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Journal article
Optimised Allocation of Distributed Generation and Electric Vehicles Integration in Microgrids: A Multi‐Criteria Approach
IET renewable power generation, Vol.19(1), e70042
2025
Abstract
This paper presents the allocation and planning of renewable distributed generations such as wind turbines and photovoltaic systems, and non‐renewable generators such as fuel cells in the microgrids that incorporate electric vehicle parking lots while considering financial, operational and voltage stability goals. The proposal is modelled in the form of a bi‐objective optimisation problem. One of the objective functions minimises the distributed generators’ planning cost and the microgrid's operational cost, while the other function maximises the microgrid's voltage stability index. The optimisation problem is solved, satisfying technical constraints of power flow, network operational, and voltage stability limits while incorporating the models of the electric vehicles parking lots and distributed resources. The ε‐constraint‐based Pareto optimisation solution is used to determine a single‐objective formulation in the next step. Then, a fuzzy decision‐making tool derives a compromise solution. Finally, the Crow Search algorithm finds a trustable optimal solution to the problem. The performance of the proposal is validated on a standard microgrid network using numerical analyses and shows that the proposal can effectively determine the optimal economic conditions.
Details
- Title
- Optimised Allocation of Distributed Generation and Electric Vehicles Integration in Microgrids: A Multi‐Criteria Approach
- Authors/Creators
- Mehdi Veisi - Islamic Azad University Sanandaj BranchDaniel Sahebi - Vaasa University of Applied SciencesMazaher Karimi - University of VaasaFarhad Shahnia - Murdoch University, Centre for Water, Energy and Waste
- Publication Details
- IET renewable power generation, Vol.19(1), e70042
- Publisher
- Wiley
- Identifiers
- 991005765128107891
- Copyright
- © 2025 The Author(s).
- Murdoch Affiliation
- School of Engineering and Energy; Centre for Water, Energy and Waste
- Language
- English
- Resource Type
- Journal article
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