d1.4 energy saving potential report on existing technologies ......dimmer d1.4 - energy saving...

DIMMER

D1.4 - Energy Saving Potential 1

Small or medium-scale focused research project (STREP)

FP7-SMARTCITIES-2013

ICT-2013.6.4 Optimizing Energy Systems in Smart Cities

District Information Modeling and Management

for Energy Reduction

DIMMER

Project Duration: 2013.10.01 – 2016.09.30

Grant Agreement number: 609084

Collaborative Project

WP1 IREN

D1.4 Energy saving potential: report on existing

technologies and methodologies

Prepared by DIMMER Collaboration Submission date 30.09.2014 Due date 30.09.2014 Nature of the deliverable R P D O

Dissemination level PU PP RE CO

Project Coordinator: Prof. Enrico Macii, Politecnico di Torino

Tel: +39 011 564 7074

Fax: +39 011 564 7090

E mail: [email protected]

Project website address: http://dimmer.polito.it

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REVISION HISTORY

Date Version Author/Contributor Comments

28 Jul. 2014 1 D’Appolonia, Andrea Podestà Initial version with report structure

22 Aug. 2014 2 Arup, Richard Mizzi Additions as track changes to sections 1.1.3, 3.3,

4.2, 5.2, Appendix

28 Aug. 2014 3 Arup, Richard Mizzi Additions as track changes to 1.1.3

2 Sep. 2014 4 Andrea Podestà (D’Appolonia) Additions to chapter 1 and 2 – integration and

comments on sections 1.1.3, 3.3, 4.2, 5.2,

Appendix

5 Sep. 2014 5 IREN, Federico Boni Castagnetti Additions to chapter 5.1

10 Sep. 2014 6 Fraunhofer, Alexandr Krylovskiy Additions to chapter 4

20 Sep. 2014 7 University of Manchester,

Pierluigi Mancarella

Additions to paragraph

28 Sep. 2014 8 Andrea Podestà, D’Appolonia Homogenisation of the contributions,

30 Sep. 2014 9 Iren Final review

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COPYRIGHT

© Copyright 2013 DIMMER Consortium consisting of

This document may not be copied, reproduced, or modified in whole or in part for any purpose without written

permission from the DIMMER Consortium. In addition to such written permission to copy, reproduce, or modify this

document in whole or part, an acknowledgement of the authors of the document and all applicable portions of the

copyright notice must be clearly referenced.

All rights reserved.

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TABLE OF CONTENTS

Revision History................................................................................................................................... 2

Copyright ............................................................................................................................................. 3

Table of Contents ................................................................................................................................ 4

List of Figures ...................................................................................................................................... 6

List of Tables ........................................................................................................................................ 7

Abbreviations ...................................................................................................................................... 8

Definitions ........................................................................................................................................... 9

Executive summary ........................................................................................................................... 10

Introduction ...................................................................................................................................... 11

1. Energy efficiency within Districts ............................................................................................... 12

1.1. Energy efficiency at building level ............................................................................................................................... 14

1.2. Energy efficiency at distribution level ......................................................................................................................... 17

1.2.1. Electric networks ................................................................................................................................................. 17

1.2.2. Gas networks ....................................................................................................................................................... 18

1.2.3. District heating networks .................................................................................................................................... 18

1.2.4. Connectors among energy networks ................................................................................................................. 19

1.3. Energy efficiency and urban planning ......................................................................................................................... 20

2. ICT as a tool to energy efficiency ................................................................................................ 24

2.1. ICT enabled equipments benefits ................................................................................................................................ 24

2.1.1. Monitoring and control ....................................................................................................................................... 24

2.1.2. Optimization ........................................................................................................................................................ 26

2.1.3. Common features and issues on ICT systems implementation ....................................................................... 26

2.1.4. Benchmarking ...................................................................................................................................................... 27

2.2. IPMVP: energy saving measurement .......................................................................................................................... 27

3. Overview on Energy saving potential ......................................................................................... 29

3.1. The European Union case............................................................................................................................................. 30

3.2. The Italian case ............................................................................................................................................................. 35

3.3. The UK case ................................................................................................................................................................... 38

4. Districts overview ....................................................................................................................... 43

4.1. Turin district .................................................................................................................................................................. 43

4.2. Manchester district ....................................................................................................................................................... 44

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5. District Energy efficiency potential ............................................................................................ 45

5.1. Turin district .................................................................................................................................................................. 45

5.1.1. District Heating Network ..................................................................................................................................... 46

5.1.2. Energy Savings potentials: better management of the heat exchangers ........................................................ 49

5.1.3. Energy Savings potentials: Peak demand management and reduction .......................................................... 50

5.1.4. Model structure for the implementation of the DIMMER strategies .............................................................. 55

5.2. Manchester district ....................................................................................................................................................... 56

5.2.1. Heat network energy efficiency potential ......................................................................................................... 56

5.2.1. Electricity network energy efficiency potential ................................................................................................. 56

5.2.2. District intervention ............................................................................................................................................ 58

5.2.3. Literature review on integrated network modelling and distributed multi-generation ................................ 58

6. Conclusions ................................................................................................................................ 61

References ......................................................................................................................................... 62

Appendix ........................................................................................................................................... 64

Example of energy saving potential in Manchester ................................................................................................................. 64

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LIST OF FIGURES

Figure 1 – Process for visualisation of the district character ....................................................................................................... 11

Figure 2: Example of payment into an Allowable Solutions fund for energy efficiency measures ............................................ 23

Figure 3: Evolution of the energy efficiency index in Italy in 1990-2010 ..................................................................................... 35

Figure 4: UK final energy consumption per capita compared against carbon plan scenarios: 1980‑205013

........................... 38

Figure 5: 2020 Energy Efficiency Marginal Abatement Cost Curve14

........................................................................................... 39

Figure 6. Turin Polito’s District ....................................................................................................................................................... 43

Figure 7. Building selection in Turin district................................................................................................................................... 44

Figure 8: Oxford Road Corridor ...................................................................................................................................................... 44

Figure 9: Location of the substations in the Politecnico district .................................................................................................. 46

Figure 10: Building heating system PFD ........................................................................................................................................ 47

Figure 11 – Indoor temperature distribution................................................................................................................................. 47

Figure 12 – Thermal substation data gathering ........................................................................................................................... 48

Figure 13: Limitation valve’s operation – volumetric flow over time graph (Image from Siemens RVD 230 Manual) ............ 50

Figure 14: Total and individual heat demand of ten buildings with peak demand concentrated at the same hour ............... 51

Figure 15: Time shifting example on invented data..................................................................................................................... 51

Figure 16: Time shifting example on real data ............................................................................................................................. 52

Figure 17: Heat exchanger operation at different outdoor temperatures ................................................................................. 53

Figure 18: Set point reaching at different outdoor temperatures .............................................................................................. 53

Figure 19: Clean VS packed heat exchanger set point reaching ................................................................................................. 54

Figure 20: Schematic diagram of the plant rooms and buildings served by the steam heat network ...................................... 57

Figure 21: Map of buildings served by the steam heat network.................................................................................................. 57

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LIST OF TABLES

Table 1: Energy balance of the European Union in 2012 (IEA). Values in ktoe (kilo tons oil equivalent).................................. 13

Table 2: Energy consumption in the residential and commercial/public services sector in the European Union in 2012 ....... 15

Table 3: Total energy consumption and energy efficiency potential for EU-27 .......................................................................... 30

Table 4: Industrial energy consumption and energy efficiency potential for EU-27................................................................... 31

Table 5: Transportation energy consumption and energy efficiency potential for EU-27 ......................................................... 31

Table 6: Household energy consumption and energy efficiency potential for EU-27 ................................................................. 32

Table 7: Tertiary energy consumption and energy efficiency potential for EU-27 ..................................................................... 32

Table 8: Total energy consumption and energy efficiency potential for Italy ............................................................................. 36

Table 9: Industrial energy consumption and energy efficiency potential for Italy ..................................................................... 36

Table 10: Transportation energy consumption and energy efficiency potential for Italy .......................................................... 37

Table 11: Household energy consumption and energy efficiency potential for Italy ................................................................. 37

Table 12: Tertiary energy consumption and energy efficiency potential for Italy ...................................................................... 38

Table 13: Total energy consumption and energy efficiency potential for UK ............................................................................. 40

Table 14: Industrial energy consumption and energy efficiency potential for UK ...................................................................... 40

Table 15: Transportation energy consumption and energy efficiency potential for UK............................................................. 41

Table 16: Household energy consumption and energy efficiency potential for UK .................................................................... 41

Table 17: Tertiary energy consumption and energy efficiency potential for UK ........................................................................ 42

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ABBREVIATIONS

Acronym Full name

BAT Best Available Technologies

BMS Building Management System

BREEAM Building Research Establishment Environmental Assessment Methodology

C2C Capacity to Customers

CCS Carbon Capture and Storage

CMFT Central Manchester University Hospitals NHS Foundation Trust

CSP Concentrating Solar Power

DH District Heating

DHW Domestic Hot Water

EC European Commission

EE-MACC Energy Efficiency Marginal Abatement Cost Curve

EEA European Energy Agency

EECi Energy Efficient Cities initiative

EMR Electricity Market Reform

EMS Energy Management System

ENW Electricity North West

EPBD Energy Performance of Buildings Directive

ESCO Energy Service Company

EU European Union

ETS Emissions Trading System

GRP Gross Domestic Product

H2020 Horizon 2020

HPI High Policy Intensity

HVAC Heating, Ventilation and Air Conditioning

ICT Information and Communication Technologies

IEQ Indoor Environmental Quality

IEA International Energy Agency

IPCC Intergovernmental Panel on Climate Change (UN Body)

IPPC Integrated Pollution Prevention and Control (EU Directive)

ktoe kilotons oil equivalent

IPMVP International Performance Measurement and Verification Protocol

LCN Low Carbon Network

LEED Leadership in Energy and Environmental Design

LPI Low Policy Intensity

M&V Measurement and Verification

NOP Normal Open Point

ODEX Energy Efficiency Index

PEC Primary Energy Consumption

PLEEC Planning for Energy Efficient Cities

PLC Programmable Logical Controller

PV Photovoltaic

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SCADA Supervisory Control And Data Acquisition

SET-Plan Strategic Energy Technology Plan

SME2 Small and Medium-sized Enterprises

UK United Kingdom

UMIST University of Manchester Institute of Science and Technology

DEFINITIONS

Term Full name

District Subject or pilot area of enquiry

Primary Energy Primary energy is an energy form found in nature that has not been subjected to any conversion

or transformation process. It is energy contained in raw fuels, and other forms of energy

received as input to a system. Primary energy can be non-renewable or renewable. Among non

renewable there are coals, crude oil, natural gas, natural nuclear feedstock. Among renewable

there are solar energy, wind, hydro, biomasses, geothermal and tidal energy.

Secondary Energy Secondary energy is an energy form as it is used by final users. In energy statistics secondary

energy forms are normally grouped as electricity, heat and fuels.

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EXECUTIVE SUMMARY

As stated within the Description of work, Task 1.4 of the DIMMER Project treats the energy efficiency potential in general

terms, with reference to energy efficiency in districts, and then with reference to the demo sites.

Urban districts comprehend the majority of population of European Union and comprehend residential areas, commercial

activities, public services and industrial activities, and are responsible for a large part of the overall energy consumption.

An important part of transportation consumption also occurs in districts.

At European level the largest potential for reducing overall energy needs through efficiency stays in the civil sector, i.e.

households and tertiary/services.

This report starts therefore with this aspects, describing the main techniques and technologies that can be implemented

at district level, starting from buildings, passing through energy network and scaling up to urban level and to urban

planning.

Although most of the energy demand is related to energy issues (e.g. buildings’ insulation, plants’ efficiency etc.) ICT,

coupled with sensors and meters and proper energy management analysis tools, can provide a substantial contribution to

energy efficiency: ICT systems can allow a much deeper comprehension of the behaviour of energy systems, therefore

allowing their better modelling and providing important indications on the most effective measures for improving them.

ICT systems can allow a much better tuning of energy services, so that several systems traditionally operating at full load

can be tuned and used at partial load when they are not needed (such as ventilation in a room with a fraction of the

design number of occupants). At the same time, ICT systems provide a fundamental aid for optimal operation and

maintenance of systems: it can allow to promptly detect anomalous consumption of components, indicating failures, wear

or misuse. The report however also takes into consideration some issues to be taken into account when deploying more

and more pervasive sensors and ICT systems, consisting in the loss of control of too many measured parameters and

automated demand-response and the tendency of suppliers of providing vertical suites of hardware and software

components governed by proprietary protocols and systems, posing serious problems of interoperability to users.

In the report an overview of some assessments of the energy efficiency potential in Europe, in Italy and in the UK is

proposed and sided by the targets that have been set for exploiting this potential. The analysis shows the vicinity of the

potential in the three cases, although the economic structure and the typical climate conditions are quite different.

The final part of the report narrows the attention to the two demos, so that the two situations are described before and

the main actions in the demos are presented.

In Turin the boundary of the demo is clearly defined: the activities will be entirely dedicated to the local district heating

(DH) network, where ICT systems will be set in order to improve performance of DH substation thanks to a better

detection of maintenance need, thus allowing an efficiency improvement to the optimal heat exchange between network

and building. ICT will also be used to monitor internal comfort conditions and to assess which is the lowest heat flow

needed to keep them. Last but not least, an action determining a higher involvement of users will be shifting of peak heat

delivery in different buildings, which will allow reducing peak power demand and broadening the peaks thus offering a

smoother operation of the system.

In Manchester, on the other side, the interventions are not yet completely defined, but the main target will be the

involvement of final users in activities aimed at reducing electric and gas base load by limiting energy waste due to

improper behaviour.

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INTRODUCTION

As stated in the final proposal, the DIMMER project is “… a web-service oriented, open platform with capabilities of real-

time district level data processing and visualization. Thanks to the web-service interface, applications can be developed to

monitor and control energy consumption and production from renewable sources. An application can be developed to

visualize real-time energy utilization leading to a considerable educative impact”.

In this task we examine the energy efficiency potential in districts, buildings and grids and suggest innovative solutions

based on the use of sensors suggested in T1.2.

Chapter 1 provides an overview of the most important techniques and technologies to improve energy efficiency in

districts, starting from the building level and going up to the networks and to the urban planning level.

Chapter 2 describes the potential of ICT as a tool to improve energy efficiency, herein including the most important

applications of ICT for energy efficiency, the typical features and issues for ICT systems applied to energy systems and

related good practices and a description of the International Performance Measurement and Verification Protocol

(IPMVP).

Chapter 3 provides an overview of the energy efficiency potential and of the efforts done in this direction in the EU and in

the Countries involved in the DIMMER Project, namely Italy and UK.

