Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine Classifier

The system operators usually need to solve large-scale unit commitment problems within limited time frame for computation. This paper provides a pragmatic solution, showing how by learning and predicting the on/off commitment decisions of conventional units, there is a potential for system operators to warm start their solver and speed up their computation significantly. For the prediction, we train linear and kernelized support vector machine classifiers, providing an out-of-sample performance guarantee if properly regularized, converting to distributionally robust classifiers. For the unit commitment problem, we solve a mixed-integer second-order cone problem. Our results based on the IEEE 6-bus and 118-bus test systems show that the kernelized SVM with proper regularization outperforms other classifiers, reducing the computational time by a factor of 1.7. In addition, if there is a tight computational limit, while the unit commitment problem without warm start is far away from the optimal solution, its warmly started version can be solved to optimality within the time limit

A Game-Theoretic Loss Allocation Approach in Power Distribution Systems with High Penetration of Distributed Generations

Allocation of the power losses to distributed generators and consumers has been a challenging concern for decades in restructured power systems. This paper proposes a promising approach for loss allocation in power distribution systems based on a cooperative concept of game-theory, named Shapley Value allocation. The proposed solution is a generic approach, applicable to both radial and meshed distribution systems as well as those with high penetration of renewables and DG units. With several different methods for distribution system loss allocation, the suggested method has been shown to be a straight-forward and efficient criterion for performance comparisons. The suggested loss allocation approach is numerically investigated, the results of which are presented for two distribution systems and its performance is compared with those obtained by other methodologies

Association of Estrogen Receptor α Genes II and I Polymorphisms with Type 2 Diabetes Mellitus in the Inpatient Population of a Hospital in Southern Iran

BackgroundEstrogen plays a fundamental role in the pathogenesis of type 2 diabetes mellitus (T2DM). Very few studies have shown the association between estrogen receptor α (ERα), PvuII and XbaI gene polymorphisms with T2DM in both men and women. We evaluated the hypothesis that PvuII and XbaI polymorphisms of ERα gene may be associated with T2DM in adult.MethodsFrom spring of 2010 to the fall of 2011, a case-control study was performed at clinical centers of Jahrom University of Medical Sciences. We included 174 patients with T2DM including men and women and 174 age, sex, and body mass index frequency-matched health controls. We analyzed the PvuII and XbaI polymorphisms of ERα by using the polymerase chain reaction-based restriction fragment length polymorphism method.ResultsNo significant differences between demographic characteristics of control and patients groups were observed. Allele frequencies of both PvuII and XbaI polymorphisms were significantly different between patients and control subjects (P=0.014 vs. P=0.002, respectively). When the group was separated into women and men, logistic regression analysis of genotype distribution of PvuII (pp vs. Pp+PP) in both sexes revealed that there was no significant association of PvuII genotype with men (odds ratio [OR], 1.67; confidence interval [CI], 0.86 to 3.28; P=0.89) and women (OR, 0.96; CI, 0.53 to 1.74; P=0.12).ConclusionPvuII and XbaI polymorphisms in ERα are related with T2DM in the inpatient population