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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

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Listed:
  • Ju, Liwei
  • Zhao, Rui
  • Tan, Qinliang
  • Lu, Yan
  • Tan, Qingkun
  • Wang, Wei

Abstract

Power-to-gas (P2G) provides a new means for accommodating abandoned new energy that will support the optimal operation of virtual power plants (VPPs) in the future. In this study, a novel structure of a P2G-based virtual power plant (GVPP) is designed. A flexible risk aversion model for GVPP operation is proposed with two objectives—maximum operation profit and minimum operation risk. In the model, the conditional value at risk method and robust optimization theory are utilized to reflect uncertainty risks. To solve the multi-objective model, a solution algorithm was constructed for iteratively obtaining the optimal weight coefficient for transforming the multi-objective model into a single-objective model based on a payoff table and a rough set method. Four simulation cases were set for comparative analysis based on a nine-node energy hub system. The results show the following outcomes: (1) GVPP realizes the complementary utilization of distributed energy and forms a power-gas-power recycling mode, (2) the proposed model can provide an effective decision-making tool for different risk-attitude decision makers by setting a reasonable confidence level and robust coefficient, (3) P2G and price-based demand response (PBDR) improve the grid-connected space of clean energy, in particular, PBDR improves the flexibility of system operation and reduces operation risk, and (4) if the maximum emission trade allowance (META) is considered, P2G preferentially converts carbon dioxide into methane when the META is lower, while clean energy is preferentially used to satisfy load demand and the methane produced by P2G can be sold to the natural gas network when the META is higher. Overall, the proposed optimal decision model achieves the maximum utilization of clean energy to obtain higher economic benefits while rationally controlling operation risks; hence, providing reliable support for decision makers.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:250:y:2019:i:c:p:1336-1355
    DOI: 10.1016/j.apenergy.2019.05.027
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    as
    1. He, Liangce & Lu, Zhigang & Zhang, Jiangfeng & Geng, Lijun & Zhao, Hao & Li, Xueping, 2018. "Low-carbon economic dispatch for electricity and natural gas systems considering carbon capture systems and power-to-gas," Applied Energy, Elsevier, vol. 224(C), pages 357-370.
    2. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    3. Ju, Liwei & Tan, Zhongfu & Li, Huanhuan & Tan, Qingkun & Yu, Xiaobao & Song, Xiaohua, 2016. "Multi-objective operation optimization and evaluation model for CCHP and renewable energy based hybrid energy system driven by distributed energy resources in China," Energy, Elsevier, vol. 111(C), pages 322-340.
    4. Zamani, Ali Ghahgharaee & Zakariazadeh, Alireza & Jadid, Shahram, 2016. "Day-ahead resource scheduling of a renewable energy based virtual power plant," Applied Energy, Elsevier, vol. 169(C), pages 324-340.
    5. Guandalini, Giulio & Campanari, Stefano & Romano, Matteo C., 2015. "Power-to-gas plants and gas turbines for improved wind energy dispatchability: Energy and economic assessment," Applied Energy, Elsevier, vol. 147(C), pages 117-130.
    6. van Leeuwen, Charlotte & Mulder, Machiel, 2018. "Power-to-gas in electricity markets dominated by renewables," Applied Energy, Elsevier, vol. 232(C), pages 258-272.
    7. Zakariazadeh, Alireza & Homaee, Omid & Jadid, Shahram & Siano, Pierluigi, 2014. "A new approach for real time voltage control using demand response in an automated distribution system," Applied Energy, Elsevier, vol. 117(C), pages 157-166.
    8. He, Yong & Liao, Nuo & Zhou, Ya, 2018. "Analysis on provincial industrial energy efficiency and its influencing factors in China based on DEA-RS-FANN," Energy, Elsevier, vol. 142(C), pages 79-89.
