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Optimal sizing and placement of energy storage systems and on-load tap changer transformers in distribution networks

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  • Iria, José
  • Heleno, Miguel
  • Cardoso, Gonçalo

Abstract

The large-scale deployment of distributed energy resources will produce reverse power flows, voltage, and congestion problems in the distribution networks. This paper proposes a novel optimization model to support distribution system operators planning future medium voltage distribution networks characterized by high penetration of behind-the-meter distributed energy resources. The optimization model defines the optimal mix, placement, and size of on-load tap charger transformers and energy storage devices with the objectives of mitigating network technical problems and minimizing both investment and operation costs. The proposed optimization model relaxes the non-convex formulation of the optimal power flow to a constrained second-order cone programming model and exactly linearizes the non-linear model of the on-load tap changer transformer via binary expansion scheme and big-M method. These two transformations reduce the computational burden of the optimization allowing it to be applicable to real-scale distribution grids, as demonstrated by the results. The numerical results also show that the joint optimization of energy storage devices and on-load tap changer transformers produces a more affordable and flexible planning strategy than the individual optimization of the technologies.

Suggested Citation

  • Iria, José & Heleno, Miguel & Cardoso, Gonçalo, 2019. "Optimal sizing and placement of energy storage systems and on-load tap changer transformers in distribution networks," Applied Energy, Elsevier, vol. 250(C), pages 1147-1157.
    • Handle: RePEc:eee:appene:v:250:y:2019:i:c:p:1147-1157
    DOI: 10.1016/j.apenergy.2019.04.120
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    Cited by:

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    2. Iria, José & Scott, Paul & Attarha, Ahmad, 2020. "Network-constrained bidding optimization strategy for aggregators of prosumers," Energy, Elsevier, vol. 207(C).
    3. Wei, Jingdong & Zhang, Yao & Wang, Jianxue & Cao, Xiaoyu & Khan, Muhammad Armoghan, 2020. "Multi-period planning of multi-energy microgrid with multi-type uncertainties using chance constrained information gap decision method," Applied Energy, Elsevier, vol. 260(C).
    4. Kwang-Hoon Yoon & Joong-Woo Shin & Tea-Yang Nam & Jae-Chul Kim & Won-Sik Moon, 2022. "Operation Method of On-Load Tap Changer on Main Transformer Considering Reverse Power Flow in Distribution System Connected with High Penetration on Photovoltaic System," Energies, MDPI, vol. 15(17), pages 1-17, September.
    5. Li, Jinghua & Lu, Bo & Wang, Zhibang & Zhu, Mengshu, 2021. "Bi-level optimal planning model for energy storage systems in a virtual power plant," Renewable Energy, Elsevier, vol. 165(P2), pages 77-95.
    6. Kim, Hakpyeong & Choi, Heeju & Kang, Hyuna & An, Jongbaek & Yeom, Seungkeun & Hong, Taehoon, 2021. "A systematic review of the smart energy conservation system: From smart homes to sustainable smart cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    7. Marija Miletić & Hrvoje Pandžić & Dechang Yang, 2020. "Operating and Investment Models for Energy Storage Systems," Energies, MDPI, vol. 13(18), pages 1-33, September.
    8. Coelho, António & Iria, José & Soares, Filipe, 2021. "Network-secure bidding optimization of aggregators of multi-energy systems in electricity, gas, and carbon markets," Applied Energy, Elsevier, vol. 301(C).
    9. Zhou, Ze & Liu, Zhitao & Su, Hongye & Zhang, Liyan, 2022. "Integrated pricing strategy for coordinating load levels in coupled power and transportation networks," Applied Energy, Elsevier, vol. 307(C).

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