A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids

Ovidiu Ivanov, Bogdan-Constantin Neagu, Gheorghe Grigoras, Florina Scarlatache and Mihai Gavrilas
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Ovidiu Ivanov: Power Engineering Department, Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania
Bogdan-Constantin Neagu: Power Engineering Department, Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania
Gheorghe Grigoras: Power Engineering Department, Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania
Florina Scarlatache: Power Engineering Department, Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania
Mihai Gavrilas: Power Engineering Department, Gheorghe Asachi Technical University of Iasi, 700050 Iasi, Romania

Mathematics, 2021, vol. 9, issue 19, 1-17

Abstract: The global climate change mitigation efforts have increased the efforts of national governments to incentivize local households in adopting PV panels for local electricity generation. Since PV generation is available during the daytime, at off-peak hours, the optimal management of such installations often considers local storage that can defer the use of local generation to a later time. The energy stored in batteries located in optimal places in the network can be used by the utility to improve the operation conditions in the network. This paper proposes a metaheuristic approach based on a genetic algorithm that considers three different scenarios of using energy storage for reducing the energy losses in the network. Two cases considers the battery placement and operation under the direct control of the network operator, with single and multiple bus and phase placement locations. Here, the aim was to maximize the benefit for the whole network. The third case considers selfish prosumer battery management, where the storage owner uses the batteries only for their own benefit. The optimal design of the genetic algorithm and of the solution encoding allows for a comparative study of the results, highlighting the important strengths and weaknesses of each scenario. A case study is performed in a real distribution system.

Keywords: residential electricity distribution networks; renewable generation sources; energy storage; optimization; multipurpose algorithm; genetic algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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