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A robust optimization model for Optimal Feeder Routing and Battery Storage Systems design

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  • Zdraveski, Vasko
  • Todorovski, Mirko

Abstract

The increasing integration of small-scale distributed generation units and electric vehicle present significant challenges to the design of urban electrical distribution networks (DN), introducing considerable uncertainties. This paper addresses these complex challenges by proposing an Optimal Feeder Routing and Battery Storage System (OFRBSS) model. The model offers a single-stage, long-term optimization framework that simplifies load uncertainty to two values – minimum and maximum power demand – thereby enhancing simplicity and efficiency in modelling power demand uncertainty. Leveraging the Column and Constraint Generation algorithm, the OFRBSS model optimizes feeder routing and battery storage system installation, enabling distribution system operators to meet operational constraints under worst power demand conditions. This research bridges existing gaps in integrating optimal DN planning, battery storage system sizing, and placement while addressing the critical aspect of power demand uncertainty. The three-level OFRBSS model is transformed into a two-level counterpart through the application of Karush–Kuhn–Tucker conditions.

Suggested Citation

  • Zdraveski, Vasko & Todorovski, Mirko, 2024. "A robust optimization model for Optimal Feeder Routing and Battery Storage Systems design," Applied Energy, Elsevier, vol. 374(C).
    • Handle: RePEc:eee:appene:v:374:y:2024:i:c:s0306261924013618
    DOI: 10.1016/j.apenergy.2024.123978
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    References listed on IDEAS

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    1. Liang, Hejun & Pirouzi, Sasan, 2024. "Energy management system based on economic Flexi-reliable operation for the smart distribution network including integrated energy system of hydrogen storage and renewable sources," Energy, Elsevier, vol. 293(C).
    2. Motalleb, Mahdi & Reihani, Ehsan & Ghorbani, Reza, 2016. "Optimal placement and sizing of the storage supporting transmission and distribution networks," Renewable Energy, Elsevier, vol. 94(C), pages 651-659.
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