Helsinki University of Technology

Department of Engineering Physics and Mathematics

Hannele Holttinen

Hourly wind power variations and their impact on the Nordic power system operation

Licenciate Thesis

December, 2003

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HELSINKI UNIVERSITY OF TECHNOLOGY DEPARTMENT OF ENGINEERING PHYSICS AND MATHEMATICS

ABSTRACT OF LICENTIATE’S THESIS Author: Hannele Holttinen

Department: Department of Engineering Physics and Mathematics

Major subject: Advanced Energy Systems

Minor subject: Energy Economics and Power Plant Engineering

Title: Hourly wind power variations and their impact on the Nordic powersystem operation

Title in Finnish: Laajamittaisen tuulivoiman tuntivaihtelut ja niiden vaikutusPohjoismaiden sähköjärjestelmään

Chair: Tfy-56 Advanced Energy Systems

Supervisor: Prof. Peter Lund

Instructor: Doc. Ritva Hirvonen

Abstract:

The variations of wind power production will increase the flexibility neededin the system, when significant amounts of load is covered with wind power.When we are studying the incremental effects that varying wind powerproduction imposes on the power system, it is important to study the system asa whole: only the net imbalances have to be balanced by the system. Largegeographical spreading of wind power will reduce variability, increase predictability and decrease the occasions with near zero or peak output. The goal of this work was to estimate the increase in hourly load followingreserve requirements, based on real wind power production and synchronous hourly load data in the 4 Nordic countries. As an interim result, properstatistical properties of large scale wind power production data were lookedfor, from the statistical analyses of the data sets available for Nordic countries. The main conclusions of this study are that the hourly variations of largescale wind power in the Nordic countries stay 98 % of time inside ± 5 % ofinstalled capacity. This will be seen as an increase in the hourly load followingrequirements of the power system, at the stage when wind power is producing a significant share of the electrical consumption. At a 10 % penetration level(wind power production of gross demand) this is estimated as 1.5...4 % ofinstalled wind capacity, taking into account that load variations are more predictable than wind power variations.

Number of pages 81p.+app.9 p. Keywords wind power, energy system, hourlyvariations

Department fills

Approved Library code

3

Teknillinen korkeakoulu

Teknillisen fysiikan ja matematiikan osasto LISENSIAATTITYÖN TIIVISTELMÄ

Tekijä Hannele Holttinen

Osasto Teknillinen fysiikka ja matematiikka

Pääaine Energiateknologiat

Sivuaine Energiatalous ja voimalaitostekniikka

Työn nimi Laajamittaisen tuulivoiman tuntivaihtelut ja niiden vaikutusPohjoismaiden sähköjärjestelmään

Title in English Hourly wind power variations and their impact on the Nordic powersystem operation

Professuurin koodi ja nimi

Tfy-56 Energiateknologiat

Työn valvoja Prof. Peter Lund

Työn ohjaaja Doc. Ritva Hirvonen

Tiivistelmä

Tuulivoiman vaihtelut lisäävät sähköjärjestelmän tarvitsemaa joustavuuttasiinä vaiheessa, kun huomattava osuus sähkön kulutuksesta tuotetaantuulivoimalla. Kun tarkastellaan tuulivoiman aiheuttamia lisävaatimuksiajärjestelmään, on tarkasteltava kokonaisuutta: jokaista tuulivoiman vaihtelua ei tarvitse säätää, ainoastaan järjestelmän näkemät nettovaihtelut. Tuulivoimanhajauttaminen eri alueille vähentää tuulivoiman kokonaistuotannonvaihteluita, parantaa ennustettavuutta sekä vähentää ajanjaksoja jolloin tuulivoimatuotanto on lähellä nollaa tai huipputehoa. Tutkimuksen tavoitteena oli arvioida tuulivoiman aiheuttama lisäystunnittaisen säätöreservin tarpeeseen perustuen toteutuneisiin tuulivoimantuotannon ja sähkönkulutuksen aikasarjoihin Pohjoismaissa. Arvion taustaksitehtiin tuulivoimatuotantoaineiston tilastollinen analyysi. Työn päätuloksena todettiin Pohjoismaiden tuulivoiman tuntivaihteluidenpysyvän 98 % ajasta välillä ± 5 % asennetusta kapasiteetista. Suurimmat vaihtelut näkyvät tunnittaisen säätöreservin lisäyksenä sitä enemmän mitäsuurempi osa tuulivoimalla sähköntarpeesta tuotetaan. Kun tuulivoiman osuuson 10 % vuotuisesta sähkönkulutuksesta, reservitarpeen lisäykseksi arvioitiin 1,5–4 % asennetusta tuulivoimakapasiteetista. Tässä on huomioitu kuormavaihteluiden tuulivoimaa parempi ennustettavuus.

Sivumäärä 81 s.+liitt.9 s. Avainsanat tuulivoima, sähköjärjestelmä, tuntivaihtelut

Täytetään osastolla

Hyväksytty Kirjasto

Table of Contents Foreword.....................................................................................................................................6

Nomenclature..............................................................................................................................7

1 Introduction ........................................................................................................................8

1.1 Wind power production ..............................................................................................9

1.2 The area of study: the Nordic power system ..............................................................9

1.3 Power system operation............................................................................................10

2 The impacts of wind power on the power system ............................................................14

2.1 Previous work ...........................................................................................................15

2.2 The aim of this work.................................................................................................17

3 Data used in this study......................................................................................................18

3.1 Data handling principles...........................................................................................19

3.2 Data set for Finland ..................................................................................................21

3.3 Data set for Denmark................................................................................................23

3.4 Data set for Sweden..................................................................................................24

3.5 Data set for Norway..................................................................................................24

3.6 Long term yearly production data ............................................................................25

4 Large scale wind power production..................................................................................26

4.1 Basic statistics of the wind power production data used ..........................................28

4.2 Frequency distributions of wind power production..................................................31

4.3 Seasonal variation of wind power production ..........................................................33

4.4 Diurnal variation of wind power production ............................................................35

4.5 Persistence of wind power production......................................................................37

4.5.1 Duration of calms .............................................................................................37

4.5.2 Peaks of wind power production ......................................................................38

4.6 Correlation of wind power production .....................................................................39

4.7 Short term variations of wind power production......................................................42

4.7.1 The in-hour variations ......................................................................................42

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4.7.2 The hourly variations........................................................................................43

4.7.3 Variations for longer time scales ......................................................................48

4.8 Predictability of wind power production ..................................................................49

5 Representative data for large scale wind power production.............................................51

5.1 Representativeness of the study years ......................................................................51

5.2 Representativeness of the geographical spreading of data .......................................52

6 Wind power production and load .....................................................................................55

6.1 Basic statistics of the hourly load time series...........................................................56

6.2 Correlation of load and wind power .........................................................................58

6.3 Temperature dependence..........................................................................................59

6.4 Instant penetration level of wind power ...................................................................61

6.5 Wind power during peak load ..................................................................................63

6.6 Hourly variations of load..........................................................................................65

7 Increase in net load variations by wind power .................................................................67

7.1 Wind power increasing the largest hourly variation in the system ..........................69

7.2 Wind power increasing the hourly variations in the system.....................................70

7.3 Wind power increasing the unexpected hourly variations of load ...........................72

8 Summary and conclusions................................................................................................75

References ................................................................................................................................78

List of Appendices

1. Wind power production curves, 4 countries and total Nordic production, years 2000-01.

2. Hourly variations of wind power production 4 countries, years 2000-01.

3. Hourly load data, 4 countries and total Nordic load, year 2000.

4. Hourly variations of load, 4 countries and total Nordic load variations, years 2000-01.

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Foreword

This licenciate thesis has been carried out at Technical Research Centre of Finland VTT, wind power team1. The work was partly financed by Fortum Säätiö (Fortum Foundation), Nordic Energy Research and EU, through research project WILMAR (Wind power Integration in Liberalised electricity markets), with Finnish Energy Industries Federation Finergy co-funding.

First of all I want to thank the wind power producers that have given hourly production data from their wind parks, as well as power companies that have given wind speed measurement series, without which this study would not have been possible.

My supervisor professor Peter Lund and my instructor docent Ritva Hirvonen2 have given me valuable comments for the work, for which I am grateful. This work is a fruit of Nordic co-operation – visiting the research institutes and power companies in Denmark, Norway and Sweden has given me a better opportunity to obtain data for this study, as well as interesting discussions on the impacts of wind power on the power system. The wind power team as well as the energy systems group at VTT has been a good working environment, thank you colleagues! Special acknowledgement goes to Göran Koreneff, who has made load forecast estimates for Finland used in this work.

Last but not least, my family has given me the hugs and kisses needed to keep me going. Special thanks for the patience of my daughters Sara and Meri, not getting angry when the mother had eaten the last chocolate biscuits. And to my dear husband Esa, for his love and impatience.

Espoo, December 2003 Hannele Holttinen

1 VTT Processes, Energy production research area, Distributed energy group 2 docent at Power Systems Laboratory of Helsinki University of Technology, currently working as Head of Unit, Natural Gas and Electricity Transmission, Energy Market Authority EMA

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Nomenclature

Alphabets

d distance between two sites

D decay constant

i index, hour

I increase in variations

L load, electric consumption

n number of data points in time series

NL net load (load – wind power production)

p wind power production relative to installed capacity

P wind power production

P TOT nominal power of wind power (installed capacity)

rxy cross correlation

x data value in time series 1

y data value in time series 2

Greek letters

σ standard deviation

µ mean value of time series

∆ hourly variation

Abbreviations

DC direct current

CHP combined heat and power production

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1 Introduction

Integration of wind power in large power systems is mainly subject to theoretical studies, as wind power penetration levels are still modest. Even though the penetration in areas like West Denmark is already high (about 20 % of yearly electricity consumption), wind power presents only 1–2 % of Nordel or Central Europe (UCTE) systems.

Wind power production is characterised by variations on all time scales: seconds, minutes, hours, days, months and years. Even the short term variations are to some extent unpredictable. These are the main reasons why large scale wind power production poses a challenge to the rest of the energy system. To what extent this will be a problem, depends on how large a share is produced by wind power, as well as on the power system in question: the inherent load variations and flexibility of the production capacity mix.

For the power system, the relevant wind power production to study is that of larger areas. This means large geographical spreading of installed wind power, which will reduce the variability and increase the predictability of wind power production. Not taking this into account can result in an exaggeration of the impacts of wind power. On the other hand, assuming that the smoothing effect of large scale wind power will take care of all problems, will result in an underestimation of the impacts of wind power.

This study is one step in the way of quantifying the impacts of large scale wind power on the operation of power system, based on existing production and load data on an hourly level. Detailed statistical analyses of hourly wind power production are presented. The aim is to see how large scale, regional wind power production looks like compared with that of a single wind farm, and going further, how wind power production from the whole Nordic area looks like compared with the production from one country only.

After looking at the wind power production on its own, the analysis is broadened to contain the electricity system. Wind power production is studied together with the electrical demand, i.e. the load. The aim of the study is to look at the effects of large scale wind power production on power system operation. The main focus is on the hourly variations of wind power and their effects on the needs for flexibility for the electricity system.

We start by introducing some basic properties of wind power production, the area of the study, as well as a short description of power system operation. Chapter 2 describes the impacts of wind power to power systems, and sets this study into perspective. Chapter 3 presents the data used in this study. Chapter 4 illustrates the patterns and smoothing effect of large scale wind power production, with a short description of the predictability. Chapter 5 gives an overview of representativeness of large scale wind power data, setting the data used in this study in perspective. Chapters 6 to 7 deal with the variations of wind power production seen from the electric system point of view, and chapter 8 concludes the findings of this study.

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1.1 Wind power production

Wind power is usually referred to as its nominal installed capacity, the peak power. The nominal power is usually reached only for less than 5 % of time. For the power system, wind power can also be seen as production resource with a (low) average power and a large variation.

Wind power production is highly dependent on the wind resource of the site. Therefore both the average production, distribution of production, as well as seasonal and diurnal variation can look very different in different sites and areas of the world. For most sites on land, the average power as % of nominal capacity is between 20–40 %. This can be expressed as full load hours of 1800–3500 h/a (full load hours is the annual production divided by nominal capacity). Offshore wind power production, or some extremely good sites on land, can reach up to 4000–5000 full load hours (average production 45–60 % of nominal capacity).

Full load hours is a theoretical figure only, for comparison of different power plants, so it does not tell how many hours the power plant is actually operating. For wind power, which operates most of the time at less than half of the nominal capacity, the turbines will typically produce power during 6000–8000 hours a year (70–90 % of time). Also, the full load hours of wind power can be influenced by the design of wind turbines (generator size compared to wind turbine rotor swept area). The values given here are the result of standard economic optimisation of investment costs versus production.

For comparison: for combined heat and power production (CHP) full load hours range typically between 4000–5000 h/a, for nuclear power 7000–8000 h/a and for coal fired power plants 5000–6000 h/a.

The available wind resource will vary from year to year. Wind power production during one year ranges typically between ± 15 % of the average long term yearly production (Ensslin et al, 2000; Giebel, 2001).

1.2 The area of study: the Nordic power system

The joint, liberalised Nordic electricity market covers Norway, Sweden, Finland and Denmark.

Eastern part of Denmark is part of the Nordel system, and Western part of Denmark is part of the Central Europe UCTE system. They are not connected by a transmission line, but are both connected to Sweden and Germany, and West Denmark also to Norway by a DC link. The production mix is shown in Fig.1. A large share of hydro power is characteristic for the Nordic area: Norway covers almost 100 %, Sweden almost 50 % and Finland almost 20 % of electricity consumption by hydro power.

The installed wind power capacity in the beginning of year 2003 was 2200 MW in West Denmark, 573 MW in East Denmark, 345 MW in Sweden, 97 MW in Norway and 41 MW in

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Finland (Eltra, 2003; Elkraft 2003; Carlstedt, 2003; Laakso, 2003; Windpower Monthly, 2003). In Western part of Denmark, system integration of wind power is already reality, whereas in other countries it is a subject for discussion. In Denmark, the scheduling of production units takes into account wind power production, and prediction methods together with the hourly trade in the spot and regulation markets are used in order to accommodate the substantial share of wind power in the system (Eriksen et al, 2002).

44 %50 %

6 %

99%

0,7 %

71 TWh

158 TWh

122 TWh

36 TWh88 %

12 %

8 TWh

82 %

18 %

55 %23 %

20 %

395 TWh

Hydro power

Nuclear powerThermal power

1,6 %

Wind power and geothermal

19 %

30 %51 %

Figure 1. Electricity production in the Nordic countries in 2001. (Source: Nordel/Finergy)

1.3 Power system operation

Electric power systems include power plants, consumers of electric energy and transmission and distribution networks connecting the production and consumption sites. This constantly fluctuating interconnected system should maintain balance between production and consumption of electric energy. Faults and disturbances should be cleared with smallest disadvantage in the delivery of electricity.

Power systems comprise a wide variety of generating plant types, which have a range of capital and operating costs. The operation of power system involves providing a total amount of electricity, at each instant, corresponding to a varying load from the electricity consumption. To make this cost effectively, the power plants running at low operational costs will be kept running almost all the time (base load demand), and the power plants with higher costs will be run only when the load is high. When ignoring second order costs (like start-up, shutdown, reserves), plants can be stacked in merit order, where production with low marginal costs run first. Wind power plants (as well as other variable sources like solar and tidal) have very low marginal costs, usually assumed as 0, so they come to the top of the merit order, that is, their power is used whenever available (Grubb, 1991). The electricity markets

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operate in a similar way, at least theoretically. The price the producers bid to the market is slightly higher than their marginal cost, because it is cost effective for the producers to operate as long as they get a price higher than their marginal costs. When the market is cleared, the power plants operating at lowest bids come first.

The failure to keep the electricity system running has high and costly consequences, thus the reliability of the system has to be kept at a very high level. Security of supply needs to be maintained for both short and long term. This means maintaining both flexibility and reserves necessary to keep the system operating under a range of conditions, also in peak load situations. These conditions include credible plant outages, as well as predictable and uncertain variations in demand and in primary generation resources including wind.

The operational reserves are divided into different categories according to the time scale they are operating. Roughly, a division to regulation (seconds-minutes; primary reserve) and load following (from minutes to hours; secondary reserve) can be made. Actually load following is done partly beforehand as scheduling and dispatch of power plants according to load forecast, and partly by secondary reserve to balance the load forecast errors. Power system operation involves also the start-ups and shut downs of slower power plants, called unit commitment, in the time scale of 3…12 hours.

The operation of the power system has to be guaranteed also in the free electricity markets. Usually Independent System Operator (ISO) as a system responsible grid company takes care of the whole system using active and reactive power reserves to maintain system reliability, voltage and frequency. The scheduling and dispatch of the power plants (unit commitment and load following according to load forecasts) can be dealed with in the electricity markets, for example the Nordpool ELSPOT market in the Nordic countries. Contracts between some producers (and consumers) and system operators are made to allocate the primary and secondary reserves. The secondary reserves can also be dealed partly in the markets, at a special regulation market. In the Nordic countries, the system operators maintain a joint regulation market since 2003 (Nordpool, 2003).

The power system, which is operated synchronously, has same frequency. Frequency shows balance or imbalance between production and consumption in the power system. With nominal frequency (in Europe 50 Hz) the production and consumption (including losses in transmission and distribution) are in balance. When frequency is below 50 Hz the consumption of electric energy is higher than the production. If frequency is above 50 Hz the consumption of electric energy is lower than the production. Frequency deviates from the nominal value the less the better the balance between production and consumption can be maintained. For example in Nordic Power System, the frequency is allowed to range between 49,9 Hz and 50,1 Hz (Fingrid, 2003). Frequency of the system is maintained within allowed limits by using primary reserve in power plants. It is activated automatically by frequency fluctuations.

