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Typical scenario set generation algorithm for an integrated energy system based on the Wasserstein distance metric

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  • Fu, Xueqian
  • Guo, Qinglai
  • Sun, Hongbin
  • Pan, Zhaoguang
  • Xiong, Wen
  • Wang, Li

Abstract

The stochastic fluctuation characteristics of intermittent renewable energy sources and energy loads, as well as their multi-energy interactions and dependencies, have negligible effects on the operation and analyses of integrated energy systems. Determining how to model the probability characteristics of such systems with high calculation accuracy using limited scenarios is a major difficulty of uncertainty description. This study proposes the use of an optimum quantile method based on the Wasserstein distance metric to generate a typical scenario set in an integrated energy system considering energy correlations based on weather conditions. The use of discrete variables, as opposed to continuous variables based on sampling techniques such as Monte Carlo simulations, sets this study apart from other studies. The uncertainties of a typical network containing power, heat, and gas are analysed, and the results show that the proposed method can produce a typical scenario set with good precision.

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  • Fu, Xueqian & Guo, Qinglai & Sun, Hongbin & Pan, Zhaoguang & Xiong, Wen & Wang, Li, 2017. "Typical scenario set generation algorithm for an integrated energy system based on the Wasserstein distance metric," Energy, Elsevier, vol. 135(C), pages 153-170.
    • Handle: RePEc:eee:energy:v:135:y:2017:i:c:p:153-170
    DOI: 10.1016/j.energy.2017.06.113
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    Cited by:

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    2. Fu, Xueqian & Li, Gengyin & Zhang, Xiurong & Qiao, Zheng, 2018. "Failure probability estimation of the gas supply using a data-driven model in an integrated energy system," Applied Energy, Elsevier, vol. 232(C), pages 704-714.
    3. Fu, Xueqian & Zhang, Xiurong, 2019. "Estimation of building energy consumption using weather information derived from photovoltaic power plants," Renewable Energy, Elsevier, vol. 130(C), pages 130-138.
    4. Chi, Lixun & Su, Huai & Zio, Enrico & Zhang, Jinjun & Li, Xueyi & Zhang, Li & Fan, Lin & Zhou, Jing & Bai, Hua, 2020. "Integrated Deterministic and Probabilistic Safety Analysis of Integrated Energy Systems with bi-directional conversion," Energy, Elsevier, vol. 212(C).
    5. Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
    6. Qin, Chao & Yan, Qingyou & He, Gang, 2019. "Integrated energy systems planning with electricity, heat and gas using particle swarm optimization," Energy, Elsevier, vol. 188(C).
    7. Fu, Xueqian & Zhang, Xiurong, 2018. "Failure probability estimation of gas supply using the central moment method in an integrated energy system," Applied Energy, Elsevier, vol. 219(C), pages 1-10.
    8. Markos A. Kousounadis-Knousen & Ioannis K. Bazionis & Athina P. Georgilaki & Francky Catthoor & Pavlos S. Georgilakis, 2023. "A Review of Solar Power Scenario Generation Methods with Focus on Weather Classifications, Temporal Horizons, and Deep Generative Models," Energies, MDPI, vol. 16(15), pages 1-29, July.
    9. Zhong, Shengyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Deng, Shuai & Li, Yang & Hussain, Sajjad & Wang, Xiaoyuan & Zhu, Jiebei, 2021. "Quantitative analysis of information interaction in building energy systems based on mutual information," Energy, Elsevier, vol. 214(C).
    10. Fu, Xueqian & Li, Gengyin & Wang, Huaizhi, 2018. "Use of a second-order reliability method to estimate the failure probability of an integrated energy system," Energy, Elsevier, vol. 161(C), pages 425-434.
    11. Fu, Xueqian & Zhang, Xiurong & Qiao, Zheng & Li, Gengyin, 2019. "Estimating the failure probability in an integrated energy system considering correlations among failure patterns," Energy, Elsevier, vol. 178(C), pages 656-666.
    12. Hu, Jinxing & Li, Hongru, 2022. "A transfer learning-based scenario generation method for stochastic optimal scheduling of microgrid with newly-built wind farm," Renewable Energy, Elsevier, vol. 185(C), pages 1139-1151.

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