This paper presents a real time strategy for optimal power flow in presence of storage devices and wind turbine driven by Doubly Fed Induction Generators. These elements work in cooperation defining a dynamic bus where the generated power is subject to temporal constraints, which establish a coupling between traditional power flow problems related to consecutive time periods; further the uncertainty in wind power generation forecasts requires a continuous update of the planned power profiles, in order to guarantee a dynamic equilibrium among demand and supply. Model predictive control is used for this purpose, considering the dynamic equations of the storage and the wind turbine rotor as prediction models. A proper target function is introduced in order to find a trade-off between the need of minimizing generation costs and the excursions of the storage state of charge and the wind turbine angular speed from reference states. In the case study under consideration storage, wind turbines and a traditional synchronous generator are operated by the Transmission System Operator in the form of a Virtual Power Plant working as slack bus to cover network losses. The proposed approach is validated on simulation basis.

Real time optimal power flow integrating large scale storage devices and wind generation / DI GIORGIO, Alessandro; Liberati, Francesco; Lanna, Andrea. - ELETTRONICO. - (2015), pp. 480-486. (Intervento presentato al convegno 23rd Mediterranean Conference on Control and Automation, MED 2015 tenutosi a Torremolinos; Spain nel 2015) [10.1109/MED.2015.7158794].

Real time optimal power flow integrating large scale storage devices and wind generation

DI GIORGIO, ALESSANDRO
;
LIBERATI, FRANCESCO;LANNA, ANDREA
2015

Abstract

This paper presents a real time strategy for optimal power flow in presence of storage devices and wind turbine driven by Doubly Fed Induction Generators. These elements work in cooperation defining a dynamic bus where the generated power is subject to temporal constraints, which establish a coupling between traditional power flow problems related to consecutive time periods; further the uncertainty in wind power generation forecasts requires a continuous update of the planned power profiles, in order to guarantee a dynamic equilibrium among demand and supply. Model predictive control is used for this purpose, considering the dynamic equations of the storage and the wind turbine rotor as prediction models. A proper target function is introduced in order to find a trade-off between the need of minimizing generation costs and the excursions of the storage state of charge and the wind turbine angular speed from reference states. In the case study under consideration storage, wind turbines and a traditional synchronous generator are operated by the Transmission System Operator in the form of a Virtual Power Plant working as slack bus to cover network losses. The proposed approach is validated on simulation basis.
2015
23rd Mediterranean Conference on Control and Automation, MED 2015
Demand Response; Energy Storage System; Model Predictive Control; Renewable Energy Sources; Smart Grid; Control and Optimization; Control and Systems Engineering
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Real time optimal power flow integrating large scale storage devices and wind generation / DI GIORGIO, Alessandro; Liberati, Francesco; Lanna, Andrea. - ELETTRONICO. - (2015), pp. 480-486. (Intervento presentato al convegno 23rd Mediterranean Conference on Control and Automation, MED 2015 tenutosi a Torremolinos; Spain nel 2015) [10.1109/MED.2015.7158794].
File allegati a questo prodotto
File Dimensione Formato  
DiGiorgio_Real-time-optimal_2015.pdf

solo gestori archivio

Note: Articolo pubblicato
Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 378.79 kB
Formato Adobe PDF
378.79 kB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/856604
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 10
social impact