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XUE Yixun, DU Yuan, WANG Ke, CHANG Xinyue, DENG Lirong, SUN Hongbin. Distributed Unit Commitment for Integrated Electric and Heating System Considering Reconfiguration of District Heating Network[J]. Proceedings of the CSEE, 2025, 45(10): 3698-3708. DOI: 10.13334/j.0258-8013.pcsee.232504
Citation: XUE Yixun, DU Yuan, WANG Ke, CHANG Xinyue, DENG Lirong, SUN Hongbin. Distributed Unit Commitment for Integrated Electric and Heating System Considering Reconfiguration of District Heating Network[J]. Proceedings of the CSEE, 2025, 45(10): 3698-3708. DOI: 10.13334/j.0258-8013.pcsee.232504

Distributed Unit Commitment for Integrated Electric and Heating System Considering Reconfiguration of District Heating Network

  • With the widespread adoption of combined heat and power (CHP) technology, the coupling between power grids and heating networks has become increasingly integrated. Heating networks can dynamically adjust their configurations through valve operations, enhancing the operational flexibility and start-stop dynamics of coupling equipment such as CHP units. This capability provides a novel approach to power system congestion management and facilitates the integration of renewable energy. This paper introduces a unit commitment model that incorporates the coordination of transmission networks and reconfigurable heating networks. To accommodate the heterogeneous nature of these systems, an extended heterogeneous decomposition algorithm is developed based on the generalized master-slave splitting theory, enabling distributed solutions for both networks. To address the non-convexities arising from binary decision variables— specifically, unit commitment and valve switching—a scalable coordinate descent procedure (CDP) is designed to ensure the finite convergence of the extended heterogeneous decomposition (HGD) algorithm, particularly in large-scale systems. Finally, numerical simulations are conducted to validate the benefits of heating network reconfiguration in alleviating transmission congestion, reducing wind curtailment, and optimizing system-wide operational costs. The results also demonstrate the effectiveness and computational efficiency of the proposed distributed algorithm compared to traditional methods.
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