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Distributionally robust planning for integrated energy systems incorporating electric-thermal demand response

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  • He, Shuaijia
  • Gao, Hongjun
  • Wang, Lingfeng
  • Xiang, Yingmeng
  • Liu, Junyong

Abstract

Demand response (DR) can achieve the optimal planning of integrated energy systems (IESs) for improving the economic performance and sustainable development of IESs. However, the integration of renewable energy imposes great uncertainties on IESs planning. In this context, electric-thermal DR is implemented to maximize the net annual profit during the planning period of electric-thermal IES. For avoiding the new electric load peak, electric price-based DR is modified by integrating the peak-valley difference constraint and cost. Meanwhile, the inertia of thermal loads measured by the predicted mean vote (PMV) is considered as the thermal DR measure. In order to deal with uncertainties of wind power, distributionally robust optimization (DRO) method is developed. Especially, the first- and second-moment uncertainties of wind power are considered in the uncertainty set. Further, based on the conditional value at risk (CVaR) and duality theories, the DRO planning model is reformulated to a tractable second order conic programming problem. Finally, the whole planning model is solved by the CPLEX solver. Case studies and comparative analysis are performed based on a representative test system. Numerical results show that the proposed model is effective in improving the net annual profit and solution robustness, and it significantly outperforms other methods.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220318909
    DOI: 10.1016/j.energy.2020.118783
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    Cited by:

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    5. Zhang, Jinliang & Liu, Ziyi, 2024. "Low carbon economic scheduling model for a park integrated energy system considering integrated demand response, ladder-type carbon trading and fine utilization of hydrogen," Energy, Elsevier, vol. 290(C).
    6. Pan, Chongchao & Jin, Tai & Li, Na & Wang, Guanxiong & Hou, Xiaowang & Gu, Yueqing, 2023. "Multi-objective and two-stage optimization study of integrated energy systems considering P2G and integrated demand responses," Energy, Elsevier, vol. 270(C).
    7. Jie Zhu & Buxiang Zhou & Yiwei Qiu & Tianlei Zang & Yi Zhou & Shi Chen & Ningyi Dai & Huan Luo, 2023. "Survey on Modeling of Temporally and Spatially Interdependent Uncertainties in Renewable Power Systems," Energies, MDPI, vol. 16(16), pages 1-19, August.
    8. He, Shuaijia & Gao, Hongjun & Chen, Zhe & Liu, Junyong & Zhao, Liang & Wu, Gang & Xu, Song, 2022. "Low-carbon distribution system planning considering flexible support of zero-carbon energy station," Energy, Elsevier, vol. 244(PB).
    9. He, Shuaijia & Gao, Hongjun & Liu, Junyong & Zhang, Xi & Chen, Zhe, 2022. "Distribution system planning considering peak shaving of energy station," Applied Energy, Elsevier, vol. 312(C).
    10. 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).
    11. Fan, Wei & Tan, Qingbo & Zhang, Amin & Ju, Liwei & Wang, Yuwei & Yin, Zhe & Li, Xudong, 2023. "A Bi-level optimization model of integrated energy system considering wind power uncertainty," Renewable Energy, Elsevier, vol. 202(C), pages 973-991.
    12. Zhang, Houwang & Wu, Qiuwei & Chen, Jian & Lu, Lina & Zhang, Jiangfeng & Zhang, Shuyi, 2023. "Multiple stage stochastic planning of integrated electricity and gas system based on distributed approximate dynamic programming," Energy, Elsevier, vol. 270(C).
    13. Han, Fengwu & Zeng, Jianfeng & Lin, Junjie & Gao, Chong & Ma, Zeyang, 2023. "A novel two-layer nested optimization method for a zero-carbon island integrated energy system, incorporating tidal current power generation," Renewable Energy, Elsevier, vol. 218(C).
    14. He, Shuaijia & Gao, Hongjun & Tang, Zao & Chen, Zhe & Jin, Xiaolong & Liu, Junyong, 2023. "Worst CVaR based energy management for generalized energy storage enabled building-integrated energy systems," Renewable Energy, Elsevier, vol. 203(C), pages 255-266.
    15. Liang, Weikun & Lin, Shunjiang & Liu, Mingbo & Sheng, Xuan & Pan, Yue & Liu, Yun, 2023. "Risk assessment for cascading failures in regional integrated energy system considering the pipeline dynamics," Energy, Elsevier, vol. 270(C).

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