An IGDT-Based Day-ahead Optimal Energy Scheduling Strategy in a Virtual Power Plant

As grid-connected variable renewable energy (VRE) increases, especially in the distribution network, dealing with the uncertainty, variability, and consequently flexibility requirements is becoming an urgent challenge to the power system operators. Virtual power plant (VPP) can group different kinds of small-scale distributed energy resources (DERs), which are usually unseen by the system operators, as a single unit to participate in energy markets and system management. Therefore, an optimal energy management problem of a VPP is addressed in this paper for energy and operating reserve (OR) scheduling. The studied VPP is a cluster of dispersed generating units (including dispatchable and stochastic power), flexible loads, as well as storage units. Since the uncertainty could come from the stochastic power production, load demand, and electricity price, information gap decision theory (IGDT) is applied in the unit commitment procedures to minimize the VPP operating cost to deal with price uncertainty, while both power production and load forecasting uncertainties are covered by OR services. Finally, the proposed method is applied with a case study, and optimal decisions are investigated.