenegro territory (territory, infrastructures, climate, population, industry, agriculture and forestry, tourism, energy) are characterized, as relevant to the present renewable energy assessment. Most of the data are derived from recent statistical reports (MONSTAT, 2005), as summarized in the following table.

Table 2.1 : Republic of Montenegro Key Statistical Data

Mun

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[in

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Cut

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of T

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[m

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Andrijevica 9 247 1,285 5,785 1,073 - 1 65 762 23,839

Bar 101 746 11,426 40,037 13,719 101,188 59 768 1,168 2,065

Berane 74 1,089 7,560 35,068 11,776 1,981 16 291 3,018 36,099

Bijelo Polje 192 2,020 11,807 50,284 15,883 2,819 62 476 6,210 48,664

Budva 65 195 5,468 15,909 10,918 302,600 168 1,561 250 294

Cetinje 68 1,583 5,400 18,482 15,137 7,588 19 260 509 10,025

Danilovgrad 55 737 4,192 16,523 5,208 86 6 102 575 16,224

Herceg Novi 107 1,149 10,405 33,034 12,739 144,591 221 1,060 137 529

Kolašin 33 350 2,889 9,949 2,989 1,335 3 246 957 41,658

Kotor 40 401 5,950 22,947 1,331 53,893 44 408 227 3,096

Mojkovac 27 259 2,207 10,066 4,120 1,356 5 139 621 12,307

Nikšic 194 4,627 20,096 75,282 58,212 3,761 71 822 3,813 48,854

Plav 25 295 2,291 13,805 3,615 54 7 112 1,975 21,134

Pljevlja 62 833 11,287 35,806 21,377 1,961 13 283 1,975 134,157

Plužine 5 207 1,536 4,272 1,494 468 1 125 341 30,033

Podgorica 580 7,776 52,446 169,132 136,473 20,994 267 1,792 4,721 33,143

Rožaje 114 549 4,074 22,693 9,121 1,946 9 180 605 38,691

Šavnik 2 22 895 2,947 570 258 3 18 323 6,077

Tivat 34 198 3,972 13,630 9,467 36,003 40 310 95 60

Ulcinj 49 237 4,813 20,290 10,828 80,971 98 844 3,423 636

Žabljak 10 38 1,326 4,204 1,937 12,599 12 95 197 19,580

TOTAL 1,846 23,558 171,325 620,145 347,987 776,452 1,125 9,957 31,902 527,165

Notes Year 2004

Year 2003

Year 2003

Year 2003

Year 2003

First 9 Months 2005

Year 2004

Year 2003

Year 2004

Year 2004

Reference: Monstat, 2005



2.1 TERRITORY The Republic of Montenegro, which declared its independence from the Republic of Serbia-Montenegro in June 2006, extends over an area of 14,026 km2, 210 of which are occupied by internal waters (Figure 2.1).

The terrain is typically rugged and steep and is mostly covered by forests, crops and pastures.

Figure 2.1 : Map of Montenegro

The northern region is mountainous and alpine, intersected by steep and narrow river valleys, glacial lakes and forests. The highest point is the Bobotov Kuk, in the Durmitor range, having an elevation of 2,522 meters a.s.l.



The only plain (green portion in the lower part of Figure 2.1), having a relatively big extension, is located around Podgorica, the capital city, and partially surrounds the coast of the Skadar Lake.

The Skadar Lake, only 40 km far from the sea, is the largest fresh water body (391 km2 surface) in the Balkans and is rich in flora and fauna (its adjacent marshland is one of the most extensive in the Mediterranean).

The inland area is separated from the seashore by a karst coastal range (Dinaric Alps) which extends parallel to the Adriatic coast forming a backbone to the Balkans region.

The narrow coastal area (almost 300 kilometers long) provides excellent facilities for bathing, diving, sailing and almost all water sports, taking advantage from the mild Mediterranean climate.

2.2 INFRASTRUCTURES Montenegro has two airports, in Podgorica and Tivat. The Port of Bar, at the entrance to the Adriatic, is equipped for handling around 5 million tons of goods annually, relying on a fleet with the total carrying capacity of more than 1,000,000 tons.

The road network of Montenegro extends over 5,000 km, 1,700 km of which are modern arterial and regional roads, while the rest are local. The total extension of the railroad system is of the order of 250 kilometers, most electrified. The railway junction in Podgorica connects the inland with the Adriatic sea (Bar harbor), whereas the railroad Podgorica-Bozaj connects Montenegro with the neighboring Albania.

2.3 POPULATION Population presently accounts for 620,000 inhabitants, with an average density of about 45 inhabitants per square kilometer, i.e. of the order of one half of the European Union average.

A former country of farmers (62% of the population in 1953), Montenegro has experienced a rapid urbanization - in 2003 the agricultural population accounted for only 2.5% of the overall population, with 56% of the population living in urban centers (Table 2.1).

Figure 2.2 compares the population in the 21 municipalities forming the Montenegro territory: most of the population is resident in the Podgorica area, in the Nikšic municipality, in the Bar harbor area and in the municipalities (Pljevlja, Bijelo Polje and Berane) located along the northern border with Serbia.

2.4 CLIMATE Montenegro's lower areas enjoy a Mediterranean climate, having dry summers and mild, rainy winters. Temperature varies greatly with elevation. Podgorica, lying near sea level, is noted for having the warmest July temperatures in the Serbia and Montenegro area, averaging 27 °C.

The coastal regions generally enjoy mild winters and hot summers, with sea temperatures ranging from 10-12°C in winter to 25-28°C in summer.

Cetinje, in the Karst at an elevation of 650-700 meters asl, has a temperature 5 °C lower. January temperatures range from 8 °C at Bar on the southern coast to -3 °C in the northern mountains.



Figure 2.2 : Population

Montenegro's mountainous regions receive some of the highest amounts of rainfall in Europe. Annual precipitation at Crkvice, in the Karst above the Gulf of Kotor, is as much as 4,928 millimeters. Like most areas along the Mediterranean Sea, precipitation occurs principally during the cold part of the year, but in the higher mountains a secondary summer maximum is present. Snow cover is rare along the Montenegrin coast; it averages 10 days in the karstic depressions and increases to 120 days in the higher mountains.

2.5 INDUSTRY Over the last 50 years, industry played a strong role in the economic development of Montenegro. In that period (as per the official data, e.g. http://www.montenegro.yu/english/ekonomija/eko-nomija.htm), the growth of the power industry, metallurgy (steel and aluminum), and transport infrastructure were making the basis for the present industrial production (e.g. 400,000 tons of crude steel, 1,000,000 tons of bauxite, 280,000 tons of alumina, 100,000 tons of aluminum, 75,000 tons of sea salt and



2,700,000 tons of coal per year). The power plants (hydro-electric power plants of Perucica and Piva, and the thermoelectric power plant of Pljevlja) produce around 3 TWh per year.

Together with these basic production activities, a variety of industries (e.g. metal-processing, engineering, wood-processing, textile, chemical, leather and footwear, ready-made clothes, household appliances, construction and forestry machines) are located in the Montenegrin territory, mainly (Figure 2.3) in Podgorica, Nikšic, and also in Cetinje, Bijelo Polje and Berane.

Figure 2.3 : Active Population and Industry Sector



2.6 AGRICULTURE AND FORESTRY Agricultural lands are located (Figure 2.4) in the flat areas around Podgorica and Ulcinj, in the Nikšic area and in the region close to the northern border with Serbia (Pljevlja, Bijelo Polje and Berane).

Forests and woodlands (significant producer of biomass) cover the area of 720,000 hectars, about 51% of the total surface of the country; of these, the major part (572,000 hectars) is in the North-East.

Figure 2.4 : Agriculture Vocation

The total standing stock in forests of Montenegro is estimated at 72,056,699 m3 of which conifers constitute 29,527,555 m3 (41%) and deciduous trees 42,529,144 m3 (59%). Figure 2.5 shows the rate of cutting trees (as a measure of the forestry utilization) over the 21 municipalities. The highest production is shown by the municipalities located along the northern border with Serbia (Pljevlja, Bijelo Polje, Berane and Rožaje) as well as in the Kolašin and Nikšic areas.



Figure 2.5 : Forestry Utilization

2.7 TOURISM The geographical position of the country, a coastline of almost 300 kilometers length and a great variety of cultural, historical and natural sites such as high mountains, deep valleys, glaciers, or a largely diverse flora and fauna, make Montenegro very attractive for the tourism industry.

Tourism is key to Montenegro, as its Government marked tourism as one of the strategic industries in the country. Currently (year 2005 data), the country can count (Ministry of Tourism, 2005) on over 5.3 million of overnight stays per year (30% international tourists) and on 820,000 tourist per year (33% international).

Figure 2.6 shows that the coastal municipalities (Herceg Novi, Kotor, Tivat, Budva, Bar and Ulcinj), which altogether account for the 93% of the tourists, represent the most attractive area for the tourism. Additional contributions are exhibited by Podgorica (3%), the capital city, and Žabljak (2%), where the Durmitor natural area is located.



Figure 2.6 : Tourism Sector

2.8 ENERGY

2.8.1 Energy Regulatory Framework

Some progress has been recently made in the field of energy in Montenegro. However, the adoption of relevant acquis for the creation of an effectively liberalized energy market in electricity and gas is underway, notably on electricity tariffs.

The Energy Law of Montenegro has been adopted in June 2003. The objectives of this Law are to ensure a safe, secure, reliable energy supply at fair prices, taking into account, among the other, the efficient use of energy and comprises the following activities:

• electricity production, transmission, distribution and supply on market;

• organization and function of electricity market;

• oil derivates and gas transportation, distribution, storage and trading.



According to the directions contained in the Energy Law, the Government shall, through the Ministry:

• realize energy efficiency policies and encourage the conservation of energy resources;

• encourage and advise on energy efficiency and the rational use of energy;

• develop and promote incentives for the efficient use of energy and renewable resources;

• promote the increased use of renewable energy sources and alternative energy sources for generation in the internal market; and

• manage funds contributed for the purpose of energy conservation and energy efficiency.

A new institution, which will be responsible for the implementation of this law is the Regulatory Agency for Energy that was activated in early 2004. According to this Agency eligible customer are defined by the law according to the level of annual energy consumption. The threshold is set at 25 GWh in electricity, 50 million m3 of natural gas, and 5,000 GJ of heat.

At the beginning of year 2006, the Energy Policy of Montenegro has been adopted with the goal and objective, among other, to provide institutional and financial incentives to improving energy efficiency and reducing energy intensity in all sectors, from generation to consumption of energy.

In October 2005, the Government of Montenegro has adopted the so-called Energy Efficiency Strategy of Montenegro. The next step of the Government and the other bodies in the country is to implement that Strategy. Municipality will take a significant role in that process.

In October 2006, the Government of Montenegro has ratified the Energy Community South East Europe (ECSEE) Treaty, signed in Athens, Greece on October 25, 2005 and entered into force on July 1, 2006. The treaty, which effectively sets up a European Energy Community, aims at establishing a single regulatory framework for trading energy across southeast Europe, covers the sectors electricity, natural gas and petroleum products. The treaty will ensure that signatory states will adopt EU single market regulations regarding energy (the EU acquis communitaire in the relevant fields of Energy, Environment, Competition and others).

The Government of Montenegro is working on the implementation of the acquis on the European Directive on Electricity Production from Renewable Energy Sources 2001/77/EC. The purpose of this Directive is to promote an increase of the contribution of renewable energy sources to electricity production in the internal market for electricity and to create a basis for a future Community framework.

In addition, the experts of the Government of Montenegro in 2006 started the preparation of the National Energy Strategy (NES), planned to be completed in spring 2007.

One of the main goals of the Government is to establish Energy Efficiency Unit, which will work as a part of the Ministry of Economy and which will make a better cooperation with the municipalities in order to improve energy efficiency projects.

A crucial question for Montenegrin municipalities is financing of energy efficiency projects. Therefore experiences from other countries in terms of energy efficiency funds, commercial loans, international cooperation, municipal bounds and other possibilities are very



interesting. The other obstacles for promoting energy efficiency are undercoat price of energy and lack of individual measuring.

2.8.2 Energy Production and Use

While analyzing the current power production and energy use in Montenegro, it is essential to consider that the economy of Serbia and Montenegro fell into crisis as a result of UN sanctions towards Serbia and Montenegro and the surrounding armed conflicts. During the period of the sanctions, the entire economy was in crisis. There were difficulties in exports and imports, there was insufficient energy (oil, gasoline, gas), shortage of other materials, hyperinflation, and no major investment. Recession and a decline of production were to be seen in all industrial branches. After ten years of stagnation, the economy of Serbia and Montenegro started to recover in 2000.

During 2002, the strengthening of macro economic stability of Serbia and Montenegro has continued, the process of transition has accelerated towards market economy and integration into international monetary institutions. In 2002, gross domestic production increased by 4 % compared to the previous year, with the greatest growth achieved in the trade sector. Over one half of gross domestic production is being achieved by processing industries (30,7 %) and by agriculture and forestry (24 %). The gross domestic product per capita in 2002 was $1,831. Industrial production has increased in 2002 compared to 2001 by 1,7 %, with the greatest growth achieved in processing industries (2,7 %) and mining (2,2 %), while production of electricity, gas and water declined by 2,0 %. After the significant growth in 2001 of 17,2 %, agricultural production declined 2,1 % in 2002.

Since Montenegro became an independent Republic, it has to develop its own energy supply strategy in the near future. Renewable energy resources are one of the possibilities to satisfy the energy demand of the about 620,000 inhabitants.

At present, in Montenegro there are 10 energy generation plants: two large scale hydropower, one thermal power and 7 small-scale hydro (Table 2.2). The total installed capacity is 868 MW and the combined production of all power generation facilities amounted to approximately 3,200 GWh in 2005. A further 1,300 GWh had to be imported at a cost of €40 million, resulting in a specific energy cost for imported power of 30,78 €/MWh. The average of the national energy price (households) for electricity was about 4.6 €Cent/kWh. Which in comparison to other European countries is very low. The demand for electricity per household in Montenegro is about 4,800 kWh/year, and with respect to other European countries is a very high level (e.g. Germany has an average of electricity demand per household of approximately 3,500 kWh/year).

Table 2.2 : Power Generation Plants in Montenegro ( Year 2005)

Installed Capacity [MW]

Production Mix 2005

Generation Output [GWh]

Large hydro: Perucica Piva

[5x38 + 2x58.5] 342 (3x114)

29.5 % 30.0 %

930 960

Thermal: Pljevlja (Lignite)

210 40 % 1,280

Small hydro (7 plants) 9 < 1% 21



From the point of view of installed capacity, renewable resources represent a fraction of approximately 75 %. However, in the utilization of the national generating capacity, renewables only accounted for approximately 60 % of energy generated internally, and 42 % of the total energy fed to the national grid.

Moreover, given the actual installed capacity, generating capacity in Montenegro only saw an equivalent of 3,700 hours at full capacity – 42 % of installed generating potential. This low load factor of the power supply system showed above is a consequence of the higher share of HPPs, which give a “non continuous” supply due to the hydrology, and of the special position of the peaking unit HPP Piva. In solution to this situation, both the large hydro-power plants have already obtained foreign loans for reconstruction, and tendering calls have been made.

With reference to the TPP Pljevlja, even though the future ownership is still unclear (the privatization process is ongoing), the energy production will continue also in case of higher cost of coal and non-cost-recoverable tariffs. In this view, renewable energy can play a strategic role in the framework of the directions of the Montenegrin energy legislation.



3 WIND POWER POTENTIAL ASSESSMENT

From a European, as well as a global perspective, wind power is undergoing rapid development. The global installed capacity of wind power has increased from approximately 2.5 GW in 1992 to more than 51 GW at the end of 2005, with an annual growth rate of around 30%. According to the “Global Wind Energy Market Report” (AWEA, 2004), Europe dominates the global market accounting for 72.4% of installations, Asia has a 15.9% installation share, followed by North America (6.4%) and the Pacific Region (4.1%), whereas Latin America and Africa have a 0.6% market share. The bulk of wind power capacity remains concentrated in few countries: Germany (16,629 MW), Spain (8,263 MW), the United States (6,740 MW), Denmark (3,117 MW), and India (3,000 MW). These top five countries accounted in 2004 for nearly 80% of total wind energy installation worldwide.

As a result, the exploitation of wind energy both on- and offshore have started being regularly considered by nations, also Candidate States, in the formulation of their wind energy programs when the overall wind potential over land and sea is comparable to the total national energy demand. Offshore wind energy is at an earlier stage of development than onshore, but it offers a number of advantages compared to inland installation. The growth of onshore wind energy exploitation is mainly constrained by the undesirable visual impact of massive turbines on the landscape in populated areas, while an offshore wind farm far enough from the coast has a minimised visual impact even if large wind turbines are adopted in order to increase the overall installed capacity per unit area. On the other hand, offshore development needs higher initial investments and maintenance costs than on land.

In this context, one of the main purposes of the present study is to evaluate if the Republic of Montenegro has a sufficient wind potential on- and offshore to justify further efforts towards a massive exploitation of this particular kind of renewable energy.

The scope of this section is to assess the wind energy resources in the Republic of Montenegro, on the basis of the following activities:

• methodological approach adopted for estimating the wind potential of Montenegro (Section 3.1);

• subdivision of the target area into the specific computational domains (Section 3.2);

• illustration of the numerical code used to simulate the high-resolution wind fields over each computational domain (Section 3.3);

• description of the wind aloft data used to initialize the code as well as to attribute a statistical weight to the simulations (Section 3.4);

• illustration of the numerical simulations performed over the computational domains (Section 3.5);

• estimation of the theoretical wind potential of Montenegro directly obtained from the simulations (Section 3.6);

• validation analysis performed on the measured wind data at the ground, used to tune the simulated wind fields (Section 3.7);

• elaboration of the correction factor to be applied to the simulated theoretical wind potential in order to obtain the actual theoretical wind potential of Montenegro (Section 3.8);



• identification of the main technical and natural constraints to wind farm installation and evaluation of the actual exploitable wind power of Montenegro (Section 3.9);

• description of the typical features of modern wind power plant (Section 3.10);

• preliminary economical analysis for the installation of wind farms in the Country (Section 3.11).

The relevant conclusions of the wind energy resource assessment are reported in Section 6.

3.1 METHODOLOGICAL APPROACH This analysis is based on a methodology which combines the statistical analysis of data relative to the wind speed aloft with numerical modelling of wind flows over complex terrain to realise a first-guess theoretical wind potential of the target area (the whole Montenegro). Then, wind measurements at the ground level are used to validate and refine these preliminary results, in order to obtain a final estimate of the wind potential calibrated on measured wind data. This methodology was already applied in 2004 for the realization of the Italian Wind Atlas, (see Burlando et al., 2002a; 2002b), available at the website http://www.ricercadisistema.it.

Within this study, the territory of Montenegro has been subdivided into a number of computational domains about 150×200 km2, with a grid step lower than 1 km. These domains are partially overlapped. Almost 300 three-dimensional wind flow simulations have been performed over each domain by means of the diagnostic mass-consistent code WINDS, initialised by means of wind aloft values. These data belongs to a dataset of wind speed and direction obtained, at the top of the atmospheric boundary layer (i.e. around 3000-5000 m above sea level), from the re-analyses of the European Centre for Medium-Range Weather Forecast (ECMWF), available for research purposes on the ECMWF website http://www.ecmwf.int/. The statistical analysis of these data provided the weights for the numerical simulations performed, in order to calculate the mean wind field and the maximum theoretical wind potential over Montenegro. The mean wind field and wind potential maps provided by the simulations were compared with the average wind speeds measured at 10 m above ground level (a.g.l.) at the existing measuring stations located within the target area. More specifically, measurements in the inland were provided by HIM, while offshore satellite data were provided by the National Aeronautics and Space Administration (NASA).

With the purpose of estimating the feasible wind power exploitation of Montenegro, the main constraints to the wind energy use over the territory were applied to the theoretical wind potential map. At this stage, the presence of natural parks or protected habitats, the road network, and the structure of the national power supply system have been taken into account.

3.2 COMPUTATIONAL DOMAINS The simulation procedure requires, first of all, the definition of a digital representation of the terrain within a three-dimensional computational domain. Such a domain should be extended horizontally to include the main topographical structures influencing the wind field in the area under study as well as a reasonable portion of the Mediterranean sea in front of Montenegro, and vertically to contain the atmospheric boundary layer.

The wind fields over the territory of Montenegro, with a surface of approximately 14,000 km2, are likely to be strongly influenced by the Dinaric Alps in the northern part of Albania, around 21.25°E and 41.25°N, as well as in the southern part of Bosnia and Herzegovina,



around 17.50°E and 44.25°N, therefore an overall computational domain comprehending these topographic structures should be simulated. Since the overall area cannot be enclosed in a single computational domain, we subdivided it into four computational domains.

Each computational domain has an extension of about 2°×2° in longitude and latitude respectively, and is defined on a horizontal grid with a grid-step of 30 arc-second, resulting in more than 50,000 grid nodes per domain. This spatial resolution is corresponding to the outcome available from the topographic and land cover databases of the United States Geological Survey, which were used to obtain the digital representation of the terrain all over Montenegro.

Each domain was then overlapped to the adjacent areas for a strip of about 0.5 degrees in longitude and latitude, in order to reduce the border effects generated by the simulations and to avoid discontinuities between adjacent domains. Table 3.1 summarizes the main data of the computational domains, while Figure 3.1 shows the main characteristics of each domain.

Figure 3.1 : Selected Computational Domains

The top of each computational domain was set at 5,000 m above sea level (a.s.l.) in order to include both the highest topographical elevations and the highest planetary boundary layers. The vertical discretization of the computational domains is provided by 16 conformal surfaces, or σ-levels (Phillips, 1957), spaced in order to obtain a higher resolution of the wind field close to the surface, i.e. about 150-100 m above ground level.



Table 3.1 : Main Data of the Computational Domains

Domain Longitude (°N) Latitude (°E) Nodes (n°) Resolution (m) Surface (km 2)

1 17.50-19.50 42.50-44.25 241×210 674×927 162×194

2 19.00-21.25 42.50-44.25 270×210 674×927 181×194

3 17.50-19.50 41.25-43.00 241×212 687×927 165×196

4 19.00-21.25 41.25-43.00 270×212 687×927 185×196

3.2.1 Topographic Map

The Global 30 Arc-Second Elevation Data Set (GTOPO30) is a global raster digital elevation model (DEM) derived from a variety of raster and vector sources, resulting from a collaborative effort led by the staff at the U.S. Geological Survey's (USGS) Center for the Earth Resources Observation and Science (EROS) in Sioux Falls, South Dakota. The whole dataset is available on the USGS website http://edc.usgs.gov/products/elevation/gtopo30/gtopo30.html.

GTOPO30 covers the full extent of latitude from 90° South to 90° North, and the full extent of longitude from 180° West to 180° East. The horizontal grid spacing is 30-arc seconds (8.33 × 10-3 degrees), resulting in a DEM with 21,600 rows and 43,200 columns. The data is expressed in geographic coordinates (latitude/longitude) and is referenced to the World Geodetic Survey (WGS) system of 1984 (WGS84).

The vertical units represent the elevation in meters above mean sea level. The elevation values range from – 407 to 8,752 meters. In the DEM, the ocean areas were masked as "no data" and were assigned a value of –9999, so that a land-sea mask can be obtained directly from the dataset. Lowland coastal areas have a minimum elevation of 1 meter, so it a user will reassigns the ocean value from -9999 to 0, the land boundary portrayal will be maintained. Due to the nature of the raster structure of the DEM, the islands smaller than approximately 1 km2 are not represented.

Figure 3.2 shows the topography of the target area available from the GTOPO30 data set. Note that most of the country is covered by high and extensive mountains, characterised by the presence of very high relieves with more than 2000 m high peaks all along the Dinaric Alps, intersected by river gorges and deep valleys. Larger lowland areas are to be found in the south, near the coastline. The Montenegro seacoast is a narrow strip of land running from Herceg-Novi to the Bojana river on the frontier with Albania, with the high mountains of Orjen, Lovcen and Rumija rising steeply from the sea up to more than 1000 m a.s.l. in the southernmost part.



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Figure 3.2 : Topographic Map

3.2.2 Land-sea Mask Map

A land-sea mask is a data grid that represents all land points with one (numerical) value and all water points with a different (numerical) value, being typically between 0 and 1. Specifically, within this study the land-sea mask is used by the simulation code to adopt different parameterization schemes of the wind profiles over land and sea.

The masks are usually obtained from pre-existing bathymetric data, or from an image analysis. In this study, the land-sea mask of the computational domains were derived from the GTOPO30 dataset, converting the undefined -9999 values over the Mediterranean area into the “sea flag”, and all defined values into the “land flag”. Figure 3.3 shows the resulting land-sea mask with 0 values for the land and 1 values over the sea. It has to be noted that, being the satellite unable to distinguish pixels occupied by water bodies contiguous to the sea from the sea itself, the area occupied by the final part of the Bojana River (an area reach of water bodies, such as Ulcinska Solina salt works) results in being considered as a deep fjord instead of a land area. However this discrepancy is not a problem for the numerical model, since the sea and all water bodies are considered in the same way during the simulation.



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Figure 3.3 : Land-Sea Mask

3.2.3 Land Cover Map

The Global Land Cover Characterization (GLCC) is a global land cover classification database based primarily on the unsupervised classification of 1-km Advanced Very High Resolution Radiometer (AVHRR) 10-day Normalized Difference Vegetation Index (NDVI) composites. The data used are the International Geosphere Biosphere Programme (IGBP) 1-km AVHRR 10-day composites source images from April 1992 through March 1993 (Eidenshink and Faundeen, 1994).

Among the available datasets, we have used the Land Use/Land Cover System version 2 (Anderson et al., 1976), jointly realized by the University of Nebraska-Lincoln and the Joint Research Centre of the European Commission. The database includes 24 different levels, as shown in Table 3.2.

The global dataset, referenced in geographic coordinates to the WGS84 system, can be retrieved from the website http://edcsns17.cr.usgs.gov/glcc/tabgeo_globe.html. Figure 3.4 shows the land cover of the whole territory under study available from the Land Use/Land Cover System data set of GLCC. Note that the land cover types 22-24, which are typical of very high latitudes, do not appear over the considered area, while types 2, 11 and 14 are characterized by a significant presence.



