Placement and capacity selection of battery energy storage system in the distributed generation integrated distribution network based on improved NSGA-II optimization

Abstract

The scalability of distributed generation (DG) dominated by clean energy in the distribution network is continuously increasing. Increased grid integration of DGs has aggravated the uncertainty of distribution network (DN) operation, which affects the power losses and voltage fluctuations. The battery energy storage system (BESS), as an essential part of the distribution grid, its appropriate placement and capacity selection can improve the power quality and bring economic benefits for the DGs integrated DN (DGDN). In this paper, the placement and capacity selection of BESS in the DGDN is investigated including: (1) The relationship between the placement and capacity selection of BESS with the power losses and line voltages of the DN is derived. The impact of the placement and capacity selection of BESS on the power losses and voltage fluctuations of the DGDN is analyzed. (2) In order to achieve the objectives of a minimum of power losses, voltage fluctuations and BESS capacity in the DGDN, a multi-objective model with optimized placement and capacity of the BESS is established. (3) In order to ensure the globality and uniformity of Pareto solution set, an improved algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to optimize the process of traditional NSGA-II optimization. By adding the selection coefficient of population optimal solution and the congestion distance update function, the population dispersion is expanded and the global searching capability of the algorithm is enhanced. (4) Based on the improved NSGA-II optimization, an optimal scheme of the placement and capacity of the BESS is selected based on information entropy weight (IEW)-analytic hierarchy process (AHP). Taking an IEEE 33-node DGDN as an example, the proposed algorithm is compared with the traditional NSGA-II optimization and the multi-objective particle swarm optimization (MOPSO) algorithm to verify the effectiveness and superiority of the BESS optimal placement and capacity method based on the improved NSGA-II optimization.