Optimal Power Flow Application For Active Distribution Networks Using Ant Colony System Algorithm
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Optimal Power Flow Application for Active Distribution Networks Using Ant Colony System Algorithm
This study presents an optimal power flow algorithm based on the ant colony optimization metaheuristic (ACO) for network loss minimization in active distribution networks. The algorithm based on the coupling between an estimated power flow and construction a graph which represents a finite set of discrete states in which the algorithm looks for the best solution for the objective function of the system. The control variables of the optimization problem comprise all transformer taps, and reactive load compensation system. The compensation system represents the addition of reactive power injections associated to devices able to provide volt/var support such as variable capacitors, distributed generation and electronic loads as electrical vehicles. These control variables are modeled with a set of discrete states that are modified according to the objective function evaluated, in this case network losses. Nodal voltages and angles in all system buses correspond to the state variables of the optimization problem. The proposed application has been successfully tested in the 4-bus and 13-bus IEEE test cases and results were validated using a standard Newton-based Optimal Power Flow. As an additional contribution the proposed Ant Colony-based OPF has been applied under an multiobjective approach considering also the investment cost of the reactive support.
Frontiers in Genetics Algorithm Theory and Applications
This book reviews recent advances in theory and applications of genetic algorithm (GA). The book is composed of five parts; Part 1 of the book involves the chapters about the advances in GA theory. Part 2 concerns applications in health, society, and economy. Part 3 has an inclusive focus on application in power systems, and Part 4 concerns the applications of GA in electrical vehicle industries. Finally, Part 5 includes applications in signal and image processing.
Swarm, Evolutionary, and Memetic Computing
This volume constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2014, held in Bhubaneswar, India, in December 2014. The total of 96 papers presented in this volume was carefully reviewed and selected from 250 submissions for inclusion in the proceedings. The papers cover a wide range of topics in swarm, evolutionary, memetic and other intelligent computing algorithms and their real world applications in problems selected from diverse domains of science and engineering.