Table 3.2 : Land Use/Land Cover levels

Value Description

1 Urban and Built-Up Land

2 Dry land Cropland and Pasture

3 Irrigated Cropland and Pasture

4 Mixed Dry land/Irrigated Cropland and Pasture

5 Cropland/Grassland Mosaic

6 Cropland/Woodland Mosaic

7 Grassland

8 Shrub land

9 Mixed Shrub land/Grassland

10 Savannah

11 Deciduous Broadleaf Forest

12 Deciduous Needle leaf Forest

13 Evergreen Broadleaf Forest

14 Evergreen Needle leaf Forest

15 Mixed Forest

16 Water Bodies

17 Herbaceous Wetland

18 Wooded Wetland

19 Barren or Sparsely Vegetated

20 Herbaceous Tundra

21 Wooded Tundra

22 Mixed Tundra

23 Bare Ground Tundra

24 Snow or Ice

Reference: USGS Land Use/Land Cover System



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Figure 3.4 : Land Cover Map

3.2.4 Roughness Length Map

As explained in the previous section, USGS Land Use/Land Cover System adopts 24 different types of land cover, whereas only 21 appear in the target area. Each land cover type is characterised by its own roughness, an average measure of the small-scale variations of the protrusions of the physical surface, causing an enhanced friction or drag to the fluid flow. That’s why in fluid dynamics the surface friction is usually described through the physical quantity named roughness length, expressed in meters. Moreover, different land cover types can be often considered similar as far as the surface flow resistance is concerned.

The procedure used to assign the roughness length values to the land cover types is summarized in Table 3.3. The land cover types with similar effects in terms of surface friction were grouped, since they can be represented by the same value of roughness length. At the end of this aggregation process, ten roughness classes have been considered (Table 4.3 – second column). The corresponding roughness length values, shown in Table 4.3 - third column, have been estimated taking into account the most recent classifications reported on scientific publications (Wieringa, 1993; Stull, 1988; Wieringa, 2001; Davenport et al., 2000; Baklanov et al., 2003). The last column reports the displacement levels associated to the different roughness length classes, as illustrated in the following section.



Table 3.3 : Roughness Length Classes for the Land C over Types

USGS Land Use/Land Cover System z0 classes z0 (m) d (m)

Water Bodies 1 0.001

Irrigated Cropland and Pasture 2 0.005

Herbaceous Wetland Herbaceous Tundra Grassland

3 0.01

Cropland/Grassland Mosaic 4 0.06

Dry land Cropland and Pasture Mixed Dry land/Irrigated Cropland and Pasture

5 0.2

Shrub land Mixed Shrub land/Grassland Barren or Sparsely Vegetated

6 0.4

Cropland/Woodland Mosaic Savannah Wooded Wetland Wooded Tundra

7 0.5

0

Deciduous Broadleaf Forest Deciduous Needle leaf Forest

8 0.7

Evergreen Broadleaf Forest Evergreen Needle leaf Forest Mixed Forest

9 0.8 5

Urban and Built-Up Land 10 0.9 8


Figure 3.5 shows the map of roughness length for the area of interest, obtained after processing the land cover types of the Land Use/Land Cover System data set of GLCC.

3.2.5 Displacement Level Map

A terrain characterised by close roughness elements causes the air flux to undergo a dynamic effect, is occur in presence of a surface with a roughness z0, but placed at a height d above ground level. This typically happens either on densely vegetated lands or on urban areas, where the average level of roofs acts as a terrain displaced upward by the distance d. Therefore, the length d is named displacement level, expressed in meters.

For the purpose of this study, it was assumed a displacement level value equal to 5 m for forests and to 8 m for urban areas, as shown in Table 3.3. The corresponding displacement level map is shown in Figure 3.6.



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Figure 3.5 : Roughness Length Map

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Figure 3.6 : Displacement Level Map



3.3 SIMULATION CODE We simulated three-dimensional wind fields over the territory of the Republic of Montenegro through the diagnostic mass-consistent model WINDS (Wind–field Interpolation by Non-Divergent Schemes), developed by the Department of Physics of the University of Genoa (Ratto et al., 1990; Ratto et al., 1994; Georgieva et al., 2003).

During the last decade, this code has been extensively used for many geophysical and engineering applications, like dispersion modelling on the local scale (Canepa and Builtjes, 2001), wind energy potential evaluations (Burlando et al., 2002a; 2002b; 2002c; Cassola et al., 2006) and applied wind engineering problems (Castino et al., 2003). The code has been validated both against wind tunnel data (Trombetti et al., 1991) and data obtained from field campaigns in coastal and mountainous terrains (Canepa et al., 1999).

The diagnostic models are simple and easy-to-use numerical codes, used to simulate average wind fields, i.e. the deterministic three-dimensional motion field with time scales from minutes to days and spatial scales from kilometres on. The mass-consistent models are a class of diagnostic models producing three-dimensional wind fields on the basis of the satisfaction of the physical constraints of mass-conservation (Sherman, 1978; Ratto, 1994; Homicz, 2002). The WINDS code, specifically, is an evolution of the AIOLOS code (Lalas, 1985). The main difference is the insertion of proper algorithms aimed at evaluating the effects of the terrain roughness and of the Coriolis force on the variation of the wind direction with the height.

The WINDS code numerically solves the three-dimensional continuity equation and generates an Eulerian wind field by satisfying the mass-conservation constraint in every node of the computational domain. The code creates the three-dimensional wind field through a two steps procedure:

• the wind data (wind speed at the ground level, aloft wind, vertical wind profile respect to a fixed point on the ground, etc.) are interpolated over the computational domain, in order to transform the observed wind vectors in a three-dimensional “first-guess” wind field;

• the final wind field (named “non diverging” or “mass consistent”) is then calculated by imposing the constraint of mass conservation in each node of the computational grid trough an adjustment of the first-guess wind field by the minimum possible number of modifications.

The input to the WINDS code consists of:

• wind initialisation data over for the domain and stability conditions of the lower atmospheric layers;

• topography and land cover data the domain, adjusted through the roughness length and the displacement level.

The volume of the 3D simulation domain is discretized with a traditional Cartesian coordinates grid for the horizontal directions (with constant spacing, different for each direction), and with conformal coordinates along the vertical axis. As a result, the volume is represented by terrain surfaces, close to the ground surface, and by flat surfaces on the top of the computational volume. The use of terrain surfaces allows a better description of the wind fields close to the ground level, and simplifies the boundary conditions for the equations, close to the ground.



The model uses the Zilitinkevich formulas (Zilitinkevich et al., 1998) for the vertical wind profiles (dependence of velocity components versus height above ground), allowing the estimation of the wind speed in the whole boundary layer, depending on the atmospheric stability and the terrain roughness. It should be noted that this approximation implies the neglect of the baroclinic features of mid-latitude disturbances.

3.4 WIND ALOFT DATA The wind aloft data, used for the statistical analysis and for the simulations at the top of the boundary layer, originates from the “re-analyses” of the General Circulation Model of ECMWF, and represents a powerful and recent approach to climate analysis.

3.4.1 “Re-analysis” of ECMWF

In daily forecasting, the latest ground and satellite based observations are combined with a short forecast based on earlier observations in order to create the initial state for a new forecast. This initial state, called “Analysis”, describes the weather elements throughout the atmosphere and the geophysical properties of land and ocean surfaces.

In a re-analysis, the weather observations collected in past decades are fed into the current more refined forecasting system. Atmospheric and surface parameters are reconstructed for each day of the observed period. The Re-analysis differs from the traditional climatological approach since it processes a wide variety of observations simultaneously, using the physical laws embodied in the forecast model and the knowledge of the typical errors of forecasts and observations to interpret conflicting or indirect observations and fill gaps in observational coverage.

The Re-analyses are continuously updated through the meteorological simulation codes. The outcome is a database covering more than 40 years where world climatic evolution was recalculated. The database, partially available on the web for research use, covers all the atmospheric physical features and, for the purposes of this study, the wind data.

Freely available ECMWF data, at the website http://www.ecmwf.int/, are defined on a grid of 2.5° in latitude and in longitude. It should be noted that only one node of the grid is located within the area of Montenegro territory, between 18.5°E and 20.5°E and 41.5°N and 44.0°N, at 20.0°E, 42.5°N (see Figure 3.1). Therefore, we downloaded from the ECMWF website the wind speed and direction data for this node, covering a time range of 22 years from the 00 UTC of 1st January 1980, to 18 UTC of 31st December 2001. These data are reported with a frequency of 1 value each 6 hours, i.e. 4 values per day at 00, 06, 12, and 18 UTC. Such data were retrieved at the isobaric surfaces of 300, 500, and 700 hPa, corresponding to heights of about 8,000, 5,500 and 3,000 m above sea level.

3.4.2 Statistical Analysis of the Wind Aloft

From the aforementioned wind aloft data, the climatology at the reference height of 5,000 m above sea level was calculated through a linear interpolation between wind speed and direction values at the geopotential surfaces above and below this threshold.

The statistical analysis is based on the calculation of the frequency distribution of wind speed and direction, performed with wind speed classes of 1 m/s and 16 sectors, corresponding to 22.5° degrees and centred on the cardinal points (Table 3.4). The wind rose resulting from this analysis, reported in Figure 3.7, shows prevailing wind directions ranging between SSW and N, with high frequencies for wind speeds predominant stronger than 20 m/s.



Table 3.4 : Wind Speed and Direction Aloft

Direction

Spe

ed

N

NN

E

NE

EN

E

E

ES

E

SE

SS

E

S

SS

W

SW

WS

W

W

WN

W

NW

NN

W

<=1 X

>1-2 X X X X X X X X X X X X

>2-3 X X X X X X X X X X X X X X X X







>9-10 X X X X X X X X X X X X X X

>10-11 X X X X X X X X X X X X X X

>11-12 X X X X X X X X X X X X

>12-13 X X X X X X X X X X X

>13-14 X X X X X X X X X X X

>14-15 X X X X X X X X X

>15-16 X X X X X X X X X

>16-17 X X X X X X X X

>17-18 X X X X X X X X

>18-19 X X X X X X X X

>19-20 X X X X X X X

>20-21 X X X X X X X X

>21-22 X X X X X X X

>22-23 X X X X X X X

>23-24 X X X X X X

>24-25 X X X X X X

>25-26 X X X X X X

>26-27 X X X

>27-28 X X

>28-29 X

>29-30 X



Figure 3.7 : Wind Rose at 5,000 m a.s.l.

3.5 NUMERICAL SIMULATION OF THE WIND FIELDS Given the climatological character of this study and being our interest focused on strong winds, we limited our attention to the case of neutral stability of the low atmospheric levels. Moreover, the incorporation of unstable conditions would have required not available (or scarcely available) information and longer computing times.



The code was initialized by wind aloft values. Specifically 283 three-dimensional simulations for each computational domain were performed. This number turns out to be the combination of 16 direction sectors of amplitude 22.5° and a number of wind speeds variable within a direction-dependent range, in order to respect the structure of the wind rose aloft (see Figure 3.7). For instance, 30 simulations with different wind speeds aloft (from 0.5 to 29.5 m/s, every 1 m/s) were performed for the most populated sector corresponding to W (258.75°-281.25°), while only 7 simulations with wind speeds aloft corresponding to the range 2.5-8.5 m/s were performed for sector ESE (101.25°-123.75°).

The wind speed and directions used in the simulations are summarized in Table 3.4.

The 2 scenarios, wind calms (0.5 m/s) and extreme events (29.5 m/s), were considered only for the W direction, since, due to their low frequency of occurrence, their contribution to the wind potential is negligible.

The simulations performed over different computational domains corresponding to the same initialisation were then composed together through the linear spatial interpolation of the wind fields between adjacent domains over the overlapping strips. Figure 3.8 and Figure 3.9 show two examples of the overall wind field:

• at 50 m above ground level (a.g.l.) for the simulation corresponding to the wind aloft with speed equal to 9.5 m/s and direction 45.0°;

• at 10 m a.g.l. for the simulation corresponding to the wind aloft with speed equal to 8.5 m/s and direction 135.0°.

Note that the wind field becomes more uniform as the height increases.

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Figure 3.8 : Simulated Wind Field at 50 m a.g.l.



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Figure 3.9 : Simulated Wind Field at 10 m a.g.l.

3.6 THEORICAL WIND POTENTIAL A specific frequency of occurrence, f, obtained in the statistical analysis of the wind aloft (see Section 3.4), can be assigned to each simulation in order to calculate the climatological mean wind velocity through the following equation:

∑=i iivfV (1)

where fi (i=1,…,N=283) are the frequencies of the 283 wind velocities aloft, while vi are the three-dimensional simulated wind fields. Figure 3.10 shows the average wind speed at 50 m a.g.l.

Similarly, the average cubic wind velocity can be calculated according to the following relation:

∑=i iivfV 33 (2)

The theoretical wind potential, expressed in W/m2, can be calculated through the following equation:

321 VAP ρ= (3)

where ρ is the air density, assumed constant and equal to 1.225 kg/m3, corresponding to the standard value under dry conditions at sea level and at 15°C, and A is a unit surface.



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Figure 3.10 : Average wind speed at 50 m a.g.l.

The theoretical wind potential, expressed in W/m2, can be calculated through the following equation:

321 VAP ρ= (3)

where ρ is the air density, assumed constant and equal to 1.225 kg/m3, corresponding to the standard value under dry conditions at sea level and at 15°C, and A is a unit surface.

Through the three-dimensional fields V , 3V , and P, bi-dimensional maps at fixed heights above ground level can be created, after interpolation among the closer conformal surfaces.

As a result, the theoretical wind potential of Montenegro at 50 m a.g.l. is shown in Figure 3.11. It is noted that the map identifies a set of very interesting areas, both in the inland and along the coast as well as offshore. Specifically, a narrow strip along the coastline seems to be very attractive, as it is characterized by a value of wind potential around 1500 W/m2. However, these preliminary results are likely to be affected by a 30%-40% overestimation, as explained in Section 3.5.



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Figure 3.11 : Theoretical Wind Potential at 50 m a. g.l.

3.7 CORRECTION OF THE SIMULATED WIND POTENTIAL In order to validate the theoretical wind potential calculated as illustrated in Section 3.6, we statically analyzed the measurements from a number of anemometric stations in place throughout the Montenegro territory (Section 3.7.1) as well as satellite data offshore (Section 3.7.2).

In particular, following the same methodology adopted for the realisation of the Italian Wind Atlas, the simulated wind fields will be corrected through the determination of a correction factor evaluated in correspondence of a number of geographical points where available observations of the wind velocity at 10 m a.g.l. will be considered reliable for the present purposes (Section 3.7.3).

3.7.1 Statistical Analysis of Onshore Anemometric M easurements

HIM collected wind speed and direction measurements from 24 stations throughout the territory of Montenegro for more than 20 years. Specifically, the Italian experts were provided with the following time series:

• one dataset for 18 out of 24 stations, hereafter DB1;

• two different datasets for 6 out of 24 anemometric stations, DB1 and DB2.

Among all the available anemometric stations, HIM suggested to use a set of eight representative stations: Zabljak, Pljevlja, Herceg Novi, Niksic, Bar, Podgorica, Kolasin, Ulcinj. Nevertheless, all datasets (DB1 and DB2) of each available station were analyzed. The wind data were measured at 10 m above ground level.



3.7.1.1 Statistical analysis of DB1 dataset

We performed a statistical analysis of dataset DB1, corresponding to the 24 HIM anemometric stations, in order to verify the reliability of the measurements. Most of the time series resulted to be almost completely empty as far as the measures of the wind speed are concerned, with the exception of eleven stations: the eight stations suggested by HIM plus the stations at Cetinje, Play, and Mojkovac.

Table 3.5 summarizes the main characteristics of these stations. Pljevlja, Herceg Novi, Niksic, Bar, Podgorica, Kolasin, and Cetinje have recording times up to 55 years, i.e. 1950-2005. Zabljak, and Ulcinj have recorded for 25 years, i.e. 1980-2005. Play and Mojkovac are characterised by shorter recording times as the corresponding time series are 7- (1999-2005) and 5-year long, respectively. Data are collected three times per day at 7 a.m, 2 and 9 p.m.

It is noted that most stations, with the exception of Bar and Ulcinj, show unrealistically high frequency of wind calms, as well as consequent very low values of the average wind speed. This outline is particularly unexpected at the stations located along the shoreline, as Bar and Herceg Novi. Therefore, further analyses were necessary in order to verify the data reliability. In particular, the following parameters were estimated:

• the joint frequency distribution of the wind speed and direction;

• the non-directional frequency distribution of the wind speed.

Table 3.5 : Main Data of the Anemometric Stations - DB1 Dataset

Station Latitude (°N)

Longitude (°E)

Height [m a.s.l.]

Wind calms [%]

Average wind speed [m/s]

Recording time [years]

Zabljak 43°09’ 19°08’ 1458 33.3 1.9 1980-2005

Pljevlja 43°21’ 19°21’ 784 69.3 0.5 1950-2005

Herceg Novi 42°27’ 18°33’ 18 50.6 0.8 1950-2005

Niksic 42°46’ 18°57’ 647 22.0 1.7 1950-2005

Bar 42°86’ 19°06’ 4 9.1 1.4 1950-2005

Podgorica 42°26’ 19°17’ 49 43.0 0.1 1950-2005

Kolasin 42°50’ 19°32’ 944 45.4 0.9 1950-2005

Ulcinj 41°55’ 19°13’ 24 6.1 2.0 1980-2005

Cetinje 42°24’ 18°56’ 655 64.7 0.5 1951-2005

Play 42°36’ 19°56’ 908 50.7 1.4 1999-2005

Mojkovac 42°57’ 19°35’ 790 21.9 2.6 2001-2005



As an example of the aforementioned statistical analyses, the results obtained for Pljevlja are analyzed herebelow.

The Pljevlja database of wind speed and direction extends from 1950 to 2005. Nevertheless, during this period, the wind speed data hardly rise up to 3 m/s for the first 30 years, while from 1981 the same data spread considerably over a wider range of values up to 20 m/s.

The wind rose, shown in Figure 3.12, presents a systematic “up and down” behaviour in the frequencies of occurrence, even though some directions were predominant. It is worth noting again the very high number of wind calms, which represents almost 70% of the total number of measurements.

Figure 3.13 also shows the probability frequency distribution of the wind speed, which presents an oscillating behaviour with respect to some preferential wind speed values. Obviously, these variations are not realistic, since they are usually related to human systematic errors during the measurement or data transcription to digital format.

Since the remaining stations show similar features, the considerations outlined above can be applied to the whole DB1 dataset. Therefore, the available datasets were not used for the validation of the simulated wind potential.

Figure 3.12 : Pljevlja Wind Rose based on the DB1 d ataset



Figure 3.13 : Frequency Distribution of the Wind Sp eed at Pljevlja Station – DB1 Dataset

3.7.1.2 Statistical analysis of DB2 datasets

As undertaken for DB1 (see pervious section), the statistical analysis of dataset DB2 was performed, corresponding to stations Pljevlja, Herceg Novi, Bar, Podgorica, Kolasin and Ulcinj.

Table 3.6 summarises the main characteristics of DB2 dataset. The aforementioned six stations have recording times between 20 and 23 years, from the beginning of 80’s to 2003-2004. Data are collected at every hour of the day.

Table 3.6 : Main Data of the Anemometric Stations - DB2 Dataset

Station Wind Calms [%]

Average Wind Speed [m/s]

Recording Time [years]

Pljevlja 21.5 1.2 1981-2003

Herceg Novi 5.9 1.5 1982-2004

Bar 0.7 3.0 1981-2003

Podgorica 9.3 2.0 1985-2004

Kolasin 32.1 1.1 1981-2004

Ulcinj 2.4 1.5 1984-2004

The wind calms have reasonable or at least acceptable values, whereas the average wind speeds are still rather low, especially for the coastal stations Herceg Novi, Bar, and Ulcinj.



This difference might be caused by a not appropriate location of the anemometric mast, with reference to the recommendations of the World Meteorological Organisation (WMO, 1996), which suggests to place the anemometric instruments in large flat open spaces, surrounded by short vegetation like grass, up to a radius of at least 1 km. Otherwise, the measured wind speed can be influenced by local roughness elements around the site like buildings, walls, trees, forests, etc., and therefore cannot be considered reliable.

Analogously to the previous section, in the following we report the joint frequency distribution of wind speed and direction as well as the non-directional frequency distribution of wind speed for Pljevlja.

Figure 3.14 shows the Pljevlja wind rose calculated with dataset DB2. The systematic up and down behaviour is not completely removed, even though more soft than DB1.

Therefore, also BD2 cannot be considered completely reliable.

As shown in Figure 3.15, the frequency distribution of the wind speed for DB2 is more similar to the classic Weibull distribution, widely used to represents wind data.

As the quality of the data of the other stations is similar to Pljevlja data, only three stations, (i.e. Pljevlja, Bar and Podgorica) were included in the calculation of the correction factor used to obtain the final maps of wind potential of Montenegro. The other three stations, characterised by average wind speeds significantly lower than the simulated values, were considered as outliers.

Figure 3.14 : Pljevlja Wind Rose based on the DB2 D ataset



Figure 3.15 : Frequency Distribution of the Wind Sp eed at Pljevlja Station – DB2 Dataset

3.7.2 Statistical Analysis of Offshore Satellite Me asurements

In addition to the inland anemometric measurements, also satellite measurements of the wind speed and direction offshore of Montenegro were used. More precisely, we analysed the sea surface winds derived from the SeaWinds scatterometer instrument on the QuikSCAT satellite (JPL, 2001; Bourassa et al., 2003; Tang et al., 2004), belonging to the National Aeronautics and Space Administration (NASA).

The SeaWinds instrument is a specialized microwave radar that measures near-surface wind speed and direction under all weather and cloud conditions over Earth's oceans. Measures are “neutral equivalent” wind speed and direction at 10 m a.s.l., that is wind is estimated from scatterometer data assuming a neutrally stratified atmosphere. The wind speed is measured from 3 to 20 m/s with an accuracy of 2 m/s, while wind direction has an accuracy of 20 degrees. The wind vector resolution is around 25 km.

The whole database (http://podaac.jpl.nasa.gov/) is available from July 1999 to present time. Over the Mediterranean, 2 data per day, at around 4 a.m. and 5 p.m., are available at the longitude of Montenegro. As an example, the data corresponding to three points offshore are reported in Table 3.7.

The values of average wind speed calculated at 10 m a.s.l. corresponding to the available points of QuikSCAT dataset within the computational domain are shown in Figure 3.16. Note that all values lie in the range between 5 and 6 m/s.



Table 3.7 : Examples of Offshore Wind Data from Sat ellite Measurements

Station Latitude (°N) Longitude (°E) average wind speed [m/s]

recording time [years]

QuikSCAT 41.375° 17.625° 6.1 1999-2006

QuikSCAT 42.375° 17.625° 5.8 1999-2006

QuikSCAT 41.375° 18.875° 5.8 1999-2006

Reference: QuikSCAT DATASET

18.0 18.5 19.0 19.5 20.0 20.5 21.0

Longitude (deg)

41.5

42.0

42.5

43.0

43.5

44.0

Lat

itude

(de

g)

4

5

6

7

8

9

10

11

12

13[m/s]

Reference: QuikSCAT dataset

Figure 3.16 : Offshore Average Wind Speed (calculat ed by QuikSCAT dataset)

3.7.3 Correction factor

The “correction factor”, Φ, introduced in order to estimate a more realistic wind potential. The primary source of information to build up the correction factor are the local ratios of the average wind speeds in correspondence to the anemometric stations (or satellite points)



calculated from the measurements, Vm(λ,θ), where λ and θ are the longitude and latitude of the station respectively, and simulated in the same position, Vw. Then, the correction factor Φ =Vm/Vw all over the computational domains is generated by means of suitable interpolation or extrapolation algorithms, taking care that Φ is smooth enough so that the physical coherence of the wind velocity field (mass conservation) is preserved and the vertical profiles of the wind speed predicted by WINDS are retained everywhere (Burlando et al., 2002b).

As already pointed out, the simulated wind fields are expected to be generally overestimated with respect to the measured data, because the simulations were performed under neutral stability conditions only.

The values of the correction factor Φ obtained from the DB2 dataset (stations Pljevlja, Bar, and Podgorica) and from QuikSCAT dataset (three grid points shown in Table 3.7), are reported in Table 3.8.

Table 3.8 : Correction Factors for DB2 and QuikSCAT Datasets

Station Measured mean Wind Speed [m/s]

Simulated mean Wind Speed [m/s] Φ

Pljevlja 1.2 2.8 0.43

Bar 3.0 6.3 0.48

Podgorica 2.0 3.9 0.51

QuikSCAT 6.1 8.5 0.72

QuikSCAT 5.8 8.5 0.68

QuikSCAT 5.8 8.5 0.68

As anticipated in Section 3.7.1.1, the DB1 dataset was considered not reliable and therefore was not included in the calculation of the correction factor. With reference to the DB2 dataset the stations Herceg Novi, Kolasin, and Ulcinj were not considered too, as the corresponding correction factors are around 0.2, and this value is too low to be accepted without any reserve. The limited reliability of the measured wind data was highlighted by the HIM representatives at the beginning of this study (presentation held in Podgorica on July19th, 2006). The QuikSCAT dataset was used entirely, since it presents offshore values in accordance with the previous estimates of the Italian Wind Atlas.

A careful analysis of the spatial dependence of the correction factors (see Table 3.8) reveals in the inland a variability which could be defective because of the low spatial representativeness of Vm values. Furthermore, these values are systematically too low with respect to the corresponding ones obtained for the Italian Wind Atlas. Moreover, it is worth noting that the correction factor of the wind potential (proportional to 3V , see Equation (3)) is the third power of the correction factor of the average wind speed, i.e. Φ3, so that decreasing the wind speed of 1/2 corresponds to reduce the wind potential to 1/8, and so forth.

Therefore, we interpolated the Φ values all over the computational domains, taking into account:

• off-shore, the values of the correction factor calculated for QuikSCAT dataset;



• on-shore, the values of Φ at the Pljevlja, Bar, and Podgorica stations, corrected in order to respect the range of values obtained for the Italian Wind Atlas.

The final result of the interpolation process, obtained trough the radial basis function algorithm, is shown in Figure 3.17. Note that the values of the correction factor outside the borders of Montenegro have no influence on the following results.