Chapter 4 includes a description of the districts selected for implementing the demonstrators of the DIMMER Project in

Turin and in Manchester. The case studies were selected on the basis of “heterogeneity, complementariness and

replicability” as outlined in the project proposal. Concerning the complementariness of the case studies, the two sites will

have different characteristics both in terms of energy distribution as well as building usage, materials and construction

period. A special mention is required for the energy networks. More specifically regarding real-time data collection, focus

is applied to the heat network (i.e. district heating) for the Turin district, while Manchester will concentrate on electricity

and gas distribution. For both sites, access to historic energy and utility consumption data in general is available.

Chapter 5 provides an assessment of the energy efficiency potential of the demonstrators, describing the interventions to

be done at their level of definition in September 2014, and Chapter 6 includes the conclusions of this report. The figure

below illustrates the importance of having a clear understanding of the energy systems and networks in the district, in

addition to the building characteristics and materials of construction. All these aspects contribute to the ‘energy signature’

of the district.

Figure 1 – Process for visualisation of the district character

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1. ENERGY EFFICIENCY WITHIN DISTRICTS

In the following, urban districts are considered as densely populated urban areas, with predominant residential,

residential/commercial and mixed urban land use, therein excluding large industrial complexes which often are adjacent

to districts and sparsely urbanized and rural areas.

Urban districts consume enormous amounts of energy, also without considering within them the industrial energy

consumption. In the European Union about 74%1 of the population lives in urban areas. In the past decades, energy

efficiency improvements in industrial processes and social-economic evolutions, bringing to a substantial de-

industrialization of European economies drastically changed the energy uses. As a result, energy consumption in industrial

activities was overcome by both civil energy uses (residential plus commercial uses) and by transportation. Although

analyses on energy consumption dividing use among urban and rural areas are not commonly done, an analysis of energy

balances of countries provides useful hints. The following Table 1 shows the IEA synthesis2 energy balance of the European

Union.

Urban districts keep together the greatest part of civil (residential and tertiary) energy consumption, as well as relevant

parts of industrial consumption (several small to medium industrial and handicraft activities are included within the urban

texture) and of transportation energy consumption.

Within this context, the importance of districts within the challenge of reducing energy consumption, promoting the

development of clean energy and abating the dependence on fossil fuels, is crucial.

Energy is used for a broad variety of needs and services, this broad variety can however be simplified in considering the

primary energy, that is the natural energy source taken from nature for human use, final energy, i.e. the main categories

of energy in the final use, and the energy vectors used for transporting energy.

In terms of primary energy, the main energy sources are:

• Coals, whose use is currently limited, in most of Europe, to thermoelectric energy production, co-generation and

to some heavy industry processes (steelmaking and metallurgy); the combustion of coal and coal products for

other uses (i.e. space heating or stationary combustion in the industry) is limited in most European countries;

• Oil, still the most important energy source, whose products are the predominant energy source for

transportation and cover an important quote of energy needs for heating in the residential and commercial

sector, as well as several industrial energy uses and thermoelectric power production;

• Natural gas, whose use is steadily growing in substitution of coal and oil products in thermoelectric power

production, for heating purposes in the civil sector and for several industrial uses. Its use in transportation is still

limited, but it is growing;

• Nuclear energy, whose use is limited to electric production (very few plants are used for combined heat and

power production) and whose use is decreasing;

• Waste and waste derived fuels, whose combustion provides significant portions of heat and electric power in

several countries;

1 Data from the World Bank statistics (http://data.worldbank.org/topic/urban-development). This international, not

Eurostat source is chosen to allow providing a consistent comparison among all countries and geographic areas of the

world. 2 http://www.iea.org/statistics/statisticssearch/report/?country=EU28&product=balances&year=2012

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Table 1: Energy balance of the European Union in 2012 (IEA). Values in ktoe (kilo tons oil equivalent)

• Renewable energy sources, whose use is rapidly growing for distributed electricity production (small hydropower,

wind, and solar, in particular), for heat production and combined heat and power production (geothermal,

biomass) and for large size electric production (large hydropower) and transportation fuels (biomass).

In terms of final energy, the main categories of interest for a district are:

• Low temperature heat (temperature below 100 °C), used for space heating and domestic hot water (DHW)

preparation mostly. This is the most important form of final energy used, and the one providing the largest

possibilities of reducing energy use and substituting traditional sources with waste heat sources and renewable

sources;

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• Chill, mostly needed for space cooling and, to a much lesser extent, for refrigeration needs at residential,

commercial and industrial level. Cooling needs are much larger within southern Europe, but large commercial and

office buildings regularly need cooling even in cold climates;

• Mechanical force, needed to satisfy most of the common energy uses within residential, commercial, industrial

and transportation. Such energy service is mostly provided by electricity and, in the case of transportation, by

fuels. This type of energy uses are the most valuable ones, since whereas theoretically fuel energy can be

transformed into thermal energy with 100% efficiency, when providing force through thermal conversion of

primary energy (coal, oil, gas, biomass, nuclear source) or their derived (coal by-products, oil derivates, waste

derived fuels etc.) the limits posed by the 2nd

principle of thermodynamics impose much lower maximum

theoretical efficiencies, which in practice bring to: 55-58% in thermoelectric conversion from natural gas

combined cycle power plants, 30-45% in thermoelectric conversion from oil, coal and simple cycle gas power

plants, 25-35% in thermonuclear electric production and 15-30% in vehicles’ engines. The passage from electric

energy production to the final conversion into mechanical force implies in practice further transportation and

transformation losses, which commonly range between 10 and 30%. The same occurs in transportation between

energy produced at the engine shaft and the final use at the wheels.

Other important categories of final energy are medium temperature heat (temperature between 100 and 500 °C) and high

temperature heat (above 500 °C), but their use is mostly related to the industrial sector (the only exception is cooking in

the civil sector).

Energy vectors are the forms in which energy is commonly transported: electricity and fuels and thermal energy vectors

are the most important ones. Thermal energy vectors are those used in district heating and cooling. For district heating,

hot water, superheated water and steam are the most common vectors used. In district cooling the typical vectors are

chilled water (for systems having heat exchangers only at the end users), superheated water and steam (for systems

having absorption chillers at the end users).

In order to simplify statistics and needs, however, statistics mostly reduce final energy to the most commonly used

vectors, so that in energy statistics the final energy is divided into electricity, recovered heat (from cogeneration and

waste heat sources) and fuels.

The challenge of drastically reducing energy consumption in districts passes through different levels: the level of buildings,

the level of distribution networks, that must be able to distribute energy more efficiently and recover waste energy and

energy surpluses and that of urban planning. These three levels are described in deeper detail in the following three

paragraphs.

1.1.Energy efficiency at building level

Energy demand of buildings accounts roughly 40%3 of total energy demand within the European Union. The improvement

of efficiency performance of buildings is being one of the main targets of the European Union policies on energy.

Energy efficiency is being pursued towards the reduction of demand – in terms of space heating, ventilation, cooling,

lighting, electric appliances – and in terms of efficiency of energy transforming apparels (boilers for heating and domestic

water preparation, heat pumps, electric appliances etc.). A further path is related to distributed energy generation at

building level; this is not an energy efficiency measure but it implies, indirectly, an improvement in network efficiency

thanks to the reduction of the distance between producer and consumer.

Table 2 shows an elaboration of the already mentioned IEA energy balance, limited to the residential sector and to the

commercial and public services sector, the two main sectors represented within district.

3Concerted Action EPBD website, http://www.epbd-ca.eu/

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This table shows that the largest part of energy used in buildings is thermal energy: fuels are used for this, as well as heat

from district heating, renewable energies and firewood (wood is the main component, within these sectors, of the

“biofuels and waste” category); also a large part of electricity is used for thermal uses: domestic hot water production,

space heating through electric heaters and heat pumps, cooling. Cooling in particular is a main electric energy use in the

commercial sector, whereas its importance in the residential sector is still relatively limited and diffused only in southern

Europe.

Residential Commercial and

public services

ktoe % ktoe %

Coal 9278 3% 1134 1%

Oil products 37776 13% 16239 11%

Natural gas 108413 37% 45804 31%

geothermal, solar etc 1515 1% 522 0%

biofuels and waste 39254 14% 2981 2%

heat from district heating 21649 7% 9141 6%

electricity 71247 25% 72660 49%

Total 289131 100% 148480 100%

Table 2: Energy consumption in the residential and commercial/public services sector in the European Union in 2012

(Elaboration from IEA data)

Thus, the predominant energy use at building level is space heating and cooling. Both needs are largely determined by the

building envelope, the main element determining energy exchange between internal and external environments: buildings

with reduced energy demand must necessarily have an efficient envelope, able to minimize heat exchange by

transmission, to maximize solar gains in the cold seasons, to screen excess heat in the hottest periods, to exploit the

thermal capacity of the building at best. The key elements of an efficient building envelope are:

- a thick, low conductivity thermal insulation layer along the external opaque surfaces;

- the absence of thermal bridges;

- high performance windows and glazed surfaces;

- a different distribution of glazed surfaces depending on the exposition;

- the presence of shading structures to prevent overheating from direct sunlight in the hot season.

These key elements obviously change in terms of qualitative and quantitative features, as well as in terms of relative

importance, depending on the local climate: Europe spans from sub-tropical to arctic climates, so that depending on this

the optimal parameters may change. For example, “high performance window” may be a triple glazed window with low-e

glasses and heavy noble gas in the air chambers to minimize transmittance in some climates or a simpler double glazed

window with normal glasses to maximize solar gains in other climates.

The second field of intervention for having more efficient buildings are active systems for warranting internal comfort:

heating, ventilation and air conditioning systems are present in nearly all buildings and their efficiency in converting

energy into the needed service varies. The way in which these systems are designed and operate and their components

has a deep impact on the energy figure of buildings, and especially in large tertiary buildings their importance may

overcome that of envelope features in determining energy performance of the whole structure. Active systems are also

those where sensors, actuators and distributed management systems play a major role.

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A third “hardware” field of energy efficiency in buildings is related to the use of distributed energy sources. Within this

field solar thermal systems, solar photovoltaic systems and micro-CHP systems are the most important technological

possibilities. Although distributed energy production cannot itself be considered as an energy efficiency action in a direct

way (energy efficiency means providing an energy service with a lower energy input, not substituting the input with a

cleaner one), efficiency comes from a more efficient use of fuel (in the case of micro-CHP) and from the reduction of the

distance between production and consumption: distribution and transmission losses account for 5-10% in the best electric

and district heating grids, although these losses are often larger.

Technical interventions on buildings are those determining the real consumption of the building; however, some

important “software” interventions proved to be extremely effective in determining the increase of efficiency in buildings.

Among these, aimed at providing a stronger engagement of owners and occupants of buildings in the challenge of abating

energy consumption, are related to energy labelling of buildings. Historically, energy labelling was first implemented on a

large scale for some electric appliances in the late nineties and had an extraordinary success in driving a strong reduction

of energy consumption of refrigerators, washing machines and other white goods. The power of the energy label is the

ability to communicate the energy performance of a good to the buyer (or the future user) in a simple way, without the

need of a deep knowledge on energy themes. This induced a deep change in the market driven by the sensibility of buyers

to the energy consumption and the relative operation cost. Energy labelling schemes on buildings were introduced in the

EU legislation by the EU directive 2002/91 and then integrated by Directive 2010/31 and are now increasingly

implemented in the European Union and the energy certificate is becoming mandatory for all buildings and housing units

throughout the European Union. This certificate has not a unique form in all the Union, the shape of it is defined by

national and local regulations and it is defined by legislation. To date, the energy certificate is effective and reasonably

precise, especially in the residential sector, for assessing space heating demand and, to some extent, domestic hot water

demand, whereas efforts still have to be done to improve the significance of certificates in the cooling season, as well as

for developing effective labelling schemes in commercial and large office structures. Aside of this type of labelling, other

kinds of third-party, voluntary certification schemes exist for environmental labelling of buildings (among all, LEED4 and

BREEAM5 certification).

As already mentioned, energy labelling of buildings is working very well especially for space heating and domestic hot

water preparation and in the residential sector, but not so well for other uses. The main reason for this is related to the

longer history of attempts for assessing correctly the real use of energy in colder climates (i.e. in central and northern

European countries), and under well standardized conditions of use achieving results in good agreement with measured

values of consumption. The difference among a calculation of consumption under standard conditions and the real

consumption is not only due to possible over- or underestimation of parameters describing the building and its plants, but

also to the differences between the “standard” conditions and the real conditions, whose average values and statistical

variations – due to the type of activity in the commercial sector, to the possibilities of plant regulation, to the automation

systems of HVAC systems, to the perceived comfort conditions – are much less defined. This issue, the benchmarking of

energy demand, comfort parameters, internal loads, crowding and other parameters influencing the energy behaviour of

buildings will be crucial, in the next years, to be able in determining more precisely the typical values of energy demand of

4 Leadership in Energy and Environmental Design (LEED) is a set of rating systems for the design, construction, operation,

and maintenance of green buildings, homes and neighborhoods. LEED has been developed by the U.S. Green Building

Council (USGBC), a private non-profit organization that promotes sustainability in how buildings are designed, built, and

operated. LEED rating system covers energy efficiency and ecological footprint, and is used and recognized internationally. 5 BREEAM (Building Research Establishment Environmental Assessment Methodology), is a broadly recognized method of

assessing, rating, and certifying the sustainability of buildings and the first in the world to be developed in 1990. It was

published by the Building Research Establishment (BRE), a UK based organization working on research, consultancy and

testing for the construction and built environment sectors.

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buildings and units with different end users and in the cooling season, and to be able to classify the energy performance of

buildings.

The strategies for improving energy efficiency in buildings are different for renovation of buildings and for new buildings.

The experimentations in the past decades and their development have developed several schemes of low consumption

buildings. The most interesting, which are now driving policies on sustainable housing and construction in Europe, are the

passive house concept (a very low energy consumption house, where energy need for space heating is below 15 KWh/m2

year) and the quasi-zero energy building (i.e. a low energy consumption building whose residual energy demand is

compensated by the energy production through active systems (solar systems, geothermal energy etc.), are now driving

the EU policies as regards to the new buildings. Improving energy efficiency of existing buildings during renovation can

reduce their consumption in a sensible way, although the final result can rarely achieve that on a new low energy building.

The most important actions for increasing energy efficiency in buildings are:

• External envelope insulation, which is often possible during external renovation of facades and roofs. This can

reduce losses through the envelope of two to five times on buildings without insulating layers;

• Insulation of the internal side of external walls and roof during internal renovation. This intervention is less

effective than external insulation since normally a larger number of thermal bridges remain necessarily, but the

result is anyway effective;

• Windows substitution;

• Renovation of heating, cooling and ventilation plants, which normally allows significant savings at a much lower

cost than when renovating envelope;

• Substitution of lighting systems and electric appliances with higher efficiency ones.

Among the indicated interventions, those related to plants and electric systems are the ones where the development of

sensors, actuators and ICT systems is developing the deeper changes and the best opportunities for energy efficiency.

