Meer consumptie van windenergie met evenveel wind(turbines) mogelijk?

Michiel

Kenis

De masterproef kadert binnen de huidige maatschappelijke uitdaging richting duurzame energieproductie, met een focus op windenergieproductie. Het onderzoekt potentiële verbeteringen in de penetratie van windenergie in de groothandelselektriciteitsmarkt, meer bepaald de day-ahead

elektriciteitsmarkt. Hierbij onderzoekt de masterproef de invloed van een regelgevend kader waarin individuele windenergievoorspellingen van diverse windenergieproducenten onderling uitgewisseld en gedeeld kunnen worden.

Specifiek wordt een model geconstrueerd dat het strategische energiemanagement van elke energieproducent in de groothandelselektriciteitsmarkt simuleert. Hierbij brengt elke producent een strategisch offer uit op de day-ahead markt aan een bepaalde strategische minimumprijs. Het principe van vraag en aanbod zorgt tot slot voor een correcte marktwerking. De masterproef gaat echter nog een stap verder en breidt het model verder uit, waarbij de hoeveelheid informatie die een energieproducent bezit over het strategisch gedrag van zijn concurrerende energieproducenten een rol speelt.

Dit model wordt toegepast op een fictief elektriciteitssysteem, geïnspireerd door de Belgische Belpex markt. Naast waardevolle inzichten in het strategisch biedgedrag van energieproducenten op de markt, worden de principes van marktmacht en imperfecte competitie zichtbaar. Wanneer elke energieproducent het strategisch gedrag van zijn concurrerende energieproducenten kan simuleren omdat elke energieproducent zijn individuele productievoorspelling deelt, dan zal de penetratie van windenergie in de day-ahead elektriciteitsmarkt lager zijn. Dit omwille van lagere offers van de producenten op de markt, zodat een hogere elektriciteitsprijs veroorzaakt kan worden. Additioneel wordt de geldigheid van bovenstaande conclusie getest bij veranderende parameters (risicoaversie, onzekerheid in windenergievoorspellingen,...).

Onderzoek naar de invloed van informatie-delende energieproducenten is relatief recent. De masterproef stelt mede daarom een uniek model voorop in de academische literatuur. Het model kan dienen voor energieproducenten die hun strategisch energiemanagement wensen te optimaliseren (via een verhoogde gerealiseerde winst) door rekening te houden met eventuele informatie over concurrerende producenten via historische data of geografische correlaties.

Bibliografie

[1] S. Gabriel, A. Conejo, J. Fuller, B. Hobbs, and C. Ruiz, Complementarity modeling in energy markets, vol. 180. Springer Science and Business Media, 2012.

[2] L. Exizidis, J. Kazempour, P. Pinson, Z. De Greve, and F. Vallee, “Sharing wind power forecasts in electricity markets: A numerical analysis,” Applied Energy, vol. 176, pp. 65–73, 2016.

[3] L. Exizidis, J. Kazempour, P. Pinson, Z. De Greve, and F. Vallee, “Impact of public aggregate wind forecasts on electricity market outcomes,” IEEE Transactions on Sustainable Energy, vol. 8, no. 4, pp. 1394–1405, 2017.

[4] E. Delarue, “Modeling electricity generation systems: Development and applicationofelectricitygenerationoptimizationandsimulationmodels,withparticular focus on co2 emissions,” 2009. Ph.D. dissertation, KU Leuven, Leuven, Belgium.

[5] V. Masson-Delmotte, P. Zhai, H. Portner, D. Roberts, J. Skea, P. Shukla, Pirani, W. Moufouma-Okia, C. Pean, R. Pidcock, S. Connors, J. Matthews, Y. Chen, X. Zhou, M. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterﬁeld, 2018: Global warming of 1.5C. An IPCC Special Report on the impacts of global warming of 1.5C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and eﬀorts to eradicate poverty. IPCC, 2018. Tech. Rep.

[6]EuropeanCommission,“2020climateandenergypackage.”https://ec.europa. eu/clima/policies/strategies/2020_en. [Online; accessed 13-May-2019].