18.0 18.5 19.0 19.5 20.0 20.5 21.0

Longitude (deg)

41.5

42.0

42.5

43.0

43.5

44.0

Lat

itude

(de

g)

Pljevlja

Bar

Podgorica

QuikSCAT

QuikSCAT

QuikSCAT

100

300

500

700

900

1100

1300

1500

1700

1900

2100

2300

2500

[m]

Figure 3.17 : Correction Factor after Interpolation

3.8 ACTUAL WIND POTENTIAL The actual wind velocity, v', for each simulation can be calculated multiplying the corresponding wind field, v, by the correction factor, obtained in the previous section, so that v'=Φv. The average wind speed is then obtained through the following relation:

∑ ′=′i iivfV

Φ (4)

where fi (i=1,…,N=283) are the frequencies of the 283 wind velocities aloft, while v'i are the three-dimensional simulated actual wind fields. Analogously to Figure 3.10, Figure 3.18 shows the map of actual average wind speed at 50 m a.g.l. all over Montenegro. Note that the colour scale is the same for both the figures to allow a better comparison between the two maps.



Similarly, the actual average cubic wind velocity ca be estimated through the following equation:

∑ ′=′i iivfV 33

3Φ (5)

Finally, the actual theoretical wind potential, expressed in W/m2, is:

321 VAP ′= ρ (6)

18.0 18.5 19.0 19.5 20.0 20.5 21.0

Longitude (deg)

41.5

42.0

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43.0

43.5

44.0

Lat

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(de

g)

4

5

6

7

8

9

10

11

12

13[m/s]

Figure 3.18 : Actual Average Wind Speed [m/s] at 50 m a.g.l.

The actual wind potential of Montenegro at 50 m a.g.l. is shown in Figure 3.19 (2 maps). Note that the colour scale in the upper map is the same of Figure 3.11 in order to allow a better comparison between the two figures; in the map below, the colour scale has been modified in order to allow a better understanding of the wind potential variability.



18.0 18.5 19.0 19.5 20.0 20.5 21.0

Longitude (deg)

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42.0

42.5

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44.0

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(de

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400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

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2800

3000[W/m ]2

18.0 18.5 19.0 19.5 20.0 20.5 21.0

Longitude (deg)

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Lat

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(de

g)

50

100

150

200

250

300

350

400

450

500

550

600

650

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[W/m ]2

Figure 3.19 : Actual Wind Potential [W/m 2] at 50 m a.g.l.

Note: the top map has the same colour scale of the Figure 3.11, the bottom map has a more appropriate colour scale.



3.9 EXPLOITABLE WIND POTENTIAL The maps obtained applying the WINDS code, subsequently corrected with measured data, clearly show the windiest areas. They are located at the ridges and tops of mountain ranges (see Figure 3.18), with wind speeds gradually decreasing from the coast towards the East. Obviously, these areas are not entirely suitable to wind power exploitation, since some social, natural and financial constraints can occur. Therefore, in order to estimate the actually exploitable areas of Montenegro, the following constraints were taken into account:

• height above sea level;

• road network and railways;

• electric power supply system;

• natural or protected areas.

3.9.1 Constraints to Wind Power Exploitation

The areas at heights over 1,800 m a.s.l., shown in Figure 3.20, were excluded from the analysis considering that, at high altitudes, technological and economical constraints decrease the attractiveness of the investments on wind energy generation.

18.0 18.5 19.0 19.5 20.0 20.5 21.0

Longitude (deg)

41.5

42.0

42.5

43.0

43.5

44.0

Lat

itude

(de

g)

Figure 3.20 : Areas with Heights over 1800 m a.s.l.

The second constraint to be considered is the presence of roads and railways all over the country, as shown in Figure 3.21, in order to verify their closeness to the windiest areas.



Figure 3.21 : Road network and Railway Lines



The electric power supply system of Montenegro and the natural parks, respectively shown in Figure 3.22 and Figure 3.23, are the remaining constraints to be analyzed.

The national power supply system (see Figure 3.22) consists of four main voltages:

• 110-35 kV (yellow);

• 110 kV (orange);

• 220 kV (green);

• 400 kV (red).

Figure 3.22 : Electric Power Supply System

The national parks represent a strong constraint for the exploitation of the wind energy. Within the Montenegro territory, the following protected areas were identified:

• Skadar Lake;

• Lovcen;

• Biogradska Gora;

• Durmitor.



Figure 3.23 : Protected Natural Areas

3.9.2 Evaluation of the Exploitable Wind Potential

The above mentioned constraints were superimposed to the wind maps shown in Figure 3.18 and Figure 3.19. The results are shown in Drawing A.1 and Drawing A.2 in the Appendix A, respectively the actual average wind speed and the average wind potential at 50 m a.g.l.

It should be noted that most of high wind speed areas located in inner Montenegro lose their appeal due the high altitude of the mountains. Also in the remaining parts of the territory, the windiest areas are located on the ridges of the mountains. Most of these areas are not crossed by the existing road network. Therefore, investments for improving the road infrastructure (and power lines) should be necessary to allow transportation of wind farm parts, such as tower pieces and blades, to the construction sites. This means that, apart for few suitable locations, small turbines (in the range of 750-1000 kW), implying small equipment to be transported, should be chosen in most of the potential sites. On the other hand, in most of the suitable locations, big wind farms, consisting of ten or more mills, would be more suitable than single, stand-alone machines, due to a faster amortization of fixed costs for the infrastructures (nearly independent by the number of turbines) allowed by a bigger power production.

Electric lines and road network are well developed in the coastal areas and in the hills and mountains around Niksic. Since these zones are also densely populated and high energy



consuming, the wind energy development of these regions will be feasible in terms of capacity and stability of the national grid.

The protected natural areas in Montenegro do not offer interesting opportunities for wind power development. Windy ridges in the Durmitor and Biogradska Gora national parks are located at high altitudes and are not served by good infrastructure. The coastal area of the Scutari Lake is not very windy, and the development of “off shore” wind farms in the lake area is not feasible for economical and technical reasons. The Lovcen national park is the only protected area offering an interesting wind potential, due to the high wind speed and the good infrastructure network.

In conclusion the most interesting areas for wind energy exploitation in Montenegro are:

• Coastal areas – this area contains the most important wind resources of the country. In particular, the Rumlja range shows the highest wind speed of the country, considering the technical, economical and ecological constraints. Another very interesting area is located on the hills behind Petrovac, crossed by a main road and two 220 KV electric lines. Apart for the Lovcen massif, excluded for ecological reasons, other interesting areas are the mountainous zones behind Herceg Novi and Orahovac. All these areas show high wind speeds, with average over 6 m/s;

• Hills around Niksic – this region is characterized by average wind speeds in the range 5.5-6.5 m/s. The presence of road network and of energy lines allow to consider this area as a potential location for future wind energy generation. Moreover, the smooth slope of the hills represents an additional favorable characteristic for the potential realization of future wind energy generation plants.

3.10 TYPICAL FEATURES OF WIND POWER PLANTS This section provides a general overview of the typical features of wind power plants, with specific attention to the common big windmills employed in modern wind farms, excluding small scale windmills (the so-called microturbines) and uncommon, experimental machines. Among the various solutions developed in the pioneering age of modern windmills (1970-1980), nowadays a specific design scheme has a predominant role in the commercial wind power sector. Figure 3.24 shows the technical layout of a turbine (U.S. Department of Energy, 2006).

The typical mill has a horizontal three bladed rotor with variable pitch, mounted on a nacelle on top of a vertical tower. Blades are generally made of fibreglass reinforced with steel or carbon fibre elements. The rotor works counter wind, and the pitch is regulated in order to maximize power production at moderate wind speeds, and to maintain constant power output at higher speeds. The nacelle can rotate horizontally to fit the changes of wind direction. It hosts the power generator and often a gearbox to multiply the rotation speed at the generator’s shaft. The tower can be a steel or steel reinforced concrete mono-pile, or a steel lattice-type tower. Nearly all the older turbines have a synchronous generator, generating power in AC at the frequency of the power grid. This limits the rotation frequency of the generator shaft (and thus, those of the rotor via the gearbox) to fixed values. For this reason, most of the newest turbine models work in a different way: the generator is DC and the power is fed to the grid via an inverter that converts it in AC at the voltage of the grid. Some types of turbines employ axial flow generators working at very low rotation speeds, so that the gearbox is eliminated. While the generator is always in the nacelle, the remaining electrical equipment is hosted in the tower or in an electrical box close to the turbine’s



foundations. The typical size of turbines varies between 500 kW and 5 MW. Hub height varies between 30 and 160 metres, and rotor diameter between 30 and 120 metres.

Reference: U.S. Department of Energy

Figure 3.24 : Technical Layout of a Wind Turbine

As already underlined, wind kinetic power is proportional to the cubic power of wind speed. Not all of this energy can be recovered by a turbine, because this would imply that speed is zero behind the turbine. This is, of course, physically impossible. The theoretical limit for extracting mechanical energy from a wind flow, that is the efficiency of a device such as a wind turbine, is known as Betz limit and is around 59%. Moreover, a real turbine facing real problems of friction and of mechanical strength follows just partially the cubic curve, and in the best conditions has a wind-to-electric power efficiency between 40 and 45%.

Figure 3.25 shows the power curve of a commercial turbine with 850 kW nominal power, the Vestas V-52, as a function of the wind speed. The various curves correspond to different tolerable noise levels.



Figure 3.25 : Power Curve of a Typical Commercial W ind Turbine (Vestas V-52)

At very low wind speeds, power production is zero. The energy production starts at a speed comprised between 3 and 4 m/s. This value is called cut-in speed. As wind speed arises, the power curve follows a cubic shape. A real wind turbine is a compromise between mechanical strength, needed as the wind speed rises, and lightness, needed to lower frictions and to rapidly fit with wind speed variations, as well to avoid oversizing of the overall structure. On the other side, very high wind speeds, although meaning big power, happen with very low frequency, therefore their contribution to the yearly energy production is minimal. For these reasons, blades are designed for optimal power output with mild winds, and above a given value of speed (typically between 12 and 15 m/s) the pitch is gradually varied in order to maintain the power output at the nominal value. Thus, the power output remains approximately constant even at high wind speed, up to the so-called cut-off speed (between 25 and 30 m/s), at which the rotor is stopped for safety reasons.

As shown in the power curve, the power production strongly depends on wind speed, and as a consequence the average yearly power output is related to the mean wind speed. The ratio between the average wind power delivered by a plant installed in a certain location and the nominal power is called capacity factor. Wind power plants in Europe have capacity factors between 15 and 40%, although most of them are characterized by values between 20 and 30%. Of course, the economic feasibility of a wind power plant in locations with small capacity factors strongly depends on the local energy prices.

The typical cost of a windmill, including equipment, installation and civil and electrical engineering works is around 1 million Euro per MW nominal power, while yearly operation



maintenance costs are around 1% of the investment costs. The typical lifetime is usually estimated to be around 20 years.

Wind power plants can be made of only one turbine, but very often if the territory allows it, wind farms made of many turbines are built. In a wind farm with many windmills it is important to avoid mutual interference between them, so that usually a distance of 5-10 rotor diameters is kept in the dominant direction of wind and a distance of 2-3 rotor diameters is kept in the perpendicular direction. In mountain areas the dominant wind direction is typically that perpendicular to the ridges. As an example, a windy ridge of the coastal mountains could host one 1MW wind turbine (rotor diameter around 60 m) every 180 m, so that 5 to 6 turbines could be installed every km of ridge.

3.11 PRELIMINARY ECONOMICAL ANALYSIS FOR WIND POWER SYSTEMS In this paragraph we undertook a preliminary analysis of the economical aspects related to the practical installation of wind power plants in Montenegro. The economical feasibility of the investment mainly depends on two key factors: the energy purchase price and the capacity factor. It is noted that the investment does not significantly change with different wind conditions (although infrastructural situation of the site has a strong influence), since the main difference is given by in the obtainable revenues. Introducing the constraint of economic sustainability of the plant installation, the suitable areas for wind power production restrict to those with capacity factors – or, equivalently, actual wind potentials – above a given threshold. The lower is the power purchase price, the higher is the threshold, and consequently the lower is the exploitable wind potential.

In the following we assume an investment of one million Euro per MW nominal power, and a yearly operation and maintenance cost of 10,000 Euro (see previous section). An operational lifetime of 20 years is considered, while a discount factor equal to 8.5% is assumed (IMF, 2005; EBRD, 2005).

From the technical point of view, three different scenarios were considered, i.e. capacity factors of 20, 25 and 30%, roughly corresponding to average wind speeds around 6.1, 6.4 and 7 m/s at 50 m a.g.l. The correspondence between the average wind speed and the capacity factor slightly varies depending on the model of turbine. In our case study we considered the Vestas V-52 turbine. An on-line calculator of this relation for many commercial wind turbines can be found, for example, on the website of the Danish Wind Industry Association (http://www.windpower.org/en/tour/wres/pow/index.htm).

From a financial point of view, two energy selling prices were considered, on the basis of the unofficial information gathered at the Montenegrin Ministry of Economy: 0.045 €/KWh, as current tariff without any form of incentive, and 0.08 €/KWh, a possible future including State incentives for renewable energy production. As suggested by the Montenegrin Ministry of Economy, the incentive period was assumed equal to 10 years.

On this basis, the following 4 scenarios were considered in the analysis:

• Scenario 1: capacity factor of 30%, energy price without incentives (0.045 €/KWh);

• Scenario 2: capacity factor of 20%, energy price with incentives (0.08 €/KWh);

• Scenario 3: capacity factor of 25%, energy price with incentives (0.08 €/KWh);

• Scenario 4: capacity factor of 30%, energy price with incentives (0.08 €/KWh).

Table 3.9 summarizes the main economic parameters for the scenarios mentioned above.



Table 3.9 : Main Economic Parameters

Scenario 1 Scenario 2 Scenario 3 Scenario 4

Nominal Power (kW) 1,000 1000 1000 1000

Capacity Factor (%) 30 20 25 30

Discount Factor (%) 8.5 8.5 8.5 8.5

Energy Price (€/kWh) (*) 0.045 0.08 0.08 0.08

Investment Cost (€) 1,000,000 1,000,000 1,000,000 1,000,000

Yearly Production (kWh) 2,628,000 1,752,000 2,190,000 2,628,000

Yearly Power Revenues (€) 118,000 79,000 98,000 118,000

Yearly Incentives Revenues (€) (**) - 140,000 175,000 210,000

Yearly Maintenance Costs (€) 10,000 10,000 10,000 10,000

Internal Rate of Return (IRR) (%) 8.8 9.4 13.7 17.8

Net Present Value (NPV) (€) 25,000 54,000 340,000 630,000

Pay Back Period (PB) (years) 19 17 9 7

(*) Scenario 2, 3 and 4 are based on an energy price including possible future incentives

(**) For the first 10 years of the operational lifetime

Scenario 1 is based on the current energy price without incentives and good wind conditions (capacity factor of 30%), with speed around 7 m/s. Although positive technical settings, the low revenues implies a long payback period (19 years). A more attractive payback period of 10 years can be obtained with a capacity factor of 42%, corresponding to average wind speeds above 8.5 m/s. It should be noted that very few locations in Montenegro can offer such profitable wind conditions, (also in the windiest site, the Rumlja range), therefore without incentives the financial feasibility of the investment will be hardly guaranteed.

In Scenarios 2, 3 and 4 we assumed a future possible energy price including incentives for renewable energy production (0.08 €/KWh), associated with capacity factors of 20, 25 and 30%.

The preliminary financial analysis estimates payback periods ranging from 7 (Scenario 4) to 17 (Scenario 2) years. Assuming an operational lifetime of 20 years or more, we can consider 7 years as an acceptable payback period for private investors, while a timeframe of about 9 years should be suitable for public entities, such as EPCG or other public companies or institutions. Moreover, it should be considered that the baseline price of electricity is likely to increase in the next years, providing additional guarantees to the financial institutions interested to stable, middle-term investments.



On this basis, and under the general assumptions of a maximum payback period equal to 10 years, we can estimate two wind power production cases:

1. High Productivity Potential, considering areas where the capacity factor is above 30%; 2. Medium Productivity Potential, considering areas where the capacity factor is above

25%.

High Productivity Potential . These areas are very limited and concentrated in the coastal mountain ranges. The broadest areas are the highest ridges of the Rumlja chain, the Mount Lisinj (close to Bar) and the Lovcen National Park. Some smaller zones are present in the Lodvica range above Sutomore, on the crest above Petrovac, on that north-east of Kotor, on the tops of mount Subra and Orjen close to the border with Bosnia. In the inland, only the tops of mount Velji Garac (west to Danilovgrad), and a small part of the Njegos ridge (west of Niksic) show wind speeds above 7 m/s. Altogether, these ridges cover a length of approximately 80 km and assuming that about 25% of the overall extent can be used, due to the infrastructural and natural constraints, around 20 km of crests could host wind turbines. Under this hypothesis, as mentioned in the previous section, about 5 turbines rating 1 MW or 6 Vestas V52 rating 850 KW nominal power could be installed, resulting in 5 MW/km. This means that under the above mentioned assumptions the overall potential wind power would be of 5*20 = 100 MW. Being the capacity factor equal to 30% or more, an average power production around 30 MW can be estimated, corresponding to a yearly production of 265 GWh/year. This energy output would be approximately 6-7% of total yearly power consumption of Montenegro, considering a gross power consumption of 6,500 kWh per inhabitants per year (IPA, 2005).

Medium Productivity Potential . Considering a wider range of capacity factor, i.e. minimum equal to 25% (wind speed > 6.4 m/s), the extent of the suitable areas for wind power installation increase significantly. More specifically, the whole coastal area from the Rumlja range to Kotor’s gulf become attractive for wind energy production, and the overall zone between Orjen and Mount Subra can be considered interesting. In the inland, a larger portion of the Njegos chain and of Velji Garac appears suitable for wind power potential. The long ridge of the Golja Range (North West of Niksic), as well as other small spots around Podgorica (Kamenik, Prekornica, Mount Humorahovski) and Niksic (Vojnik mountains, Mount Ruzica) are also suitable areas. Under the same assumptions considered for the High Productivity Potential, the overall gross wind power could increase up to approximately 400 MW, 100 MW deriving from high productivity areas and 300 MW from medium productivity areas. The net potential power output was estimated considering a capacity factor of 30% for the high productivity areas (100*0.3=30MW, i.e. 265 GWh/year) and a capacity factor of 25% for the remaining medium productivity zones (300*0.25=75MW, corresponding 660 GWh/year). Through this approach, the potential wind energy generation is estimated to be approximately 925 GWh/year, equal to about 20-25% of total yearly energy consumption of Montenegro, considering a gross power consumption of 6,500 kWh per inhabitants per year (IPA, 2005).

According to the rough estimation illustrated above, the Republic of Montenegro shows a wind potential of 100 MW considering only the windiest areas (wind speeds above 7 m/s) and an overall potential of 400 MW taking also into account the zones with medium potentiality. This potential energy output could provide up to 20-25% of the yearly power consumption of the Country.

It should be noted that our analysis is based on safe assumptions, and the actual wind speeds are likely to be higher than the estimated values. Moreover, the turbine towers height are



referred to an hub height of 50 m while, by using higher towers, it would be possible to exploit stronger winds, obtaining as a consequence higher capacity factors.

On this basis, the technical exploitable potential could be even higher, even though the poor infrastructure network in the most useful areas could be a serious constraint for wind energy generation. Anyway, taking into consideration that the preliminary anemometric measurements have an average duration of 1-2 years and the plant installation is quite fast, wind farms with a suitable power potential could become a reality in few years, if a new regulatory and incentivation framework will be approved.

Figure 3.26 shows the wind speed intervals and the corresponding capacity factor intervals considered in the overall wind power potential evaluation. As already pointed out, the assumed correspondence between wind speed and capacity factor is referred to the Vestas V-52 turbine.

Figure 3.26 : Wind Speed - Capacity Factor Ranges ( Vestas V-52 turbine)



4 SOLAR ENERGY ASSESSMENT

The energy from the Sun that keeps our planet warm exceeds by far the current primary energy supply used by mankind for its comfort, leisure and economic activities. It also exceeds vastly other energy sources at ground level such as geothermic or tidal energy, nuclear power and fossil fuel burning. Sunrays also drive hydraulics, wind and wave power and biomass growth.

Solar energy is thus the primary energy source on our planet’s surface - and exceeds 8,000 times our primary energy supply.

The drawbacks are well-known: the solar radiation reaching the earth is very dilute (only about 1 kWth per square meter), intermittent (available only during day-time), and unequally distributed over the surface of the earth (mostly between 30° north and 30° south latitude).

Various technologies, however, can be used to overcome the difficulties in making sunlight a usable form of energy for all purposes.

Solar “thermal” energy designate all technologies that collect solar rays and transform their energy into usable heat, either for directly satisfying heating needs (notably space heating, water heating – and space cooling) or for producing electricity and fuels. The latter includes concentrating solar power technologies, and other concepts such as solar updraft towers and ocean thermal energy. This report considers only direct forms of solar energy usable for low temperature use such as space heating and preparation of hot water.

Within this study, the solar potential of Montenegro was assessed on the basis of ground based and satellite data. Unfortunately, a correlation between both kinds of data was not scientifically justified as ground based data were incomplete. In a second part, the solar thermal technology (low temperature applications) is presented in terms of technique, applications and costs.

In order to know if such a technology is suitable for Montenegro, two case studies were performed, one aimed at households and the other aimed at the tourism sector, one of Montenegro’s most important source of revenues. While energy production levels are high, economic savings do not exist because of the high investment costs and because of the very low price of energy in Montenegro. Minimum price levels for economic viability are given.

In a last section, various prospects for the future development of solar energy are stated with strengths and weaknesses of the country.

The relevant conclusions of the solar energy resource assessment are reported in Section 6.

4.1 METHODOLOGICAL APPROACH The estimation of the solar thermal energy potential of a country is somewhat different from other studies. Indeed, solar thermal energy for low temperature applications is a market driven energy dedicated to individuals or industries in order to help them saving heat and thus energy. In other cases, such as wind or photovoltaic energy, most projects in Europe are grid-connected and are considered as private power producers as they generate some power that will then be sold to the grid manager. As a consequence, this difference prevents the calculation of the total area that can be covered by solar thermal collectors and how much energy can be drawn from them – a common case for Photovoltaic for instance. In the case



of solar thermal energy, the potential is dependant on the demand as the heat produced has to find a local use.

In addition, the system has to bring a benefit for the user. Experience shows that there is always a solar thermal market as soon as the final user, and buyer of the system, immediately benefits from the system.

As a consequence, our approach was based on the determination of the benefits for the users and the actions to take in order to create them or emphasize them.

In details, our methodology was built upon the following steps:

• analysis of the solar resource and comparison with surrounding countries (Section 4.2);

• analysis of the situation of solar thermal energy in Montenegro (Section 4.3);

• evaluation of the main market sectors for solar thermal energy in Montenegro (Section 4.4);

• estimation of the technical and economic performance of solar thermal systems for the main market sectors in Montenegro, in terms of savings and benefits for the users (Section 4.5-4.7);

• evaluation of the future development of Solar Thermal in Montenegro and proposition of actions to be taken (Section 4.8).

4.2 SOLAR POTENTIAL

4.2.1 Climate Overview

Montenegro’s topography is very rough and mountainous. Indeed, most of the country (60%) lies at an altitude of more than 1000 m. In the centre and in the south east, there are some flat areas where the capital city is located, while in the very south is the coastal region along the Adriatic Sea.

The contrasts in the land (topography) are related to climate. While the Mediterranean climate prevails at the seaside, in the closest hinterland, only few air-distance kilometers away, the dominant climate is continental. The Dinaric range of mountains (Orjen, Lovcen and Rumija) rises steep above the coast, and just like a gigantic backdrop largely prevents the penetration of the Mediterranean climate into the inland. At the seaside region the average July temperatures are between 23.4ºC and 25.6ºC. Summers are usually long and dry, winters short and mild. Along the valley of the Bojana River, over the basin of Lake Skadar and upstream the Moraca, waves of intense heat penetrate to Podgorica, making it the warmest city in Montenegro, and one of the warmest in the Balkans.

In central Montenegro, in the regions of Zetska and Bjelopavli ka plains, July temperatures are 26.4ºC (in Podgorica), and 25.4ºC (in Danilovgrad). The absolute maximum can sometimes reach 40ºC. The average January temperatures are around 5ºC, with the absolute minimum is -10ºC.

In the region of high limestone mountains, the climate is typically sub alpine – with cold, snow-abundant winters and moderate summers. While along the Montenegrin seaside and in the basin of Lake Skadar snow is a rarity, on Mt. Durmitor it can fall up to five meters.

In the northern parts of Montenegro, and particularly in the high mountains, due to low evaporation, the snow remains for several months and sometimes even over the whole year.

In fact, the Montenegrin climate can be divided in three zones:



• mountainous region in the north where the climate is continental with cold winters and hot, humid summers with well distributed rainfall;

• central portion or plains, with a continental and Mediterranean climate;

• coastal region with an Adriatic climate resulting in hot and dry summers and autumns and mild winters.

The climate data for the coastal and mountainous regions is shown in Table 4.1 and Figure 4.1.

Table 4.1 : Climate Data for the Coastal and Mounta inous regions

Coast Mountains

Min. °C Max °C Sunshine Hours Min. °C Max °C Sunshine

Hours January 4 12 116 -7 2 84 February 5 12 119 -6 4 96 March 7 15 165 -3 8 137 April 10 19 194 1 12 159 May 14 22 254 5 18 192 June 17 26 289 8 21 204 July 19 29 338 9 23 162 August 19 29 312 9 24 246 September 17 26 248 6 20 194 October 13 22 192 2 15 159 November 9 14 121 -1 9 97 December 6 13 106 -5 3 75

Figure 4.1 : Temperature Profile for the Coastal an d Mountainous Regions

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4.2.2 Solar Radiation Data

4.2.2.1 Ground Based Data

Database

Three sets of ground based data collected by HIM were made available. These data cover different periods and needed to be reviewed and evaluated before they could be used.

The available ground based data came from three meteorological stations located in Bar, Podgorica and Zabljak.

As the exact coordinates of the stations were not know, it was decided to estimate them based on the NASA Coordinates System Centre, as summarized in the table below.

