Title
Risk Adverse Virtual Power Plant Control in Unsecure Power Systems
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.
Year
DOI
Venue
2018
10.1109/MED.2018.8442768
2018 26th Mediterranean Conference on Control and Automation (MED)
Keywords
Field
DocType
unsecure Power Systems,VPP,traditional power plant,wind turbine,Fed Induction Generators,virtual slack bus functions,electricity transmission networks,network losses,generation schedule,unsecured power plants,short term notifications,possible malicious/natural adverse events,dynamic optimal power flow problem,realtime Model Predictive Control framework,nonlinear plant,wind turbines,expensive legacy power plants,pollutant legacy power plants,risk adverse Virtual Power Plant Control,large scale Virtual Power Plant
Computer science,Model predictive control,Electric power system,Electric power transmission,Control engineering,Slack bus,Virtual power plant,Distributed generation,Wind power,Power station
Conference
ISSN
ISBN
Citations 
2325-369X
978-1-5386-7499-4
1
PageRank 
References 
Authors
0.35
11
3
Name
Order
Citations
PageRank
Alessandro Giuseppi13610.37
Roberto Germanà210.35
Alessandro Di Giorgio3447.39