Multi-Objective Optimization for Wind Farm Based on Improved Wild Horse Optimizer

In order to improve the economics of wind farm operation after energy storage access and to promote the safe and stable operation of the network connected by renewable energy, a multi-objective optimization method for wind farm with energy storage based on the improved wild horse optimizer (IWHO) is presented. In order to address the premature maturity and convergence of the original wild horse optimizer due to the lack of population diversity and local optimality, the IWHO produces an initial population with high ergodicity through Tent chaotic mapping then using asymmetric crossover and the constraints transformed into adaptive penalty functions, improving the optimization performance. Finally, the IWHO is verified to have good search performance by simulating the example of IEEE 33-bus with wind turbines and energy storage, which is regarded as a wind farm.