Table 4.2 : Estimated Coordinates of the Meteorolog ical Stations

Station Longitude Latitude

Bar 19.08 42.08

Podgorica 19.28 42.45

Zabljak 19.23 43.12

Note: (WGS 84 system)

The measured data (Excel files) are a collection of tables, one for each year, with entries for months (January to December) and days (1 to 31). Each cell of each table represents then a particular day (referenced with day, month, year) and was filled with the cumulated amount of solar global radiation received by the measurement devices. For each station, two different units systems were used: cal.cm-2 (1977-1979) and J.cm-2 (from 1980)1. For the latter, for the station close to Podgorica, from the mid-eighties the unit was given in WRR(J) but was in reality J.cm-2 (Table 4.3). This change in unit is likely due to a change in instrumentation.

No information was given about the measurement instruments, therefore their technical specifications, accuracy and ranges are unknown. For one station, an example of the data recording was given (see Figure 4.2).

Figure 4.2 : Example of Trace Recorder for the Meas ured Solar Radiation

1 The units should have been cal x (cm-2.d-1) and Jx (cm-2.d-1)



Data recording is done via a trace recorder on paper and the values are then read by an operator and finally collected in tables for every hour of the year. It seems that this method was used all the time in all three stations. But as a matter of fact, this is not exactly known.

Although covering a period of time of several years, the data sets were considered as incomplete as many months were missing and even years in some cases. Generally, many days were missing for a majority of months. This has certainly a non-negligible influence on the quality of the database, various months being deviated on one side or another. As the distribution of the missing data is not uniform over the years and seasons, the different lacks of data do not counterbalance each other.

Table 4.3 : Available Ground Based Solar Data

Station Type of Data Period Unit

Bar 1/1980 – 10/1990 J.cm-2

12/1977 – 12/1979 Cal.cm-2

1/1980 – 12/1983 J.cm-2 Podgorica

1/1984 – 12/1986

+1-7/1990 WRR(J)

9/1977 – 12/1979 Cal.cm-2 Zabljak

Global Solar Radiation, summed per day

1/1/1980 – 12/2005 J.cm-2

Accuracy

It is doubtful that the ground based data has a good accuracy. Indeed, as stated before, poor information has been delivered regarding the equipment and methodology. In addition, the recording of the data is performed thanks to a trace recorder and the reading of an operator. The tracing of the recorder brings already a large inaccuracy (width of the pen, placement of the paper, calibration, offset, low accuracy of the machine itself, etc.), increased by the reading of the operator whose error can be estimated at 5% on Figure 4.2.

Reliability

The reliability of this database comes here into question because of the lack of data (months and years missing) and the lack of information regarding the measurement themselves.

4.2.2.2 Satellite Based Data

Database

The solar maps are based on the Helioclim-1 (HC-1) database. This database has been developed by the Télédétection & Modélisation group at the Centre d'Energétique of Ecole des Mines de Paris - Armines in France. The team of scientists have produced data of solar radiation, namely databases and time-series of irradiance or irradiation. These databases are produced by the processing of satellite images, especially from the Meteosat series of satellites. The databases are called HelioClim.

Presently, the database HC-1 is available. It is made of daily irradiation (daily sum of irradiance), covering Europe, Africa, the Mediterranean Basin, the Atlantic Ocean and part of the Indian Ocean. Period runs from 1985 onwards. The Meteosat data are routinely received from Eumetsat and processed every month by using the method Heliosat-2.



Accuracy

The current values of the HC-1 database are calculated with a good accuracy thanks to years of research and development. As of now, the global accuracy is very close to the kind of uncertainty that can be found between two measurement devices installed at the same site.

The table below presents results of a comparison made between HC-1 data and measurements of the World Meteorological Organization (WMO) radiometric network in Europe (55 sites) and in Africa (35 sites) during the period 1994-1997.

As shown in Table 4.4 the Root Mean Square Error (RMSE) of the data does not exceed 15% which is a good value for solar radiation measurements. As a matter of fact, satellite based solar databases have been considered as reliable and accurate for some years now and are used daily for the implementation of many projects around the world.

Table 4.4 : Comparison between Satellite and Ground - based Data

Mean value Bias RMSE

Daily Values 4848 Wh/m2

17453 kJ/ m2

202 W/m2

24 86 1

840 3024 35

5-days Sums 22.6 kWh/m2

81.5 MJ/m2 0.1 0.4

3.2 11.6

10-days Sums (pentads)

45.4 kWh/m2

163.3 MJ/m2 0.2 0.7

6.0 21.7

Monthly Sums 145 kWh/m2

497 MJ/ m2 0.7 2.6

17 60

Resolution

The original resolution of the HC-1 database is given at 10 km meaning that values are valid for pixels of 10 x 10 km in size.

The units given in the maps and colours are given in Wh per m² and per day : Wh/m² d.

4.2.2.3 Data Correlation

Nowadays, the assessment of solar energy requires the use of ground based and satellite data, in order to regroup the accuracy of ground based measurement with the availability of satellite data for all points of a territory. This was the method used in this study for Montenegro.

A correlation between the 2 sets of data requires the calculation of correlation factors by using the ground base of data and the satellite data for the same coordinates. Then, using a large number of factors, it is possible to rescale the values for the mapping of a complete territory or better, a series of factors for different areas, by interpolation mainly.

Unfortunately, in the present case, the only three data sets available were incomplete and with an unknown reliability. In addition, as the correlation process has to cover the same period of time for all stations, many years of data were to be discarded, making the database even less complete. The overlapping with the HC-1 database was therefore very short.



Nevertheless, a correlation process has been tested and led to the following results as shown in Figure 4.3. The data from the different stations do not correlate the same way with the satellite data and exhibit very large deviation factors, mostly between 10% and 20% in absolute terms. Certain values even exceed 20%.

As HC-1 database runs over 20 years and is known for its reliability, the procession of a correlation of HC-1 with the terrain data would have been a regression in terms of accuracy and visibility of long-term effects.

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Figure 4.3 : Average Deviation - Satellite and Grou nd Based Data

As the ground based data sets do not correlate similarly with the satellite data, the calculation of a unique correlation factor is impossible. Therefore, this process should have been performed by calculating separate factors and by applying them to the ground based in association with an interpolation. However, as the influence of the factors decreases with an inversely proportional relation to the distance, this would have led to the creation of flakes on the solar maps. These flakes would have then be centered on the three points where the stations are.

As a result, solar mapping was processed by using satellite data and local calculation by using ground based data after they were “cleaned” and thoroughly evaluated. Only the most complete time periods of the ground based data sets were used.

4.2.3 Solar Mapping

4.2.3.1 Methodology

As stated in the previous section it is considered that processing a correlation between ground based data and satellite data makes little sense both in statistical and physical ways. Therefore, the generation of solar maps which can be used as shape files for GIS systems was based on the HC-1 database only.



4.2.3.2 Solar Maps

The solar maps were produced with the values of global radiation on a daily basis. As it would be impossible to generate maps for every day and as the goal of the study is to draw trends and general conclusions, 13 maps were produced, as follows:

• one map representing the daily solar radiation averaged over a year. This map allows the user to quickly know what is the average daily radiation value over Montenegro;

• twelve maps of daily solar radiation averaged over a month. These maps are more accurate and allow to obtain values for every typical month of the database. They allow the perception of seasonal trends and minimum and maximum values.

The coordinate system was chosen as WGS 84 in order to keep up with international standards.

All the solar maps outlined above (high-quality georeferenced GEOTIFF format) are showed in Figures B.1 ÷ B.13 in the Appendix B.

4.2.4 Analysis of the Solar Resource

Due to the size of the country, great differences in average solar radiation cannot be observed, especially in the annual average solar radiation (see Appendix B – Figure B.13). However, different trends can be drawn from the data collected for the surrounding countries.

Montenegro shows a very good potential for solar energy systems, since the annual number of sunshine hours is more than 2,000 hours for most part of the country and even 2,500 along the coast (HIM, 2006). Indeed, Montenegro has one of the greatest solar energy potential in the South-Eastern Europe. According to the Meteonorm database, it ranks above its neighbours, as showed in Table 4.5 and Figure 4.4.

Table 4.5 : Solar Radiation for the Major Cities in the Balkan Region

Country City / Station Annual Amount of Solar Energy in kWh/(m².y)

Montenegro Podgorica 1602

Serbia Belgrade 1336

Macedonia Skopje 1368

Croatia Zagreb 1209

Greece Athens 1564

Bosnia and Herzegovina Sarajevo 1263

Italy Rome 1561

Albania Tirana 1562

Reference: Meteonorm, 2006



Reference: Meteonorm, 2006

Figure 4.4 : Map of Solar Radiation in Eastern Euro pe

As stated in Section 4.2.1, Montenegro can be divided in three main regions:

• Coastal in the south;

• Central plains;

• Mountainous regions in the north.

The coastal regions enjoy more than 2,500 hours of sunshine a year2 and a very high level of solar radiation in the summer, late spring and early fall.

The plains also exhibit a large number of hours of sunshine3 but somewhat lower than on the coast. In winter, this region receive a similar amount of solar radiation to that of the coast but relatively lower in summer.

The annual sunshine hours in Montenegro are shown in Figure 4.5.

2 Ulcinj : 2559 hours of sunshine per year (average 1970-2000), Bar : 2543 hours of sunshine per year (average

1970-2000) 3 Podgorica : 2479 hours of sunshine per year (average 1970-2000), Niksic : 2223 hours of sunshine per year

(average 1970-2000)



Figure 4.5 : Annual Sunshine Hours

The major difference lies between these two first regions and the mountainous regions of the north where sunshine can be scarce4. Indeed, the number of hours of sunshine is much lower and the solar irradiance drops in summer compared to that of the plains and the coast. In winter, it seems that the amount of solar energy is larger as it can be seen on the maps.

The ground based data measured by the three meteorological stations (see Section 4.2.2.1) were plotted for the period 1980-1986. The results are shown in Figure 4.6, in terms of average daily solar radiation per month.

After this assessment, it appears that the theoretical solar potential of the coastal and central regions of Montenegro is high. In fact, the amount of solar radiation is comparable to that of Greece and Southern Italy where solar thermal systems are widely used.

From a technical point of view and based on this potential, the use of solar thermal energy in Montenegro is recommended.

4 Pjevlja : 1587 hours of sunshine per year (average 1970-2000), Kolasin : 1741 hours of sunshine per year

(average 1970-2000), Zabljak : 1925 hours of sunshine per year (average 1971-2000)



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Figure 4.6 : Average Daily Solar Radiation 1980 - 1 9865 – Ground-based Data

4.3 SOLAR THERMAL ENERGY At present, solar heating provides by far the largest solar contribution to energy needs. The main technologies belong to either “passive” and “active” solar energy forms. Passive solar energy relates to the design of buildings collecting and transforming solar energy used for passive heating, day lighting and natural ventilation. Active solar energy relates to the use of solar collectors for water or space heating purposes, active solar cooling, heat pumps, desalinization and industrial high temperature heat.

Solar thermal energy covers a wide range of applications. In the present report, two main applications are presented: passive solar architecture and active solar heat. Passive solar architecture will be only presented on the general side, while active solar heat will be presented and applied to different cases.

4.3.1 Passive Solar Energy

Passive solar energy does not show up in energy statistics as collecting data would be hugely expensive, requiring close building by building examination. Passive solar energy is usually considered from the demand side as part of energy savings potential rather than from the supply side. Through a combination of a high-performance thermal envelope, efficient systems and devices, and full exploitation of the opportunities for passive solar energy, 50 % to 75% of the energy needs of buildings as constructed under normal practice can be either eliminated or satisfied through passive solar means. In the industrialised world, buildings use 35 % to 40 % of total primary use of energy. Letting the sun heat buildings in winter, and letting daylight enter them to displace electric lighting, are the least cost solar energy forms.

5 This plot can only be taken as an indication as many months were missing



4.3.1.1 Current situation in Montenegro

The level of energy efficiency in Montenegro is low and therefore also the use of passive solar architecture (United Nations Economic Commission for Europe, 2002). There have been no incentives for saving energy and there are no energy-saving programmes. Indeed, houses and apartments are the largest source of household energy consumption and the introduction of solar architecture techniques would allow great savings in terms of energy and pollution.

The abandonment of traditional building practices since World War II has resulted in a decline in housing quality, with accompanying increases in energy inefficiency. In addition, newer houses are larger than older ones, are not positioned to take advantage of the energy-conserving characteristics of their natural surroundings and are less suitable for the efficient provision of energy services.

Following the 1973 international energy shock, the flow of international financing to Yugoslavia increased, spurring a boom in housing construction. Houses were built based on the promise of cheap energy. This pattern continued in the 1980s. The combination of relatively inexpensive energy and building materials (bricks, cement and concrete) and a lack of knowledge led to the construction of houses with poor thermal characteristics (see Figure 4.7). Low-quality doors and windows were used, and ventilation or more sophisticated systems were often not incorporated at all.

Figure 4.7 : Communist-era Buildings Figure 4.8 : Typical houses

Therefore, in Montenegro heating houses not connected to the district heating system, requires more than 200 kilowatt-hours (kWh) of energy per square meter per winter on average (see Figure 4.8). Apartments require less energy per square meter than houses, since the area of outer walls and roof per dwelling is smaller. District heating companies are able to heat apartments using just 115 kWh per square meter during the winter (still much higher than the 50–80 kWh used to heat apartments in Northern Europe, where the winters are longer and more severe than in Montenegro). The use of solar architecture techniques, together with energy saving procedures, would help to reduce these numbers of



consumption. The share of income spent by households in space heating would be then greatly lowered and injected in the local economy.

4.3.1.2 Technology Overview

Passive solar heating (see Figure 4.9) can involve extensive sun-facing glazing, various wall- or roof-mounted solar air collectors, double-façade wall construction, air-flow windows, thermally massive walls behind glazing, or preheating of ventilation air through buried pipes.

Lighting and ventilation can be directly supplied through solar energy: interior light through a variety of simple devices that concentrate and direct sunlight deep into a building, and ventilation through the temperature and hence pressure differences that are created between different parts of a building when the sun shines.

Figure 4.9 : Typical Solar House

Very often traditional materials and knowledge can be a great source of inspiration for designers and architects. This may be especially true in hot climate where a reasonable level of comfort can be provided with a variety of energy-efficient devices high-inertia materials to avoid using air-conditioning systems.

This does not preclude, however, the use of modern softwares or up-to-date materials, with emerging new standardised materials for walls, doors and windows. For example, spectrally selective windows can maximise sunlight to replace lighting while minimising increased cooling requirements from solar radiation. Electro-chromatic windows have small voltages that allow the window to change from a clear to a transparent coloured state, or vice versa. They can minimise both winter heating requirement and summer cooling needs. Another technology under development is thermo chromatic glazing, which automatically permits penetration of solar radiation only when heating is desired, eliminating the need for sensors.



Passive solar architecture often uses specific tools for the simulation of the thermal behaviour of buildings. Thermal simulation usually requires a lot of expertise and a good understanding of building physics. There are two categories of tools which can simulate the thermal behaviour of buildings and their energy performance.

Simple interface-driven tools can be used to get a rough idea of the expected building performance, even though the results must be seen as a preliminary estimation of the performance of a real building under real conditions. Examples of such tools include: Summer2, NHER and Seri-Res.

More complex softwares often use text files as input, and if there are graphical user interfaces, they can be rather crude. Owing to the complexity of the problem, more input data is required, and the model requiring a great expertise from the operator: the model can become significantly elaborated. The following tools fall under this category: ESP-r, Transys and Apache.

4.3.1.3 Simulation methods

Simulation methods employ weather data (either from a weather tape or generated internally) on an hourly basis to calculate heat and or ventilation flows through a building. They therefore generally require greater computing power and complexity of operation than steady state methods. The existing models can be divided in easy-to-use:

• COMFIE (French, Armines Ecole de Mines), low cost;

• CLA (CIEMAT), Spain;

• S3PAS (CIEMAT), Spain;

• TAS (UK) models heat and ventilation flows using a simple graphical user interface (GUI) to predict heating and cooling loads and temperatures-Commercial and standard professional program;

• IES (UK) a family of computational methods for thermal, ventilation, radiation used for plant sizing, energy load, temperature and comfort calculations;

and complex:

• ESP-r: a research based program available at no cost;

• Energy+ based on DOE2 and BLAST: the LBLL, US Department of Energy programme, available at low cost.

4.3.2 Active Solar Heat

Small scale, low temperature solar thermal systems can supply heat for domestic hot water and space heating in residential, commercial and institutional buildings, schools, hotels, swimming pools, crop drying, industrial process heat, desalination plants and solar-assisted district heating. The main collector technologies include unglazed, glazed flat plate and evacuated tubes. The technology may be considered mature but continues to improve. Aluminium, being cheaper and lighter than copper, is being used in manufacturing absorbers. Soldering and ultrasonic welding are now the most common way of assembling absorbers but laser welding technology makes it possible to have a perfectly smooth absorber surface and obtain a homogenous colour.

Active solar thermal system are virtually used in every country of the world at different levels of sophistication. Even in countries far north or south with very low levels of solar radiation, solar thermal systems can be found. This technology has been proven reliable and



efficient for decades and is now gaining much interest because of the rise in energy prices and increasing awareness of global warming.

4.3.2.1 Current Situation in Montenegro

In Montenegro, solar energy represents an important energy potential still insufficiently utilized for local needs. The solar energy use in comparison with other European countries is low, and limited to heat production, mainly for hotels and buildings in the coastal region of the Adriatic Sea (Recover, 2005) Solar thermal energy was relatively widely used on the seaside of Montenegro before 1990, mainly for the production of sanitary hot water for hotels and residential and military premises. According to the information collected, there was a large production and installation of solar thermal systems in the Balkans during the 80’s thanks to political line of action, in response to the oil crisis. This political action disappeared after the collapse of the communist regimes.

Different reports indicate a large numbers of collectors installed in the former Republic of Yugoslavia6 over the past decades, ranging between 250,000 and 300,000 m².

According to the available information at the Montenegrin Ministries, the current area of installed collectors in Montenegro is about 11,000 m2, with approximately installed power equal to 5,500 kW (IPA, December 2005). Other sources (RENEUER, 2005) confirmed that the total area of installed collectors reached 10-15,000 m2 in the 90’s.

Considering the theoretical number of 11,000 m², and the current population, it was estimated a ratio of about 17 m² every 1,000 inhabitants. Since usually countries can reach several hundreds m²/1,000 habitants (Austria, Greece, German, Cyprus, etc.), Montenegro has got the potential for the installation of a great number of collectors.

A solar thermal systems manufacture known as “Elastik Solarni Kolektori” used to be located in Podgorica but it was shut down few years ago. The former Republic of Serbia-Montenegro used to have total of 9 collector manufacturers7; they had difficulties keeping on producing during the last years and it is unclear if they all survived the recent crisis.

No production capacity remains in Montenegro, however there is still some expertise in Serbia, especially with the company Nissal, an important aluminium product manufacturer. Nevertheless, skilled manpower is still present in Montenegro, since a large numbers of technicians and engineers were trained in the firm formerly in Podgorica, therefore is present the necessary workforce for any solar project in Montenegro.

Although the internal production is over the distribution of solar collectors still continues with import of products from Nissal (Serbia), Riesol (Germany) or Centrometal (Croatia)8.

The public prices of collectors were collected and are shown in Table 4.6.

6 Unclear if it only concerns Serbia and Montenegro 7 It is unclear if this number comes from the Communist-era or not 8 Nissal is probably the only real producer



Table 4.6 : Examples of Public Prices for Solar Col lectors in Montenegro

Producer / OEM Area Price Price per m²

Centrometal (Croatia) 2 m² 480 € 240 €

1,5 m² 300 € 200 € Nissal (Serbia)

2 m² 400 € 200 €

Riesol (Germany) 2 m² 550 € 275 €

Prices for Centrometal and Riesol are common prices that can be found in Western Europe for German or Austrian products. Nissal supplies less expensive collectors because of their lower quality and lower level of technology. The lower standard of living of Montenegro compared to its richer neighbours does not seem to influence prices as most products are imported. This is a common pattern in the solar business.

4.3.2.2 Technology Overview

Solar heating systems are generally composed of solar thermal collectors, a fluid system to move the heat from the collector to its point of usage, and a reservoir to stock the heat for subsequent use. The systems may be used to heat domestic hot water or a swimming pool, or to provide heat for a heating circuit. The heat can also be used for industrial applications or as an energy input for other uses such as cooling equipment.

In many climates, a solar heating system can provide a very high percentage (50 to 75%) of domestic hot water energy. In many northern European countries, combined hot water and space heating systems provide 15-25% of the total home heating energy.

The residential solar thermal installations can be subdivided in two kind of systems: compact and pumped. Both systems include an auxiliary energy source (electric heating element or connection to a gas or fuel oil central heating system), activated when the water in the tank falls below a minimum temperature, usually equal to 50°C. Hence, hot water is always available.

In order to heat water using solar energy, a collector is placed on - or forms - the roof of a building, or on a wall facing the sun. In some cases, the collector may be free-standing. The working fluid is either pumped (active system) or driven by convection (passive system) through it.

The collector could be made of a simple glass topped box with copper pipes in it, or a set of metal tubes surrounded by an evacuated (near vacuum) glass cylinder. A parabolic mirror can also be added to concentrate the sun's light on the tube.

A simple water heating system pumps cold water out to a collector to be heated, the heated water flows back to a collection tank. This type of collector can provide enough hot water for a family, for very little or no monthly cost.

Heat is stored in a hot water tank. The volume of this tank will be larger with solar heating systems in order to allow for bad weather, and because the optimum final temperature for the absorber is lower than a typical immersion or combustion heater.

The working fluid for the absorber may be the hot water from the tank, but more commonly (at least in pumped systems) is a separate loop of fluid containing anti-freeze and a corrosion



inhibitor which delivers heat to the tank through a heat exchanger (a coil of copper tubing within the tank).

If a central heating system is also present and heats water, then either the solar heat will be concentrated in a pre-heating tank that feeds into the tank heated by the central heating, or the solar heat exchanger will be lower in the tank than the hotter one. It is important to remember, however, that the critical need for central heating, is during the night when there is no sunlight and in winter when solar gain is lower. Therefore, solar water heating for washing and bathing is often a better application than central heating, because supply and demand are better matched.

The water from the collector can reach very high temperatures in good sunshine, or if the pump fails. Designs should allow for relief of pressure.

Compact system (Figure 4.10) consists of a tank for the heated water, a solar collector, and connecting pipes (all pre-mounted in a frame). Based on the thermo siphon principle, the water flows upward when heated in the panel. When this water enters the tank (positioned higher than the solar panel), it expels some cold water from inside so that the heat transfer takes place without the need for a pump. A typical system for a four-person home in a sunny region consists of a tank of 150 to 300 liters and three to four square meters of solar collector panels.

Direct compact systems are not suitable for cold climates if they are made of metals. During the night the remaining water can freeze and damage the panels, and the storage tank is exposed to the outdoor temperatures that will cause excessive heat losses on cold days. Some compact systems have a primary circuit. The primary circuit includes the collectors and the external part of the tank. Instead of water, a non-toxic antifreeze liquid is used. When this liquid is heated up, it flows to the external part of the tank and transfers the heat to the water placed inside. However, direct systems are slightly cheaper and more efficient.

Figure 4.10 : Solar Compact Systems



A compact system can avoid up to 4.5 tonnes a year of gas emissions. In order to meet the requirements of the Kyoto Protocol, several countries are offering subsidies to the end user. Some systems can work for up to 25 years with minimum maintenance. These systems can be redeemed in six years, and achieve a positive balance of energy (energy used to build them minus energy they save) after 1.5 years. Most part of the year, when the electric heating element is not working, these systems do not use any external source for power (as water flows due to thermosyphon principle).

Normally flat solar thermal collectors are used, but compact systems using vacuum tube collectors are also available on the market, providing a higher heat yield per square meter at a cost comparable to the flat collector systems.

In the forced circulation systems the primary circuit (heat transfer) liquid is pushed by a pump. The installation of forced circulation systems is necessary in all situations where the accumulator tank cannot be positioned at a level higher than the solar collector. This type of system is composed of solar collectors, tanks installed in appropriate technical areas and a pump controlled by an electronic device (Figure 4.11).

Before sunrise, the collectors are not energized and so the pump is not in function. Once the sun has risen, the temperature of the heat transfer liquid passing through the collectors becomes higher than the water in the tank. In these conditions, the electronic device sends a signal to the pump, which activates the circulation of the transfer fluid, transferring the heat to the water held in the storage tank.

After a sunny day, the hot water in the tank, having accumulated a certain amount of energy, is hot. If the available solar energy is insufficient, there will be alternating periods of pump operation, directly proportional to the rays emitted by the sun. When the sun sets, the liquid passing through the collectors becomes cold causing the pump to stop.

Usually these systems have a hot water storage tank with two heat exchange units, the solar exchanger placed at the bottom and an integration exchanger (part of the auxiliary heating system) placed in the upper part. The upper stratification of the hot water in the tank allows to take advantage of solar energy thanks to the return of low temperatures fluid to the collectors. When required, the auxiliary heating system warms up just a sufficient amount of water. Usually, when a boiler contains more than 1000 l, it has three heat exchange units, two of which are used for the exchange of solar energy and one for the integration of the boiler.

The most commonly used solar collector is the insulated glazed flat panel. Less expensive panels, like polypropylene panels (for swimming pools) or higher-performing collectors (like evacuated tube collectors), are sometimes used.

4.3.2.3 Solar Thermal Heating Collectors

The solar collector absorbs solar radiation, converts it into heat, and transfers useful heat to the solar system. There are a number of different design concepts for collectors: besides simple absorbers used for swimming pool heating, more sophisticated systems have also been developed for higher temperatures, such as integral storage collector systems, flat-plate collectors, evacuated flat-plate collectors and evacuated-tube collectors. Although commercial integral storage collectors do exist, no significant numbers have been sold, and so these are not described in detail here.



Figure 4.11 : Pumped Solar Thermal System

There are three main solar thermal collectors:

• plastic;

• flat plate;

• evacuated tube.

The plastic collectors (such as polypropylene, EPDM or PET plastics), used for extending the swimming season in swimming pools, consist of tubes or formed panels through which water is circulated and heated by the sun's radiation. In some countries heating an open-air swimming pool with non-renewable energy sources is not allowed, and then these inexpensive systems offer a good solution. This panel is not suitable for year round uses like providing hot water for home use, primarily due to its lack of insulation, which reduces its effectiveness when the ambient air temperature is lower than the temperature of the heated fluid.

