Two-stage Optimal Control of Virtual Power Plant based on Improved Economic Model Predictive Control

Abstract

In order to cope with the adverse effects of the disordered operation of hydropower units on the economic operation of Guangxi Power Grid, this paper proposes a virtual power plant based on improved economic model predictive control. The model consists of two parts: day-ahead rolling optimization and intra-day real-time feedback correction. The day-ahead rolling optimization aims to maximize the operating profit of the virtual power plant as the day-a-day scheduling goal. The day-ahead large-scale scheduling plan is formulated through multi-step rolling solution, in which the prediction domain length is adaptively selected according to the wind power output prediction domain error; the intra-day feedback correction takes into account the operating cost, Use the rolling optimization stage control plan as a benchmark to adjust the operating status of the equipment to deal with the uncertain changes in the small time scale of wind power. The analysis of multi-scenario calculation examples shows that the proposed scheduling model can realize the economic operation of virtual power plants, effectively cope with the uncertainty of wind power output, and realize the controllable adjustment of hydropower units, which verifies the feasibility and correctness of the model.