Two-level optimization model for day-ahead peak shaving market considering reliability and customer satisfaction of load aggregators
CSTR:
Author:
Affiliation:

(Electrical and Automation College, Wuhan University, Wuhan 430072, China)

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    The large- scale grid connection of new energy brings huge challenges to the peak load regulation of power systems.The peak load regulation space of the traditional power supply side is gradually exhausted, an efficient market mechanism is urgently needed to guide the flexible loads on the user side to actively participate in the peak load regulation market to maintain the security and stability of the power grid. Combined with the actual situation of users, considering user satisfaction and load demand, the market mechanism of flexible load aggregation including electric vehicles,electric heating, and energy storage participating in day- ahead peak load regulation was proposed, and the bi- level optimization model of aggregator participating in day-ahead peak shaving regulation market clearing and flexible load scheduling on the user side was established. The upper-level model considers the influence of the reliability of load aggregators and conducts day- ahead market clearing with the objective of the lowest dispatching cost. The lower·level model considers user satisfaction and calls user-side resources in the form of signing a contract. The simulation results verify the feasibility of combining different loads for joint dispatch, indicating that the strategy proposed can significantly reduce the cost of dispatch, ensure the reliability of peak shaving in the market, and ensure the safe and reliable operation of the power grid.

    Reference
    Related
    Cited by
Get Citation

蔡博武,廖 菲,杨 军.考虑负荷聚合商诚信度及用户满意度的日前调峰市场双层优化模型[J].电力需求侧管理英文版,2022,24(5):29-35.

Copy
Related Videos

Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:June 29,2022
  • Revised:July 27,2022
  • Adopted:
  • Online: September 27,2022
  • Published:
Article QR Code