Multi-objective energy management in a renewable and EV-integrated microgrid using an iterative map-based self-adaptive crystal structure algorithm

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 8, 2024

Abstract The use of plug-in hybrid electric vehicles (PHEVs) provides a way to address energy and environmental issues. Integrating large number PHEVs with advanced control storage capabilities can enhance the flexibility distribution grid. This study proposes an innovative management strategy (EMS) using Iterative map-based self-adaptive crystal structure algorithm (SaCryStAl) specifically designed for microgrids renewable sources (RESs) PHEVs. goal is optimize multi-objective scheduling microgrid wind turbines, micro-turbines, fuel cells, solar photovoltaic systems, batteries balance power store excess energy. aim minimize operating costs while considering impacts. optimization problem framed as nonlinear constraints, fuzzy logic aid decision-making. In first scenario, optimized all RESs installed within predetermined boundaries, in addition grid connection. second operates turbine at rated power. third case involves integrating into three charging modes: coordinated, smart, uncoordinated, utilizing standard RES SaCryStAl showed superior performance operation cost, emissions, execution time compared traditional CryStAl other recent methods. proposed achieved optimal solutions scenario cost emissions 177.29 €ct 469.92 kg, respectively, reasonable frame. it yielded values 112.02 196.15 respectively. Lastly, achieves 319.9301 €ct, 160.9827 128.2815 uncoordinated charging, coordinated smart modes Optimization results reveal that outperformed evolutionary algorithms, such differential evolution, CryStAl, Grey Wolf Optimizer, particle swarm optimization, genetic algorithm, confirmed through test cases.

Язык: Английский

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Optimal scheduling and management of grid‐connected distributed resources using improved decomposition‐based many‐objective evolutionary algorithm

IET Generation Transmission & Distribution, Год журнала: 2024, Номер 18(16), С. 2625 - 2649

Опубликована: Июль 16, 2024

Abstract This paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, flexibility. A novel multi‐objective optimization framework is introduced this study minimize supply costs, emissions, losses while improving voltage deviation (VD) stability index (VSI). The proposed comprising normal boundary intersection (NBI) decomposition‐based evolutionary algorithms (DBEA) determines optimal siting sizing renewable‐based distributed resources, considering load demand variations intermittency solar outputs. comparative analysis establishes that strategy performs better than many contemporary algorithms, specifically when all objective functions are optimized simultaneously. validation was carried out on standard IEEE‐33 bus test network, which demonstrates significant percentage savings costs (49.6%), emission rate (62.2%), loss (92.3%), along enormous improvements VSI (91.9%) VD (99.8953%). obtained results categorically underline efficiency, robustness approach employed any complex network multiple renewable sources systems.

Язык: Английский

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Optimal day-ahead scheduling of microgrid equipped with electric vehicle and distributed energy resources: SFO-CSGNN approach

Journal of Energy Storage, Год журнала: 2024, Номер 102, С. 113933 - 113933

Опубликована: Окт. 5, 2024

Язык: Английский

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Optimal Scheduling of Integrated Energy System Considering Electric Vehicle Battery Swapping Station and Multiple Uncertainties

World Electric Vehicle Journal, Год журнала: 2024, Номер 15(4), С. 170 - 170

Опубликована: Апрель 18, 2024

In recent years, there has been rapid advancement in new energy technologies aimed at mitigating greenhouse gas emissions stemming from fossil fuels. Nonetheless, uncertainties persist both the power output of sources and load. To effectively harness economic operational potential an Integrated Energy System (IES), this paper introduces enhanced uncertainty set. This set incorporates N-1 contingency considerations nuances source–load distribution. framework is applied to a robust optimization model for Electric Vehicle (EV-IES), which includes Battery Swapping Station (EVBSS). Firstly, establishes IES EVBSS, then proceeds classifies schedules large-scale battery groups within these stations. Secondly, proposes account status multiple units system. It also considers characteristics loads. Additionally, it takes into consideration state multi-interval distribution characteristics. Subsequently, multi-time-scale optimal scheduling established with objective minimizing total cost IES. The day-ahead fully multivariate solution employs Nested Column Constraint Generation (C&CG) algorithm, based on discrete variables model. intraday reallocates each unit results scheduling. Finally, simulation demonstrate that proposed method reduces conservatism set, ensuring stable operation EV-IES while meeting demands electric vehicle users.

Язык: Английский

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Integrated Energy System Optimization for Electric Vehicles and Demand Response within Carbon Trading Mechanism

Journal of Electrical Engineering and Technology, Год журнала: 2024, Номер 19(7), С. 4013 - 4025

Опубликована: Апрель 18, 2024

Язык: Английский

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