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Production Planning of a Wind Farm Based on Wind Speed Forecasting

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Production Planning of a Wind Farm Based on Wind Speed Forecasting Lari J¨arvenp¨a¨a Production Planning of a Wind Farm Based on Wind Speed Forecasting Faculty of Electronics, Communications and Automation Thesis submitted for examination for the degree of Master of Science in Technology. Espoo 12.4.2010 Thesis supervisor: Prof. Heikki Koivo Thesis instructor: M.Sc.(Tech.) Arto Tuominen Aalto University School of Science A? and Technology aalto university abstract of the school of science and technology master's thesis Author: Lari J¨arvenp¨a¨a Title: Production Planning of a Wind Farm Based on Wind Speed Forecasting Date: 12.4.2010 Language: English Number of pages: 13+93 Faculty of Electronics, Communications and Automation Department of Automation and Systems Technology Professorship: Systems Technology Code: AS-74 Supervisor: Prof. Heikki Koivo Instructor: M.Sc.(Tech.) Arto Tuominen In recent years, the use of wind power has expanded significantly in many countries and shall continue on that track in the future as well. In the everyday operation of wind power producers, predicting the future output of wind power is of key interest. The structure of electricity markets imposes upon the producer to fore- cast the future production level, which thus forces the producer to be subject to possible deviations. These deviations lead to economic losses; therefore there is a strong need to predict as accurately as possible. After briefly introducing wind power in general as well as explaining the structure of electricity markets, this thesis presents some of the current state-of-the-art wind power prediction models and uses as a reference the current forecasting performance from a wind farm case study. The reference model is based directly on a turbine power curve. Based on these results it then develops forecasting models for making short-term (up to two days) predictions of wind power output. The main focus is on advanced artificial intelligence-based autoregressive models enhanced with numerical weather prediction information. The prediction of wind speed and production is rather intractable by nature; therefore there cannot be any model that would lead to zero errors of prediction. For evaluating this omnipresent uncertainty, methods for estimating prediction errors which would lead to more efficient wind power trading and management are also presented and tested. The performance of the implemented models is measured and compared to the reference model. The results show that the use of an advanced forecasting model decreases the prediction errors and thus also the related economical losses. The results have a general value; nevertheless they, as well as the model configurations presented here, have to be considered as case-specific. Keywords: wind farm, wind power prediction, wind speed forecasting, prediction uncertainty, electricity markets, support vector machine aalto-yliopisto diplomityon¨ teknillinen korkeakoulu tiivistelma¨ Tekij¨a:Lari J¨arvenp¨a¨a Ty¨onnimi: Tuulivoimapuiston tuotannonsuunnittelu tuuliennusteiden pohjalta P¨aiv¨am¨a¨ar¨a:12.4.2010 Kieli: Englanti Sivum¨a¨ar¨a:13+93 Elektroniikan, tietoliikenteen ja automaation tiedekunta Automaatio- ja systeemitekniikan laitos Professuuri: Systeemitekniikka Koodi: AS-74 Valvoja: Prof. Heikki Koivo Ohjaaja: DI Arto Tuominen Viime vuosina tuulivoiman k¨aytt¨o on lis¨a¨antynyt merkitt¨av¨asti monessa maassa ja saman trendin oletetaan jatkuvan my¨os tulevaisuudessa. Tulevan tuotannon ennustaminen on tuulivoiman tuottajille ensiarvoisen t¨arke¨a¨a, sill¨a s¨ahk¨omarkkinoiden rakenne pakottaa tuulivoimatuottajat ennustamaan tulevan tuotantonsa etuk¨ateen.Tuulivoimatuottajien on siis hyv¨aksytt¨av¨a,ett¨amahdol- liset ennustepoikkeamat johtavat taloudellisiin tappioihin. T¨am¨anvuoksi tuotta- jilla on tarve tehd¨aennusteensa mahdollisimman tarkasti. T¨am¨adiplomity¨oesittelee ensin yleisesti tuulivoimaa ja s¨ahk¨omarkkinoidenraken- netta. T¨am¨an j¨alkeen ty¨oss¨a k¨ayd¨a¨an l¨api useita viimeisimpi¨a tuulituotan- non ennustemalleja. Lis¨aksity¨oss¨aesitell¨a¨aner¨a¨antuulivoimapuiston nykyinen suoraan tuuliturbiinin tehok¨ayr¨a¨anperustuva ennustemenetelm¨aja arvioidaan sen tarkkuutta. N¨aihintuloksiin perustuen ty¨oss¨akehitet¨a¨anennustemalleja, joiden avulla voidaan tehd¨alyhyen ajan (alle kahden p¨aiv¨an)tuulituotannon en- nusteita. P¨a¨aasiallisenamallien luokkana ovat kehittyneet teko¨alyyn perustuvat autoregressiiviset mallit, joita t¨aydennet¨a¨annumeeristen s¨a¨aennusteiden infor- maatiolla. Tuulennopeuden ja tuotannon ennustaminen on kuitenkin varsin hankalaa, joten ei ole olemassa mallia, jonka avulla p¨a¨ast¨aisiint¨aydellisiinennusteisiin. Jotta ennusteen ep¨avarmuutta voitaisiin arvioida ja tuulituotantoa myyd¨a ja hal- lita