Scenario Analysis And Stochastic Programming Of Wind-Integrated Power Systems

Intermittency in an electric power system with high-level penetration of wind energy results in both variability and uncertainty in the availability of undispatchable wind power. With the increasement of wind power’s penetration rate in China, the issue of wind power integration becomes more intractable. It is a frontier problem for the electric power industry and academia around the world to incorporate uncertainty and variability of wind power during planning and operation of power systems.This paper designs a decision-making framework for the wind-integrated power system (WPS) based on the scenario analysis and stochastic programming. Further, the scenario generation methods of wind power are studied from two aspects, i.e. static scenario and dy-namic scenario according to the correlations of random variables. The static and dynamic scenarios of wind power are respectively applied to "decision-making of optimal access point for wind power transmission corridor" and "stochastic unit commitment". The scena-rio-based stochastic programming approaches for WPS are studied. The contributions of this paper are briefly summarized as follows:A. Scenario generation:Currently, the great variety of the point forecast methods and applied locations results in no universally applicable theoretical distribution. To deal with the aforementioned issues, it is proposed to use the empirical cumulative distribution function as the input of Latin Hypercube Sampling to characterize the uncertainty of wind power. This method is applicable for the static scenario generation of single random variable or multiple independent random variables. The available scenario generation methods in the literature cannot well fit the distribution of wind power variations. What’s more, the scenario genera-tion method for wind power point forecast, which is widely used in the industry, is incom-plete. Hence, this paper proposes a dynamic scenario generation method of wind power based on the statistical uncertainty and variability. The proposed method characterizes the forecast error of point forecast via empirical distributions of a set of forecast bins and as-sumes that wind power fluctuations over unit interval follow t location-scale distribution. An inverse transform sampling from a multivariate normal distribution is adopted to generate a large number of wind power scenarios. The covariance matrix of the multivariate normal distribution is estimated to fit the distribution of historical wind power fluctuations.B. Static scenarios’application in stochastic programming:The static scenarios and stochastic programming method are applied to the decision-making of optimal access point for wind power transmission corridor in one province of China. It is found that the diversi- fied mode of WPS operation may influence the decision-making result. The uncertainty of WPS’s operation mode is characterized as multi-scenarios. This paper proposes a scena-rio-based stochastic programming methodology for selecting the proper access point.C. The application of dynamic scenario, i.e. stochastic unit commitment: The traditional stochastic unit commitment cannot guarantee the operational reliability if system operators only consider most likely scenarios. Extreme events, in which the net-load exceeds the upper or lower available generation or ramping boundaries of committed units, are generally rare but they should not be ignored. To incorporate the effects of extreme scenarios, a two-phase stochastic unit commitment methodology is proposed that is designed to identify extreme boundary scenarios caused by wind power uncertainty and variability. The modified stochas-tic unit commitment method can significantly reduce the amount of lost load and wind cur-tailment, as well as the overall system costs. The proposed stochastic unit commitment mod-el is built upon the mixed integer linear programming formulation that can be solved by CPLEX quickly and accurately.