A multivariate statistical method for risk parameter scenario generation and renewable energy bidding in electricity markets

• DOI: 10.3389/fenrg.2023.1326613
• Publication Date: 2023-11-29T05:39:51Z
• Authors: Yingchun Feng; Jie Fan; Bo Gao; Yu Jiang; Jianrun Chen; Rui Zhang; Min Chen
• Keywords: Economics and Econometrics; terms--correlated parameters; Renewable Energy, Sustainability and the Environment; wind power bidding; Energy Engineering and Power Technology; 02 engineering and technology; stochastic optimization; General Works; Fuel Technology; A; electricity market; 0202 electrical engineering, electronic engineering, information engineering; multivariate statistical method

To maximize the expected profits and manage risks of renewable energy system under electricity market environment, scenario-based- stochastic optimization model can be established to generate bidding strategies, in which probabilistic scenarios risk parameters are usually obtained by using statistical or machine learning methods. This paper proposes a practical multivariate method for parameter scenario generation, is used wind faced with uncertain prices power productions, it considers correlation between dependent historical data directly. The probabilities containing correlated calculated histograms, asymmetric different existing preserved. Additionally, order make problem large numbers tractable, reduction trim down number. By solving problem, optimal day-ahead curves generated, Douglas–Peucker algorithm fit according requirements. Case studies based on real world markets performed prove effectiveness proposed generation strategies. Finally, conclusions suggestions future research works provided.