Abstract
In this paper, the Unit Commitment (UC) problem is presented and solved, following an innovative approach based on a metaheuristic procedure. The problem consists on deciding which electric generators must be committed, over a given planning horizon, and on defining the production levels that are required for each generator, so that load and spinning reserve requirements are verified, at minimum production costs. Due to its complexity, exact methods proved to be inefficient when real size problems were considered. Therefore, heuristic methods have for long been developed and, in recent years, metaheuristics have also been applied with some success to the problem. Methods like Simulated Annealing, Tabu Search and Evolutionary Programming can be found in several papers, presenting results that are sufficiently interesting to justify further research in the area. In this paper, a resolution framework based on GRASP - Greedy Randomized Adaptive Search Procedure - is presented. To obtain a general optimisation tool, capable of solving different problem variants and of including several objectives, the operations involved in the optimisation process do not consider any particular characteristics of the classical UC problem. Even so, when applied to instances with very particular structures, the computational results show the potential of this approach.

Abstract
This paper presents a new and efficient methodology for network reconfiguration with optimal power flow based on Benders Decomposition approach. The objective minimizes the power losses, balancing load among the feeders and subject to the constraints: capacity limit of the branches, minimal and maximal limits of the substation or generator, minimum deviation of the nodes voltages and radial operation of the networks. A variant of the generalized Benders decomposition algorithm is applied for solving the problem, since the formulation can be embedded under two stages. The first one is the Master problem and Is formulated as Mixed Integer non-Linear Programming. This stage determines the radial topology of the distribution network. The second stage is the Slave problem and is formulated as a non-Linear Programming problem. This stage is used to determine the feasibility of the Master problem solution by means of an Optimal Power Flow and provides information to formulate the linear Benders cuts. The model is programmed in GAMS mathematical modeling language. The effectiveness of the proposal is demonstrated through an example extracted from the specialized literature.

Abstract
This paper presents a new and efficient methodology for distribution network reconfiguration integrated with optimal power flow (OPF) based on a Benders decomposition approach. The objective minimizes power losses, load balancing among feeders, and is subject to constraints: capacity limit of branches, minimum and maximum power limits of substations or distributed generators, minimum deviation of bus voltages, and radial optimal operation of networks. A specific approach of the Generalized Benders decomposition algorithm is applied to solve the problem. The formulation can be embedded under two stages: the first one is the Master problem and is formulated as a mixed integer nonlinear programming problem. This stage determines the radial topology of the distribution network. The second stage is the Slave problem and is formulated as a nonlinear programming problem. This stage is used to determine the feasibility of the Master problem solution by means of an OPF and provides information to formulate the linear Benders cuts that connect both problems. The model is programmed in the General Algebraic Modeling System. The effectiveness of the proposal is demonstrated through three examples extracted from the literature.

Abstract
The coordination between wind farms and pumping storage units increases the wind farm's controllability and maximizes the profit. In literature, several optimization algorithms were proposed for deriving the optimal coordination between wind farms and storage units. However, no attention has been given to operational management strategies for following the strategy that results from the optimization phase. This paper presents three possible heuristic strategies for managing the wind-hydro system during the operational day according to a day-ahead optimized strategy. Moreover, a chance-constrained based optimization algorithm, that includes wind power uncertainty, is also described. The algorithms are tested in a real case-study.

Abstract
The participation of an EV aggregator in the electricity market for purchasing electrical energy requires an algorithm for managing the EV charging during the operational day. In this paper the coordination of EV for minimizing the deviation between bid and consumed electrical energy is studied and compared with an uncoordinated strategy. Two algorithms are proposed: a heuristic algorithm that dispatches the EV for each time interval separately, and another one, formulated as an optimization problem for dispatching the EV considering all the time intervals. Furthermore, the aggregator architecture is compared with an autonomous architecture where each EV operates and participates in the market individually. The results, for a realistic case-study, show that the aggregator with an optimized coordination strategy achieves the lowest deviation cost and magnitude.

Abstract
It is foreseeable that electricity retailers for electrical mobility will be market agents. These retailers are electric vehicle (EV) aggregation agents, which operate as a commercial middleman between electricity market and EV owners. Furthermore, with the foreseen evolution of the smart-grid concept, these agents will be able to control the EV charging rates and offer several ancillary services. This paper formulates an optimization problem for the EV aggregation agent participation in the day-ahead and secondary reserve market sessions. Forecasting issues are also discussed. The methodology was tested for two years (2009 and 2010) of the Iberian market, considering perfect and naïve forecast for all variables of the problem. © 2011 IEEE.