Deng, Haotian, Jiang, Jing, Qian, Haiya and Sun, Hongjian (2022) A Microgrid Management System Based on Metaheuristics Particle Swarm Optimization. In: 2022 the 6th International Conference on Smart Grid and Smart Cities. International Conference on Smart Grid and Smart Cities (ICSGSC) . IEEE, Piscataway, US, pp. 126-131. ISBN 9781665487849, 9781665487832, 9781665487825
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Abstract
Microgrid is playing an increasingly important role in making the utility grid more intelligent and efficient, since it can make better use of the renewable energy resources to simultaneously relieve the grid supply pressure and reduce carbon emissions. Innovations in electric technologies, information and communication technologies can facilitate better management of the power transmission and distribution in the microgrid. This paper proposes an optimization strategy, which considers distributed generations, photovoltaics and wind turbines, based on particle swarm optimization for the management of the microgrid. Simulation results demonstrate that with the optimal generation resources management and the effective use of demand side management in the microgrid, the proposed strategy can reduce electricity costs by 29.283% and 32.158% on weekdays and weekends, respectively.
Item Type: | Book Section |
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Additional Information: | Funding information: This work was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 872172. TESTBED2 project.; 2022 the 6th International Conference on Smart Grid and Smart Cities (ICSGSC); Chengdu, China; 22-24 Oct 2022 |
Uncontrolled Keywords: | microgrid, demand side managementoptimization, optimization, photovoltaics, wind turbines |
Subjects: | G900 Others in Mathematical and Computing Sciences H800 Chemical, Process and Energy Engineering |
Department: | Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering |
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Depositing User: | John Coen |
Date Deposited: | 11 Aug 2022 10:50 |
Last Modified: | 07 Dec 2022 12:15 |
URI: | https://nrl.northumbria.ac.uk/id/eprint/49824 |
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