Fig. 2 shows an example of the actual load in the system during 3 hours compared to hourly forecasted load denoting forecast errors and short-term load deviations in the system. An example of how the operational reserves operate, is illustrated in Fig.3. It shows the activation of reserves and frequency of the system as a function of time when a large power plant is disconnected from the power system. Activation of reserves divides the reserves into primary

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reserve, secondary reserve (also called fast reserve) and long-term reserve (also called slow reserve or tertiary reserve). Secondary reserve is active or reactive power activated in 10 to 15 minutes after the occurrence of frequency deviation from nominal frequency. It replaces the primary reserve and it will be in operation until long-term reserves substitute it as seen from Fig.3. The secondary reserve consists mostly of rapidly starting gas turbine power plants, hydro (pump) storage plants and load shedding. Usually every country in interconnected power system has a secondary reserve, corresponding to the amount of disconnected power during the dimensioning fault (usually loss of largest power unit) within the country in question. To provide sufficient secondary power reserve system operators may take load forecast errors into account. In this case the total amount of the secondary reserve may reach a value corresponding to about 1.5 times the largest power unit (Holttinen & Hirvonen, 2004).

Time / hours

Forecast error

Forecast

Load(actual)

Power

Short-term load deviation

Figure 2. Example of actual load in the system during 3 hours compared to forecasted load.

50 Hz Time

Time0HoursSeconds

Pow

er

Minutes

Frequency

Freq

uenc

y

Load

Primary reserve

Secondaryreserve

Long term reserve

Kinetic energy Frequency dependentload decrease

Figure 3. Activation of power reserves and frequency of power system as a function of time when a large power plant is disconnected from the power system (Source: Hirvonen, 2000).

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The system works for the consumers, and they also pay the system cost in their tariffs, like they pay for the production, distribution and taxes. It is not usually necessary to allocate these system costs to a certain producer or consumer. However, when we are talking about larger changes in the system, introduced by large scale wind power for example, it is appropriate to consider also the changes needed for the system, as well as the possible extra costs related to these changes.

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2 The impacts of wind power on the power system

Wind speed varies in all time scales, and this has different effects on the power system. Wind gusts cause second to minute variations, which time scale is relevant for frequency control (primary reserve). Changing weather patterns can be seen in the hourly time series of wind power production. This is the time scale from secondary reserve, load following to unit commitment. Diurnal cycle is present in this time scale, too, whereas the seasonal cycle and annual variations are relevant for the long term adequacy studies. For system planning, extreme variations of large-scale wind power production are of importance, together with the probability of the variations. The system impacts of wind energy are presented schematically in Fig. 4. These impacts are divided into two: short term, balancing the system in the operational time scale (minutes to hours), and long term, providing enough power and energy in peak load situations.

For the operation of power systems, the variations from day to day, hour to hour and minute to minute are of interest. The stops of the power plants do not impose as large variations as the wind variability for large scale wind power, where a single turbine is less than 1 % of capacity (for example, a 2 MW turbine in a country with 1000 MW wind power).

SHORTTERM

EFFECTS

LONGTERM

EFFECTS

Voltage management: Reactive reserve. WF can provide. Local or system area. Time scale up to some minutes

Reserves: Load following and regulation (WF can provide partly)System area. Time scale some minutes to one hour

Cycling losses: Unoptimal use of thermal/hydro capacity. System area. Time scale 1…24 hours

Discarded energy: wind power exceeds the amount system can absorb..System area. Time scale some hours

System reliability: Adequacy of power (capacity credit of WP)System area. Time scale one to some years

Transmission/distribution losses (or benefits). System/local area. Time scale 1…24 hours

Figure 4. System impacts of wind power (WP) and wind farms (WF), causing integration costs. Part of the impacts can be beneficial for the system, and wind power can have a value, not only costs.

When studying power system impacts of wind energy, we are referring to a larger area than just one wind farm. The relevant system area to look at varies according to the impact studied. For voltage management, only areas near wind power plants should be considered. There should be enough reactive reserve in the system during disturbances, but it should mainly be managed locally. For intra-hour variations, frequency control and load following, the

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synchronously operated system forms a relevant area. DC links connecting synchronously operated areas can also be automised to be used for primary power control; their power reserve capacity is usually, however, only allocated as emergency power supply. For day-ahead hourly production, the electricity market is a relevant area: for example Nordic power market includes countries situated in different synchronous systems. When looking at a large interconnected area, it has to be taken into account that benefits exist when there are no bottlenecks of transmission.

When we are studying the incremental effects that varying wind power production imposes on the power system, it is important to study the system as a whole: power system is there for all production units and loads, and only the net imbalances have to be balanced by the system. The overall system reliability should be held on a same level before and after wind power, and study the requirements for the power system from this perspective.

There are means to reduce the variations of wind power production. Staggered starts and stops from full power as well as reduced (positive) ramp rates can reduce the most extreme fluctuations, in magnitude and frequency, over short time scales (Kristoffersson et al, 2002). This is at the expense of production losses, so any frequent use of these options should be weighed against other measures (in other production units) in cost effectiveness.

2.1 Previous work

Studies of large scale wind power production, its variability and effects on energy system have been carried out to some extent in the 1990’s and increasingly in the first years of the new millennium. The first comprehensive article about the system impacts of wind power was by Grubb, 1991, considering the UK power system.

The extent of wind power variability has been the subject of several studies. European meteorological station wind data for one year has been used in two studies, however, not covering all of the Nordic countries (Giebel, 2001; Landberg, 1997). In the Netherlands, a conscientious work on analysing wind speed data was done, including variability and persistence (van Wijk, 1990). In Ireland the variations of dispersed wind power production as well as diurnal dependence has been studied with 5 year wind speed data across the island (Hurley & Watson, 2002). For the Nordic countries, a study based on Reanalysis (weather prediction) long term 12-hourly wind speed data was made (Giebel, 2000) looking at the longer (12- and 24-hour) variations and correlation of production. For Finland, yearly and monthly wind power variations were studied in Holttinen et al, 1996 and 3-hourly variations based on data for 5 geographically dispersed weather stations by Tammelin & Nurmi, 2001.

All the studies above have been based on wind speed data from several geographically dispersed measurement masts, converting wind speeds first to higher altitude hub height wind speeds and then to the production of a single wind turbine using a power curve. There are possible caveats first of all in upscaling the wind to higher altitudes, as the wind profile is dependent on atmospheric conditions (van Wijk, 1990) and secondly in using a single point measurement to represent a wind farm stretching from one to several kilometres in dimensions. Studies based on wind power production, however, are more scarce, due to the

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fact that large scale wind power production has only started to emerge in the past few years. There are, however, some studies using wind power production data. In the Netherlands, variability and persistence of dispersed wind farm data of 250-500 kW turbines (van Zuylen et al, 1994) confirm most of the wind speed data analyses of van Wijk, 1990, however, indication of somewhat less variability when using wind turbine data can be seen. In Germany, statistical analyses from production and measured wind speed data have been made in conjunction with a comprehensive 10 year follow up project of the 250 MW programme (ISET, 2002; Ensslin et al, 2000). Annual, seasonal, diurnal and hourly variations are one result of this activity. Faster measurements of this data were further analysed to look at the trends of power fluctuations as well as fast regulation needs of wind power (Ernst, 1999). A study of the smoothing effect and its saturation was made for Northern part of Germany (Focken et al, 2001). Fast ramp rates (1 second to 1 hour) for a large wind farm have been recorded in USA (Parsons et al, 2001; Poore & Randall, 2001).

Taking the analyses of wind power production further, there are studies combining the variations of wind power and electrical load. In Norway, wind speed data was analysed in conjunction with peak load periods, concluding also a decrease in the variability of geographically dispersed wind power production (Alm & Tallhaug, 1993). First analyses of the Nordic data set analysed in this study were presented in (Holttinen, 2002). Statistical analysis of the variability of wind power together with load variations has been presented in (Persaud et al, 2000) and (Milligan, 2003).

First experiences from West Denmark and Northern coast of Germany have shown that when significant amounts of electrical demand are covered with wind power, increased flexibility is needed in the system. This is first seen as increased transmission with neighbouring countries or areas (Eriksen et al, 2002; Lund & Münster, 2003; Holttinen & Pedersen, 2003). Studies for island systems like Crete and Ireland have shown that some of the wind power produced will have to be discarded if the penetration is more than 10 % of yearly gross demand (CER/OFGEM, 2003; Giebel, 2001).

As a conclusion of several studies in USA (Milligan et al, 2002; Hirst, 2002), it has become clear that to estimate the impacts of wind power on the power system, the wind induced imbalances have to be treated together with aggregated system imbalances. Estimating the increased reserve requirements have resulted in very small impact on regulation time scale (Ernst, 1999; Parsons et al, 2003) and increasing impact on load following time scale with increasing penetration (Milborrow, 2001; Milligan, 2003). The studies mostly suffer from lack of detailed, representative data for both the large scale wind power production and the load from the same area, so they give conservative estimates.

Power system studies are often carried out as the system was operating before liberalisation of electricity markets. As wind power is new to actors in the electricity market, market rules that treat wind fairly, neither subsidising nor penalising its operation have not yet been developed. Some methods for looking at how the imbalance due to variability and unpredictability of wind come into the markets have been suggested, using either an hour-ahead market or intra-hour and hourly imbalance payments (Hirst, 2002).

The influence of wind power to the adequacy of power production in the system is not the goal of this work. However, as some analyses touch upon this subject, a short overview on

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this work is given here. It has been shown in several studies that when the capacity of a variable source is small (low system penetration) the capacity value equals that of a completely reliable plant generating the same average power at times when the system could be at risk (Milligan, 2000; Giebel, 2001; Peltola & Petäjä, 1990). As the penetration increases, variable sources become progressively less valuable for saving thermal capacity. According to studies made, this will be a level of about 10–15 % of installed capacity. Dispersion of wind power and a positive correlation between wind power and demand increase the value of wind for the system. At large penetrations, the capacity credit tends towards a constant value, that is, there is no increase in the capacity credit when increasing the wind power capacity. This will be determined by the loss-of-load-probability without wind energy and the probability of zero wind power. (Giebel, 2001). As wind energy correlates only weakly with hydro power production, wind energy added to the system can have considerably higher energy delivery value than adding more hydro (Söder, 1999).

2.2 The aim of this work

This work is not covering all impacts of wind power on the power system described in Fig. 4. It is based on a data set of realised hourly wind power production values from two example years. The study is mainly concentrating on the extent of wind power hourly variations, and their impacts on the secondary reserve, or hourly load following reserve of the power system (Short tem effects --> Reserves in Fig.4). Some of the analyses of hourly data give also insight to the adequacy of power systems (Long term effects --> System reliability in Fig.4).

A large part of the work includes analysing the large scale hourly wind power production as such. It is common sense as well as proven by earlier studies, that geographical spreading of wind power will reduce variability. However, the quantification of this phenomenon is not straightforward. This is a relevant research topic in itself, needed in order to find out what kind of input data for wind power should be used when studies of wind power in power systems are made.

After analysing the example years of wind power data available, the representativeness of the data is judged. This is done first as to how well the two example years are representing the average/low/high wind years. Then the wind power production data is judged in how well it represents a large scale wind power production, that is, can it be upscaled to present a significant portion of the electricity system without upscaling large variations present in data of only few wind farms.

Wind power is studied together with data of electricity consumption from the same area and example years, to see how much the variations of wind power production affect the varying load the power system sees. That is, how much addition of wind power will increase the variations that the power system has to deal with in an hourly level. First the variations are taken as measure of how much extra reserve is required due to wind power. Secondly, an effort to take into account the different properties of predictability for load and wind variations is presented.

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3 Data used in this study

The data used in this study is the measured output of wind power plants and wind parks (Fig.5). Realised hourly wind power production time series from 4 Nordic countries were collected. The total electricity consumption from the countries, also as hourly time series, was obtained to see the effect of wind variations compared to load variations. Some available hourly temperature time series were used to study the temperature dependence. Data was collected for years 1999 to 2001. As data from Denmark was only available from 2000 onwards, and data for Norway and Sweden was limited for 1999, most of the analyses were made for two years of data, 2000-2001.

The advantage of using realised wind power production data is to get the real wind farm output. When converting wind speed data to power production, there will always be some error, especially if the single point measurement is to represent a larger wind farm area. There will also be the effect of technical availability in the data, some of the turbines being serviced or faulty.

Figure 5. Data for hourly wind power production was available from 21 sites in Finland, 5 sites in Sweden, 6-12 sites in Norway (the lighter coloured sites only for part of the time) and the aggregated total production of hundreds of sites in Denmark West and East.

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As wind power production data is limited in Finland, Norway and Sweden, also hourly wind speed measurement data was used to complement the production data. An effort was made to make single measurement point data represent wind farm production.

Nordic data set was formed from the data sets of the 4 countries. The production at each hour was a simple average of the % of capacity production of the 4 countries. In terms of capacity this would mean setting for example 3000 MW in each country, a total of 12 000 MW. This is somewhat theoretical, as Denmark is now dominating the installed wind power, and probably will be for quite some time. The wind energy potential, however, is probably as large in all the 4 countries, when taking the offshore wind power potential in Sweden and Finland into account.

The rest of this chapter describes in detail the wind power data used in this study. It starts with outlining the data handling principles. Also the aggregation and upscaling done to represent large scale wind power production in each country is described.

3.1 Data handling principles

The extensive work for gathering data included a check up to make sure that the time shifts from winter and summertime were taken the same way in all time series collected, to keep all hourly values synchronous. Also the time zone difference for Finland was taken into account when outlining the Nordic data.

For wind power production time series in Finland, Sweden and Norway, the available data presented far less than 100 MW of capacity. This means that these time series had to be upscaled more than 10-fold, to make a large scale wind power production time series for the countries. Upscaling the hourly values means upscaling also the hourly variations. Real large scale wind power production would mean that the output would be smoothened out hundreds or thousands of turbines situated in tens or hundreds of sites. An example of the problem is illustrated in Fig. 6, from real data in Denmark. Upscaling data from a single site would give us a different kind of hourly time series – more pronounced peaks and variations – than the real, 500 MW data shows. This is why several, geographically dispersed sites were looked for to make the aggregate time series for the countries. Also, data from single wind speed measurement points were smoothed out before using as data for a larger wind farm.

The time series of only few turbines were checked for longer downtimes of turbines. This was done for several sites in Finland, and the one production series for Norway. Upscaling one wind farm data of 2...8 MW to 50...300 MW means that a large amount of turbines would suddenly be unavailable simultaneously for a long period. The technical availability of wind turbines is usually quite high, more than 95 % is reported from Sweden and Germany (Carlstedt, 2003; ISET, 2002). For hundreds of single turbines, on the average only less than 5 % of the turbines will be unavailable at the same time.

There were 2 wind speed series for Finland and one for Sweden. Most of the data for Norway was as wind speed time series. The wind speed was converted to wind power production. First the wind speed was smoothed out by taking a 2-hour-sliding-average for each hour. This

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smoothened wind speed was converted to power production using an aggregated, multi-turbine power curve, Fig.7 (Nørgaard, 2003).

For single turbine data, the same kind of smoothing was done, by first converting back to wind speed, and then applying the same method as for the wind speed time series (only one site in Finland).

0

50000

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1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361 385 409 433 457 481 505 529 553 577 601

East Denmark (500 MW)15 MW wind farm upscaled to 500 MW

Figure 6. An example of wrong upscaling: a single site would see more variations, peaks and calms than dispersed, large scale wind power production (here 500 MW, 200 x 100 km2).

0200400600800

1000120014001600180020002200

0 3 6 9 12 15 18 21 24 27 30

Wind speed (m/s)

Win

d tu

rbin

e po

wer

out

put (

kW)

Multiturbine power curveOriginal power curve

Figure 7. To convert the wind speed time series to wind farm power production, a multiturbine power curve was used, smoothing out the production near the cut-in (3 m/s) and cut out (25 m/s) wind speeds compared with a single turbine power curve.

The focus in this study is on the variations of wind power production. Basically the error in the data sets comes from not having tens of aggregated wind farm time series available to

21

represent a combined production of a country. The data handling procedure trying to smooth out some of the variations in a point wind speed measurement data is artificial and will introduce some error to the data sets. The smoothing of the wind speeds, by sliding 2 hour averages, make the most of the reduction of variability. The use of a multiturbine power curve will mostly affect the time series near the cut in wind speeds, above 22 m/s (Fig. 7). All in all, as the yearly energy production will remain the same in the time series, this procedure will mainly affect the variability of the production, and the error is considered small. For Finland data set, this procedure was done for 15 % of the data (3 time series representing 150 MW of a total of 1000 MW). For Norway nearly all data was handled this way, so there is probably error involved, however, as described before, compared to the original error of not having enough time series data, this procedure will reduce the error, not increase it.

To compare the data sets of different installed capacity, they were represented as relative production, as % of installed capacity. It could be useful to represent the data relative to average power instead of maximum power, installed capacity. However, as the average power is changing from year to year, the nominal power is here chosen as a relative measure:

TOT

ii P

Pp = , (1)

where pi is the relative production for hour i as % of capacity, Pi is the production MWh/h for hour i and PTOT is the installed capacity.

3.2 Data set for Finland

Even though the amount of wind power in Finland, 41 MW, is still modest, the capacity installed is well spread along the long coastline and Lapland fells. As a courtesy of 10 wind power producers, and 2 power companies with wind speed measurements in high masts, wind power production data was available from a total of 55 turbines on 21 sites and wind speed data was available from 2 sites (in the internet, only Lumituuli, 2003). The data is presented in Table 1 and Fig. 8.