1.2.Energy efficiency at distribution level

Within this paragraph energy distribution is discussed, as regards fixed networks: electricity, gas and district

heating/cooling. Distribution of liquid and solid fuels commonly occurs via rail or road, with a few pipelines regarding oil

products. Losses of fuel in these infrastructures are very limited under normal operation (minimal evaporation and

pouring losses occur systematically, the other losses being accidental), and energy efficiency is determined by

fuel/electricity consumption in transportation.

1.2.1. Electric networks

Efficiency of the European electric networks is among the most efficient in the world, with losses around 5.82% and values

at Country level spanning from 3.12% (Slovakia) to 14.44% (Croatia)6. Aside losses due to failures, typical of countries with

high losses, losses are due to unbalancing of reactive power consumption, to ohmic losses along the lines, to losses in

transformation stations. In an efficient network, failures are quickly repaired, there is a careful active management of

reactive power though compensation systems and the best compromise is found between length of lines (tending to

increase losses) and lines capacity (overloaded lines have higher losses), lines voltage and voltage elevation/reduction

passages (the higher the voltage, the lower the losses, although this implies normally a larger number of transformation

passages). Narrowing the distance between electricity production and consumption is an effective strategy to reduce

losses, since ohmic and transformation losses are both reduced. In this sense, distributed power generation with small

6 IEA statistics, OECD/IEA, http://www.iea.org/stats/index.asp

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plants connected to the distribution grid at low voltage and electricity used by the producers-consumers (the so-called

prosumers in the smart grid language) themselves or by their closer neighbours is by itself a tool to increase grid efficiency.

The challenge for energy grids, and in particular of electric grids in the next future is related to the development of smart

grids: the rapidly growing electric production from distributed, unpredictable power sources – such as solar or wind

systems –, whose production follows weather conditions and not power demand is stressing the traditional grid

management practices and calls for new solutions. In smart grids, the control of grid stability will be managed thanks to an

informatics network siding the traditional power grids, and calling both the operation of traditional plants and the

operation of programmable power loads. New features of grids will occur, such as electricity buffers, production forecast

systems linked to weather forecasts and dynamic pricing, favouring energy use in moments of high availability and low

demand and discouraging consumption in situations of low production and high demand, with larger and less stable

variations than occurring today.

1.2.2. Gas networks

The largest supply areas of Europe for gas are the Northern Sea, former Soviet countries and North Africa. In gas networks

large diameter, high pressure and gas speed pipelines connect the production areas to the use areas covering thousands

of miles. Transmission pipelines are sided by ship gas transport. Here natural gas is liquefied into LNG (Liquid Natural Gas)

at cryogenic temperatures at shipping harbours, transported by gas carrier ships and brought to gas phase again into

receiving LNG terminals at arrival harbours, where it enters the transmission network again. Transport of liquid gas is

more expensive, in terms of energy and of cost, than pipelines, but it allows the access to a larger number of possible

suppliers thus reducing risks of price changes and gas availability due to geopolitical tensions. From the transmission

network gas passes at local level in distribution networks, where pressure is reduced to low values and chemical additives

are added to odorize gas. Losses due to leaks are minimal in gas networks, due to the very high care in avoiding and

repairing them for safety reasons.

Energy losses in transmission and distribution losses are due to gas compression at the production sites, gas pumping

stations along the lines, gas free decompression without energy recovery at pressure reduction station. For LNG

transportation, large energy inputs are needed for the liquefaction process at shipping harbours to bring gas at cryogenic

temperatures and further losses occur from refrigeration systems on LNG carrier ships. Part of the liquefaction energy is

normally recovered at regasification terminals at arrival harbours.

At the user side, pressure reduction normally occurs through simple lamination valves with no energy recovery. An

emerging technology is the mechanical energy recovery from the pressure drop trough turboexpander systems7

generating electricity, which is the only possible energy efficiency improvement possibility at district level in gas

distribution networks.

1.2.3. District heating networks

District heating networks bring heat from large production systems, where heat often comes as a by-product of industrial

processes (waste incineration, refineries, cement plants etc) or of power generation, to the end users in districts. The heat

carrier is normally hot water, superheated water or steam. Losses occur by heat transmission through pipes walls, by

water or steam leaks due to holes on pipes, heat exchangers and fittings and, in case of steam systems, by condensate

return lines and by damaged steam traps. In an efficient DH network, losses due to damages are limited by careful

maintenance and losses due to thermal transmission are minimized thanks to high insulation levels of piping. In a well

maintained DH network, losses are kept below 10% of thermal energy entered into the network.

7 An example of turboexpander system being installed is in the Genova demonstrator of the EU Celsius research project,

http://www.celsiuscity.eu/

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Aside improving maintenance, efficiency of district heating increases by maximising the ratio between the number of

users and the length of the network. Transmission losses are commonly dependent on lines length and relatively

independent from the demand (a small increase of losses, in absolute terms, occurs in high demand moments due to fact

that in these periods the grid manager often increases delivery temperature), thus these losses tend to be perceptually

higher in periods with lower demand (typically in summer).

Despite good maintenance, losses due to leaks in DH networks keep being a main problem also in the best cases. Typical

strategies to detect leaks, put in place since long times, are related to colouring water flowing in DH pipes (this allows

immediately detecting failures on heat exchangers, and permits a quicker detection of leaks along lines) and to measuring

delivery and return flow rates. Efficiency reduction due to wear and aging of components in DH networks is one of the

fields where ICT technologies and the availability of more precise sensors play a major role in improving energy efficiency.

The installation of meters along networks to detect anomalies in flow rate, pressure and temperature is today much

cheaper than in the past, with more precise measurements and more powerful automatic monitoring systems able to

detect anomalous behaviours and report them to the DH managers. The improvement of these systems is one of the most

important areas of technological development of DH systems in general and one of the most important research areas for

the DIMMER Project.

A further problem of efficiency loss within DH networks is related to fouling on heat exchangers. This problem is quite

difficult to detect without sensors on heat exchangers, but its effect is a serious efficiency reduction due to the reduction

of thermal exchange efficiency. This is also an issue that can be detected through the deployment of a pervasive sensors

network along the DH lines – heat exchangers are present in a small number between the primary and secondary

distribution networks, but there is at least one heat exchanger at the substation serving each end user. This problem is

also one of the most important that are being investigated by the DIMMER Project, with particular reference to the Turin

Demo.

A large field of energy efficiency improvement along DH systems is related to shaving demand peaks. In the majority of DH

systems, part of the heat comes from cheap waste heat sources (industries or power plants), that produce it as a by-

product at a certain rate regardless of the heat need of the city. Very often the waste heat sources are sufficient to cover a

part of demand, but often the peak load has to be covered in a more expensive way through dedicated boilers. Reducing

demand peaks and shifting demand on different times is therefore a way to increase the waste heat use and to reduce

primary energy use. Normally, fluctuations in heat demand decrease with the size of the DH system. At network level,

strategies that are effective in shaving peaks are related to mixing users with different heat demand profiles and different

peak demand hours and to storing heat. Heat storage is in use in several DH systems, among which Torino District Heating

system.

1.2.4. Connectors among energy networks

Energy networks are connected by transformations of energy: electricity is mostly produced by fuels and in particular by

natural gas, through thermoelectric systems converting fuel energy into heat and part of the heat into electricity. In

cogeneration both electricity and waste heat are used. Electric heaters convert electricity into heat, and heat pumps can

produce heat and chill from electricity with an extremely high conversion factor.

A relatively new field of investigation is related to the possibility of connecting the different energy networks present

within a district – electricity, gas and district heating – through any of these technologies in order to be able to

compensate or flatten a demand peak on one network by producing the needed service through the use of a different

network. This is another field of investigation within the DIMMER project.

There are several possibilities to put into relationship the different networks in a district and even more to do it exploiting

buffers to store the different forms of energy, and the need for such interrelation is becoming more and more important

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along with the development of distributed energy production from unmanageable renewable energy sources and

distributed micro-cogeneration.

A non exhaustive list of these possibilities, still far to be evaluated in terms of overall effectiveness since their combination

at district level is still far to be considered a mature energy strategy, includes:

- Using heat pumps for feeding DH networks during DH demand peaks, but out of electric peaks: in several DH

systems one of the most important peaks occurs in the morning. In the early morning electricity demand is

typically very low, and the availability of power in these moments can be used instead of peak gas boilers to feed

the DH system;

- Exploiting the nearly contemporary peaks of cooling demand and PV production: this issue is quite natural, and –

far from being an issue of importance limited to a single district – the widespread development of distributed PV

generation in Italy and Spain strongly decreased in the past few years the power demand to thermoelectric plants

in the daily peak in summer, that was the most difficult and expensive demand peak to be satisfied by the power

grid until a few years before;

- Developing DH systems to exploit waste heat to cover baseload only, and using distributed heat generators for

covering peaks. This technique, far from being broadly implemented, can offer customers very cheap heat when

possible, leaving them the responsibility of being able to cover their demand peak in another way. This system

can allow the development of new district heating systems with lower capital investment in places where heat

was previously produced by distributed boilers (an example of this is the “seed district heating system” in the

Islington Council, London);

- Using solar thermal systems in homes served by DH systems, and selling the excess production to the grid to feed

neighbours in case of overproduction (some examples of this are present in the Gothenburg DH system);

- Exploiting “smart” electric water boilers for heating DHW at a higher temperature than usual during low

electricity demand hours and avoiding the resistance to work during peak hours. This concept can be expanded to

space heating with heat pumps, possibly coupled with heat buffers;

- Exploiting CHP systems at the service of DH networks not only to serve the network itself, but also to support

district electric substation

- Exploiting electric vehicles as an electric buffer, recharging them during low electricity demand times.

1.3.Energy efficiency and urban planning

Urban planning is one of the keys for developing energy efficient districts because:

1. Energy efficiency at building level is commonly achieved through urban planning tools, such as building codes and

regulations on construction and renovation of buildings at municipal, regional and national level;

2. Also the way in which energy networks develop is also defined through urban planning;

3. The shape of a city, of a district or of a village influence its energy profile.

A sustainable, energy efficient urban planning aims at easing choices and lifestyle of people oriented to more sustainable

behaviours. In terms of energy, the shape and density of a city dramatically influences, in particular, the transportation

model. Sustainable, energy efficient mobility shall be based on public and electric transportation and on soft mobility

(walking and cycling). The typical features of an energy efficient urban planning are: promotion of a compact urban

development with few voids in the urban texture, a mixed land use with residential activities mixed with public services

and commercial activities and the favouring of medium sized buildings.

A compact urban development, with fast public transportation lines travelling on dedicated ways, is more efficient than

sparse urban development since the higher inhabitants density allows to have shorter distances (favouring soft mobility)

and permits to public transportation companies to serve more passenger on a less extensive network, thus allowing the

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economic sustainability of more frequent services and the use of more sustainable transportation systems with larger

capacity: electric systems such as trains, metros, tramways, trolley buses become more convenient than traditional buses.

This urban planning model started dominating planning in Central and Northern Europe from the Eighties, when the

previously car based approaches started showing their limits in terms of environmental problems and of infrastructural

needs. In this model, cars must complement a main urban mobility demand satisfied by other means. Reduced distances

also allow more sustainable possibilities for private motor vehicles, since with small distances the typical limitations of

short range of electric vehicles are not a serious limit. A compact urban development has also some other energy

advantages: networks, and especially district heating (and cooling, if it makes sense in the context) can serve more people

with a shorter overall length, and this reduces losses.

A mixed land use allows to reduce the distance that people must cover for going to work and for daily needs (public

services, entertainment, shops), thus favouring soft mobility and public transportation choices rather than the use of car.

Medium sized buildings, with a few storeys and medium plant surface, have energy advantages both over individual

homes (a smaller surface to volume ratio reduces thermal losses) and over large buildings (overheating problems are

reduced, so that the need for air conditioning decreases, the need of mechanical plants is smaller and that of artificial

lighting decreases).

Urban planning approaches and their influence on increased energy efficiency are ultimately driven by energy policy set at

EU, national and local levels. For example, the EU has made renewable energy, energy efficiency and measures to achieve

a transition to a low carbon economy key priorities from a both policy and an investment perspective.

The UK government has set a legally binding target of 80% reduction in carbon dioxide emissions (compared to those of

1990) by 2050, as it has been planned by the European Union in its whole. As part of this, the UK Carbon Plan sets out the

need to have emissions from electricity to be near to zero by 20508. The Electricity Market Reform (EMR) puts in place

measures to attract the £110 billion investment required by 2020; this is needed to replace current electricity generating

capacity with greener and more reliable supplies at the lowest possible cost.

To shape the delivery of this, urban planning approaches need to be driven by robust planning policy that influences the

energy transition towards low and zero carbon decentralised energy infrastructure. Urban planning also needs to include

the retrofitting of the existing urban fabric. In the UK the Government has developed policies in the National Planning

Policy Framework to explain how urban development should be planned to reduce carbon emissions and protect the

environment. To reduce carbon emissions from buildings, the Government developed several key policy actions including:

- The requirement that local planning authorities make sure that new developments are energy efficient

- That all new homes to be zero carbon from 2016 and are considering extending this to include all other buildings

from 2019

- The introduction of the Green Deal to enable people to pay for home improvements over time using savings on

their regular energy bills

- The development of Energy Performance Certificates and Display Energy Certificates to ensure there is

improved data and monitoring of the energy efficiency of domestic and non-domestic buildings

- The introduction of the Code for Sustainable Homes which provides a single national standard for the design and

construction of sustainable new homes.

Of particular note is an emerging policy called Allowable Solutions9. As part of the journey to zero carbon homes the

Government has strengthened the energy performance requirements in the Building Regulations for new homes and will

be implementing zero carbon homes from 2016. Allowable Solutions are part of this strategy to give developers an

8 The UK Carbon Plan: Delivering a Low Carbon Future (2011)

9 www.zerocarbonhub.org/zero-carbon-policy/allowable-solutions

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economical way of compensating for the CO2 emissions reductions that are difficult to achieve through normal design and

construction on site. Developers who opt to use Allowable Solutions can use four routes which are not mutually exclusive.

1. Undertake more/all carbon abatement on site through connected measures (e.g. a heat network).

2. Meet the remaining carbon abatement requirement themselves through off-site carbon abatement actions - the

“do-it-yourself” option (e.g. retrofitting existing buildings).

3. Contract with a third party Allowable Solutions private sector provider or work with the local authority.

4. Payment into a fund which then invests in carbon abatement projects (see example in Figure 2).

An overview of European initiatives promoting sustainable urban planning is provided below.

Energy Efficient Cities initiative [EECi]

The Energy Efficient Cities initiative [EECi] is a cross-disciplinary research project at the University of Cambridge. The EECi

aims to strengthen the UK's capacity to address energy demand reduction and environmental impact in cities, by research

in building and transport technologies, district power systems, and urban planning. The EECi was originally funded by a

£2.9 million EPSRC Science and Innovation Award spanning 2007 to early 2014.

Energy Cities

Energy Cities is the European Association of local authorities in energy transition. From 2013 to 2015, Energy Cities is

under the Presidency of the City of Heidelberg (Germany) with a Board of Directors from 11 European cities. The

association created in 1990 represents now more than 1,000 towns and cities in 30 countries.

