Multi-objective optimal allocation of renewable distributed generation units in a distribution network under high penetration of plug-in hybrid electric vehicles

This paper presents a novel multi-objective framework for efficiently allocating renewable distribution generation (RDG) in electric distribution systems (EDSs) amidst high plug-in hybrid electric vehicle (PHEV) penetration. The study incorporates a practical PHEV demand model considering various st...

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Bibliographic Details
Published in:Electrical engineering Vol. 107; no. 5; pp. 6075 - 6097
Main Authors: Sankar, Matta Mani, Chatterjee, Kalyan
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2025
Springer Nature B.V
Subjects:
Algorithms
Costs
Decision making
Distributed generation
Economics and Management
Electric power demand
Electric vehicles
Electrical Engineering
Electrical Machines and Networks
Energy Policy
Engineering
Hybrid electric vehicles
Multiple objective analysis
Objectives
Optimization
Optimization algorithms
Optimization techniques
Original Paper
Pareto optimum
Power Electronics
System effectiveness
Vehicle-to-grid
Plug-in hybrid electric vehicle (PHEV)
Technique for order preference by similarity to the ideal solution (TOPSIS)
Distributed generation (DG)
Electric distribution system
Multi-objective artificial hummingbird algorithm (MOAHA)
ISSN:0948-7921, 1432-0487
Online Access:Get full text
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Summary:This paper presents a novel multi-objective framework for efficiently allocating renewable distribution generation (RDG) in electric distribution systems (EDSs) amidst high plug-in hybrid electric vehicle (PHEV) penetration. The study incorporates a practical PHEV demand model considering various stochastic parameters. It investigates the impact of PHEV adoption on EDS via simulations with three demand response levels, including grid-to-vehicle and vehicle-to-grid options. Unlike traditional RDG studies focusing solely on technical or economic objectives, the proposed framework optimizes both aspects simultaneously using a posteriori multi-objective methodology. The multi-objective problem is addressed using the multi-objective artificial hummingbird algorithm (MOAHA), and the best trade-off solution from Pareto optimal solutions is selected using the technique for order preference by similarity to the ideal solution. The framework is validated on 33-bus, 69-bus and 118-bus benchmark electric distribution test systems, offering three distinct optimal solutions for each system to accommodate diverse decision-maker preferences. The reduction in energy loss after allocation of RDGs in the presence of PHEVs is observed to be 70.65%, 75.84% and 31.30%, respectively, for 33-bus, 69-bus and 118-bus test systems. Finally, the effectiveness of MOAHA is demonstrated through comparative analysis with other leading optimization algorithms.
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ISSN:0948-7921
1432-0487
DOI:10.1007/s00202-024-02849-z