The majority of solar collectors that are sold in many countries are of the flat-plate variety. The main components are a transparent front cover, a collector housing and an absorber (Figure 4.12). The absorber, inside the flat plate collector housing, converts sunlight to heat and transfers it to water in the absorber tubes. As the collector can reach stagnation temperatures up to 200°C (i.e. when no water flows through), all the materials used have to resist to such heat. Therefore, the absorber is usually made of metal materials such as copper, steel or aluminium (Figure 4.13). The collector housing can be made of plastic, metal or wood, and the glass front cover must be sealed so that heat does not escape, and dirt, insects or humidity do not get into the collector itself. Many collectors also have controlled ventilation, so as to avoid condensation inside the glass front cover. The collector housing is highly insulated at the back and sides, keeping heat losses low. However, there are still some collector heat losses, mainly due to the temperature difference between the absorber and ambient air, and these are subdivided into convection and radiation losses. The former are



caused by air movements, while the latter are caused by exchange of heat by radiation between the absorber and the environment.

A glass layer covers the collector helping to prevent most of the convection losses. Furthermore, the glass cover reduces heat radiation from the absorber into the environment like a greenhouse. However, the glass also reflects a small part of the sunlight, which does not reach the absorber.

Figure 4.14 shows the processes occurring at a flat-plate collector. Insulating materials like glass wool, rock wool, glass fiber or fiberglass are used.

Figure 4.12 : Flat-Plate Collector

Figure 4.13 : Absorber

Figure 4.14 : Flat Plate Collector Scheme



Evacuated tube collectors are made of a series of modular tubes, mounted in parallel, whose number can be added or reduced as hot water delivery needs change. This type of collector consists of rows of parallel transparent glass tubes, each of which contains an absorber tube (in place of the absorber plate to which metal tubes are attached in a flat-plate collector). The tubes are covered with a special light-modulating coating. In an evacuated tube collector, sunlight, passing through an outer glass tube, heats the absorber tube contained within it (Figure 4.15 and Figure 4.16).

Figure 4.15 : Typical Scheme of an Evacuated Tube C ollector

Figure 4.16 : Evacuated Tube

Collector

Figure 4.17 : Collecting Tube and the Heat Exchangers

Reference: Schott Solar AG

Two types of tube collectors are distinguished by their heat transfer method: the simplest pumps a heat transfer fluid (water or antifreeze) through a U-shaped copper tube placed in each of the glass collector tubes. The second type uses a sealed heat pipe that contains a liquid that vaporizes as it is heated. The vapour rises to a heat-transfer bulb that is positioned outside the collector tube in a pipe through which a second heat transfer liquid (the water or



antifreeze) is pumped. For both types, the heated liquid then circulates through a heat exchanger (Figure 4.17) and gives off its heat to water that is stored in a storage tank (which itself may be kept warm partially by sunlight). Evacuated tube collectors heat to higher temperatures, with some models providing considerably more solar yield per square meter than flat panels. However, they are more expensive and fragile than flat panels.

4.3.2.4 Selective Absorbers

Black materials absorb sunlight very well, and heat up as a result. Since metallic materials do not naturally have a black surface, they need to be coated for selective absorption. Black, temperature-resistant lacquer can serve this purpose, but there are specific materials for absorber coating. If a black surface heats up, it emits part of the heat energy again as heat radiation, as can be shown with electrical hotplates: when the hotplate is on, heat radiation can be felt on the skin without touching the hotplate itself. A black lacquered absorber shows the same effect, transferring only part of the absorbed heat to the water that flows through the absorber tubes, while radiating some heat back into the environment.

The level of absorption indicates the amount of short-wave solar radiation being absorbed (i.e. not reflected). As the absorber warms up to a temperature higher than the ambient temperature, it gives off a great part of the accumulated solar energy in the form of long-wave heat rays. The ratio of absorbed energy to emitted heat is indicated by the degree of emission. In order to reduce energy loss through heat emission, the most efficient absorbers have a selective surface coating. This coating enables the conversion of a high proportion of the solar radiation into heat, simultaneously reducing the emission of heat.

The usual coatings provide a degree of absorption of over 90%. Solar paints, which can be mechanically applied to the absorbers (with either brushes or sprays), are less or not at all selective, as they have a high level of emission. Galvanically applied selective coatings include black chrome, black nickel, and aluminium oxide with nickel. Relatively new is a titanium-nitride-oxide layer, which is applied via steam in a vacuum process. This type of coating stands out not only because of its quite low emission rates, but also because its production is emission-free and energy efficient.

4.3.2.5 Efficiency, Capacities, Output

In order to compare collectors, test institutions usually estimate efficiency curves based on measurements of the collector performance. These curves are given for different irradiances E and a variety of temperature differences between collector TC and ambient air TA. The commonly used empirical equation for the collector efficiency ηC is:

ηC = η0-(a1·(TC-TA)+a2·(TC-TA)²)/E

The three parameters η0, a1 and a2 are estimated by collector test measurements; η0 is also referred to as optical efficiency. Figure 4.18 shows typical collector efficiencies for a flat-plate collector. The thermal losses increase when the temperature difference between collector and ambient air rises. At low solar irradiances, the efficiency decreases at a faster rate. For instance, at a solar irradiance of only 200 W/m², the collector efficiency becomes zero even at a low temperature difference (about 40°C).

The heat loss is indicated by the thermal loss factor or k-value (a1 in the present case). This is given in watt per m2 of collector area and the particular temperature difference (in °C) between the absorber and its surroundings. The higher the temperature difference, the more heat is lost. Above a specific temperature difference, the amount of heat loss equals the



energy yield of the collector, so that no energy at all is delivered to the solar circulation system. A good collector will have a high conversion factor and a low k-value (Table 4.7).

Figure 4.18 : Typical Collector Efficiencies for a Flat-Plate Collector

Table 4.7 : Technical Specifications of Solar Colle ctors

Type of Collector Conversion Factor Thermal loss factor (W/m 2°C) Temp. Range (°C)

Uncovered absorber 0.82 – 0.97 10 – 30 Up to 40

Flat-plate 0.66 – 0.83 2.9 – 5.3 20 - 80

Evacuated-plate 0.81 – 0.83 2.6 – 4.3 20 – 120

Evacutaed-tube 0.62 – 0.84 0.7 – 2.0 50 – 120

Reservoir Collector About 0.55 About 2.4 20 – 70

Air collector 0.75 – 0.90 8 – 30 20 - 50

The conversion factor mentioned in Table 4.7 is the optical efficiency η0 showed in Figure 4.18, representing the ratio between the energy absorbed by the absorber and the solar energy arriving on the solar collector. The conversion factor can be also defined as the collector’s efficiency when the outside temperature is equal to the temperature of the absorber (no thermal losses).

The efficiency of solar collectors (heat delivered divided by incident solar energy) depends on the design parameters of the collector and on the whole system features. Average values of efficiency for solar water heating systems range between 50% and 80%, depending on solar radiation on site, meaning that in some cases, almost all needs for hot water can be covered by the solar loop. Solar collectors of all types have a nominal peak capacity of about



0.7 kWhth.m-2 under standard conditions9. However, the estimated annual solar thermal

energy production from the collector areas in operation depends on the solar radiation available, the outside temperature and the solar thermal technology used. For example, estimated annual yields for glazed flat-plate collectors are 1,000 kWhth.m

-2 in Israel, 700 kWhth.m

-2 in Australia, 400 kWhth.m-2 in Germany and 350 kWhth.m

-2 in Austria – where they reaches 550 kWhth.m

-2 for vacuum collectors and 300 kWhth.m-2 for unglazed collectors.

The desired temperature range of the material to be heated is the most important factor in choosing the correct type of collector. Moreover the amount of radiation on that spot, the exposure to storms, and the amount of space must be carefully considered when a solar array is designed. The specific costs of collectors are also important. Evacuated-tube collectors are substantially more expensive than flat-plate collectors or even plastic absorbers. However, a good collector does not guarantee a good solar system, since all components should be of high quality and similar capacity and strength.

Large collective solar systems with a collector surface from 100 m2 to 1,000 m2 could be installed in big buildings, district networks, hospital, hotels, etc. They are combined solar thermal system that thanks to the large dimension, can guarantee a significant reduction of the consumption. The water storage can be either seasonal or daily. In the first case the summer solar thermal energy is kept through a big storage tank for the cold season. Normally starting from minimum needs of 1,500 MWh per year, the larger the system the higher the savings. The table below shows costs, energy savings and technical characteristics of the two typical large collective solar systems.

Table 4.8 : Typical Large Collective Systems

With Daily Storage With Seasonal Storage

Minimum flat number > 30 flats > 60 people

> 100 flats

Collector Surface 0,8-1,2 m2 per person 1,5-2,5 m2/(MWh.year)

Storage Volume 50-60 l/m2 1.5-2.5 m3/m2

Energy saving 600-900 kWh/(m2.year) 400-700 kWh/(m2.year)

Energy saving: - Domestic hot water

Total domestic hot water and space heating

60-80 % 20-40 %

50-80 %

Cost 500-600 €/m2 900-1000 €/m2

Reference: Ambiente Italia, 2002

4.3.2.6 Solar Systems Dimensioning

Taking into consideration domestic hot water systems, an average demand of 50 l per person per day can be estimated. If hot water has to be used also for supplying clothes washers and / or dish washing machines, about 20 l per each wash has to be added.

9 Standard Test Conditions: 1000 W m-², 25°C.



Starting from these indications, the daily hot water demand per family can be calculated and this information can be used for proper dimensioning of the solar collector area to be installed. In the case of Montenegro, based on reference values for collector surface in similar zones and by considering the case of a flat collector south oriented with a tilt angle of 30°, it is estimated that the collector’s area should be around as 0.8÷1 m² per 50 litre and per day.

These reference values allow to completely meet the daily demand in summer, when all hot water can be provided by the solar thermal system. The values referred to flat plate collectors, have to be reduced (about 30% less) in case of evacuated tube collectors.

For a correct dimensioning of a solar system for hotels it is necessary to consider the average value of daily demand calculated over the summer season (from May to August), estimated to be around 80÷90 l per guest and per day. As for the storage tank, it is necessary to consider a volume of about 50÷70 l/m2 of collector surface. The surface of the heat exchanger should be about 0.4 m2/m2 of collector surface.

4.3.2.7 Cost Breakdown

As stated before, the purchase and installation of a solar thermal system is the most important obstacle to the development of the use of this source of energy. However, the situation differs depending on the country. Indeed, in Greece, a thermo-siphon system for one family unit of 2.4 m2 collector and 150 l tank costs € 700. In Germany or Northern Italy, where solar radiation is lower, a similar system (4÷6 m2 and 300 l tank) costs around € 4,500. From Table 4.9 to Table 4.11 are reported the cost breakdown for typical solar systems in European Countries. Table 4.12 summarizes the average specific costs for typical solar thermal systems.

Table 4.9 : Cost Breakdown of a Standard Thermosyph on System in Greece

Component Price (VAT excl.) Share

Material (2,4 m² + 150 litre storage) 191 € 33%

Labour (manufacturer) 58 € 10%

Promotion and General Expenses 250 € 43%

Labour and Installation (Installer) 81 € 14%

TOTAL 580 100%

Reference: EBHE, 2003

Table 4.10 : Cost Breakdown for a Typical Forced-Ci rculation System in Italy


Material (5 m² + 300 litre storage) 3160 € 80%


TOTAL 3920 100%

Reference: ASTER, 2002



Table 4.11 : Cost Breakdown for a Typical Thermosyp hon System in Cyprus


Material (1,5 m² + 150 litre storage) 427 € 50%


Gross Profit 214 € 25%

TOTAL 855 € 100%

Reference: Applied Energy Centre & Cyprus Institute of Energy, 2001

Table 4.12 : Specific costs for Typical Solar Therm al Systems

Individual Individual Large Scale

Type Thermosyphon Forced Circulation Forced Circulation

Size 3-4 people 4-6 people 60 people

Collector Surface 2,4 m² 4-5 m² 0,8-1,2 m²/person

Storage Volume 150 litres 300 litres 50-60 l/m²

Specific Costs (VAT excl.)

500 €/m² (250 €/m²)10

750 €/m² 500-600 €/m²

The collector surfaces showed in the above Table, relevant to the individual cases, have been estimated on the basis of the collector’s area recommended in Section 4.3.2.6 (approximately 0.8 ÷ 1 m2 per 50 litre of storage volume).

4.3.2.8 Solar Market in Europe

Over the last decade, the European solar thermal market has been experiencing a constant growth (see Figure 4.19). In 2005, it passed the cape of 2 million m² of newly installed collectors, reaching a yearly growth of 26%.

Leading countries for the use of solar thermal technology are located in Northern and Southern Europe. In the first group, the most important countries are Germany and Austria where high quality, high efficiency and expensive systems are installed. The second group includes manly Greece or Spain, heavy users of solar thermal technology. In those countries the solar system does not require high levels of performance, thanks to the higher solar radiation and milder climate conditions. Therefore, the systems have lower efficiency but are more price competitive.

The general trend in Europe is to incentive the use of solar thermal energy. It is believed that this trend will also apply to Montenegro. When the market will develop in Montenegro, it will be like in Spain, Greece or Cyprus, because of the similar solar and economic conditions. Indeed, as Montenegro enjoys large amounts of solar radiation and milder weather, high performance systems with high selective coating, very thick insulation and sophisticated regulation systems will not be required.

10 Market price in Greece : very mature market (with high level of competition and low margins) and large

number of important local manufacturers



Reference: ESTIF, June 2006

Figure 4.19 : Solar Thermal Market in Europe since 1990

4.4 MARKET SECTORS IN MONTENEGRO Two main market sectors can be identified in Montenegro as possible targets for solar water heating systems:

• residential sector;

• tourism sector.

Besides these sectors, analyzed in details in the following sections, it is possible to consider also public buildings, air conditioning and industrial process heating. Although at the moment these applications are not very common in Montenegro, the potential is extremely high. As for air conditioning, the demand for cooling is growing in coastal regions because of the tourism industry, as well as the costs both for the user and for the environment. The use of solar technologies could therefore represent an interesting alternative to conventional systems.

4.4.1 Residential Sector

The analysis of the peculiar situation of Montenegrin households is necessary in order to understand both the current situation of the solar thermal systems and the perspectives / hindrances for the future development.

In 2003, there were 190,212 households in Montenegro equal to a population of about 620,145. Their distribution is presented in Table 4.13 (Statistical Yearbook, 2005).

In more than 90% of households in Montenegro (UNDP, 2004) water is heated by immersing an electric heater (with an average age of 12 years) into a water tank with a capacity of less than 80 litres. Most of the tanks are domestically produced, with poor or damaged insulation and inadequate controls, and cannot meet the hot water needs of households with more than three members. These tanks are very expensive to use and difficult to control, maintain and use. Basic maintenance is not carried out.



Table 4.13 : Distribution of Households per Region

Location Year 2003 Year 2004 %

Mountainous Regions 58,510 59,492 30.8

Andrijevica 2,017 2,051 1.1

Berane 10,875 11,057 5.7

Bijelo Polje 14,129 14,366 7.4

Zabljak 1,353 1,376 0.7

Kolasin 3,230 3,284 1.7

Mojkovac 2,919 2,968 1.5

Plav 4,760 4,840 2.5

Plzine 1,352 1,375 0.7

Pljevlja 11,376 11,567 6.0

Rozaje 5,576 5,670 2.9

Savnik 923 938 0.5

Plain regions 82,817 84,206 43.5

Danilovgrad 5,057 5,142 2.7

Niksic 21,479 21,839 11.3

Podgorica 50,382 51,227 26.5

Cetinje 5,899 5,998 3.1

Coastal Regions 48,885 49,705 25.7

Bar 13,796 14,027 7.3

Budva 5,440 5,531 2.9

Kotor 7,385 7,509 3.9

Tivat 4,675 4,753 2.5

Ulcinj 6,271 4,376 3.3

Herceg Novi 11,318 11,508 6.0

Total Montenegro 190,212 193,403 100

Reference: Statistical Yearbook, 2005

In 2001, the electricity consumption of households for the preparation of domestic hot water reached the value of 187 GWh (Ian Pope Associates, 2005). By estimating that 90% of the households use an electric water heater for this purpose, the yearly electric consumption of an average Montenegrin household for preparing sanitary hot water is around 1,087 kWh. This number is very low and represents only 2 h per day for a 1,5 kW electric heater.

4.4.2 Tourism Sector

The tourism sector and in particular hotels along the Montenegrin coastline is another key target group. Several solar thermal systems have been installed in the past decades but their number and state of operation are not known.



Tourism represents an important component of the Montenegrin economy and a vital source of revenue. In 2005, more than 800,000 tourists visited Montenegro for a total number of more than 5,1 million overnights. Hotels themselves have a total capacity of about 76,047 beds, other accommodations accounting for about 41,764 units.

Table 4.14 represents the regional distribution of accommodation capacities and overnights, showing that the largest share of accommodation capacities and overnights are on the coastal area.

Figure 4.20 shows that almost the whole tourism activity in Montenegro is concentrated along the coast with about 96% of both accommodation capacities and overnight stays. Moreover, the highest number of the visits over the year is concentrated during the summer season, which accumulate about 83% of all overnights.

Table 4.14 : Accommodation Capacities and Tourist O vernights (year 2005)

No. of Beds Share Overnights Share

Coastal Area 112,892 95.8% 4.983,967 96.7%

Central Area 2,983 2.5% 120,397 2.3%

Mountainous Area 1,989 1.7% 49,909 1.0%

Montenegro 117,811 100.0% 5,154,273 100.0%

Reference: Howarth Consulting Zagreb, 2005

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

Janu

ary

Febru

ary

Mar

chApr

ilM

ayJu

ne July

Augus

t

Septe

mbe

r

Octobe

r

Novem

ber

Decem

ber


Figure 4.20 : Overnights Seasonality – Coastal Area (year 2004)



4.5 CASE STUDY 1: SOLAR THERMAL ENERGY FOR HOUSEHOL DS In this section, three basic technical case studies were developed, one for each main regions of Montenegro. Solar thermal systems were designed and their production of hot water, and thus the savings generated, simulated depending on the local conditions. These data were then used to perform a financial analysis in order to evaluate the economic viability of the solar thermal systems in Montenegro.

4.5.1 Location and Meteorological Data for Simulati on

The simulations of the three regions were based on information relative to one precise location in each region: Bar for the coastal region, Podgorica for the central region and Zabljak for the mountainous region.

In the previous chapters, some information about global solar radiation has been provided. In order to run correct simulations, information about water temperatures (tap water), wind speed and air temperature should also be known. These data were not available for the target sites and it was necessary to gather information from other locations with similar characteristics. Therefore, some information (temperature of water, humidity and wind speed) for the case of the Coastal Region was taken from Split in Croatia and for that of the mountainous Region from Pristina in Kosovo.

4.5.2 Basic Technical Case Study

As no data was available specifically for each region regarding the demand and production of domestic hot water, the same assumptions were taken as inputs for the simulation in all three regions, as shown in the table below.

Table 4.15 : Input Values for the Residential Case Study

Parameter Value

Number of persons per Households 4

Daily demand of hot water 200 l per day (50 l per person per day)

Temperature of hot water 50°C

Period of use per year 12 months

Rate of Occupancy 100%

Table 4.16 summarizes the main technical data used in the case study for the three regions (coastal, central and mountainous). Note that the indicated temperature of hot water is a recommended value.

For the Central and Coastal regions, a typical thermosyphon system was designed, while in the mountainous areas, due to the cold climate, a forced-circulation installation was simulated with a sizing of 1m² of collector area per person. In addition, as the inclination of the collectors was taken as 35° for the compact systems (typical inclination), a tilt of 43° (value of latitude) has been chosen for the mountain region in order to favour collection of solar heat in the winter season. Moreover, the tank for the forced circulation system is larger in order to store as much solar energy as possible due to the low level of resource. The



typical alternative option to a solar system in a household is an electric boiler (average efficiency of 95%).

The assumed values of Optical Efficiency and Thermal Loss Factor (K factor), are typical values for these kind of equipment. It is noted that solar collectors can reach Optical Efficiency values higher than 0.8, and K values lower than 3.5, however these collectors are much more expensive because of their higher technical level. In this case study we assumed to use the same collectors for both thermosyphon and forced circulation systems.

Table 4.16 : Assumed Technical Data for the Solar T hermal Systems

Component Coastal Region Central Region Mountainous Region

Type Thermosyphon Thermosyphon Forced Circulation

Collector 2.4 m² 2.4 m² 4 m²

Optical Efficiency 0.77 0.77 0.77 K Factor 5.00 5.00 5.00

Storage Tank 150 litres 150 litres 300 litres

Heat Exchanger Efficiency

80% 80% 80%

Back-up Electric Electric Electric

Efficiency 95% 95% 95%

Latitude 42.1° 42.5° 43.1°

Collectors tilt 35° 35° 43°

Temperature of Tap Water

Minimum: 12.9°C

Maximum: 19.2°C

Minimum: 11.6°C

Maximum: 18.8°C

Minimum: 1.7°C

Maximum: 8.4°C

The results of the simulation for the three solar thermal systems are summarized in Table 4.17, calculated using the software available at the International Clean Energy Decision Support Centre Website (RETScreen International, 2006).

Solar system efficiency represents the ratio between the solar energy arriving on the collectors and the energy supplied to the water by the solar system, i.e. the percentage of solar energy that the system can collect and transfer to the water.

The solar fraction is the ratio between the energy supplied to the water by the solar system and the total energy demand in terms of hot water. Such value allow to estimate the percentage of the energy demand that has to be supplied by the electric water heater back-up, or light fuel heater back-up.

Since the solar systems for the three residential cases have similar components, we assumed to use the same solar system efficiency value. On the contrary, the solar fractions are different because they are related to the amount of energy produced, which depends on the size of the system.

The coastal and central regions have similar energy production and solar fraction, while the solar fraction on the mountain is higher. In the mountains region, the equipment is much



larger and therefore, despite lower insulation values and lower specific energy production, it can collect more solar energy and cover a larger part of the energy demand (70%), delivering a greater total amount of solar energy to the domestic hot water (DHW). The values of 58% and 56% are referred to two identical systems but with different solar radiation.

Table 4.17 : Estimated Solar Energy for the Three R esidential Case Studies

Coastal Region

Central Region

Mountainous Region

Specific Energy production [kWh/m²] 703 688 668

Solar System efficiency 39% 39% 39%

Solar Fraction 58% 56% 70%

Solar Energy production [kWh/year] 1,690 1,650 2,67 0

4.5.3 Costs Assessment

The costs assessment is based on information gathered in Montenegro and on our experience of such systems. It was decided that a standard normal case for Europe would be applied to Montenegro in terms of type of system, use and price range.

In Europe there are two types of solar thermal installations in terms of prices: Northern European type or Southern European type. The price of the products are dependant on the country of production. In fact, as an example, German products will remain at the same price if sold in Germany or in Spain, the same for Montenegro. Generally speaking, the market is composed of two categories, German-like and Spanish-like products. The German-like approach was considered more suitable, since these products were already sold on the Montenegrin market.

Table 4.18 summarizes the estimated prices of solar collectors for the residential sector for the three regions (coastal, central and mountainous). The estimated specific costs were already presented in Table 4.12. The costs of maintenance as 3% come from experience and from the following considerations:

• there are no parts requiring regular maintenance;

• the heat transfer fluid should be changed every three years;

• some annual visit are foreseen, including the change of some small parts every couple of years.

Other solar thermal systems could be imported from the Far East, especially from China which is a very large source of thermosyphon systems with vacuum tubes. These pieces of equipment are cheap and manufactured in great numbers. However, their quality is very far from matching European quality standards and, in any case, they are still not (or very little) imported in Europe and not certified.

An additional supplier of solar thermal equipment could be Turkey. In this case, the equipment is of low quality and does not respond to European quality standards (very few are certified in Europe). In addition, these systems in Turkey are often thermosyphon systems of the direct type, therefore the sanitary water directly flows into the collector, and the installation is shut down during winter in order to prevent damages from freezing. This case was not considered as suitable for Montenegro.



Table 4.18 : Estimated Price for the Three Resident ial Case Studies

Component Coastal Region

Central Region

Mountainous Region

Type Thermosyphon Thermosyphon Forced

Circulation

Collector 2,4 m² 2,4 m² 4 m²

Storage Tank 150 litres 150 litres 300 litres

Specific Price in €/m², installation included

500 500 750

Total price in €, installation included 1,200 1,200 3,000

Annual Maintenance - in % of initial investment - in €

3% 36

3% 36

3% 90

4.6 CASE STUDY 2: SOLAR THERMAL ENERGY FOR THE TOUR ISM SECTOR

4.6.1 Meteorological Data for Simulation

The simulations of the three regions were based on information relative to one precise location in the coastal region: Bar. As in the previous section, some other information were taken from Split in Croatia.

4.6.2 Basic Technical Case Study

Based on the report “Howarth Hotel Industry Survey Montenegro 2005” (Howarth Consulting Zagreb, 2005) a basic large-scale solar thermal installation was designed.

On the seaside of Montenegro, most hotels are large and can accommodate numerous tourists. In 2004, the average number of beds in this region was equal to 346 and was assumed as 350 in the simulation (see Table 4.19). The rate of occupancy of this typical hotel was based on the overnight statistics given in Figure 4.20. However, the very low rates of occupancy during the period between November and April were taken as zero. Indeed, large hotels on the seaside are very likely to close during winter. Occupancy 100% was given for the two months of summer, July and August (see Table 4.20).

Table 4.19 : Input Values for the Tourism Sector

Condition Value

Capacity in Number of Guests 350

Daily demand of hot water 80 l per person per day

Temperature of hot water 50°C

Period of use per year 12 months

Rate of Occupancy Variable (see Table 4.20)




Table 4.20 : Rate of Occupancy of a "Standard" Hote l in the Coastal Region

Real ROO Smoothened ROO

January 2.4% 0

February 3.5% 0

March 2.0% 0

April 4.8% 0

May 28.6% 29.0%

June 65.1% 65.0%

July 99.6% 100.0%

August 100% 100.0%

September 63.5% 63.0%

October 21.0% 21.0%

November 2.8% 0

December 2.8% 0


Note: ROO = Rate of Occupancy

In the following table are shown the main technical data used for the simulation of the tourism sector case study.