In 2012, Energy Cities initiated a process aimed at making and debating proposals for accelerating the energy transition of

European cities and towns. These proposals are based on innovative approaches, new ideas and groundbreaking practices.

They provide practical answers and link today’s action to the long-term vision of a low energy city with a high quality of life

for all. Proposals for energy-efficient urban planning developed by Energy Cities include:

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Figure 2: Example of payment into an Allowable Solutions fund for energy efficiency measures

- Make planning system drive territory’s energy transition

- Prepare an energy retrofitting plan for the whole building stock

- Ensure that new neighbourhoods are “100%” renewable

- Plan modal shift to sustainable transport

- Transform railway stations into territorial structuring hubs

- Implement goods delivery schemes

Planning for Energy Efficient Cities (PLEEC)

The PLEEC project is funded by the EU Seventh Framework Programme uses an integrative approach to achieve the

sustainable, energy–efficient, smart city. By coordinating strategies and combining best practices, PLEEC aims to develop a

general model for energy efficiency and sustainable city planning. This will be achieved by connecting scientific excellence

and innovative enterprises in the energy sector with ambitious and well-organised cities.

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2. ICT AS A TOOL TO ENERGY EFFICIENCY

The development of information and communication technology, as well as the availability of cheaper and more precise

sensors, allows today the widespread use of several energy efficient technologies, services and solutions that were not

available until a few decades ago. ICT itself cannot be considered the most important technological sector on abating

energy demands, however it can enable large savings especially at the level of plants management thanks to improved

control and monitoring, allowing a much better matching between the need of an energy service and the service provided.

2.1.ICT enabled equipments benefits

In this paragraph the benefits enabled by ICT are subdivided into three categories: those related to monitoring and control

of energy systems, those allowing optimization of systems and the possibility of improving benchmarking thanks to the

collection of an extremely large abundance of data.

2.1.1. Monitoring and control

One of the guiding sayings of resource efficiency is “what gets measured, gets controlled”. The deployment of sensors and

monitoring systems enabled by ICT development brings this concept to a much finer granularity than in the past. This is

sided by the ease of remote monitoring, allowing information to pass from the plant to its manager without the need of

visits with minimal additional communication infrastructure. In the following, the most important services enabled by ICT

are described.

Monitoring of environment/comfort conditions and occupation

The possibility of easily collecting environmental parameters (temperature, humidity, light) and of the real use of an area

(through movement and presence sensors) and transmitting them allows a very fine tuning of energy services, arriving to

the level of the single room. Such signals can be used to manage ventilation, air conditioning, lighting, humidification and

other electric systems typical of building plants, but also movable shading structures or electrochromic glazings being part

of the envelope of the building. Some sensor based control systems like these are already in use by decades (e.g. the

thermostatic control of a heating system in an office), but the traditional local control systems do not allow the plant

manager to record and detect if the plant works correctly or not since in these systems there is no centralized data

collection and analysis ICT enables from one side local control of a much larger number of parameters within smaller

volumes, and from the other it allows to fully understand the operation of complex systems thanks to data concentration

and analysis.

Measurement of production and consumption breakdown

Another important aspect of monitoring equipment is to understand the real operation of each component and thus to

correctly allocate energy consumptions and economic costs. An example can be provided by a centralized space heating

system in a large building block (but the same example is valid in case the building block is connected to the district

heating and a substation is in place of the centralized boiler), shared by several housing units. Traditionally such a system

had a simple regulation of delivery water temperature exiting the boiler, and energy consumption was only measured at

the fuel meter. Building occupants could only regulate temperature by opening radiators’ valves (or opening windows). At

present, such a system can be renovated by installing on each radiator thermostatic valves (automatic actuators with

manual regulation) and a heat cost allocator (a small computer measuring radiator temperature and calculating the heat

flow from it to the environment, sending then a signal to a centralized data management and elaboration system that is

usually remotely controlled via a web interface). In this way, it is possible to regulate more precisely and in an automatic

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way the internal comfort, and the data management system knows the consumption in each room. In a public building

(i.e. a school, or a public office) such system indicates immediately where heating is left on even if the room is not used, or

where a room needs too much heat indicating some anomaly (e.g. improper behaviour of occupants, windows left open

etc.). A different example is given by the monitoring systems installed on PV plants, where it is possible to continuously

monitor production of each string and detect different behaviour of strings due to shadowing, different heat removal,

different modules characteristics and other causes.

Monitoring of equipment operation

Sensors installed specifically on equipment (e.g. delivery and return temperatures and flow rate from a boiler) or for

environmental control (e.g. a light sensor) allow to always monitor the correct operation of equipment or problem caused

by equipment improper operation or by failures, thus allowing the quick detection of the need for maintenance of

cleanliness. In the example of the building block, the main boiler (or the group of boilers) serving the heating system can

be easily equipped with two thermocouples, one flow meter and a pressure transducer at the main delivery and return

pipelines of the heating system and with a fuel flow meter, with data being communicated to the same data management

system (in the following BMS, Building Management System) collecting data from the radiators. In this way, the plant

manager is able to constantly know if the boiler works properly, if its efficiency is not dropping and if the distribution

pumps work as they should. In the example of the photovoltaic plant, a monitoring system can quickly detect any anomaly

due to dirt or a damage, and thus call for maintenance immediately.

Equipment control

ICT can of course be used bi-directionally, not only measuring field parameters and monitoring them at central level but

also deciding actions at central level and executing them at local level, substituting, siding or forcing on field retroaction

systems. For example, HVAC systems where temperature is controlled by locally installed thermostats can be remotely

forced not to work in case the area is not occupied, or heating/cooling can be anticipated in some parts of a building and

delayed in other parts in order to reduce overall peak demand.

Billing

In cases where a plant serves several subjects (building occupants, companies etc) ICT can be used to divide more

correctly the operation costs (or earnings). In the example of the building block with centralized heating systems and

thermostatic valves and heat cost allocators on each radiator, the ICT system makes it possible to share the heating cost

among building occupants proportionally to the individual consumption. In the reality, in this case heat billing usually

comprehends a fixed quote taking into account for maintenance expenses and heat exchange among housing units and a

consumption quote proportional to energy use; in any case the implementation of such systems on building blocks having

centralized space heating systems brings alone savings between 15 and 20% due to the fact that people pay for what they

use and that regulation is simpler. Although similar cost allocation systems existed also before ICT, the absence of

communication systems made it necessary the visit of an operator every year, reading consumption from each radiator of

the building with much higher service costs and a much slower detection of changes or of problems.

Forecasting and planning of energy use and/or production

The implementation of ICT allows making systems more flexible and adaptable to changes. This means that a set point of

systems, timetables, and operation tables of systems can be quickly changed following routine changes in the use of

internal spaces of buildings and other structures.

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2.1.2. Optimization

Collecting and recording large amounts of data in a building or other complex energy system allows a much deeper

knowledge of the system behaviour, thus orienting technological choices in case of renovation or system upgrade and

allowing the gradual optimization of equipment choices.

Optimization of equipment choice occurs since operation always shows that some assumptions done during design

underestimated some factors and overestimated other, and that large safety margins were taken in order to avoid

problems during operation. Monitoring provides important indications that allow reducing margins – which always mean

larger installation costs and poorer operation performance – and better sizing the components of plants and the features

of buildings.

In terms of ordinary use, optimization brings to different, more efficient choices in systems management that would not

be possible without the deep knowledge of the system behaviour thanks to pervasive sensing networks. A typical example

is the management of the HVAC system of an office building served by a condensing boiler or a heat pump at the morning

start-up: these systems have higher efficiency at partial load, and only through a deep knowledge – only possible by ICT -

of the building behaviour it is possible to optimize the space heating (or cooling) ramp, finding the best compromise

between a quick warm-up (or cool down) time at high heating (cooling) power rate, with generator operating at low

efficiency and a long, smooth warming up (cooling time) time with a higher generator efficiency but higher losses due to

higher temperature difference between internal and external environments for a longer time.

2.1.3. Common features and issues on ICT systems implementation

Development of ICT systems allowed the broad extension of control systems on energy systems, with an improvement of

service quality, energy and cost performance. The improvements are achieved thanks to:

- the improvement of sensors and actuators;

- the reduction of their cost;

- the development of wireless sensors (and sometimes actuators) for many applications, with secure radio

communication systems and autonomous power supply through PV cells or long duration battery;

- the possibility of developing extremely sophisticated control algorithms through digital systems, computers and

PLCs;

- the possibility of recording and transmitting large amounts of data in secure way through private or public wired

or wireless networks;

- the development of refined analysis and reporting software tools, often operable also via web interface,

automatically elaborating information and distilling the most significant information and patterns of interest for

the system manager and for the user.

The spreading use of ICT in energy systems, also at final user level, poses however some issues which are tricky and still

call for standardized solution. Among the various problems, two are common issues faced when implementing ICT in

energy systems: the excess of control and the interoperability of software and hardware tools.

Excess of control occurs when sensors and feedback systems are not well designed, and not all the practical situations are

considered. A typical example of this problem can occur in large office buildings where HVAC systems where the

contemporaneous use of heating and cooling is possible and were a room-by room control of environmental parameters

occurs. These situations are not uncommon for two reasons: first, cooling systems are often equipped with post-heating

batteries to allow humidity control of internal air and improve comfort; second, in large buildings it is not uncommon that

rooms located along external walls may require some heating and internal rooms may require at the same time some

cooling because the internal heat sources are significant (lighting, electric appliances, computers, crowding) and there is

no dissipation of this heating. In such situation, it is common to install a large number of local HVAC system terminals for

regulating temperature and humidity at room level or at area level with a regulation based on local measurement of these

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parameters. If this pervasive sensors and actuators network has not a proper centralized management system, analyzing

and understanding what is happening at local level and eventually forcing local control systems, it often occurs that in a

room generating a slight excess of heat air cooling is on, and in the neighbouring one, facing some minimal heat losses,

heating is on. This situation may bring in some conditions (due to air circulation or rooms use) to a vicious circle where

heating system mostly works to compensate heat losses caused by air cooling occurring in the neighbouring room, with

the consequence of wasting large amounts of energy without detecting the problem. A good way to avoid this kind of

problem is to design measurement and control systems keeping the same level of measurement and control for

environmental parameters and for energy flows.

Whereas the problem just described is a technological problem, interoperability of ICT tools for energy management is

more a commercial problem, which however can have serious consequences. In the past two decades companies have

started offering the market more and more sophisticated ICT solutions including sensors, actuators, communication

systems and data management and analysis software able to solve an enormous amount of problems. The problem is

however that these systems often rely on proprietary communication protocols, and analysis and control software is

closed and does not permit to understand the internal way of working of algorithms. This poses a series of problems to

installers, energy managers and end users: what to do if a component breaks and it relies on systems which became

obsolete or the company producing it went out of production? How to expand the system if the new functions are not

supported by the supplier of the rest of the system? How to check if a newly operating system works correctly in all

conditions or if some settings are wrong (such question seems stupid, but it is not in complex systems)? These and other

related questions are real problems which are still not resolved. Although there are some open systems, large suppliers

tend to supply complete proprietary systems for both hardware and software to warrant long term supply and assistance

contracts with customers, and these solutions are often more fascinating and complete than open solutions. As a result,

energy management systems are often not complete or not updated or a series of different management systems operate

independently (with typical conflict problems when the same service has different controls with badly defined hierarchy)

on the same building or energy system.

2.1.4. Benchmarking

The deployment of pervasive ICT based management on complex energy systems, offering advanced data analytics is

building enormous data bases that offer the possibility of important statistical elaborations, providing more precise

reference data in a vast variety of conditions. Although most of these data are private, and this kind of analysis is limited

to elaboration of energy service companies and plant managing companies managing large numbers of energy systems,

but large data bases are being built also by universities, research centres, standardization and certification institutions and

public bodies, and it is expected that these data will help to fulfil in the next few years the current lack of benchmarking

data not allowing reliable energy classification for commercial, office, small handicraft activities and extending

classification to important energy services such as cooling.

2.2.IPMVP: energy saving measurement

The International Performance Measurement and Verification Protocol (IPMVP) defines standard terms and suggests

best practices for quantifying the results of energy efficiency investments and increase investment in energy and water

efficiency, demand management and renewable energy projects.

IPMVP is a widely referenced framework for assessing energy or water savings, and is used especially for assessing and

reporting savings under ESCO contracts. It is a high level framework, not providing specific project design but presenting a

common terminology and defining full disclosure to allow a rational discussion on issued related to measurement and

verification (in the following, M&V), which often are cause of discussions disputes.

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Measurement of savings of an energy efficiency project is tricky to be defined, and a common terminology and total

access to measure data are a pre-requisite for assessing energy efficiency. For technical discussion on a specific

intervention, IPMVP must be sided by energy engineering skills.

The main IPMVP is to provide guidelines for M&V practice, in order to reassure the public about savings reports. IPMVP

stresses on some main features of saving reports, such as the need to define a baseline (to be corrected depending on a

clear set of operational parameters of the system subject to the interventions) and to adjust raw differences in energy use

according to changes in conditions along with the savings reporting periods. IPMVP is being used as a reference guideline

framework within performance contracting industry in the USA, in M&V practices in several European Projects and in the

EPC industry worldwide.

IPMVP is currently in its fourth edition and is freely available at www.evo-world.org under the Products tab. IPMVP is

prepared in three Volumes10

:

Volume I Concepts and Options for Determining Energy and Water Savings

Volume I defines terminology and suggests good practices for documenting the effectiveness of energy or water efficiency

projects that are implemented in buildings and industrial facilities. These terms and practices help managers to prepare

M&V Plans, which specify how savings will be measured for each project. The successful M&V Plan enables verification by

requiring transparent reports of actual project performance. The Preface of Volume 1, 2007, summarizes its contents, and

is quoted below.

Volume II Indoor Environmental Quality (IEQ) Issues

Volume II reviews IEQ issues as they may be influenced by an energy efficiency project. It highlights good project design

and implementation practices for maintaining acceptable indoor conditions under an energy efficiency project. It advises

on means of measuring IEQ parameters to substantiate whether indoor conditions have changed from the conditions of

the baseline when determining savings.

Volume III Applications

Volume III contains specific application guidance manuals for Volume I. The two current applications manuals address new

building construction (Part I) and renewable energy additions to existing facilities (Part II). This Volume is expected to be

an area of continued development as more specific applications are defined, or country-specific sections are contributed.

10

The description of the volumes contents is taken from the EVO website

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3. OVERVIEW ON ENERGY SAVING POTENTIAL

In this section an analysis of the energy saving potential is provided. The section reports information regarding the

development of energy efficiency policies and technologies at EU and national level for Italy and UK, and provides a

quantitative evaluation of the energy saving potential.

