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<p>This study proposes a stochastic multi-objective optimization method to enhance the energy storage systems (ESSs), along with wind and photovoltaic renewable energy sources in distribution networks, accounting for uncertainties in renewable power and network load. The multi-objective function involves reducing the costs of energy loss, emissions, and both investment and operational expenses related to energy resources and storage systems. An enhanced meta-heuristic optimization technique, called the improved artificial hummingbird algorithm, is introduced based on a spiral motion approach to address premature convergence and determine the decision variables, including the location and size of the energy storage system, as well as the photovoltaic and wind energy resources in the distribution network. The simulation outcomes are obtained in various scenarios that incorporate renewable resources. With the participation of the stationary ESS, the costs of energy loss and emission are reduced by 31.47% and 1.11%, respectively. Also, the placement of the Mobile ESS with solar and wind resources was presented, and the energy loss cost and cost of emission were reduced by 36.50% and 1.29%, respectively.</p>

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Optimal Planning and Operation of the Smart Electrical Distribution Network Considering Stochastic Optimization Modeling and Energy Storage Systems

  • 期刊论文

  • 发表日期：

    2025年8月15日

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  • Ming Hung Lin,
  • Mohammad Sassani,
  • Navid Golchin,
  • Yeganeh Jabbari,
  • Zulxumorxon Boymatova,
  • Jabbarov Umarbek Rustambekovich,
  • Yuldoshev Jushkinbek Erkaboy Ugli,
  • Saodat Atajanova,
  • Yakitjon Turdiyeva

摘要

This study proposes a stochastic multi-objective optimization method to enhance the energy storage systems (ESSs), along with wind and photovoltaic renewable energy sources in distribution networks, accounting for uncertainties in renewable power and network load. The multi-objective function involves reducing the costs of energy loss, emissions, and both investment and operational expenses related to energy resources and storage systems. An enhanced meta-heuristic optimization technique, called the improved artificial hummingbird algorithm, is introduced based on a spiral motion approach to address premature convergence and determine the decision variables, including the location and size of the energy storage system, as well as the photovoltaic and wind energy resources in the distribution network. The simulation outcomes are obtained in various scenarios that incorporate renewable resources. With the participation of the stationary ESS, the costs of energy loss and emission are reduced by 31.47% and 1.11%, respectively. Also, the placement of the Mobile ESS with solar and wind resources was presented, and the energy loss cost and cost of emission were reduced by 36.50% and 1.29%, respectively.