Work

Multi-objective optimization framework for coordinated integration of electric vehicle charging stations and distributed generators in microgrids

Year: 2026

Type: article

Abstract: The rapid growth of electric vehicles(EVs) increases operational stress on radial distribution networks, raising active power losses, degrading the voltage profile, and reducing stability margins. This paper presents a multi-objective particle swarm optimization(PSO) framework for the coordinated placement and sizing of electric vehicle charging stations(EVCS) and distributed generation(DG) in radial AC microgrids. The problem is formulated as a constrained mixed-integer nonlinear program in which inverter-interfaced photovoltaic and permanent magnet synchronous generator(PMSG) wind units provide active and reactive power. A normalized weighted objective minimizes active power loss and the voltage deviation index while maximizing the voltage stability index, subject to voltage, thermal, and capacity limits, and is evaluated using a backward/forward sweep power flow. The framework is validated on the IEEE 33-bus and IEEE 69-bus systems through three scenarios of increasing coordination: EVCS placement only, partial DG integration, and full coordinated allocation. Under full coordination, active power losses are reduced by 84.0% on the 33-bus system and by 90.3% on the 69-bus system, and the minimum bus voltage is raised to 0.985 and 0.994 p.u., within the ± 5% regulatory band. Over 30 independent runs, PSO consistently outperforms Harris Hawks Optimization(HHO) in mean active loss and attains the lowest overall active loss solution on both networks, while Teaching-Learning-Based Optimization(TLBO) achieves a lower mean loss and markedly lower run to run variance than PSO under full coordination, particularly on the IEEE 69-bus system, as confirmed by the Wilcoxon signed-rank test applied to active power loss. The results indicate that coordinated EVCS-DG planning provides a computationally tractable and constraint compliant tool for the joint deployment of charging and generation assets in modern distribution networks.

Source: Next Energy

DOI: 10.1016/j.nxener.2026.101042

Authors: Amal Kadri, Abdelhadi Raihani, Khalili Tajeddine, Hassan Ouabi, Meryem Hamidi

Institutions: École Normale Supérieure de l'Enseignement Technique de Mohammedia, Université Hassan II Mohammedia, University of Hassan II Casablanca

Language: English

Topic: Optimal Power Flow Distribution

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,280