The figures about savings which are presented are largely based on data from the “Interactive database on Energy Saving

Potentials in EU Member States” (http://www.eepotential.eu/potentials.php), a large database covering the whole EU-28

plus Norway, Iceland and Liechtenstein. The source of data is chosen due to its authority and to the possibility of

comparing the situations of different countries in a homogeneous way. These data are here provided for assessing a

comparison with the targets sets at EU and national level about energy efficiency increase, and thus to permit the

evaluation on the level of commitment in achieving these targets.

The database uses as a reference year for assessing energy efficiency potential year 2004, so that the database projection

for year 2015 is also presented.

The database considering four scenarios:

- The autonomous scenario, used as a reference scenario to assess potential in the other scenarios considers that

technology diffusion is driven in an autonomous way. It takes into account the development in terms of

demographic drivers, technology diffusion, renovation and demolition rates, new builds, changes in energy prices

etc. This scenario considers the impacts of policies previous to the base year;

- The low policy intensity scenario considers that diffusion of the best available energy saving technologies (BAT)

beyond the autonomous diffusion is driven by increase of energy prices and by low level energy efficiency

policies. Barriers to energy efficiency diffusion persist in this scenario, including non-economic barriers such as

information deficits, administrative barriers etc.

- The high policy intensity scenario considers additional technology diffusion of BAT to the maximum possible

rates from the economic point of view. It considers cost effectiveness from a country perspective, assuming that

a high policy intensity reduces transaction costs and removes barriers for the consumer by suitable measures.

- The technical scenario considers a full technology diffusion of BAT at the maximum technically possible rate. This

means that in this scenario all investments along with the normal renovation rate in each sector move to BAT.

This scenario is a hypothetical maximum that will never be reached, but that poses a realistic upper limit to the

potential of energy saving technologies currently available on the market.

An estimate of the “remaining” energy efficiency potential at present day is herein presented assuming the high policy

intensity scenario as representative of the evolution that occurred in after 2014. This choice has been considered because:

- Considering a higher efficiency reached today, the remaining potential in the forthcoming years is estimated in a

conservative way;

- Several EU policies were truly implemented with strong commitment by countries, with results overcoming the

more optimistic estimates that could have been done ten years ago. This is the case of PV and wind energy

production, and this partially compensates the slower changes that occurred in other sectors such as the

residential sector;

- The global economic crisis was completely unexpected before 2008, so that energy consumption dropped

especially in transportation and industry independently from environmental policies.

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3.1.The European Union case

The European Union and its member states are among the most committed bodies in contrasting climate change through

energy efficiency, development of renewable energy sources and cleaner energy production from conventional source. EU

considers energy efficiency and the development of a cleaner economy necessary not only for environmental reasons, but

also as a target to improve energy self-sufficiency and a key factor for industrial leadership and economic competitiveness.

EU is seeking ambitious targets in terms of greenhouse gases emissions reduction: 20% reduction in 2020 respect to the

reference year 2007, 30%-40% in 2030 and -80% in 2050.

The instruments that EU is using to achieve these targets span from the international agreements, where the Member

States are among the most proactive countries in pushing for ambitious targets in the next successor of the Kyoto

Protocol, funding to research and industrial innovation, medium and long term plans supported by EU Directives, which

are then implemented as binding legislation in the Member States. In the following, the energy efficiency potential

assessment is presented, followed then by the most important instruments that are being set for achieving EU’s targets:

The SET Plan, the 20-20-20 Directive, Horizon 2020, the energy efficiency related directives issued in the past 10 years.

EU-27 energy efficiency potential

The following tables shows the energy efficiency potential achievable through the Bats in Europe, sector by sector, in

terms of final energy (fuels and electricity). The tables are elaborated starting from the already mentioned energy

efficiency database, and indicate the energy efficiency potential respect to the autonomous scenario and the variation,

sector by sector, of energy demand respect to the 2004 reference year and the 2015 HPI scenario. Table 3 shows the total

energy demand for EU-27. The baseline scenario foresees a fair growth in the total energy demand. In the lowest policy

intensity scenario, in 2030 the total energy consumption would have grown by 1% from 2004 and of 6% taking 2015 HPI as

the reference. The reduction respect to the baseline scenario would be of 14%. These figure increases to 22% in the high

policy intensity scenario and to 32% in the technical scenario. Considering that the more likely situation is intermediate

between LPI and HPI scenarios, it can be considered that thanks to energy efficiency energy demand should be in 2020

about 20% lower than what it would be in absence of EU policies.

Unit 2004 2015 2020 2030

Baseline Consumption Ktoe 1135752 1192105 1241851 1331770

LPI Scenario

Consumption Ktoe 1135753 1111004 1120247 1148319

Potential % 0% -7% -10% -14%

variation from 2004 % 0% -2% -1% 1%

variation from 2015 HPI % 3% 4% 6%

HPI Scenario

consumption Ktoe 1135753 1080040 1065861 1032133

potential % 0% -9% -14% -22%

variation from 2004 % 0% -5% -6% -9%

variation from 2015 HPI % 0% -1% -4%

Technical scenario

consumption Ktoe 1135753 1037387 997035 911735

potential % 0% -13% -20% -32%

variation from 2004 % 0% -9% -12% -20%

variation from 2015 HPI % -4% -8% -16%

Table 3: Total energy consumption and energy efficiency potential for EU-27

This assessment is of course an assessment with its limits; the more interesting aspect of this evaluation is however

related to the evaluation of potential at sector level. In particular, whereas at present the growth of industrial energy

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demand appears to be overestimated, the saving potential appears to be realistic: the EU IPPC (Integrated Pollution

Prevention and Control) directive already had started requiring improvements in environmental and energy performance

of large industries at the time when the assessment was done, so that its important effect is already considered within the

baseline scenario.

Unit 2004 2015 2020 2030

Autonomous consumption ktoe 344161 397336 427267 482989

LPI Scenario consumption ktoe 344161 380274 401941 445309

potential % 0% -4% -6% -8%

variation from 2004 % 0% 10% 17% 29%


HPI Scenario consumption ktoe 344161 378083 399111 441606

potential % 0% -5% -7% -9%

variation from 2004 % 0% 10% 16% 28%


Technical scenario consumption ktoe 344161 372178 388623 418705

potential % 0% -6% -9% -13%

variation from 2004 % 0% 8% 13% 22%

variation from 2015 HPI % -2% 3% 11%

Table 4: Industrial energy consumption and energy efficiency potential for EU-27

The effect of energy efficiency on transportation, mostly given by the substitution of vehicles with less fuel consuming

vehicles, is also notable, but the foreseen growth in the sector, in terms of passengers-kilometre and of tons-kilometre-

transportation nearly compensates the large savings achieved by the sector.

Unit 2004 2015 2020 2030

autonomous consumption ktoe 342689 356704 386338 438787


potential % 0% -11% -14% -15%

variation from 2004 % 0% -8% -3% 8%



potential % 0% -13% -18% -24%

variation from 2004 % 0% -9% -8% -3%



potential % 0% -17% -23% -30%

variation from 2004 % 0% -13% -13% -11%


Table 5: Transportation energy consumption and energy efficiency potential for EU-27

The results of the assessment are much more interesting for the residential, the most important when considering

districts, where the potential at 2030 is assessed between 20 and 60%. Considering the energy efficiency improvement

already occurring through policies issued before 2004, the savings to be expected for this sector are expectable between

40 and 50%. The tertiary sector, also provides interesting margins of improvement, with a saving potential estimable

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between 20 and 25% (keeping an intermediate evaluation between low policy intensity scenario and high policy intensity

scenario), and a saving in absolute terms of around 10-15%.

households Unit 2004 2015 2020 2030



potential % 0% -4% -7% -17%

variation from 2004 % 0% -11% -19% -35%



potential % 0% -11% -19% -42%

variation from 2004 % 0% -18% -29% -55%



potential % 0% -16% -29% -66%

variation from 2004 % 0% -22% -38% -73%


Table 6: Household energy consumption and energy efficiency potential for EU-27

Tertiary Unit 2004 2015 2020 2030



potential % 0% -8% -14% -22%

variation from 2004 % 0% 1% -2% -6%

variation from 2015 HPI % 2% 0% -4%


potential % 0% -11% -17% -29%

variation from 2004 % 0% -2% -5% -14%



potential % 0% -16% -25% -37%

variation from 2004 % 0% -8% -15% -24%


Table 7: Tertiary energy consumption and energy efficiency potential for EU-27

The 2020 Climate and Energy Package

The climate and energy package11

is a set of binding legislation which aims to ensure the European Union meets its climate

and energy targets for 2020. These are the “20-20-20” targets for 2020 are:

- A 20% reduction in EU greenhouse gas emissions from 1990 levels. This target might improve to 30% by 2020 if

other major economies in developed and developing countries committed themselves to undertake a global

emissions reduction effort.;

- Raising the share of EU energy consumption produced from renewable resources to 20%;

11

More information on the package at http://ec.europa.eu/clima/policies/package/index_en.htm

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- A 20% improvement in the EU's energy efficiency. The 20-20-20 targets represent an integrated approach to climate and energy policy that aims to combat climate change,

increase the EU’s energy security and strengthen its competitiveness.

The “20-20-20” targets are part of the “Europe 2020” strategy for smart, sustainable and inclusive growth, and in this

sense the package is considered not only an initiative on environmental and energy themes, but a strategy to improve

energy independence, to foster competitivity and to promote employment, with the estimation of about 400,000 new

jobs created in the renewable energy sector only.

The climate and energy package is articulated into four measures:

1. The reform of the EU Emissions Trading System (EU-ETS), aimed at cutting industrial GHG emissions more cost

effectively. The main changes consider the substitution of national emissions caps with a single, EU-wide cap, that

will be progressively reduced down to 21% below the 2005 level in 2020. The free allocation of allowances will be

substituted by the mechanism of auctioning, and the sectors and gases covered by the scheme will be

broadened;

2. The introduction of national targets for non-EU ETS emissions. These targets cover the sectors not covered by

the emission trading systems, including housing, agriculture, waste and transport with the exclusion of aviation.

These sectors produce about 60% of EU’s total emissions. National targets are differentiated according to

Member States’ relative wealth. Emissions must be reported under the EU monitoring mechanism.

3. The setting of National renewable energy targets, differentiated country by country depending from their

starting point and from their potential, ranging from 10% (Malta) to 49% (Sweden)

4. The creation of a legal framework for carbon capture and storage.

The climate and energy package does not directly address the energy efficiency target. This major target is specifically

treated by the Energy Efficiency Plan and by the Energy Efficiency Directive, which are described below.

The Energy Efficiency Plan 2011

This plan aims at promoting a resource efficient economy, implementing a low carbon system, improving EU’s energy

independence and strengthening the security of energy supply. The plan proposes actions at several levels:

- Fostering low energy consumption in the construction sector, through the removal of existing economic,

technical and cultural obstacles, the dedicated training of architects, engineers and technicians, and through a

major development of ESCO schemes in the field;

- Developing a competitive, clean industry, through the replacement of inefficient equipment and the adding of

new, efficient production capacity and infrastructures, the recovery of waste heat streams from electricity and

industrial production, the diffusion of cogeneration, through the creation of new instruments to better allocate

the financial value of energy and to gradually shift profits and fees from delivered energy to delivered, efficient

energy services and, finally through the systematic diffusion of energy audits within small and medium-sized

enterprises (SMEs)

- Adapting national and European financing through the intensification of energy taxation and carbon taxes;

- Reinforcing the approach of the “Ecodesign” Directive and defining stricter standards on appliances, improving

customers’ understanding of ecolabelling and promoting the diffusion of intelligent energy meters

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The Energy Efficiency Directive

The Directive 2012/2712

, the energy efficiency directive, entered into force on 4 December 2012 and was ther modified

into Directive 2013/12 to take into account the entrance of Croatia and thus to adapt projections for EU-27 to EU-28. Most

of its provisions will have to be implemented by the Member States by 5 June 2014.

The Directive establishes a common framework of measures for the promotion of energy efficiency within the Union in

order to ensure the achievement of the Union’s 2020 20 % headline target on energy efficiency and to pave the way for

further energy efficiency improvements beyond that date.

All EU-28 countries are required to use energy more efficiently at all stages of the energy chain, from the transformation

of energy and its distribution to its final consumption. The new Directive will also help remove barriers and overcome

market failures that impede efficiency in the supply and use of energy. It also provides for the establishment of indicative

national energy efficiency targets for 2020.

New measures include:

- The legal definition and quantification of the EU energy efficiency target as the ''Union's 2020 energy

consumption of no more than 1 474 Mtoe primary energy or no more than 1 078 Mtoe of final energy''. With the

accession of Croatia the target was revised to "1 483 Mtoe primary energy or no more than 1 086 Mtoe of final

energy''.

- The obligation on each Member State to set an indicative national energy efficiency target in the form they

prefer (e.g. primary/final savings, intensity, consumption) and, by 30 April 2013, to notify it together with its

'translation' in terms of an absolute level of primary energy consumption and final energy consumption in 2020.

- The obligation on Member States to achieve certain amount of final energy savings over the obligation period

(01 January 2014 – 31 December 2020) by using energy efficiency obligations schemes or other targeted policy

measures to drive energy efficiency improvements in households, industries and transport sectors;

- Major energy savings for consumers: easy and free-of-charge access to data on real-time and historical energy

consumption through more accurate individual metering will now empower consumers to better manage their

energy consumption.

- The obligation for large enterprises to carry out an energy audit at least every four years, with a first energy

audit at the latest by 5 December 2015. Incentives for SMEs to undergo energy audits to help them identify the

potential for reduced energy consumption.

- Public sector to lead by example by renovating 3% of buildings owned and occupied by the central

governments starting from 01 January 2014 and by including energy efficiency considerations in public

procurement – insofar as certain conditions are met (e.g. cost-effectiveness, economic feasibility) – so as to

purchase energy efficient buildings, products and services.

- Efficiency in energy generation: monitoring of efficiency levels of new energy generation capacities, national

assessments for co-generation and district heating potential and measures for its uptake to be developed by 31

December 2015, including recovery of waste heat, demand side resources to be encouraged.

Roadmap for moving to a competitive low-carbon economy in 2050

The roadmap is a communication issued by European Commission in 2011 aimed at looking beyond the short term, the

approaching 2020, in order to set a cost-effective path for achieve deep emission cuts, as needed from all major

economies to hold global warming below 2°C compared to pre-industrial times.

12

Information here is taken from European Commission website on the page of this directive,

http://ec.europa.eu/energy/efficiency/eed/eed_en.htm

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The roadmap suggests to cut EU emissions to 80% below 1990 levels through reductions within European Union, with the

intermediate milestones of 40% as a target by 2030 and 60% by 2040.

3.2.The Italian case

In Italy the lack of internal resources and a high fiscal load on energy products have historically pushed the national

industry to save energy, and historically the energy intensity in Italy has been among the lowest in Europe. In the past

twenty years, further achievements in energy efficiency occurred especially in the industrial and residential sectors,

whereas changes in transportation were limited due to a reduction of energy intensity in freight transportation caused

mostly by changes in logistics organization, where the diffusion of “just-in-time” delivery reduced the average load of

freight vehicles. The evolution of energy intensity in Italy is indicated by the graph in Figure 3.

