Modeling of Wind/Environment/Economic Dispatch in power system and solving via an online learning meta-heuristic method

Abstract

This paper addresses the effect of the wind power units into the classical Environment/Economic Dispatch (EED) model which called hereafter as Wind/Environment/Economic Dispatch (WEED) problem. The optimal dispatch between thermal and wind units so that minimized the total generating costs are considered as multi objective model. Normally, the nature of the wind energy as a renewable energy sources has uncertainty in generation. Therefore, in this paper, use a practical model known as 2m-point to estimate the uncertainty of wind power. To solve the WEED problem, this paper proposed a new meta-heuristic optimization algorithm that uses online learning mechanism. Honey Bee Mating Optimization (HBMO), a moderately new population-based intelligence algorithm, shows fine performance on optimization problems. Unfortunately, it is usually convergence to local optima. Therefore, in the proposed Online Learning HBMO (OLHBMO), two neural networks are trained when reached to the predefined threshold by current and previous position of solutions and their fitness values. Moreover, Chaotic Local Search (CLS) operator is use to develop the local search ability and a new data sharing model determine the set of non-dominated optimal solutions and the set of non-dominated solutions to kept in the external memory. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as a decision-making technique is employed to find the best solution from the set of Pareto solutions. The proposed model has been individually examined and applied on the IEEE 30-bus 6-unit, the IEEE 118-bus 14-unit, and 40-unit with valve point effect test systems. The robustness and effectiveness of this algorithm is shows by these test systems compared to other available algorithms.