The current electrical grid is undergoing a deep renovation that poses new problems in terms of technologies, communication and control. The increasing level of penetration of renewable energy is leading towards the concept of distributed energy production, and it is expected that Virtual Power Plants (VPPs) will play an important role in the future smart grid. The stochastic nature of the power flows in the VPP, caused by the fluctuating availability of renewables, by the users’ demand and by the energy market price, complicates the task of power balancing for the VPP. This paper proposes the use of simple learning mechanisms to support power scheduling decisions and to improve a correct supply of the connected loads.

SVM Methods for Optimal Management of a Virtual Power Plant

CRISOSTOMI, EMANUELE;TUCCI, MAURO;RAUGI, MARCO
2013

Abstract

The current electrical grid is undergoing a deep renovation that poses new problems in terms of technologies, communication and control. The increasing level of penetration of renewable energy is leading towards the concept of distributed energy production, and it is expected that Virtual Power Plants (VPPs) will play an important role in the future smart grid. The stochastic nature of the power flows in the VPP, caused by the fluctuating availability of renewables, by the users’ demand and by the energy market price, complicates the task of power balancing for the VPP. This paper proposes the use of simple learning mechanisms to support power scheduling decisions and to improve a correct supply of the connected loads.
Crisostomi, Emanuele; Tucci, Mauro; Raugi, Marco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/208330
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