Integrated Intelligent Energy ›› 2026, Vol. 48 ›› Issue (5): 10-18.doi: 10.3969/j.issn.2097-0706.2026.05.002

• Energy Storage Technology • Previous Articles     Next Articles

Bi-level trading decision-making model for electric energy-frequency regulation markets

GUO Binglin1(), ZHANG Jingyu2(), ZANG Qiyong2,*()   

  1. 1 Shandong Electric Power Engineering Consulting Institute Company LimitedJinan 250013, China
    2 School of Nuclear Science and EngineeringNorth China Electric Power UniversityBeijing 102206, China
  • Received:2025-07-29 Revised:2025-08-11 Published:2025-11-24
  • Contact: ZANG Qiyong E-mail:guobinglin007@outlook.com;poptnt@163.com;zangqiyong@ncepu.edu.cn
  • Supported by:
    National Key R&D Program Project(2019YFE03110000);National Key R&D Program Project(2019YFE03110003);State Power Investment Group's Science and Technology Project(042500105290)

Abstract:

Energy storage(ES), as a new type of single-technology business entity with flexible regulation and rapid response capabilities, can simultaneously participate in multi-market trading such as electric energy and frequency regulation. To address the insufficient coordination of existing models, a bi-level optimization model was constructed for ES power stations to jointly participate in electric energy and frequency regulation markets. The upper level aimed to maximize revenue by optimizing bidding and scheduling strategies, while the lower level simulated market clearing with the goal of minimizing system electricity purchase costs. By introducing complementary slackness conditions and strong duality theory, the model was transformed into a mixed-integer linear programming problem, ensuring both solvability and scalability. Simulation results based on an IEEE 30-bus system demonstrated that the proposed strategy could reduce system electricity purchase costs by approximately 5.8% and increase the revenues of ES power stations by over 20%, thereby effectively enhancing resource allocation efficiency and economic benefits for market participants. This model can provide decision-making support and mechanism references for new entities such as ES power stations in multi-market trading.

Key words: energy storage system, multi-market trading, bi-level optimization model, mixed-integer linear programming, market clearing

CLC Number: