Multi-objective coordinated optimization of renewable-based EV charging stations with user-centric scheduling and grid support in distribution networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F25%3A10259314" target="_blank" >RIV/61989100:27730/25:10259314 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2772671125002189" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2772671125002189</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.prime.2025.101111" target="_blank" >10.1016/j.prime.2025.101111</a>

Result language

angličtina

Original language name

Multi-objective coordinated optimization of renewable-based EV charging stations with user-centric scheduling and grid support in distribution networks

Original language description

The rapid proliferation of Electric Vehicles (EVs) imposes significant challenges on modern distribution networks, including increased power losses, voltage instability, peak demand stress, and economic impacts on both grid operators and EV users. Existing EV charging strategies often fail to address these challenges simultaneously while considering technical, economic, and user-centric objectives. This study proposes a novel multi-objective optimization framework for coordinated EV charging management that simultaneously minimizes power losses and voltage deviations, maximizes EV charging station (EVCS) profitability through dynamic pricing and Vehicle-to-Grid (V2G) support, and reduces EV user costs while accounting for battery degradation. EVCSs are supplied via a hybrid renewable system integrating photovoltaic (PV), wind generation, and battery energy storage systems (BESS), reducing grid dependency and enhancing local energy sustainability. A recently developed Cheetah Optimization Algorithm (COA) is employed as an energy management system (EMS) to optimally schedule EV charging/discharging under realistic operational constraints, while Monte Carlo Simulation (MCS) models uncertainties in renewable DG generation and stochastic EV arrivals to ensure robust operation under variable conditions. Comparative benchmarking against GA, PSO, and NSGA-II demonstrates COA&apos;s superior solution quality, faster convergence, and computational efficiency. Simulation results on IEEE 33-bus and 118-bus systems indicate that, under coordinated optimal conditions (Scenario 1), network losses reduce by 15–17%, voltage profiles improve by 40–45%, EVCS profitability increases by 55–60%, and total EV operating costs decrease by 40–45% compared to uncoordinated stressed operations (Scenario 2). Even under dynamic, price-responsive operations (Scenario 3), COA maintains 10–12% lower losses, 25–30% improved voltage stability, 20–25% higher EVCS profitability, and 20–25% reduced EV costs. These results highlight COA&apos;s ability to simultaneously enhance technical performance, economic efficiency, and user-centric operation, providing a scalable, resilient, and practical solution for integrating high EV penetration with renewable energy in future smart grids.

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

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OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

<a href="/en/project/TN02000025" target="_blank" >TN02000025: National Centre for Energy II</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

e-Prime - Advances in Electrical Engineering, Electronics and Energy

ISSN

2772-6711

e-ISSN

2772-6711

Volume of the periodical

14

Issue of the periodical within the volume

101111

Country of publishing house

GB - UNITED KINGDOM

Number of pages

31

Pages from-to

1-31

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-105015864258