In the following, the analysis of energy efficiency in Italy based on the European “data base on energy savings potential” is

provided.

Figure 3: Evolution of the energy efficiency index in Italy in 1990-2010

13

Energy efficiency potential in Italy

The following tables show the energy efficiency potential achievable through the Bats in Italy, sector by sector, in terms of

final energy (fuels and electricity). As for the EU-27 the tables are elaborated starting from the energy efficiency database,

and indicate the energy efficiency potential respect to the autonomous scenario and the variation, sector by sector, of

energy demand respect to the 2004 reference year and the 2015 HPI (high policy intensity) scenario. Table 8 shows the

total energy demand for Italy. The baseline scenario foresees a fair growth in the total energy demand. In the lowest

policy intensity scenario, in 2030 the total energy consumption would have grown by 1% from 2004 and of 7% taking 2015

HPI as the reference. The reduction respect to the baseline scenario would be of 14%. These figure increases to 21% in the

high policy intensity scenario and to 29% in the technical scenario. Considering that the more likely situation is

13

Data from “Energy Efficiency Policies and Measures in Italy”, ODYSSEE- MURE 2010 Intelligent Europe Project, ENEA.

Available online at http://www.odyssee-mure.eu/publications/national-reports/energy-efficiency-italy.pdf.

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intermediate between LPI and HPI scenarios, it can be considered that thanks to energy efficiency energy demand should

be in 2020 about 15-20% lower than what it would be in absence of policies.

Unit 2004 2015 2020 2030

baseline Consumption ktoe 131789 133251 136683 142910

LPI Scenario Consumption ktoe 131789 123862 122829 122885

Potential % 0% -7% -10% -14%

variation from 2004 % 0% -6% -7% -7%



potential % 0% -8% -13% -21%

variation from 2004 % 0% -7% -10% -14%



potential % 0% -12% -18% -29%

variation from 2004 % 0% -11% -15% -23%


Table 8: Total energy consumption and energy efficiency potential for Italy

This assessment an assessment made in 2007 starting from 2004 data and has some limits (economy in EU had not the

steady growth used to build these scenarios and faced the crisis), the more interesting aspect of this evaluation is however

related to the evaluation of potential at sector level. In particular, at present the growth of industrial energy demand

appears to be overestimated, but the saving potential appears in relative terms to be realistic: the EU IPPC (Integrated

Pollution Prevention and Control) directive already had started requiring improvements in environmental and energy

performance of large industries at the time when the assessment was done, so that its important effect is already

considered within the baseline scenario.

Unit 2004 2015 2020 2030

Autonomous consumption ktoe 344161 397336 427267 482989


potential % 0% -4% -6% -8%

variation from 2004 % 0% 10% 17% 29%



potential % 0% -5% -7% -9%

variation from 2004 % 0% 10% 16% 28%



potential % 0% -6% -9% -13%

variation from 2004 % 0% 8% 13% 22%


Table 9: Industrial energy consumption and energy efficiency potential for Italy

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Unit 2004 2015 2020 2030

baseline consumption ktoe 43149 47173 49716 55099


potential % 0% -4% -6% -7%

variation from 2004 % 0% 5% 9% 18%



potential % 0% -5% -6% -8%

variation from 2004 % 0% 4% 8% 18%



potential % 0% -6% -8% -12%

variation from 2004 % 0% 3% 6% 12%


Table 10: Transportation energy consumption and energy efficiency potential for Italy

The results of the assessment are much more interesting for the residential sector, the most important when considering

districts, where the potential at 2030 is assessed between 19 and 65%. Considering the energy efficiency improvement

already occurring through policies issued before 2004, the savings to be expected for this sector are expectable between

30 and 40% respect to 2015 HPI scenario. The tertiary sector also provides interesting margins of improvement, with a

saving potential estimable between 11 and 31% (keeping an intermediate evaluation between low policy intensity

scenario and high policy intensity scenario), and a saving in absolute terms of around 15%.

Unit 2004 2015 2020 2030



potential % 0% -4% -8% -19%

variation from 2004 % 0% -13% -21% -40%



potential % 0% -9% -18% -42%

variation from 2004 % 0% -17% -30% -57%



potential % 0% -16% -29% -65%

variation from 2004 % 0% -23% -40% -74%


Table 11: Household energy consumption and energy efficiency potential for Italy

DIMMER


Unit 2004 2015 2020 2030



potential % 0% -9% -15% -24%

variation from 2004 % 0% -6% -11% -18%



potential % 0% -12% -18% -30%

variation from 2004 % 0% -8% -14% -25%



potential % 0% -17% -27% -41%

variation from 2004 % 0% -14% -23% -36%


Table 12: Tertiary energy consumption and energy efficiency potential for Italy

3.3. The UK case

The UK has committed to reducing greenhouse gas emissions by 80% by 2050 (from the 1990 baseline), and energy

efficiency will have to increase dramatically across all sectors to achieve this target. The Government has set out scenarios

for 2050 which imply a per capita demand reduction of between 21% and 47% relative to a 2011 baseline, shown below.14

Figure 4: UK final energy consumption per capita compared against carbon plan scenarios: 1980‑205014

14

DECC Energy Efficiency Strategy statistical summary, Nov 2012

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The Energy Efficiency Marginal Abatement Cost Curve (EE-MACC) presented below estimates the annual energy savings by

2020 through implementing energy efficiency measures.15

It is based on detailed modelling of ambitious scenarios for the

potential for investment in energy efficiency from different sectors of the economy. The more cost-effective a measure,

the closer it is to the left-hand side of the chart. It shows that that by 2020 the UK could be saving 196TWh annually

through socially cost-effective investment in energy efficiency. That is around 11% lower than the business as usual

baseline.

Figure 5: 2020 Energy Efficiency Marginal Abatement Cost Curve15

Energy efficiency potential in the UK

The following tables show the energy efficiency potential achievable through the Bats in the UK, sector by sector, in terms

of final energy (fuels and electricity). As for the EU-27 and Italy the tables are elaborated starting from the energy

efficiency database, and indicate the energy efficiency potential respect to the autonomous scenario and the variation,

sector by sector, of energy demand respect to the 2004 reference year and the 2015 HPI (high policy intensity) scenario.

Table 13 shows the total energy demand for the UK. The baseline scenario foresees a fair growth in the total energy

demand. In the lowest policy intensity scenario, in 2030 the total energy consumption would have grown by 1% from 2004

and of 7% taking 2015 HPI as the reference. The reduction respect to the baseline scenario would be of 14%. These figure

increases to 21% in the high policy intensity scenario and to 29% in the technical scenario. Considering that the more likely

situation is intermediate between LPI and HPI scenarios, it can be considered that thanks to energy efficiency energy

demand should be in 2020 about 15-20% lower than what it would be in absence of policies.

15

DECC Policy paper: Energy Efficiency Strategy: The Energy Efficiency Opportunity in the UK, Nov 2012

The x-axis measures the size of the energy saving in a given year, relative to the level of energy consumption that would

be seen in the absence of these measures. The y-axis represents the cost effectiveness of a measure, defined as the net

present value divided by the lifetime energy savings. This cost-effectiveness metric represents the net cost of saving a

MWh of energy over the lifetime of the project. Measures that are below the line have negative costs over their lifetime,

which means that the discounted sum of benefits outweighs the discounted costs of that measure.

DIMMER


Unit 2004 2015 2020 2030



potential % 0% -7% -10% -14%

variation from 2004 % 0% -6% -7% -8%



potential % 0% -9% -14% -23%

variation from 2004 % 0% -8% -11% -18%



potential % 0% -13% -20% -34%

variation from 2004 % 0% -12% -17% -30%


Table 13: Total energy consumption and energy efficiency potential for UK

This assessment an assessment made in 2007 starting from 2004 seems more correct than in the case of Italy: the

industrial sector in 2004 in Italy had a relatively higher importance in the economy then in UK; since then industrial

production in Italy was strongly affected by the crisis whereas the UK industry could benefit of the weakness of the pound

favouring exports, so that UK industrial sector is now stronger than it was ten years ago.

The saving potential appears in relative terms to be realistic: the EU IPPC (Integrated Pollution Prevention and Control)

directive already had started requiring improvements in environmental and energy performance of large industries at the

time when the assessment was done, so that its important effect is already considered within the baseline scenario.




Unit 2004 2015 2020 2030



potential % 0% -4% -5% -7%

variation from 2004 % 0% 2% 5% 12%



potential % 0% -5% -6% -7%

variation from 2004 % 0% 1% 4% 11%



potential % 0% -6% -9% -14%

variation from 2004 % 0% -1% 1% 3%


Table 14: Industrial energy consumption and energy efficiency potential for UK

DIMMER


Unit 2004 2015 2020 2030



potential % 0% -11% -14% -15%

variation from 2004 % 0% -13% -11% -7%



potential % 0% -13% -18% -24%

variation from 2004 % 0% -14% -16% -17%



potential % 0% -17% -23% -31%

variation from 2004 % 0% -19% -21% -24%


Table 15: Transportation energy consumption and energy efficiency potential for UK

The results of the assessment are also for the UK much more interesting for the residential sector, where the potential at

2030 is assessed between 14 and 63%. Considering the energy efficiency improvement already occurring through policies

issued before 2004, the savings to be expected for this sector are expectable between 30 and 40% respect to 2015 HPI

scenario. The tertiary sector also provides interesting margins of improvement, with a saving potential estimable between

5 and 21% (keeping an intermediate evaluation between low policy intensity scenario and high policy intensity scenario),

and a saving in absolute terms of around 10%.

Unit 2004 2015 2020 2030



potential % 0% -3% -5% -14%

variation from 2004 % 0% -8% -13% -27%



potential % 0% -7% -14% -35%

variation from 2004 % 0% -12% -21% -45%



potential % 0% -12% -24% -63%

variation from 2004 % 0% -17% -31% -69%


Table 16: Household energy consumption and energy efficiency potential for UK

DIMMER


Tertiary Unit 2004 2015 2020 2030



potential % 0% -9% -14% -23%

variation from 2004 % 0% 1% -2% -7%



potential % 0% -10% -16% -28%

variation from 2004 % 0% -1% -4% -14%



potential % 0% -16% -24% -35%

variation from 2004 % 0% -7% -13% -22%


Table 17: Tertiary energy consumption and energy efficiency potential for UK

DIMMER


4. DISTRICTS OVERVIEW

4.1.Turin district

In Turin, the Polito’s District has been chosen as the DIMMER demonstrator. As shown in Figure 6, the selected district is

located near the city center next to the Polytechnic University of Turin. The district is fully served by the heating network

and comprised by a balanced presence of both public (schools, city administration) and private (residential) buildings,

which enables the diversity of the possible studies in energy efficiency that can be carried out on its basis.

Figure 6. Turin Polito’s District

Inside the district, seven representative buildings have been selected as shown in Figure 7:

1. The Polytechnic University of Turin;

2. Primary school Michele Coppino;

3. Kindergarten Paolo Braccini;

4. Commune di Torino – Direzione Smart City;

5. Student Accommodation Renato Einaudi;

6. Residential building, Via Pigafetta 52;

7. Residential building, Corso Mediterraneo 130;

With the exception of the building 5 (Student Accommodation Renato Einaudi), all of the selected buildings are connected

to the district heating network, which is the main focus of energy efficiency studies in the Turin demonstrator. A more

detailed overview of the selected buildings, as well as the district characteristics in general, have been provided in the

deliverable D1.1.

DIMMER


Figure 7. Building selection in Turin district

4.2.Manchester district

The Oxford Road Corridor area (shown in the figure to

the right) has been chosen as a district for DIMMER.

The Oxford Road Corridor (‘the Corridor’) is home to

the University of Manchester, the Manchester

Metropolitan University and the Central Manchester

University Hospitals NHS Foundation Trust (CMFT) –

making the Corridor not only the largest education

campus in the UK but also the largest clinical academic

campus in Europe.

The DIMMER study will focus on University of

Manchester buildings where a large proportion of the

building stock was constructed during 1960-1979,

although a recent capital programme has changed the

profile somewhat.

Figure 8: Oxford Road Corridor

DIMMER


5. DISTRICT ENERGY EFFICIENCY POTENTIAL

In this chapter, the efficiency targets described in D1.1 per each DEMO site have been included, to cover all the aspects

involved in the scope of the project.

5.1.Turin district

Turin Demonstrator energy savings potential will be evaluated considering the portion of district heating network which

supplies heat to the selected district. It has been considered that district heating is the energy system which best matches

with the efficiency studies and actions of the DIMMER project, providing significant results at utility, public bodies and

final customer’s level.

The surroundings of Politecnico Campus are connected to the district heating network with a market penetration of about

50%, which slightly differs from the district heating total market penetration in Turin (almost 60%). Turin owns the largest

district heating network in Italy, with 474 km of dual piping and a heating capacity corresponding with a volume of around

56 million m3. The network is made up of a primary line or backbone (large-diameter pipes) with several “thermal hubs”(a

thermal hub is a conventional point/knot of the network backbone or primary lines from which secondary lines - small-

diameter pipes that reach the various buildings - branch off). The network is fed by 3 Combined Cycle Gas Turbine (CCGT)

Plants (total of 1200 MWe), peak boilers (more than 1000 MWth) and thermal energy storages (12500 m3).

In terms of district heating network, Polito’s District is made up of several thermal hubs: BCT 418 (the one that feeds

Politecnico Campus), BCT 413, BCT 411, BCT 412, BCT 410, BCT 414, BCT 410 and other smaller ones. Some of these hubs

have similar features in terms of customer typology, power installed and network pipe length: this gives the possibility for

setting up a representative Demonstrator with some of them and a well-defined Validation Set with some of the others for

a comparison of energy-efficiency actions taken and validation of results.

DIMMER


Figure 9: Location of the substations in the Politecnico district

The final definition of the two groups (the Demonstrator and the Validation Set), that is the choice of the most

appropriate thermal hubs, will take into account several aspects:

• Heterogeneity of building typologies and uses among the chosen demonstrator but comparability with the other

parts of the area and/or city;

• Market penetration of the various thermal hubs;

• New-generation remote control of the various thermal substations (which are constituted of a single heat

exchanger in the basement of each served building), equipped with energy-saving management options;

• Good exploitation of the thermal hubs hydraulic and thermal capacity and absence of network failures or

weaknesses (due both from design or operation phases);

• Evaluation and comparison of consumption data (based also on historical sets) of the various thermal hubs.

5.1.1. District Heating Network

Turin District heating network in Politecnico District will be the focus of the job. Substantial energy saving potential can be

evaluated and energy policies can be defined once the equipment will be put in place in the thermal substations and in

some reference buildings and apartments.

As described in D1.1, the following works will be necessary in order to exploit the energy saving potential of the district

heating network:

• Installation of a temperature sensor on the return of the building network: each building heat exchanger is

already equipped with three temperature sensors on the secondary network delivery land return lines and on the

building network delivery line (see figure below). Installing the fourth temperature sensor on the return line of

DIMMER


the building network will help managing the heat exchanger operation & maintenance and implementing some

energy savings actions.

