Multi-Objective Optimization Strategy for Wind-Photovoltaic-Pumped Storage Combined System Based on Gray Wolf Algorithm

Abstract

The output of wind power and photovoltaic has the characteristics of randomness, volatility, and intermittency. Direct grid connection will lead to a lower power generation income of the power station, a greater volatility of grid connection of electric energy, and more wind and photovoltaic power discards, resulting in lower carbon emission reductions. The addition of pumped storage power plants effectively reduces the above impacts. Therefore, this paper studies the application scenario of wind photovoltaic and pumped storage combined power generation, establishes a multi-objective optimization model that comprehensively considers the three objectives of maximizing the economic benefits of the combined system, minimizing the system power fluctuation, and maximizing carbon emission reduction, and converts the multi-objective problem into a single objective problem for solution by normalization. In this paper, the gray wolf algorithm, which can realize the adaptive adjustment of local search and global search, is used to simulate and optimize the grid connected power of wind power, photovoltaic, and pumped storage. The optimization results show that the established model can effectively improve the economic benefits of the system and greatly reduce the fluctuation of power grid connection. In addition, the efficient use of new energy also greatly improves the carbon emission reduction capacity of the joint system, which proves that the model has high feasibility.

Keywords

• carbon emission reduction
• multi-objective optimization
• normalization
• gray wolf algorithm