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1.中国矿业大学电气工程学院,江苏省徐州市 221116
2.南方电网科学研究院有限责任公司,广东省广州市 510663
Received:07 April 2025,
Revised:2025-07-16,
Online First:18 August 2025,
Published:25 November 2025
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袁志鑫,韩丽,王晓静等.计及网荷热惯性的综合能源系统低碳优化调度[J].电力系统自动化,2025,49(22):145-155.
YUAN Zhixin,HAN Li,WANG Xiaojing,et al.Low-carbon Optimal Scheduling of Integrated Energy System Considering Thermal Inertia of Grid and Load[J].Automation of Electric Power Systems,2025,49(22):145-155.
袁志鑫,韩丽,王晓静等.计及网荷热惯性的综合能源系统低碳优化调度[J].电力系统自动化,2025,49(22):145-155. DOI: 10.7500/AEPS20250407007.
YUAN Zhixin,HAN Li,WANG Xiaojing,et al.Low-carbon Optimal Scheduling of Integrated Energy System Considering Thermal Inertia of Grid and Load[J].Automation of Electric Power Systems,2025,49(22):145-155. DOI: 10.7500/AEPS20250407007.
针对热网传输延迟引发的用户低碳响应时滞问题,提出一种计及网荷热惯性的综合能源系统低碳优化调度方法。首先,基于流量分段法构建热网热惯性与碳流模型,并设计基于负荷碳势分布的碳价定价机制,以解决热网传导碳信号时的延迟问题;其次,结合热舒适度理论建立建筑热惯性模型,揭示用户热负荷调整与室内温度变化的动态关系,据此构建时滞依赖的热需求价格弹性模型;最后,建立以系统运营商运行成本和用户用能成本最优为目标的双层优化模型,通过售能价格和负荷响应协同迭代求解。仿真结果表明,所提方法可提升用户热负荷曲线与新能源出力峰谷的时空匹配度,实现新能源消纳与用户用能体验的兼顾。
To address the low-carbon response delay of users caused by the transmission delay of the heating network
a low-carbon optimal scheduling method for the integrated energy system considering thermal inertia of grid and load is proposed. Firstly
the thermal inertia model and carbon flow model of the heating network are constructed based on the flow segmentation method
and the carbon pricing mechanism based on the load carbon potential distribution is designed to solve the delay problem of carbon signal transmission in the heating network. Secondly
a building thermal inertia model is established by combining the thermal comfort theory to reveal the dynamic relationship between the user thermal load adjustment and the room temperature change
and a model of heat demand price elasticity with time lag dependence is constructed accordingly. Finally
a bi-level optimization model with the objective of minimizing the operating cost of system operators and the energy cost of users is established
which is solved through coordinated iteration of the energy pricing and load response. The simulation results show that the proposed method can improve the spatiotemporal alignment between the thermal load curves of users and the peaks and valleys of renewable energy output
achieving a balance between renewable energy accommodation and user energy consumption experience.
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