Figure 10: Building heating system PFD

• Indoor temperature sensors: some of the buildings will be equipped with temperature sensors in various parts of

their volumes in order to precisely map their internal temperature distribution. Such analysis is important in

order to fully understand the opportunities and constraints for the DIMMER strategies. Some buildings have been

already identified, but the idea is to define a wider range of “reference buildings” that could cover a large number

of existing typologies in terms of construction categories and expected uses.

Figure 11 – Indoor temperature distribution

These sensors will provide several data that will be exploited during the DIMMER project together with other input data,

which are:

• Buildings 3D thermal modelling in the selected area: thanks to Politecnico expertise, the Turin Demonstrator will

also rely on a deep architectural and construction knowledge of the buildings using BIM (Building Information

Modelling); a fundamental input in order to define energy behaviours and consumptions of the selected

buildings.

• Thermal substations’ near-real-time consumptions: for each building equipped with a remote controlled thermal

substation it will be possible to gather consumption and operation data, together with external temperature

coming from the substation specific weather station, with a recording period of five minutes.

Figure 12 shows an example of the data that will be collected: in this case the heat exchanger is due to start

6:30AM (grey line). The thermo-regulator opens the valve (green line) and the hot water flows from the network

pipes at the temperature of 114 °C (red line). The indoor temperatures grow till the building network forward

DIMMER


temperature reaches its set point: the regulator closes the valve and maintains the temperature (blue and pink

lines).

Figure 12 – Thermal substation data gathering

• District heating network technical properties and layout: as-built district heating network data (layout and

design) will also be available as input for the hydraulic and thermal dynamic modelling of the Politecnico District

network.

Each reference building of the District, whose input data listed above can be collected, will be characterized in detail in

terms of thermal energy behaviour and consumption.

The basic idea of this first step of the project in Turin demonstrator is to collect a set of data and to assess thermal

behaviours as representative as possible of the various typologies of buildings in the district. This information will be used

to obtain correlations between the temperatures registered at the substation (plus the external temperature) and an

average indoor temperature. Temperature deviations inside the building will be captured through the building model,

verified through measurements and related with the building type and geometry (e.g. shape factor, etc.). In this way, it is

expected that the same energy saving actions can be implemented to all the other buildings of the same typologies not

equipped with indoor sensors and evaluating the results, without penalizing the internal comfort.

A dynamic model of the building will be created on the basis of the measurements, able to correlate an average internal

temperature with the time evolution of the heat flow supplied at the substation and with the external temperature. The

model uses two characteristic constants: the global heat transfer coefficient and the characteristic time constant. The first

parameter is evaluated when the building reaches quasi-steady state conditions (e.g. afternoon operation), while the

second parameter is evaluated during the evening shut-down.

This model is used to obtain an optimal profile of the heat flow that minimizes the annual thermal energy request without

penalizing the internal comfort level.

A thermo-fluid-dynamic model of the network is built in order to relate the thermal request of the buildings in the

network hubs to the thermal request to the plants. This model is required since the thermal requests of the users

DIMMER


“propagate” throughout the network, depending on the water velocity in the various pipes and are smoothed by the

thermal capacity of the network itself.

The model considers the continuity and momentum equations in steady-state and the energy equation in transient

conditions. Inputs for the model are the thermal profile for the selected buildings and the thermal request of the other

thermal hubs, besides the network physical characteristics, topology, and so forth.

The model can be used in order to investigate the effects of possible variations in the thermal request profiles in terms of

primary energy consumption at the thermal plant.

5.1.2. Energy Savings potentials: better management of the heat exchangers

Based on the points listed above, it will be possible to test different energy-efficiency actions in the various buildings and

evaluate the direct results on the reference buildings (that is the change in comfort set point in different sectors of a

building with indoor temperature sensors). In addition, it will be possible to evaluate the indirect results on the other

buildings, based on the information gathered with the four temperature sensors placed at each building heat exchanger

and the thermal behaviour curves and coefficients that result from these measurements.

In order to positively test energy-saving actions, the heat exchanger parameters should be always under control and, most

importantly, the exchanger must always be perfectly functioning. As a result, IREN and Politecnico di Torino will develop a

new software tool/algorithm capable of:

1. Real-time calculation of heat exchangers performances and quantification of the fouling conditions. Energy-

savings functions will be applied only to the “good” heat exchangers, that are able to support the improved

strategies without affecting the comfort conditions in the building. The proposed strategies will be disabled in the

case something wrong occurs;

2. Daily calculation of the optimal operation parameters (e.g. comfort set points) and remote-uploading on each

system;

3. Analyze and compare results and performances of old and new actions or programs on each heat exchanger.

The “smart management” of the heat exchangers described is a mandatory step towards a tangible improvement in

thermal energy provision. In particular the following benefits are envisaged:

1. More accurate and efficient maintenance programs for the heat exchangers based on real-time data gathering

and evaluation of packing (fouling);

2. Use of energy-saving option provided by the thermal substations vendors: most of the new substations are

equipped with Siemens RVD2xx series regulator. This device controls all activities. Optimization functions are

available but only with some sensors installed on the substation. In particular they have a function of limitation of

power and flow by checking temperature status called “Maximum limitation of the temperature differential (DRT

function)”.

Maximum limitation of the temperature differential generally ensures that a smaller amount of heat will be

drawn from the network or that the volumetric flow will be throttled when heat is demanded for the first time in

the morning, when the pipes have not yet reached their normal operating temperature (prevention of idle heat

and unnecessary supply of heat back to the network). In addition, maximum limitation of the temperature

differential:

• acts as a dynamic limitation of the return temperature;

• shaves peak loads.

DIMMER


Figure 13: Limitation valve’s operation – volumetric flow over time graph (Image from Siemens RVD 230 Manual)

The difference between the return temperature on the building circuit and the return temperature on the district heating

circuit is usually from 2 to 5 °C and depends on the type of heat exchanger.

5.1.3. Energy Savings potentials: Peak demand management and reduction

The EASY Arithmetic sample

As an example of potential peak management technique that would be carried out in the DH network, Figure 14 below we

shows ten numerical series with the same starting hour, representing the hourly thermal demand for ten building thermal

substations, and peaks concentrated at step 5 for each building. The flow peak request from the network is the arithmetic

sum of each peak. The series of the data of the single building overlap.

Figure 15 shows the effect on total demand (the clear blue line) after having shifted the peak time of the single buildings,

changing the starting hour. The total peak power demand is much lower and the peak is broader, meaning a smoother

operation of the system with higher use of CHP and lower use of peak boilers.

DIMMER


Figure 14: Total and individual heat demand of ten buildings with peak demand concentrated at the same hour

Figure 15: Time shifting example on invented data

TIME SHIFTING

DIMMER


Test on real data series

The idea described above was assessed by simulations. Real data of heat demand from 64 buildings were used to set up a

simulation of the effect of shifting demand peaks.

Figure 16: Time shifting example on real data

It is not easy to obtain significant savings because it is not possible to shift the start times too much as customers’

requests must be fulfilled. Taking that into account, it was estimated that a reduction of 5% in flow could be reached by

shifting start times by no more than 25 minutes from nominal times.

Moreover, in winter, after the peak time the exchangers continue to draw heat from the network to keep the indoor

temperatures stable. The requests differ for each building, but they are always related to the external temperature.

Below is a sample of the same building heat exchanger in two different external temperature conditions.

DIMMER


Figure 17: Heat exchanger operation at different outdoor temperatures

If the external temperature is high, the flow after the peak demand is low and the peak duration is relatively shorter; if the

external temperature is lower, the peak duration is longer and the heat request following the peak is more substantial.

Therefore, the higher is the temperature, the better will be the results of time shifting policies, since the power (and flow

rate) needed to maintain the temperature set points after the peak is lower.

As expected, the plant reaches its set point at different hours if the external temperature is different, as displayed in the

graph below.

Figure 18: Set point reaching at different outdoor temperatures

0

0,5

1

1,5

2

2,5

3

3,5

4

FLOW m³/h at ext.

Temp 5 °C

FLOW m³/h at ext.

Temp 0 °C

Hours

0

10

20

30

40

50

60

70

SET POINT at 5° c

SET POINT at 0° C

T_FORW 5° C

T_FORW 0° C

Hours

Flo

w

Te

mp

era

ture

F

low

DIMMER


The time to reach the comfort condition can also increase if the heat exchanger is dirty (fouled, also said packed). Figure

19 displays the effect of fouling: on the upper figure temperatures corresponding with a heat exchanger in good

conditions are shown; on the lower figure temperatures corresponding with a fouled heat exchanger are shown. In

particular, it is possible to notice that the transient evolution is faster in the first case, as the steady state is reached after

about 1.5 hours, while in the second case the steady state is not reached after 2 hours. Moreover, return temperature on

the primary circuit is just slightly larger than that on the secondary circuit in the case of a clean heat exchanger, while this

temperature difference is much larger in the case of a fouled heat exchanger. This means that fouled heat exchangers

make increase the return temperature on the district heating network, which affects the energy performance of the entire

system, i.e. the primary energy required to supply heat to the users increases.

Figure 19: Clean VS packed heat exchanger set point reaching

The difference between the two return temperatures (the blue lines) is higher in packed conditions.

To summarize, multiple factors can be considered in order to improve the management of the Peak Energy request:

• Starting Time;

• Weather temperature;

• Heat Exchange maintenance.

All these factors can be evaluated by the field sensors as mentioned above.

Data will be sent to the software optimization algorithm that will suggest, based on the daily data analysis, the optimal

hours to switch on each exchanger in the district network, in order to minimize the peak request. Using IREN remote

control system, the regulator parameters and the starting hours for the following day (based on the day before data

DIMMER


analysis) can be sent from the central server to all the heat exchangers. Particular attention will be paid to the software

architecture because of the big amount of data that must be processed in a short time: other specific external conditions,

such as particular customers’ requests and other technical factors, must be taken into account, too.

A correct evaluation of the primary energy savings will be obtained by applying the transient model of the network, which

allows transforming the thermal energy profile of each single user into the thermal load profile of the plants. The overall

system management will be conducted by proposing thermal profiles to the users that are able to maximize the heat

production from the combined cycles and reduce the boiler utilization, without affecting the desired comfort conditions of

the users. Combined cycles are characterized by a much larger primary energy efficiency in the thermal production than

boilers, therefore a significant reduction of fuel consumption can be achieved.

5.1.4. Model structure for the implementation of the DIMMER strategies

This model aims at calculating the primary energy savings that can be achieved by applying the DIMMER strategies to the

selected districts. The model receives the thermal request profiles of the buildings as the input. The model considers the

district heating network in order to transform the thermal request of users into thermal load of the plants. A plant model

allows one to calculate the fuel consumption associated with the thermal load.

First step: thermal substation model

This model includes a compact building model and the heat exchanger model. The building model is described in D1.3.1. It

allows one to check the possible effects of variations in the thermal request profiles of the buildings on the average

internal temperature. The proposed thermal request profile is acceptable if the internal temperature is kept at the same

level as that in the initial profile.

The heat exchanger model transforms the thermal request into mass flow rate request on the primary side (i.e. in the

district heating network). This model does not consider possible perturbations on the district heating network caused by

variations in the thermal request profiles. This means that the temperature on the supply line is assumed as the same that

would occur if the thermal requests were not modified. Such assumption allows one simple optimization of the changes in

the thermal request profiles at district level. This is a constrained optimization performed by setting the minimum and

maximum time shift of the thermal profiles. These constraints should be verified by considering possible perturbations.

Second step: network model at thermal barycentre level

The second step considers full fluid dynamic and thermal model of the district heating network of a thermal barycentre.

This is the distribution network from the main pipeline (also called the “transport network”) to the various users located in

an area. The distribution network is characterized by smaller diameters than the main network. This causes a completely

different transient behaviour.

The full topology of the network is considered. Fluid dynamic model is considered in steady state, since the characteristic

propagation velocity of pressure disturbances (the sound speed) is very large compared with the fluid velocity. The

thermal model is considered in transient condition. This allows one to calculate the temperature distribution as the

function of time and particularly:

1. The temperature distribution as the function of time on the supply line during night operation (when fluid is

characterized by very small or zero velocity)

2. The temperature distribution on the return line, particularly during the operation when heating systems are

started.

DIMMER


Third step: network model

The network model considers the main pipeline. It receives the input from distribution networks as the boundary

conditions and allows one to calculate the thermal load of the power plants. Thermal load is obtained on the basis of the

mass flow rate in each plant and the difference between supply and return temperature. The latter is calculated by the

thermal model considering mixing, heat transport, heat losses and transient effects.

Fourth step: plant model

The last step considers the various thermal plants and allows one to obtain the primary energy consumption associated

with heat generation. In the case of boilers, the model only accounts for their efficiencies. In the case of cogeneration

plants the model is more complex, since the separate contribution of fuel consumptions due to heat and electricity

productions is calculated.

The model also includes the thermo-fluid dynamic model of the storage systems. These systems are characterized by time

shift between generation and use. Charging is mainly operated through hot water produced by cogeneration systems,

while the discharge is generally operated when the operation of auxiliary systems (boilers) would be required. The model

accounts for thermal degradation of water in the tanks due to temperature gradients and mixing effects.

5.2.Manchester district

5.2.1. Heat network energy efficiency potential

The amount of renewable heat generated across Greater Manchester is not currently accurately known, but the Greater

Manchester Climate Change Strategy and Implementation Plan16 contains a target of 3 TWh of renewable heat

production by 2020 (equivalent to 12% of the current use of 25.5 TWh). A recent study17 concluded that Greater

Manchester needs to increase the rate of deployment of low carbon and decentralised energy, particularly heat, across

the conurbation.

The DIMMER Manchester district includes heat networks whereby centralized boiler plants deliver heat to multiple

buildings through buried pipework. There are three separate systems: a Steam system on the west side of Oxford Road;

and a Low Temperature Hot Water system and a High Temperature Hot Water system on the east side of Oxford Road.

At present, these heat networks utilize boilers as the heat-generating technology, i.e. not CHP, also known as

cogeneration. Utilizing or expanding these heat networks with the use of CHP technology presents an opportunity for

increasing the efficiency of heat and electricity supply to the district.

Figure 20 and Figure 21 are related to the steam heat network. On the left, a schematic diagram of the plant rooms and

buildings served by the network, and on the right these buildings presented spatially on a map.

5.2.1. Electricity network energy efficiency potential

Currently there is an estimated 0.54TWh of renewable electricity generated across Greater Manchester17

. The Greater

Manchester Climate Change Strategy and Implementation Plan16

contains a target of 1 TWh of renewable electricity by

2020 (equivalent to about 8% of the current use of 11.8 TWh).

Part of the role of the regions Distribution Network Operator (Electricity North West, ENW) is to plan for the future and

invest money into the region’s electricity network. ENW is looking at how the electricity network can be developed to

meet future demand as we more and more renewable energy is used from sources such as solar panels, heat pumps and

wind turbines.