The maximum distance between the sites is 1000 km North-South and 400 km West-East. For year 1999, when some of the turbines were erected during the year, first part of the year was taken from wind speed measurements of the Finnish Meteorological Institute, from the synoptic stations measuring a 10-minute wind speed value every 3 hours.

As the data was used to represent large scale wind power production, it was upscaled. For this upscaling, first a 1000 MW wind power production series was produced, so that it would represent the geographic distribution of a potential wind power production in Finland: Lapland and Åland archipelago and the Southern coast were reduced to a tenth of large scale capacity each, and the West coast was given the bulk of wind power production (300 MW in the South and 400 MW in the North of West coast). Upscaling is presented in Table 1.

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Lapland100 MW

100

Åland100 MW

100

5050

5050

5050

200

Gulf of Finland100 MW

Gulf of Bothnia South,

300 MW

Gulf of Bothnia North,

400 MW

5050

Wind speed dataSingle turbine data2-8 turbine data

Figure 8. Time series collected for wind power production in Finland.

Table 1. Wind power production data from Finland, and upscaling to 1000 MW wind power.

Site (region) Turbines /wind speed Data MW Upscaled to Kotka (South coast, East) 2 x 1 MW 2.0 MW 50 MWLoviisa (South coast, East) Hourly wind speed 100 m.a.g.l 2.0 MW 50 MWÅland archipelago (South coast, West)

12 turbines on 7 sites (80 km): 225 kW Sottunga island, 500 kW Eckerö, 500 kW Kökar island, 4 x 600 kW Lemland, 2 x 500 kW+600 kW Finström, 500 kW Vårdö island, 600 kW Föglö island

6.32 MW 100 MW

Uusikaupunki (West cst, South) 2 x 1.3 MW 2.6 MW 50 MWEurajoki (West coast, South) Hourly wind speed 100 m.a.g.l 2.0 MW 50 MWPori (West coast, South) 8 x 1 MW 8.0 MW 100 MWNärpiö (West coast, South) 1 x 750 kW 0.75 MW 50 MWKorsnäs (West coast, South) 4 x 200 kW 0.8 MW 50 MWKalajoki, Siikajoki, Hailuoto (West coast, North)

10 turbines on 4 sites (100 km): 2 x 300 kW Kalajoki, 2 x 300 kW and 2 x 600 kW Siikajoki (2 sites), 2 x 300 kW + 2 x 500 kW Hailuoto

4 MW 200 MW

Lumijoki (West coast, North) 1 x 660 kW 0.66 MW 50 MWOulunsalo (West coast, North) 1 x 1.3 MW 1.3 MW 50 MWKuivaniemi (West coast, North) 6 x 750 kW 3 MW 100 MWLapland 8 turbines on 2 sites (100 km):

5 x 600 kW Olos, 2 x 450 kW + 600 kW Lammasoaivi

4.5 MW 100 MW

TOTAL 55 turbines on 21 sites + 2 wind speed measurement sites

38 MW 1000 MW

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3.3 Data set for Denmark

For Denmark, the system operators Eltra (West DK) and Elkraft System (East DK) have hourly production data available at their internet sites, starting from year 2000 (Eltra, 2003; Elkraft, 2003). The maximum distance between the sites in West Denmark is roughly 300 km North-South and 200 km West-East. For the Eastern part, the dimension is about 200 km North-South and 100 km West-East. Bornholm island, South of Sweden, is a part of East Denmark.

Danish data is representing the realised production of thousands of turbines and hundreds of sites. However, there has been a significant increase in wind power capacity during the two years: from 1740 MW in start of 2000 to 2524 MW at the end of year 2001 (West Denmark: 1340 MW in the start of 2000, 1790 MW in the start of 2001 and 1970 MW in the start of 2002, East Denmark 390, 503 and 554 MW respectively).

To be correct in converting the hourly production in MWh/h to relative production, as % of capacity, exact data on each wind farm’s network connection would be needed. This means making an hourly PTOT time series in formula 1, PTOTi. If the information on capacity addition (or reduction as some old wind turbines have been taken from operation) was not correct, a step up in the MW time series at a wrong hour could distort the real production time series. This would either add more variations or damp the real variations from one hour to the next.

Exact daily data on capacity development in East Denmark was obtained for year 2001. For West Denmark, and for year 2000 of East Denmark, no exact data on capacity was available. For these data sets, an approximate hourly MW series has been constructed to convert the data to % of capacity. For West Denmark, the capacity has been rising at an average rate of 50 kW/hour in 2000 and 13 kW/hour in January 2001, after which a constant capacity has been used. For East Denmark, the capacity has been rising at an average rate of 16 kW/hour in 2000.

Looking at the capacity increase in Denmark (Elkraft, 2003; Eltra, 2003), it has been quite linear. The error made here would stay below 50 MW at any hour (the difference between the approximation used here and the real life). This results in a maximum 3 % error in the hourly % of capacity values (for example, the production level 84.7 % would in real life be 82.4 % or vice versa) in the beginning of year 2000 (total capacity 1740 MW) and a maximum 2 % error at the end of year 2000 (total capacity 2330 MW). The error for the hourly variations are even smaller, as the capacity increase in practise comes as 1-3 turbines at a time, when the test operation of a wind farms starts. Assuming a maximum 10 MW instantaneous capacity increase in an hour, this would be seen as a 0.5 % error in the hourly variation, either overestimating an upward variation or underestimating a downward variation in the data set used in this study. There is no detectable error in data in the situation where there is in real life no increase in the capacity from one hour to the next – an assumed 60 kW increase in capacity is 0.004 % of the total capacity in the beginning of year 2000 and 0.003 % at the end of year 2001.

The advantage of the procedure used here is that we get a better knowledge of how much, as % of capacity, the production has been.

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3.4 Data set for Sweden

For Sweden, wind power production data was acquired from 2 sites in Southern Sweden (West and South coast) and 2 sites on the island of Gotland (East coast). From the Northern part, only one wind speed measurement time series was acquired (SMHI, 2003). From the Central part of Sweden, no data was available for this study. This is due to late start of hourly data registration of wind farm production data in Sweden, for most wind farms this was first started in 2001.

The South Sweden area is about 300 km North-South and 400 km West-East. Taking the one wind speed measurement available from North of Sweden, this increases the North-South dimension to 1300 km.

For upscaling, a 1000 MW wind power production series was produced, representing the geographic distribution of potential wind power production in Sweden. Most of the capacity was assumed in Southern Sweden with 300 MW West coast, 300 MW South coast and 300 MW Gotland island. 100 MW was assumed in Northern Sweden (1 wind speed series).

3.5 Data set for Norway

For Norway, wind power production data was acquired from one site. However, the data had missing periods especially for year 2001. Two wind speed measurement time series were acquired from potential wind power sites in Middle and South Norway, covering parts of years 1999 and 2000.

Norwegian meteorological institute (NMI) data was well representative for wind power production: it is measured hourly and with high average wind speeds. 5 sites along the coastline were used for 2000 and 11 sites for year 2001.

Norway is the largest country when considering the largest dimensions between the potential wind farm sites: about 1400 km North-South and 700 km West-East. The South Norway area is about 500 km North-South and 150 km West-East, Middle Norway 300 and 100 km, and North Norway 400 and 400 km respectively.

For up-scaling, Norway was divided to 3 regions, first aggregating the available data as simple averages per site for each region South, Middle and North Norway. The total wind power production was also a simple average: same amount of wind power was assumed to South, Middle and North Norway.

In Norway data, there were several periods of high wind speeds above the cut out wind speed of wind turbines (Fig.7). Especially during the first months of 2000 (7.- 8.1., 3.-4.2.,10.-11.2.) and November, 2001 (3.11. and 10.-11.11.).

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3.6 Long term yearly production data

Long term data was used to determine the representativeness of the wind resource for the example years.

Yearly wind production index data was acquired from existing national wind energy statistics (Laakso, 2003; Carlsted, 2003 and Naturlig energi). Production index data was used because the yearly wind production data (also available from statistics) needs to be corrected for the capacity built during the year. As exact average capacity is not known, but only the capacity in the beginning and end of years, this would result in errors trying to make a representative and comparable figure for the yearly production. Also, the wind index data reaches farther back in time than the production data, which has only started in the 90’s for Finland and late 80’s for Sweden and Denmark. Norway wind index data does not yet exist.

Wind power production index is a measure of one year’s production compared with the long term average production. For Denmark and Sweden, this is derived by looking at the production of reference turbines, operating since the end of 1980’s. Production index for one year is the production of those turbines that year divided by the average production of those turbines over a long reference time period. Index of 100 % means that the production during the year has been the same as for long term average. For Finland, the production indices are calculated from the Finnish Meteorological Institute (FMI) wind speed measurements along the coast, converting the wind speed to power production. In Finland, the coastal areas South and West experience somewhat different wind resource variations, that is why the production indices are presented for 4 sites (Laakso, 2003). The long term average period is 15 years, 1987-2001, over which period the wind index is on the average 100 %.

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4 Large scale wind power production

Examples of the data sets in this study are presented in Fig. 9 for February, 2000. Graphs of yearly data for years 2000 and 2001, for the 4 countries and their combination, is presented in Appendix 1.

When studying the effects of wind power on power systems, the wind power data has to represent large scale wind power production. This means the production of hundreds (or thousands) of turbines, tens (or hundreds) of sites. Geographical spreading of production evens out the total production from an area. Duration of calms will be substantially decreased, as the wind blows almost always at some part of the system area (Giebel, 2001). Maximum production level will not reach installed nominal capacity, as the wind will not blow as strongly at all sites simultaneously, and of hundreds or thousands of WTs not all are technically available at each instant. The extent of the smoothing effect of wind power production depends mainly on number of sites and distribution of sites over the area, as well as spatial correlation between the production of the sites (Focken et al, 2001).

In this chapter, a closer look on large scale wind power production is taken, analysing the patterns of wind power production and how the aggregation of production from a larger area affects these patterns. The smoothing effect can be seen from most of the statistical analyses presented in this chapter. The representativeness of the data sets and the example years are discussed in chapter 5.

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Nordic, average 39 %

0 %

20 %

40 %

60 %

80 %

100 %

1 24 47 70 93 116 139 162 185 208 231 254 277 300 323 346 369 392 415 438 461 484 507 530 553 576 599 622 645 668 691

Denmark, average 40 %

0 %

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1 24 47 70 93 116 139 162 185 208 231 254 277 300 323 346 369 392 415 438 461 484 507 530 553 576 599 622 645 668 691

Finland, average 30 %

0 %

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1 24 47 70 93 116 139 162 185 208 231 254 277 300 323 346 369 392 415 438 461 484 507 530 553 576 599 622 645 668 691

Norw ay, average 46 %

0 %

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1 24 47 70 93 116 139 162 185 208 231 254 277 300 323 346 369 392 415 438 461 484 507 530 553 576 599 622 645 668 691

Sw eden, average 40 %

0 %

20 %

40 %

60 %

80 %

100 %

1 24 47 70 93 116 139 162 185 208 231 254 277 300 323 346 369 392 415 438 461 484 507 530 553 576 599 622 645 668 691

Figure 9. Hourly wind power production in February, 2000. The production is as % of installed capacity (y-axis).The average production during the month is denoted above the curve.

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4.1 Basic statistics of the wind power production data used

The average production from all seasons in the study period is shown in Table 2. The 12 months are divided into seasons as the following: spring is March, April and May; summer is June, July and August; autumn is September, October and November; winter is January, November and December.

First of all, the difference in wind resource is notable: Norway has an excellent wind resource, with average production of nearly 33 % of capacity compared with 22–24 % for the other Nordic countries. Denmark has here the lowest production rates, as % of capacity. This is probably due to the data containing also sites in the inland and sites with older turbines with 20-40 m towers: the rotors are not reaching as good wind resource as the new, 60-100 m high MW scale turbines. The production in 2000 and 2001 does not yet have large offshore wind power included, with better wind resource (2 x 160 MW wind farms erected in 2002 and 2003).

It can be seen from Table 2, that year 2000 was considerably more windy than year 2001, especially winter and spring months. The difference is most pronounced in Denmark and Sweden and less pronounced in Norway. The production during the summer months is 60–80 % of the yearly average and production during the winter months is 110–140 % of the yearly average (Table 2).

Table 2. Average wind power production in the Nordic countries in the study period. Wind power production is presented as relative production, % of installed capacity.

Nordic Denmark Finland Norway Sweden

Years 2000-2001 25.5 % 22.2 % 23.3 % 32.6 % 23.9 %

Year 2000 27.1 % 24.1 % 24.4 % 33.8 % 26.0 %

Year 2001 24.0 % 20.1 % 22.2 % 31.4 % 21.9 %

Winter 2000 35.8 % 33.5 % 29.2 % 45.0 % 35.5 %

Spring 2000 26.3 % 20.8 % 26.6 % 33.5 % 24.7 %

Summer 2000 17.9 % 17.9 % 16.2 % 21.5 % 16.1 %

Autumn 2000 28.3 % 24.6 % 25.4 % 35.6 % 27.8 %

Winter 2001 28.8 % 21.8 % 23.6 % 41.8 % 27.8 %

Spring 2001 20.5 % 18.8 % 19.7 % 24.6 % 18.9 %

Summer 2001 17.8 % 15.2 % 17.9 % 21.8 % 16.4 %

Autumn 2001 29.5 % 25.0 % 26.5 % 37.7 % 27.7 %

The basic statistics of the yearly time series are presented in Table 3. Wind power production from the 4 countries and the combination are shown. As a comparison, data from one site is shown.

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Table 3. Descriptive statistics of hourly wind power production in the Nordic countries for years 2000-2001. Wind power production is presented as relative production, % of installed capacity. The width of the areas are presented as largest distance (km) North-South and West-East.

Single site

Denmark Finland Norway Sweden Nordic

Largest distance NS-WE - 300-300 1000-400 1400-700 1300-400 1700-1100

Mean 25.9 % 22.2 % 23.3 % 32.6 % 23.9 % 25.5 %

Median 14.9 % 14.4 % 18.8 % 28.9 % 17.4 % 22.2 %

Standard Deviation 28.2 % 21.4 % 17.9 % 20.1 % 20.6 % 15.5 %

Range 105.0 % 92.7 % 91.1 % 92.8 % 98.5 % 83.6 %

Minimum 0.0 % 0.0 % 0.0 % 0.3 % 0.0 % 1.3 %

Maximum 105.0 % 92.7 % 91.1 % 93.1 % 98.5 % 84.0 %

Largest(24) 102.8 % 89.0 % 84.1 % 89.0 % 94.1 % 80.8 %

Smallest(24) 0.0 % 0.0 % 0.3 % 0.9 % 0.0 % 1.9 %

To take a closer look at the regional wind power production, the same statistics are presented in Table 4, for the regions of the countries (Denmark and Sweden 2 regions; Norway 3 regions and Finland 5 regions). For the regions, there is clearly not as good smoothing effect seen, except for the real data for Denmark East and West.

Table 4. Descriptive statistics of hourly wind power production in the different regions of Nordic countries. Years 2000-2001.

South Norway

Middle Norway

North Norway

DK East DK West South Sweden

North Sweden

FI South coast East

FI South coast West

FI West coast South

FI West coast North

FI Lapland

NS-WE 500-150 300-100 400-400 200-100 300-200 300-400 - 20-50 50-80 250-30 150-50 100-100

Mean 31.7 % 37.7 % 28.4 % 20.7 % 22.7 % 23.8 % 28.6 % 22.0 % 25.9 % 23.6 % 23.8 % 21.3 %

Median 26.8 % 28.5 % 22.9 % 12.4 % 14.8 % 16.2 % 15.1 % 12.8 % 17.9 % 17.0 % 17.2 % 14.6 %

StDev 24.5 % 33.1 % 22.7 % 21.7 % 22.0 % 22.2 % 31.4 % 23.9 % 24.7 % 22.0 % 22.1 % 20.8 %

Range 100.0 % 100.0 % 100.0 % 92.3 % 94.0 % 98.6 % 100.0 % 102.1 % 98.7 % 99.4 % 99.4 % 97.5 %

Minimum 0.0 % 0.0 % 0.0 % 0.0 % 0.0 % -0.1 % 0.0 % -1.0 % 0.0 % -0.1 % -0,1 % 0.0 %

Max 100.0 % 100.0 % 100.0 % 92.3 % 94.0 % 98.5 % 100.0 % 101.1 % 98.7 % 99.3 % 99.3 % 97.5 %

Largest (24)

99.3 % 100.0 % 98.9 % 89.9 % 90.8 % 96.6 % 100.0 % 100.8 % 96.7 % 95.4 % 95.4 % 90.0 %

Smallest (24)

0.0 % 0.0 % 0.0 % 0.0 % 0.0 % 0.0 % 0.0 % -0.4 % 0.0 % 0.0 % -0,1 % 0.0 %

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The median is the value in the middle, when sorting all the values in an increasing or decreasing order. For wind power production it is typical that median is lower than the mean value. Most of the time, the production is less than average. When aggregating production from a larger area, the median gets closer to the mean value.

The smoothing effect can be seen in the range of the production, the maximum and minimum encountered during the years. For the total Nordic time series the production never goes to 0, however, the lowest production is only 1 % of installed capacity. For one country, the production can go to 0, even to a slightly negative value, due to consumption of electricity at the power plants. However, the 24th smallest production value is above 0 (Table 3), so there are less than 24 hours of 0 production during the two years. The maximum production from geographically dispersed wind power production stays under 90 % for the Nordic countries. For a single country, it seldom passes 90 %, as can be seen from the 24th largest value that are below 90 % for the countries. Sweden is here an exception, which can be explained by the data being 90 % from the Southern part of Sweden from only 4 sites. Even if we are talking about large scale wind power production, the production range will still be large compared with other production forms: maximum production will be 3–4 times the average production, depending on the area (Table 3; Giebel, 2000).

