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Systematic planning of power-to-gas for improving photovoltaic acceptance rate: Application of the potential RES penetration index

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  • Son, Yeong Geon
  • Choi, Sungyun
  • Aquah, Moses Amoasi
  • Kim, Sung Yul

Abstract

This paper proposes a novel method for integrating renewable energy sources (RES) and power-to-gas (P2G) technologies to optimize energy utilization efficiency as RES capacity increases. The method introduces the potential RES penetration index (PRPI) to select the appropriate capacity of P2G facilities for improved RES acceptance rates. In order to establish input constraints for RES and P2G facilities, this study determines the saturation point of RES (R-SP) and the saturation point of P2G (PG-SP). Additionally, the paper introduces the marginal capacity rate of RES to P2G (MCRG) as a useful tool for evaluating the adequacy of planned facilities. This paper's index is limited to PV as a renewable energy source. By incorporating PRPI into the input plan of P2G facilities, the proposed method improves PV's acceptance rate, contributes to facilities' economic planning, and streamlines optimization complexity. Previous studies have demonstrated the potential for P2G technology to optimize the integration of PV into the grid. However, the proposed method considers the PV's acceptance rate and provides system planners with necessary information on RES and P2G facility capacity. The method proposed in this paper can be extended to include other RES.

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  • Son, Yeong Geon & Choi, Sungyun & Aquah, Moses Amoasi & Kim, Sung Yul, 2023. "Systematic planning of power-to-gas for improving photovoltaic acceptance rate: Application of the potential RES penetration index," Applied Energy, Elsevier, vol. 349(C).
    • Handle: RePEc:eee:appene:v:349:y:2023:i:c:s0306261923009753
    DOI: 10.1016/j.apenergy.2023.121611
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    References listed on IDEAS

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    1. Son, Yeong Geon & Oh, Byeong Chan & Acquah, Moses Amoasi & Kim, Sung Yul, 2023. "Optimal facility combination set of integrated energy system based on consensus point between independent system operator and independent power producer," Energy, Elsevier, vol. 266(C).
    2. Greiml, Matthias & Fritz, Florian & Kienberger, Thomas, 2021. "Increasing installable photovoltaic power by implementing power-to-gas as electricity grid relief – A techno-economic assessment," Energy, Elsevier, vol. 235(C).
    3. Ju, Liwei & Zhao, Rui & Tan, Qinliang & Lu, Yan & Tan, Qingkun & Wang, Wei, 2019. "A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response," Applied Energy, Elsevier, vol. 250(C), pages 1336-1355.
    4. Yue, Meiling & Lambert, Hugo & Pahon, Elodie & Roche, Robin & Jemei, Samir & Hissel, Daniel, 2021. "Hydrogen energy systems: A critical review of technologies, applications, trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    5. Nastasi, Benedetto & Mazzoni, Stefano & Groppi, Daniele & Romagnoli, Alessandro & Astiaso Garcia, Davide, 2021. "Solar power-to-gas application to an island energy system," Renewable Energy, Elsevier, vol. 164(C), pages 1005-1016.
    6. Fambri, Gabriele & Diaz-Londono, Cesar & Mazza, Andrea & Badami, Marco & Sihvonen, Teemu & Weiss, Robert, 2022. "Techno-economic analysis of Power-to-Gas plants in a gas and electricity distribution network system with high renewable energy penetration," Applied Energy, Elsevier, vol. 312(C).
    7. Yeong-Geon Son & Eun-Tae Son & Moses-Amoasi Acquah & Sung-Hoon Choo & Hyun-Sik Jo & Ji-Eun Lee & Dong-Min Kim & Sung-Yul Kim, 2022. "Independent Power Producer Approach to Optimal Design and Operation of IES with Wind Power Plants," Energies, MDPI, vol. 16(1), pages 1-14, December.
    8. Buttler, Alexander & Spliethoff, Hartmut, 2018. "Current status of water electrolysis for energy storage, grid balancing and sector coupling via power-to-gas and power-to-liquids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2440-2454.
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    1. Tahir, Muhammad Faizan & Yousaf, Muhammad Zain & Tzes, Anthony & El Moursi, Mohamed Shawky & El-Fouly, Tarek H.M., 2024. "Enhanced solar photovoltaic power prediction using diverse machine learning algorithms with hyperparameter optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    2. Yan, Yamin & Wang, Yumeng & Yan, Jie & Zhang, Haoran & Shang, Wenlong, 2024. "Wind electricity‑hydrogen-natural gas coupling: An integrated optimization approach for enhancing wind energy accommodation and carbon reduction," Applied Energy, Elsevier, vol. 369(C).

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