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<p>As the integration of distributed generation (DG) and smart grid technologies grows, the need for enhanced reliability and efficiency in power systems becomes increasingly paramount. Energy storage systems (ESS) play a crucial role in achieving these objectives, particularly in enabling effective islanding operations during emergencies. This research leverages genetic algorithms to identify optimal combinations of ESS units and strategic load curtailment techniques to mitigate potential contingencies. The results demonstrate that integrating ESS significantly improves network reliability, optimizes infrastructure utilization, and is particularly beneficial in regions with high commercial and industrial load percentages. Moreover, the study underscores the critical role of load shedding in balancing reliability improvements and the sizing of DG units, offering valuable insights for future energy management approaches. By employing binary load curtailment strategies, the research determines the optimal location and size of ESS and DG units within the distribution network. The analysis reveals that ESS integration leads to a reduction in net annual costs while providing valuable insights into network reliability through the assessment of expected energy not supplied. Two case studies comparing various storage technologies with a base case without ESS highlight the cost-effectiveness of enhancing system reliability through distributed storage allocation. The study also emphasizes the significance of load shedding in balancing reliability enhancement and DG sizing, examining the effects of DG placement across different network buses. Finally, this article investigates the impact of diverse load mixes, indicating the need for larger ESS units as the proportion of commercial and industrial loads increases, accompanied by rising costs. Through these comprehensive analyses, the study offers valuable insights into optimizing the placement of distributed storage units and improving the reliability of distribution systems.</p>

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Optimizing the placement of distributed energy storage and improving distribution power system reliability via genetic algorithms and strategic load curtailment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月18日

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  • Anfaj Islam,
  • Souman Rudra,
  • Mohan Lal Kolhe

摘要

As the integration of distributed generation (DG) and smart grid technologies grows, the need for enhanced reliability and efficiency in power systems becomes increasingly paramount. Energy storage systems (ESS) play a crucial role in achieving these objectives, particularly in enabling effective islanding operations during emergencies. This research leverages genetic algorithms to identify optimal combinations of ESS units and strategic load curtailment techniques to mitigate potential contingencies. The results demonstrate that integrating ESS significantly improves network reliability, optimizes infrastructure utilization, and is particularly beneficial in regions with high commercial and industrial load percentages. Moreover, the study underscores the critical role of load shedding in balancing reliability improvements and the sizing of DG units, offering valuable insights for future energy management approaches. By employing binary load curtailment strategies, the research determines the optimal location and size of ESS and DG units within the distribution network. The analysis reveals that ESS integration leads to a reduction in net annual costs while providing valuable insights into network reliability through the assessment of expected energy not supplied. Two case studies comparing various storage technologies with a base case without ESS highlight the cost-effectiveness of enhancing system reliability through distributed storage allocation. The study also emphasizes the significance of load shedding in balancing reliability enhancement and DG sizing, examining the effects of DG placement across different network buses. Finally, this article investigates the impact of diverse load mixes, indicating the need for larger ESS units as the proportion of commercial and industrial loads increases, accompanied by rising costs. Through these comprehensive analyses, the study offers valuable insights into optimizing the placement of distributed storage units and improving the reliability of distribution systems.