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An intelligent decentralized energy management strategy for the optimal electric vehicles' charging in low‐voltage islanded microgrids.

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Title: An intelligent decentralized energy management strategy for the optimal electric vehicles' charging in low‐voltage islanded microgrids.
Authors: Boglou, Vasileios1 (AUTHOR) vboglou@ee.duth.gr, Karavas, Christos‐Spyridon2,3 (AUTHOR), Karlis, Athanasios1 (AUTHOR), Arvanitis, Konstantinos2 (AUTHOR)
Source: International Journal of Energy Research. Mar2022, Vol. 46 Issue 3, p2988-3016. 29p.
Subjects: Microgrids, Electric power system reliability, Energy management, Hybrid electric vehicles, Electric vehicles, Electric power consumption, Clean energy, Cognitive maps (Psychology)
Abstract: Summary: The expected significant growth in global electricity demand, followed by the adoption of green energy sources, led to the modernization of the energy distribution grids, such as the development of microgrids that can improve the reliability of the electric power system. Meanwhile, the increased integration of electric vehicles is expected to have a negative impact on power quality, normal operation, and investment costs of the microgrid. In this paper, a microgrid topology was studied for the islanded operation of a low‐voltage distribution network. The deployment of such energy management systems is essential to guarantee the safe and reliable operation of isolated microgrids. Hence, a decentralized energy management system, based on multi‐agent systems, was developed for the efficient charging of electric vehicles, by expanding a state‐of‐the‐art fuzzy logic controller‐based energy management strategy, in combination with a charging power controller, based on fuzzy cognitive maps. According to the authors' best knowledge, this is the first time that fuzzy cognitive maps theory is introduced in EVs' charging management problems. The performance of the proposed multi‐agent decentralized energy management system presented significant reduction in the investment costs of the microgrid, as compared with the use of a sole fuzzy logic controller‐based energy management strategy. The total cost of the microgrid for a 20‐year investment period is decreased by approximately 8.8%. Furthermore, the incorporation of the fuzzy cognitive maps‐based charging power controller leads to a significant amount of chargeable EVs. The mean chargeable EVs increased by 31%. Finally, the proposed energy management system reduces the peak load and load variances approximately 17% and 29%, respectively, without shifting and delaying the charging of the EVs. Hence, the proposed novel charging management system offers an intelligence approach for islanding distribution grids including the high penetration of electric vehicles by presenting operational and financial benefits. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Summary: The expected significant growth in global electricity demand, followed by the adoption of green energy sources, led to the modernization of the energy distribution grids, such as the development of microgrids that can improve the reliability of the electric power system. Meanwhile, the increased integration of electric vehicles is expected to have a negative impact on power quality, normal operation, and investment costs of the microgrid. In this paper, a microgrid topology was studied for the islanded operation of a low‐voltage distribution network. The deployment of such energy management systems is essential to guarantee the safe and reliable operation of isolated microgrids. Hence, a decentralized energy management system, based on multi‐agent systems, was developed for the efficient charging of electric vehicles, by expanding a state‐of‐the‐art fuzzy logic controller‐based energy management strategy, in combination with a charging power controller, based on fuzzy cognitive maps. According to the authors' best knowledge, this is the first time that fuzzy cognitive maps theory is introduced in EVs' charging management problems. The performance of the proposed multi‐agent decentralized energy management system presented significant reduction in the investment costs of the microgrid, as compared with the use of a sole fuzzy logic controller‐based energy management strategy. The total cost of the microgrid for a 20‐year investment period is decreased by approximately 8.8%. Furthermore, the incorporation of the fuzzy cognitive maps‐based charging power controller leads to a significant amount of chargeable EVs. The mean chargeable EVs increased by 31%. Finally, the proposed energy management system reduces the peak load and load variances approximately 17% and 29%, respectively, without shifting and delaying the charging of the EVs. Hence, the proposed novel charging management system offers an intelligence approach for islanding distribution grids including the high penetration of electric vehicles by presenting operational and financial benefits. [ABSTRACT FROM AUTHOR]
ISSN:0363907X
DOI:10.1002/er.7358