Improved evolutionary algorithms with application to smart grid demand response management

D.Ing. (Electrical Engineering)Abstract: Evolutionary Algorithms (EAs) refer to a group of optimization strategies which are based on Darwin’s theory of natural selection. According to Darwin, attributes of an organism’s genotype are shared among its offspring when mating with another organism occurs. Through this continuous process of mating, combination (or recombination), mutation and selection, it becomes possible to obtain offspring with an optimal balance of gene attributes for both parent organisms. EAs include Evolutionary Strategies (ES), Evolutionary Programming (EP), Genetic Algorithms (GAs), and Differential Evolution (DE). This thesis presents a collection of articles which detail improvements to the performance of existing EAs (mainly based on GAs and DE). The motivation for this research arises from the fact that evolutionary algorithms suffer from inadequacies in handling multiple objectives and constraints which frequently occur in real world problems. In particular, Pareto-based GAs experience difficulty in balancing convergence and diversity in the presence of multiple time-varying objectives and constraints. DE encounters difficulty in finding the global optimum for multiple mutation vectors. This thesis proposes several niching-based strategies to improve the tradeoff between convergence and diversity for GAs. It also presents a 2-archive approach to improve crossover and recombination for DE with multiple mutation vectors. These improved algorithms are tested on both static and dynamic mathematical models representing selected aspects of smart grid, namely: distributed energy resource (DER) allocation, cost function minimization, and demand response management. Results show that the improved EAs provide better optimized parameters for the selected mathematical grid models compared to already existing algorithms. In particular, the improved EAs optimize reference point placement, utilize a pointer-based archiving approach, and adaptively vary crossover rate in order to achieve optimal convergence and diversity of the search population. With respect to GAs, the articles generally adopt a Pareto-based approach to search the solution space. Results obtained from applying a number of improved EA approaches demonstrate their effectiveness. The research in this thesis also details various directions for future research which are discussed at the end of the thesis.Evolutionary programming (Computer science