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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Vydané v:	Electrical engineering Ročník 107; číslo 5; s. 6075 - 6097
Hlavní autori:	Sankar, Matta Mani, Chatterjee, Kalyan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2025 Springer Nature B.V
Predmet:	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
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Abstract	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.
AbstractList	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.
Author	Sankar, Matta Mani Chatterjee, Kalyan
Author_xml	– sequence: 1 givenname: Matta Mani surname: Sankar fullname: Sankar, Matta Mani email: che.shankar@gmail.com organization: Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Department of Electrical Engineering, BIT Sindri – sequence: 2 givenname: Kalyan surname: Chatterjee fullname: Chatterjee, Kalyan organization: Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines)
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SubjectTerms	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
Title	Multi-objective optimal allocation of renewable distributed generation units in a distribution network under high penetration of plug-in hybrid electric vehicles
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