Optimization methods for electric power systems: An overview

Power systems optimization problems are very difficult to solve because power systems are very large, complex, geographically widely distributed and are influenced by many unexpected events. It is therefore necessary to employ most efficient optimization methods to take full advantages in simplifying the formulation and implementation of the problem. This article presents an overview of important mathematical optimization and artificial intelligence (AI) techniques used in power optimization problems. Applications of hybrid AI techniques have also been discussed in this article

A Biased Random Key Genetic Algorithm Approach for Unit Commitment Problem

A Biased Random Key Genetic Algorithm (BRKGA) is proposed to find solutions for the unit commitment problem. In this problem, one wishes to schedule energy production on a given set of thermal generation units in order to meet energy demands at minimum cost, while satisfying a set of technological and spinning reserve constraints. In the BRKGA, solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval [0, 1]. The GA proposed is a variant of the random key genetic algorithm, since bias is introduced in the parent selection procedure, as well as in the crossover strategy. Tests have been performed on benchmark large-scale power systems of up to 100 units for a 24 hours period. The results obtained have shown the proposed methodology to be an effective and efficient tool for finding solutions to large-scale unit commitment problems. Furthermore, from the comparisons made it can be concluded that the results produced improve upon some of the best known solutions

Advanced Methods for Evolutionary Optimisation

In this paper we present two advanced methods for evolutionary optimisation. One method is based on Parallel Genetic Algorithms. It is called Co-operating Populations with Different Evolution Behaviours (CoPDEB), and allows each population to exhibit a different evolution behaviour. Results from two problems show the advantage of using different evolution behaviour on each population. The other method concerns application of GAs on constrained optimisation problems. It is called the Varying Fitness Function (VFF) method and implements a fitness function with varying penalty terms, added to the objective function for penalising infeasible solutions, in order to assist the GA to easily locate the area of the global optimum. Simulation results on two real world problems show that the VFF method outperforms the classic static fitness function implementations. 1. Introduction Using a serial Genetic Algorithm with a static quality function is a wise decision in a great number of optimisatio..