A Day-Ahead Wind Power Scenario Generation, Reduction, and Quality Test Tool

Abstract

During the last decades, thanks to supportive policies of countries and a decrease in installation costs, total installed capacity of wind power has increased rapidly all around the world. The uncertain and variable nature of wind power has been a problem for transmission system operators and wind power plant owners. To solve this problem, numerous wind power forecast systems have been developed. Unfortunately none of them can obtain absolutely accurate forecasts yet. Thus, researchers assumed that wind power generation is a stochastic process and they proposed a stochastic programming approach to solve problems arising from the uncertainty of wind power. It is well known that representing stochastic process by possible scenarios is a major issue in the stochastic programming approach. Large numbers of scenarios can represent a stochastic process accurately, but it is not easy to solve a stochastic problem that contains a large number of scenarios. For this reason scenario reduction methods have been introduced. Finally, the quality of this reduced scenario set must be at an acceptable level to use them in calculations. All of these reasons have encouraged authors to develop a wind power scenario tool that can generate and reduce the scenario set and test the quality of it. The developed tool uses historical data to model wind forecast errors. Scenarios are generated around 24 day-ahead point wind power forecasts. A fast forward reduction algorithm is used to reduce the scenario set. Two metrics are proposed to assess the quality of the reduced scenario set. Site measurements are used to test the developed wind power scenario tool. Results showed that the tool can generate and reduce the scenario set successfully and the proposed metrics are useful to assess the quality.

Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty

“…For horizon t, s realizations of the variable V = Φ −1 [F t ] are obtained by applying the inverse of the Weibull cumulative distribution function (CDF) Φ [45]. Finally, the scenarios are transformed to wind power by (5). The process is repeated until period T and number of scenarios N S are reached.…”

“…The resulting set has statistical characteristics similar to those of the original set. A practical scenario-reduction technique based on the Kantorovich distance is used to obtain an appropriate number of scenarios [5,45]. It can be express as:…”

“…In the literature, there is a broad range of methods for forecasting wind speed and converting these values to wind power forecasts using power curves [1,2,5]. A widely used approach to represent renewable energy uncertainties is the creation of a set of time-series called scenarios for forecasting the future [2].…”

Day-ahead stochastic scheduling of integrated multi-energy system for flexibility synergy and uncertainty balancing

“…The gas storage balance and capacity limits are presented in Eqs. (30) and (36). Due to only one process being available at a time, the binary variables for incoming and outgoing gas to and from the gas storage are included in Eqs.…”

“…Table 1 lists some of the existing SG and SR methods along with their applications in energy systems and optimization models. Based on the work in [36]- [38], and the literature review, it can be concluded that, significant error in the results can appear by using the wind speed to generate scenarios for wind power, due to inaccuracies in the wind to power conversion. Moreover, the forecast error of wind power is dynamic and the temporal correlation due to wind variability cannot be neglected.…”