Title :
Wind power trading in power energy market
Author :
Heydari, Ramiyar ; Hasanpour, Somayeh
Author_Institution :
Electr. Dept., Sadjad Inst. of higher Educ., Mashhad, Iran
Abstract :
With rapid increase in wind power penetration into the power grid, wind power forecasting is becoming increasingly important to power system operators and electricity market participants. Wind power in large scale in electricity market, has some drawbacks, such as uncertainties in generation. In this paper, in order to, simulating wind power plane accuracy, Weibull probability density function is used. Weibull PDF Parameters are forecasted by combination of Simulated Annealing and Artificial Neural Network (SA-ANN) in real case wind speed of Khorasan, Iran. The results illustrate, proposed method have reliable solution for Weibull PDF parameters. Finally, simulated energy market show, entry of the proposed wind energy plan, into the power energy market, increased competition belong other market players and decreased power energy price.
Keywords :
Weibull distribution; load forecasting; neural nets; power engineering computing; power generation economics; power markets; simulated annealing; wind power plants; SA-ANN; Weibull PDF parameters; Weibull probability density function; artificial neural network; electricity market participants; power energy market; power forecasting; power grid; power system operator; simulated annealing; wind energy plan; wind power penetration; wind power trading; wind speed; Artificial neural networks; Electricity supply industry; Probability density function; Wind energy; Wind farms; Wind power generation; Wind speed; Power energy market; Simulated anneaaling; Weibull probablity density function; Wind farms; artificial neural network;
Conference_Titel :
Environment and Electrical Engineering (EEEIC), 2014 14th International Conference on
Conference_Location :
Krakow
Print_ISBN :
978-1-4799-4661-7
DOI :
10.1109/EEEIC.2014.6835865
