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Volume 14
Issue 18
10.3390/pr14182967
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Multi-Objective Dung Beetle Optimization-Based Optimal Scheduling Strategy for Active Distribution Networks with Large-Scale Electric Vehicle Integration

by
Zesheng Hu
1,*,
Kaikai Wang
1,
Zhaorui Lu
1,
Fei Zhao
2,
Zhenfei Ma
2,
Xingtao Tian
2 and
Zening Li
2
1
Economic and Technological Research Institute of State Grid Shanxi Electric Power Co., Ltd., Taiyuan 030021, China
2
College of electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
*
Author to whom correspondence should be addressed.
Processes 2026, 14(18), 2967; https://doi.org/10.3390/pr14182967 (registering DOI)
Submission received: 7 August 2026 / Revised: 10 September 2026 / Accepted: 14 September 2026 / Published: 17 September 2026
(This article belongs to the Special Issue Energy Systems Improvement, Conversion and Low-Carbon Development)
Versions Notes

Abstract

The large-scale integration of electric vehicles (EVs) can increase load fluctuations, operating costs, and security risks in active distribution networks (ADNs). To address these challenges, this study proposes a multi-objective optimal scheduling strategy based on a Multi-Objective Dung Beetle Optimization (MODBO) algorithm. A Monte Carlo simulation is first used to model stochastic EV charging behavior, followed by representative scenario selection using a minimum-distance criterion. A multi-objective scheduling model is then established considering photovoltaic utilization, ADN operating cost, system net-load variance, and voltage deviation. Case studies on a modified IEEE 33-bus system with 200 EVs show that uncoordinated charging increases the maximum load from 5672 kW to 6321 kW and raises the net-load variance to 4.15. With coordinated scheduling, the proposed method reduces the maximum load to 5672 kW and the variance to 1.29, while achieving 96.69% photovoltaic utilization and an operating cost of CNY 17,573. The results demonstrate that the proposed strategy effectively coordinates EV charging with distributed energy resources, mitigates load fluctuations, and improves the operational performance of ADNs.
Keywords: electric vehicles; active distribution network; multi-objective optimization; multi-objective dung beetle optimization; coordinated scheduling; photovoltaic utilization electric vehicles; active distribution network; multi-objective optimization; multi-objective dung beetle optimization; coordinated scheduling; photovoltaic utilization

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MDPI and ACS Style

Hu, Z.; Wang, K.; Lu, Z.; Zhao, F.; Ma, Z.; Tian, X.; Li, Z. A Multi-Objective Dung Beetle Optimization-Based Optimal Scheduling Strategy for Active Distribution Networks with Large-Scale Electric Vehicle Integration. Processes 2026, 14, 2967. https://doi.org/10.3390/pr14182967

AMA Style

Hu Z, Wang K, Lu Z, Zhao F, Ma Z, Tian X, Li Z. A Multi-Objective Dung Beetle Optimization-Based Optimal Scheduling Strategy for Active Distribution Networks with Large-Scale Electric Vehicle Integration. Processes. 2026; 14(18):2967. https://doi.org/10.3390/pr14182967

Chicago/Turabian Style

Hu, Zesheng, Kaikai Wang, Zhaorui Lu, Fei Zhao, Zhenfei Ma, Xingtao Tian, and Zening Li. 2026. "A Multi-Objective Dung Beetle Optimization-Based Optimal Scheduling Strategy for Active Distribution Networks with Large-Scale Electric Vehicle Integration" Processes 14, no. 18: 2967. https://doi.org/10.3390/pr14182967

APA Style

Hu, Z., Wang, K., Lu, Z., Zhao, F., Ma, Z., Tian, X., & Li, Z. (2026). A Multi-Objective Dung Beetle Optimization-Based Optimal Scheduling Strategy for Active Distribution Networks with Large-Scale Electric Vehicle Integration. Processes, 14(18), 2967. https://doi.org/10.3390/pr14182967

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