基于改进黏菌算法的风光电并网双层储能容量优化

Optimization of Tow-Level Energy Storage Capacity of Grid-Connected Wind and Solar Power Based on Improved Slime Mold Algorithm

为了提高并入大量风光电配电网的供电可靠性和经济性,提出一种配电网双层混合储能容量优化策略。采用自适应小波包分解方法得到风光电目标并网功率以及储能系统容量初次分配;通过考虑配电网网损和储能系统综合成本,分别建立了综合成本模型和配电网储能优化配置模型;给出一种自适应对抗黏菌-粒子群双层优化混合算法(adaptive opposition slime mould algorithm-particle swarm optimization hybrid algorithm,AOSMA-PSO),上层优化储能系统成本函数,下层优化配电网网损函数,通过上下层反复迭代得到最优混合储能容量配置方案,并对该方案进行最终储能修正,以达到风电平抑的标准。通过MATLAB并以IEEE33节点算例进行仿真验证,结果表明:所提方法和模型不仅能够有效降低混合储能综合成本,还降低了配电网网络损耗,提高了配电网的供电可靠性;通过对不同算法进行对比,验证了所提算法在收敛速度和寻优方面的优势。

To improve the reliability and economy of the power supply of the distribution network connected with a large number of wind and solar power,an optimization strategy of two-level hybrid energy storage capacity of distribution network is proposed in this paper.First,the adaptive wavelet packet decomposition method is used to obtain the target grid-connected power of wind and solar power and the initial capacity allocation of energy storage system;second,by considering the network loss of the distribution network and the comprehensive cost of energy storage system,the comprehensive cost model and the optimal allocation model of energy storage of distribution network are established respectively;third,the model that two-level Adaptive Opposition Slime Mold Algorithm-Particle Swarm Optimization hybrid algorithm(AOSMA-PSO)is proposed where the upper layer optimizes energy storage system cost function,and the lower level optimizes active distribution network loss function.The optimal hybrid energy storage capacity allocation scheme is obtained through repeated iteration of the upper and lower layers,the final energy storage correction of this scheme is carried out to meet the wind power stabilization standard.Finally,the simulation verification is conducted using MATLAB with IEEE33 node as the example.The results show that the proposed method and model can not only effectively reduce the comprehensive cost of energy storage,but also reduce the network loss of the distribution network and improve the power supply reliability of the distribution network;Comparison of different algorithms also verifies the advantages of the proposed algorithm in terms of convergence speed and optimization.