Key points are not available for this paper at this time.
This study proposes a double-stage double-layer optimization model for a virtual power plant (VPP) consisting of interconnected microgrids (IMGs) with integrated renewable energy sources (RESs) and energy storage systems (ESSs) to realize demand-side ancillary service, considering intra energy sharing among the IMGs within the VPP. In particular, the first stage, day-ahead scheduling, is carried out to predict the hourly electricity consumption baseline and regulation capacity for the next day, the latter of which results in a reward from the market operator. In the second stage, real-time power consumption control is performed by following the dynamic regulation (or RegD) signal. The second stage is further divided into two layers: the upper layer distributes demand response (DR) signals from the main grid according to the electricity unit price of each microgrid (MG) and exchanges electricity among MGs based on a new energy sharing mechanism to reduce RegD-following violations. The lower layer performs real-time power consumption control for each MG to minimize operation costs. The overall goal is to maximize the reward in the day-ahead stage and minimize the RegD-following violation penalty in the real-time stage, so as to minimize the overall operation cost of the VPP. The optimization is written in five objective functions, which are solved using mixed integer linear programming (MILP) in Gurobi solvers. Extensive simulation and comparison studies are carried out, and numerical results show that compared with traditional MG operations, VPPs comprised of IMGs can reduce operation costs and provide better frequency support for the grid through superior RegD signal following performances.
Liu et al. (Thu,) studied this question.