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This paper proposes a practical and effective model for the generation forecasting of a wind farm with an emphasis on its scheduling and trading in a wholesale electricity market. A novel forecasting model is developed based on indepth investigations of meteorological information. This model adopts a two-stage hybrid network with Bayesian clustering by dynamics and support vector regression. The proposed structure is robust with different input data types and can deal with the nonstationarity of wind speed and generation series well. Once the network is trained, we can straightforward predict the 48-h ahead wind power generation. To demonstrate the effectiveness, the model is applied and tested on a 74-MW wind farm located in the southwest Oklahoma of the United States. Index Terms--Machine learning, meteorology, nonstationarity, wind generation forecasting.

The development of wind power generation has rapidly progressed over the last decade. With the advancement in wind turbine technology, wind energy has become competitive with other fuel-based resources. The fluctuation of wind, however, makes it difficult to optimize the usage of wind power. The current practice ignores wind generation capacity in the unit commitment (UC), which discounts its usable capacity and may cause operational issues when the installation of wind generation equipment increases. To ensure system reliability, the forecasting uncertainty must be considered in the incorporation of wind power capacity into generation planning. This paper discusses the development of an artificial-neural-network-based wind power forecaster and the integration of wind forecast results into UC scheduling considering forecasting uncertainty by the probabilistic concept of confidence interval. The data from a wind farm located in Lawton City, OK, is used in this paper. Index Terms--Artificial neural network (ANN), confidence interval, short-term wind power forecast, wind forecast uncertainty.

A significant portion of the operating cost of utilities comes from energy production. To minimize the cost, unit commitment (UC) scheduling can be used to determine the optimal commitment schedule of generation units to accommodate the forecasted demand. The load forecast is a prerequisite for UC planning. The projected load of up to seven days is important for the allocation of generation resources. Hour-ahead forecast is used for optimally dispatching online resources to supply the next hour load. This paper addresses the systematic design of a multistage artificial-neural-network-based short-term load forecaster (ANNSTLF). The developed ANNSTLF engine has been utilized in a real utility system. The performance analysis over the past year shows that a majority of the forecast error was detected in a consistent period with a large temperature forecast error. The enhancement of ANNSTLF is proposed to improve the forecasting performance. The comparison of forecasting accuracy due to this enhancement is analyzed. Index Terms--Neural network, short-term load forecasting, unit commitment (UC) scheduling, weather forecast.

Unit commitment scheduling of the utility company relies upon the forecast of the demand, demand pattern, availability and capacity of the generators, minimum/maximum up and down time of the generators, and heat rate. According to the experiences of a local utility company, the difference of the fuel cost can reach a million dollars per day with different unit commitment scheduling. Accurate hour-ahead and day-ahead demand forecasting play important roles for proper unit commitment scheduling. This paper describes the procedure to improve the unit commitment scheduling by using the hour-ahead and day-ahead results from the newly developed neural network based short-term load forecasting program in the supervisory control and data acquisition and energy management system. Comparison of field records is also provided. Index Terms--Neural networks, production cost, short-term load forecast, unit commitment (UC).

Following in the steps of the gas industry, the traditional paradigm of the vertically integrated electric utility structure has begun to change. In the United States, the Federal Energy Regulatory Commission has issued several rules and Notices of Proposed Rulemaking to set the road map for the deregulated utility industry. The crisis in California has drawn great attention and sparked intense discussion within the utility industry. One general conclusion is to rejuvenate the idea of integrated resource planning and promote the distributed generation via traditional or renewable generation facilities for the deregulated utility systems. Wind generation is one of the most mature and cost-effective resources among different renewable energy technologies. Recently, several large-scale wind generation projects have been implemented in the U.S. and other parts of the world. Similar to other new generation facilities, the impacts of a large-scale wind generation on the system operation, voltage profile, and system security have to be investigated and studied. Remedies for possible operation issues have to be evaluated and implemented. This paper discusses the impact study of connecting a 120-MW wind farm into the transmission system of a utility company within the Southwest Power Pool. Index Terms--Dynamic stability, generation interconnection, induction generator, system impact study.

This paper proposes a practical and effective model for the generation forecasting of a wind farm with an emphasis on its scheduling and trading in a wholesale electricity market. A novel forecasting model is developed based on indepth investigations of meteorological information. This model adopts a two-stage hybrid network with Bayesian clustering by dynamics and support vector regression. The proposed structure is robust with different input data types and can deal with the nonstationarity of wind speed and generation series well. Once the network is trained, we can straightforward predict the 48-h ahead wind power generation. To demonstrate the effectiveness, the model is applied and tested on a 74-MW wind farm located in the southwest Oklahoma of the United States. [ABSTRACT FROM AUTHOR]

Unit commitment scheduling of the utility company relies upon the forecast of the demand, demand pattern, availability and capacity of the generators, minimum/maximum up and down time of the generators, and heat, rate. According to the experiences of a local utility company, the difference of the fuel cost can reach a million dollars per day with different unit commitment scheduling. Accurate hour-ahead and day-ahead demand forecasting play important roles for proper unit commitment scheduling. This paper describes the procedure to improve the unit commitment scheduling by using the hour-ahead and day-ahead results from the newly developed neural network based short-term load forecasting program in the supervisory control and data acquisition and energy management system. Comparison of field records is also provided. [ABSTRACT FROM AUTHOR]

Following in the steps of the gas industry, the traditional paradigm of the vertically integrated electric utility structure has begun to change. In the United States, the Federal Energy Regulatory Commission has issued several rules and Notices of Proposed Rulemaking to set the road map for the deregulated utility industry. The crisis in California has drawn great attention and sparked intense discussion within the utility industry. One general conclusion is to rejuvenate the idea of integrated resource planning and promote the distributed generation via traditional or renewable generation facilities for the deregulated utility systems. Wind generation is one of the most mature and cost-effective resources among different renewable energy technologies. Recently, several large-scale wind generation projects have been implemented in the U.S. and other parts of the world. Similar to other new generation facilities, the impacts of a large-scale wind generation on the system operation, voltage profile, and system security have to be investigated and studied. Remedies for possible operation issues have to be evaluated and implemented. This paper discusses the impact study of connecting a 120-MW wind farm into the transmission system of a utility company within the Southwest Power Pool. [ABSTRACT FROM AUTHOR]

This paper presents an approach to work sampling involving a mixture of fixed and random sampling. Fixed-interval sampling is more efficient in terms of providing more precise information for the same number of observations. Random sampling avoids the possibilities of sampling times being anticipated and the sampling schedule coinciding with some rhythm in the sampled process. Using a mixture of fixed and random sampling provides a smooth transition from fixed to random sampling and allows the practitioner to select a mixture level appropriate for his or her purposes. The efficiency of the mixture sampling method is analyzed by using an alternating Poisson process model for the sampled process. The mixture sampling method eliminates the observation-free period after every observation that was provided by the previously proposed delayed-sampling method. Although the mixture sampling method is slightly less efficient than the delayed-sampling method, its advantage is clear when anticipation of observation times is a concern, and it is always more efficient than random sampling. [ABSTRACT FROM AUTHOR]