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A Mixed Integer Quadratic Program for Valuing the Impact of Price and Forecast Uncertainty for Wind Generators (2312.03600v1)

Published 6 Dec 2023 in eess.SY and cs.SY

Abstract: Owners of wind power plants are exposed to financial risk in wholesale electricity markets due to the uncertain nature of wind forecasts and price volatility. In the event of a wind shortfall, the plant may have to repurchase power at a higher price in the real-time market. However, reducing the power offered in the day-ahead market may also be interpreted by regulators as physical withholding. We formulate and solve a mixed-integer quadratic program (MIQP) that prices the uncertain portion of a wind generator's forecast to hedge against uncertainties and which addresses concerns around withholding. We exploit the structure of the MIQP inputs to introduce additional constraints to improve computation time. Additionally, we provide a qualitative approach for generators and regulators to interpret the results of the MIQP. Finally, we simulate a real-world application for a wind farm in New York using past wind forecasts and NYISO prices.

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References (9)
  1. A. A. Thatte, L. Xie, D. E. Viassolo, and S. Singh, “Risk Measure Based Robust Bidding Strategy for Arbitrage Using a Wind Farm and Energy Storage,” IEEE Transactions on Smart Grid, vol. 4, no. 4, pp. 2191–2199, Dec. 2013.
  2. G. Ruan, H. Zhong, B. Shan, and X. Tan, “Constructing Demand-Side Bidding Curves Based on a Decoupled Full-Cycle Process,” IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 502–511, Jan. 2021.
  3. S.-E. Fleten and E. Pettersen, “Constructing bidding curves for a price-taking retailer in the norwegian electricity market,” IEEE Transactions on Power Systems, vol. 20, no. 2, pp. 701–708, May 2005.
  4. X. Yin, M. D. Ilić, and B. Sinopoli, “Toward design of risk-based real-time dispatch at value,” in 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Feb. 2015, pp. 1–5.
  5. D. Shen and M. Ilic, “Valuing Uncertainties in Wind Generation: An Agent-Based Optimization Approach,” in 2023 American Control Conference (ACC), May 2023, pp. 1237–1242.
  6. H. Shin, D. Lee, and R. Baldick, “An Offer Strategy for Wind Power Producers That Considers the Correlation Between Wind Power and Real-Time Electricity Prices,” IEEE Transactions on Sustainable Energy, vol. 9, no. 2, pp. 695–706, Apr. 2018.
  7. “The Guide to Energy Market Manipulation,” Global Competition Review, Tech. Rep., 2018.
  8. R. T. Rockafellar and S. Uryasev, “Optimization of conditional value-at-risk,” The Journal of Risk, vol. 2, no. 3, pp. 21–41, 2000.
  9. R. Carmona and X. Yang, “Joint Stochastic Model for Electric Load, Solar and Wind Power at Asset Level and Monte Carlo Scenario Generation,” Sep. 2022.

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