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

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 whole...

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Bibliographic Details
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
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Summary: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.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2018.2890714