DocumentCode :
640991
Title :
Hybrid model for hourly forecast of photovoltaic and wind power
Author :
Duong Minh Quan ; Ogliari, E. ; Grimaccia, F. ; Leva, S. ; Mussetta, M.
Author_Institution :
Dipt. di Energia, Politec. di Milano, Milan, Italy
fYear :
2013
fDate :
7-10 July 2013
Firstpage :
1
Lastpage :
6
Abstract :
High penetration of solar and wind power in the electricity system provides a number of challenges to the grid such as grid stability and security, system operation, and market economics. Ones of the considerable problems of solar and wind systems, they depend on the weather, as compared to the conventional generation. As we know, the balance in managing load and generated power in energy system is very important. If the power which is supplied from solar and wind perfectly predictable, the extra cost of operating power system with a large penetration of renewable energy will be reduced. Since, the accurate and reliable forecasting system for renewable sources represents an important topic as a major contribution for increasing non-programmable renewable on over the world. The target of this research is to describes the advanced hybrid evolutionary techniques of computational intelligence applied for PV as well as wind power forecast. The evaluation of this investigation is obtained by comparing different definitions of the forecasting error. Moreover, the meaning of NWP (numerical weather prediction) values based on meteorological information on solar and wind power forecasting at Italy has been highlighted in this research.
Keywords :
load forecasting; power grids; power system security; power system stability; solar power; wind power; computational intelligence; electricity system; forecasting system; grid security; grid stability; hybrid evolutionary; market economics; meteorological information; numerical weather prediction; photovoltaic power; power forecasting; power system operation; renewable energy; renewable sources; solar power; wind power forecast; Forecasting; Predictive models; Wind forecasting; Wind power generation; Wind speed; Fuzzy; Hybrid techniques; Neural Networks; PV forecasting; Wind power;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems (FUZZ), 2013 IEEE International Conference on
Conference_Location :
Hyderabad
ISSN :
1098-7584
Print_ISBN :
978-1-4799-0020-6
Type :
conf
DOI :
10.1109/FUZZ-IEEE.2013.6622453
Filename :
6622453
Link To Document :
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