[7] European Commission, “2050 long-term strategy.” https://ec.europa.eu/ clima/policies/strategies/2050_en. [Online; accessed 13-May-2019].

[8] M. Aglietta and G. Bai, “China’s 13th ﬁve-year plan. in pursuit of a moderately prosperous society,” CEPII Research Center, vol. 1, 2016.

[9] T. Brijs, “Electricity storage participation and modeling in short-term electricity markets,” Ph.D. dissertation, KU Leuven, Leuven, Belgium.

[10] Elia, “Wind-power generation data.” http://www.elia.be/en/grid-data/ power-generation/wind-power. [Online; accessed 13-May-2019].

[11] European Commission, “Commission regulation (eu) no 543/2013.” http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX: 32013R0543&qid=1477468359304&from=EN. [Online; accessed 13-May-2019].

[12] B. Hobbs, C. Metzler, and J. Pang, “Strategic gaming analysis for electric power systems: An mpec approach,” IEEE Transactions on Power Systems, vol. 15, no. 2, pp. 638–645, 2000.

[13] C. Ruiz and A. Conejo, “Pool strategy of a producer with endogenous formation of locational marginal prices,” IEEE Transactions on Power Systems, vol. 24, no. 4, pp. 1855–1866, 2009.

[14] L. Baringo and A. Conejo, “Strategic oﬀering for a wind power producer,” IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4645–4654, 2013.

[15] T. Dai and W. Qiao, “Trading wind power in a competitive electricity market using stochastic programming and game theory,” IEEE Transactions on Sustainable Energy, vol. 4, no. 3, pp. 805–815, 2013.

[16] L. Baringo and A. Conejo, “Oﬀering strategy of wind-power producer: A multistage risk-constrained approach,” IEEE Transactions on Power Systems, vol. 31, no. 2, pp. 1420–1429, 2016.

[17] T. Dai and W. Qiao, “Optimal bidding strategy of a strategic wind power producer in the short-term market,” IEEE Transactions on Sustainable Energy, vol. 6, no. 3, pp. 707–719, 2015.

[18] R. Sioshansi, “When energy storage reduces social welfare,” Energy Economics, vol. 45, pp. 106–116, 2014.

[19] A. Shahmohammadi, R. Sioshansi, A. Conejo, and S. Afsharnia, “Market equilibria and interactions between strategic generation, wind, and storage,” Applied Energy, vol. 220, pp. 876–892, 2018.

[20] A. Shahmohammadi, R. Sioshansi, A. Conejo, and S. Afsharnia, “The role of energy storage in mitigating ramping ineﬃciencies caused by variable renewable generation,” Energy Conversion and Management, vol. 162, pp. 307–320, 2018.

[21] P. Zou, Q. Chen, Q. Xia, G. He, C. Kang, and A. Conejo, “Pool equilibria including strategic storage,” Applied Energy, vol. 177, pp. 260–270, 2016.

[22] M. Banaei, M. Buygi, and H. Zareipour, “Impacts of strategic bidding of wind power producers on electricity markets,” IEEE Transactions on Sustainable Energy, vol. 31, no. 6, pp. 4544–4553, 2016.

[23] Gurobi, “Gurobi optimizer.” http://www.gurobi.com/products/ gurobi-optimizer. [Online; accessed 14-May-2019].

[24] A. Lahouar and J. Slama, “Hour-ahead wind power forecast based on random forests,” Renewable Energy, vol. 109, pp. 529–541, 2017.

[25] X. Zhao, S. Wang, and T. Li, “Review of evaluation criteria and main methods of wind power forecasting,” Energy Procedia, vol. 12, pp. 761–769, 2011.

[26] J. Jung and R. Broadwater, “Current status and future advances for wind speed and power forecasting,” Renewable and Sustainable Energy Reviews, vol. 31, no. 16, pp. 762–777, 2014.

[27] M. Lei, L. Shiyan, L. Chuanwen, Hongling, and Z. Yan, “A review on the forecasting of wind speed and generated power,” Renewable and Sustainable Energy Reviews, vol. 13, no. 4, pp. 915–920, 2009.

