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Multiobjective Methodology for Assessing the Location of Distributed Electric Energy Storage

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Table of contents.pdfChapter: Multiobjective Methodology for Assessing the Location of Distributed Electric Energy Storage, pp 227–238 Book: Applications of Evolutionary Computation Part of the book series: Lecture Notes in Computer Science (LNTCS,volume 9028)214.71 KBAdobe PDF Download

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The perception of the associated impacts among possible management schemes introduces a new way to assess energy storage systems. The ability to define a specific management scheme considering the different stakeholder objectives, both technical and economic, will increase the perception of available installation options. This paper presents a multiobjective feasibility assessment methodology using an improved version of the Non-dominated Sorting Genetic Algorithm II, to optimize the placement of electric energy storage units in order to improve the operation of distribution networks. The model is applied to a case study, using lithium-ion battery technology as an example. The results show the influence of different charging/discharging profiles on the choice of the best battery location, as well as the influence that these choices may have on the different network management objectives, e.g. increasing the integration of renewable generation. As an additional outcome, the authors propose a pricing scheme for filling the present regulatory gap regarding the pricing scheme to be applied to energy storage in order to allow the exploitation of viable business models.

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18th European Conference on the Applications of Evolutionary Computation, Evo Applications 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings

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Energy profiles Energy service Energy storage Genetic algorithms NSGAII Power distribution networks

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Springer Nature

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