16

http://manchesterismyplanet.com/gmccsip 17

New Economy Working Paper: Powering Greater Manchester: how will we fuel our future?, Jun 2014.

DIMMER


Figure 20: Schematic diagram of the plant rooms

and buildings served by the steam heat network

Figure 21: Map of buildings served by the steam heat

network

ENW has been granted around £10 million from Ofgem’s Low Carbon Network (LCN) Fund to develop its ground-breaking

Capacity to Customers (C2C)18

initiative which aims to:

• Release previously untapped emergency network capacity for everyday use;

• Enable users to make savings by changing the way they use electricity;

• Prevent huge infrastructure improvement costs being passed on to customers; and

• Deliver vital benefits to the region and to the whole of the UK.

The region’s high voltage networks are often interconnected by a ‘normal open point’ (NOP) which is only used in the

event of a network fault or planned outage. Nearly half of the city circuits do not suffer faults and one third only

experience faults lasting 1 - 2 hours once every 5 years. Closing the NOP allows all customers affected by a fault or outage

to be re-supplied from the alternative circuit. By redesigning the network to allow the NOP to be run closed, the two

circuits can be joined and release their full capacity.

18

http://www.enwl.co.uk/c2c

DIMMER


5.2.2. District intervention

The example in the appendix illustrates a relatively simple intervention that lead to behaviour change at one of the

buildings at the University of Manchester.

5.2.3. Literature review on integrated network modelling and distributed multi-generation

Several approaches for modelling the integration of energy systems and networks at various levels have been published.

General modelling framework examples include energy hubs19

, multi-energy systems and distributed multi-generation20,21

,

community energy systems22

, smart energy systems23

, and integrated energy systems24

. These different frameworks and

modelling approaches may be useful to drive the creation of an integrated district energy system model with the aim of

studying different options to increase the economic and environmental performance of a neighbourhood.

A generic framework for steady-state analysis and optimisation of energy systems was investigated by Geidl and

Andersson19

. The coupling between multiple energy carriers was modelled using energy hubs. Using the energy hubs

concept, input power of electricity, natural gas and district heat is converted to electricity and heat output power through

an efficiency coupling matrix. The model showed the potential for reduction of overall energy cost and emissions.

Smart multi-energy and distributed multi-generation systems were described. In multi-energy systems, coupling of

electricity, heating, cooling and gas networks takes place through various distributed technologies such as CHP, micro-

CHP, heat pumps, solar thermal, photovoltaic and energy storage systems. A holistic overview from an energy,

environmental, and techno-economic perspective was provided.

An extension to flexible distributed multi-generation of electricity and heat with district level CHP and heat pumps was

developed, showing how a smart coordination of multi-energy component in a district can bring impressive economic and

environmental benefits25

. Such high-efficiency flexible multi-energy plants prove to be very effective also from a planning

perspective and in the presence of different market conditions. Further economic benefits could be accrued by a district if

providing real time demand response services. Smart Polygeneration microgrids have already been realized and are under

testing26

.

Various methods have been developed to investigate combined electricity and natural gas networks19, 27, 28

, in which gas

turbine generators (which could in case be CHP ones) provide the linkage between gas and electricity networks. An

19

M. Geidl and G. Andersson, "Optimal Power Flow of Multiple Energy Carriers," Power Systems, IEEE Transactions on, vol.

22, pp. 145-155, 2007. 20

G. Chicco and P. Mancarella, "Distributed multi-generation: A comprehensive view," Renewable and Sustainable Energy

Reviews, vol. 13, pp. 535-551, 2009. 21

P. Mancarella, "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, vol. 65, pp. 1-17,

2014. 22

P. Mancarella and G. Chicco, "Distributed Multi-Generation: energy models and analyses": Nova Publisher, New York,

2009. 23

H. Lund, A. N. Andersen, P. A. Østergaard, B. V. Mathiesen, and D. Connolly, "From electricity smart grids to smart

energy systems – A market operation based approach and understanding," Energy, vol. 42, pp. 96-102, 2012. 24

CHPA, "Integrated Energy: The role of CHP and district heating in our energy future," The Combined Heat and Power

Association (CHPA), 2010. 25

T. Capuder and P. Mancarella, Techno-economic and environmental modelling and optimization of flexible distributed

multi-generation options, Energy, Volume 71, 15 July 2014, Pages 516–533. 26

F. Delfino, L. Barillari, F. Pampararo, M. Rossi, A. Zakariazadeh, P. Molfino, A. Podestà, A. Venturin, N. Robertelli, The 5th

International Conference on Development and assessment of Decentralized Energy Management System in a smart

Microgrid, Information, Intelligence, Systems and Applications, IISA 2014, Pages 125-130. 27

A. Seungwon, L. Qing, and T. W. Gedra, "Natural gas and electricity optimal power flow," Transmission and Distribution

Conference and Exposition, IEEE PES, vol. 1, pp. 138-143, 2003.

DIMMER


approach was described29

to execute a single gas and power flow analysis in a unified framework based on the Newton-

Raphson formulation. Likewise, there have been a few studies that have investigated combined electricity and heat

networks, e.g. again the energy hub model19

, the energy interconnector model30

, and an integrated optimal power flow

for electricity and heat networks31

. On the other hand, the impact of electric heat pumps and distributed CHP on LV

networks was evaluated, showing how a smart combination of these technologies could reduce the arising network

impact (particularly severe in the case of heat pumps32

). A range of micro CHP sizes and penetration levels and the

relevant network impacts were also investigated33

, where it is shown that running a micro CHP unit instead of a boiler will

tend to increase the volumes of gas delivered to consumers, which could potentially affect the operation of the gas

network. This will have to be properly considered in planning studies at a district level. Strategic techno-economic analysis

of heat network options in different areas was carried out34

. All these studies point out the benefits of integrated network

analysis or design.

The simultaneous transmission of heat, electricity, and chemical energy in one single device was modelled and analysed30

.

The integration of technical design, greenhouse gas emission analysis and financial analysis for integrated community

energy systems was modelled by Rees35,36

. In these models the electrical, thermal and gas power flows were calculated

independently and linked through generating units.

The role of the conversion components (CHP units, heat pumps and electric boilers) within integrated network studies was

investigated in several publications, i.e., in terms of optimal control strategies37

, the arising economic value38

and the

impact of future heat demand39

. It may be concluded from the various studies that diversifying the heat delivery options

within a district or city with integrated electricity, heat and gas system modelling may facilitate the development of a low

carbon energy system40,41,42

.

28

M. Chaudry, N. Jenkins, and G. Strbac, "Multi-time period combined gas and electricity network optimisation," Electric

Power Systems Research, vol. 78, pp. 1265-1279, 2008. 29

A. Martinez-Mares and C. R. Fuerte-Esquivel, "A Unified Gas and Power Flow Analysis in Natural Gas and Electricity

Coupled Networks," Power Systems, IEEE Transactions on, vol. 27, pp. 2156-2166, 2012. 30

P. Favre-Perrod, "Hybrid energy transmission for multi-energy networks," PhD Thesis, ETH, 2008. 31

B. Awad, M. Chaudry, J. Wu, and N. Jenkins, "Integrated optimal power flow for electric power and heat in a microgrid,"

Prague, 2009. 32

A. Navarro-Espinosa and P. Mancarella, Probabilistic modelling and assessment of the impact of electric heat pumps on

low voltage electrical distribution networks, Applied Energy, Volume 127, 15 August 2014, Pages 249–266. 33

T. Sansawatt, J. R. G. Whiteford, and G. P. Harrison, "Assessing the impact of micro CHP on gas and electricity

distribution networks," in Universities Power Engineering Conference (UPEC), 2009 Proceedings of the 44th International,

2009, pp. 1-5. 34

A. Ahmed and P. Mancarella, Strategic techno-economic assessment of heat network options for distributed energy

systems in the UK, Energy, Volume 75, 1 October 2014, Pages 182–193. 35

M. T. Rees, J. Wu, B. Awad, J. Ekanayake, and N. Jenkins, "A modular approach to integrated energy distribution system

analysis," 17th Power Systems Computation Conference, Stockholm Sweden, 2011. 36

M. T. Rees, J. Wu, B. Awad, J. Ekanayake, and N. Jenkins, "A total energy approach to integrated community

infrastructure design," Power and Energy Society General Meeting, 2011 IEEE, 2011. 37

H. Lund and E. Münster, "Integrated energy systems and local energy markets," Energy Policy, vol. 34, pp. 1152-1160,

2006. 38

P. Meibom, J. Kiviluoma, R. Barth, H. Brand, C. Weber, and H. V. Larsen, "Value of electric heat boilers and heat pumps

for wind power integration," Wind Energy, vol. 10, pp. 321-337, 2007. 39

R. Sansom, "The impact of future heat demand pathways on the economics of low carbon heating systems," BIEE

Conference, 2012. 40

J. Arran and J. Slowe, "2050 Pathways for Domestic Heat Final Report " Delta Energy & Environment Ltd, 2012. 41

J. Speirs, R. Gross, S. Deshmukh, P. Heptonstall, L. Munuera, M. Leach, and J. Torriti, "Heat delivery in a low carbon

economy," BIEE Conference, 2010.

DIMMER


Flows of energy through the chains of conversion components were visually mapped43,44

. The benefit is to identify the

technical areas with the largest energy flows, to deliver the largest energy efficiency gains. Again, this can be useful to

identify possible intervention strategies to improve a district’s environmental performance by targeting the weakest or

the most strategic areas.

42

P. Dodd, "Delivering the low carbon energy jigsaw. Will the technology and incentive pieces fit," 2012. 43

J. M. Cullen and J. M. Allwood, "Theoretical efficiency limits for energy conversion devices," Energy, vol. 35, pp. 2059-

2069, 2010. 44

J. M. Cullen and J. M. Allwood, "The efficient use of energy: Tracing the global flow of energy from fuel to service,"

Energy Policy, vol. 38, pp. 75-81, 2010.

DIMMER


6. CONCLUSIONS

The extensive use of sensors, meters, ICT and data management tools can strongly improve energy efficiency in districts at

several levels: buildings, networks, energy products and services.

The most effective fields of application of ICT are four:

1. The possibility of extending control of energy system at a much higher level of definition with a higher ease than

with traditional systems;

2. A deeper comprehension of real behaviour of complex energy systems, highlighting phenomena that were not

foreseen during design and/or construction and providing high definition information to face these problems

through system management and/or by proper interventions in case of retrofitting;

3. A much better tuning of energy services, so that several systems traditionally operating at full capacity, regardless

of their need, can be tuned and used at partial load when they are not needed (such as ventilation in a room with

a fraction of the design number of occupants);

4. An optimal operation and maintenance of systems: ICT can allow to promptly detect anomalous consumption of

components, indicating failures, wear or misuse.

In general, it is foreseen that ICT will have a primary role in exploiting the large energy efficiency potential in Europe, as

well as in its Member States Italy and UK. It will be the enabling technology in the evolution of today’s power grids into

smart grids, able to manage the use of unprecedented amounts of unpredictable renewable power sources through smart

management of shiftable loads, electricity storage and smart management of diffused, adjustable power sources.

Aside this role that will allow a large increase of the share of renewable energy in the EU energy mix, ICT will allow

improving the quality of energy services and increase their efficiency.

Within the Turin demo, a novel ICT system will be introduced to allow a better comprehension of the district heating

system behaviour and a better management of it. Sets of temperature sensors distributed on some target buildings will

allow to discover which are the patterns in DH energy delivery that allow minimizing the energy use, keeping the required

service quality standard. New temperature sensors at substation level will allow the understanding of the need for

maintenance and cleaning of heat exchangers, with an optimization of their efficiency. The combination of this

information will allow establishing the way to best exploit DH network dynamics and the anticipation or delivery of some

minutes of peak service in each individual building to cut the overall peak demand and broaden the peak, with the effect

of reducing the need of energy from peak boilers exploiting in a better way cogeneration heat.

In Manchester attention will be focussed on electricity and gas systems. In particular, the attention will be focussed on

base loads, which appear to be very high at times when university structures are not used. The action in this demo will be

mainly addressed towards final use appliances and to the engagement of final users in taking behaviours aimed at

reducing electric and gas base load by limiting energy waste.

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APPENDIX

Example of energy saving potential in Manchester

The example in the box below illustrates a relatively simple intervention that lead to behaviour change at one of the

buildings at the University of Manchester.

Introduction

The University of Manchester identified that the energy use in the Ferranti Building is

increasing. There may be good reasons for this such as increased occupancy and

research activity, however, examination of the daily electricity profile shows that there

is a high load at night (base load) and even over Christmas, so it is thought that energy

use can be reduced in this building.

Overview of Ferranti Building

The Ferranti Building is located in the North Campus of the University of

Manchester, part of the former UMIST campus. It was built in 1969, a

reinforced concrete block design, with most of the building comprising three

floors, with 6 floors in some areas, and a gross floor area of 18541 m2.

The building includes teaching facilities and labs, including for High Voltage

Laboratory at the western end which is clad in aluminium.

Typical energy use

The following charts illustrate the electricity use for the building. The electrical demand varies in a typical day with a peak

of about 65kW and a night base load of 25 to 40 kW. The average daily demand graph shows that the daily peaks occur

between 09:00 and 16:00, which coincides with the times of higher occupancy levels and usage for a university building

with teaching and research facilities.

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The charts below illustrate that electricity demand is generally slightly higher during the winter months than the summer

months.

Electricity use over Christmas holiday period

It can be seen from the chart below that although the peak demand reduced during the days between 24th

to 26th

December 2013, the base load of demand remained fairly constant.

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Description of intervention

In order to understand the level of control users have over the base load, for one night (Thursday 10th

July 2014) building

users were asked to make every effort to switch off any electrical devices overnight, including computers, printers, phone

chargers, drinks machines and laboratory equipment.

The following information was publicized prior to 10th

July.

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Results of intervention

Between 8pm on 10th

July and 8am on 11th

July, the building electricity use reduced by 34% compared to the period 8pm

on 9th

July and 8am on 10th

July. This is shown in the chart below with different lines for the two night-time periods.

Some of this saving appears to have been maintained. The chart below shows the same time period but for the ‘average of

week before’ (which is 3rd

to 9th

July inclusive) and ‘average of week after’ (which is 10th

to 16th

July inclusive). There was a

20% reduction in electricity use for the time period in the two weeks. The implication here could be that some equipment

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was switched off on 10th

July and remained switched off, or that a better practice was maintained where equipment was

switched off routinely at the end of the day.

District-wide intervention

This building energy use characteristic of high base load electricity use during the night time is not unique to the Ferranti

building. Other buildings present a similar pattern; a sample of these is presented below.

The Mill

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Paper Science Building

Manchester Business

School West

James Chadwick Building

Precinct 1

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Stopford Building

d1.4 energy saving potential report on existing technologies ......dimmer d1.4 - energy saving...

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