This invention discloses a multi-objective collaborative optimization control method and
system for
energy storage systems. The method establishes an
optimization problem model encompassing three objectives: economic cost, battery life degradation, and deviation from
safety constraints. Local state data and external
environmental data are fused to form a real-time
optimization problem. The upper layer automatically determines the
Pareto optimal weight vector by solving the
minimum norm convex combination of the gradient vectors of each objective, and obtains a uniformly covered
Pareto optimal solution set by combining angle sector partitioning and hole-oriented repair. The middle layer achieves efficient
collaboration through a distributed ADMM that allocates adaptive step sizes based on the rated capacity of each
energy storage unit and embeds a recursive
momentum estimator, outputting a globally coordinated
charge and discharge plan. This invention solves the problems of manually preset multi-objective search weights, uneven solution set coverage, low convergence efficiency of distributed collaborative control of multiple
energy storage units, and poor robustness under extreme operating conditions.