Multi-Objective Collaborative Optimization of Distribution Networks with Energy Storage and Electric Vehicles Using an Improved NSGA-II Algorithm

Runquan He (), Jiayin Hao, Heng Zhou and Fei Chen
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Runquan He: Maoming Power Supply Bureau, Guangdong Power Grid Co., Ltd., Maoming 525000, China
Jiayin Hao: Maoming Power Supply Bureau, Guangdong Power Grid Co., Ltd., Maoming 525000, China
Heng Zhou: Maoming Power Supply Bureau, Guangdong Power Grid Co., Ltd., Maoming 525000, China
Fei Chen: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Energies, 2025, vol. 18, issue 19, 1-17

Abstract: Grid-based distribution networks represent an advanced form of smart grids that enable modular, region-specific optimization of power resource allocation. This paper presents a novel planning framework aimed at the coordinated deployment of distributed generation, electrical loads, and energy storage systems, including both dispatchable and non-dispatchable electric vehicles. A three-dimensional objective system is constructed, incorporating investment cost, reliability metrics, and network loss indicators, forming a comprehensive multi-objective optimization model. To solve this complex planning problem, an improved version of the NSGA-II is employed, integrating hybrid encoding, feasibility constraints, and fuzzy decision-making for enhanced solution quality. The proposed method is applied to the IEEE 33-bus distribution system to validate its practicality. Simulation results demonstrate that the framework effectively addresses key challenges in modern distribution networks, including renewable intermittency, dynamic load variation, resource coordination, and computational tractability. It significantly enhances system operational efficiency and electric vehicles charging flexibility under varying conditions. In the IEEE 33-bus test, the coordinated optimization (Scheme 4) reduced the expected load loss from 100 × 10 −4 yuan to 51 × 10 −4 yuan. Network losses also dropped from 2.7 × 10 −4 yuan to 2.5 × 10 −4 yuan. The findings highlight the model’s capability to balance economic investment and reliability, offering a robust solution for future intelligent distribution network planning and integrated energy resource management.

Keywords: energy storage; distribution network; optimal scheduling; coordinated response; NSGA-II; grid-based distribution network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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