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Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration

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dc.citedby16
dc.contributor.authorALAhmad A.K.en_US
dc.contributor.authorVerayiah R.en_US
dc.contributor.authorShareef H.en_US
dc.contributor.authorid58124002200en_US
dc.contributor.authorid26431682500en_US
dc.contributor.authorid57189691198en_US
dc.date.accessioned2025-03-03T07:42:47Z
dc.date.available2025-03-03T07:42:47Z
dc.date.issued2024
dc.description.abstractIn this paper, we formulate a stochastic long-term optimization planning problem that addresses the cooperative optimal location and sizing of renewable energy sources (RESs), specifically wind and photovoltaic (PV) sources and battery energy storage systems (BESSs) for a project life span of 10-years. The aim is to enhance the integrated capacity of green energy in the electric power distribution system (DS) while adhering to topological, technical, and economic constraints and considering the annual load growth. Moreover, to account for uncertainties related to various input random variables such as wind speed, solar irradiation, load power, and energy prices, Monte Carlo Simulation (MCS) is employed to generate multiple scenarios. The backward reduction method (BRM) is then applied to streamline the number of generated scenarios, reducing computational efforts. To solve the optimization planning model, a hybrid optimization algorithm is proposed, combining the non-dominating sorting genetic algorithm (NSGAII) and multi-objective particle swarm optimization (MOPSO). This hybrid approach aims to simultaneously minimize three long term objective functions from the economic, environmental, and technical point of view: total expected investment, operational, and carbon emission cost, power loss, and voltage deviation. The effectiveness of the planning model and the performance of the solver method are validated using the 69-bus benchmark test system. The adopted system is configured into three cases, including basic DS, DS with RESs, and DS with a combination of RESs and BESSs. Simulation results demonstrate the capability of the proposed planning model in achieving the following improvements: RESs without ESS achieved 3.35 MVA penetration while reducing DS dependency by 31.44 %. Moreover, the technical objectives improved: power loss by 39.14 % and voltage deviation by 45.45 %. Post-BESS deployment, green energy capacity reached 3.65 MVA, enhancing technical objectives by 3.74 % and 9.00 %, with a marginal 0.82 % expense increase compared to the case with RESs alone. ? 2024 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo111868
dc.identifier.doi10.1016/j.est.2024.111868
dc.identifier.scopus2-s2.0-85192048658
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85192048658&doi=10.1016%2fj.est.2024.111868&partnerID=40&md5=27c4e0375c38a3d82d018a9d870c77f6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36508
dc.identifier.volume90
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Energy Storage
dc.subjectBattery storage
dc.subjectBenchmarking
dc.subjectCharging (batteries)
dc.subjectDistributed power generation
dc.subjectElectric loads
dc.subjectGenetic algorithms
dc.subjectIntelligent systems
dc.subjectInvestments
dc.subjectMonte Carlo methods
dc.subjectMultiobjective optimization
dc.subjectOperating costs
dc.subjectParticle swarm optimization (PSO)
dc.subjectScreening
dc.subjectSecondary batteries
dc.subjectStochastic systems
dc.subjectWind
dc.subjectBattery energy storage systems
dc.subjectCharging and discharging control strategy
dc.subjectCharging and discharging controls
dc.subjectControl strategies
dc.subjectDistribution systems
dc.subjectGreen energy
dc.subjectHybrid optimization algorithm
dc.subjectLong term planning
dc.subjectRenewable energy source
dc.subjectUncertainty
dc.subjectRenewable energy
dc.titleLong-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetrationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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