Another trend of smoothing can be seen in the standard deviation values. Standard deviation tells about the variability of the hourly time series, it is the average deviation from mean value:

)1()( 22

−Σ−Σ

=nn

xxnσ (2)

The reduction in variability, measured as the reduction in standard deviation, is depicted in Fig.10.

Standard deviation of hourly production

0 %

20 %

40 %

60 %

80 %

100 %

120 %

0 200 400 600 800 1000 1200 1400

Average diameter of area (km)

stde

v as

% o

f si

ngle

win

d fa

rm s

tdev

East Denmark Total DKFinlandNorwaySwedenNordic

Figure 10. Reduction in variability of wind power production: reduction in standard deviation of hourly time series taken from different areas, as relative to a single site standard deviation (28 % of capacity). Data from year 2001.

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For a single turbine, the standard deviation is close to 30 %, somewhat larger than the mean. For a country, the standard deviation gets closer to 20 %, for a larger country like Norway and Finland, where the sites are spread 1000 km apart, the standard deviation is less than 20 %. For the total Nordic time series, the standard deviation is close to 15 % (Table 3). In Fig. 10 it can be seen that Denmark, Finland and Nordic data represent a reduction, whereas specifically the Norwegian data series shows far more variations than those of considerably smaller East Denmark. This was as expected, as the Norwegian data for the areas consists of 2–5 time series only. For the Nordic data, also a data set where wind power was concentrated in Denmark (half of the capacity) was made, and there the reduction in standard deviation is to 60 % of the single site value, compared with the nearly to 50 % for an evenly distributed wind power production (Fig. 10).

4.2 Frequency distributions of wind power production

To take closer look at wind power production, the hourly production of years 2000 and 2001 are plotted as frequency distributions. In Fig.11 the data is grouped with the scale in x axis as following: 0 means the number of values below or equal to 0 ; 5 % means the number of values above 0 and below or equal to 5 % etc.

0 %

5 %

10 %

15 %

20 %

25 %

0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 %

production, % of capacity

frequ

ency

, % o

f tim

e

Single wind farm, average 22 %

0 %

5 %

10 %

15 %

20 %

25 %

0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 %

production, % of capacity

frequ

ency

, % o

f tim

e

Norway, average 33 %

Denmark, average 22 %

0 %

5 %

10 %

15 %

20 %

25 %

0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 %

production, % of capacity

frequ

ency

, % o

f tim

e

Nordic, average 26 %

0 %

5 %

10 %

15 %

20 %

25 %

0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 %

production, % of capacity

frequ

ency

, % o

f tim

e

Finland, average 23 %

Sweden, average 24 %

Figure 11. Frequency distribution of wind power production from one site, from a country and for a theoretical total Nordic production. Example years 2000 and 2001. The production values at x axis note the upper value of the range.

32

It can be seen in Fig.11, that large scale production of wind power means shifting the most frequent ranges to the middle of the graph. For the single site, the production is almost half of the time below 10 % of capacity. For the wind power scatter to all Nordic countries, the production is most of the time in between 5...30 % of capacity, and is seldom below 5 % or above 70 % of capacity.

The probability of wind power production can also be presented as a duration curve. The duration curve of power production is often used in the energy sector to illustrate the time the power plant produces a certain power level. In Fig.12, the Nordic wind power production for year 2000 is shown chronologically (the varying curve) and as a duration curve, where the production values are sorted in descending order before drawing the curve. The duration curve does not tell about the correlation between consecutive values, for this a persistence study is made separately in chapter 4.5.

0 %

20 %

40 %

60 %

80 %

100 %

1 741 1481 2221 2961 3701 4441 5181 5921 6661 7401 8141

hour

prod

uctio

n (%

of c

apac

ity)

2000

Figure 12 Example of data for this study: the total Nordic wind power production, as a chronological time series and as a duration curve.

0 %10 %20 %30 %40 %50 %60 %70 %80 %90 %

100 %

1 741 1481 2221 2961 3701 4441 5181 5921 6661 7401 8141

hour

prod

uctio

n %

of c

apac

ity

Nordic, average 26 %

Denmark, average 22 %

Single turbine, average 26 %

Figure 13. The effect of geographical spreading is to flatten the duration curve of wind power production. Example of year 2000 hourly data, where wind energy distributed to all 4 Nordic countries is compared with one of the wind farms and one of the countries (Denmark). Average production for the curves is denoted in the legend text.

33

In Fig. 13, the smoothing effect is presented as duration curves. The duration curves for the countries are presented in Fig. 14. Here again it can be seen that the Norwegian wind power production is at a higher level than for the other countries, and also the smoothing effect is stronger. The Danish wind power production shows less smoothing effect than the data sets for the other countries. This is due to Denmark being far smaller area than the other countries.

0 %10 %

20 %30 %

40 %50 %

60 %70 %

80 %90 %

100 %

1 741 1481 2221 2961 3701 4441 5181 5921 6661 7401 8141

hour

prod

uctio

n %

of c

apac

ity

Norway, average 33 %Sweden, average 24 %Denmark, average 22 %Finland, average 23 %

Figure 14. Duration curves for the wind power production in the 4 Nordic countries, year 2000 data. Average production is denoted in the legend text.

4.3 Seasonal variation of wind power production

In Central and Northern Europe, there is a distinct seasonal variation in wind power production: more production in winter than in summer. For example in the Nordic countries, 60–70% of yearly production comes during 6 winter months (Fig.15). The production during the winter months is 110–140 % of the yearly average and production during the summer months 60–80 % of the yearly average (Table 2). This is also reflected in the range of production values, for example, the hourly data for Nordic countries for these example years 2000 and 2001 ranges between 1...61 % in the summer and 2...85 % in the winter.

Frequency distributions for the 4 seasons are presented in Fig.16. Duration curves for summer and winter are presented in Fig.17 for Denmark and the combined Nordic wind power production.

34

0 %

5 %

10 %

15 %

1 2 3 4 5 6 7 8 9 10 11 12

Month

% o

f yea

rly p

rodu

ctio

n /c

onsu

mpt

ion

Electricconsumption2002Averageproduction1995-20021995

1996

1997

1998

1999

2000

2001

2002

Figure 15. Seasonal variation of total wind power production in Finland in 1997-2001. Average of 1992-2001 is shown (line) together with the electric consumption (dotted line).

0 %

5 %

10 %

15 %

20 %

25 %

30 %

0 %

10 %

20 %

30 %

40 %

50 %

60 %

70 %

80 %

90 %

100 %

production level (% of capacity)

frequ

ency

(% o

f tim

e)

Nordic spring, average 24 %

Nordic summer, average 18 %

Nordic autumn, average 29 %

Nordic winter, average 32 %

Nordic 2000-2001, average 26 %

0 %

5 %

10 %

15 %

20 %

25 %

30 %

0 %

10 %

20 %

30 %

40 %

50 %

60 %

70 %

80 %

90 %

100 %

production level (% of capacity)

frequ

ency

(% o

f tim

e)

Finland spring, average 23 %

Finland summer, average 17 %

Finland autumn, average 26 %

Finland winter, average 26 %

Finland 2000-2001, average 23 %

0 %

5 %

10 %

15 %

20 %

25 %

30 %

0 %

10 %

20 %

30 %

40 %

50 %

60 %

70 %

80 %

90 %

100 %

production level (% of capacity)

frequ

ency

(% o

f tim

e)

Denmark spring, average 20 %

Denmark summer, average 17 %

Denmark autumn, average 25 %

Denmark winter, average 28 %

Denmark 2000-2001, average 22 %

0 %

5 %

10 %

15 %

20 %

25 %

30 %

0 %

10 %

20 %

30 %

40 %

50 %

60 %

70 %

80 %

90 %

100 %

production level (% of capacity)

frequ

ency

(% o

f tim

e)

Norway spring, average 29 %

Norway summer, average 22 %

Norway autumn, average 37 %

Norway winter, average 43 %

Norway 2000-2001, average 33 %

Figure 16. Difference in frequency distributions of wind power production for seasons: the lower production rates have a higher probability in summer, and higher production rates are more probable in winter.Average production during the seasons is denoted in the legend text.

35

0 %10 %20 %30 %40 %50 %60 %70 %80 %90 %

100 %

1 741 1481 2221 2961 3701hour

prod

uctio

n (%

of c

apac

ity)

Denmark, summer, average 17 %

Nordic, summer, average 18 %

Denmark, winter, average 28 %

Nordic, winter, average 32 %

Figure 17. The wind power production is higher during the 3 winter months (upper curves: January, February and December) than the 3 summer months (lower curves: June, July, August). Duration curves for production in 2000 and 2001.

4.4 Diurnal variation of wind power production

Wind is driven by weather fronts and a daily pattern caused by the sun, so depending whether one of these dominates there is either significant or hardly any diurnal pattern in the production. Diurnal variation can also be due to local phenomena, for example in California passes there are morning and evening peaks when wind blows to and from the desert and the sea. In Europe, there is a tendency for winds starting to blow in the morning and calming down in the evening (Ireland: Hurley and Watson, 2002; Germany: Ensslin et al, 2000). In Northern Europe this is mostly pronounced during the summer (Fig.18).

In winter there is not a clear diurnal variation to be seen, except for slightly in Denmark (the uppermost curves in Fig. 18 graphs). In summer, the average production at 11…18 hours is on the average above 20 % of capacity compared with less than 15 % of capacity during the night. Wind power production in Denmark and Sweden experience a more pronounced diurnal variation, whereas the sites in the northern part of Finland, Sweden and Norway do not experience any detectable diurnal variation.

The diurnal variation here is presented in Central Europe time, as is used in Denmark, Norway and Sweden. The hours have a shift for summer time in the spring and back to normal time in the autumn (Fig 18). For the single sites with whole year data, the hours are in normal time. A shift in the peak can be seen for the single site data in Fig.19, where data for all countries are in the same graph. The sun rises from the East, warming up Finland first (peak at 10-12 Central Europe time, 11-13 Finnish time), Sweden next (peak at 11-13), Denmark (peak at 13-15) and lastly Norway (peak at 14-17). For Norwegian data, the smoothing made to single wind speed series to represent wind farm data, makes the peak shift somewhat to later than it should be (2-hour-sliding average of wind speeds was done).

36

0 %

5 %

10 %

15 %

20 %

25 %

30 %

35 %

0 2 4 6 8 10 12 14 16 18 20 22

hour of day

prod

uctio

n (%

of c

apac

ity)

All data Denmark,average 22 %

Spring Denmark,average 20 %

Summer Denmark,average 17 %

Autumn Denmark,average 25 %

Winter Denmark,average 28 %

0 %

5 %

10 %

15 %

20 %

25 %

30 %

35 %

0 2 4 6 8 10 12 14 16 18 20 22hour of day

prod

uctio

n (%

of c

apac

ity) All data Finland,

average 23 %Spring Finland,average 23 %Summer Finland,average 17 %Autumn Finland,average 26 %Winter Finland,average 26 %

10 %

15 %

20 %

25 %

30 %

35 %

40 %

45 %

0 2 4 6 8 10 12 14 16 18 20 22

hour of day

prod

uctio

n (%

of c

apac

ity) All data Norway,

average 33 %Spring Norway,average 29 %Summer Norway,average 22 %Autumn Norway,average 37 %Winter Norway,average 43 %

0 %

5 %

10 %

15 %

20 %

25 %

30 %

35 %

0 2 4 6 8 10 12 14 16 18 20 22

hour of day

prod

uctio

n (%

of c

apac

ity) All data Sweden,

average 24 %Spring Sweden,average 22 %Summer Sweden,average 16 %Autumn Sweden,average 28 %Winter Sweden,average 32 %

Figure 18. For the Nordic countries, diurnal variation is more pronounced in summer time

0 %

5 %

10 %

15 %

20 %

25 %

30 %

35 %

0 2 4 6 8 10 12 14 16 18 20 22

hour of day

prod

uctio

n (%

of c

apac

ity) All data Nordic,

average 26 %Spring Nordic,average 26 %Summer Nordic,average 18 %Autumn Nordic,average 26 %Winter Nordic,average 26 %

Single sites

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0 2 4 6 8 10 12 14 16 18 20 22

hour of day

prod

uctio

n (%

cap

acity

)

North NorwaySouth NorwaySouth SwedenNorth SwedenWest DenmarkSouth Finland

Figure 19. Diurnal variation of wind power production for some example sites. For North Norway, Sweden and Finland, the diurnal variation is practically non existent also for summertime.

37

4.5 Persistence of wind power production

Frequency distributions and duration curves give some idea of how often certain production levels occur. However, for a varying power production like wind power, also persistence in different production levels is of interest – how long does a certain production level last?

There are two special cases, presenting the greatest challenges in integration of wind power in the system: duration of calms or low wind power production, as well as occurrence of the peaks, which are specially pronounced in wind power production. This analysis gives also insight into how the example years 2000 and 2001 differ in this respect.

4.5.1 Duration of calms

Duration of calms has here been defined as time when wind power production is less than 1 % of capacity. As the average production is of the order of 20–25 % of capacity, this can also be put as about 4–5 % of average production. Additionally low production persistence has been studied: when wind power production is less than 5 % of capacity (roughly 20 % of average production). Production level of 10 % of capacity is already almost half of average production, and wind power production is almost a third of the time below 10 % level (for the total Nordic production, almost 15 % of time, Fig. 11). That is why it is not considered as a calm period.

0

2

4

6

8

10

12

14

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

duration of calm, production less than 1 % of cap (hours)

num

ber o

f per

iods

2000, total 92 hours2001, total 88 hours

Finland

0

2

4

6

8

10

12

14

1 4 7 10 13 16 19 22 25 28 31 34duration of calm, production less than 1 % of cap (hours)

num

ber o

f per

iods

2000, total 404 hours2001, total 430 hours

Denmark

0

2

4

6

8

10

12

14

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

duration of calm, production less than 1 % of cap (hours)

num

ber o

f per

iods

2000, total 22 hours2001, total 11 hours

Norway

02468

101214161820

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

duration of calm, production less than 1 % of cap (hours)

num

ber o

f per

iods

2000, total 134 hours2001, total 144 hours

Sweden

Figure 20. Duration of calms for wind power production, number of different length periods when production below 1 % of capacity. The graphs for countries do not all have the same scale.

38

In Denmark, the production was below 1 % of capacity a total of nearly 5 % of time, where as for the larger areas of Finland and Sweden, this was about 1 % of time. For Norway, the calms were very rare (0.2 % of time). The longest duration of calm (production below 1 % of capacity) was 35 hours for Denmark in 2000. For Sweden, it was 18 hours, for Finland 14 hours and for Norway 5 hours. In Fig.20 it can be seen that for Norway, the total amount of hours below 1 % of capacity is half in 2001 compared to 2000, this can be explained by more data series for year 2001. For a total Nordic data set, there were no calms, the production is always above 1 % of capacity. The production was below 5 % of capacity about 2 % of time (Fig.21).

There are not significantly more and longer calms in year 2001 data than in year 2000 data. This is somewhat surprising: as year 2001 was a lower wind year than year 2000 (Table 2), so intuitively also the calm periods could have been more.

02468

1012141618202224

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

duration of low production, less than 5 % of cap (hours)

num

ber o

f per

iods

2000, total 200 hours

2001, total 216 hours

Figure 21.Duration of low production in a total Nordic wind power production, number of different length periods when production less than 5 % of capacity.

4.5.2 Peaks of wind power production

Peak production has here been studied for the level of above 75 % of capacity. As the average production is of the order of 25 % of capacity, this can also be defined as roughly three times the average production.

In 2000, there was one long period with high wind power production exceeding 75 % of capacity: 37 hours in Finland, 34 hours in Sweden and Denmark and 27 hours in Norway. In addition, there were 1-3 periods of about 20 hours long high production. For the Nordic data, peaks of more than 75 % are rare, 84 hours in 2000 and 34 hours in 2001, with maximum duration of 14 hours.

In all the countries except Norway, there are more and longer periods of peak power production in year 2000 data than in year 2001 data. This was as expected, as year 2000 saw a better wind resource (Table 2) so also the high wind periods are supposedly more.

39

Duration of peaks

0

2

4

6

8

10

12

14

1 4 7 10 13 16 19 22 25 28 31 34 37 40

duration of peak, production more than 75 % of cap (hours)

num

ber o

f per

iods

2000, total 91 hours2001, total 33 hours

Finland

Duration of peaks

0

2

4

6

8

10

12

14

1 4 7 10 13 16 19 22 25 28 31 34 37 40

duration of peak, production more than 75 % of cap (hours)

num

ber o

f per

iods

2000, total 372 hours2001, total 223 hours

Denmark

Duration of peaks

02468

1012141618202224

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

duration of peak, production more than 75 % of cap (hours)

num

ber o

f per

iods

2000, total 211 hours2001, total 295 hours

Norway

Duration of peaks

02468

1012141618202224

1 4 7 10 13 16 19 22 25 28 31 34 37 40

duration of peak, production more than 75 % of cap (hours)

num

ber o

f per

iods

2000, total 396 hours2001, total 166 hours

Sweden

Figure 22. Length of high wind power production periods in example years 2000 and 2001.

