Non-Pareto Genetic Algorithm for Optimal Planning of Multi-Type Energy Resources in Active Distribution Networks

Abstract

Based on summarizing and analyzing the typical applications of energy storage, the study established a model for an active distribution network, and analyzed the technical and economic benefits of its access to the distribution network. In addition, considering the economic and technical requirements of multiple types of energy, ensure the stable and continuous operation of multiple types of energy, and build an optimal configuration model for multiple types of energy. To achieve a reliable solution to the model, a non-Pareto genetic algorithm (NSGA-II) is designed to obtain the optimal Pareto solution set for multi-type energy location and volume schemes. The proposed solution algorithm has a rich individual update mechanism and an advanced Pareto solution set storage and screening mechanism, which can effectively solve the problem. Furthermore, idea point decision making (IPDM) has been designed to select the best compromise solution in Pareto non-dominated solution set. Finally, based on the IEEE-33 node standard test system, the input source-load uncertainty scenario set is used to construct the distribution network operation scenario, and the configuration model is solved. The results show that NSGA-II can obtain a Pareto front with better solution quality and a more uniform distribution. After accessing the battery energy storage systems (BESS), the annual total power fluctuation and peak-valley difference of daily maximum load have been reduced by 19.25% and 11.8% respectively.