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Optimal Energy Management for Virtual Power Plant Considering Operation and Degradation Costs of Energy Storage System and Generators

Author

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  • Kanjanapon Borisoot

    (Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Rittichai Liemthong

    (Business Development Engineer, Sermsang Power Corporation Public Company Limited, Bangkok 10300, Thailand)

  • Chitchai Srithapon

    (Department of Electrical Engineering, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden)

  • Rongrit Chatthaworn

    (Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
    Center for Alternative Energy Research and Development, Khon Kaen University, Khon Kaen 40002, Thailand)

Abstract

Even though generating electricity from Renewable Energy (RE) and electrification of transportation with Electric Vehicles (EVs) can reduce climate change impacts, uncertainties of the RE and charged demand of EVs are significant challenges for energy management in power systems. To deal with this problem, this paper proposes an optimal energy management method using the Virtual Power Plant (VPP) concept for the power system considering solar PhotoVoltaics (PVs) and Electric Vehicle Charging Stations (EVCS). The Differential Evolution (DE) algorithm is applied to manage energy in the power system to minimize the operation cost of generators and degradation costs in Energy Storage Systems (ESS) and generators. The effectiveness of the proposed approach is examined and tested on the IEEE 24 bus Reliability Test System (RTS 24) using the MATPOWER tool on the MATLAB program for calculating Optimal Power Flow (OPF). In this study, two situations before and after applying the proposed method are considered. The simulation results demonstrate that a branch constraint violation occurs before using optimal energy management using the VPP concept. In order to solve this issue, the DE algorithm for optimal energy management using the VPP concept is applied by dividing the studied case into two subcases as follows. For the first subcase, two objectives consisting of the minimization of the generator operation cost and the minimization of the battery degradation cost in ESS are considered. In the second case, three objectives comprising the two mentioned objectives with the minimization of generator degradation cost are considered. The results demonstrate that branch constraint violations can be avoided by applying optimal energy management using the VPP concept. This study also suggests considering the generator degradation cost in the objective function, which can minimize the total costs by 7.06% per day compared with the total cost of the first case.

Suggested Citation

  • Kanjanapon Borisoot & Rittichai Liemthong & Chitchai Srithapon & Rongrit Chatthaworn, 2023. "Optimal Energy Management for Virtual Power Plant Considering Operation and Degradation Costs of Energy Storage System and Generators," Energies, MDPI, vol. 16(6), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2862-:d:1101979
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    References listed on IDEAS

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    1. Zhang, Xiongwen & Tan, Siew-Chong & Li, Guojun & Li, Jun & Feng, Zhenping, 2013. "Components sizing of hybrid energy systems via the optimization of power dispatch simulations," Energy, Elsevier, vol. 52(C), pages 165-172.
    2. Partha Pratim Dey & Dulal Chandra Das & Abdul Latif & S. M. Suhail Hussain & Taha Selim Ustun, 2020. "Active Power Management of Virtual Power Plant under Penetration of Central Receiver Solar Thermal-Wind Using Butterfly Optimization Technique," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    3. Zuoyu Liu & Weimin Zheng & Feng Qi & Lei Wang & Bo Zou & Fushuan Wen & You Xue, 2018. "Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading," Energies, MDPI, vol. 11(6), pages 1-19, June.
    4. Li, Shuijia & Gong, Wenyin & Hu, Chengyu & Yan, Xuesong & Wang, Ling & Gu, Qiong, 2021. "Adaptive constraint differential evolution for optimal power flow," Energy, Elsevier, vol. 235(C).
    5. Naval, Natalia & Sánchez, Raul & Yusta, Jose M., 2020. "A virtual power plant optimal dispatch model with large and small-scale distributed renewable generation," Renewable Energy, Elsevier, vol. 151(C), pages 57-69.
    6. Peng Cheng & Hai Wang & Vladimir Stojanovic & Fei Liu & Shuping He & Kaibo Shi, 2022. "Dissipativity-based finite-time asynchronous output feedback control for wind turbine system via a hidden Markov model," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(15), pages 3177-3189, November.
    7. Roth, Ian F. & Ambs, Lawrence L., 2004. "Incorporating externalities into a full cost approach to electric power generation life-cycle costing," Energy, Elsevier, vol. 29(12), pages 2125-2144.
    8. Shafiekhani, Morteza & Ahmadi, Abdollah & Homaee, Omid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal bidding strategy of a renewable-based virtual power plant including wind and solar units and dispatchable loads," Energy, Elsevier, vol. 239(PD).
    9. Kasaei, Mohammad Javad & Gandomkar, Majid & Nikoukar, Javad, 2017. "Optimal management of renewable energy sources by virtual power plant," Renewable Energy, Elsevier, vol. 114(PB), pages 1180-1188.
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    1. Myada Shadoul & Rashid Al Abri & Hassan Yousef & Abdullah Al Shereiqi, 2024. "Designing a Dispatch Engine for Hybrid Renewable Power Stations Using a Mixed-Integer Linear Programming Technique," Energies, MDPI, vol. 17(13), pages 1-27, July.

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