Abstract
This paper presents a new and efficient methodology for voltage control and network reconfiguration of distribution networks using fuzzy rules, EPSO and graph theory. A simpler representation of the network is built through a graph, were all the network loops are identified, both closed and open loops. This information is incorporated into the EPSO operators to create feasible solutions for the topological problem, avoiding convergence problems and reducing the computational burden. The initial EPSO population is created from the initial feasible solution, using appropriated heuristics to create feasible and possibly better initial solutions. At the same time a heuristic based strategy is used to perform local voltage control actions. Finally a fuzzy inference based algorithm is employed to achieve the optimal transformer and capacitor bank tap position. The proposed methodology was tested in a 13-bus test system, and in a real distribution system with 3200 buses.

Abstract
This paper aims at presenting a Home Energy Management System ( HEMS) module capable of scheduling electric water heater ( EWH) appliances in order to optimize the PV self-consumption. A multi-period optimization model is presented. Laboratory tests were conducted to validate the model and to demonstrate the capability of this HEMS module to address recent challenges of self-consumption in a domestic environment. A commercial EWH device developed by Bosch communicating with the HEMS module is used to perform the tests.

Abstract
This paper describes the application of the clonal selection algorithm to the reconfiguration problem of distribution networks considering non-uniform demand levels. The Clonal Algorithm, CLONALG, is a combinatorial optimization technique inspired in the immunologic bio system and it aims at reproducing the main properties and functions of this system. The reconfiguration problem of distribution networks with non-uniform demand levels is a complex problem that aims at identifying the most adequate radial topology of the network that complies with all technical constraints in every demand level while minimizing the cost of active losses along an extended operation period. This work includes results of the application of the Clonal algorithm to distribution systems with 33, 84 and 136 buses. These results demonstrate the robustness and efficiency of the proposed approach.

Abstract
This paper describes the application of the specialized genetic algorithm of Chu-Beasley to solve the Distribution System Reconfiguration, DSR, problem considering different demand scenarios. This algorithm is an approach inspired in the natural selection and evolution of species. The reconfiguration problem of distribution networks taking into account different demand scenarios aims at identifying the most adequate radial topology of a distribution system assuming that this topology is used for all demand scenarios under study. This search is driven by the minimization of the cost of energy losses in the network along a full operation year. The performance of the algorithm is evaluated considering test systems having 33, 70, 84 and 136 buses and a real system with 417 buses. The obtained results confirm the robustness and efficiency of the developed approach and its potential to be used in distribution control centers.

Abstract
This paper describes a hybrid tool to perform Static Transmission Expansion Planning, STEP, studies and its application to the Garver6-Bus academic system and to the Southern Brazilian Transmission equivalent real system. The developed STEP tool integrates two phases as follows. The first one uses Constructive Heuristic Algorithms (CHA) to reduce the search space, and the second uses Particle Swarm Optimization (PSO) to identify the final solution. This hybridization between CHAs and PSO proved to be very effective and shows good performance to reduce the size of the STEP search space and to identify good quality solutions. These are relevant issues given the combinatorial nature of investment problems leading to the explosion of the number of alternative plans, one of the greatest difficulties faced in this planning problem.

Abstract
In a restructured electricity sector, day-ahead markets can be modeled as a game where some players - the producers - submit their proposals. To analyze the companies' behavior we have used the concept of Nash equilibrium as a solution in these multi-agent interaction problems. In this paper, we present new and crucial adaptations of two well-known mechanisms, the adjustment process and the relaxation algorithm, in order to achieve the goal of computing Nash equilibria. The advantages of these approaches are highlighted and compared with those available in the literature.