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Optimal scheduling of electro-thermal system considering refined demand response and source-load-storage cooperative hydrogen production

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  • Yang, Lijun
  • Jiang, Yaning
  • Chong, Zhenxiao

Abstract

How to further increase the level of wind power consumption is a hot research topic in constructing new power systems around the world. It's a consensus that the electrolytic hydrogen production's participation in the power system can further improve the consumption of new energy. This paper proposes a new optimal scheduling strategy for the electro-thermal system, considering refined demand response and source-load-storage cooperative hydrogen production to increase the large-scale wind power consumption. Firstly, to alleviate the impact of wind power fluctuations on hydrogen production, a “wind-storage” combined hydrogen production module is proposed based on the characteristics of hydrogen energy to ensure the safe operation. Secondly, a refined demand response model is built to optimize the electric load curve through price and incentive guidance, to improve the flexibility of “source-load” coordination deeply. Then, to address the issue of wind power competition caused by benefits conflicts of system operation and hydrogen production, a Bi-level scheduling model of source-load-storage cooperative operation is established to achieve the maximum tracking of wind power, while ensure the interests of both sides. Simulation results show that the proposed strategy can balance the system operation cost and hydrogen production benefits, improve hydrogen revenue by 24%. And even if the capacity of hydrogen production device is low, 100% consumption of wind power can be achieved. This study is heuristic for the large-scale wind power consumption in the multi-energy coupled environment, and provides an idea for operation of new energy power system.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:renene:v:215:y:2023:i:c:s096014812300736x
    DOI: 10.1016/j.renene.2023.05.103
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    References listed on IDEAS

    as
    1. Khalili, Reza & Khaledi, Arian & Marzband, Mousa & Nematollahi, Amin Foroughi & Vahidi, Behrooz & Siano, Pierluigi, 2023. "Robust multi-objective optimization for the Iranian electricity market considering green hydrogen and analyzing the performance of different demand response programs," Applied Energy, Elsevier, vol. 334(C).
    2. Dong, Xiangxiang & Wu, Jiang & Xu, Zhanbo & Liu, Kun & Guan, Xiaohong, 2022. "Optimal coordination of hydrogen-based integrated energy systems with combination of hydrogen and water storage," Applied Energy, Elsevier, vol. 308(C).
    3. Saberi-Beglar, Kasra & Zare, Kazem & Seyedi, Heresh & Marzband, Mousa & Nojavan, Sayyad, 2023. "Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads," Applied Energy, Elsevier, vol. 329(C).
    4. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
    5. Liu, Ming & Wang, Shan & Zhao, Yongliang & Tang, Haiyu & Yan, Junjie, 2019. "Heat–power decoupling technologies for coal-fired CHP plants: Operation flexibility and thermodynamic performance," Energy, Elsevier, vol. 188(C).
    6. Bornapour, Mosayeb & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2019. "An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in micro grids," Renewable Energy, Elsevier, vol. 130(C), pages 1049-1066.
    7. Kim, Min Jae & Kim, Tong Seop & Flores, Robert J. & Brouwer, Jack, 2020. "Neural-network-based optimization for economic dispatch of combined heat and power systems," Applied Energy, Elsevier, vol. 265(C).
    8. Yang, Xiaohui & Wang, Xiaopeng & Leng, Zhengyang & Deng, Yeheng & Deng, Fuwei & Zhang, Zhonglian & Yang, Li & Liu, Xiaoping, 2023. "An optimized scheduling strategy combining robust optimization and rolling optimization to solve the uncertainty of RES-CCHP MG," Renewable Energy, Elsevier, vol. 211(C), pages 307-325.
    9. Shen, Bo & Ghatikar, Girish & Lei, Zeng & Li, Jinkai & Wikler, Greg & Martin, Phil, 2014. "The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges," Applied Energy, Elsevier, vol. 130(C), pages 814-823.
    10. Huang, Chunjun & Zong, Yi & You, Shi & Træholt, Chresten & Zheng, Yi & Wang, Jiawei & Zheng, Zixuan & Xiao, Xianyong, 2023. "Economic and resilient operation of hydrogen-based microgrids: An improved MPC-based optimal scheduling scheme considering security constraints of hydrogen facilities," Applied Energy, Elsevier, vol. 335(C).
    11. Gao, Shuang & Li, Hailong & Hou, Yichen & Yan, Jinyue, 2023. "Benefits of integrating power-to-heat assets in CHPs," Applied Energy, Elsevier, vol. 335(C).
    12. Mimica, Marko & Dominković, Dominik Franjo & Capuder, Tomislav & Krajačić, Goran, 2021. "On the value and potential of demand response in the smart island archipelago," Renewable Energy, Elsevier, vol. 176(C), pages 153-168.
    13. Salvia, Monica & Reckien, Diana & Pietrapertosa, Filomena & Eckersley, Peter & Spyridaki, Niki-Artemis & Krook-Riekkola, Anna & Olazabal, Marta & De Gregorio Hurtado, Sonia & Simoes, Sofia G. & Genele, 2021. "Will climate mitigation ambitions lead to carbon neutrality? An analysis of the local-level plans of 327 cities in the EU," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    14. Richard Oberdieck & Nikolaos A. Diangelakis & Styliani Avraamidou & Efstratios N. Pistikopoulos, 2017. "On unbounded and binary parameters in multi-parametric programming: applications to mixed-integer bilevel optimization and duality theory," Journal of Global Optimization, Springer, vol. 69(3), pages 587-606, November.
    15. 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).
    16. Yu, Mengmeng & Lu, Renzhi & Hong, Seung Ho, 2016. "A real-time decision model for industrial load management in a smart grid," Applied Energy, Elsevier, vol. 183(C), pages 1488-1497.
    17. Xu, Qingyang & Sun, Feihu & Cai, Qiran & Liu, Li-Jing & Zhang, Kun & Liang, Qiao-Mei, 2022. "Assessment of the influence of demand-side responses on high-proportion renewable energy system: An evidence of Qinghai, China," Renewable Energy, Elsevier, vol. 190(C), pages 945-958.
    18. Han, Ouzhu & Ding, Tao & Zhang, Xiaosheng & Mu, Chenggang & He, Xinran & Zhang, Hongji & Jia, Wenhao & Ma, Zhoujun, 2023. "A shared energy storage business model for data center clusters considering renewable energy uncertainties," Renewable Energy, Elsevier, vol. 202(C), pages 1273-1290.
    19. Liu, Ming & Wang, Shan & Yan, Junjie, 2021. "Operation scheduling of a coal-fired CHP station integrated with power-to-heat devices with detail CHP unit models by particle swarm optimization algorithm," Energy, Elsevier, vol. 214(C).
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    1. Xiaozhi Gao & Han Xiao & Shiwei Xu & Hsiung-Cheng Lin & Pengyu Chang, 2024. "What Is the Optimal Solution for Scheduling Multiple Energy Systems? Overview and Analysis of Integrated Energy Co-Dispatch Models," Energies, MDPI, vol. 17(18), pages 1-25, September.
    2. Wu, Qiong & Chen, Min & Ren, Hongbo & Li, Qifen & Gao, Weijun, 2024. "Collaborative modeling and optimization of energy hubs and multi-energy network considering hydrogen energy," Renewable Energy, Elsevier, vol. 227(C).

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