Table 4.21 : Technical Data for the Tourism Sector

Component Technical Specifications

Type Forced Circulation, on-roof, standing

Collector 230 m²

Optical Efficiency 0.77

K Factor 5.00

Storage Tank 15,000 litres

Heat Exchanger Efficiency 80%

Back-up Heating Fuel

Efficiency 90%

Latitude 42.1°

Collectors tilt 35°

Temperature of Tap Water

Minimum: 12.9°C

Maximum: 19.2°C



The results of the simulation for the tourism sector are summarized in Table 4.22. Similarly to the residential case study (see Section 4.5), a software available at the International Clean Energy Decision Support Centre Website (RETScreen International, 2006) was used.

On the basis of the design options available within the software and on practical and site-specific considerations, a surface collector of 230 m² and a tank of 15,000 litres were selected.

As typically used for a solar system in a hotel, a heating fuel fired boiler (average efficiency of 90%) was considered for the backup. The values of Optical Efficiency and K factor assumed for the tourism sector case study are equal to the residential case study hypothesis.

As shown in Table 4.22, the performance of this installation is expected to be quite high with a system efficiency of about 44% and a solar fraction of 62%, meaning that two third of the needed hot water can be produced through the solar energy. The quantity of solar energy delivered is obviously very large with more than 74 MWh over the 6 months but would be more than double over a complete year. It is important to note that the level of specific energy production is low because the system only runs half of the year and at partial load for some other months. If the system would operate at full capacity, then this number would reach about 764 kWh per m².

Table 4.22 : Estimated Solar Energy for the Tourism Sector

Specific Energy production [kWh/m²] 323

Solar System efficiency [%] 44%

Solar Fraction [%] 62%

Solar Energy production [kWh/year] 74,350

4.6.3 Cost Assessment

For large-scale installations, the average cost in Europe is about 550 €/m² (Ambiente Italia, 2002). This price does not include the complete installation, since most hotels already have their own heating system. The main economic features for the three case studies, already discussed in Section 4.5.3 and Section 4.6.2, are summarized in the table below.

Table 4.23 : Estimated Price for the Tourism Sector Case Study

Component Estimated Specifications

Type Forced Circulation

Collector 230 m²

Storage Tank 15,000 litres

Specific Price in €/m², installation included 550

Total price in €, installation included

126,500

Annual Maintenance - in % of initial investment - in €

3%

3,800



4.7 ECONOMIC PERFORMANCE OF SOLAR SYSTEMS As far as economic aspects are concerned, solar thermal systems offer the opportunity to save on traditional energy bills for the whole lifetime of the plant, thus totally recovering, after some years, the costs of the initial investment.

The economic benefits are greater if the system does not need to be integrated with other conventional heating systems, for instance in the case of installations for domestic water, in open swimming pools or in summer houses, where all the heating needs can be provided by solar radiation. However, even when the solar system is just auxiliary, it allows economic advantages and it is possible recovering the investment costs.

In order to analyze the economic benefits of solar thermal systems, the pay-back period shall be taken into consideration. The pay-back period, i.e. the time required to recover investment costs through avoided or reduced energy costs, can usually vary between 5 to 15 years, which is significant considering that the solar system will allow reduction of energy costs for its whole life, which is normally 20-30 years.

The pay-back period, depends on the following factors:

• the price of the replaced energy sources (electricity, natural gas, gas oil, bottled gas, coal, gasoline, etc), including the different taxes applied at regional level and for different energy applications (residential, industrial);

• the level of annual consumption.

4.7.1 Methodology

In this section, the option of solar-assisted provision of hot water is compared to a conventional hot water system in terms of investment, installation and operation costs. The cases of application investigated are a typical Montenegrin household and a typical large-scale hotel at the Montenegrin seaside. The conventional system chosen for comparison should represent the technical alternative which would usually have been chosen if the solar heating system would not have been implemented. In a Montenegrin household, the typical alternative option for providing hot water is an electrical boiler. These installations cover around 90% of the energy supply for hot water in Montenegro's households. In a large-scale hotel as characteristic for Montenegro's seaside, the alternative to the solar hot water system would be a heating-fuel-fired boiler.

To make the solar system and the alternative system comparable in terms of costs, both analyzed systems are designed to deliver the same energy service. With hot water being the primary service, the amount of heat produced for heating water is the same for both technical alternatives in the analysis. To be able to deliver this service at any time during the year, the solar water heater must be designed with a back-up heater. The back-up heater is assumed to be electrical in the household application, and heating-fuel-fired in the case of the large-scale hotel.

Designed for the same service, solar system and electrical boiler can be compared in terms of their actual costs, calculated by discounting all relevant costs for energy service provision arising during the economic lifetime of the facility. The direct costs considered in this analysis are the investment cost, the fuel cost or cost of electricity (if electricity is the main energy source for service provision), and the operation and maintenance cost (fixed and other variable).



4.7.2 Case Study 1: Residential Sector

4.7.2.1 Potential Savings

In order to calculate the potential savings generated by a solar system, we assumed that the solar installation will cover a part of the energy needs required for heating up the water in the tank. The results of the simulation are presented in Figure 4.21.

0

20

40

60

80

100

120

140

160

0 250 500 750 1000 1250 1500 1750 2000 2250 2500 2750 3000 3250 3500 3750 4000

Annual Consumption in kWh

Ann

ual S

avin

gs in

Eur

os

Solar Fraction of 50%Solar Fraction of 60%Solar Fraction of 70%

Figure 4.21 : Estimated Costs Savings - Residential Sector

The simulation gives an idea of the savings that can be done per year depending on the system and the consumptions. A solar fraction of 70% means that 70% of energy demand is supplied by the solar system, and the rest 30% is supplied by an electric back-up or a light fuel heater back-up.

This simulation is based on an average electricity price of 4.85 c€/kWh. As a result of the low cost of electricity, the savings generated are quite low compared to other European countries. Therefore, also the economic viability of the case study appears low.

4.7.2.2 Economic Analysis

The target service for the cost comparison is the delivery of heat for hot water provision of a typical household in central Montenegro. For this case study the selected location is Podgorica. The alternative electric system is assumed to be a 80 litre drum electric water heater which reaches a system efficiency of 95%. In the solar case, it is replaced by a 2.4 m² solar system with an electrical back-up heater. The energy needed to heat up the water amounts at 2,960 kWh/y.



This value, an output from the used software (RETScreen International, 2006), depends on the demand of hot water per day, on the rate of occupancy (100%, 7 days a week) and on the hot water temperature. Estimating an average efficiency of 95%, the conventional system needs 3,115 kWh/y of electricity for that service.

The basic data for the cost analysis are summarized in Table 4.24. A discount rate of 8.5% is chosen (IMF, 2005; EBRD, 2005) reflecting a calculation in constant prices. For the same reason, no electricity tariff increase has been applied. The economic life time of evaluation amounts to 15 years, assuming that the final value after 15 years of the installation is zero. The currency of comparison is Euro and the base year for evaluation is 2007 (beginning of year).

Table 4.24 : Basic Data for the Residential Case St udy

Discount Rate [%] 8.5

Economic lifetime [Years] 15

Heating energy for hot water [kWh/y] 2,960

Electricity Tariff [c€/kWh] 4.85

Electricity Tariff Increase [%/year] 0

As shown in Table 4.24, 2,960 kWh/y is the overall amount of energy necessary to heat up the water. In the solar system scheme, the solar thermal collectors can cover only a part of the overall energy demand. Therefore, the solar system needs an electric back-up equipment, whose consumption is estimated to be 1,308 kWh/y.

Table 4.25 summarizes the preliminary cost estimation for two different supply alternatives for the household application. The operation and maintenance cost are assumed to be 3% of the investment cost per year, on the basis of the experience and the considerations already illustrated in Section 4.5.3.

Table 4.25 : Preliminary Cost Estimation - Resident ial Case Study

Unit Solar hot

water system

Electrical boiler system

Electricity demand for heating kWh/y 1,308 3,115

Total investment and installation cost Euro 1,200 334

Fixed operation and maintenance cost Euro/y 36 10

Other variable operation and maintenance costs (i.e. excluding cost for fuel or electricity)

Euro/y 0 0

The price of total investment and installation cost for electrical boiler system (334 Euro) is referred to a 80 liter middle class electric boiler with a 20% overpricing for installation.

The estimation of the investment and the operation and maintenance costs for the solar system has been already illustrated in the previous sections (see Table 4.18).



On the basis of these assumption, the Net Present Value for the two systems was preliminary estimated. The results showed that, from the financial point of view, the electrical boiler system would be characterized by a more favorable Net Present Value (2,096 Euro) if compared with the solar thermal system (1,786 Euro), since the lower initial investment cost of the electrical boiler over-compensate the higher yearly cost for electricity purchase.

As no increase in electricity cost over time has been applied, any future increase in the general electricity tariff will make the solar hot water system more attractive compared to the electrical system. At a tariff of 6.76 c€/kWh both systems reveal the same present value of costs. With a higher electricity tariff, the solar system would become more convenient from the cost perspective than the electrical boiler.

4.7.3 Case Study 2: Tourism Sector

4.7.3.1 Potential Savings

In the tourism industry and for large hotels, our approach was based on the use of heating oil, since, for large scale installations, this fuel is more common than electricity. An estimation of the potential savings depending on the level of consumption and the size of the solar fraction was performed and is presented below. It should be noted that the cost of the equipment for a single installation is beyond the sum of the potential savings. The graphical representation of the potential savings is presented in Figure 4.22.

0

1000

2000

3000

4000

5000

6000

7000

8000

0 5000 10000 15000 20000 25000 30000

Annual Heating Oil Consumption in liter

Ann

ual S

avin

gs in

Eur

os

Solar Fraction of 50%Solar Fraction of 60%Solar Fraction of 70%

Figure 4.22 : Estimated Costs Savings - Tourism Sec tor

In general terms, as shown in Figure 4.23 for a 60% solar fraction, the comparison of a solar system against heating oil would generate higher savings than against electricity. However, the absolute value of these savings is low if compared to other European Countries, where fuels and electricity are not subsidized and/or heavily taxed.



0

20

40

60

80

100

120

140

160

180

0 1000 2000 3000 4000 5000

Energy Demand in kWh

Sav

ings

in E

uro

Electricity - Efficiency 95%

Heating Oil - Efficiency 80%

Figure 4.23 : Estimated Costs Savings vs. Electrici ty and Heating Oil - 60% Solar Fraction

4.7.3.2 Economic Analysis

The target service for the cost comparison is the delivery of heat for hot water provision of a typical large-scale hotel on the Montenegrin seaside. The location for this case study is Bar. The solar system consists of a 230 m² solar collector. We assumed that 62% of the energy needed to heat up the water is supplied by the solar installation, the rest is provided by a heating fuel back-up firing, taking into account a system efficiency of 80% for the fossil fuel system and a lower heating value of 10 kWh per liter heating fuel. The conventional, alternative system is a hot water boiler fired with heating fuel. The system efficiency of the installation is assumed to be 80%. The energy needed to heat up the water amounts at 121,000 kWh/y.

This value, an output from the used software (RETScreen International, 2006) , depends on the demand of hot water per day, on the rate of occupancy (100%, 7 days a week) and the desired hot water temperature.

The basic data for the cost analysis of the large-scale hot water application are summarized in Table 4.26. As applied in the residential case study, a discount rate of 8.5% is chosen, reflecting a calculation in constant prices. The economic life time of the large-scale system is assumed to be 20 years, while the base year for evaluation is 2007 (beginning of year).

The heating fuel price, 27.2 c€/l, is based on the average price of “Mazout oil” gathered from the MME, i. e. 340 €/kg. The conversion was based on a specific weight of 0.8 kg/l.

Table 4.27 summarizes the preliminary cost estimation for two energy supply alternatives for a large scale hotel. Since no units of the conventional hot water system are replaced by the solar thermal system, only the additional investment and the operation and maintenance costs connected to the solar system are considered in this cost comparison. The additional operation and maintenance costs are assumed to be only 1% of the investment cost per year, due to a higher maintenance efficiency compared with the household system.



Table 4.26 : Basic Data for the Large Scale Hotel C ase Study

Basic Data

Discount Rate 8.5 %

Period under Consideration 20 Years

Heating energy for hot water 121,000 kWh/y

Lower heating value of heating fuel 10 kWh/l

Mean fuel efficiency 80 %

Heating fuel price 27.2 c€/l

Fuel price increase 0 %/year

Table 4.27 : Preliminary Cost Estimation – Large Sc ale Hotel Case Study

Unit Solar Hot Water System

Heating Fuel System

Fuel demand for heating liter/y 5,696 15,000

Additional investment cost of the solar system

Euro 126,500 -

Additional fixed operation and maintenance cost of the solar system

Euro/y 1,265 -

Other variable operation and maintenance costs Euro/y 0 0

The fuel demand for heating was calculated by the software already mentioned (RETScreen International ,2006). The estimation of the investment cost for the solar system has been already illustrated in the previous sections (see Table 4.23).

As shown in Table 4.26, the demand of heating water is about 120 MWh/year and the efficiency of the boiler is 80%, therefore the primary energy demand is approximately 150 MWh/year. By assuming that the energy content of the fuel is 10 kWh/l, the fuel consumption is estimated to be approximately 15,000 liters per year.

As the solar installation covers a part of the load, it does not need to heat 15,000 liters but only 5,696 liters.

In this case, we assumed the addition of the solar loop onto the existing boiler (via a large plate heat exchanger), therefore all operation and maintenance costs (apart from the fuel costs) related to the boiler are not considered.

Therefore, considering the high initial investment required for the solar system, this option appears not suitable from the economical point of view.

No increase in heating fuel cost over time was applied. Any future increase of the fuel supply cost will make the solar hot water system more attractive compared to the heating-fuel system. It is noted that only at a fuel cost equal to 1.46 Euro per liter the high initial



investment for the solar system can be compensated by the fuel cost savings during operation. In conclusion, according to the assumptions considered in the case studies, the solar system turns out to be more expensive even considering a heating fuel price increase.

4.8 DEVELOPMENT PERSPECTIVES

4.8.1 Solar Thermal Technologies and Applications

The internal production of solar systems does not exist any more. Nowadays, some equipments are being sold and installed, mainly coming from Germany and Austria, but the quantities are still very limited.

However, as already mentioned in Section 4.3.2, the necessary skilled workforce for any solar project should be theoretically available in Montenegro, since a large number of engineers and technicians were trained in the firm formerly in Podgorica. Efficiency improvement, quality enhancement, design and monitoring optimisation are necessary in order to make the solar technology economically sustainable and commercially competitive.

The most important potential market for solar heating plants is represented by the small systems used for residential hot water supply. In this context, compact thermo-siphon systems will be widely spread along the coast, while forced circulation systems will be preferred in mountainous areas.

Flat plate collectors will strongly dominate the market. Evacuated tube collectors could gain a significant share of the market in the sector of large scale installations. Combi-systems for water and space heating will be barely used.

Large collective systems in Montenegro have a great potential and, if a solar thermal energy experiences a boom in the country, then they will have a large share of the total installed capacity.

4.8.2 Local Employment

In Europe, with a turnover close to 2 billion €, the sector of solar thermal energy employs more than 20,000 people. ESTIF states that if appropriate political and market conditions were to be developed, some 580,000 full-time job opportunities would be created by the year 2030 (ESTIF, 2006). Solar thermal jobs create local income sources and are easily targeted for regional development. This case would definitely apply to Montenegro in the development of the use of solar thermal energy.

From the industrial point of view, solar thermal technology is based on very simple principles. The trend towards high-efficiency systems and the automation of the production process leads to ever more sophisticated products and high-tech manufacturing methods. Over half of the turnover is related to marketing, distribution, design and installation of the systems. Correspondingly, solar thermal is a job machine for the local and national develop-ment, as most of these jobs are inherently local.

Regarding the creation of employment, estimations can only be taken within a wide range depending on the expected development, manufacturing technology used in production and share of imports and exports. Indeed, a market development without any local production would obviously create only jobs in the installation and distribution branch.



Still according to ESTIF, the employment created by solar thermal would be about 1 full-time job for every 100 m² installed, this number being an “aggregation” of all jobs in production, marketing, administration and installation and is an average for the EU-15.

With specific reference to Montenegro, several basic estimation of the potential employment were performed by assuming different levels of solar thermal integration rate over ten years (see Table 4.28).

Table 4.28 : Estimation of Potential Employment

Installed area per 1000 inhabitants after 10 years

Average Annual Rate of Installation

Total Number of Jobs in the Solar Thermal

Branch

100 m² (current level of Germany) 6,200 m² 50-100

300 m² (current level of Greece / Austria)

18,600 m² 150-200

600 m² (current level of Cyprus) 37,200 m² 350-400

Since the numbers given above are a rough estimation based on the ESTIF data, they cannot give any indication about the share of export of local production with respect to the installed area.

It is interesting to note that if 100% of Montenegrin households will be equipped with a 2,4 m² collectors in the next ten years, then the penetration ratio would become 735 m²/000 persons and would lead to the creation of about 750-800 jobs.

Regarding future production capabilities in Montenegro, the establishment of a local manufacture is relatively unlikely. Indeed, the solar industry is entering a phase of consolidation and the number of actors is diminishing year after year, while manufactures are getting larger and larger. In order to “survive”, a production unit would have to start with a very large output in order to be competitive, to “weigh” in the market and to be cost-effective. To do so, this unit would have to be export oriented as the Montenegrin market would only allow, at a maximum, an off-take of 10-20,000 m², which will be insufficient for a company to be competitive in Europe in the middle-term. Nowadays, the main producers operate at output levels way beyond 100,000 m²/year (430,000m²/year for the largest representing 21,5% of the newly installed area in Europe).

Because of the rising costs of transportation and because only 100-150 collectors can be transported in a truck, there are plans from the big companies to replicate production units closer to the promising markets, especially when local workforce is less expensive. As manufacturing capabilities are very limited in Eastern Europe and in the Balkan region, the markets promising and the workforce less expensive than in Western Europe, one or two manufacturers might be interested in setting up a local production unit dedicated to these markets. However, to realize this industrial development, the Government of Montenegro shall introduce attractive mechanisms for the potential foreign or local investors.

4.8.3 Regulation, Incentives and Promotion

At this moment, in Montenegro no incentives for thermal solar energy production are in place. A strategy and a dedicated legislation are under development. Similarly, there are no investment incentives for the development of innovative applications for solar systems.



The Montenegrin Ministry of Economy, according to the Energy Law, is responsible for the national energy policy, while the Energy Regulator Agency is in charge of the implementation of the energy legislation and regulations together with the regulatory aspects of the policy.

As far as solar energy is concerned, it is not mentioned in any official document as an energy source.

The users must be informed in order to understand the benefits of using a solar system. The public awareness is necessary, as far as the residential sector is concerned.

The involved entities must also be well informed about market and technical opportunities. Education at all levels must be introduced and continuous training for designers, investors and decision makers must be provided.

4.8.4 Market Conditions

As already explained in this study, solar thermal systems, when compared to conventional fuels, have the basic advantage of being a renewable energy source and thus environmentally friendly. This advantage cannot be quantified in terms of cost and is related to the public welfare. The Government has the responsibility to incentive and support any available means the solar thermal systems viability in the market. This support ensures fair competition by safeguarding the public interest.

Moreover, the Government has several reasons to support renewable energy sources in general, in order to keep a long-term view of environmental, social and economic developments. For example, it is crucial that certain targets in emissions reduction are met as defined under international agreements, such as the Kyoto Protocol. Also, conventional fuel prices are projected to follow an increasing trend.

Additionally, manufacturers (if any) and distributors must make more efforts to overcome the difficulties and promote the solar systems in every sector. The private initiative remains the driving force of a healthy market competition towards the benefit of the consumer.

The Montenegrin energy market is passing through a period of transition, in accordance to its recent history. These changes can be summarized in the trends of deregulation and liberalization.

A short SWOT analysis (strength, weakness, opportunities, threats), was performed, and the following points were pointed out.

Strengths- weaknesses

• Montenegro has excellent solar radiation levels which would allow to reach very high energy yields;

• the large amount of radiation received and the high number of sunshine hours on the coast would allow the use of equipment from Southern Europe. Their lower quality and level of performance would not suffer from the climatic conditions and they very cost-competitive;

• there was (and still is) already a solar thermal market in Montenegro and therefore, some experience and expertise is available;



• a limited (in terms of potential) domestic market has not allowed high productivity procedures to be applied and the cost of solar thermal systems remains probably high. The state support is currently a necessity;

• there is no manufacturing capacity in Montenegro;

• energy prices in general are very low and downsize the economic value of the savings generated by the solar installations;

Opportunities – threats;

• the growth of the market will result in economies of scale in production and in a reduction of the solar thermal systems cost;

• the Government must provide the market with proper incentives, with the purpose of supporting the environmental protection in the country;

• new financing techniques should be introduced, helping to overcome investment obstacles. The experience of other countries in the field can surely be useful;

• solar thermal systems are a good solution for cheap, abundant and environmentally friendly energy for heating water.

The negative and positive aspects of the Montenegrin solar market are summarized in Table 4.29.

Table 4.29 : Strengths and Weaknesses of the Monten egrin Solar Market

NEGATIVE POSITIVE

No specific regulation

No incentive

No Promotion

Very Low awareness of population

Limited domestic market

Good solar Resource

Large Tourism Market

Easy retrofitting of HHs

Expertise available

Solar Thermal technology reliable and technically viable

Energy Prices will rise in the middle term



5 BIOMASS TO ENERGY POTENTIAL ASSESSMENT

The target of this section was to work out a biomass to energy resource assessment in the Republic of Montenegro. The study was based on the data gathered from the Statistical Yearbook of Montenegro (MONSTAT, 2005) and from the Global Land Cover 2000 (GLC_2000) data.

The relevant concluding remarks for the biomass energy resource assessment are reported in Section 6.

At present there are 10 power generation plants in operation, nine of them running on hydro-power. All units run at low efficiency. Overall Montenegro is a net importer of electricity, dependent on other countries. The energy demand per household is much higher than the demand of central European countries. According to the last census on population in 2004, about 620.533 people lived in the territory of Montenegro.

Population density, the average number being 45 inhabitants per km2, makes Montenegro one of the least densely populated countries in Europe. Forest and water resources are among the most important natural resources in Montenegro, and are very important from the standpoint of future economic development. Furthermore, these resources are exposed to manifold pressures, which are likely to cause or have already caused their unsustainable use. The pressures primarily include unplanned and excessive exploitation of forests and watercourses, as well as plant and animal species whose habitats are either woods or waters, nevertheless, just over 7% of the territory of Montenegro is protected in order to maintain biological diversity.

Until recent times there has been no systematic annual monitoring of changes in the territory covered with forests in Montenegro; although new forestry policies, capacity building and execution of a Global Land Cover (GLC) mapping project are defined as a priority of the Development and Poverty Reduction Strategy (DPRS). At the same time, the Ministry of Agriculture, Forestry and Water Management is tipped to optimize forestry management and land-use planning in the state.

With regard to the agricultural sector, Montenegro is a net importer of agricultural foodstuffs, and in 2004 only 8,133 ha have been used to produce cereals and maize, yielding a total output of 16,100 tons. From this harvest there is no possibility to calculate a biomass potential for renewable energy use, but Montenegro has, with further development, the potential to sustainable harvest production from forestry and agricultural systems as an energy resource.

5.1 BIOMASS-TO-ENERGY CONVERSION TECHNOLOGIES Mankind from the beginning has used biomass, and wood still is the most important cooking fuel world-wide. Through the depletion of fossil fuels and the impact of the corresponding CO2 emissions on global warming, biomass has gained new interest and innovative developments are taking place on all levels from biomass production to conversion technologies. Figure 5.1 shows the most important options from biomass resources to energy conversion. The following subsections describe the following main conversion pathways: direct combustion of biomass, and anaerobic digestion.



5.1.1 Direct combustion

Direct combustion is the most important way to produce electricity and heat from biomass. Biomass is burned directly to produce hot water or steam. Technical devices are widely distributed with thermal capacities ranging from a few kW in household stoves up to heating plants with several tens of MW. The generated heat can be used directly for household (e.g. cooking) or industrial applications (e.g. process heat), or for electricity production. From a thermodynamic point of view, the highest process efficiencies are obtained if both heat and electric power are generated and used in one process. The conversion efficiencies vary from 8 to 18 percent for simple stoves used traditionally in developing countries up to approximately 80 percent for modern combined heat and power plants.

Figure 5.1 : Options for Biomass Energy Combustion

For electricity production, different technologies are available, as illustrated below:

• conventional water/steam cycle: Rankine cycle operated with a (continuous) turbo engine, i.e. a steam turbine. By far, it is the most wide-spread technology for electricity generation, both in industrial and developing countries. The process efficiencies vary from around 10 percent for small steam turbines with simple blade design or single stage (approximately 150 kW up to 5 MW) up to around 30 percent for plants with 15 MW and more, where more sophisticated heat exchanger and steam turbine technologies are applied. Although direct combustion followed by a conventional steam cycle is up to now the most widely applied process, there are several



disadvantages related to its application, arising especially in developing countries: i) for small sizes plants (<10 MW), electric efficiency decreases significantly, ii) steam/water cycle plants should operate in almost continuous operation. If there is only very low power base load required, as often the case in mini-grids, or there is no grid at all, this technology is not suitable, iii) considerable experience is required for operation and maintenance;

• steam (piston) engines: Rankine cycle operated with a (discontinuous) reciprocating engine. Available in the range from 25 kW to 1,500 kW, with efficiencies around 10%. A spilling engine reacts insellustrated belownsible to fluctuating steam conditions, can operate with saturated steam and additionally its efficiency remains almost constant under partial load operation;

• organic Rankine cycle (ORC): the organic Rankine cycle (ORC) is similar to the cycle of a conventional steam turbine, except for the fluid that drives the turbine, which is a high molecularignificantly mass organic fluid. The selected working fluids allow to exploit efficiently low temllustrated belowperature heat sources to produce electricity. The typical range of power output per unit is from few kW up to 3 MW electric power;

• stirling process: an indirectly fired engine with a closed gas volume as working medium. This process is particularly interesting for developing countries because of its simplicity, especially for small installations (3 to 50 kW). However, in spite of the interesting concept and high status of development, the Stirling engine is actually produced in small series only.

5.1.2 Biofuels

In general, the production of three types of biofuels is realized on an industrial scale (Table 5.1). Biofuels usually are generated from large-scale plantations of crops specifically suited for Biofuels production (energy crops). For example, for a small ethanol factory, more than 10,000 ha land have to be cultivated. Ethanol producing factories cost several million Euro even with small-scale plants of 30÷60 m³/day.