tehokkaammin, t¨am¨aty¨oesittelee my¨osmenetelmi¨aennustevirheiden esti- moimiseksi. K¨aytt¨o¨onotettujenmallien suorituskyky¨amitataan ja verrataan verrokkimalliin. Tulokset osoittavat, ett¨akehittyneiden ennustemallien avulla ennustevirheet ja n¨aist¨ajohtuvat taloudelliset tappiot pienenev¨at.T¨ass¨aty¨oss¨aesitellyill¨atuloksilla ja mallirakenteilla on yleist¨aarvoa, mutta niit¨apit¨a¨atulkita tapauskohtaisesti. Avainsanat: tuulivoimapuisto, tuulivoimaennustaminen, tuulennopeuden en- nustaminen, ennusteen ep¨avarmuus, s¨ahk¨omarkkinat, tukivek- torikone iv Preface Yes, 'n' how many times can a man turn his head, Pretending he just doesn't see? The answer, my friend, is blowin' in the wind, The answer is blowin' in the wind. { Bob Dylan This LATEX-written Master's thesis is made as an assignment for the power pro- curement department of the Finnish power producer Pohjolan Voima. Today, the company possesses the largest share of wind power production resources in Finland; in addition it also has vast wind power investment plans sighted for the future. To the best of my knowledge, this thesis is the first one written specifically for the needs of a Finnish wind power producer. In this way, the thesis for its part collaborates to the scientific work done in the field and serves as a guidebook for other wind power operators as well. My hope is that this continues to be a guide in the future as well, as a high level of generality is preserved throughout the thesis. There are several persons to whom I wish to express my sincere gratitude. First, I would like to thank my instructor Arto Tuominen, whose expertise especially in the area of electricity markets has been of great value in all phases of the work. Secondly, my supervisor professor, Ph.D. Heikki Koivo deserves the highest praise for being very encouraging, and providing me with new ideas and thoughts. I would also like to thank D.Sc. Vesa Hasu for providing me with his expertise on meteorology. My superior Jussi Hintikka and my colleagues Mikko Rajala, Anu Hyv¨onen,Lauri Luopaj¨arviand Raine Laaksonen also deserve my special recognition. Working with such enthusiastic people has been a true pleasure for me. Finally, I wish to express my humble gratitude and respect towards my dear family and friends. It is with your love, care and support that I have been able to come this far. I am convinced that you will keep me going in the future as well, wherever I will be. Helsinki, April 12, 2010 Lari J¨arvenp¨a¨a v Contents Abstract ii Abstract (in Finnish) iii Preface iv Contentsv Symbols and abbreviations vii List of figures xi List of tables xiii 1 Introduction1 2 Wind power4 2.1 Wind as a source of electricity......................4 2.2 Wind power intermittence........................8 3 Electricity markets 11 3.1 Electricity wholesale markets....................... 11 3.1.1 Spot markets........................... 12 3.1.2 Intraday markets......................... 13 3.2 Balancing supply and demand...................... 15 3.2.1 Regulatory market........................ 15 3.2.2 Balancing cost........................... 16 3.3 Wind power producer in electricity markets.............. 18 3.4 Trading under uncertainty........................ 20 4 Wind power forecasting methods 26 4.1 Numerical weather prediction...................... 26 4.2 Wind power forecasting.......................... 28 4.3 Literature review of wind power forecasting methods......... 32 4.3.1 Na¨ıve reference methods..................... 32 4.3.2 Physical methods......................... 33 vi 4.3.3 Statistical and AI methods.................... 34 4.3.4 Hybrid and ensemble methods.................. 38 4.3.5 Probabilistic methods....................... 41 5 Case study on a real wind farm 44 5.1 Data collection.............................. 44 5.2 Wind characteristics at a real site.................... 45 5.3 Evaluation of current wind power forecasting procedure........ 50 6 Implementation of an advanced wind power forecasting procedure 58 6.1 Design paradigms............................. 58 6.2 Description of the models........................ 60 6.3 Implementation.............................. 64 7 Results 67 7.1 Performance of the implemented models vis-`a-vis reference model.............................. 67 7.2 Effect on net revenue........................... 77 7.3 Estimating the uncertainty........................ 78 8 Conclusions 82 8.1 Summary................................. 82 8.2 Guidelines for future........................... 83 References 87 vii Symbols and abbreviations Symbols w wind speed d wind direction E energy m mass ρ air density A area (of a wind turbine rotor plane) L length $ pressure y_ time derivative of y η efficiency y∗ optimal value of y ν Weibull distribution shape parameter & Weibull distribution scale parameter a price area h hour D(x) inverse demand function, gives
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