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Optimal capacity of storage systems and photovoltaic systems able to control reactive power using the sensitivity analysis method

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  • Kim, Insu

Abstract

As weather-dependent distributed renewable energy resources (RERs) such as photovoltaic (PV) systems and wind farms have increasingly been connected to distribution networks, energy storage systems able to compensate intermittency in their power generation may be required. Moreover, such RERs can participate in reactive power control upon voltage regulation. Thus, the problem of optimizing the capacity of storage systems for RERs with the capability of reactive power control is necessary for planning, maintaining, or upgrading a distribution network. The objective of this study is to optimize the capacity of storage systems for RERs, particularly PV inverters with the capability of reactive power control in this study. For this purpose, this study proposes the power-flow algorithm able to optimize reactive power amount to be either consumed or injected by PV systems and a hybrid multi-objective sensitivity analysis algorithm that optimizes the capacity of PV and storage systems. The proposed algorithm includes an objective function that minimizes voltage variations and capital costs of PV and storage as well as maximizes energy savings and peak load reduction. Then, it successfully optimizes the capacity of PV and storage systems in the well-known IEEE test feeders.

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  • Kim, Insu, 2018. "Optimal capacity of storage systems and photovoltaic systems able to control reactive power using the sensitivity analysis method," Energy, Elsevier, vol. 150(C), pages 642-652.
    • Handle: RePEc:eee:energy:v:150:y:2018:i:c:p:642-652
    DOI: 10.1016/j.energy.2017.12.132
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    References listed on IDEAS

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    Cited by:

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    2. Barukčić, M. & Hederić, Ž. & Hadžiselimović, M. & Seme, S., 2018. "A simple stochastic method for modelling the uncertainty of photovoltaic power production based on measured data," Energy, Elsevier, vol. 165(PB), pages 246-256.
    3. Donghyeon Lee & Seungwan Son & Insu Kim, 2021. "Optimal Allocation of Large-Capacity Distributed Generation with the Volt/Var Control Capability Using Particle Swarm Optimization," Energies, MDPI, vol. 14(11), pages 1-19, May.
    4. Merad, Faycel & Labar, Hocine & Samira KELAIAIA, Mounia & Necaibia, Salah & Djelailia, Okba, 2019. "A maximum power control based on flexible collector applied to concentrator solar power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 315-331.
    5. Jaemin Park & Haesung Jo & Insu Kim, 2021. "The Selection of the Most Cost-Efficient Distributed Generation Type for a Combined Cooling Heat and Power System Used for Metropolitan Residential Customers," Energies, MDPI, vol. 14(18), pages 1-25, September.
    6. Mahdavi, Sajad & Hemmati, Reza & Jirdehi, Mehdi Ahmadi, 2018. "Two-level planning for coordination of energy storage systems and wind-solar-diesel units in active distribution networks," Energy, Elsevier, vol. 151(C), pages 954-965.
    7. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.
    8. Wang, Chutong & Zhang, Xiaoyan & Wang, Yucui & Xiong, Houbo & Ding, Xi & Guo, Chuangxin, 2023. "Pricing method of electric-thermal heterogeneous shared energy storage service," Energy, Elsevier, vol. 281(C).

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