Table 5.1 : Biofuel Applications

Biofuel Type Production Use Plant Oil Mechanical or chemical extraction (or

combination of both) from oil, containing plants such as rape seed, Jatropha, Oil Palm

Substitute for diesel fuel in transport sector or small scale electricity generation after slight technical modifications, as well as cooking fuel.

Biodiesel Produced from plant oil via (large-scale) transesterification and refining.

Sale to the transport sector as a direct substitute for diesel fuel.

Bio-Ethanol Anaerobic fermentation of sugar in a water solution, distillation, rectification for high purity. Either produced directly from sugar containing biomass such as sugar cane, or indirectly from startch containing biomass such as cerial or corn after polysaccharide decomposition.

Substitute for gasolina either in a misture (<5% without modification) or pure in modified gasoline engines. Also used as cooking fuel.



5.1.3 Energy Crops

The term energy crops refer to non-food agricultural or non timber forestry plantations to harvest energy rich products like wood / lingo-celluloses, oil seed and starch and sugar containing plant parts (grains, tubers, stem, shoots, etc).

Energy crops are used as fuel (wood) or to be processed into fuel (alcohol, vegetable oil, bio diesel, synthetic biomass-to-liquid fuels, bio gas/methane, charcoal). Energy crops have some special characteristics different from species used for other purposes, and generally the purpose of production changes. Typical examples of species used for energy crop production area are:

• oil palm;

• sugar cane;

• rapeseed;

• maize;

• cereals;

• physic nut;

• eucalyptus;

• elephant grass;

• castor oil plant.

Currently no crops are cultivated in Montenegro with the primary purpose of supplying fuel or energy. The only exemption is represented by trees in woodlands, while agro-forestry and forests are managed to provide also traditional fuels like fire wood and charcoal. This kind of multi purpose use of forest based resources is generally not included under the term energy crops.

5.2 BIOMASS POTENTIAL ESTIMATION

5.2.1 Definition of Biomass Potential

5.2.1.1 Theoretical biomass potential

The theoretical biomass potential describes, in a given region within a certain period, theoretically and physically exploitable energy potentials, productions and/or offers (e.g. whole energy stored in the plant material from 1 ha in 1 year). It is determined only by the given physical borders of utilization and represents the upper limits of the theoretical realizable contribution to the energy production. Only a small fraction of the theoretical biomass potential can be exploited. No practical relevance comes regarding the actual usability of renewable energy from biomass. The theoretical potential is not used in the following analysis.

5.2.1.2 Technical biomass potential

The technical potential describes the fraction of the theoretical potential, which is utilizable, taking into account the main technical constraints. In addition, structural and ecological restrictions, as well as legal default, are taken into consideration. The technical potential is not considered in the following analysis.



5.2.1.3 Economical biomass potential

The economic potential represents the temporal fraction of the theoretical biomass potential, internalizing local costs and conditions for a given place. It is limited by the technical potential, which can be opened up in each location, and does not follow principles of long term sustainable resource management. The economic potential is therefore not considered in the following.

5.2.1.4 Exploitable and sustainable biomass potential

The exploitable potential describes that fraction of the economic potential, which can be harvested under sustainable conditions. Therefore, the exploitable potential is smaller than the other potentials. It can be bigger only if administrative measures give high incentives for the use of renewable energy. The exploitable potential should be considered in the way of sustainability. This potential is considered in the following sections.

5.2.2 Methodology of Biomass Potential Estimation

Different biomass resources are used in energy production. All biomass has a specific energy heating value, depending on the energy density and on the moisture content of the biomass under reference. Additionally, biomass processed for conversion into Biofuels (e.g. Bioethanol and Biodiesel) has specific yield factors that depend on starch/sugar or oil content. When estimating the potential in terms of renewable biomass fuel, the annual increment, or yield that can be sustainable harvested is multiplied by the energy content or yield factor, obtaining an annual amount of energy.

5.2.2.1 Forestry and wood production

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

5

5,5

0 5 10 15 20 25 30 35 40 45 50 55 60

Water content %

low

er H

eatin

g V

alue

(kW

h/kg

)

Hardwood Coniferous Wood

Figure 5.2 : Lower Heating Value for different Type s of Biomass



For our estimations of wood potential, we use a 20% water content, corresponding to a heating value of 4 kWh/kg wood. We considered for 1m³ wood an average mass of 500 kg.

Also, we used for further estimations the lower heating values presented in Figure 5.2, while Table 5.2 shows average estimation of upper heating values for different biomass.

Table 5.2 : Upper Heating Values for different Type s of Biomass

Upper heating value Moisture content

Wood residuals 4.3 MWh/t 15 - 20%

Beeches 5.2 MWh/t 15 - 20%

Oaks 5.3 MWh/t 15 - 20%

Conifers 5.42 MWh/t 15 - 20%

Larch 5.3 MWh/t 15 - 20%

Fir 5.4 MWh/t 15 - 20%

5.2.2.2 Agricultural production

For the biomass from agricultural production it is not easy to define the conversion factors for further resource yield estimations. We used equivalents to give the potential from agro - biomass. For example we used an average of cereal input of 2.5 kg to produce 1 litre of Bioethanol, which is the same as 2.4 t/m3.

Table 5.3 : Examples for Biomass Equivalents from A gricultural Production

Upper heating value Moisture content Energy Factor

Cereals /crop 4.2 kWh/kg 12 % 2.4 – 2.7 kg = 1 l

Ethanol

Maize 5.2 kWh/kg 12 % 2.4 – 2.7 kg = 1 l

Ethanol

Rape seed 6.83 kWh/kg 9 % 1 l / 1 l

5.2.3 Topographic Features

The topography of Montenegro is largely mountainous, with heights from 762 to 2,438 m above sea level. The most famous peak is Mount Lovçen (1,749 m), called the “black mountain” because of its basaltic rock, from which the region’s name is derived.

The land is part of the limestone Karst Plateau along the Adriatic. The few arable regions are in river valleys, mainly along the River Zeta, on the plain around Lake Scutari in the south-west, and near the town of Cetinje.

A 3-D topographic view of the Montenegro territory is shown in Figure 5.3.



Figure 5.3 : 3-D Topographic Map

5.2.4 Global Land Cover Classification

The European Commission’s Joint Research Centre realized a new Global Land Cover classification for the year 2000 (GLC_2000). The project was carried out to provide accurate baseline land cover information to the International Conventions on Climate Change, the Convention to Combat Desertification, the “Ramsar” Convention and the Kyoto Protocol. Furthermore, the GLC_2000 land cover database has been chosen as a core dataset for the Millennium Ecosystems Assessment.

The 2-D land use map of the Montenegro territory is shown in Figure 5.4.



Reference: GLC_2000

Figure 5.4 : 2-D land-use map

More specifically, the GLC_2000 dataset is a main input dataset to define the boundaries between ecosystems such as forest, grassland, and cultivated systems. In contrast to former global mapping initiatives, the GLC_2000 project is a bottom up approach to global mapping. In this project more than 30 research teams have been involved, contributing to 19 regional windows. Each defined region was mapped by local experts, which guaranteed an accurate classification, based on local knowledge. Each regional partner used the VEGA2000 dataset, providing a daily global image from the Vegetation Sensor onboard the SPOT4 satellite. Each partner also used the Land Cover Classification System (LCCS) produced by FAO and United Nations Environment Programme, UNEP, (Di Gregorio and Jansen, 2000), which ensured that a standard legend was used over the globe. This hierarchical classification system allowed each partner to choose the most appropriate land cover classes which best describe their region, whilst also providing the possibility to translate regional classes to a more generalized global legend.



Using the data from the GLC_2000 sources, 16 out of 19 classes occurred in the mosaic of the Montenegrin vegetation types, also including the urban areas, as summarized in Table 5.4.

Table 5.4 : Vegetation Types

Number Vegetation Type %

1 cropland/natural vegetation mosaics 26,9

2 Croplands 24,2

3 mixed forest 13,5

4 deciduous broadleaf forest 12,7

5 woody savannas 6,4

6 Savannas 4,4

7 Grasslands 3,6

8 evergreen needle leaf forest 3,4

9 Water 1,5

10 open scrublands 1,4

11 urban and built-up 1,4

12 evergreen broadleaf forest 0,2

13 barren or sparsely vegetated 0,1

14 permanent wetlands 0,1

15 closed scrublands 0,1

16 deciduous needle leaf forest 0,1

17 snow and ice 0

18 IGBP water bodies 0

19 fill value 0

Reference: GLC_2000

The rendering of GLC_2000 data reveals the heterogeneous nature of the Montenegrin landscape. It is difficult to isolate large belts or expanses of a defined land cover. The limited resolution of 800x1000 m², means that a pixel (800x1000 m²) can be unspecific: i.e. for lands with a mosaic of croplands, forests, shrub land, and grasslands in which no one component comprises more than 60% of the landscape, the pixel is classified as Cropland/Natural Vegetation Mosaics. This is a problem for areas where the different vegetation types changes quickly in one pixel, which means that the resolution is too low for an appropriate classification of land cover with respect to the conditions and topography in Montenegro and results in unspecific data.

The result of the GLC mapping is that nearly 27 % of the landscape is not clearly categorized as “cropland/natural vegetation mosaics”, which distorts the represented land use proportions



in Montenegro. Nevertheless, the data were taken as the basis for our calculations. Only four classifications individually account for more than 10% of the whole landscape, covering together more than 75% of the area.

As shown in Figure 5.5, forests represent 35,9%, cropland 28% and at least 33% of the types were unspecific, because they were a mixture between cropland and natural vegetation. The three percent of biomass potential exclusion were only urban areas: national parks were not included.

36%

28%

33%

3%

Forests Cropland Cropland / Natural Vegetation Mosaics

Biomass Potential Exclusion

Reference: GLC_2000

Figure 5.5 : Area Percentages considered for Biomas s Potential Assessment

5.2.4.1 Biomass potential – exclusion areas

Land utilization for productive use must also compete with other needs of the state and ecosystem, as well as natural features of the landscape. Figure 5.6 and Table 5.5 present land excluded from biomass production, mapping urban development, protected areas and water bodies. It should be noted that nearly 10% of the national territory is not usable for biomass production.

Table 5.5 : Areas not usable for Biomass Exploitati on

km² % of the Montenegrin Territory

Urban 200 1.4

Lakes 503 3.6

National parks 908 6.6

Reference: MONSTAT, 2005



Figure 5.6 : Areas not considered for Biomass Poten tial Exploitation

5.3 BIOMASS FROM FOREST PRODUCTION Forests offer an equity rich resource for economic and social development. Effective management of forest resources provides a track for local employment creation, and exploitation of a renewable resource. At the same time, forests are natural habitats with a sensitive ecological balance. The sustainable management of forests can guarantee stable employment and income, domestic and industrial fuel needs, forest products, products of agriculture, stable soils, clean and plentiful water, biodiversity, and mitigation of greenhouse gas emissions.

The last forest resource inventory for Montenegro was realized in 1979. The objective of the assessment was to initiate a methodical and well-organized system for forest management and resource planning. The development of new and balanced forestry policies was to be built on this inventory.

Data on growing stock obtained in 1979 was gathered on the basis of a common methodology drawn up by the former Federal Statistical Bureau. Forests with management plans were developed via their own internal statistics, while private forests or areas where internal data were provided by assessment methods. This data was used to determine the



area, wood volume, increment, types of trees, sylviculture form, age, etc. Cadastral data were used for assessing forest area (where it was not otherwise determined). The inventory included forest area, wood volume and increment for all forests. In addition to forests, unstocked forest areas (managed by forest enterprises), plantations of deciduous and coniferous species, permanent communication structures and public roads, which pass through forests, were inventoried. During the inventory, the following assumptions were defined:

• a forest is a forest area exceeding 0,5 ha;

• hedgerows, municipal parks, forest nurseries and groups of trees on areas smaller then 0,5 ha are not considered forests;

• plantations are forests raised from selected plant sources with intensive high yield measures (short rotation);

• unstocked forest land includes land which yields the best results if forests are growing on it.

5.3.1 Forest Types

Forests and woodland cover 54% (743,609 ha) of the total area of the Republic of Montenegro. Out of the total woodland surface, forests cover the area of 39%, which means 546,000 ha, while under grown forest land covers 122,737 ha. The average size of private forests holdings is small (about 0,5 hectares). Private forests tend to contain timber of poor quality, and are largely unproductive. Due to their small size and low productivity, owners cannot afford to pay for professional management of their forests. The timber produced is used mainly for fuel wood.

The total standing stock in the Montenegro forests is estimated at 72,056,699 m3. According to the national regulations, forests as a public wealth are to be renewed, maintained and used in order to ensure their sustainable protection, growth of their natural values and ecological functions, sustainable and functional utilization protection from negative effects.

About 212,000 hectares are covered with high economic forests that can be used as raw materials for wood-processing industries. A substantial portion of the forest resources is placed in the underdeveloped Northern region.

The main issues related to forest management are:

• small size of private owned forests;

• unplanned cutting (wood is mainly used as timber or firewood) and poor enforcement of regulations;

• inappropriate forest exploitation methods and poor technical equipment.

Wood-processing industry is in a very bad condition, due to the problems and challenges now being addressed in the forestry industry, with particular reference to forest management.

The forest potential is seriously damaged by forest fires, along with forest illnesses caused by the air pollution in certain municipalities. Major pollution sources include thermal-power



plant and Pljevlja coal mine, plants of large industrial facilities (aluminium plant in Podgorica, iron and steel works in Nikšić). Pathogenic micro flora and vermin are also taking their toll on the productivity of the forests. Uncontrolled fires are also eating away at the resource potential; in year 2000 more than 250 forest fires occurred, in which almost 2,000 hectares were burnt down, and around 150,000 m3 of wood were destroyed or damaged.

The optimization of forest and water resources management requires significant changes to both the legislative and institutional framework. With regard to water resources management, for instance, harmonization with the EU standards and introduction of the river-basin integral management will bring about major modifications. The legislation and institutional reform process relevant to forest resources is more advanced, but some major changes still lie ahead. One of the key issues is the need to set concessions and other forms of compensations order to reach a sustainable resource use.

5.3.2 Environmental Advantages of Wood Biomass to E nergy

The main environmental advantages of the utilization of wood biomass are:

• Renewables: the main environmental advantages of the utilization of wood biomass are :wood fuel has several environmental advantages compared with fossil fuels. Wood can be continually replenished, which leads to a sustainable and dependable supply. However, proper forest management must be undertaken in order to ensure that growing conditions are not degraded during biomass production;

• Carbon emissions: there is little net production (~5%) of carbon dioxide (CO2), the major greenhouse gas, from wood combustion because the CO2 generated during combustion of the wood equals the CO2 consumed during the lifecycle of the tree;

• Heavy metals and sulphur: wood fuel contains minimal concentrations of heavy metals and sulphur; wood fuel does not add to acid rain pollution.

• Minimal bottom ash: particulate emissions from wood are controllable through standard emission control devices such as bag houses, cyclone separators, and electronic precipitators. Usually wood ash is less than 1% of the weight of the wood, and sometimes ash may be used as fertilizer.

5.3.3 Energy Potential from Forests

The theoretical energy potential of Montenegrin forests have been estimated on the basis of the data presented in the paper: “Forest and Forest Production Country Profile: Serbia and Montenegro 2005” (FAO, 2005). The annual increment of forests in Montenegro considered within this study is equal to 2.6 m3/ha/y. This value is considerably lower than the average increases of central European forests (between 7-13 m³/ha/year). This is due to the low amount of total standing tree-biomass (m³) per hectare in Montenegro, if compared with the central European forests (300-500 m³/ha).

As the current utilization of wood from forest is about 1.03 m³/ha/y, the biomass potentially available results to be 1.57 m3/ha/y. Since the overall forest area is equal to 546,000 ha, the estimated amount of biomass available for energy production is approximately 850,000 m³/year.

In addition, we can also consider the residues from the wood industry. This quantity can be estimated to be around 170,000 m³/year, as 30% of the wood currently used.



From the above, the overall biomass potentially available for energy production is up to approximately 1,000,000 m³/year.

This value must be considered as a preliminary estimation due to the limited set of available data. As an example, the information collected for the Berane region are discussed in the following.

The Montenegrin Ministry of Economy recently issued a technical assessment of the wood waste available in Montenegro (MME, 2006). Table 5.6 and Table 5.7 summarize the relevant wood waste data.

Table 5.6 : Forest Characterization Data

Municipality Wood type

Gross cutting

mass m 3

Technical Logs % m3

Cell. Fire Wood

% m3

Waste % m3

conifer 48,229 64 30,866 16 7,717 20 9,646 Rožaje

deciduous 7,088 48 3,402 32 2,268 20 1,418

conifer 40,854 64 26,146 16 6,537 20 8,171 Berane

deciduous 16,854 48 8,090 32 5,393 20 3,371

conifer 7,185 64 4,598 16 1,150 20 1,437 Andrijevica

deciduous 31,033 48 14,896 32 9,930 20 6,207

Total 151,243 87,998 32,995 30,250

Reference: MME, 2006

Table 5.7 : Sawmill Waste

No. Type of Technical Logs

Quantity m3

Sawmill Waste % m3

1 conifer 61,610 35 21,563

2 deciduous 26,388 45 11,875

Total 87,998 33,438

Reference: MME, 2006

It should be noted that the wood data enclosed in the Government inventory (MME, 2006) does not always match with the field information collected at a local level during the site visits held by the Italian experts on September 2006. With reference to the municipalities of Berane, Andrijevica, and Rozaje, the MME document reports an annual wood waste available for energetic fuel from forest and sawmills respectively equal to 30,250 m³ and 33,438 m³ (total: 63,688 m³). On the other hand, the specific data received from the target municipalities during



the site visit, as shown in Table 5.8, indicates a total amount of wood available for energetic fuel from forest and sawmills of approximately 31,600 m³/year.

Therefore, a reliable inventory of the wood waste available in the Country can be built only with careful surveys and meetings with the local relevant institutions.

Table 5.8 : Wood Waste available for Energetic Fuel

No. Municipality Wood Waste m3

1 Rožaje 18,400

2 Berane 10,800

3 Andrijevica 2,400

Subtotal 31,600

4 Bijelo Polje 11,520

5 Plav 14,000

Total 57,120

Wood pelleting

It is not foreseen at this time that Montenegro will have an economically exploitable potential for a wood pellet industry. The increase in production costs caused by this additional processing step cannot be justified in the current unexploited market for wood chips. As an example, considering a 6 t/h plant, the cost of pelletisation represents around 38% of the total production costs; whereas for production of wood chips, the chipping only represents approximately 12% of the total production costs (including transport and storage). Therefore, the available wood biomass can be more effectively used as untreated residues, or as wood chips. Furthermore, the current technological level and efficiency of the Montenegrin forest industry cannot compete with the highly mechanized forest industries of countries such as Finland and Austria.

5.4 BIOMASS FROM AGRICULTURE Agricultural areas in Montenegro cover approximately 518,067 ha (MONSTAT, 2005) equivalent to the 38% of the total country surface. The average agricultural area per capita as about 0.84 ha. In Europe only Ireland has larger agricultural area per capita (1.10 ha), while the average (EU-25) is 0.36 ha.

In the last five years no significant changes occurred in the extension of the total agricultural areas in Montenegro. Agriculture in Montenegro is still facing problems with fragmented land and ownership structure, poor level of technology, with a consequent low level of productivity and a low level of incomes for people employed in this sector. The latest available data on structure of agricultural households, collected in 1991, report an average size of farms below 9 hectares.

Crop production is mostly aimed at livestock consumption or household consumption and only 8% of the total production is sold on the market.



At the moment there are no integrated systems to take biomasses from agricultural systems to produce heat or power as renewable energy sources.

The biomass potential from agriculture in Montenegro was estimated by using the yields of farming and the agricultural area (MONSTAT, 2005) and also GLC_2000 data. The yields of farming resulted to be very poor, and therefore also the estimated energy potential is low.

5.4.1 Main Agricultural Areas

Hilly-mountainous zone and a lack of lowland are the main limiting factors for the agriculture development. Therefore, the agricultural production in Montenegro is based on small-scale family households.

Green lands (permanent pastures and meadows) are the most frequent types, representing the 87% of the whole agricultural land (518,067 ha). Green lands were normally used to produce feedstock for livestock.

Figure 5.7 shows the types of agricultural lands of Montenegro. Even thought the agriculture in Montenegro will be modernized in the future, strong limitations will derive from the topography, since mountainous green land is not convertible into arable land.

Permanent Pastures

62%

Meadows25%

Permanent Crops3%

Arable Land9%Swamps

1%

Arable Land Permanent Crops Meadows Permanent Pastures Swamps


Figure 5.7 : Agricultural Land - Production Units

5.4.2 Arable Land

In 2004, 31,902 ha of arable land were used as sown areas. The main products were cereals, industrial crops like tobacco, vegetable crops and fodder crops. Approximately 28% of total arable land (46,888 ha) are non-cultivated areas or areas used for extensive agricultural production. The production of vegetables is characterized by the lack of intensive arable land production and inadequate sowing and irrigation structure. Since vegetable production in



Montenegro is considered as a secondary activity the production is low, designated for local market only.

5.4.2.1 Production of cereal crops

As shown in Table 5.9, in 2004 only 5,819 ha were used for cereal production (MONSTAT, 2005), equal to the 12.6% of the whole arable area and only 1.1% of the agricultural land.

Table 5.9 : Cereal Crop Production - year 2004

Cereal Harvested Area (ha) Total Yield (t) Yield pe r Hectare (t)

Maize 3,217 9,641 2.99

Wheat 1,123 3,437 3.09

Ray 112 256 2.28

Barley 1,026 1,960 1.91

Oats 341 666 1.95

Total 5,819 15,960

In comparison to central European agricultural systems the yields were very poor and average production in wheat and maize has been only approximately 3 t/ha (Table 5.9), 3 times less than in more exploited agricultural systems. This is related to the low access to high-technical agricultural machinery (fertilization, harvesting, sawing and irrigation). The graphical representation of the average yield of the main cereal crops in 2004 is shown in Figure 5.8.

0

0,5

1

1,5

2

2,5

3

t / h

a

Wheat Maize Ray Oats

Wheat Maize Ray Oats

Figure 5.8 : Average Yield of the Main Cereal Crops

Montenegro is a net importer of food products. Although agro-food trade is growing, the import deficit is also growing. Taking 2004 as the first representative year regarding availability of reliable data, the total exchange of agro-food was € 259.5 x 106, and the



import deficit was € 146.5 x 106. This deficit was evident in all product groups except tobacco.

5.4.2.2 Pastures and natural meadows

The percentage of the pastures and natural meadows in the whole of the agricultural land is the largest, representing about 87% of the total agricultural area. Production on meadows almost doubled compared to 1992, while production on pastures increased about 30%.

5.4.2.3 Production of Fodder crops

Table 5.10 : Production of Fodder Crops

Clover Lucerne Forage beet

Yield, tons Yield, tons Yield, tons Area harvested total per ha

Area harvested Total per ha


858 3,802 4.43 3033 11,784 3.88 272 3,453 12.69

Fodder maize Meadows Pastures

Yield, tons Yield, tons Yield, tons Area harvested total per ha


Area harvested Total per

ha

56 473 8.45 128,261 241,669 1.88 326,620 127,118 0.39


Table 5.11 : Production of Vegetable crops

Potatoes Onion Garlic

Yield, tons Yield, tons Yield, tons Area

harvested total per ha



10,350 117,039 11.31 646 3,261 5.05 191 501 2.62

Tomatoes Paprika





993 22,818 22.97 805 16,092 19.99 1382 40,647 29.41

Beans Peas Cabbage and kale





768 2,218 1.72 212 578 2.73 1,804 25,015 13.87




5.4.2.4 Orchards

All fruits, except for citrus and oranges, are cultivated in gardens or on a small-scale without the application of agro-technological measures (tillage, fertilizing, cutting, pest and frost protection, irrigation), so the fruitfulness is low and alternative (every second or third year). This is the main reason why, along with weather conditions, fruit production varies significantly.

According to statistical data, in 2003 orchards (and olive trees), covered an area of about 9,580 ha.

Table 5.12 : Fruit Production

Apples Pears

Number of trees Yield Number of trees Yield

Total Of

productive age

Total tons Kg per tree Total

of productive

age Total tons Kg per

tree

450,305 385,244 3,980 10.4 223,905 183,005 1,647 9

Cherries Peaches


Total Of

productive age


of productive


tree

127,386 111,885 1,495 13.4 211,400 197,960 3,842 19.4

Plums Citrus Fruit


Total Of

productive age


of productive


tree

1,353,596 1,200,835 6,155 5.1 292,774 231,769 6,859 29.6

Walnuts Figs


Total Of

productive age


of productive


tree

55,800 46,340 476 10.3 219,530 204,790 4,811 23.5


5.4.2.5 Olive Production

With 3,200 ha, olive production covers one third of the total fruit area in Montenegro. There are 412,000 olive trees on the coast of Montenegro.



Table 5.13 : Olive production - Year 2004

Olives

Number of trees Yield

Total Of productive

age Total tons

Kg per tree

443,041 406,621 2,533 6.2


Trees are 100 years old and older are predominant. About 70% of trees are traditional ones, while there are less than 10% young trees. Autochthonous varieties amount to 90% (zutica and other), which are mainly used for the production of oil and conserved fruit in the traditional way.

Despite a good potential for production of olive oil of excellent quality, the existing capacities are not adequately used (below 50%).

The olive residues, or “cake”, represent a good potential source of renewable energy.

The residue from olive oil production constitutes more than 80% by mass of the olives collected. This residue contains vegetable water, pulp and stone from the olives. An attractive feature of the solid component of the residue is the calorific value. This solid residue is quite dense (515 kg/m3) and has a heating value of 20 MJ/kg.

Two processes can be used:

• agglomeration of the residues into fuel blocks to be transported and utilised as solid fuel, also known as briquetting;

• direct firing of the olive residue or co-firing with coal.

Considering the losses due to for wastage transport and handling, together with the relatively low efficiency of typical small-scale power generation systems, it is estimated that approximately 20-25% of the energy available in the olive residues could be transformed in electricity. On this basis, approximately 2,533 tonnes a year of waste could yield approximately 3,000 MWh/y; equivalent to meet the power demand of about 625 Montenegrin houses.

