电力建设2026，Vol.47Issue(7)：141-153,13.DOI:10.12204/j.issn.1000-7229.2026.07.011
一种基于动态模型在线自更新的新能源制蓄热调控策略
An Online Self-Updating Control Strategy for New Energy Storage Based on Dynamic Modelling
摘要
Abstract
[Objective]The stochastic volatility of renewable energy sources presents challenges to the safe and stable operation of the new-type power system.Electric thermal storage systems have emerged as high-quality resources for accommodating variable renewable energy generation and have been widely deployed across the Three-North regions of China.However,the parameters of heat storage media vary with temperature,causing parameter drift in electric thermal storage equipment during operation and resulting in deviations between actual regulation performance and expected outcomes.Accordingly,this paper proposes a control strategy for electric thermal storage equipment that explicitly accounts for parameter variations in the heat storage medium.[Methods]First,a parametric dynamic model of electric thermal storage equipment is established considering the heat transfer process.Subsequently,an online parameter identification algorithm based on parameter projection is developed to address the parameter drift.Building upon this foundation,a coordinated regulation framework for renewable energy and electric thermal storage is constructed within a measurement-identification-control architecture that considers parameter variations of the heat storage medium.An adaptive model predictive control algorithm with online self-updating of the dynamic model is designed to accommodate the time-varying characteristics of model parameters of electric thermal storage equipment.[Results]Numerical examples verify that the proposed method effectively mitigates parameter drift in electric thermal storage equipment.Compared with the traditional model predictive control,the proposed adaptive model predictive control reduces the root mean square error of heat storage temperature prediction from 23.22 ℃ to 1.06 ℃,a reduction of 95.4％.The daily power procurement cost of the system drops from CNY 431.32 to CNY 341.45,representing a decrease of 20.8％.[Conclusions]The proposed method demonstrates superior performance in control accuracy and economic efficiency compared with conventional approaches.It provides effective support for flexible regulation and cost-effective operation of electric thermal storage systems under high renewable energy penetration.关键词
新能源消纳/电蓄热/参数辨识/模型预测控制Key words
new energy accommodation/electric thermal storage/parameter identification/model predictive control分类
信息技术与安全科学引用本文复制引用
杨智健,仪忠凯,徐英,冉晓贺,李宝聚,孙勇..一种基于动态模型在线自更新的新能源制蓄热调控策略[J].电力建设,2026,47(7):141-153,13.基金项目
国家自然科学基金项目(U25A20337) This work is supported by National Natural Science Foundation of China(No.U25A20337). （U25A20337）
