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Incentive-based integrated demand response for multiple energy carriers under complex uncertainties and double coupling effects

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  • Zheng, Shunlin
  • Sun, Yi
  • Li, Bin
  • Qi, Bing
  • Zhang, Xudong
  • Li, Fei

Abstract

Incentive-based integrated demand response has been recognized as a power tool to mitigate supply-demand imbalance in integrated energy systems with high penetration of renewable energy resources. However, complex uncertainties including output uncertainty of renewable energy sources in supply side and responsiveness uncertainty of consumers in demand side and double coupling effects including energy conversion effect in multi-energy aggregator side and appliance coupling effect in consumer side have been a central challenge to design incentive strategies of integrated demand response programs. In this paper, considering the above two uncertainties and two coupling effects, an incentive-based integrated demand response model for multiple energy carriers is proposed. Besides, our model improves the conventional model of energy storage unit to cope with the balancing power deviation from both under-target response and over-target response. In addition, the applicability of integrated demand response is enhanced to be applicable to scenarios where both curtailment integrated demand response programs and absorbing integrated demand response programs are planned simultaneously by adding dynamic parameters. The model is formulated as a bi-level stochastic programming problem based on uncertain programming theory, and corresponding equivalent model is also given to solve the problem effectively. Finally, simulation results demonstrate that the proposed model can achieve a win-win situation between multi-energy aggregator and consumers, showing merits in decreasing the total costs and the risk costs of multi-energy aggregator and increasing the profits of consumers.

Suggested Citation

  • Zheng, Shunlin & Sun, Yi & Li, Bin & Qi, Bing & Zhang, Xudong & Li, Fei, 2021. "Incentive-based integrated demand response for multiple energy carriers under complex uncertainties and double coupling effects," Applied Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316457
    DOI: 10.1016/j.apenergy.2020.116254
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    as
    1. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    2. Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
    3. Imani, Mahmood Hosseini & Ghadi, M. Jabbari & Ghavidel, Sahand & Li, Li, 2018. "Demand Response Modeling in Microgrid Operation: a Review and Application for Incentive-Based and Time-Based Programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 486-499.
    4. Wang, Dan & Hu, Qing'e & Jia, Hongjie & Hou, Kai & Du, Wei & Chen, Ning & Wang, Xudong & Fan, Menghua, 2019. "Integrated demand response in district electricity-heating network considering double auction retail energy market based on demand-side energy stations," Applied Energy, Elsevier, vol. 248(C), pages 656-678.
    5. 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.
    6. Liu, F. & Tait, S. & Schellart, A. & Mayfield, M. & Boxall, J., 2020. "Reducing carbon emissions by integrating urban water systems and renewable energy sources at a community scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    7. 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.
    8. 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.
    9. Fernández-Guillamón, Ana & Gómez-Lázaro, Emilio & Muljadi, Eduard & Molina-García, Ángel, 2019. "Power systems with high renewable energy sources: A review of inertia and frequency control strategies over time," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    10. Zeng, Qing & Fang, Jiakun & Li, Jinghua & Chen, Zhe, 2016. "Steady-state analysis of the integrated natural gas and electric power system with bi-directional energy conversion," Applied Energy, Elsevier, vol. 184(C), pages 1483-1492.
    11. Lu, Qing & Lü, Shuaikang & Leng, Yajun, 2019. "A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response," Energy, Elsevier, vol. 175(C), pages 456-470.
    12. Su, Yongxin & Zhou, Yao & Tan, Mao, 2020. "An interval optimization strategy of household multi-energy system considering tolerance degree and integrated demand response," Applied Energy, Elsevier, vol. 260(C).
    13. Wang, Jianxiao & Zhong, Haiwang & Ma, Ziming & Xia, Qing & Kang, Chongqing, 2017. "Review and prospect of integrated demand response in the multi-energy system," Applied Energy, Elsevier, vol. 202(C), pages 772-782.
    14. Yan, Xing & Ozturk, Yusuf & Hu, Zechun & Song, Yonghua, 2018. "A review on price-driven residential demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 411-419.
    15. Liu, Wenxia & Huang, Yuchen & Li, Zhengzhou & Yang, Yue & Yi, Fang, 2020. "Optimal allocation for coupling device in an integrated energy system considering complex uncertainties of demand response," Energy, Elsevier, vol. 198(C).
    Full references (including those not matched with items on IDEAS)

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