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Long-term optimal planning of distributed generations and battery energy storage systems towards high integration of green energy considering uncertainty and demand response program

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dc.citedby10
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.authorid58510833400en_US
dc.contributor.authorid26431682500en_US
dc.contributor.authorid6602912020en_US
dc.contributor.authorid59312509000en_US
dc.date.accessioned2025-03-03T07:42:05Z
dc.date.available2025-03-03T07:42:05Z
dc.date.issued2024
dc.description.abstractUtilizing renewable energy sources (RESs) offers a pathway towards a cleaner and more sustainable future by reducing carbon emissions, enhancing energy generation independently from conventional methods, and driving innovation in green technologies. Motivated by these goals, this paper introduces a long-term Mixed-Integer Nonlinear Programming (MINLP) multi-objective stochastic optimization planning model to increase the penetration of green energy in the distribution system (DS). The model integrates wind and solar Photovoltaic (PV) distributed generations (DGs) and battery energy storage systems (BESSs). It simultaneously minimizes three long-term objectives: total cost, power loss, and voltage deviation by determining the optimal locations and sizes for wind-DGs, PV-DGs, and BESSs. Additionally, the model incorporates a demand response program (DRP) to enhance green energy integration further. To address uncertainties in wind speed, solar irradiation, load demands, and energy prices, Monte Carlo Simulation (MCS) is employed. Scenario reduction through the Backward Reduction Algorithm (BRA) manages computational complexity. To solve the proposed model, a hybrid approach combining Non-Dominated Sorting Genetic Algorithm II (NSGAII) and Multi-Objective Particle Swarm Optimization (MOPSO) is employed. The proposed model has been considered planning for ten years, and this was simulated and validated on the IEEE 33-bus radial DS using MATLAB R2023b. Four cases were studied to demonstrate the proposed model's effectiveness: base case, DGs, DGs-BESSs, and DGs-BESSs-DRP. The results showed that the model substantially reduces total system cost by 26.27 %, power loss by 50.79 %, and voltage deviation by 47.53 % compared to the base case. Moreover, the integration of DRP significantly increased the green energy penetration by 6.52 % compared to the case without DRP. ? 2024 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo113562
dc.identifier.doi10.1016/j.est.2024.113562
dc.identifier.scopus2-s2.0-85203071101
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85203071101&doi=10.1016%2fj.est.2024.113562&partnerID=40&md5=d4e81c9d2b5194ef1a700ae1b2776216
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36361
dc.identifier.volume100
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Energy Storage
dc.subjectBattery storage
dc.subjectClean energy
dc.subjectDistributed energy
dc.subjectMixed-integer linear programming
dc.subjectParticle swarm optimization (PSO)
dc.subjectStochastic programming
dc.subjectBattery energy storage systems
dc.subjectDemand response programs
dc.subjectGreen energy
dc.subjectHybrid non-dominated sorting genetic algorithm
dc.subjectLong-term optimal planning
dc.subjectMulti objective particle swarm optimization
dc.subjectNon-dominated sorting genetic algorithms
dc.subjectOptimal planning
dc.subjectPhotovoltaic distributed generations
dc.subjectUncertainty
dc.titleLong-term optimal planning of distributed generations and battery energy storage systems towards high integration of green energy considering uncertainty and demand response programen_US
dc.typeArticleen_US
dspace.entity.typePublication
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