基於改進粒子群算法的風光互補發電系統儲能容量優化配置

Capacity Optimization of Hybrid Energy Storage in Wind and Photovoltaic Complementary Power Generation System Based on Improved Particle Swarm Optimization Algorithms

為了提高獨立風光互補發電系統的電能品質和經濟效益，通常需要採用儲能技術。儲能系統的容量配置是儲能的關鍵技術問題。本文根據風光互補發電-儲能系統聯合運行的特點，考慮運行過程中儲能裝置能量的動態變化過程，建立了以混合儲能裝置的全生命週期成本(Life Cycle Cost, LCC)最低為目標函數，以獨立風光互補發電系統中負荷缺電率(Loss of Power Supply Probability, LPSP)和能量損失率(Loss of Produced Power Probability, LPPP)為約束條件的儲能容量優化配置模型。針對粒子群演算法局部搜索能力不足和易陷入局部最優解的問題，提出了改進粒子群演算法並利用該演算法對儲能容量優化配置問題進行求解。通過算例結果分析，證明該優化模型及改進算法的正確性和有效性。

In order to improve the power quality and economic benefits of independent wind and photovoltaic complementary power generation system, the energy storage technology is normally required. The key issue of energy storage technology is the capacity configuration of energy storage systems. According to the characteristics of combined operation of wind and solar complementary power generation and energy storage systems, and considering the dynamic change process of energy storage during operation, an optimal configuration model of energy storage capacity is established in this paper with an objective function of minimizing the life cycle cost (LCC) of hybrid energy storage devices, and operational indicators of loss of power supply probability (LPSP) and loss of produced power probability (LPPP) in an independent wind/solar complementary power generation system as constraints. An improved particle swarm optimization algorithm is proposed to solve the energy storage capacity optimization problem due to insufficient local search ability and easy to fall into local optimal solutions of particle swarm optimization algorithms. The correctness and effectiveness of the optimization model and the improved algorithm are verified through analyzing the results of an example.