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.

I. Introduction

Using wind turbines are one of the most important initiatives to achieve the “peak carbon, carbon neutral” strategy, which can effectively reduce carbon emissions and achieve eco-friendly and low carbon goals. With the large number of wind turbines in the grid, the problem of renewable energy consumption and voltage out of limit is becoming more and more significant, which brings hidden problems to the safety and economy of the wind farms. With a huge occupancy of renewable energy sources in the grid, the deployment of energy storage in the wind farms and their coordinated control with other distributed power sources is one of the most effective ways to solve the consumption issues [1]. On the other hand, in order to improve the consumption of the wind by adjusting the output power of the storage, taking into account the impact of the energy storage on the voltage amplitude of the wind farms and the economic indicators such as the additional network loss caused by the energy storage access is necessary. Traditional non-heuristic optimisation algorithms are difficult to meet the needs of actual operation.