5.4.3 Agricultural Land Use

In Montenegro there are huge differences in the agricultural land use at a local level. Out of the 21 municipalities, 5 have more than 50% of the whole agricultural area of Montenegro. The average agricultural land use is nearly 14% (see Figure 5.9).

The overall land area in Montenegro is covered with different vegetation types (GLC_2000 Data). Figure 5.9 shows the comparison among forest use and agricultural land by municipalities (MONSTAT, 2005). It is noted that about 39% of the whole Montenegrin territory is covered by forests.



0%

5%

10%

15%

20%

25%

30%

TIVAT

BIJELO

POLJ

E

ULCIN

J

ROŽAJE

PLJEVLJ

A

BERANE

ANDRIJEVIC

A

DANILOVGRAD

PLAV

ŽABLJAK

PODGORICA

ŠAVNIK

MOJK

OVAC

BUDVA

KOLAŠIN

HERCEG NOVI

BAR

PLUŽIN

E

KOTOR

CETINJE

Figure 5.9 : Agricultural Land Use by Municipalitie s

Note: the blue area is the Skadar Lake

Figure 5.10 : Comparison between forests and cropla nds



5.4.4 Energy Potential from Agricultural Production

As illustrated in the previous sections, Montenegro shows a certain biomass potential from agricultural residues. In 2004 only 4,340 ha were used to produce cereals and maize, achieving a total yield of 13,000 tons. Table 5.14 summarizes the theoretical biomass potential from the main agricultural productions.

Table 5.14 : Biomass Potential from Agricultural Pr oduction

Yield (t) Energy Factor Potential

Maize 9,641 2.4 ÷ 2.7 kg = 1 l Ethanol 24,100 m3

Ethanol

Wheat 3,437 2.4 ÷ 2.7 kg = 1 l Ethanol 8,590 m3 Ethanol

Hey 12,000 4 MWh/t 15.8 GWh*

Walnut 476 6.16 MWh/t 0.96 GWh*

Olive residues 2,533 4.05 MWh/t 3.4 GWh*

* the estimation includes the efficiency of the combustion power plant

In principle, the total yearly production of Ethanol is equal to 32,690 m3 . In comparison to the low yields of cereal production it is probably not possible to produce high yields in oil seed systems. An estimate for rape seed yields should not be higher than 1.5 tons a hectare. By a 40 % oil content of the seeds the yield per hectare will not be higher than 600 to 800 litre. For one litre of Biodiesel it is at least necessary to use 1 litre of rape oil. In conclusion one hectare of rape seed is not equivalent to more than 600 litres of Biodiesel.

Other potential fuels (hey, walnut and olive residues) are theoretically available (the total energy production could be approximately 20.2 GWh/y). Currently the whole hey-produced on meadows is used as feedstock’s. Walnuts are usually sold on the regional food markets, while the olive residues are mainly used as feedstock for animals.

5.5 PRELIMINARY ECONOMICAL ANALYSIS Currently no biomass combustion power plant running on wood have been realized in Montenegro. However, giving that a good biomass potentiality has been assessed, particularly for forestry resources and wood waste, a preliminary economical analysis for three specific case studies was developed. The theoretical simulation of the three case studies foresees a technical configuration based on a steam-boiler-system with a condensation power turbine. Three different electrical outputs (2 MW, 5 MW and 10 MW) were considered.

It should be noted that the actual lack of operating biomass to energy plants in Montenegro affect the reliability of the analysis, mainly due to the uncertain evaluation of relevant input data, above all the unit cost of the feedstock (wood biomass).

In the following, the main technical assumptions for the three case studies are presented.



Power Plant No. 1(2 MWe):

• output: 2 MWel is approximately equivalent to 7÷10 MWth (range from between 25÷30% efficiency);

• feedstock: wood biomass, energy content 4 kWh/kg, water content 20%;

• plant availability at nominal capacity: 7,800 h/y.

Based on the assumptions outlined above, the feedstock demand would be 17,000 t/y, or 34,000 m³. The power output of the plant would be approximately 15.6 GWh/y, able to meet the energy needs of 3,250 typical Montenegrin households (average need 4,800/kWh/year).

Power Plant No. 2 (5 MWe):


• feedstock: wood biomass, energy content 4 kWh/kg, water content 20%;


Based on the assumptions outlined above, the feedstock demand would be 40,000 t/y, or 80,000 m³. The power output of the plant would be approximately 39 GWh/y, able to meet the energy needs of 8,100 typical Montenegrin households (average need 4,800/kWh/year).

Power Plant No. 3(10 MWe):


• feedstock: Wood biomass, energy content 4 kWh/kg, water content 20%;


Based on the assumptions outlined above, the feedstock demand would be 80,000 t/y, or 160,000 m³. The power output of the plant would be approximately 78 GWh/y, able to meet the energy needs of 16,200 typical Montenegrin households (average need 4,800/kWh/year).

The moisture content affects the energy density of the biomass feedstock and varies depending on how the biomass stock is handled, thus the required input volume of the feedstock varies. For example, freshly cut wood can have a moisture content between 70%÷80%, while harvested wood stored outside for one year will have a moisture content of approximately 50%. Further reductions in moisture content require that the wood is stored under a roof, whereby at the end of the third year the moisture content between 7÷10%. The appropriate heating value for the biomass feedstock is chosen relative to the water content. For example, woodchips have an average water content of 45%, or a heating value of 2.5 kWh/kg. To produce the same energy, it is necessary to intake much more biomass while running on woodchips.

From the financial point of view, an operational lifetime of 20 years is considered, while a discount factor equal to 8.5% is assumed (IMF, 2005; EBRD, 2005). Two energy selling prices were used as input data, on the basis of the unofficial information gathered at the Montenegrin Ministry of Economy: 0.045 €/KWh, as current tariff without any form of incentive, and 0.08 €/KWh, a possible future including State incentives for renewable energy production. As suggested by the Montenegrin Ministry of Economy, the incentive period was assumed equal to 10 years.



An average cost of wood equal to 30 €/m3 (unloaded at the power plant) was assumed (MME, 2006). This unit cost was provided by the Montenegrin Ministry of Energy and was therefore used as input data in the analysis, since was the only ‘official’ information received. However, it is noted that the typical European unit cost for wood is approximately 40 €/t (Bartolazzi, 2006), thus significantly lower than the value reported in the MME document (MME, 2006). The preliminary financial analysis illustrated below is therefore based on very pessimistic assumptions, resulting in not attractive economic indicators. We believe further on site investigations are necessary in order to verify the actual unit cost of the feedstock to be used in the potential power plants.

The financial feasibility of the three power plants has been estimated taking into account a first scenario without incentives, that foresees revenues from selling of electricity generated by the plant and delivered into the National Power grid, and a second scenario with incentives, that includes revenues from State incentives for renewable energy production.

Both scenarios are characterized by low values of IRR and NPV, and by pay back periods higher than 20 years (operational lifetime), therefore not attractive both for private investors and for other public companies or institutions such as EPCG.

The minimum value of the electricity price able to guarantee the financial feasibility (i.e. payback of 7 years) of the three power plants, is in the range 0.12 ÷ 0.14 €/kWh (the current price is 0.045 €/kWh). In these hypothetical scenarios, payback period and IRR become respectively 7 years and 14%. The preliminary financial analysis showed that the proposed scenarios could represent an attractive investment only in case that the current electricity price will be significantly increased.

As already highlighted, the price of wood is another important parameter to be carefully evaluated within the financial analysis, therefore the possibility to obtain wood at a lower cost shall be verified. Several options could be considered, for instance the creation of a consortium among the local authorities or medium-long term contracts with reliable suppliers.

In order to increase the financial sustainability of the plant, other alternative plant configuration were generally analyzed, as illustrated below.

Refuse Derived Fuel

The use of Refuse Derived Fuel (RDF), mixed in a certain percentage with wood as fuel could represent a significant attractive if the Montenegrin Government will introduce State incentives (i.e. the electricity producers could be paid if they use RDF as feeding fuel for the power plants).

The term ‘RDF’ usually refers to the segregated high calorific fraction of processed Municipal Solid Waste (MSW); RDF is the drier solid fraction usually with a higher calorific value. RDF can be produced from MSW through a number of different processes mainly consisting of:

• separation at source;

• sorting or mechanical separation;

• size reduction;

• separation, screening and blending;

• drying and palletizing;



• packaging and storage.

High calorific fractions from processed Municipal Solid Waste (MSW) and industrial wastes are being used both in dedicated energy-to-waste plants and as fuel substitutes in industrial processes.

RDF is also incinerated in district heating plants mainly for heat production. The plants are normally smaller than plants operated for electricity generation.

Co-incineration of RDF with waste wood in small size (less than 20 MW) district heating plants is widespread in North European Countries relying on grate combustion technology. The amount of RDF is usually 10 to 30% of the fuel mass flow to the boiler.

Combined Heat and Power plant (CHP)

A second alternative scenario for increasing the attractive of the investment is the combined production of power and heat, where the heat can be used for industrial purposes (i.e. pulp and paper, tires, and cement industry), or for community heating (i.e. District Heating).

CHP, also known as “cogeneration”, is a method of generating both usable electricity and heat at the same time and includes a prime mover driving an electric generator.

Electric power is always generated while thermal energy may be in the form of steam, hot water, chilled water, or a combination of one or more of these options. The heat generated is utilized via a heat recovery system. The fuel used may be biomass, or woodchips, oil or landfill gas.

The key design factor for a successful on site cogeneration plant is to match the energy balance of the plant to the user requirements. This is commonly achieved by dimensioning the plant in accordance with the users heat demand, and selling the electricity surplus to the Grid, or other external consumer. Significant savings can be made, due to the utilization of the heat from generating electricity, and the elimination of transmission and distribution losses of the grid network.

Most countries in Europe today have a specific objective in their energy policy to increase the percentage of electrical power production from cogeneration installations. Due to the obvious benefits of cogeneration, many countries in Europe have introduced specific legislation to allow cogeneration plants to export surplus energy to the Grid. Some countries have special incentive programs to promote the use of cogeneration energy by giving favorable prices for electricity exported to the Grid.

The main advantages of cogeneration systems are:

• High Energy Efficiency: the high efficiency of cogeneration plants makes this technique a serious competitor for conventional energy generation. The efficiency could be 90%, in comparison with conventional generation which converts only about 35% of the fuel into usable energy, the heat being lost to the atmosphere;

• Low Emissions: the positive environmental impact of cogeneration, i.e. the reduction in emission of greenhouse gases, in particular CO2, has made cogeneration an important element in the world’s energy policy to counter these problems;

• Energy Cost Savings: substantial savings in energy cost through cogeneration provides an additional competitive edge for commercial and industrial users.

The cogeneration technology applied to the district heating systems could become an interesting investment for many towns in Montenegro. District heating system based on



cogeneration are designed to take advantage of large scale energy production, compared to local heat production. Such systems are built with an accumulation tank and auxiliary boiler in addition to the cogeneration plant.

Accumulation tanks are installed with the aim of optimizing the income from electricity generation by running the plant during the hours of the day characterized by high energy price and low thermal demand. The heat can then be accumulated and pumped into the system during the periods of the day when the electricity price is low. Auxiliary boilers are used either to boost the thermal capacity of the district heating system during periods of peak heat demand, or, if necessary, to boost the heat from the accumulation tanks, or simply as back-up to the cogeneration plant.



6 CONCLUSIONS AND RECOMMENDATION

The main objective of this report is to provide a first Renewable Energy Resource Assessment for the Republic of Montenegro, with specific reference to wind, solar and biomass renewable energy sources.

The project team assembled a clear overview and information resource on current and potential renewable energy in Montenegro based on the following activities:

• Collection of data required for renewable energy resource assessment The data were very wide-ranging, as they included information from local authorities such as climatological data, land use/land cover data, forestry data, households and tourism infrastructures data, energy tariffs and incentives, etc.;

• Estimate and map resources The next stage involved producing estimates of the total or ‘technical’ resource – referred to in the study brief as the theoretical potential, analyzing their geographical spread on the Montenegrin territory;

• Taking account of constraints and incompatibilities Grid connection constraints, as well as planning and environmental considerations (in particular, some factors such as, the site complexity and topography, site accessibility and roads, the presence of natural parks, railway lines, grid network) will all reduce the availability of renewable energy, either by preventing physical access to the resource or by creating non-viable commercial conditions;

• Final Assessment Based on the results of the mapping activities, a final evaluation of the actual potential for each of the renewable sources analyzed was provided, in order to promote the inclusion of alternative renewable energy sources into the energy matrixes of the country.

The overall purpose of the project was to thereby supporting the reshaping of policies aimed at integrating the renewable energy development in the Montenegrin economy. The Government of Montenegro is doing a considerable effort toward the promotion and development of renewable energy sources:

• the Energy Law of Montenegro, adopted in June 2003, states that the Government shall, through the Ministry, “develop and promote incentives for the efficient use of energy and renewable resources” and “promote the increased use of renewable energy sources and alternative energy sources for generation in the internal market”;

• within this framework, in early 2004 a new institution, the Regulatory Agency for Energy, which will be responsible for the general implementation of the law and specifically of the measures on renewable energy, was activated;

• in October 2005, the Government of Montenegro has adopted the so-called Energy Efficiency Strategy of Montenegro, that explicitly assumes to increase “participation” of renewable resources in the next 5 years;

• at the beginning of year 2006, the Energy Policy of Montenegro has been adopted with the goal and objective, among other, to provide institutional and financial incentives to improving energy efficiency and reducing energy intensity in all sectors, from generation to consumption of energy. The Energy Policy explicitly includes among the objectives, the “creation of conditions for higher utilization of renewable energy resources, combined production of electric and heat energy and utilization of fossil fuels through clean technologies” and the “sustainable production and utilization of



energy in relation to environmental protection, and international cooperation in this field, particularly in reduction of GHG emissions”.

The main findings of the study are summarized in the following paragraphs, also providing, where needed, suggestions and recommendations for further activities.

6.1 WIND ENERGY POTENTIAL The estimate of the countrywide wind energy potential was first performed and the results presented in the format of countrywide wind maps, showing the average wind speed and the average wind power at a reference height (50 meters) above ground level.

Following the wind energy potential mapping, a more detailed technical potential evaluation was conducted in order to properly take into account all the main restrictions that can reduce the potential of exploitation of the wind resource. Wind measurements at the ground were also used to validate and further refine the preliminary results above in order to obtain a final estimate of the annual wind energy production calibrated on actual wind data.

Montenegro shows a good potential for wind energy systems in specific portions of its territory. From the analysis, the wind speed turns out to be lower than 5 meters per second in most of the Montenegro, thus in a typical range of Northern Italy and Central Europe. However, the estimated values were increasing to 5-7 meters per second moving toward the sea, reaching 7-8 meters per second in promising areas along the coast, where typical values of South Italy, Greece and Spain are noticed.

Typical values of the actual wind potential is of the order of 100÷300 W/m2, raising in those specific windiest areas, located at the ridges and tops of mountain ranges, to more than 400 W/m2 i.e. typical values of the Southern part of the European countries faced to the Mediterranean sea.

The analysis showed that most of high wind speed areas located in inner Montenegro lose their appeal due to the high altitude of the mountains. Also in the remaining parts of the territory, the windiest areas are located on the ridges of the mountains. Most of these areas are not crossed by the existing road network. Therefore, investments for improving the road infrastructure (and for power lines) should be necessary to allow transportation of wind farm parts, such as tower pieces and blades, to the construction sites. This means that, apart for few suitable locations, small turbines (in the range of 750÷1000 kW), implying small equipment to be transport, should be chosen in most of the potential sites. This means that medium size wind farms, consisting of ten or more mills, would be more suitable than single machines. In this way fixed investments in infrastructure would be more conveniently shared by a number of turbines served by the same power line and access road.

On the basis of these results, by defining a minimum energy potential, which is required for an economical operation of wind turbines, all the coastal as well as the offshore areas seem to be promising as they show values as high as 6 meters per second. The territory around Niksic can be considered interesting also, with average wind speed values in the range 5.5-6.5 meters per second.

A preliminary analysis of the economical aspects related to the practical installation of wind power plants in Montenegro was also performed. Various scenarios with different capacity factors (20, 25 and 30%), corresponding to different average wind speeds at 50 m a.g.l (6.1, 6.4 and 7 m/s), for the wind turbine Vestas V-52, 850 kW nominal power, were analyzed.



In order to evaluate the most suitable areas for wind power installations, the following assumptions were made:

• high productivity potential, considering areas where the capacity factor is above 30%;

• medium productivity potential, considering areas where the capacity factor is above 25%.

According to the preliminary estimation undertaken, the Republic of Montenegro shows a wind potential of 100 MW considering only the windiest areas (wind speeds above 7 m/s) and an overall potential of 400 MW taking also into account the zones with medium potentiality. This potential energy output could provide up to 20-25% of the yearly power consumption of the Country.

In order to allow the actual exploitation of the wind energy potential in the Country, a set of priority actions should be implemented in the close future. More specifically, the following initiatives are recommended:

• within this study, the comparison of the simulation results with the ground measured wind data highlighted the need of further analyses in order to refine the estimation and to avoid the risk of underestimating the actual wind potential. At the scope, more reliable measured wind data are necessary, through the installation of new anemometric stations or the collection of ground measures at the airports, where the instruments are usually located in open zones and properly maintained;

• the grid cell used for the elaboration of the wind maps is around one km, an area that allows a good estimation of wind potential but is too wide for the precise location of a wind farm. The realization of higher resolution maps (with grid steps around 100 m) would allow a more precise identification of the best suitable sites for wind farms installation;

• the implementation of one or more pilot projects for the realization of wind power plants represents the most useful step to verify the actual feasibility of the exploitation of the wind energy potential. To this purpose, the installation of accurate wind speed meters on the selected sites will be necessary. Moreover, a pilot project would allow the identification of other important possible criticalities, not highlighted in this study, such as logistics problems, grid interconnection issues and potential impacts of wind power plants on the local environment and landscape. Finally, the practical development of a wind project would increase the local knowledge and expertise in this field, that represents a fundamental requirement for a broad development of wind energy in the country.

6.2 SOLAR ENERGY POTENTIAL As part of this study, digital maps of global solar radiation over the whole territory of Montenegro were created. The solar radiation maps show the theoretical solar potential of the country, i.e. the available global solar radiation on a site over a certain period of time (unaccounted for are all technical and economical restrictions). In particular, solar mapping is a means of showing the solar potential of buildings, in order to identify which ones are suitable for retrofitting to solar energy, particularly solar domestic hot water.

Due to the size of the country, great differences in average solar radiation were not observed. Montenegro has one of the greatest solar energy potential in the South-Eastern Europe: it ranks above its neighbours, as the annual amount of solar energy estimated in Podgorica, of



the order of 1,600 kWh/(m²·d), is greater than the corresponding reference values for Athens, Rome and Tirana (of the order of 1,560 kWh/(m²·d)), Belgrade and Skopje (of the order of 1,350 kWh/(m²·d)) and of Zagreb and Sarajevo (of the order of 1,200-1,250 kWh/(m²·d)).

The Montenegrin coastal regions enjoy more than 2,500 hours of sunshine a year and a very high level of solar radiation in the summer, late spring and early fall. The plains also exhibit a large number of hours of sunshine but somewhat lower than on the coast. In winter, this region receives a similar amount of solar radiation to that of the coast but relatively lower in summer. The major difference lies between these two first regions and the mountainous regions of the North, where sunshine can be scarce.

After this assessment, it appears that the pure solar potential of the coastal and central regions of Montenegro is high. In fact, the amount of solar radiation is comparable to that of Greece and Southern Italy, where solar thermal systems are widely used. From a technical point of view and based on this potential, the use of solar thermal energy in Montenegro was recommended. Within this study, the potential of use of solar energy was assessed in two of the most promising sectors: solar thermal energy for households and solar thermal energy for the tourism industry. The evaluation was conducted on the basis of the mapping results and of the evaluation of the seasonal and yearly needs in terms of hot water for typical households in the coastal, central and mountainous areas. Specific aspects, such as investment and operational and maintenance costs, were also taken into account.

Concerning the tourism sector, that is expected to grow rapidly in the next years, a similar approach was adopted, considering the different seasonal domestic hot water demand. The evaluation was mainly focus on the coastal area, i.e. the region where tourists mostly concentrate.

If the development of solar thermal energy becomes one important governmental action in the future, then the decision-makers need to have available a better overview of the market and possibilities in order to create the suitable set of regulations, laws and support schemes. Therefore a follow-up to this work are suggested to be undertaken:

• a complete market and sector study has to be performed in order to thoroughly analyze the history of solar thermal in Montenegro in terms of quantities, quality and regulation. This study should focus on the commercial but also on the technical sides. Regarding the present situation, the current level of expertise should be evaluated and the origin and specifications of the equipment installed documented. The complete potential for solar heating and cooling should be evaluated in all sectors: residential, tourism, industry and public buildings;

• a second study should also be carried out with focus on European regulations and market. By basing itself on an analysis of best practices and the results of the previously mentioned piece of work, this study would present the best solutions for Montenegro in terms of regulation, law, incentives and development and awareness programs;

• an exemplar show case project could be put in place. This project should be state-of-the-art and include solar heating and cooling, and passive solar architecture; it should be a public building which can be visited by the public.

6.3 BIOMASS ENERGY POTENTIAL The biomass energy potential assessment was focused on forestry resources, wood waste and agriculture.



For the forestry, the evaluation was based on the data provided by the competent authorities (forestry institutes, etc.). Concerning the wood waste, the available data on the wood industry sector in Montenegro were evaluated, with specific reference to the identification, description and evaluation of the local sector of activities (sawmills, other wood processing industries, etc), the assessment of production capabilities and capacities, the estimate of the amount of wood waste generated, etc. Concerning agriculture, the potential was limited to the estimation of the possibility to produce bio-fuel.

Both the forestry and the agricultural sector in Montenegro presents interesting margin of improvements: on the basis of the data available, the agriculture and forestry yields are significantly lower (from 2 to 3 times) than the typical values in Central Europe.

However, for both sectors there is the possibility to improve yields. In the agricultural sector, it is recommended the use of modern and improved technologies, such as fertilizers and effective irrigation techniques. In order to facilitate such improvements, specific incentives should be provided by the Government, since the farmers do not have the economic resources necessary to implement the technology enhancements. In the forestry sector, a good potential could be theoretically available, since large portions of the Montenegro territory are covered by woods. One of the most useful development could be the application of short rotation forestry for wood biomass production, together with a more effective forests management. However, a number of issues need to be addressed before short-rotation forestry could be widely established, i.e. the potential impacts on biodiversity, archaeology and the landscape, and the high water use required by some species. The prices for wood are increasing on the European market very fast. Nowadays, products from the wood industry are viable to be exported to other countries, due to the higher price level abroad.

Taking into account the possible development of forestry sector, specific feasibility studies are needed. At present the cost for electric energy in Montenegro is very low, while the buying price for imported electricity is approximately 4÷6 €Cent/kWh (it should be noted that this price is varying on a daily basis) versus a national selling price of electricity of about 4.5 €Cent/kWh.

Within this study, a preliminary economical analysis for three specific case studies was developed. Three different electrical outputs (2 MW, 5 MW and 10 MW) were considered, all based on the use of wood biomass. Two energy prices were taken into account: a first scenario without incentives, that foresees revenues from selling of electricity generated by the plant and delivered into the National Power grid (0.045 €/kWh), and a second scenario with incentives, that includes revenues from State incentives for renewable energy production (0.08 €/kWh). The unit cost of the feedstock (wood biomass) used in our analysis (30 €/m3) was estimated in accordance to the only ‘official’ information available (MME, 2006), even though the typical European unit cost for wood is significantly lower (approximately 40 €/t). Therefore, in order to have a more refined estimation of the financial feasibility of biomass plants in the Country, further investigations on the actual unit cost of the feedstock are required.

The results of the preliminary analysis showed that the proposed scenarios could represent an attractive investment only in case that the current electricity price will be significantly increased. In order to improve the financial sustainability of the plant, other alternative plant configurations were preliminary evaluated (i.e. use of Refuse Derived Fuel Combined Heat and Power plant, etc.).



In order to have a more refined estimation of the financial feasibility of biomass plants in the Country, a more reliable inventory of wood wastes (to be developed in close cooperation with local relevant institutions) and specific feasibility studies are required.

6.4 CONCLUDING REMARKS The analysis of the conditions and potential of using renewable energy sources show that renewable energy can play a significant role in the fuel energy infrastructure in Montenegro. However, there are a number of barriers that prevent greater utilization of renewable energy in Montenegro and in general in the Balkans area:

• very low prices of traditional fuel energy sources;

• lack of finance and absence of investors interested in investing in these technologies;

• absence of a complete legislative base to promote renewable energy;

• lack of information to the population, not aware of the opportunities of renewable energy.

Within the study, whenever possible the analysis was completed by a preliminary assessment of the investment opportunities, carried out using information on the most appropriated technology available in the market for every specific case.

For the development of wind and biomass energy sources, feasibility studies are needed to assess the actual suitability of specific interventions from the technical-economic perspective. In the solar energy sector, since distributed systems in the household and tourism sectors are expected to eventually take place, a specific market study and an exemplar show case project could support and accelerate the actions of the decision-makers.

In principle direct comparisons between conventional and renewable technologies, in terms of the financial return on investment, do not favor renewables, and several factors must be accounted for, such as:

• relatively high capital costs per unit of output, often either because further technological development is needed or because the system is too small to benefit from economies of scale;

• grid connection costs may also be high;

• because of the small size of many renewable energy projects, project planning and development costs are also often high;

• the price of electricity is maintained at an artificially low level, reducing the value of the product;

• unlike other fuels like oil and coal, which can be transported to central generating facilities, renewables are best converted into electricity where the resource is located (because many renewable resources are relatively dispersed and are often located in remote areas, transmission and connection costs are high);

• a high level of both real and perceived risk is associated with the construction and operation of many renewable energy projects, due to lack of experience of these sources, particularly with regard to the reliability of resource availability (e.g. wood biomass), and therefore of the project value;

• fluctuating power outputs from renewable electricity generation may reduce the security of electricity supplied through the grid, often resulting in the need for back-up systems which may increase the overall costs of the system.


Italian Ministry for the Environment, Land and Sea Pag. R-1 Renewable Energy Resource Assessment, Republic of Montenegro Wind, Solar and Biomass Energy Assessment

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