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Optimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variables

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dc.citedby13
dc.contributor.authorALAhmad A.K.en_US
dc.contributor.authorVerayiah R.en_US
dc.contributor.authorRamasamy A.en_US
dc.contributor.authorMarsadek M.en_US
dc.contributor.authorShareef H.en_US
dc.contributor.authorid58124002200en_US
dc.contributor.authorid26431682500en_US
dc.contributor.authorid16023154400en_US
dc.contributor.authorid26423183000en_US
dc.contributor.authorid57189691198en_US
dc.date.accessioned2025-03-03T07:44:02Z
dc.date.available2025-03-03T07:44:02Z
dc.date.issued2024
dc.description.abstractThis paper formulates a mixed integer non-linear probabilistic optimization planning problem to determine the optimal location, power rating and capacity of compressed air energy storage system (CAES) for a hybrid power system that includes wind and photo-voltaic (PV) energy sources. The Quasi-Monte Carlo simulation (QMCS) method is adopted to generate multiple scenarios for a combination of wind, PV, load and electricity price uncertainties. Also, Cholesky decomposition is adopted to preserve the actual correlation coefficients among the generated input stochastic variables. Moreover, the QMCS method is combined with the probabilistic load flow (PLF) to track the actual output variables. Three constrained incompatible non-linear objective functions are to be minimized simultaneously including, the total expected planning and operation cost of all generation sources, total expected power losses and the total expected voltage deviation. This optimization problem is solved by the hybrid non-dominated sorting genetic algorithm (NSGAII) and the multi-objective particle swarm optimization (MOPSO). The IEEE 118-bus system is adopted as the large-scale testing system to assess the performance of the proposed methodology and the convergence capability of the hybrid algorithm in rejecting the disturbances in the system caused by the existence of 132 different input correlated stochastic variables. The simulation results show that utilizing bulk CAESs can decrease the dependency on the thermal generators by 15.0984 % and decrease the total investment and operation cost by 25.5026 % compared to the case without utilizing any ESS technology. Also, the hybrid NSGAII-MOPSO proved its capability to converge successfully and reject all the input disturbances which could affect its performance. Moreover, the results show that the voltage on each bus in all scenarios remains within the limits in the presence of large input disturbances. ? 2024 Elsevier Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo110615
dc.identifier.doi10.1016/j.est.2024.110615
dc.identifier.scopus2-s2.0-85183204487
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85183204487&doi=10.1016%2fj.est.2024.110615&partnerID=40&md5=acadc122547d08ddeabcd0100ce31592
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36704
dc.identifier.volume82
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Energy Storage
dc.subjectCompressed air
dc.subjectCompressed air energy storage
dc.subjectElectric energy storage
dc.subjectElectric loads
dc.subjectElectric power system planning
dc.subjectGenetic algorithms
dc.subjectInteger programming
dc.subjectIntelligent systems
dc.subjectInvestments
dc.subjectMultiobjective optimization
dc.subjectNonlinear programming
dc.subjectOperating costs
dc.subjectParticle swarm optimization (PSO)
dc.subjectPower quality
dc.subjectPressure vessels
dc.subjectScreening
dc.subjectStochastic systems
dc.subjectCompressed air energy storage system
dc.subjectCorrelated stochastic variable
dc.subjectMulti objective particle swarm optimization
dc.subjectMulti-objective particle swarm optimization
dc.subjectNon-dominated sorting genetic algorithm (NSAGII)
dc.subjectNon-dominated sorting genetic algorithms
dc.subjectOptimal planning
dc.subjectOptimal planning problem
dc.subjectPlanning problem
dc.subjectProbabilistic load flow
dc.subjectQuasi-Monte Carlo simulation
dc.subjectStochastic variable
dc.subjectStorage systems
dc.subjectMonte Carlo methods
dc.titleOptimal planning of energy storage system for hybrid power system considering multi correlated input stochastic variablesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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