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Microgrid operation relying on economic problems considering renewable sources, storage system, and demand-side management using developed gray wolf optimization algorithm

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  • Wang, Xuebin
  • Song, Wenle
  • Wu, Haotian
  • Liang, Haiping
  • Saboor, Ahmed

Abstract

Density of renewable energy sources in distribution systems has developed a novel structure, called microgrid (MG), which consists of small-scale power grids with controllable and uncontrollable loads. MGs are a combination of different distributed generation resources (DGRs) which act as a controllable system at the distribution voltage level and supply power or heat to a group of local loads. Due to the high fluctuations of available power at the distribution voltage level, MGs may fail to supply major consumers. Therefore, several MGs are used by dividing consumers into smaller units; each unit is supplied by one MG. In this study, renewable sources such as wind turbine (WT), photovoltaic (PV) cell, and hydrogen storage system are considered as MGs. A novel energy management strategy is proposed using hydrogen storage system and considering uncertainties of renewable sources in the MG. This strategy aims to minimize the operating costs of batteries and hydrogen storage systems as well as unsupplied and surplus energy costs considering load supply constraints. Various resource constraints are employed in the proposed strategy. Moreover, demand response (DR) program is applied for MG optimal operation. The proposed model is implemented on a system using MATLAB software and meta-heuristic gray wolf optimization (GWO) algorithm. The results indicate applying hydrogen storage system and demand-side management (DSM) program could reduce the final cost of MGs and the proposed method has high efficiency in solving complex problems.

Suggested Citation

  • Wang, Xuebin & Song, Wenle & Wu, Haotian & Liang, Haiping & Saboor, Ahmed, 2022. "Microgrid operation relying on economic problems considering renewable sources, storage system, and demand-side management using developed gray wolf optimization algorithm," Energy, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:energy:v:248:y:2022:i:c:s0360544222003759
    DOI: 10.1016/j.energy.2022.123472
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    References listed on IDEAS

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    2. Gelchu, Milky Ali & Ehnberg, Jimmy & Shiferaw, Dereje & Ahlgren, Erik O., 2023. "Impact of demand-side management on the sizing of autonomous solar PV-based mini-grids," Energy, Elsevier, vol. 278(PA).
    3. R, Revathi & N, Senthilnathan & V, Kumar Chinnaiyan, 2024. "Hybrid optimization approach for power scheduling with PV-battery system in smart grids," Energy, Elsevier, vol. 290(C).
    4. Zhou, Yanting & Ma, Zhongjing & Zhang, Jinhui & Zou, Suli, 2022. "Data-driven stochastic energy management of multi energy system using deep reinforcement learning," Energy, Elsevier, vol. 261(PA).

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