多代理粒子群算法光储电站控制策略

Multi-agent Particle Swarm Optimization Control Strategy for Optical Storage Power Station

摘要:

针对大规模复杂系统的光储电站中光伏出力波动大、储能荷电状态和放电深度不一致,提出多代理与粒子群算法思想结合的光储电站控制策略,建立光储联合电站输出功率目标函数。算法中粒子的速度用各储能单元区的各储能换流器的充放电功率表示,粒子的位置用各储能换流器对应的荷电状态表示,经过不断优化约束条件和目标函数,寻优探索出最优解。Matlab/simulink仿真结果表明,所提出的多代理粒子群算法在储能系统作用下有效减小了功率波动率,优化了光储电站主代理对应的电池荷电状态。

Abstract:

For the large-scale complex system, the photovoltaic output fluctuates greatly, and the state of charge (SOC) and the depth of discharge are inconsistent. A control strategy of optical storage power station based on multi-agent and particle swarm optimization is proposed. The output power objective function of the optical storage combined power station is established. The particle velocity in the algorithm is expressed by the charge and discharge power of each PCS in each energy storage unit area, and the particle position is represented by the SOC corresponding to each PCS. Through continuous optimization of constraints and objective functions, the optimal solution is found. The simulation results of Matlab/Simulink show that the proposed multi-agent particle swarm optimization algorithm effectively reduces the power fluctuation rate and optimizes the SOC of battery corresponding to the main agent of the optical storage power station.