    9. Qiao, Zheng & Guo, Qinglai & Sun, Hongbin & Pan, Zhaoguang & Liu, Yuquan & Xiong, Wen, 2017. "An interval gas flow analysis in natural gas and electricity coupled networks considering the uncertainty of wind power," Applied Energy, Elsevier, vol. 201(C), pages 343-353.
    10. Zhang, Xiaojin & Bauer, Christian & Mutel, Christopher L. & Volkart, Kathrin, 2017. "Life Cycle Assessment of Power-to-Gas: Approaches, system variations and their environmental implications," Applied Energy, Elsevier, vol. 190(C), pages 326-338.
    11. Götz, Manuel & Lefebvre, Jonathan & Mörs, Friedemann & McDaniel Koch, Amy & Graf, Frank & Bajohr, Siegfried & Reimert, Rainer & Kolb, Thomas, 2016. "Renewable Power-to-Gas: A technological and economic review," Renewable Energy, Elsevier, vol. 85(C), pages 1371-1390.
    12. Qu, Kaiping & Yu, Tao & Zhang, Xiaoshun & Li, Haofei, 2019. "Homogenized adjacent points method: A novel Pareto optimizer for linearized multi-objective optimal energy flow of integrated electricity and gas system," Applied Energy, Elsevier, vol. 233, pages 338-351.
    13. Wang, Xuebin & Chang, Jianxia & Meng, Xuejiao & Wang, Yimin, 2018. "Short-term hydro-thermal-wind-photovoltaic complementary operation of interconnected power systems," Applied Energy, Elsevier, vol. 229(C), pages 945-962.
    14. Sousa, Tiago & Morais, Hugo & Vale, Zita & Castro, Rui, 2015. "A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context," Energy, Elsevier, vol. 85(C), pages 236-250.
    15. Wang, Jianxiao & Zhong, Haiwang & Tang, Wenyuan & Rajagopal, Ram & Xia, Qing & Kang, Chongqing & Wang, Yi, 2017. "Optimal bidding strategy for microgrids in joint energy and ancillary service markets considering flexible ramping products," Applied Energy, Elsevier, vol. 205(C), pages 294-303.
    16. Shabanzadeh, Morteza & Sheikh-El-Eslami, Mohammad-Kazem & Haghifam, Mahmoud-Reza, 2017. "An interactive cooperation model for neighboring virtual power plants," Applied Energy, Elsevier, vol. 200(C), pages 273-289.
    17. Wei, Zhinong & Chen, Sheng & Sun, Guoqiang & Wang, Dan & Sun, Yonghui & Zang, Haixiang, 2016. "Probabilistic available transfer capability calculation considering static security constraints and uncertainties of electricity–gas integrated energy systems," Applied Energy, Elsevier, vol. 167(C), pages 305-316.
    18. Soares, João & Fotouhi Ghazvini, Mohammad Ali & Vale, Zita & de Moura Oliveira, P.B., 2016. "A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads," Applied Energy, Elsevier, vol. 162(C), pages 1074-1088.
    19. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    20. Tan, Zhongfu & Wang, Guan & Ju, Liwei & Tan, Qingkun & Yang, Wenhai, 2017. "Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand r," Energy, Elsevier, vol. 124(C), pages 198-213.
    21. Ju, Liwei & Zhang, Qi & Tan, Zhongfu & Wang, Wei & Xin, He & Zhang, Zehao, 2018. "Multi-agent-system-based coupling control optimization model for micro-grid group intelligent scheduling considering autonomy-cooperative operation strategy," Energy, Elsevier, vol. 157(C), pages 1035-1052.
    22. Jin, S.W. & Li, Y.P. & Huang, G.H. & Nie, S., 2018. "Analyzing the performance of clean development mechanism for electric power systems under uncertain environment," Renewable Energy, Elsevier, vol. 123(C), pages 382-397.
    23. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
    24. Zhou, Yizhou & Wei, Zhinong & Sun, Guoqiang & Cheung, Kwok W. & Zang, Haixiang & Chen, Sheng, 2018. "A robust optimization approach for integrated community energy system in energy and ancillary service markets," Energy, Elsevier, vol. 148(C), pages 1-15.
    25. Perera, A.T.D. & Nik, Vahid M. & Mauree, Dasaraden & Scartezzini, Jean-Louis, 2017. "An integrated approach to design site specific distributed electrical hubs combining optimization, multi-criterion assessment and decision making," Energy, Elsevier, vol. 134(C), pages 103-120.
    26. Jun Xie & Chi Cao, 2017. "Non-Convex Economic Dispatch of a Virtual Power Plant via a Distributed Randomized Gradient-Free Algorithm," Energies, MDPI, vol. 10(7), pages 1-12, July.
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