Risk-based optimal bidding patterns in the deregulated power market using extended Markowitz model

Bakhtiar Ostadi, Omid Motamedi Sedeh and Ali Husseinzadeh Kashan

Energy, 2020, vol. 191, issue C

Abstract: Deregulation of power industry has entailed important changes in the energy market. With the power industry being restructured, a generation company (GenCo) sells energy through auctions in a daily market, and submission of the appropriate amount of electricity with the right bidding price is important for a GenCo to maximize their profits and minimize the acceptance risk. The objective of this paper is to propose a novel approach for determination of the optimal biding patterns among GenCos in the deregulated power market using a hybrid of Markowitz Model and Genetic Algorithm (GA). While Markowitz Model as an optimization model considers the risk premium for biding patterns and GA as a search engine, considering the acceptance risk in deregulated market. A case study is used to examine the findings of the proposed approach. Also, to compare the proposed model, neural network by back propagation learning algorithm and real proposed pattern were considered. The numerical results indicate that the proposed model is statistically efficient and offers effective curves and biding patterns by lesser risk and equal profitability in day-ahead market as it is able to achieve better results compared to the neural network.

Keywords: Markowitz model; Deregulated market; Risk estimation; Value at risk (VaR); Energy generation cost (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:191:y:2020:i:c:s036054421932211x

DOI: 10.1016/j.energy.2019.116516

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