We develop a tractable and flexible data-driven approach for incorporating side information into multi-stage stochastic programming. The proposed framework uses predictive machine learning methods (such as k-nearest neighbors, kernel regression, random forests) to weight the relative importance of various uncertainty sets in robust optimization formulation. Through novel measure concentration r...

this research uses a comprehensive method to solve a combinatorial problem of distribution network expansion planning (dnep) problem. the proposed multi-objective scheme aims to improve power system's accountability and system performance parameters, simultaneously, in the lowest possible costs. the dynamic programming approach is implemented in order to find the optimal sizing, siting and...

a chaotic optimization algorithm (coa) based approach for the robust coordinated design of the upfc power oscillation damping controller and the conventional power system stabilizer has been investigated in this paper. chaotic optimization algorithms, which have the features of easy implementation, short execution time and robust mechanisms of escaping from local optimum, is a promising tool fo...

in this research optimal reconfiguration strategy of the improved srr reconfigurable mobile robot based on force-angle stability measure has been designed using genetic algorithm. path tracking nonlinear controller which keeps robot’s maximum stability has been designed and simulated in matlab. motion equations of the robot have been derived in parametric form by means of newton- euler, lagrang...

We consider an electricity generator making offers of energy into an electricity pool market over a horizon of several trading periods (typically a single trading day). The generator runs a set of generating units with given start-up costs, shut-down costs and operating ranges. At the start of each trading period the generator must submit to the pool system operator a new supply curve defining ...