Robust decomposition and tracking strategy for demand response enhanced virtual power plants

Abstract

Current scheduling strategies for flexible loads typically depend on simplified demand response (DR) models that do not take into consideration the nonlinear coupling of uncertain characteristics, leading to substantial DR deviations and hindering precise load scheduling. This paper introduces a robust decomposition and tracking strategy to address multi-dimensional DR deviations. The primary objective is to support economic and precise demand response in day-ahead scheduling within virtual power plant (VPP) management. Firstly, a multi-dimensional deviation model is proposed to capture the coupling and uncertainties across four dimensions, including time, speed, power, and energy dimensions. Then, utilizing a two-stage robust optimization approach, this strategy incorporates the deviation model to refine the decomposition and tracking processes. In the decomposition stage, the strategy optimizes scheduling commands for flexible loads and energy storage, aiming to enhance the overall benefits of the VPP. In the tracking stage, energy storage effectively compensates for DR deviations, thereby minimizing the VPP's net deviations. Finally, the effectiveness and robustness of this strategy are verified by utilizing historical data from Northern China. The optimization result demonstrates notable advantages, including a 12.0% cost reduction for the VPP and compensation of 75.81 MWh in DR deviations compared to traditional approaches. Additionally, a case study comparing various VPP configurations highlights the heating load VPP as the most economically viable option, priced at ¥94,200.