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A two-stage distributionally robust optimization model for P2G-CCHP microgrid considering uncertainty and carbon emission

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  • Siqin, Zhuoya
  • Niu, DongXiao
  • Wang, Xuejie
  • Zhen, Hao
  • Li, MingYu
  • Wang, Jingbo

Abstract

The utilization of energy efficient combined cooling heat and power (CCHP) microgrid systems provide an opportunity for us to considering both the increase of economic benefits and environmental costs, simultaneously. The goal of this paper is to propose a P2G-CCHP microgrid system integration framework, connect power to gas (P2G) devices to CCHP microgrid, and provide a two-stage distributionally robust optimization (DRO) model to solve the problem of economic dispatch. DRO model uses the Wasserstein metric to extract the ambiguity set of the probability distribution information of the wind power and photovoltaic output uncertainty. And environmental cost is also considered in the optimal operation of the system. In addition, based on the strong duality theory, the DRO model is transformed into an easy-to-solve MILP problem. The simulation results show that: 1) The introduction of a P2G device can effectively improve the electrical-gas coupling of the P2G-CCHP microgrid system, and improve the stability and economy of the system operation. 2) Considering environmental cost, the pollutant emission of the P2G-CCHP microgrid system is significantly reduced, which ensures the low-carbon operation of system. 3) The DRO model can resist the interference of uncertain wind power and PV output with relatively low conservatism and computational complexity and has the characteristics of data-driven.

Suggested Citation

  • Siqin, Zhuoya & Niu, DongXiao & Wang, Xuejie & Zhen, Hao & Li, MingYu & Wang, Jingbo, 2022. "A two-stage distributionally robust optimization model for P2G-CCHP microgrid considering uncertainty and carbon emission," Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:energy:v:260:y:2022:i:c:s0360544222016991
    DOI: 10.1016/j.energy.2022.124796
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    as
    1. Cui, Qiong & Ma, Peipei & Huang, Lei & Shu, Jie & Luv, Jie & Lu, Lin, 2020. "Effect of device models on the multiobjective optimal operation of CCHP microgrids considering shiftable loads," Applied Energy, Elsevier, vol. 275(C).
    2. Kostevšek, Anja & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Papa, Gregor & Petek, Janez, 2016. "The concept of an ecosystem model to support the transformation to sustainable energy systems," Applied Energy, Elsevier, vol. 184(C), pages 1460-1469.
    3. Li, Yanbin & Zhang, Feng & Li, Yun & Wang, Yuwei, 2021. "An improved two-stage robust optimization model for CCHP-P2G microgrid system considering multi-energy operation under wind power outputs uncertainties," Energy, Elsevier, vol. 223(C).
    4. Varasteh, Farid & Nazar, Mehrdad Setayesh & Heidari, Alireza & Shafie-khah, Miadreza & Catalão, João P.S., 2019. "Distributed energy resource and network expansion planning of a CCHP based active microgrid considering demand response programs," Energy, Elsevier, vol. 172(C), pages 79-105.
    5. Aghamohamadi, Mehrdad & Mahmoudi, Amin, 2019. "From bidding strategy in smart grid toward integrated bidding strategy in smart multi-energy systems, an adaptive robust solution approach," Energy, Elsevier, vol. 183(C), pages 75-91.
    6. Liang, Weikun & Lin, Shunjiang & Lei, Shunbo & Xie, Yuquan & Tang, Zhiqiang & Liu, Mingbo, 2022. "Distributionally robust optimal dispatch of CCHP campus microgrids considering the time-delay of pipelines and the uncertainty of renewable energy," Energy, Elsevier, vol. 239(PC).
    7. Wolfram Wiesemann & Daniel Kuhn & Melvyn Sim, 2014. "Distributionally Robust Convex Optimization," Operations Research, INFORMS, vol. 62(6), pages 1358-1376, December.
    8. Alola, Andrew Adewale & Akadiri, Seyi Saint, 2021. "Clean energy development in the United States amidst augmented socioeconomic aspects and country-specific policies," Renewable Energy, Elsevier, vol. 169(C), pages 221-230.
    9. Blanco, Herib & Faaij, André, 2018. "A review at the role of storage in energy systems with a focus on Power to Gas and long-term storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1049-1086.
    10. Zhang, Yan & Meng, Fanlin & Wang, Rui & Kazemtabrizi, Behzad & Shi, Jianmai, 2019. "Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid," Energy, Elsevier, vol. 179(C), pages 1265-1278.
    11. Wang, Luhao & Li, Qiqiang & Ding, Ran & Sun, Mingshun & Wang, Guirong, 2017. "Integrated scheduling of energy supply and demand in microgrids under uncertainty: A robust multi-objective optimization approach," Energy, Elsevier, vol. 130(C), pages 1-14.
    12. He, Shuaijia & Gao, Hongjun & Wang, Lingfeng & Xiang, Yingmeng & Liu, Junyong, 2020. "Distributionally robust planning for integrated energy systems incorporating electric-thermal demand response," Energy, Elsevier, vol. 213(C).
    13. Muhammad Jabir & Hazlee Azil Illias & Safdar Raza & Hazlie Mokhlis, 2017. "Intermittent Smoothing Approaches for Wind Power Output: A Review," Energies, MDPI, vol. 10(10), pages 1-23, October.
    14. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    15. Rakkyung Ko & Sung-Kwan Joo, 2019. "Stochastic Mixed-Integer Programming (SMIP)-Based Distributed Energy Resource Allocation Method for Virtual Power Plants," Energies, MDPI, vol. 13(1), pages 1-10, December.
    16. Moretti, Luca & Martelli, Emanuele & Manzolini, Giampaolo, 2020. "An efficient robust optimization model for the unit commitment and dispatch of multi-energy systems and microgrids," Applied Energy, Elsevier, vol. 261(C).
    17. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.
    18. Erick Delage & Yinyu Ye, 2010. "Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems," Operations Research, INFORMS, vol. 58(3), pages 595-612, June.
    19. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
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    7. Zhou, Kaile & Fei, Zhineng & Hu, Rong, 2023. "Hybrid robust decentralized optimization of emission-aware multi-energy microgrids considering multiple uncertainties," Energy, Elsevier, vol. 265(C).
    8. Lv, Shuaishuai & Wang, Hui & Meng, Xiangping & Yang, Chengdong & Wang, Mingyue, 2022. "Optimal capacity configuration model of power-to-gas equipment in wind-solar sustainable energy systems based on a novel spatiotemporal clustering algorithm: A pathway towards sustainable development," Renewable Energy, Elsevier, vol. 201(P1), pages 240-255.
    9. Dong, Yingchao & Zhang, Hongli & Ma, Ping & Wang, Cong & Zhou, Xiaojun, 2023. "A hybrid robust-interval optimization approach for integrated energy systems planning under uncertainties," Energy, Elsevier, vol. 274(C).
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