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Electricity Virtual Bidding Strategy Via Entropy-Regularized Stochastic Control Method

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  • Zhou Fang

Abstract

We propose a virtual bidding strategy by modeling the price differences between the day-ahead market and the real-time market as Brownian motion with drift, where the drift rate and volatility are functions of meteorological variables. We then transform the virtual bidding problem into a mean-variance portfolio management problem, where we approach the mean-variance portfolio management problem by using the exploratory mean-variance portfolio management framework

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  • Zhou Fang, 2023. "Electricity Virtual Bidding Strategy Via Entropy-Regularized Stochastic Control Method," Papers 2303.02303, arXiv.org.
    • Handle: RePEc:arx:papers:2303.02303
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    References listed on IDEAS

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    1. Birge, John R. & Hortaçsu, Ali & Mercadal, Ignacia & Pavlin, J. Michael, 2018. "Limits to arbitrage in electricity markets: A case study of MISO," Energy Economics, Elsevier, vol. 75(C), pages 518-533.
    2. Ehsan Samani & Mahdi Kohansal & Hamed Mohsenian-Rad, 2021. "A Data-Driven Convergence Bidding Strategy Based on Reverse Engineering of Market Participants' Performance: A Case of California ISO," Papers 2109.09238, arXiv.org.
    3. Koichiro Ito & Mar Reguant, 2016. "Sequential Markets, Market Power, and Arbitrage," American Economic Review, American Economic Association, vol. 106(7), pages 1921-1957, July.
    4. Haoran Wang, 2019. "Large scale continuous-time mean-variance portfolio allocation via reinforcement learning," Papers 1907.11718, arXiv.org, revised Aug 2019.
    5. John R. Birge & Ali Hortaçsu & J. Michael Pavlin, 2017. "Inverse Optimization for the Recovery of Market Structure from Market Outcomes: An Application to the MISO Electricity Market," Operations Research, INFORMS, vol. 65(4), pages 837-855, August.
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