Worst-case conditional value-at-risk based bidding strategy for wind-hydro hybrid systems under probability distribution uncertainties

Yangyang Liu, Zhongqi Shen, Xiaowei Tang, Hongbo Lian, Jiarui Li and Jinxia Gong

Applied Energy, 2019, vol. 256, issue C

Abstract: It is challenging for renewable power (such as wind power) to participate in electricity markets, because of various uncertainties in terms of prices and power generation fluctuations. Further, the exact probability distributions of random variables are difficult to specify, leading to problems and errors with respect to the bidding strategy and risk management conducted by power generation companies. To overcome these issues, a risk averse bidding strategy is proposed to allow a wind-hydro hybrid system to participate in an electricity market when only partial information is available about the underlying probability distributions of random variables. A mixture distribution structure is employed to model multiple distributional uncertainties for the hybrid system, and the worst-case conditional value-at-risk is used to measure the hybrid system’s risk considering the distributional uncertainties. This bidding strategy provides a solution that allows power generation companies to manage their distributional uncertainties in electricity markets, especially for renewable power with low accuracy forecasts. This method can estimate the benefits of forecast accuracy improvement and predictions’ probability information on generation companies. Compared with the stochastic bidding strategy, the proposed bidding strategy obtains robuster results for distributions to achieve better risk management, as illustrated by the study case.

Keywords: Bidding strategy; Distributionally robust optimization; Hybrid renewable energy system; Mixture distribution; Risk management; Worst-case conditional value-at-risk (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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DOI: 10.1016/j.apenergy.2019.113918

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