On the Data-Driven Modeling of Price-Responsive Flexible Loads: A Fast BayesianOptimization Approach
针对电力系统中价格响应型柔性负荷建模困难的问题，提出了一种数据驱动识别框架和贝叶斯优化算法，能处理预测和测量误差，适用于大规模样本场景。
Flexible loads (FLs) in power and energy systems, such as interruptible and transferable loads, are critical flexibility resources for mitigating power imbalances. Despite their potential, accurate modeling of these loads remains challenging and has not received sufficient attention, hindering their effective integration into system-level operational and decision-making frameworks. To bridge this gap, this article develops a data-driven identification framework and algorithm for price-responsive FLs (PRFLs). First, we introduce PRFL models that capture both static and dynamic decision mechanisms governing their response to electricity price variations. Second, we develop a data-driven identification framework that explicitly incorporates forecast and measurement errors. In particular, we provide a theoretical analysis to quantify the statistical impact of such noise on parameter estimation. Third, leveraging the bilevel structure of the identification problem, we propose a Bayesian optimization (BayOpt)-based algorithm that is scalable to large-sample regimes and can provide posterior identifiability certificates as a byproduct. Numerical tests demonstrate the effectiveness and advantages of the proposed approach.