An aggregator-oriented hierarchical market mechanism for multi-type ancillary service provision based on the two-loop Stackelberg game. (1st October 2022)

• Record Type: Journal Article
• Authors: Li, Jiamei; Ai, Qian; Yin, Shuangrui; Hao, Ran
• Is Part Of: Applied energy. Volume 323(2022)
• Publication Date: 2022-10-01
• Subjects: Market mechanism -- Multi-type ancillary services -- Two-loop Stackelberg game -- Pricing method -- Market clearing -- Demand flexibility; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.119644
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A tri-layer ancillary service market mechanism is presented for demand flexibility. A two-loop Stackelberg game depicts the interactions between the market subjects. Multi-type ancillary services are considered in the game model. The two-loop Stackelberg game equilibrium is proved. A distributed iterative interaction method is presented to clear the multi-type ancillary service market. Abstract: In order to utilize demand-side flexibility and meet system requirements, the aggregator (AGG) has become a vital role in the ancillary service market. To reflect the willingness of each market subject, a tri-layer ancillary service market framework for ancillary service market operator (ASMO), AGG, and users is proposed in this paper, which contains the interactions between different layers including ASMO and AGG, AGG and users. We develop a detailed market timeline that takes multi-type ancillary services into account. Moreover, due to the hierarchical market structure, we conduct a two-loop Stackelberg game to depict the effect of each market subject's strategic behavior. Based on the game models of all the market subjects, the existence and uniqueness of the game equilibrium point are proved. In addition, considering the correlation between multi-type ancillary services, the clearing process is decomposed into two sub-problems and a distributed joint market clearing based on the particle swarm optimization algorithm is presented. Case study shows that the proposed method is profitable for all the market subjects, the market timeline is reasonable, and the joint clearing method is effective.