An improved multi-objective particle swarm optimization for battery energy storage allocation in microgrids: A Pareto-optimal approach to voltage stability and loss minimization

Because of the fast response and flexible adjustment, battery energy storage systems (BESS) are crucial in the grid-scale application to eliminate unbalanced power, which is primarily caused by the intermittency of renewable energy sources and the volatility of load demand. An optimal dispatch of BESS leads to satisfied power quality as well as minimized costs. In this article, a comprehensive analysis of the BESS optimal model is developed, which integrates node voltage fluctuation, network loss, and comprehensive battery cost. Additionally, a novel improved multi-objective particle swarm optimization algorithm is proposed to solve the aforementioned model and determine the optimal location, capacity, and power output of the BESS. Specifically, a similarity assessment mechanism, which is based on the Euclidean distance between particles and the global optimal solution, is proposed to achieve dynamic adaptive adjustment of inertial weights. Thus, a weighted individual optimal update strategy is introduced to search for the optimal solution of individual particles. Moreover, a dual-population cooperative evolution architecture, balancing the convergence and the diversity, is designed to obtain an accurate and well-distributed approximation of the true Pareto frontier of the algorithm. Finally, comparative simulations of multi-objective particle swarm optimization, nondominated sorting genetic algorithm II, multi-objective evolutionary algorithm based on decomposition (MOEA/D), and the proposed algorithm were conducted on the Deb-Thiele-Laumanns-Zitzler-5 benchmark and the Institute of Electrical and Electronics Engineers-33 node system. The simulation results validate the effectiveness of the proposed method.