Analysis of the applicability and results of swarm intelligence tools for the positioning of Energy Storage Systems
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Fecha
2024-12
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Elsevier Ltd
Resumen
The integration of renewable energy is transforming traditional energy systems, blurring the distinction between producers and consumers and shifting towards a distributed grid network. This change demands innovative approaches to optimize Energy Storage Systems (ESS) and manage grid incidents efficiently, all without significant infrastructural changes. While optimization algorithms like Swarm Intelligence are gaining traction, critical aspects, such as worst-case scenario analysis in distribution networks, remain underexplored. This study addresses this gap by applying stochastic optimization techniques to determine the optimal placement and capacity of ESS in a medium voltage radial distribution system, using the IEEE 33-bus model. The findings highlight the importance of considering worst-case scenarios, offering a balanced evaluation of current methodologies. This research provides valuable insights for improving system flexibility and resilience, contributing to more effective and practical energy optimization strategies in real-world applications.
Palabras clave
Distribution systems
Electrical systems
Energy storage systems
Optimization
Stochastic algorithms
Electrical systems
Energy storage systems
Optimization
Stochastic algorithms
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Materias
Cita
Divasson-J, A., Santamaria, I. A., Landaribar, M. T. B., & Aguirre, P. C. (2024). Analysis of the applicability and results of swarm intelligence tools for the positioning of Energy Storage Systems. International Journal of Electrical Power and Energy Systems, 163. https://doi.org/10.1016/J.IJEPES.2024.110343