Title :
Methods for energy price prediction in the Smart Grid
Author :
Crisostomi, Emanuele ; Tucci, Mauro ; Raugi, Marco
Author_Institution :
Dept. of Energy & Syst. Eng., Univ. of Pisa, Pisa, Italy
Abstract :
This paper compares different strategies for the prediction of energy prices. This feature is very important to support the Energy Management System for the computation of optimal power flows in a smart grid framework, e.g., in a Virtual Power Plant. The paper compares simple strategies like the typical one based on the assumption that the prices of the following day will remain the same of the current day, with more complicated approaches, like the Kalman Filter and empirical strategies that also include the information of the current day of the week. The performance of the different algorithms are thoroughly discussed and compared on real data taken from the Italian energy market.
Keywords :
Kalman filters; energy management systems; load flow; power plants; power system economics; smart power grids; Italian energy market; Kalman Filter; empirical strategy; energy management system; energy price prediction method; optimal power flow; smart grid framework; virtual power plant; Equations; Kalman filters; Mathematical model; Optimization; Power generation; Prediction algorithms; Vectors; Kalman Filter; Optimal Power Flow Scheduling; Virtual Power Plant;
Conference_Titel :
Innovative Smart Grid Technologies (ISGT Europe), 2012 3rd IEEE PES International Conference and Exhibition on
Conference_Location :
Berlin
Print_ISBN :
978-1-4673-2595-0
Electronic_ISBN :
2165-4816
DOI :
10.1109/ISGTEurope.2012.6465774