[28] A. Tascikaraoglu and M. Uzunoglu, “A review of combined approaches for prediction of short-term wind speed and power,” Renewable and Sustainable Energy Reviews, vol. 34, no. 12, pp. 243–254, 2014.

[29] M. Ranaboldo, G. Giebel, and B. Codina, “Implementation of a model output statistics based on meteorological variable screening for short-term wind power forecast,” Wind Energy, vol. 16, no. 6, pp. 811–826, 2013.

[30] J. Torres, A. Garcia, M. Blas, and A. Francisco, “Forecast of hourly average wind speed with arma models in navarre (spain),” Solar Energy, vol. 79, no. 1, pp. 65–77, 2005.

[31] A. Sfetsos, “A novel approach for the forecasting of mean hourly wind speed time series,” Renewable Energy, vol. 27, no. 2, pp. 163–174, 2002.

[32] L. Xie, Y. Gu, X. Zhu, and M. Genton, “Short-term spatio-temporal wind power forecast in robust look-ahead power system dispatch,” IEEE Transactions on Smart Grids, vol. 5, no. 1, pp. 511–520, 2014.

[33] Y. Heredia, V. Nunez, and J. Shulcloper, Progress in Artiﬁcial Intelligence and Pattern Recognition: 6th International Workshop, IWAIPR 2018, Havana, Cuba, Proceedings. Springer International. September 2426, 2018.

[34] G. Chang, H. Lu, P. Wang, Y. Chang, and Y. Lee, “Gaussian mixture modelbased neural network for short-term wind power forecast,” International Transactions on Electrical Energy Systems, vol. 27, no. 6, pp. 2320–2332, 2017.

[35] F. Pelletier, C. Masson, and A. Tahan, “Wind turbine power curve modelling using artiﬁcial neural network,” Renewable Energy, vol. 89, pp. 207–214, 2016.

[36] L. Yang, M. He, J. Zhang, and V. Vittal, “Support-vector-machine-enhanced markov model for short-term wind power forecast,” IEEE Transactions on Sustainable Energy, vol. 6, no. 3, pp. 791–799, 2015.

[37] Q. Xu, D. He, N. Zhang, C. Kang, Q. Xia, J. Bai, and H. J., “A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining,” IEEE Transactions on Sustainable Energy, vol. 6, no. 4, pp. 1283–1291, 2015.

[38] K. Methaprayoon, C. Yingvivatanapong, W. Lee, and L. JR., “An integration of ann wind power estimation into unit commitment considering the forecasting uncertainty,” IEEE Transactions on Industrial Applications, vol. 43, no. 6, pp. 1441–1448, 2007.

[39] V. Pappala, I. Erlich, K. Rohrig, and J. Dobschinski, “A stochastic model for the optimal operation of a wind-thermal power system,” IEEE Transactions on Power Systems, vol. 24, no. 20, pp. 940–950, 2009.

[40] H. Bludszuweit, J. Dominguez-Navarro, and A. Llombart, “Statistical analysis of wind power forecast error,” IEEE Transactions on Power Systems, vol. 23, no. 3, pp. 983–991, 2008.

[41] M. Lange, “On the uncertainty of wind power predictions: Analysis of the forecastaccuracyandstatisticaldistributionoferrors,”SolarEnergyEngineering, vol. 127, no. 2, pp. 177–184, 2005.

[42] M.Zugno,J.Morales,P.Pinson,andH.Madsen,“Poolstrategyofaprice-maker wind power producer,” IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 3440–3450, 2013.

[43] A. Fabbri, T. Roman, J. Abbad, and V. Quezada, “Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market,” IEEE Transactions on Power Systems, vol. 20, no. 3, pp. 1440–1446, 2005.

[44] K. Bruninx and E. Delarue, “A statistical description of the error on wind power forecasts for probabilistic reserve sizing,” IEEE Transactions on Sustainable Energy, vol. 5, no. 3, pp. 995–1002, 2014.

[45] J.Wu, B.Zhang, H.Li, Z.Li, Y.Chen, andX.Miao, “Statisticaldistributionfor wind power forecast error and its application to determine optimal size of energy storage system,” Electrical Power and Energy Systems, vol. 55, pp. 100–107, 2014.

