Impact of wind forecast error statistics upon unit commitment

Abstract

Abstract-Driven by a trend towards renewable forms of generation, in particular wind, the nature of power system operation is changing. Systems with high wind penetrations should be capable of managing the uncertainty contained within the wind power forecasts. Stochastic unit commitment with rolling planning and input scenarios, based on wind forecasts, is one way of achieving this. Here a scenario tree tool is developed which allows forecast error statistics to be altered and facilitates the study of how these statistics impact on unit commitment and system operation. It is shown the largest individual impact on system operation is from the inclusion of variance and that variance, kurtosis and skewness together produced the error information with the lowest system cost. Similar impacts for inaccurate error statistics are observed but generalisation of these results will need more studies on a range of test systems.

Selecting the forecast interval of wind generations

“…In short-term generation dispatch problems, wind generation forecast error is usually approximated by a Gaussian distribution [3]. With this assumption, constraint (1.2) becomes…”

Day-ahead scheduling of large numbers of thermostatically controlled loads based on equivalent energy storage model

“…Recently, renewable energies, such as wind and solar energy, have garnered significant attention. However, due to the uncertainty and intermittency of these energies, their high penetration will bring about challenges for power grid scheduling [1,2]. Demand response (DR) enables customers to participate in power system scheduling through price or incentive, and plays a more and more important role in shaving the peak load, restraining the fluctuation caused by new energy, and so on [3][4][5][6].…”