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Optimal load dispatch of energy hub considering uncertainties of renewable energy and demand response

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  • Lu, Xinhui
  • Li, Haobin
  • Zhou, Kaile
  • Yang, Shanlin

Abstract

As an important modeling method for multi-energy systems, the energy hub (EH) plays an important role in the planning and operation research of multi-energy systems. In this regard, this paper proposes an optimal load dispatch model for the EH system by considering the coupling relationship of electric and thermal energy. The target of the proposed model is to reduce the total cost of the EH system, including resource cost, carbon emission cost, demand response (DR) cost, and system maintenance cost. An adjustable robust optimization method is presented to model the uncertainty of renewable energies (REs) and DR programs. Through duality theory, the proposed robust optimization problem is transformed into a mixed-integer linear programming problem and solved efficiently by a commercial solver. This paper carries out simulation experiments on three different cases with or without energy storage systems and DR programs. The simulation results show that the total cost of the EH system can be effectively reduced by installing energy storage systems and implementing DR programs. At the same time, the results also show that some economic benefits of the EH system need to be sacrificed to deal with the uncertainty of REs and DR programs.

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  • Lu, Xinhui & Li, Haobin & Zhou, Kaile & Yang, Shanlin, 2023. "Optimal load dispatch of energy hub considering uncertainties of renewable energy and demand response," Energy, Elsevier, vol. 262(PB).
    • Handle: RePEc:eee:energy:v:262:y:2023:i:pb:s0360544222024501
    DOI: 10.1016/j.energy.2022.125564
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    References listed on IDEAS

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    2. Xiong, Yongkang & Zeng, Zhenfeng & Xin, Jianbo & Song, Guanhong & Xia, Yonghong & Xu, Zaide, 2023. "Renewable energy time series regulation strategy considering grid flexible load and N-1 faults," Energy, Elsevier, vol. 284(C).
    3. Chen, Minghao & Sun, Yi & Xie, Zhiyuan & Lin, Nvgui & Wu, Peng, 2023. "An efficient and privacy-preserving algorithm for multiple energy hubs scheduling with federated and matching deep reinforcement learning," Energy, Elsevier, vol. 284(C).

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