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Optimal scheduling of multi-energy type virtual energy storage system in reconfigurable distribution networks for congestion management

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  • Aghdam, Farid Hamzeh
  • Mudiyanselage, Manthila Wijesooriya
  • Mohammadi-Ivatloo, Behnam
  • Marzband, Mousa

Abstract

The virtual energy storage system (VESS) is one of the emerging novel concepts among current energy storage systems (ESSs) due to the high effectiveness and reliability. In fact, VESS could store surplus energy and inject the energy during the shortages, at high power with larger capacities, compared to the conventional ESSs in smart grids. This study investigates the optimal operation of a multi-carrier VESS, including batteries, thermal energy storage (TES) systems, power to hydrogen (P2H) and hydrogen to power (H2P) technologies in hydrogen storage systems (HSS), and electric vehicles (EVs) in dynamic ESS. Further, demand response program (DRP) for electrical and thermal loads has been considered as a tool of VESS due to the similar behavior of physical ESS. In the market, three participants have considered such as electrical, thermal and hydrogen markets. In addition, the price uncertainties were calculated by means of scenarios as in stochastic programming, while the optimization process and the operational constraints were considered to calculate the operational costs in different ESSs. However, congestion in the power systems is often occurred due to the extreme load increments. Hence, this study proposes a bi-level formulation system, where independent system operators (ISO) manage the congestion in the upper level, while VESS operators deal with the financial goals in the lower level. Moreover, four case studies have considered to observe the effectiveness of each storage system and the simulation was modeled in the IEEE 33-bus system with CPLEX in GAMS.

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  • Aghdam, Farid Hamzeh & Mudiyanselage, Manthila Wijesooriya & Mohammadi-Ivatloo, Behnam & Marzband, Mousa, 2023. "Optimal scheduling of multi-energy type virtual energy storage system in reconfigurable distribution networks for congestion management," Applied Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:appene:v:333:y:2023:i:c:s0306261922018268
    DOI: 10.1016/j.apenergy.2022.120569
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    References listed on IDEAS

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    Cited by:

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    3. Farshad Khavari & Jay Liu, 2024. "A Hydrogen-Integrated Aggregator Model for Managing the Point of Common Coupling Congestion in Green Multi-Microgrids," Energies, MDPI, vol. 17(16), pages 1-20, August.
    4. Wang, Bangyan & Wang, Xifan & Wang, Zhiwei & Wei, Chengxiao & Zhang, Xiao-Ping & Zhou, Mo & Gao, Jiawen & Han, Zhentao, 2024. "A four-stage fast reliability assessment framework for renewables-dominated strong power systems with large-scale energy storage by temporal decoupling and contingencies filtering," Applied Energy, Elsevier, vol. 362(C).
    5. Qitong Fu & Zuoxia Xing & Chao Zhang & Jian Xu, 2024. "A Review and Prospective Study on Modeling Approaches and Applications of Virtual Energy Storage in Integrated Electric–Thermal Energy Systems," Energies, MDPI, vol. 17(16), pages 1-21, August.
    6. Zare Oskouei, Morteza & Gharehpetian, Gevork B., 2024. "Flexibility enhancement of multi-district DISCOs considering a trade-off between congestion and extractable reserve capacity from virtual energy storage systems," Applied Energy, Elsevier, vol. 353(PB).
    7. Lin, Xiaojie & Lin, Xueru & Zhong, Wei & Zhou, Yi, 2024. "Multi-time scale dynamic operation optimization method for industrial park electricity-heat-gas integrated energy system considering demand elasticity," Energy, Elsevier, vol. 293(C).
    8. Yang, Lijun & Jiang, Yaning & Chong, Zhenxiao, 2023. "Optimal scheduling of electro-thermal system considering refined demand response and source-load-storage cooperative hydrogen production," Renewable Energy, Elsevier, vol. 215(C).
    9. Takele Ferede Agajie & Armand Fopah-Lele & Isaac Amoussou & Ahmed Ali & Baseem Khan & Om Prakash Mahela & Ramakrishna S. S. Nuvvula & Divine Khan Ngwashi & Emmanuel Soriano Flores & Emmanuel Tanyi, 2023. "Techno-Economic Analysis and Optimization of Hybrid Renewable Energy System with Energy Storage under Two Operational Modes," Sustainability, MDPI, vol. 15(15), pages 1-31, July.
    10. Pinciroli, Luca & Baraldi, Piero & Compare, Michele & Zio, Enrico, 2023. "Optimal operation and maintenance of energy storage systems in grid-connected microgrids by deep reinforcement learning," Applied Energy, Elsevier, vol. 352(C).

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