4.6 Correlation of wind power production

Cross-correlation (rx,y) is a measure of how well two time series follow each other. It is near the maximum value 1 if the ups and downs of the production occur simultaneously, near the minimum value –1 if there is a tendency of decreasing production from one site when increasing production at the other site, and close to zero if the two are uncorrelated, and the ups and downs of production do not follow each other at two sites.

yx

n

iyixi

yx

yxnr

σσ

µµ∑=

−−= 1

,

))((1

, (3)

where µ denotes the average, σ the standard deviation and n the number of points in the time series.

Correlation can also be calculated for a single time series but with time lags. This is called autocorrelation. For wind power production, the autocorrelation decreases soon with increasing time lag, already at 12 hour lag the correlation becomes weak (Pryor & Barthelmie, 2001)

If wind production data is not correlated, there can be strong winds in one place at the same time as weak winds in the other. When distributing wind power production to a larger area,

40

the total production will be smoother and less variable, if the correlation between the sites is low.

The cross-correlations were calculated for all sites in the Nordic countries for one year, 2001, when the data available included most sites, altogether 33 time series. Some of the time series were aggregated production data from a larger area, for which the coordinates were estimated from the centre of the area. The results are presented in Fig. 23. The cross-correlation decreases fast at first, rxy =0.7 for distance of about 100 km and 0.5 for distance of about 300 km, after which the decrease is slower.

There is significant variation in the cross correlation coefficients for a similar distance, as is expected. The correlation becomes weak, below 0.5, with distances above 200-500 km. When local phenomena influence the wind resource, the winds do not correlate with sites even some 200 km apart. In Fig. 23, the lowest cross-correlations are slightly negative, for Finnish Lapland with Southern Norway sites. For the westernmost site in Southern Norway, the correlation is weak for all other sites, the lowest points in Fig. 23 for distances of 200...800 km come from there. Slightly negative correlations between two points in Europe have been reported from weather data from Ireland/Portugal (1500 km apart) and Spain/Greece (3000 km apart) (Giebel, 2001). The results from correlation between weather station wind speed based data calculated from 9 years in Finland are similar to the ones here for year 2001 (Tammelin&Nurmi, 2001). There is not a significant change in correlation coefficients calculated from different years. A year of hourly data contains enough different weather situations to be able to determine the correlation between the wind power production at different sites.

The cross-correlation can be modelled by exponential fitting, decay parameters (D) of 500…700 have been reported (Giebel, 2001). For this data, D= 500 fits the data (Fig. 23).

-0,1

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 200 400 600 800 1000 1200 1400 1600 1800 2000distance between sites, d (km)

corre

latio

n co

effic

ient

y=exp(-d/500)

Figure 23. Cross correlation coefficients for the sites in the Nordic data for year 2001.

Looking at large scale wind power production in the countries and regions, the correlations are calculated for 2 years of data (2000-2001) and presented in tables 5-6. For the four

41

countries, Swedish and Danish wind power production is correlated (with the assumption here that most of the Swedish wind power is in the Southern part of Sweden). Wind power production in the other countries is only weakly correlated, with lowest correlation between Denmark and Finland.

Table 5. Cross correlation coefficients between wind power production in the Nordic countries.

Norway Denmark Sweden Finland

Norway 1.00

Denmark 0.37 1.00

Sweden 0.41 0.77 1.00

Finland 0.44 0.33 0.45 1.00

Taking a closer look at the regions in the Nordic countries, the largest correlation is again for wind power production in West and East Denmark and South Sweden. These are the areas with least distance apart. Also the two areas in Southern part of Finland are strongly correlated. For other areas, the correlation is not strong. There is weak correlation (0.4...0.5) between the areas in Lapland (the Northern part of Norway, Sweden and Finland), between Southern Norway and Denmark/South Sweden, between South Sweden and the Southern areas of Finland, between the northernmost West coast of Finland and Lapland, Southern and Western parts of Finland, and between Middle and North Norway. There is practically no correlation between Lapland (North Norway, Sweden and Finland) and the Southern areas (Denmark, South Sweden, Norway and to some extent South Finland).

Table 6. Cross correlation coefficients between the regional wind power production in the Nordic countries.

NO South

NO Middle

NO North

DK East Elkraft

DK West (Eltra)

SE South

SE North

FI South coast East

FI South coast West

FI West coast South

FI West coast North

FI Lap-land

NO S 1.00

NO M 0.33 1.00

NO N 0.21 0.41 1.00

DK E 0.37 0.16 0.15 1.00

DK W 0.48 0.21 0.16 0.86 1.00

SE S 0.40 0.26 0.20 0.78 0.75 1.00

SE N 0.12 0.32 0.41 0.13 0.15 0.17 1.00

FI SE 0.22 0.24 0.16 0.26 0.26 0.38 0.26 1.00

FI SW 0.26 0.33 0.23 0.31 0.32 0.50 0.28 0.52 1.00

FI WS 0.28 0.33 0.28 0.30 0.33 0.42 0.32 0.52 0.71 1.00

FI WN 0.18 0.23 0.37 0.22 0.21 0.25 0.31 0.33 0.33 0.57 1.00

FI Lappi 0.07 0.11 0.37 0.08 0.06 0.07 0.39 0.13 0.13 0.23 0.41 1.00

42

4.7 Short term variations of wind power production

For power system operation, the variations from day to day, hour to hour and minute to minute are of interest. The larger the area, the longer time scales are affected by smoothing effect. Inside a WF, all the WTs will experience different gusts (seconds), but the hourly wind power production will see approximately the same ups and downs. In a larger area covering several hundreds of km, the weather fronts causing high winds will not pass simultaneously but the good and poor months will occur same time. This can be seen in Fig. 24, where the decreasing correlation of the variations is depicted for different time scales (Ernst, 1999). The correlation is here calculated for the differences between consecutive production values (∆P). For the time series of production values (P), the correlation does not decrease as rapidly as shown here (Fig.23).

0

0.2

0.4

0.6

0.8

1

0 100 200 300 400 500 600Distance (km)

Cor

rela

tion

coef

ficie

nt fo

r the

var

iatio

ns

4h average

12h average

1h average30min average

5min average

2h average

Figure 24. Variations will smooth out faster when the time scale is small. Correlation of variations for different time scales, example from Germany. (Source: B.Ernst,1999)

4.7.1 The in-hour variations

Already the inertia of large rotating blades of a wind turbine will smooth out the very fast gusts of wind. For variable speed wind turbines, the second-to-second variations will be absorbed in the varying speed of the rotor. For a wind farm, the second-to-second variations will smoothed out, as the same gusts will not occur simultaneously at all turbines, situated several hundred meters apart.

The extreme ramp rates recorded from one 103 MW wind farm are 4…7 % of capacity in a second, 10…14 % of capacity in a minute and 50…60 % of capacity in an hour (Parsons et al, 2001). These examples are from a limited area compared with system operation: large wind

43

farm or 3 smaller wind farms some 10 km apart. For a larger area of geographically dispersed WFs, the second and minute variations will not be significant.

For the 15 min variations in Denmark, the production can vary 8.4 % of capacity 6 times per month, and the maximum is 11 % (Nordel, 2000). This is not as much as for the hourly variations, as seen in the following chapter.

There are means to reduce the fast variations of wind power production. Staggered starts and stops from full power as well as reduced (positive) ramp rates could reduce the most extreme fluctuations, in magnitude and frequency, over short time scales (Kristoffersson et al, 2002). This is at the expense of production losses, so any frequent use of these options should be weighed against other measures (in other production units) in cost effectiveness.

4.7.2 The hourly variations

The hourly variation is here defined as the power difference between two consecutive hours. It is here measured relative to the nominal capacity, to compare it with several countries with different amounts of capacity installed.

1−−=∆ iii PPP ; 1−−=∆ iii ppp (4)

-15 %

-10 %

-5 %0 %

5 %

10 %

15 %

hour

ly v

aria

tion

(% o

f cap

)

2000

-15 %

-10 %

-5 %

0 %

5 %

10 %

15 %

1 741 1481 2221 2961 3701 4441 5181 5921 6661 7401 8141

hour

hour

ly v

aria

tion

(% o

f cap

.)

2001

Figure 25. Hourly variations from Nordic wind power production, chronological time series and duration curve, years 2000 and 2001.

44

For large scale, dispersed wind power production there will be a significant smoothing effect in the hourly variations. The correlation of the variations between two WTs decreases faster than the correlation of the production. For hourly variations, the correlation becomes weak already in distances less than 100 km (Fig.24, Ernst, 1999). Correlation of hourly variations for the countries and regions were calculated, and most of them were between –0.01 and 0.04, so there is no correlation between the hourly variations. Hourly variations in East and West Denmark are weakly correlated (0.46). For the other closest regions, South Sweden/Denmark, South Norway/West Denmark as well as the Western part of Finland, the correlation of variations is below 0.2.

In Figs 25 and 26 the amount of hourly variations are shown as duration curves. From the hourly time series of wind power production, the hourly variation as the difference between the production at consecutive hours, and these values have been sorted in descending order. In the figure, 0 % means that the power production keeps on the same level and does not vary from one hour to another, positive values indicate situations when wind power production is increasing, and negative values for decreasing production.

-25 %-20 %-15 %-10 %-5 %0 %5 %

10 %15 %20 %25 %

hour

ly v

aria

tion

(% c

ap)

NordicDenmarkSweden

-25 %-20 %-15 %-10 %

-5 %0 %5 %

10 %15 %20 %25 %

1 741 1481 2221 2961 3701 4441 5181 5921 6661 7401 8141

hour

hour

ly v

aria

tion

(% c

ap)

NordicNorwayFinland

Figure 26. Variation of wind power production from one hour to the next. Duration curve of variations, as % of installed capacity, for the Nordic countries, year 2001.

45

In Appendix 2, the hourly variations of wind power production for years 2000 and 2001 are shown for the 4 countries.

Largest hourly variation is about ± 30 % of capacity when the area is in the order of 200x200 km2 (like West/East Denmark), ± 20 % of capacity when the area is in the order of 400x400 km2 (like Germany, Denmark, Finland, Iowa USA) and about ± 10 % in larger area covering several countries, like the four Nordic countries (ISET, 2002; Holttinen, 2002; Milligan & Factor, 2000). For this Nordic data, largest hourly variations are 11 % up and 10 % down. For Norway and Sweden, despite the large area, the variations are higher than for Denmark and Finland. This is due to limited number of sites in the data sets. The Nordic variations are probably overestimated due to this.

These are the extreme values, for most of the time the hourly variations will stay inside ± 5 % of installed capacity (Fig. 26 and Table 7). It is notable, that as the average production is about 25 % of capacity, this 5 % of capacity represents 20 % of average power. For the countries, the hourly variations are more than 5 % of capacity 6…20 % of time. For Denmark this is 10 % of time, so probably the large variations of Norway and Sweden data sets are due to too few time series in the countries to represent the variations right. Omitting Norway and Sweden, the conclusion is that the hourly variations of large scale wind power production are about 90 % of time between ± 5 % of capacity and 99 % of time between ± 10 % of capacity. For the total Nordic time series the hourly variations are about 98 % of time between ± 5 % of capacity (Table 7).

Table 7. Largest hourly variations in the wind power production for Nordic countries, years 2000 and 2001.Maximum variations are as % of installed capacity. The portion of time that the variations are more than 5 or 10 % of capacity is also presented.

Nordic, evenly Denmark Finland Sweden Norway

2000 2001 2000 2001 2000 2001 2000 2001 2000 2001

max up-variation

10.9 % 11.4 % 20.1 % 16.7 % 14.0 % 16.2 % 33.5 % 34.5 % 26.9 % 24.8 %

max down-variation

-12.2 % -10.1 % -23.1 % -18.0 % -15.5 % -14.9 % -31.1 % -31.6 % -21.2 % -19.6 %

above 5 % 1.1 % 0.8 % 4.9 % 4.0 % 3.0 % 3.2 % 10.0 % 8.8 % 10.1 % 6.2 %

below -5 % 1.0 % 0.7 % 4.9 % 3.7 % 3.2 % 3.0 % 9.6 % 8.2 % 10.5 % 5.7 %

above 10 % 0.0 % 0.0 % 0.6 % 0.5 % 0.2 % 0.2 % 2.2 % 1.9 % 1.9 % 0.7 %

below -10 % 0.0 % 0.0 % 0.5 % 0.4 % 0.2 % 0.2 % 1.8 % 1.7 % 1.7 % 0.6 %

The difference between year 2000 and 2001 is not very pronounced. For Norway, the difference is the largest, which is probably due to better data set for year 2001 (more sites). The largest variation in Denmark was Tuesday evening 8.2.2000 at 21-22 hours up and Sunday afternoon 30.1.2000 at 15-16 hours down. For the Nordic data set largest up-variation was 15.11.2001, and surprisingly during the night, at 01-02 hours. This was due to wind power increasing in all countries simultaneously, but for Norway, the increase was 25 % of capacity, which is probably overestimated. The largest down-variation was Wednesday

46

15.3.2001, at 15-16 hours, when there was a large variation (20 %) in Denmark simultaneously with nearly 20 % variation in Norway and more than 10 % variation in Sweden. If more representative data for Norway was available, the largest Nordic variations would probably stay in ±10 % of capacity.

Probability of significant variations is a function of production level. Significant changes occur most probably when wind farms are operating between 25...75 % of capacity, as this is the steep part of the power curve when changes in wind speed produce largest changes in power output of the turbines (Poore & Randall, 2001). For large scale wind power, the production is rarely above 75 %, so an analysis to Nordic data was done for the production level of above 20 % of capacity (at the first hour). Hourly variations were analysed for these periods only. Example from duration curve of variations is shown in Fig. 27. It can be seen, when comparing for all the hourly variations in Fig. 26, that the large variations occur nearly twice as often for the countries when looking this way (Table 8). For the total Nordic data set the difference is not that large.

Table 8. Largest hourly variations in the wind power production for Nordic countries, when taking only the periods with production more than 20 % of capacity (years 2000 and 2001).

Nordic Denmark Finland Norway Sweden

time above 20 % 56.09 % 40.40 % 47.64 % 66.81 % 44.71 %

max up-variation 11.4 % 20.1 % 16.2 % 26.9 % 33.5 %

max down-variation -12.2 % -23.1 % -15.5 % -21.2 % -31.6 %

time above 5 % 1.5 % 9.0 % 5.1 % 10.1 % 14.6 %

time below - 5 % 1.6 % 9.7 % 6.0 % 11.3 % 17.1 %

time above 10 % 0.0 % 1.2 % 0.4 % 1.7 % 3.7 %

time below - 10 % 0.0 % 1.2 % 0.3 % 1.7 % 3.8 %

Denmark, -23 %…+20 %

-25 %-20 %-15 %-10 %

-5 %0 %5 %

10 %15 %20 %25 %

1 741 1481 2221 2961 3701 4441 5181 5921 6661hour

hour

ly v

aria

tion

(% o

f cap

Figure 27. Duration curve of hourly variations, when the initial production level has been above 20 % of capacity. Data from years 2000 and 2001. (The x scale is not the same as in Fig.26, as the total amount of hours with this production level is not as much as the total number of hours in 2001.)

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Reductions in standard deviation for hourly time series were presented in chapter 4.1 before, this is a measure of reduced variability in the time series with geographic dispersion of wind power. The standard deviation of hourly time series will reduce from 25–30% for a single site to 18–22 % for a larger area, that is to 70–80 % of the single site value (Fig.10 and Focken et al, 2001). For the Nordic area, the reduction is to almost half of the one site value (σ = 15 %).

The standard deviation of the time series of fluctuations ∆P will decrease even faster, from about 10 % for a single turbine to less than a third (3 %) for an area like West Denmark (Milborrow, 2001). For Nordic data, the reduction in maximum variations and standard deviation of variations is presented in Fig.28. The Norway and Sweden data give again larger standard deviation values than Denmark and Sweden, due to lack of real large scale wind power data.

Maximum hourly variation

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East Denmark Total DKFinlandNorwaySwedenNordic

Figure 28. Maximum hourly variation in wind power production for the data for Nordic countries.

As can be seen from Fig. 28, the smoothing effect is more pronounced with more turbines and more separation. The smoothing effect of a specified area has a limit, that is, the time series will not get smoother if more and more turbines are added from the same area. For Germany, for example, it has been estimated that 30 sites will be enough to get the low variations (Focken et al, 2001). After saturation, the only way to increase the smoothening will be to increase the area – which has a limit somewhere, too. In Fig. 28 it is also obvious that increasing the area from that of Denmark, the decrease in the statistical parameters shown here is slower.

Diurnal variations in output can help indicate when significant changes in output are most likely to occur (Poore & Randall, 2001). The average hourly variations of wind power production are zero – there are as much up and down variations. However, when plotting the average hourly variation as of time of day, the average is no longer zero for all hours of the day. There are more upward changes during the morning hours and more downward changes during the afternoon hours, as can be seen in Fig. 29. This is more pronounced during summer, as is the diurnal variation of the production (chapter 4.4). Also the maximum variations in the data set occur in morning hours for the upward changes and in the evening hours for the downward changes. The maximum variations are less in summer.

48

-4 %

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Summer 2000

Figure 29. Diurnal dependence of variations. All data and summer 2000. Above: average hourly variations, as of time of day. Below: maximum variations, up (positive) and down (negative).

4.7.3 Variations for longer time scales

For longer time scales, 4…12 h variations, short term prediction tools for wind power give valuable information on the foreseeable production levels, and expected variations of wind power production.

From the Nordic data set, the maximum 4- and 12-hour-variations are presented in table 9. The range of 4 hour variations is about ± 50 % of capacity for one country. This has also been reported for a longer following period from Germany (ISET, 2002). For the Nordic area it is ± 35 % of capacity according to this 2-year data set.

