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Long-term optimal planning for renewable based distributed generators and plug-in electric vehicles parking lots toward higher penetration of green energy technology

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dc.citedby3
dc.contributor.authorALAhmad A.K.en_US
dc.contributor.authorVerayiah R.en_US
dc.contributor.authorShareef H.en_US
dc.contributor.authorRamasamy A.en_US
dc.contributor.authorid59312509000en_US
dc.contributor.authorid26431682500en_US
dc.contributor.authorid57189691198en_US
dc.contributor.authorid16023154400en_US
dc.date.accessioned2025-03-03T07:41:40Z
dc.date.available2025-03-03T07:41:40Z
dc.date.issued2024
dc.description.abstractDue to the extensive pollution generated by conventional fuel-based power systems, there has been a significant shift in global focus toward increasing the adoption of renewable energy sources (RESs) through renewable-based distributed generation (DG), particularly wind and solar photovoltaic (PV) systems. Additionally, the electrification of the automotive sector, aimed at reducing pollution, is driving a rapid increase in electric vehicles (EVs). A critical element of this transition is the development of efficient infrastructure for plug-in electric vehicle parking lots (PEV-PLs). A collaborative planning model is essential to address the impact of integrating RESs and PEV-PLs into the electric power distribution system (DS) over the long term. This paper introduces a long-term mixed-integer non-linear (MINL) optimization planning model designed to optimize the planning and operation of RESs, including wind and PV sources, alongside PEV-PLs infrastructure. The goal is to increase the penetration of renewable energy and EVs within the DS while adhering to security constraints. The optimization model features three non-linear, incompatible objective functions: minimizing overall strategic expected investment, maintenance, emission, and operational costs; long-term power loss; and voltage deviation. Moreover, to ensure realism, the model incorporates uncertainties related to stochastic variables such as the intermittent nature of RESs, EV energy and time variables, loads, and energy price fluctuations, using Monte Carlo Simulation (MCS) and the backward reduction method (BRM). A hybrid optimization algorithm addresses the proposed objectives, combining the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) to minimize the three distinct objective functions concurrently. The effectiveness of the planning model is validated using the 69-bus benchmark test system, with four configurations tested: case 1 (the base case), case 2 (the base case with RESs (wind and PV)), case 3 (the base case with RESs and PEV-PLs), and case 4 (the base case with RESs, PEV-PLs, and a higher number of EVs). The impact of RESs on DS operation, PEV-PLs on RES penetration levels and DS operation, and the effect of increased EV penetration on the integrated capacity of RESs and DS operation are thoroughly investigated. Simulation results demonstrate that the optimal integration of 5 PEV-PLs, accommodating a fleet of 107 PEVs with wind and PV DGs, increases the RES penetration level from 3.35 MVA to 3.85 MVA compared to the case with RESs alone. Moreover, integrating PEV-PLs with RESs results in a 51.00 % reduction in overall operational costs, a 37.55 % reduction in overall planning and operation costs, a 52.82 % reduction in total carbon emissions, and a 45.85 % reduction in total voltage deviation. ? 2024 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo114057
dc.identifier.doi10.1016/j.est.2024.114057
dc.identifier.scopus2-s2.0-85206171317
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85206171317&doi=10.1016%2fj.est.2024.114057&partnerID=40&md5=39db94bf707b58dc803366ed4a3d256f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36245
dc.identifier.volume102
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Energy Storage
dc.subjectAir cushion vehicles
dc.subjectAir quality
dc.subjectBenchmarking
dc.subjectDistributed energy
dc.subjectDistributed power generation
dc.subjectGeophysical prospecting
dc.subjectInteger programming
dc.subjectLight pulse generators
dc.subjectLinear programming
dc.subjectPhosphate deposits
dc.subjectPlug-in electric vehicles
dc.subjectPlug-in hybrid vehicles
dc.subjectRenewable energy
dc.subjectStochastic models
dc.subjectSurface water resources
dc.subjectSurface waters
dc.subjectTabu search
dc.subjectDistributed generation
dc.subjectElectric vehicle
dc.subjectLong term planning
dc.subjectLong term planning model
dc.subjectMeta-heuristic optimization techniques
dc.subjectParking lots
dc.subjectPlanning models
dc.subjectPlug in electric vehicle parking lot
dc.subjectPlug-ins
dc.subjectRenewable energy source
dc.subjectUncertainty models
dc.subjectVehicle parking
dc.subjectSolar power generation
dc.titleLong-term optimal planning for renewable based distributed generators and plug-in electric vehicles parking lots toward higher penetration of green energy technologyen_US
dc.typeArticleen_US
dspace.entity.typePublication
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