Risk Adverse Virtual Power Plant Control in Unsecure Power Systems. Giuseppi, A., Germana, R., & Di Giorgio, A. 2018. doi abstract bibtex This paper presents a control strategy for enabling a large scale Virtual Power Plant (VPP) constituted by a traditional power plant, distributed Energy Storage Systems (ESSs) and wind turbine driven Doubly Fed Induction Generators (DFIGs) to virtual slack bus functions in electricity transmission networks. The VPP in question is in charge of covering the network losses and a portion of the day ahead generation schedule of unsecured power plants, in presence of short term notifications about possible malicious/natural adverse events affecting them. The objective is pursued by integrating a dynamic optimal power flow problem in a realtime Model Predictive Control framework, and applying a second level of control aimed at keeping the dynamics of the real nonlinear plant subject to wind turbulence in line with the dynamics of the MPC model. Simulation results provide a proof of the proposed concept, showing as the joint coordination of storage devices and wind turbines can be part of the task of providing support actions to the network traditionally delivered by expensive and pollutant legacy power plants. © 2018 IEEE.
@CONFERENCE{Giuseppi2018210,
author={Giuseppi, A. and Germana, R. and Di Giorgio, A.},
title={Risk Adverse Virtual Power Plant Control in Unsecure Power Systems},
journal={MED 2018 - 26th Mediterranean Conference on Control and Automation},
year={2018},
pages={210-216},
doi={10.1109/MED.2018.8442768},
art_number={8442768},
abstract={This paper presents a control strategy for enabling a large scale Virtual Power Plant (VPP) constituted by a traditional power plant, distributed Energy Storage Systems (ESSs) and wind turbine driven Doubly Fed Induction Generators (DFIGs) to virtual slack bus functions in electricity transmission networks. The VPP in question is in charge of covering the network losses and a portion of the day ahead generation schedule of unsecured power plants, in presence of short term notifications about possible malicious/natural adverse events affecting them. The objective is pursued by integrating a dynamic optimal power flow problem in a realtime Model Predictive Control framework, and applying a second level of control aimed at keeping the dynamics of the real nonlinear plant subject to wind turbulence in line with the dynamics of the MPC model. Simulation results provide a proof of the proposed concept, showing as the joint coordination of storage devices and wind turbines can be part of the task of providing support actions to the network traditionally delivered by expensive and pollutant legacy power plants. © 2018 IEEE.},
keywords={Asynchronous generators; Electric energy storage; Electric load flow; Electric machine control; Electric power system control; Electric power transmission; Model predictive control; Predictive control systems; Virtual storage; Wind turbines, Control strategies; Distributed energy storage systems; Doubly fed induction generators; Dynamic optimal power flow; Electricity transmission networks; Generation schedules; Virtual power plants; Virtual power plants (VPP), Electric power transmission networks},
document_type={Conference Paper},
}
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