Comparative Study of Multi-energy Multi-objective Optimal Scheduling in Microbalance Area Based on Several Meta-heuristic Algorithms

Facing the requirement of efficient balanced operation of multiple distributed energy sources and loads, the multi-objective optimal scheduling task for microbalanced area grids relies on more advanced algorithms day by day. Some metaheuristic intelligent self-trending optimization methods such as particle swarm algorithms, dung beetle optimization algorithms, etc., have been used to improve energy scheduling efficiency. Along these works, this paper further extends and generalizes some recently proposed metaheuristic algorithms, e.g., Walrus optimization, Optical microscope optimization, and Crayfish optimization, to investigate which type of methods are more effective in solving the energy scheduling optimization problem in microbalancing zones. Simulation of the algorithm shows that the Walrus optimization method performs better for optimal energy scheduling of microbalanced area in terms of convergence speed and optimization capability level.