
1. 太原理工大学电气与动力工程学院, 山西省 太原市 030024
2. 国网新疆电力调度控制中心, 新疆维吾尔自治区 乌鲁木齐市 830002
Received:07 December 2023,
Published Online:08 May 2024,
Published:20 June 2025
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Dong ZHU, Wuhui CHEN, Xiaolong GUO, et al. Hybrid Time Scale Scheduling Strategy for Thermal Storage Electric Heating and Demand Response to Synergize Wind Power Consumption[J]. Proceedings of the CSEE, 2025, 45(12): 4728-4739.
Dong ZHU, Wuhui CHEN, Xiaolong GUO, et al. Hybrid Time Scale Scheduling Strategy for Thermal Storage Electric Heating and Demand Response to Synergize Wind Power Consumption[J]. Proceedings of the CSEE, 2025, 45(12): 4728-4739. DOI: 10.13334/j.0258-8013.pcsee.232694.
冬季供暖期西北某省级电网电采暖负荷占比较高,加之风电渗透率攀升,现有调度策略未能充分发挥蓄热电采暖和负荷侧资源的互补调节能力,导致系统灵活性不足,弃风问题严重。为解决这一问题,该文基于蓄热电采暖电热转换和储热特性,并根据不同能量传输及供需差异性选取匹配的时间尺度,建立蓄热电采暖和需求响应(demand response,DR)资源在日前、日内和实时阶段消纳风电的协同调度机制,提出蓄热电采暖和需求响应资源协同消纳风电的混合时间尺度优化调度策略。最后,在实际算例中进行仿真分析,结果表明,该文调度策略不仅能够发挥蓄热电采暖和需求响应资源在长时间尺度上的削峰填谷能力,优化负荷曲线,而且能在短时间尺度上协调两者调节能力,较好地平衡风电预测误差,提升系统风电消纳水平和运行经济性。
During the winter heating period
the proportion of electric heating load in a provincial power grid in northwest China is relatively high. In addition
the penetration rate of wind power is increasing
and the existing scheduling strategy has not fully utilized the complementary regulation ability of thermal storage electric heating and load side resources
resulting in a lack of system flexibility and a serious problem of wind abandonment. To solve this problem
this article selects a matching time scale based on different energy transmission and supply-demand differences
based on the characteristics of thermal energy conversion and storage in thermal storage electric heating. A collaborative scheduling mechanism is established for the consumption of wind power by thermal storage electric heating and demand response (DR) resources in the day-ahead
intra-day
and real-time stages. A hybrid time scale optimization scheduling strategy is proposed for the collaborative consumption of wind power by thermal storage electric heating and demand response resources. Finally
simulation analysis is conducted in actual examples. The results demonstrate that the proposed scheduling strategy not only leverages the peak-shaving and valley-filling capabilities of thermal storage electric heating and demand response resources over long timescales to optimize the load curve
but also coordinates their adjustment capabilities over short timescales to balance wind power prediction errors
thereby enhancing both the system's wind power integration level and operational efficiency.
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