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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20200 - Electrical engineering, Electronic engineering, Information engineering

Projekt

<a href="/cs/project/TN02000025" target="_blank" >TN02000025: Národní centrum pro energetiku II</a><br>

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

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

ISSN

2772-6711

e-ISSN

2772-6711

Svazek periodika

14

Číslo periodika v rámci svazku

101111

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

31

Strana od-do

1-31

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105015864258