In power systems with high penetration of wind generation, probabilistic scenarios are generated for use in stochastic formulations of day-ahead unit commitment problems. To minimize the expected cost, the wind power scenarios should accurately represent the stochastic process for available wind power. We employ some statistical evaluation metrics to assess whether the scenario set possesses desirable properties that are expected to lead to a lower cost in stochastic unit commitment. A new mass transportation distance rank histogram is developed for assessing the reliability of unequally likely scenarios. Energy scores, rank histograms and Brier scores are applied to alternative sets of scenarios that are generated by two very different methods. The mass transportation distance rank histogram is best able to distinguish between sets of scenarios that are more or less calibrated according to their bias, variability and autocorrelation. ABSTRACTIn power systems with high penetration of wind generation, probabilistic scenarios are generated for use in stochastic formulations of day-ahead unit commitment problems. To minimize the expected cost, the wind power scenarios should accurately represent the stochastic process for available wind power. We employ some statistical evaluation metrics to assess whether the scenario set possesses desirable properties that are expected to lead to a lower cost in stochastic unit commitment. A new mass transportation distance (MTD) rank histogram is developed for assessing the reliability of unequally likely scenarios. Energy scores, rank histograms, and Brier scores are applied to alternative sets of scenarios that are generated by two very different methods. The MTD rank histogram is best able to distinguish between sets of scenarios that are more or less calibrated according to their bias, variability and autocorrelation.

Probabilistic wind power scenarios constitute a crucial input for stochastic day-ahead unit commitment in power systems with deep penetration of wind generation. To minimize the expected cost, the scenario time series of wind power amounts available should accurately represent the stochastic process for available wind power as it is estimated on the day ahead. The high computational demands of stochastic programming motivate a search for ways to evaluate scenarios without extensively simulating the stochastic unit commitment procedure. Reliability of wind power scenario sets can be assessed by statistical verification approaches. In this study, we examine the relationship between the statistical evaluation metrics and the results of stochastic unit commitment. Lack of uniformity in a mass transportation distance rank histogram can eliminate scenario sets that might lead to either excessive no-load costs of committed units or high penalty costs for violating energy balance. Event-based metrics can help to predict the cost performance of the remaining scenario sets.

2.1 Introduction 9 2.2 Verification of scenarios 2.2.1 Energy scores 2.2.2 Distance-based rank histograms 2.2.3 Event-based verification 2.3 Wind power scenario generation methods 2.3.1 Wind power scenario generation by quantile regression with Gaussian copula approach 2.3.2 Wind power scenario generation by epi-spline approximation approach 2.4 Example application of the verification approaches iv 2.4.1 The BPA dataset 2.4.2 Verification of BPA scenarios 2.5 Conclusions References CHAPTER 3. RELIABILITY OF WIND POWER SCENARIOS AND STOCHASTIC