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Multi-time scale optimal scheduling of integrated electricity and district heating systems considering thermal comfort of users: An enhanced-interval optimization method

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  • Liu, Xinrui
  • Hou, Min
  • Sun, Siluo
  • Wang, Jiawei
  • Sun, Qiuye
  • Dong, Chaoyu

Abstract

Due to the high integration of renewable energy, renewable energy curtailment has become a key issue that needs to be solved urgently. Aiming at the areas with the characteristics of curtailed wind energy and strong thermal demand, a multi-time scale optimal scheduling model for improving the wind power integration in heating seasons was proposed. In the framework, the enhanced-interval optimization method (EIOM) was applied, by considering the uncertainties of wind power and loads in an integrated electricity and district heating system. Considering the dynamic characteristics of the heating network and the thermal inertia of building, the factors affecting users comfort are analyzed. Then the comfort model parameters based on different user types are designed. The EIOM was reformulated by decomposing into two sub-models. A test system including a 30-node electric network and two 15-node heating networks was used to apply the proposed model. The simulation proves that the EIOM can solve the impact of load uncertainty and obtain the optimal operation cost.

Suggested Citation

  • Liu, Xinrui & Hou, Min & Sun, Siluo & Wang, Jiawei & Sun, Qiuye & Dong, Chaoyu, 2022. "Multi-time scale optimal scheduling of integrated electricity and district heating systems considering thermal comfort of users: An enhanced-interval optimization method," Energy, Elsevier, vol. 254(PB).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pb:s0360544222012142
    DOI: 10.1016/j.energy.2022.124311
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    2. Wang, Xiaojing & Han, Li & Wang, Chong & Yu, Hongbo & Yu, Xiaojiao, 2023. "A time-scale adaptive dispatching strategy considering the matching of time characteristics and dispatching periods of the integrated energy system," Energy, Elsevier, vol. 267(C).
    3. Yang, Xiaohui & Wang, Xiaopeng & Deng, Yeheng & Mei, Linghao & Deng, Fuwei & Zhang, Zhonglian, 2023. "Integrated energy system scheduling model based on non-complete interval multi-objective fuzzy optimization," Renewable Energy, Elsevier, vol. 218(C).
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    5. Li, Ruiqi & Ren, Hongbo & Wu, Qiong & Li, Qifen & Gao, Weijun, 2024. "Cooperative economic dispatch of EV-HV coupled electric-hydrogen integrated energy system considering V2G response and carbon trading," Renewable Energy, Elsevier, vol. 227(C).
    6. Zhou, Yanting & Ma, Zhongjing & Zhang, Jinhui & Zou, Suli, 2022. "Data-driven stochastic energy management of multi energy system using deep reinforcement learning," Energy, Elsevier, vol. 261(PA).
    7. Ghasemnejad, Homayoun & Rashidinejad, Masoud & Abdollahi, Amir & Dorahaki, Sobhan, 2024. "Energy management in citizen energy communities: A flexibility-constrained robust optimization approach considering prosumers comfort," Applied Energy, Elsevier, vol. 356(C).

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