Optimal Integration of EV Charging Stations Into Distribution Network Planning and Operation

This article studies optimal planning and operation of electric vehicle (EV) charging stations within power distribution networks, which is crucial due to the growing penetration of EVs and distributed energy resources. Traditional approaches for planning and operation can be suboptimal and lead to significant grid upgrade costs. To tackle this, we propose an integrated framework that jointly optimizes the location, sizing, and operation of battery energy storage systems and EV charging stations. Our multiobjective optimization model minimizes power losses, operational costs, and environmental impacts while maximizing system reliability. We use genetic algorithms and deep deterministic policy gradients to solve this problem, departing from conventional peak-demand-based designs and leveraging typical demand profiles for battery energy storage system sizing. Real-time controllers are incorporated to adjust charging rates dynamically, responding to real-time variations. The proposed framework, validated on realistic test networks, demonstrates improved efficiency and stability, supporting sustainable and resilient grid operations.

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Publication Details

Journal
IEEE Transactions on Industrial Informatics
Published
2025-06-17
DOI
https://doi.org/10.1109/tii.2025.3574435
Citations
13
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
9.50
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article

Optimal Integration of EV Charging Stations Into Distribution Network Planning and Operation

Mousa Alizadeh, Lasantha Meegahapola, Mahdi Jalili, Ali Moradi Amani et al.
13 citations
IEEE Transactions on Industrial Informatics
Electric Vehicles and Infrastructure
9.50
article

Optimal Integration of EV Charging Stations Into Distribution Network Planning and Operation

Mousa Alizadeh, Lasantha Meegahapola, Mahdi Jalili, Ali Moradi Amani, Oliver Hill
article en
13 citations

Abstract

This article studies optimal planning and operation of electric vehicle (EV) charging stations within power distribution networks, which is crucial due to the growing penetration of EVs and distributed energy resources. Traditional approaches for planning and operation can be suboptimal and lead to significant grid upgrade costs. To tackle this, we propose an integrated framework that jointly optimizes the location, sizing, and operation of battery energy storage systems and EV charging stations. Our multiobjective optimization model minimizes power losses, operational costs, and environmental impacts while maximizing system reliability. We use genetic algorithms and deep deterministic policy gradients to solve this problem, departing from conventional peak-demand-based designs and leveraging typical demand profiles for battery energy storage system sizing. Real-time controllers are incorporated to adjust charging rates dynamically, responding to real-time variations. The proposed framework, validated on realistic test networks, demonstrates improved efficiency and stability, supporting sustainable and resilient grid operations.

IEEE Transactions on Industrial InformaticsVol. 21(9)
Curtin University (AU), RMIT University (AU)
Openalex Percentile: Top 2%
Electric Vehicles and Infrastructure
9.50
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