Introduction of an efficient incentive for investment in wind turbines based on system dynamics modelling approach

Document Type : Original Article

Authors

Department of electrical engineering, University of Guilan, Rasht, Iran

Abstract

Due to the stochastic nature of wind energy, allocating an appropriate investment incentive for wind generation technology (WGT) is a complicated issue. We propose an improvement on the traditional incentive, known as capacity payment mechanism (CPM), to reward the wind generators based on their performance exogenously affected by the wind energy potential of the location where the turbines are installed, and therefore, lead the investments towards locations with more generation potential. In CPM, a part of investment cost of each generator is recovered through fixed payments. However, in our proposal, wind generators are rewarded according to dynamic forecasts of the wind energy potential of the wind farm where they are located. We use an auto-regressive moving average (ARMA) model to forecast the wind speed fluctuations in long-term while capturing the auto-correlation of wind velocity variation in consecutive time intervals. Using the system dynamics (SD) modelling approach a competitive electricity market is designed to examine the efficiency of the proposed incentive. Performing a simulation analysis, we conclude that while a fixed CPM for wind generation can decrease the loss of load durations and average prices in long-term, the proposed improvement can provide quite similar results more efficiently.

Keywords

References

[1] F. Olsina, F. Garcés, H. Haubrich, “Modeling long-term dynamics of electricity markets”, Energy Policy, vol. 34, no. 12, pp. 1411-1433, 2006.
[2] J. Williams, R. Ghanadan, “Electricity reform in developing and transition countries: A reappraisal”, Energy, vol. 31 no. 6-7, pp. 815-844, 2006.
[3] L. de Vries, R. Hakvoort, “The Question of Generation Adequacy in Liberalized Electricity Markets”, In 26th Annual Conference International Association for Energy Economics (IAEE), June 2003, Prague, Czech, pp. 1-10.
[4] M. Fürsch, S. Nagl, D. Lindenberger, “Optimization of power plant investments under uncertain renewable energy deployment paths: a multistage stochastic programming approach”, Energy Systems, vol. 5, pp. 85-121, 2014.
[5] A. Ford, “Cycles in competitive electricity markets: A simulation study of the western United States”, Energy Policy, vol. 27, no. 11, pp. 637-658, 1999.
[6] H. Brøndbo, A. Storebø, T. Boomsma, C. Skar, S. Fleten, “A real options approach to generation capacity expansion in imperfectly competitive power markets”, Energy Systems, vol. 11, pp. 515-550, 2020.
[7] M. Assili, D. Javidi, R. Ghazi, “An improved mechanism for capacity payment based on system dynamics modeling for investment planning in competitive electricity environment”, Energy Policy, vol. 36, no. 10, pp. 3703-3713, 2008.
[8] J. Valinejad, T. Barforoshi, “Evaluating the impacts of incentives on generation capacity
investment under uncertainties in electricity markets”, Tabriz Journal of Electrical Engineering, vol. 46, no. 1, pp. 357-368, 2016 (in Persian).
[9] A. Hamidi, J. Beiza, T. Abedinzadeh, A. Daghighi, “Damping adaptive neural controller design in HVDC based on offshore wind turbine to improve power system stability”, Tabriz Journal of Electrical Engineering, vol. 51, no. 2, pp. 233-242, 2021.
[10] E. Alishahi, M. Parsa Moghaddam, M. Sheikh-El-Eslami, “A system dynamics approach for investigating impacts of incentive mechanisms on wind power investment”, Renewable Energy, vol. 37, no. 1, pp. 310-317, 2012.
[11] H. Ibrahim, M. Ghandour, M. Dimitrova, A. Ilinca, J. Perron, “Integration of Wind Energy into Electricity Systems: Technical Challenges and Actual Solutions”, Energy Procedia, vol. 6, pp. 815-824, 2011.
[12] S. Gary, E. Larsen, “Improving firm performance in out-of-equilibrium, deregulated markets using feedback simulation models”, Energy Policy, vol. 28, no. 12, 845-855, 2000.
[13] D. Bunn, E. Larsen, “Sensitivity of reserve margin to factors influencing investment behaviour in the electricity market of England and Wales”, Energy Policy, vol. 20, no. 5, pp. 420-429, 1992.
[14] L. de Vries, P. Heijnen, “The impact of electricity market design upon investment under uncertainty: The effectiveness of capacity mechanisms”, Utilities Policy, vol. 16, no. 3, pp. 215-227, 2008.
[15] C. Han, D. Hur, J. Sohn, J. Park, “Assessing the Impacts of Capacity Mechanisms on Generation Adequacy With Dynamic Simulations”, IEEE Transactions on Power Systems, vol. 26, no. 4, pp. 1788-1797, 2011.
[16] M. Petitet, D. Finon, T. Janssen, “Capacity adequacy in power markets facing energy transition: A comparison of scarcity pricing and capacity mechanism”, Energy Policy, vol. 103, pp. 30-46, 2017.
[17] J. Sterman, “System Dynamics Modeling: Tools for Learning in a Complex World”, California Management Review, vol. 43, no. 4, 2001.
[18] R. Billinton, H. Chen, R. Ghajar, “A sequential simulation technique for adequacy evaluation of generating systems including wind energy”, IEEE Transactions on Energy Conversion, vol. 11, no. 4, pp. 728-734, 1996.
[19] P. Giorsetto, K. Utsurogi, “Development of a New Procedure for Reliability Modeling of Wind Turbine Generators”, IEEE Transactions on Power Apparatus and Systems, vol. PAS-102, no. 1, pp. 134-143, 1983.
[20] D. Brown, “Capacity payment mechanisms and investment incentives in restructured electricity markets”, Energy Economics, vol. 74, pp. 131-142, 2018.
[21] S. Oren, “Generation Adequacy via Call Options Obligations: Safe Passage to the Promised Land”, The Electricity Journal, vol. 18, no. 9, pp. 28-42, 2005.
[22] A. Sarkar, G. Gugliani, S. Deep, “Weibull model for wind speed data analysis of different locations in India”, KSCE Journal of Civil Engineering, vol. 21, pp. 2764-2776, 2017.
[23] A. Ghosh, “A FORTRAN program for fitting Weibull distribution and generating samples”, Computers & Geosciences, vol. 25, no. 7, 1999.
[24] R. Kumar, A. Sharma, P. Tewari, “Cost analysis of a coal-fired power plant using the NPV method”, Journal of Industrial Engineering International, vol. 11, pp. 495-504, 2015.
[25] J. Sterman, “Systems Simulation. Expectation formation in behavioral simulation models”, Behavioral Science, vol. 32, no. 3, pp. 190-211, 1987.
TABRIZ JOURNAL OF ELECTRICAL ENGINEERING
Volume 52, Issue 3 - Serial Number 101
October 2022
Pages 177-188

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