Dynamic bidding strategy for a demand response aggregator in the frequency regulation market. (15th May 2022)

• Record Type: Journal Article
• Authors: Liu, Xin; Li, Yang; Lin, Xueshan; Guo, Jiqun; Shi, Yunpeng; Shen, Yunwei
• Is Part Of: Applied energy. Volume 314(2022)
• Publication Date: 2022-05-15
• Subjects: Demand response aggregator -- Copula function -- Regulation market -- Bidding strategy -- Dynamic optimisation; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.118998
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A dynamic optimal bidding strategy for a DRA in the regulation market is proposed. A risk quantitative index considering the multivariate correlation is constructed. A compensation mechanism is designed for balancing risks and benefits. A baseline dynamic adjustment method is proposed for optimizing the bidding strategy. The proposed methods are beneficial for users and DRA. Abstract: As a low-cost flexible resource, dynamic controllable load on the demand side offers potential for great application prospects in power system frequency regulation. To overcome the risks of various uncertain factors in electricity markets and realize the economic benefits of demand response, this study proposed a dynamic bidding strategy for demand-side resources to participate in the frequency regulation market by a demand response (DR) aggregator. A correlative uncertainty model of the market price and frequency regulation demand was constructed employing the copula function, while the corresponding copula conditional value-at-risk model was used as a market risk measurement index to quantify the DR aggregator's decision risk. Consequently, an objective function that maximises the profit of the DR aggregator was established. Simultaneously, based on the analysis of the response potential of demand-side resources, a time-varying compensation method for the DR was proposed, and the bidding decision of the DR aggregator was dynamically optimised considering load deviation. Finally, case studies demonstrated that the accuracy and rationality of the uncertainty modelling are improved. The proposed dynamic optimisation method resulted in an increase of 16 % in operating profits. In addition, the revenue of users increased by 12 %. The impact of different risk preferences and the correlation between the stochastic electricity price and frequency regulation demand on the optimal decision result was analysed, based on which the manager of the DR aggregator can make decisions under different situations.