Bidding strategy for wind power and Large-scale electric vehicles participating in Day-ahead energy and frequency regulation market. (1st July 2023)

• Record Type: Journal Article
• Authors: Zhang, Qian; Wu, Xiaohan; Deng, Xiaosong; Huang, Yaoyu; Li, Chunyan; Wu, Jiaqi
• Is Part Of: Applied energy. Volume 341(2023)
• Publication Date: 2023-07-01
• Subjects: Bidding strategy -- Wind power -- Electric vehicle -- Vehicle to grid -- Energy and frequency regulation market; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2023.121063
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A two-tier market model with Nash-Stackelberg game among WPP, EVA and power trading center is established to simulate the multi-entity market game process in the day-ahead energy-FR market, which gives full play to the complementary regulating effect of system load and frequency by WP and EVs through market regulation and maximizes the benefits of market participants. A data-driven classification and aggregation method of EV is proposed. The intuitive and rapid classification of EVs, and the rapid and accurate solution of EV optimization model are realized by single-layer traversal solving for sensitivity and multi-layer iteration solving for combination division. The interaction between EV and WP in the energy-FR market is further revealed by comparing and analyzing the bidding strategies of EV and WP under price-taker mode, independent bidding mode, and cooperative bidding mode. Abstract: Aiming at the problem of insufficient research on the interactions of various participants in energy and frequency regulation (FR) market that takes into account the participation of wind power (WP) and large-scale electric vehicles (EV), a bidding strategy for WP and large-scale EVs in day-ahead energy-FR market is proposed in this paper. Firstly, based on the analysis of the influence factors of the whole process EV behavior boundaries, a classification and aggregation method of EV cluster is proposed. Then, considering EV battery loss and wind power deviation penalty, a two-layer model is established. The upper layer is the bidding model of maximum revenue of wind power producer (WPP) and electric vehicle aggregator (EVA), and the lower layer is the clearing model with the lowest system operation cost for the power trading center. The competitive relationship between EVA and WPP is described based on Nash game. Finally, the simulated results show that the suggested classification and aggregation method can achieve fast and accurate solution of large-scale electric vehicles optimization model, and the independent bidding mode is more consistent with the optimal market operation compared with the price taker bidding mode and the collaborative bidding mode, which can protect the benefit for each participant.