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面向中长期电力供需平衡的季节性储热建模与优化规划方法OA

Modeling and Optimal Planning Method for Seasonal Thermal Storage Towards Medium-and Long-term Electricity Supply-Demand Balance

中文摘要英文摘要

新能源高占比和终端用能低碳电气化是未来新型电力系统供需侧演化的重要特征,但清洁供热需求与新能源出力之间季节错峰、难以匹配的矛盾愈加严重.文中聚焦于通过季节性储热技术实现热量的长周期存储,以促进新能源的大规模消纳与清洁热能的跨季利用.首先,建立了面向优化规划的季节性储热精细化模型,以描述不同季节下储热罐的时变热损耗特征.在此基础上,将所构建的季节性储热模型嵌入电力规划问题,实现季节性储热在系统范围内的优化配置.最后,基于修正的HRP-38节点算例系统分析论证了建模方法的有效性,并对季节性储热在提升新能源消纳与清洁供热方面的作用进行了讨论.

High proportion of renewable energy and low-carbon electrification of end-use energy have become critical features in the evolution of the supply-demand dynamics in the future new power systems.However,the seasonal mismatch contradiction between clean heating demand and renewable energy output has become increasingly severe.This paper focuses on achieving long-term thermal storage through seasonal thermal storage technology to facilitate large-scale accommodation of renewable energy and cross-seasonal utilization of renewable thermal energy.First,a refined seasonal thermal storage model for the optimal planning is established to describe the time-varying heat-loss characteristics of thermal storage tanks across seasons.On this basis,the established seasonal thermal storage model is embedded into the power planning problem to achieve optimal configuration of seasonal thermal storage within the system.Finally,a modified HRP-38-bus test system is used to analyze and validate the effectiveness of the proposed modeling method,and the role of seasonal thermal storage in enhancing renewable energy accommodation and renewable heating is discussed.

罗嘉骏;姜海洋;兰焮尧;王佳昕;苏运;杜尔顺;张宁

新型电力系统运行与控制全国重点实验室(清华大学),北京市 100084新型电力系统运行与控制全国重点实验室(清华大学),北京市 100084新型电力系统运行与控制全国重点实验室(清华大学),北京市 100084新型电力系统运行与控制全国重点实验室(清华大学),北京市 100084国网上海市电力公司,上海市 200122低碳能源实验室(清华大学),北京市 100084新型电力系统运行与控制全国重点实验室(清华大学),北京市 100084

新型电力系统新能源季节性储热供热电力供需平衡优化规划精细化模型

new power systemrenewable energyseasonal thermal storageheatingelectricity supply-demand balanceoptimal planningrefined model

《电力系统自动化》 2026 (16)

21-32,12

国家重点研发计划资助项目(2022YFB2403300)国网上海市电力公司科技项目(SGSHDK00DWJS2310470)国家自然科学基金资助项目(72242105). This work is supported by National Key R&D Program of China(No.2022YFB2403300),State Grid Shanghai Electric Power Company(No.SGSHDK00DWJS2310470),and National Natural Science Foundation of China(No.72242105).

10.7500/AEPS20250722001

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