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Two-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfiguration

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dc.citedby4
dc.contributor.authorBa-swaimi S.en_US
dc.contributor.authorVerayiah R.en_US
dc.contributor.authorRamachandaramurthy V.K.en_US
dc.contributor.authorALAhmad A.K.en_US
dc.contributor.authorPadmanaban S.en_US
dc.contributor.authorid58510833400en_US
dc.contributor.authorid26431682500en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid59312509000en_US
dc.contributor.authorid59219326900en_US
dc.date.accessioned2025-03-03T07:42:01Z
dc.date.available2025-03-03T07:42:01Z
dc.date.issued2024
dc.description.abstractThis work presents a stochastic two-stage mixed-integer nonlinear programming (MINLP) optimization model for the long-term planning of a distribution system (DS) to improve renewable energy integration over a ten-year period. The outer-stage problem simultaneously minimizes the long-term expected planning costs, power losses, and voltage deviations by determining the optimal sizing and placement of renewable energy resources (RESs), such as solar photovoltaic distributed generators (PV-DGS), wind-DGs, and battery energy storage systems (BESSs). In contrast, the inner-stage problem emphasizes the reduction of hourly operational expenses, power losses, and voltage deviations through the identification of optimal scheduling for demand response programs (DRPs) and network reconfiguration (NR). The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized to address the outer-stage optimization problem. Multi-Objective Particle Swarm Optimization (MOPSO) is employed to address the inner-stage issue. In both phases, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is utilized at the conclusion of each iteration to identify the ideal solution from a collection of non-dominated solutions. Monte Carlo simulation (MCS) is utilized to model the system's unknown factors, including solar radiation, wind speed, load demand, and energy pricing. Subsequently, the backward reduction algorithm (BRA) is employed to streamline the resulting scenarios into a more feasible and representative subset, therefore mitigating excessive computational effort. The suggested model is validated utilizing the IEEE 33-bus DS developed in MATLAB R2023b. Simulation outcomes from various case studies indicate that incorporating optimal DRP and NR scheduling into a hybrid system of RESs and BESSs enhances renewable energy penetration by 17.39% compared to the case utilizing just BESSs. Moreover, the established model, featuring a wind-DG/PV-DG/BESS/DRP/NR configuration, achieves significant improvements in all objective functions, including a 31.14% reduction in total system cost, a 61.67% decrease in power loss, and a 58.11% improvement in voltage deviation, compared to the base case. ? 2024 The Author(s)en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo100766
dc.identifier.doi10.1016/j.ecmx.2024.100766
dc.identifier.scopus2-s2.0-85207794449
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85207794449&doi=10.1016%2fj.ecmx.2024.100766&partnerID=40&md5=a9ce8160f5350c3ec3bdc720c718b7fd
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36344
dc.identifier.volume24
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleEnergy Conversion and Management: X
dc.subjectAssociative storage
dc.subjectAsynchronous generators
dc.subjectBattery storage
dc.subjectCompact disks
dc.subjectDepreciation
dc.subjectGeophysical prospecting
dc.subjectGeothermal fields
dc.subjectInteger programming
dc.subjectMineral exploration
dc.subjectNonlinear programming
dc.subjectNuclear batteries
dc.subjectOrgans (musical instruments)
dc.subjectParallel architectures
dc.subjectPhosphate deposits
dc.subjectSolar energy
dc.subjectStrategic planning
dc.subjectSurface waters
dc.subjectWater wells
dc.subjectWind power
dc.subjectBattery energy storage systems
dc.subjectDemand response programs
dc.subjectLong-term optimal planning
dc.subjectNetworks reconfiguration
dc.subjectOptimal planning
dc.subjectPenetration level
dc.subjectPowerloss
dc.subjectRenewable energy penetration level
dc.subjectRenewable energy penetrations
dc.subjectVoltage deviations
dc.subjectStochastic programming
dc.titleTwo-stage strategic optimal planning of distributed generators and energy storage systems considering demand response program and network reconfigurationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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