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dc.contributor.advisorSood, Vijay
dc.contributor.authorAli, Mohammad Yasin
dc.date.accessioned2019-10-23T19:30:34Z
dc.date.accessioned2022-03-29T16:49:31Z
dc.date.available2019-10-23T19:30:34Z
dc.date.available2022-03-29T16:49:31Z
dc.date.issued2019-08-01
dc.identifier.urihttps://hdl.handle.net/10155/1094
dc.description.abstractAn Energy Management System (EMS) is required to control the flow of power and match generation with the load within a microgrid during grid-connected and islanded modes of operation. In grid-connected mode, a microgrid draws/supplies power from/to the main grid, depending on the generation and load requirements, and with suitable market policies to maximize the efficiency/cost etc. Likewise, it can separate itself from the main grid whenever a drastic power quality event (such as a fault occurs in the main grid) and continues to supply power to critical loads. An optimization algorithm is needed to minimise the cost of the energy drawn from the grid, generated within the grid and consumed by the loads. In this thesis, two optimization techniques namely Particle Swarm Optimization (PSO) and Differential Evolution (DE) are used to optimize an EMS for a generic MG comprised of Combined Heat and Power (CHP) plant, Diesel generator, Natural gas-fired generator, Photovoltaic (PV) generator and Wind generator. The EMS is tested for both grid-connected and islanded modes of operation to demonstrate the effectiveness of the optimization algorithms. In grid connected mode, the comparison of the most optimal utilization of grid during on- and off-peak hours and achieve the lowest operational cost. Likewise, for islanded mode of operation the comparison between the utilization of the three generators to match the load demand and achieve the lowest operational cost.en
dc.description.sponsorshipUniversity of Ontario Institute of Technologyen
dc.language.isoenen
dc.subjectParticle swarm optimizationen
dc.subjectDifferential evolutionen
dc.titleEnergy management system of a microgrid with distributed generationen
dc.typeThesisen
dc.degree.levelMaster of Applied Science (MASc)en
dc.degree.disciplineElectrical and Computer Engineeringen


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