Temporal correlation studies In Ref performance

Temporal correlation studies: In Ref. [14], performance of persistent and Grey predictors for short-term forecast of the wind power production is compared, and impact of the wind power volatility and the forecast accuracy on market prices is evaluated. The results demonstrate that improvement of the wind forecast accuracy does not necessarily result in more accurate market prices. In Ref. [15], an agent-based modeling approach is adopted to simulate day-ahead electricity market. Within this computational laboratory, impact of short-term wind forecast accuracy as well as wind penetration level on market prices and net revenues of the wind ATC 0175 is assessed. The results show that the application of more accurate wind forecast method is effectively beneficial to increase the net revenues of the wind producers. In Ref. [16], Northern European day-ahead and regulating power markets are modeled to evaluate the impact of large-scale wind power production on system imbalance, while the wind power production is forecasted by using high resolution numerical weather prediction models. In Ref. [17], a probabilistic model is developed to analyze long-term effects of the wind power production on market prices and revenues of the wind producers. In this simulation, a combination of time series models and Weibull distribution is used to model variability of the wind power production. The simulation concludes that the variability of energy prices can be decreased by the increase of geographical dispersion of wind farms. In Ref. [18], the performance of different alternative schemes of system cost minimization within day-ahead energy market is studied. In this reference, the wind variability is modeled using autoregressive moving average (ARMA) models. In Ref. [19], time series models are used to examine medium- and long-term effects of large-scale wind penetration on market prices, reliability of supply and revenues of dispatchable generating units. The findings show that the increase of wind penetration level can reduce the market prices, and promote the reliability of supply in medium-term, while in long-term horizon such effects may not be necessarily realized. In Ref. [20], an out-of-sample chronological simulation is run to compare two families of offering strategies (i.e., deterministic and stochastic programming methods) for a wind producer in a pool market. In this study, ARMA models are fitted to real-world wind data and used to prepare single-point forecast and temporally correlated scenarios of the wind production for both deterministic and stochastic programming methods, respectively.