Integration and Operation of Energy Storage Systems in Active Distribution Networks: Economic Optimization via Salp Swarm Optimization DOI Creative Commons
Brandon Cortés-Caicedo, Santiago Bustamante-Mesa, David Leonardo Rodríguez-Salazar

et al.

Electricity, Journal Year: 2025, Volume and Issue: 6(1), P. 11 - 11

Published: March 6, 2025

This paper proposes the integration and operation of lithium-ion battery energy storage systems (ESS) in active distribution networks with high penetration distributed generation based on renewable energy. The goal is to minimize total system costs, including purchasing at substation node, as well ESS integration, maintenance, replacement costs over a 20-year planning horizon. proposed master–slave methodology uses Salp Swarm Optimization Algorithm determine location, technology, daily schemes, combined successive approximation power flow compute objective function value enforce constraints. approach employs discrete–continuous encoding, reducing processing times increasing likelihood finding global optimum. Validated 33-node test adapted Medellín, Colombia, outperformed five metaheuristic algorithms, achieving highest annual savings (USD 16,605.77), lowest average cost 2,964,139.99), fastest time (345.71 s). results demonstrate that enables network operators reduce effectively, offering repeatability, solution quality, computational efficiency.

Language: Английский

Integration and Operation of Energy Storage Systems in Active Distribution Networks: Economic Optimization via Salp Swarm Optimization DOI Creative Commons
Brandon Cortés-Caicedo, Santiago Bustamante-Mesa, David Leonardo Rodríguez-Salazar

et al.

Electricity, Journal Year: 2025, Volume and Issue: 6(1), P. 11 - 11

Published: March 6, 2025

This paper proposes the integration and operation of lithium-ion battery energy storage systems (ESS) in active distribution networks with high penetration distributed generation based on renewable energy. The goal is to minimize total system costs, including purchasing at substation node, as well ESS integration, maintenance, replacement costs over a 20-year planning horizon. proposed master–slave methodology uses Salp Swarm Optimization Algorithm determine location, technology, daily schemes, combined successive approximation power flow compute objective function value enforce constraints. approach employs discrete–continuous encoding, reducing processing times increasing likelihood finding global optimum. Validated 33-node test adapted Medellín, Colombia, outperformed five metaheuristic algorithms, achieving highest annual savings (USD 16,605.77), lowest average cost 2,964,139.99), fastest time (345.71 s). results demonstrate that enables network operators reduce effectively, offering repeatability, solution quality, computational efficiency.

Language: Английский

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