A Collaborative Optimization Approach for Configuring Energy Storage Systems and Scheduling Multi-Type Electric Vehicles Using an Improved Multi-Objective Particle Swarm Optimization Algorithm

Energy storage systems (ESS) and electric vehicles (EVs) play a crucial role in facilitating the grid integration of variable wind and solar power. Despite their potential, achieving coordinated operational optimization between ESS and heterogeneous EV fleets to maintain grid stability under high re...

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Bibliographic Details
Published in:Processes Vol. 13; no. 5; p. 1343
Main Authors: Liu, Yirun, Wu, Xiaolong
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.05.2025
Subjects:
Algorithms
Alternative energy sources
Automobiles, Electric
Carbon
Collaboration
Costs
Efficiency
Electric vehicles
Electrical loads
Energy consumption
Energy management systems
Energy storage
Genetic algorithms
Green technology
Integration
Mathematical optimization
Monte Carlo method
Monte Carlo simulation
Multiple objective analysis
Optimization
Particle swarm optimization
Photovoltaic cells
Probabilistic models
Renewable resources
Scheduling
Solar energy
Wind
Wind power
China
ISSN:2227-9717, 2227-9717
Online Access:Get full text
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Summary:Energy storage systems (ESS) and electric vehicles (EVs) play a crucial role in facilitating the grid integration of variable wind and solar power. Despite their potential, achieving coordinated operational optimization between ESS and heterogeneous EV fleets to maintain grid stability under high renewable penetration poses a complex technical challenge. To address this, this study develops an integrated optimization framework combining ESS capacity planning with multi-type EV scheduling strategies. For ESS deployment, a tri-objective model balances cost, wind–solar integration, and electricity deficit. A Monte Carlo simulation algorithm is used to simulate different probabilistic models of charging loads for multiple types of EVs, and a bi-objective optimization approach is used for their orderly scheduling. An improved multi-objective particle swarm optimization (IMOPSO) algorithm is proposed to resolve the coupled optimization problem. Case studies reveal that the framework achieves annual cost reductions, enhances the wind–solar integration rate, and minimizes the power deficit in the system.
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr13051343