The maximum 12-hour variation for the Nordic area is ± 50 % of capacity. Taking larger areas, like the Northern Europe, and more years of data, ± 30 % change in production 12 hours ahead occurs about once a year (Giebel, 2000).

49

Table 9. Maximum variations from the Nordic wind power production (hourly values from years 2000 and 2001).

Nordic Denmark Finland Norway Sweden

4 hour variations: max down -31.1 % -61.9 % -35.9 % -45.2 % -56.6 %

4 hour variations: max up 34.1 % 52.9 % 51.6 % 55.2 % 65.1 %

12 hour variations: max down -45.0 % -73.6 % -66.6 % -84.8 % -69.1 %

12 hour variations: max up 52.7 % 79.1 % 72.9 % 74.2 % 82.5 %

4.8 Predictability of wind power production

Wind power prediction plays an important part in the system integration of large scale wind power. When the share of installed wind power is significant, the knowledge of the on-line production and predictions 1…36 hours ahead are needed. Day-ahead predictions help the scheduling of conventional units: planning the start-ups and shut-downs of slow starting units in an optimised way, keeping the units running at best possible efficiency, saves fuel and thus operational costs of the power plants. Predictions 1-2 hours ahead help keeping up the optimal amount of regulating capacity at the system operators’ use (Milligan et al, 1995). In wind power production forecasts, as is the case for load forecasts, too, the errors decrease when forecasting for a larger area (Holttinen et al, 2002).

Predictability is most important at times of high wind power production, and up to 6 hours ahead, giving enough time to react on varying production also by start-ups and shut-downs of most of the thermal power plants. An estimate of the uncertainty, especially the worst case error is also relevant information.

Forecast tools for wind power production are still under development and improvements are expected (Giebel et al, 2003). The predictions of the power production 12 hours-ahead or more rely almost entirely on meteorological forecasts for local wind speeds. In northern European latitudes, the variations of wind power production occur due to meteorological weather systems passing the area, causing high winds, which calm down again. The largest error component comes from the wind speed forecast of the Numerical Weather Prediction models. So far the accuracy of ± 2-3 m/s, ± 3-4 hours has been enough for wind speeds in weather forecasts, but electricity market (and system) requires more precise knowledge of wind power production.

An example of the forecast errors is presented in Fig. 30 for West Denmark, where the system operator Eltra is responsible for most of the wind power installed in the area. The wind power prediction tool in use in year 2001 was dated from year 1997. For Nordpool electricity market (prediction horizon 13...37 hours ahead) the mean absolute error is 8-9 % of installed capacity. However, for market operation this results in 38 % of yearly production mispredicted. For comparison, load is predicted with 1.5…3 % error (mean absolute error, as % of peak load), which results in about 5 % of yearly energy mispredicted. For prediction 2

50

hours ahead the prediction tools for wind power work significantly better (Fig.30; Holttinen et al, 2002).

For larger areas the prediction errors decrease. For East and West Denmark, for example, the errors for day-ahead predictions are to the opposite sides for about a third of time (Holttinen et al, 2002). For the distance in the direction of most weather systems passing, West-East, adding East Denmark brings 100 km, or 50 % more to West Denmark.

Eltra. Predictions for 1900 MW wind power, year 2001

-1200

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1 741 1481 2221 2961 3701 4441 5181 5921 6661 7401 8141Duration (hours)

Pred

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)

duration of predictionerror 13-37 hours aheadduration of predictionerror 2-3 hours ahead

Figure 30. Prediction errors for wind power production (state-of-the-art year 1997 for the prediction tool in use).

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5 Representative data for large scale wind power production

To study the impacts of large scale wind power production, the data should be representative – both in time and space. Depending on what impact we are looking at, we should take an average year production, or a low or high wind year, to see the extreme situations for system planning purposes. This means taking production from a representative time period to study. Depending on what impact we are studying, the wind power production time series should be representative for the area in question. For example, large scale wind power impacts on the power system operation, should take the production from large area, with proper smoothing effect present in the data. This means taking production from representative space.

Checking out the representativeness of time period studied is quite straightforward, when long term wind power data exists. This is done in chapter 5.1. For checking up the representativeness in geographical smoothing, no examples were found in the references. In 5.2 some basic parameters from chapter 4 are picked up to form a guideline in this respect.

5.1 Representativeness of the study years

Here we look at the years in question: 1999-2001. Wind power production indices from national wind power production statistics are presented in Fig. 31 (Laakso, 2003; Carlstedt, 2003; Naturlig Energi, 2003). Wind power production index is a measure of one year’s production compared with the long term average production. 100 % means that the yearly production was like the long term average. In Fig. 31 it can be seen that the yearly production varies between 80...120 %. In Finland, the coastal areas South and West experience a somewhat different wind resource variations, this is why the production indices are calculated for 4 sites (Laakso, 2003). The production indices for Finland are here calculated as weighted average of these indices, using the large scale wind power capacity distribution assumed in this study (Table 1). For Norway this analysis was not done due to lack of long term data. However, the Norwegian wind power production seems to experience the same trends as for the other Nordic countries (Table 2), even if not as strongly.

Year 1999 was less windy than long term average: 86 % in Denmark, 88 % in Finland and 94 % in Sweden (Fig. 31). Year 2000 was close to average (95 % in Denmark, 97 % in Finland, 102 % in Sweden), and year 2001 was clearly less windy than average (80 % in Denmark, 87 % in Finland, 88 % in Sweden).

The production index can be used in determining the long term average wind power production from only one year of realised production data, by dividing the year’s production with the year’s index value. For the countries presented here in Fig. 31, we get roughly 25 % of capacity as the long term average wind power production. The Danish index gives 25.3 % for both years and Finnish data would give 25.1 % from year 2000 and 25.6 % from year 2001. The Swedish production index is based on wind power production in the South of Sweden, so using the South Sweden realised production (25.5 % in 2000 and 22.2 in 2001,

52

instead of 26.0 % and 21.9 % respectively) we get 25.0 % from year 2000 and 25.2 % from year 2001 calculation.

As a total period, 2000-2001 will give a production that is less than average: 88 % of the average production in Denmark, 92 % in Finland and 95 % in Sweden. Using the years 2000 and 2001 as example years in this study, would give quite good results for Sweden, in that we are looking at a more windy period (year 2000, 102 % of average production) and a less windy period (2001, 88 % of average production) compared with long term production. For Denmark and Finland, year 2000 as a whole was not a high wind year, and as a whole period, the data in this study will underestimate the wind resource. However, as the data contains also high wind months, for example the first part of year 2000 (Fig. 15; monthly production indices in Carlstedt, 2003 and Naturlig Energi, 2003) there are also representative periods of high wind situations in the data.

Wind power production indices

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87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02year

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% =

ave

rage

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-01 DK

FISE

Figure 31. Yearly wind resource in 1987...2002, according to production statistics of wind energy in Nordic countries (DK=Denmark, FI=Finland, SE=Sweden).

5.2 Representativeness of the geographical spreading of data

Based on chapter 4 with detailed statistical analyses, it can be estimated, how well the data represents large scale wind power production. The data used for wind power fluctuations is critical in the studies for wind power impacts on power system operation. Not to upscale the fluctuations when upscaling installed wind power in the system, the statistical characteristics for large-scale production should be looked for in any simulated or meteorological data based wind power time series (Milborrow, 2001).

As Denmark data is real large scale wind power data of thousands of wind turbines, the comparisons made in chapter 4 can be used as a basis to estimate how well the data sets constructed for Norway and Sweden and Finland represent large scale wind power production.

53

Finland and Norway are considerably larger areas than Denmark, so also the smoothing effect should be stronger there. For Sweden, there is the possibility of concentrating most of the wind power capacity south of Stockholm, which means that Sweden should get closer to the same smoothing effect than in Denmark – probably more if at least some of the capacity was installed to the Central and Northern part of Sweden.

Summing up the statistical properties for an hourly time series of large scale wind power production, the following were found in chapter 4:

• Standard deviation of the hourly production series should be 20–22 % of capacity for an area like Denmark (300 x 200 km2), if larger area, then less than 20 % (Finland 18 %, Norway 20 %, Sweden 21 %, Nordic 15 %).

• Maximum hourly production should be less than 100 %: 85...95 % depending on how large the area in question is (Denmark 93 %, Finland 91 %, Norway 93 %, Sweden 98 %, Nordic 85 %).

• Duration of calms should be non existent or limited (minimum production in Denmark and Sweden 0 %, Norway and Finland 0.3 and 0.02 %, Nordic 1.3 %).

• Standard deviation of the hourly variation series should be less than 3 % of capacity (Denmark 2.9 %, Finland 2.6 %, Norway 3.9 %, Sweden 4.3 % and Nordic 1.9 %).

• The hourly variations should be in between ± 20 % of capacity, or even less if the area is larger than the size of Denmark (Denmark -23…20 %, Finland -18…16 %, Norway -21…27 %, Sweden -32…35 % and Nordic -12…11 %).

The smoothing effect is presented graphically in Figs 10 and 28 where the trends in the statistical parameters are depicted as a function of the size of the area. Finnish data set is in line with Denmark data set for reduction of standard deviation, maximum hourly variations and the standard deviation of variations. Norwegian and Swedish data sets have the statistical properties above those of Denmark. When looking at the basic statistics for the production time series, there is not a clear signal that the Norwegian and Swedish data would be unrepresentative, as taking even few time series from the countries from different locations of the area gives a basic smoothing effect in the range of production. The analysis on variability, especially the standard deviation of hourly variations, reveals the caveats of the Swedish and Norwegian data sets.

The conclusion is that the Finnish data set can be upscaled to represent large scale wind power production, whereas the Norwegian and Swedish data sets cannot. Combining the 4 data sets to form a Nordic data set probably overestimates the variations somewhat, but a continuing smoothing effect can be seen (Figs 10 and 28). It has thus been considered representative for the study of large scale wind power.

There will probably be a slight overestimation of variability for Finnish data when upscaling the data to large scale wind power production. Even for Denmark, there can be some caveats as to how well the data represents future wind power production. In the future, there will be less turbines and sites, but better production from MW scale high turbines, especially for offshore. When a substantial share of wind energy comes from large offshore wind farms, this will have an impact on the production, bringing about a less dispersed and thus more variable

54

production, but also higher duration, as there are less calms than on shore (Pryor & Barthelmie, 2001).

55

6 Wind power production and load

Wind power is a production form that partly resembles electric consumption, the load. It varies each moment with part of it being unpredictable, causing unexpected variations in the system. As an example, the wind power production in January, 2000 is presented together with the load in Fig.32. The wind power production is here upscaled for Finland, to represent approximately the same wind power penetration level3 (roughly 10 % of gross demand).

Denmark - load and wind power data from January 2000

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Figure 32. Electricity consumption (load) and wind power production in January 2000. Denmark is real data (12 % wind power). For Finland data from wind parks is scaled up to wind power penetration of about 11 % of gross demand.

The y-axis scale in the graphs is not the same, but the scale is relatively the same in the sense that it shows the load in these countries when the 10 % wind production curve is on the same

3 The share of produced wind power in the power system is here denoted as wind power penetration. The share is presented here as % of energy, yearly gross demand. Penetration as % of installed capacity is also used in some studies, which is a considerably larger figure than expressing it as % of energy.

56

level. It can be seen that the load variations in Denmark are relatively larger than in Finland, with an energy intensive industry.

In this chapter the basic patterns of electrical load, together with wind power production, are presented. The main focus is on the hourly variations and on peak load situations.

6.1 Basic statistics of the hourly load time series

Time series of load in Nordic countries, featuring also duration curves, are presented in Fig.33 for year 2001, and for year 2000 in Appendix 3. Electric load is characterised by a daily pattern, higher on weekdays than weekends (Seppälä, 1996). In addition to daily cycles, temperature effects can be seen in the graphs: the load is generally lower during summer, and different weeks in wintertime show dependence on temperature. For Finland, the holidays in midsummer and Christmas mean more pronounced dips in consumption than for the other countries. Even as the y axis scale is different in the graphs, as the scale is relative to the peak load, it can be seen that the load varies relatively more in Denmark compared with other 3 countries with energy intensive industry. Also widely used electric heating in Sweden and Norway and to a lesser extent in Finland can explain the differences. There are no major differences between the years, except for Finland where a strike in the energy intensive forest industry dropped the load by about 3000 MW for one week in April 2000 (Appendix 3).

Basic statistics of the load time series is presented in Table 10 for the years 2000 and 2001. In both Sweden and Norway the consumption is larger than Finland and Denmark together. Denmark has by far the lowest consumption, only about 10 % of the total Nordic demand. It can be seen, that electric consumption has been rising by 3 % between the years (only 1 % in Denmark, and 4 % in Finland and Sweden). The peak load has risen even more, but this is probably due to year 2000 not having extreme cold weather periods. The peak load is about 3 times larger than the minimum load. Some smoothing can be seen in the total Nordic load time series, the peak is lower and minimum load higher than the sum of the countries, as the peaks do not coincide. The Finnish load series is considerably less variable than for the other countries, as can be seen from the standard deviation relative to mean value.

Table 10. Key figures for electric load during example years 2000 and 2001.

Denmark Finland Norway Sweden Nordic

2000 2001 2000 2001 2000 2001 2000 2001 2000 2001

sum (TWh) 34.82 35.18 76.19 79.11 120.05 123.32 140.83 147.26 371.90 384.87

Peak 6284 6229 11829 12579 20420 23054 25381 26323 62265 67854

min 1964 2062 3571 3537 6832 8289 9042 9157 24586 23838

peak/min 3.2 3.0 3.3 3.6 3.0 2.8 2.8 2.9 2.5 2.8

average 3964 4016 8674 9031 13667 14078 16033 16810 42338 43935

stdev 937 927 1200 1409 2724 3327 3254 3791 7681 9076

stdev/average 23.6 % 23.1 % 13.8 % 15.6 % 19.9 % 23.6 % 20.3 % 22.6 % 18.1 % 20.7 %

57

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Figure 33. Hourly load of the Nordic countries, chronologically and as duration curves. The y-scale is different for all the graphs.

58

The total electrical consumption in the hourly time series (Table 10) is not exactly measured. This is why the electricity statistics show slightly different values (Nordel, 2003): for year 2001: 35.43 for Denmark, 81.52 for Finland, 120.36 for Norway, 148.91 for Sweden, a total of 386.22 TWh.

6.2 Correlation of load and wind power

Correlation with electrical load is important, when considering the power system effects of wind power. When there is a diurnal pattern in wind power production, coinciding with the load, like wind power production increasing in the morning and decreasing in the evening, this has a beneficial effect. In Table 11, the correlation between the electric load and wind power production is shown.

Table 11. Correlation between the electric load and wind power production in Nordic countries, years 2000 and 2001.

SE load NO load FI load WestDK load

EastDK load

DK load Nordic load

SE load 1.00

NO load 0.96 1.00

FI load 0.88 0.83 1.00

WestDK load 0.75 0.64 0.67 1.00

EastDK load 0.86 0.77 0.75 0.95 1.00

DK load 0.80 0.70 0.71 0.99 0.98 1.00

DK wind 0.20 0.20 0.22 0.20 0.22 0.21 0.21

FI wind 0.13 0.15 0.16 0.08 0.11 0.09 0.14

NO wind 0.34 0.37 0.35 0.15 0.23 0.18 0.35

SE wind 0.24 0.25 0.25 0.20 0.22 0.21 0.25

Nordic wind 0.30 0.32 0.32 0.21 0.26 0.23 0.31

The upper part of the table shows the correlation between the hourly loads in the countries. They are strongly correlated inside Denmark and between Norway and Sweden and less than strongly correlated between West Denmark and Norway/Finland. Below, the correlation between the load and wind power production is shown. For the whole data it seems that the correlation is slightly positive, but when looking at the 3 winter months only (Table 12), the correlation is zero or even slightly negative. Negative correlation means that there is a slight tendency for the wind power to decrease when load is increasing, and vice versa. For the load time series, there is a slight reduction in the correlation coefficients wintertime, compared with the values calculated for the whole data.

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Table 12. Correlation between the electric load and wind power production in Nordic countries during winter months January, February and December, 2000 and 2001.

SE load NO load FI load WestDK load

EastDK load

DK load Nordic load

SE load 1.00

NO load 0.92 1.00

FI load 0.78 0.72 1.00

WestDK load 0.82 0.73 0.64 1.00

EastDK load 0.85 0.76 0.65 0.97 1.00

DK load 0.84 0.75 0.65 0.99 0.99 1.00

DK wind -0.11 -0.13 0.01 0.09 0.04 0.07 -0.07

FI wind -0.18 -0.17 -0.07 0.01 -0.01 0.00 -0.14

NO wind -0.13 -0.11 -0.04 -0.01 -0.03 -0.02 -0.10

SE wind -0.10 -0.10 0.02 0.07 0.03 0.06 -0.06

Nordic wind -0.16 -0.15 -0.02 0.06 0.01 0.04 -0.11

6.3 Temperature dependence

In Northern Europe, the electricity demand is strongly correlated with outdoor temperature. This is why the correlation between wind power production and temperature is also relevant for the adequacy of power production, when determining the capacity value of wind power.

There is a clear negative correlation between the load and temperature (about –0.7), which means that when temperature drops the load increases. The only exception is Denmark, where the correlation is very weak. There is also a slight negative correlation between wind power and temperature, but for Denmark and Finland it is close to zero (Table 13).

In Figs 34-36, the temperature dependence is depicted for Finland and Denmark, as well as for the Nordic wind power as the function of Finnish temperature. The average wind power production at low temperatures of below –15 oC is somewhat lower than average in Finland, and these are the incidents of highest load (Fig. 34). The average wind power production in Denmark as well as the total Nordic wind power does not experience this kind of reduction (Fig. 35).

