Chitra Yingvivatanapong

An Integration of ANN Wind Power Estimation Into Unit Commitment Considering the Forecasting Uncertainty

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.

Reactive compensation techniques to improve the ride-through capability of wind turbine during disturbance

World wind energy capacity expanded at an annual rate of 25% during the 1990s. The total world wind turbine installation capacity was approximately 40 000 MW at the end of 2003. Germany has the highest installed capacity of over 10 000 MW, while Denmark, where the wind energy accounts for more than 13% of electricity consumed, has the highest wind energy level per capita. The United States is catching up in the development of wind farms, with several large-scale wind generation projects currently being materialized. Even though there is significant progress in the wind generation technology, most of the currently installed wind turbines utilize induction generators to produce the electricity. Since the induction generators do not perform voltage regulation and absorb reactive power from the utility grid, they are often the source of voltage fluctuations. It is necessary to examine their responses during the faults and possible impacts on the system stability when the percentage of the wind generation increases. This paper compares the steady-state voltage profile and the voltage ride-through capabilities of the induction-generator-based wind farms with different reactive compensation techniques.

An integration of ANN wind power estimation into UC considering the forecasting uncertainty

The development of wind generation has rapidly progressed over the last decade. With the advance in wind turbine technologies, wind energy has become competitive with other fuel-based generation resources. The fluctuation of wind, however, makes it difficult to optimize the use of wind power generation. Current practice ignores the possible available capacity of the wind generation during the unit commitment scheduling. This may cause operation issues and waste usable capacity when the installation of the wind generation increases. An accurate wind capacity forecasting is essential for efficient wind energy and capacity dispatching. To ensure the system reliability, one also has to consider the forecast uncertainty when integrating the wind capacity into generation planning. This paper discusses the development of an artificial neural network based wind forecast model with the consideration of wind generation uncertainty by using probabilistic concept of confidence interval. The data from a wind farm located in the Southern Oklahoma is used for this study

Transmission Congestion Management During Transition Period of Electricity Deregulation in Thailand

Transmission congestion management (TCM) plays a significant role in power-system operation under todays deregulated environment. Its two major functions are to maintain power system within security limits and to collect money from market participants paying back to transmission-grid investors. The TCM issue has been widely debated during the past decade. It is still an extensively discussed opened issue in the current competitive environment. In the United States, Pennsylvania New Jersey Maryland (PJM) with nodal congestion management based on renowned locational marginal price and the Electric Reliability Council of Texas with zonal congestion management are two successful stories of TCM under different operation schemes. Although the PJM model is adopted in some developing countries where the processes of restructuring of Electricity Supply Industry is still under the beginning phase, many concerns, such as advances in an information technology, energy security, social equity, price volatility, and the need to subsidize poor consumers, are necessitate factors to be considered before the establishment of TCM and settlement processes. Taking into account the above concerns, this paper proposes a TCM model for the electrical utility industry in Thailand during the transition period to the deregulated environment.

Transmission congestion management during transition of electricity deregulation in Thailand

Transmission congestion management (TCM) plays a significant role in power system operation under todays deregulated environment. Its two major functions are maintaining power system within security limits and collecting money from market participants paying back to transmission grid investors. TCM issue has been widely debated during the past decade. It is still an extensively discussed opened issue in the current competitive environment. In the United States, PJM with nodal congestion management (NCM) base on renowned location marginal price (LMP) and the Electric Reliability Council of Texas (ERCOT) with zonal congestion management (ZCM), are two successful stories of TCM under different operation schemes. Though PJM model is adopted in some developing countries where the processes of restructuring is still at the beginning phase, many social economical issues, such as advance in an information technology, energy security, social equity, price volatility, and the need to subsidize poor consumers, are necessary to be addressed before the establishment of TCM and settlement processes. Taking into account the above mentioned concerns, this paper proposes a TCM model for the electrical utility industry in Thailand during the transition period to the deregulated environment.

Closure to discussion of "Reactive compensation techniques to improve the ride-through capability of wind turbine during disturbance"

For original article by C. Chompoo-inwai et al. see ibid., vol.41, no.3, p.666-72, May/Jun. 2005 and for discussion by Shih-Min Hsu see ibid., vol.41, no.6, p.1483, Nov./Dec. 2005.

Robust algorithms for high speed voltage sags and swells detection

Short-duration variations Abstract Voltage sags and swells are important power quality issues that have been a growing concern by electric utilities and end users. Many organizations, such as Institute of Eleclronics and Electrical Engineering WEE), Electric Power Research Institute (EPRI), and manufacturers, have been using their endeavor to develop solutions. In addition, various devices have been developed to mitigate, or protect against power quality problems. Regardless the response speed of the mitigation devices, the incident and the level of voltage sags or swells have to be fast detected and accurately estimated before any solution can be deployed. This paper discusses wavelet algorithms for robust voltage sags and swells detection and estimation without frequency estimation and