一种基于动态模型在线自更新的 新能源制蓄热调控策略

[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 °C to 1. 06 °C, 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.