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Table 13. Table temperature correlation of load and wind power production in the Nordic countries, hourly data from 2000-2001

FI

temperature DK East

temperatureNO

temperature

FI temperature 1,00

DK East temperature 0,88 1,00

NO temperature 0,88 0,82 1,00

FI load -0,63 -0,56 -0,57

SE load -0,72 -0,68 -0,69

NO load -0,79 -0,76 -0,79

DK load -0,25 -0,21 -0,27

Nordic load -0,72 -0,67 -0,69

FI wind -0,09 -0,11 -0,11

SE wind -0,18 -0,14 -0,20

NO wind -0,32 -0,32 -0,28

DK wind -0,13 -0,07 -0,15

Nordic wind -0,32 -0,32 -0,28

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ak lo

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max wind powermin wind poweraverage wind powermax load

Figure 34. Temperature dependence of wind power production and load in a cold climate, example from Finland. There were 48 hours (0.2 % of time) below –23 oC and 489 hours (1.9 % of time) below –14oC during the study years 1999-2001.

61

07500

150002250030000375004500052500600006750075000

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Load

(MW

)

00,10,20,30,40,50,60,70,80,91

win

d po

wer

pro

duct

ion

(% c

ap)

Nordic load max DK wind aveNordic wind ave Nordic max wind

Figure 35.Wind power production and load in Nordic countries as a function of temperatures in Finland. Years 2000-2001.

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7000

-13 -8 -3 2 7 12 17 22 27Temperature Denmark East (C)

Load

(MW

)

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d po

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DK load max count DK wind ave Nordic wind ave

Figure 36. Temperature dependence of wind power production and load in Denmark. Years 2000-2001.

6.4 Instant penetration level of wind power

When wind power penetration is considerable, that is, wind energy produces more than 5 or 10 % of the yearly gross demand, the wind power production peaks can be clearly seen by the system. To illustrate this, instant penetration has been looked at. This is the wind energy share of a single hour’s load. This is relevant for high penetrations of wind power, when some wind energy has to be discarded in order to keep the power system stable. Example for instant penetration data is illustrated for Denmark year 2000 data, with wind power upscaled to 4000

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MW, 24 % of yearly gross demand (Fig. 37). In Table 14, periods of high instant penetration for years 2000 and 2001 are shown, upscaling the wind power production in Finland and Denmark to different levels.

Table 14. Penetration of wind power – maximum hourly penetration levels are more than 5 times higher than the average yearly value for Denmark. When the yearly penetration exceeds 20 % of gross demand, there are hours when wind power production exceeds the load (penetration > 100 %).

year

installed capacity

MW

% of yearly

demand

maximum penetration

>50 %, number of

hours

>70 %, number of

hours

>90 %, number of

hours

>100 %, number of

hours

2000 DK, MW 1740-2200 12.2 % 65 % 28 0 0 0

2000 DK, % cap 2000 12.2 % 58 % 40 0 0 0

2000 DK, % cap 3000 18.3 % 87 % 458 77 0 0

2000 DK, % cap 4000 24.3 % 116 % 1321 380 106 40

2001 DK, % cap 2000 10.1 % 58 % 24 0 0 0

2001 DK, % cap 3000 15.2 % 87 % 361 48 0 0

2001 DK, % cap 4000 20.2 % 116 % 891 277 66 24

2001 DK, % cap 5000 25.3 % 146 % 1422 691 236 138

2000 Finland 4000 11.2 % 53 % 3 0 0 0

2000 Finland 6000 16.9 % 79 % 91 4 0 0

2000 Finland 8000 22.5 % 105 % 654 58 6 3

2001 Finland 4000 9.8 % 39 % 0 0 0 0

2001 Finland 6000 14.7 % 59 % 49 0 0 0

2001 Finland 8000 19.6 % 79 % 467 18 0 0

2001 Norway 6000 13.4 % 50 % 0 0 0 0

Instant penetration from Danish real data, in MW, was 65 % in 2000 (wind 12.2 % of gross demand). The few peaks over 60 % occurred in November Sunday night, when load dropped to below 3000 MW and wind was about 1800 MW. As the capacity rose for about 300 MW during the year, and the instant penetration was reached at the end of the year, this 65 % value represents actually a situation where the yearly penetration level is more than 12 % of yearly penetration level. When looking at the Danish data as % of capacity, putting 2000 MW capacity which gives the same yearly penetration of just over 12 %, the maximum instant penetration is less than 60 %.

In West Denmark, already some hours of 100 % instant penetration of wind power have been recorded. The integration problem in Denmark involves distributed, small combined heat and power plants. This is because during cold windy winter periods the production from these weather dependent production forms sometimes exceeds the load and available transmission to neighbouring countries, when a part of the centralised thermal plants have to be in operation to provide balancing and stability for the system (Eriksen et al, 2002).

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0

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2000

3000

4000

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7000

1 49 97 145 193 241 289 337 385 433 481 529 577 625 673

hour

MW

DK wind 4000 MWDK load

Figure 37.Example of high instant penetration level of wind power. Denmark, year 2000 data, wind power upscaled to 4000 MW corresponding to 24 % of yearly gross demand. During October/Novermber, wind power production equals load during some hours in the night time.

6.5 Wind power during peak load

When studying the impact of wind power on the adequacy of power system, wind power production during the peak load periods is of crucial importance. Wind power production during the 10, 50 and 100 highest peak load hours during the example years is shown in Table 15.

Table 15. Wind power production, as % of installed capacity, during highest peak load hours.

The whole year During 10 peaks During 50 peaks During 100 peaks Average (min-max) Average (min-max) Average (min-max) Average (min-max)

Denmark 2000 24.1 % (0.0-92.7 %) 24.0 % (0.7-69.6 %) 30.5 % (0.5-86.5 %) 31.4 % (0.4-86.4 %)Denmark 2001 20.3 % (0.0-90.1 %) 36.6 % (0.2-74.4 %) 30.4 % (0.1-87.1 %) 27.8 % (0.1-87.1 %)Finland 1999 21.6 % (0.0-86.3 %) 6.9 % (4.7-10.2 %) 7.1 % (2.9-36.9 %) 8.5 % (2.2-45.9 %)Finland 2000 24.4 % (0.1-91.1 %) 36.1 % (4.4-72.4 %) 31.7 % (3.4-72.2 %) 28.7 % (3.4-75.2 %)Finland 2001 22.2 % (0.0-86.1 %) 18.5 % (3.1-37.6 %) 19.3 % (3.0-38.2 %) 17.5 % (3.0-38.2 %)Sweden 1999 24.7 % (0.0-100 %) 22.8 % (15.8-29.1 %) 20.3 % (1.9-62.8 %) 20.4 % (0.8-66.2 %)Sweden 2000 26.0 % (0.0-98.5 %) 15.8 % ( 4.5-49.8 %) 16.9 % (0.5-57.6 %) 17.6 % (0.1-77.6 %)Sweden 2001 21.9 % (0.0-95.6 %) 60.7 % (52.7-66.6 %) 35.5 % (1.4-66.6 %) 29.8 % (1.4-73.0 %)Norway 1999 29.3 % (0.3-95.5 %) 52.1 % (35.4-81.7 %) 48.5 % (14.1-81.9 %) 42.3 % (9.5-81.9 %)Norway 2000 33.8 % (0.3-93.1 %) 35.6 % ( 8.8-74.5 %) 34.8 % (8.8-74.5 %) 35.2 % (8.8-79.1 %)Norway 2001 31.4 % (0.3-92.6 %) 60.8 % (38.5-84.4 %) 54.5 % (25.5-84.4 %) 46.5 % (15.3-84.4 %)Nordic 2000 27.1 % (1.3-84.9 %) 16.4 % ( 4.1-42.1 %) 21.1 % (4.1-56.8 %) 24.4 % (4.1-71.3 %)Nordic 2001 23.4 % (1.6-90.2 %) 45.5 % (41.8-50.3 %) 35.3 % (7.5-57.1 %) 28.8 % (7.1-57.1 %)

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As wind power production from one area can be zero during wintertime, it is often assumed that wind power does not contribute to the adequacy of power production system. The so-called capacity credit of wind power is neglected. Capacity credit means the capacity that wind power can be counted on with a similar probability than conventional power units. Determining the capacity credit for wind power involves estimations of the production over a long time period, at least 10 years, as well as simulations of the system loss-of-load probability. It is therefore out of the scope of this study. However, with the hourly data from Nordic countries, it is interesting to look at the wind power production during peak load hours, based on this realised wind power production.

Years 1999 and 2001were low wind years and year 2000 was a near average wind year (chapter 5.1). However, this reflects the production during the peak load hours only for Finland. For the whole Nordic area, the production during peak load was higher than the yearly average power in 2001 and less than average in 2000.

In 1999, the highest peak occurred on 29th January, both in Finland and Sweden. The 10 highest peaks were all in two days, 28th and 29th. In Sweden (data only for South Sweden), the wind power production was close to average, but in Finland wind power production was low, less than 10 % of capacity. In Norway, the highest peak was on 15th December, all the highest peaks also within two days 15th and 16th. Wind power production (data only for Middle and North Norway) was close to average on the highest peak and higher than average during the other 9 highest peak load hours.

In 2000, the eight highest peak load hours in Sweden were on 24th January, when the wind was less than 10 % of capacity. The peaks number 9 and 10 were on 21st January, when the wind was higher than average. For Finland the highest peaks occurred on three days (21st, 24th and 25th January). For Norway and Denmark there were several days in January and December for the highest load hours, so the wind resource had also a wider range.

In 2001, the 10 highest peaks were all on 5th February in Sweden, when the wind was strong. For Norway, the peaks were during 5th and 6th, also with strong winds. For Finland, the peaks were during 3rd, 5th and 6th and the winds were part of the time low (5th morning hours) and part of the time near average production (5th evening hours and 3rd and 6th).

The average wind power production during the peak load situations (100 highest peaks) is close to average production over the year, with the exceptions of Finland 1999 and Sweden 2000 with lower production and Norway 1999 and 2001 for higher production. All in all, the table 15 gives some evidence that wind power can be counted on, with some probability greater than 0 being available during peak loads. This is also the result of several studies of wind power capacity credit (Milligan, 2000; Giebel, 2001). In Norway, previous study of wind speed data gave the result that the probability of delivering wind power during peak load was lower than on average during wintertime, but higher than on average during the whole year. Distributing the production increased the probability of wind power production during the peaks (Alm & Tallhaug, 1993).

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6.6 Hourly variations of load

The hourly variations of load follow a clear diurnal cycle (Fig. 38). The typical range of daily cycle can be estimated from the Fig.32. It is 16 000 MW for the total Nordic load, nearly 2500 MW for Denmark, 2000 MW for Finland, for Norway 2000 MW in summer and 4000 MW in winter, and for Sweden 4000 MW in summer and 6000 MW in winter.

-1000

0

1000

2000

3000

4000

5000

6000

7000

1 25 49 73 97 121 145 169Hour

MW

LoadHourly variation

Figure 38. Hourly time series of the load and its variations, Denmark first week of January, 2000.

Example of the hourly variations of load is presented in Fig.39 for Finland year 2000, for other countries and year 2001 in Appendix 4.

-1500

-1000

-500

0

500

1000

1500

1 741 1481 2221 2961 3701 4441 5181 5921 6661 7401 8141

hour

ly lo

ad v

aria

tion

(MW

)

Figure 39. Hourly load variations, example Finland 2000, chronological time series and duration curve.

Basic statistics of hourly variations are shown in Table 16. Although the load in Finland is almost twice the one in Denmark, the variations of load are nearly the same in MW.

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Table 16. Hourly variations of load in the Nordic countries.

Nordic Denmark Finland

2000 2001 2000 2001 2000 2001

max down-variation (% of peak) -7.8 % -5.4 % -13.5 % -13.4 % -8.3 % -7.2 %

max up-variation (% of peak) 10.8 % 9.0 % 18.1 % 18.3 % 9.7 % 8.2 %

max down-variation (MW) -4866 -3642 -849 -838 -985 -900

max up-variation (MW) 6698 6081 1140 1141 1144 1035

standard deviation (MW) 1446 1430 278 271 263 269

The range of hourly variations is inside ± 10 % of peak load for the total Nordic load and for Finland, for Denmark –13...+18 % of peak load. The longer variations are more than double. The maximum 12-hourly variations for Denmark are about ±2300 MW (up and down) in summer and ±2600 MW (up and down) in winter. The maximum 4-hourly upward variations are about the same, but the downward variations are –1400 MW down in summer and –1600 MW down in winter. For Finland, there is more difference between the years 2000 and 2001 for the maximum variations. The maximum 4-hourly variations are 2200...2400 MW up. The maximum downward variations are –1700 MW in summer and –2000 MW in winter. The maximum 12-hourly variations are 2600...2800 up and –2200... –2800 MW down. These are a bit larger values than the estimated typical diurnal variations, from the graphs in Fig. 33.

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7 Increase in net load variations by wind power

The additional requirements and costs of balancing the system in the operational time-scale (from several minutes to several hours) are primarily driven by fluctuations in wind generation output. Part of the fluctuations are predictable 2...40 hours ahead. The varying production pattern of wind power is changing the scheduling and unit commitment of the other production plants and use of transmission between regions – either losses or benefits are introduced to the system, compared with the situation without wind. Part of the fluctuations remain unpredicted, or mispredicted. This is what has to be handled by regulation market and balancing services (primary and secondary reserves).

To estimate the impact of wind power on power system operational reserves, it has to be studied on a control area basis. Every change in wind output does not need to be matched one-for-one by a change in another generating unit moving in the opposite direction. It is the total system aggregation that has to be balanced.

When small amounts of wind power are added to the energy system, there will be minor effects on the system behaviour. The system is dimensioned to cover the varying load, electricity consumption, at every instant. Wind power production can be seen by the system as negative consumption – when wind power is produced, the varying load that the system will see, the net load, will be sometimes slightly lower and sometimes the same as without wind. When the amount of wind is small, the effect to the net load cannot be distinguished from the difference of real load versus load prediction in normal operation of the system.

The need for regulating and load following capacity in the system increases when wind production causes larger variations to the system than the variations of existing load. The need for more flexibility in order to meet larger fluctuations in the system depends on how much wind there is in the system – what portion of consumption is covered by wind production. Also systems are different: the amount of load variations as well as the flexibility in the system differ from country to country.

In Fig.40, the same time series as in Fig.32 are shown for January, 2000, but the wind power production is subtracted from the load to show the effect of wind on the variations that the system will see. As the load in Finland varies considerably less than in Denmark, a 10 % penetration of wind would result in larger changes in the system in Finland than in Denmark. As the scale in Fig.40 is one month, 740 hours, mainly the longer term variations (12-48 h), and the changes in those, can be seen. In longer time scales there is time for the system to react to these changes – it is the time scale of electricity markets. It is clear from Fig.40, that to accommodate larger shares of wind power, good prediction models for wind power production are needed.

The short term variations were studied by hourly time series. Variations within an hour are less than the hourly variations (chapter 4.7). Therefore, hourly variations can be used as an estimate for short term variations. Because the quality of data from Norway and Sweden were judged insufficient for the hourly variation studies (chapter 5.2), the study is here made for Denmark, Finland and the total Nordic data series.

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Denmark - load and wind power data from January 2000

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1 169 337 505 673

Hour

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LoadLoad - Wind

Finland - load and upscaled wind power data (4000 MW, 11 %), January 2000

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12000

1 169 337 505 673

Hour

MW

Load - WindLoad

Figure 40. Electricity consumption (load) and the net load (wind production subtracted from load) for 2000 MW wind power in Denmark and 4000 MW wind power in Finland.

The net load hourly variations are calculated like the hourly variations (4), but now for the net load time series, where wind power production is subtracted from load:

iiiiiiiii PLPLPLNLNLNL ∆−∆=−−−=−=∆ −−− )()( 111 , (5)

where NL denotes the net load (MW), L the load (MW) and P the wind power production and i is the hour (from 2...8760 in 2001 and 2...8784 in 2000).

In Fig.41, the amount of hourly variations that the system sees is depicted, without wind (the hourly variations of the load) and with wind (the hourly variations of net load). The difference in the maximum value indicates the amount that the operating reserve capacity has to be increased. The difference in the duration curves indicates the amount that the existing reserve capacity is operating more, when wind power is added. Same capacity can in principle be used for both up and down regulation, and the variations as well as the increase should basically be symmetrical. Either up or down variations can determine the need of increase in

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the reserves. In many systems, it is the up regulation that is more critical to handle by the system.

The increase in hourly variations due to wind power is in the following chapters estimated in 3 ways. The increase in hourly variations can be taken as an estimate for increase in requirement for load following or secondary reserve in the system. The results are summarised in Table 17 at the end of 7.3.

-1500

-1000

-500

0

500

1000

1500

1 741 1481 2221 2961 3701 4441 5181 5921 6661 7401 8141

hour

varia

tion,

MW

FI load variationsFI net load variations

Figure 41. Duration curve of load variations (without wind power) and net load variations (load-wind power) example Finland, year 2000, 6000 MW wind (17 % of gross demand).

7.1 Wind power increasing the largest hourly variation in the system

Variations in net load (load minus wind production) compared to variations in load give an estimate for the needs of the system to react to large scale wind power on short term. Wind has an effect on the total amount of load following reserve capacity, if the maximum of net load variations is larger than the maximum of load variations. The largest difference in hourly variations was looked for. This is the increase of variations that the system will see.

