Advanced Genetic Algorithms for Optimal Battery Siting: A Practical Methodology for Distribution System Operators

Edward Alejandro Ortiz, Josimar Tello-Maita (), David Celeita () and Agustin Marulanda Guerra
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Edward Alejandro Ortiz: Electrical Engineering Program, Universidad Escuela Colombiana de Ingeniería Julio Garavito, Bogotá 111166, Colombia
Josimar Tello-Maita: Electrical Engineering Program, Universidad Escuela Colombiana de Ingeniería Julio Garavito, Bogotá 111166, Colombia
David Celeita: Faculty of Engineering, Universidad de la Sabana, Chía 111321, Colombia
Agustin Marulanda Guerra: Electrical Engineering Program, Universidad Escuela Colombiana de Ingeniería Julio Garavito, Bogotá 111166, Colombia

Energies, 2024, vol. 18, issue 1, 1-29

Abstract: The growing integration of renewable energy sources and the electrification of multiple sectors have heightened the need for optimized planning and operation of modern electrical distribution systems. A critical challenge for distribution network operators is enhancing the resilience and reliability of their grids by identifying effective solutions. One promising approach to achieving this is through the deployment of battery energy storage systems, which can rapidly inject power to mitigate the impacts of network disturbances or outages. This study investigates the use of advanced genetic algorithms as a practical methodology for the optimal siting of batteries in modern distribution networks. By incorporating historical data on demand and network failures, the algorithm generates statistical models that inform the optimization process. The model integrates both the technical and economic aspects of battery systems to identify locations that minimize reliability indices such as SAIDI and SAIFI, while also reducing investment costs. Tested on a real distribution system comprising 1837 nodes, the proposed approach demonstrates the ability of genetic optimization to deliver efficient solutions compared with traditional methods, providing a high likelihood of identifying strategic battery locations that respond to variable demand, system failures, and technical constraints.

Keywords: genetic optimization; battery energy storage system (BESS); distribution power systems; failure modeling; energy storage; reliability; smart grids; power system planning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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