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Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks

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  • Wang, Haixin
  • Yang, Junyou
  • Chen, Zhe
  • Li, Gen
  • Liang, Jun
  • Ma, Yiming
  • Dong, Henan
  • Ji, Huichao
  • Feng, Jiawei

Abstract

The volatility of wind power generations could significantly challenge the economic and secure operation of combined electricity and heat networks. To tackle this challenge, this paper proposes a framework of optimal dispatch with distributed electric heating storage based on a correlation-based long short-term memory prediction model. The prediction model of distributed electric heating storage is developed to model its behavior characteristics which are obtained by the auto-correlation and correlation analysis with external factors including weather and time-of-use price. An optimal dispatch model of combined electricity and heat networks is then formulated and resolved by a constraint reduction technique with clustering and classification. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the mean absolute percentage error with the proposed correlation-based long short-term memory can be reduced by 1.009 and 0.481 respectively. Compared with conventional method, the peak wind power curtailment with dispatching distributed electric heating storage is reduced by nearly 30% and 50% in two cases respectively.

Suggested Citation

  • Wang, Haixin & Yang, Junyou & Chen, Zhe & Li, Gen & Liang, Jun & Ma, Yiming & Dong, Henan & Ji, Huichao & Feng, Jiawei, 2020. "Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks," Applied Energy, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:appene:v:267:y:2020:i:c:s0306261920303913
    DOI: 10.1016/j.apenergy.2020.114879
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    References listed on IDEAS

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    Cited by:

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    3. Keke Wang & Dongxiao Niu & Min Yu & Yi Liang & Xiaolong Yang & Jing Wu & Xiaomin Xu, 2021. "Analysis and Countermeasures of China’s Green Electric Power Development," Sustainability, MDPI, vol. 13(2), pages 1-22, January.
    4. Zhang, Yijie & Ma, Tao & Yang, Hongxing, 2022. "Grid-connected photovoltaic battery systems: A comprehensive review and perspectives," Applied Energy, Elsevier, vol. 328(C).
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    6. Zhilin Lyu & Quan Liu & Bin Liu & Lijun Zheng & Jiaqi Yi & Yongfa Lai, 2022. "Optimal Dispatch of Regional Integrated Energy System Group including Power to Gas Based on Energy Hub," Energies, MDPI, vol. 15(24), pages 1-22, December.
    7. Ji, Huichao & Wang, Haixin & Yang, Junyou & Feng, Jiawei & Yang, Yongyue & Okoye, Martin Onyeka, 2021. "Optimal schedule of solid electric thermal storage considering consumer behavior characteristics in combined electricity and heat networks," Energy, Elsevier, vol. 234(C).
    8. Haichuan Zhao & Ning Yan & Zuoxia Xing & Lei Chen & Libing Jiang, 2020. "Thermal Calculation and Experimental Investigation of Electric Heating and Solid Thermal Storage System," Energies, MDPI, vol. 13(20), pages 1-20, October.
    9. Yin, Linfei & Tao, Min, 2023. "Balanced broad learning prediction model for carbon emissions of integrated energy systems considering distributed ground source heat pump heat storage systems and carbon capture & storage," Applied Energy, Elsevier, vol. 329(C).
    10. Wang, Weijun & Dong, Zeyuan, 2021. "Economic benefits assessment of urban wind power central heating demonstration project considering the quantification of environmental benefits: A case from northern China," Energy, Elsevier, vol. 225(C).
    11. Chen, Maozhi & Lu, Hao & Chang, Xiqiang & Liao, Haiyan, 2023. "An optimization on an integrated energy system of combined heat and power, carbon capture system and power to gas by considering flexible load," Energy, Elsevier, vol. 273(C).
    12. Jiawei Feng & Junyou Yang & Haixin Wang & Huichao Ji & Martin Onyeka Okoye & Jia Cui & Weichun Ge & Bo Hu & Gang Wang, 2020. "Optimal Dispatch of High-Penetration Renewable Energy Integrated Power System Based on Flexible Resources," Energies, MDPI, vol. 13(13), pages 1-19, July.
    13. Yin, Linfei & Tao, Min, 2022. "Correlational broad learning for optimal scheduling of integrated energy systems considering distributed ground source heat pump heat storage systems," Energy, Elsevier, vol. 239(PE).
    14. Shang, Ce & Ge, Yuyou & Zhai, Suwei & Huo, Chao & Li, Wenyun, 2023. "Combined heat and power storage planning," Energy, Elsevier, vol. 279(C).
    15. Li, Ling-Ling & Miao, Yan & Lim, Ming K. & Sethanan, Kanchana & Tseng, Ming-Lang, 2024. "Integrated energy system for low-carbon economic operation optimization: Pareto compromise programming and master-slave game," Renewable Energy, Elsevier, vol. 222(C).

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