• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (10): 66-76.doi: 10.12204/j.issn.1000-7229.2022.10.007

• Smart Grid • Previous Articles     Next Articles

Robust Optimal Dispatch Strategy for Battery Energy Storage System Participating in User-Side Peak Load Shifting

CHEN Ruibin(), LU Lingxia(), BAO Zhejing(), YU Miao()   

  1. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
  • Received:2022-03-11 Online:2022-10-01 Published:2022-09-29
  • Contact: YU Miao E-mail:22010042@zju.edu.cn;lulingxia@zju.edu.cn;zjbao@zju.edu.cn;zjuyumiao@zju.edu.cn
  • Supported by:
    National Natural Science Foundation of China(52077194);Zhejiang Provincial Natural Science Foundation of China(LGG22F030008)


The existing robust optimization methods for energy storage dispatch in the field of user-side peak load shifting lack the research on robust optimal scheduling of energy storage considering nonlinear multi-objective optimization model. When the range of energy storage output is directly limited by the load uncertainty, the conventional column and constraint generation algorithm cannot solve this kind of model. Aiming at these problems, considering the user-side load, battery energy storage and other constraints, a robust optimization model of energy storage system dispatch with net load variance and user-side power expenditure as optimization objectives is established, while the uncertainty of user load and new energy output is also taken into consideration. Since the decision-making and optimization results of the second stage will affect the value range of the decision-making in the first stage, it is necessary to improve the generation algorithm of columns and constraints to solve this kind of robust optimization problem. With the sub-problem solving steps in each iteration of the original column and constraint generation algorithm expanded by considering the objective function and some constraints as multiple sub-problems, solving multiple sub-problems in each iteration can effectively broaden the application scope of the algorithm. In the end, the simulation results are presented to verify the effectiveness of the method, which can be successfully applied to nonlinear multi-objective robust optimization problems without losing the performance advantages of the conventional algorithm.

Key words: battery energy storage, robust optimization, multi-objective optimization, column and constraint generation

CLC Number: