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Optimal operation of active distribution systems based on microgrid structure

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  • Haddadian, Hossein
  • Noroozian, Reza

Abstract

This paper presents a novel probabilistic-based procedure for improving the operation quality of the active distribution systems (ADSs) which have high penetration level of the renewable distributed generations (RDGs). In the proposed policy, the energy storage devices (ESDs) as important parts of the microgrid (MG) are sized and sited in ADS and the operation of ADS is performed simultaneously by integrated management and coordination of all distributed energy resources (i.e. RDGs and ESDs). To model the state of the charge (SOC) of ESDs at each hour, it is combined with the probability density functions (PDFs) of RDGs and loads by Monte Carlo simulation (MCS) algorithm. This issue has not been stated in the previous researches. Also, in an innovative approach, the operation quality is examined more accurately by testing the possibility of MG construction in the modified ADS based on technical criteria such as adequacy, efficiency, voltage profile indices. A powerful multi-objective optimization method named non-dominated genetic algorithm-II (NSGA-II) is employed for solving the problem. The results of the proposed model are compared with those of conventional operation methods by implementation on a 33-bus test ADS.

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  • Haddadian, Hossein & Noroozian, Reza, 2017. "Optimal operation of active distribution systems based on microgrid structure," Renewable Energy, Elsevier, vol. 104(C), pages 197-210.
    • Handle: RePEc:eee:renene:v:104:y:2017:i:c:p:197-210
    DOI: 10.1016/j.renene.2016.12.018
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    References listed on IDEAS

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    1. Schroeder, Andreas, 2011. "Modeling storage and demand management in power distribution grids," Applied Energy, Elsevier, vol. 88(12), pages 4700-4712.
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    Cited by:

    1. Li, Haoran & Zhang, Chenghui & Sun, Bo, 2021. "Optimal design for component capacity of integrated energy system based on the active dispatch mode of multiple energy storages," Energy, Elsevier, vol. 227(C).
    2. Wu, Zhongqun & Yang, Chan & Zheng, Ruijin, 2022. "Developing a holistic fuzzy hierarchy-cloud assessment model for the connection risk of renewable energy microgrid," Energy, Elsevier, vol. 245(C).
    3. Barra, P.H.A. & Coury, D.V. & Fernandes, R.A.S., 2020. "A survey on adaptive protection of microgrids and distribution systems with distributed generators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    4. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Træholt, Chresten, 2018. "Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage," Renewable Energy, Elsevier, vol. 123(C), pages 204-217.

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