The results for 2 years and two countries, Finland and Denmark, are presented in Fig. 42, for both the maximum upward variation (increase in downregulation) and maximum downward variation (increase in upregulation). Upscaling the wind power production and looking for the increase of maximum hourly variation in the net load time series, the curves are sometimes increasing linearly and sometimes piecewise linearly depending on what was the wind power variation in relation to the critical few hours of largest load variations. It can be seen from Fig.42, that this kind of analysis is very sensitive to the hourly data in question and can give

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very different results for different years. The increase in variations can be 0...4 % at 5 % penetration, 0...5.5 % at 10 % penetration and 2...7 % at 15 % penetration.

-2 %

0 %

2 %

4 %

6 %

8 %

10 %

0 % 5 % 10 % 15 % 20 % 25 % 30 %wind power penetration (% of gross demand)

incr

ease

in m

axim

um v

aria

tion

(% c

ap)

FI 2000 increase in downregFI 2000 increase in upregFI 2001 increase in downregFI 2001 increase upregDK 2000 increase in downregDK 2000 increase in upregDK 2001 increase in downregDK 2001 increase in upreg

Figure 42. Maximum hourly variation of net load time series compared to load time series gives the increase in variations seen by the power system. Example from upscaling wind power production data for Denmark and Finland.

7.2 Wind power increasing the hourly variations in the system

Looking at one single maximum hourly variation per year when determining the increase in the variations due to wind can overestimate the effect, especially if there is any doubt in the reliability of the data. For example, the hourly load data is based on measurements of most of the production and transmission, and part of it is estimated. This is why the largest hourly variations can be due to erroneous data.

Reserves in the power system are often determined using a probabilistic approach – to prepare for variations that are within certain limits of probability, for example covered with 99.99 % probability.

For a normally distributed probability distribution, it is known how much data is within the range of ±σ of the mean value. Taking a range of ±3σ will cover 99 %, and ±4σ will cover 99,99 % of all variations. For hourly variations, the mean value is 0.

From Fig.28 and table 16 in chapters 4 and 6 for basic statistics of wind power and load time series respectively, the standard deviation of the hourly variations can be seen. As the variations of load and wind power production can be assumed uncorrelated4, the standard deviation of net load time series can be determined by a simple square root sum of the standard deviations of load and wind power time series:

4 The use of formula (6) was checked for this data and it produced accurate results for the standard deviation of the net load

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222WLNL σσσ += (6)

Finally, the increase in the variations can be formulated as the increase in 4σ variations (Fig. 43):

)(4 LNLI σσ −= (7)

Calculating this way, I am assuming that wind power only contributes to the load following requirement by the increase due to its addition to the system, that is, wind power gets the benefit of the existing power system. In USA, different allocation methods have been elaborated (Kirby & Hirst, 2000), where the benefit of joining two varying elements is divided by the two, in this case the system would benefit a part of the addition of wind power. This would demand more from wind power than the simple increase in variations calculated here by formula 7. Both methods are numerically correct, it is a question of fairness or design of regulation payments. In the Nordic countries, different loads and production units do not pay different tariffs for the regulation burden they pose to the system. Until the reserve requirements are allocated to loads and production units it is well justified to calculate only the simple addition to reserve requirements for wind power.

0

500

1000

1500

2000

2500

-1500

-1300

-1100

-900

-700

-500

-300

-100

100

300

500

700

900

1100

1300

1500

Hourly variations (MW)

Freq

uenc

y (c

ount

)

FI load varFI net load var

52 MW152 MW

4σNL =1139 MW

4σL =1063 MW

Figure 43.An example of estimating the increase in hourly variations seen by the system. If only maximum variation is looked at, the increase is determined at the tails of the distribution (52 MW increase in up- variation and 152 MW increase in down-variation). Looking at the standard deviation of the distributions, there is a difference of 76 MW in the 4σ coverage of the variations.

72

0 %

1 %

2 %

3 %

4 %

5 %

0 % 5 % 10 % 15 % 20 % 25 % 30 %wind power penetration (% of gross demand)

incr

ease

in h

ourly

var

iatio

ns(4

sigm

a) (%

cap

)FIDKNordic

Figure 44. Increase in hourly load following requirement for wind power. Increase is relative to installed wind power capacity.

The probabilistic approach gives lower requirements than only looking at the maximum changes. The increase in variations is 0.5–1 % of installed wind power capacity at 5 % penetration (of gross demand), 1–2 % at 10 % penetration and 1.7–2.7 % at 15 % penetration. In other words, 2000 MW in Denmark increases the variations by 20 MW and the same penetration level for Finland, 4000 MW increases the variations by 80 MW. The reason why the effect of wind power on variations is smaller in Denmark than in Finland is mainly based on the relatively larger load variations in Denmark, absorbing wind variations. Finnish wind power data used here will overestimate the hourly variations of wind power to some extent, because real production from thousands of units giving 10 % of yearly electricity would see less variations than the data upscaled from 57 turbines data used here.

The same analysis was also made to the combined time series representing the Nordic wind power production. If the Nordic market area was working without bottlenecks of transmission, also the short term variations of wind power could be absorbed by the system. If the total wind power production was distributed evenly to the 4 countries, this would result in increased hourly variations in the system than the load variations today, of less than 1 % of installed capacity at 10 % wind penetration (of gross demand). In other words, 19 000 MW of wind power in the Nordic countries would increase the hourly load following requirements by about 160 MW. Also this is a conservative estimate, as half of the data, for Sweden and Norway, exaggerate the hourly variations.

7.3 Wind power increasing the unexpected hourly variations of load

The analysis in previous chapter 7.3 assumes that the hourly variations of both load and wind power production are unexpected. However, as the load with its clear diurnal pattern is easier to forecast than wind power production, this should be taken into account when analysing the increase in operating reserve requirement due to wind power (Milligan, 2003).

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For wind power, the production an hour ahead can be reasonably well forecasted by persistence, that is, taking the production level at hour i-1 for the predicted value at hour i (that is, using the hourly variation as used in previous chapters 7.2 and 7.1). The short term prediction tools can to some extent improve on this, taking into account the forecasted trend of wind speeds in the area, as well as time series techniques that have proven to work quite well for some hours ahead. The persistence is therefore a conservative estimate for the wind power production an hour ahead.

The load prediction has been studied for decades, it is well known and the predictions are quite accurate (within 1-2 %). There is a diurnal pattern and dependence of temperature in the demand for electricity. A case study for Finland year 2001 load data was carried out to estimate load forecasts. A model at VTT was used, based on calendar days of loads (from year 2000 data) and temperature (Koreneff et al, 1999; Koreneff & Kekkonen, 2000). The mean absolute error, hour ahead, was 0.7 %. This is probably lower than what is experienced in different system areas in an average (Milligan, 2003). The forecast error for the load was then compared to wind power variations. The standard deviation of forecast error was 123 MW (1 % of peak load), in comparison of 267 MW for the load hourly variations, so this method assumes that about half of the variability in load can be predicted.

Now making the same analysis as in 7.2, but using load forecast error instead of the hourly variation of load, we get the results in Table 17 for different wind power prediction error levels.

Table 17. Summary of results for the increase in hourly variations by wind power in Finland. For maximum hourly variation: if positive, the value is increasing from last hour to current hour.

Wind power MW 2000 4000 6000 Wind power penetration % of gross demand 4,9 % 9,7 % 14,6 % maximum hourly variation of wind MW 280 / -310 560 / -620 840 / -930 maximum hourly variation of load MW 1144 / -985 1144 / -985 1144 / -985 maximum hourly variation of net load MW 1138 / -1061 1191 / -1137 1385 / -1214 Increase in maximum hourly variation MW -6 / 76 47 / 152 241 / 229 stdev wind power hourly variations MW 52 103 155 stdev load hourly variations MW 266 266 266 stdev net load hourly variations MW 271 285 308 Increase in variations, 4σ, MW 20 77 167 stdev load forecast error MW 123 123 123 Increase in forecast error variations, load forecast only, 4σ, MW 41 150 298 stdev wind forecast error MW 41 82 124 Increase in forecast error variations, 4σ, MW 27 100 206

The results in Table 17 show that the results in previous chapter 7.2, based on the simple hourly variations from load and wind power time series, should be increased with 50-100 % depending on the level of wind power forecast (no forecast to hour ahead ... forecast being 20 % better than not using any). This means that when producing 10 % of yearly electricity consumption with wind power, the increase in hourly load following requirement would be

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1.5–4 % of the installed wind power, instead of 1-2 % as the result of previous chapter 7.2. More specifically, for Denmark 2000 MW of wind power would increase the load following requirement by 30-40 MW, for Finland the 4000 MW with 100–150 MW and for the Nordic countries the 19 000 MW with 240–320 MW.

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8 Summary and conclusions

The usual drawbacks of wind power from the power system point of view are that wind power production is variable, difficult to predict and cannot be counted for. However, problems of integrating intermittent sources are reduced when they are connected to large power systems, which can take advantage of natural diversity in variable sources. Large geographical spreading of wind power will reduce variability, increase predictability and decrease the occasions with near zero or peak output.

High wind power penetration will increase the flexibility needed in the system. The magnitude of the power system impacts of wind power depend on how large a share is produced by wind power, as well as on the power system in question. When we are studying the incremental effects that varying wind power production imposes on the power system, it is important to study the system as a whole: only the net imbalances have to be balanced by the system.

In this study, the focus is on the hourly time scale impacts on the power system, based on real wind power production and synchronous hourly load data. Example years of 2000 and 2001 were studied. Year 2000 was close to average wind year (95 % of long term average production in Denmark, 97 % in Finland and 102 % in Sweden). Year 2001 was clearly less windy than average (80 % of long term average in Denmark, 87 % in Finland and 88 % in Sweden).

Average production in the Nordic countries is highest in Norway (31–34 % of installed capacity), and about 22–24 % of capacity for the other countries during the example years. The seasonal variation was clearly present in the data sets, more production in winter than in summer. Wind power production in Denmark and Sweden experience a more pronounced diurnal variation, whereas the sites in the northern part of Finland, Sweden and Norway do not experience any detectable diurnal variation.

From the combined production in the Nordic countries, it can be seen that as wind power production comes from geographically distributed wind farms, the total production never reaches the total installed capacity and it is hardly ever totally calm. Production above 50 % of rated capacity is rare in summer and production above 75 % is rare in winter. The lowest hourly production was 1.3 % of capacity. The production was below 5 % of capacity about 2 % of time. There was not a significant difference between the calm periods in years 2000 and 2001. For the peak production, defined as above 75 % of capacity, there were more peaks during year 2000 than during year 2001.

Correlation for hourly wind power production is strong (more than 0.7) for distances of less than 100 km and becomes weak (below 0.5) with distances above 200-500 km. The large scale wind power production of the countries is correlated between Denmark and Sweden, and weakly correlated between the other countries. No correlation between the hourly variations of wind power production in the countries was seen in the data.

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The hourly variations of large scale wind power production are about 90 % of time between ± 5 % of capacity and 99 % of time between ± 10 % of capacity. For the total Nordic time series the hourly variations are about 98 % of time between ± 5 % of capacity.

The representativity of the constructed wind power data sets for Finland, Norway and Sweden was estimated based on the statistical properties of existing large scale wind power production data from Denmark. An hourly time series of large scale wind power production should have standard deviation of the production series less than 20 % of capacity, maximum hourly production less than 100 % (85...95 % depending on how large the area in question is), duration of calms limited or non existent, standard deviation of the hourly variation series less than 3 % of capacity and the hourly variations in between ±20 % of capacity, or even less if the area is larger than the size of Denmark (300 x 200 km2). According to these criteria, the data set for Finland is quite representative for large scale wind power production, but the data sets for Norway and Sweden are not. This is mainly discovered by the hourly variations of the production time series which is not as smooth as a large scale wind power production from thousands of turbines would be. Combining the 4 data sets to form a Nordic data set probably overestimates the variations some, but a continuing smoothing effect can be seen so it has been considered representative for the study of large scale wind power.

Electrical load is characterised by a daily pattern, higher on weekdays than weekends. In addition to daily cycles, strong temperature dependence can be seen in the Nordic countries. Wind power has a slightly positive correlation with the load, especially in Denmark. However, during the winter months, the correlation is practically non existent. The average wind power production at low temperatures of below –15 oC is somewhat lower than average in Finland, and these are the incidents of highest load. However, the average wind power production in Denmark as well as the total Nordic wind power does not experience this kind of reduction. The average wind power production in times of the highest peak load hours was near average production in the example years 2000 and 2001.

The need for more flexibility in the electricity system, due to short term variations of wind power, were estimated with the hourly time series for wind production and load. Net load variations (load minus wind production) compared to load variations give an estimate for the needs of the system to react to large scale wind power. The increase in hourly variations was estimated for Denmark and Finland. An analysis based on only the maximum hourly variation was found to be very sensitive to the hourly data in question; giving different results for different years of data, depending on what was the wind power change during the critical hours of maximum load changes. A probabilistic approach gave estimates for the range of variations, from the standard deviation (σ) values, taking ±4σ as the range that covers most variations (99.99 % of all variations are inside this range). The results were that at 5 % wind power penetration (of gross demand) the increase of variations was 0.5–1 % of installed wind power capacity, at 10 % penetration 1–2 % and at 15 % penetration1.7–.2.7 % of installed wind power capacity. The effect of wind power on variations was smaller in Denmark than in Finland. This is mainly based on the relatively larger load variations in Denmark, absorbing wind variations. If the Nordic electricity market area would be working without bottlenecks of transmission, 10 % of wind energy distributed in the area would require extra flexibility of less than 1 % of installed capacity at 10 % wind penetration (of gross demand). In other words, 19 000 MW of wind power in the Nordic countries would increase the hourly

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variations by about 160 MW. However, large scale wind power production could also increase the bottlenecks.

The estimates of increase in hourly variations do not take into account the fact that the variations are easier to predict for the load than for wind power production. To estimate the effect of load and wind forecasts to these analyses, a case for Finland year 2001 load estimates was run based on the information from previous, year 2000 load data. This analysis showed that the results above, based on the simple hourly variations from load and wind power time series, should be increased by 50–100 % depending on the level of wind power forecast (no forecast versus forecast being 20 % better than not using any). This means that when producing 10 % of yearly electricity consumption with wind power, the increase in hourly variations would be 1.5–4 % of the installed wind power, instead of 1–2 % neglecting the forecasts. More specifically, for Denmark 2000 MW of wind power would increase the hourly variations by 30–40 MW, for Finland 4000 MW wind power by 120–160 MW and for the Nordic countries 19 000 MW wind power by 240–320 MW. This can be used as an estimate for the increase in requirements for load following, or secondary reserve for the power system due to wind power.

The estimation is based on hourly wind power and load data from two years. The years were less than average wind years, meaning that the hourly variations are probably underestimated. However, the smoothing effect of thousands of wind turbines at hundreds of wind farm sites is also underestimated by the wind power data sets used here. This means that the estimates for the variations of wind power production are probably still somewhat conservative. Another basic assumption is that the hourly variations give an estimate of the short term variations relevant for operating reserve of the power system. Secondary reserve is operated in 10–15 minutes, and hourly data is used here, as 15 minute data is very limited and would not allow for a large scale system studies. However, as the wind varies less within an hour than on hourly basis, using hourly data would not underestimate the effects.

The conclusion of this study is that the hourly variations of large scale wind power will be seen as an increase in the hourly variations and thus operating reserve requirements of the power system. The impact will increase the larger share of gross demand is produced by wind power. At a 10 % wind power penetration level this is estimated as 1.5–4 % of installed wind capacity, taking into account that load variations are more predictable than wind power variations.

The cost of this increase in operating reserves, as well as the electricity market studies, focusing on longer term variations of wind power, are subjects for future work.

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APPENDIX 1 1

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APPENDIX 1 2

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Figure 1. Hourly wind power production time series for Denmark and Finland, example years 2000 and 2001.The production values as % of capacity (y-axis). On x-axis, the hour of the year is marked at 740 hour (aboutone month) intervals. Average production for the whole period 2000-2001 is denoted in the title.

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APPENDIX 1 3Sw eden, average 24 %

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Figure 2.Hourly wind power production time series for Norway and Sweden, example years 2000 and 2001. Theproduction values as % of capacity (y-axis). On x-axis, the hour of the year is marked at 740 hour (about amonth) intervals. Average production for the whole period 2000-2001 is denoted in the title.

APPENDIX 1 4

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Figure 3. Hourly wind power production time series for example years 2000 and 2001, assuming same capacityin all 4 countries Sweden, Norway, Denmark and Finland. The production values as % of capacity (y-axis). Onx-axis, the hour of the year is marked at 740 hour (about a month) intervals. Average production for the wholeperiod 2000-2001 is denoted in the title.

APPENDIX 2 1

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Figure 1.Time series for hourly variations of wind power production for Denmark and Finland, example years2000 and 2001. Positive means increasing and negative decreasing wind power production.

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Figure 2.Time series for hourly variations of wind power production for Sweden and Norway, example years2000 and 2001. Positive means increasing and negative decreasing wind power production.

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Figure 1.Hourly electricity consumption, the load, for Denmark, Finland and Norway, example year 2000.

APPENDIX 3 2

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Figure 2. Hourly electricity consumption, the load, for Norway and the combined load for the 4 Nordic countriesDenmark, Finland, Norway and Sweden , example year 2000.

APPENDIX 4 1

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Figure 1. Time series of hourly load variations, for Denmark, Finland and the combined load for the 4 Nordiccountries Denmark, Finland, Norway and Sweden , example year 2000.

APPENDIX 4 2

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Figure 2. Time series of hourly load variations, for Denmark, Finland and the combined load for the 4 Nordiccountries Denmark, Finland, Norway and Sweden , example year 2001.