A Data-Driven Model of Virtual Power Plants in Day-Ahead Unit Commitment

• Published in: IEEE transactions on power systems 2019-11, Vol.34 (6), p.5125-5135
• Main Authors: Babaei, Sadra, Zhao, Chaoyue, Fan, Lei
• Format: Article
• Language: English
• Subjects: Covariance; Distributed generation; distributionally robust optimization; Electric power distribution; Electric power generation; Electricity market; Electricity supply industry; Energy sources; Generators; Load modeling; Optimization; Physical properties; Power consumption; Power generation; Probability distribution; Uncertainty; Unit commitment; virtual power plant; Virtual power plants; Wind power
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2018.2890714

Due to the increasing penetration of distributed energy resources (DERs), power system operators face significant challenges of ensuring the effective integration of DERs. The virtual power plant (VPP) enables DERs to provide their valuable services by aggregating them and participating in the wholesale market as a single entity. However, the available capacity of VPP depends on its DER outputs, which is time varying and not exactly known when the independent system operator runs the day-ahead unit commitment engine. In this study, we develop a model to evaluate the physical characteristics of the VPP, i.e., its maximum capacity and ramping capabilities, given the uncertainty in wind power output and load consumption. The proposed model is based on a distributionally robust optimization approach that utilizes moment information (e.g., mean and covariance) of the unknown parameter. We reformulate the model as a binary second-order conic program and develop a separation framework to address it. We first solve a two-stage problem and then benchmark it with a multi-stage case. Case studies are conducted to show the performance of the proposed approach.