[46] Z.Zhang, Y.Sun, D.Gao, J.Lin, andC.L., “Aversatileprobabilitydistribution model for wind power forecast errors and its application in economic dispatch,” IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 3114–3125, 2013.

[47] B. Hodge and M. Milligan, “Wind power forecasting error distributions over multiple timescales,” 2011 IEEE Power and Energy Society General Meeting, pp. 1–8, 2011. 24-29 July 2011, San Diego, CA, USA.

[48] European Commission, “Energy, climate change, environment.” https:// ec.europa.eu/info/energy-climate-change-environment_en. [Online; accessed 15-May-2019].

[49] KU Leuven Energy Institute, “Ei-fact sheet: The current electricity market design in europe.” https://set.kuleuven.be/ei/factsheets. [Online; accessed 15-May-2019].

[50] C. Dent, J. Bialek, and B. Hobbs, “Opportunity cost bidding by wind generators in forward markets: Analytical results,” IEEE Transactions on Power Systems, vol. 26, no. 3, pp. 1600–1608, 2011.

[51] M.Rao, M.Chowdhury, Y.Zhao, T.Javidi, andA.Goldsmith, “Valueofstorage for wind power producers in forward power markets,” 2015 American Control Conference, Chicago, USA, pp. 5686–5691. 1-3 July 2015.

[52] A. Skajaa, K. Edlund, and J. Morales, “Intraday trading of wind energy,” IEEE Transactions on Power Systems, vol. 30, no. 6, pp. 3181–3189, 2015.

[53] Ventosa, Baillo, Ramos, , and Rivier, “Electricity market modeling trends,” Energy Policy, vol. 33, no. 7, pp. 897–913, 2005.

[54] S. Bigerna, “Electricity demand elasticity in italy,” Atlantic Economic Journal, vol. 40, no. 4, pp. 439–440, 2012.

[55] A.Schillemans,“Optimalbiddingstrategiesforlarge-scaleenergystoragesystem owners,” 2018. Master Thesis, KU Leuven, Leuven, Belgium.

[56] V. Ungureanu, Pareto-Nash-Stackelberg Game and Control Theory: Intelligent Paradigms and Applications, vol. 89. Springer International. Smart Innovation, System and Technologies, 2018.

[57] J. Morales, A. Conejo, and J. Perez-Ruiz, “Short-term trading for a wind power producer,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 554–564, 2010.

[58] Croatian Energy Regulatory Agency, “Methodology for determining the price for the settlement of balancing energy.,” Tech report, 2016. Zagreb, Croatia.

[59] L. Exizidis, Z. De Greve, F. Vallee, and J. Lobry, “A competitive framework to regulateday-aheadwindpowerpredictionsforoperationalplanninginelectricity markets,” 8th International Conference on Electrical and Computer Engineering, Dhaka, Bangladesh, 2014. 20-22 December 2014.

[60] EPEXspot Belgium, “Epex spot dam.” https://www.belpex.be/ trading-clearing/dam/. [Online; accessed 15-May-2019].

[61] H. Hoschle, “Capacity mechanisms in future electricity markets,” 2018. Ph.D. dissertation, KU Leuven, Leuven, Belgium.

[62] A. Dreves, F. Facchinei, C. Kanzow, and S. Sagratella, “On the solution of the kkt conditions of generalized nash equilibrium problems,” SIAM Journal on Optimization, vol. 21, no. 3, pp. 1082–1108, 2011.

[63] J. Fortuny-Amat and B. McCarl, “A representation and economic interpretation of a two-level programming problem,” The Journal of the Operational Research Society, vol. 32, no. 9, pp. 783–792, 1981.

[64] D. Luenberger and Y. Ye, Linear and nonlinear programming, vol. 116. Springer US. International Series in Operations Research and Management Science, 2016.

[65] Julia, “Julia language.” https://julialang.org/. [Online; accessed 19-May2019].

[66] K. Bruninx, “Improved modeling of unit commitment decisions under uncertainty,” 2016. Ph.D. dissertation, KU Leuven, Leuven, Belgium.