Abstract
This paper presents a new methodology for reliability evaluation of composite generation and transmission systems, based on nonsequential Monte Carlo simulation (MCS) and artificial neural network (ANN) concepts. ANN techniques are used to classify the operating states during the Monte Carlo sampling. A polynomial network, named Group Method Data Handling (GMDH), is used, and the states analyzed during the beginning of the simulation process are adequately selected as input data for training and test sets. Based on this procedure, a great number of success states are classified by a simple polynomial function, given by the ANN model, providine siginificant reductions in the computational cost. Moreover, all types of composite reliability indices (i.e., loss of load probability, frequency, duration, and energy/power not supplied) can be assessed not only for the overall system but also for areas and buses. The proposed methodology is applied to the IEEE Reliability Test System (IEEE-RTS), to the IEEE-RTS 96, and to a configuration of the Brazilian South-Southeastern System.

Abstract
A novel optimization methodology is proposed for the design of transmission line grounding systems, taking into account technical as well as economical considerations. The problem of designing the grounding systems of transmission fines is stated as a linear-integer programming problem in terms of the construction characteristics and the particular requirements of the tower grounding schemes at the supports of each of the different line sections, in order to minimize the variable investment costs, subject to the maximum allowed line outage rate due to the lightning activity. The mathematical statement of the problem allows solutions in which the transmission tower footing resistance changes along the line, depending on the cost and on the particular characteristics of each tower grounding, assuring however, that the average behavior enforces the desired outage rate due to lightning activity, selecting the complementary electrode scheme required at each tower. The methodology is tested on a real case consisting of a 230 kV transmission line, 85.4 Km long, with 180 towers. The linear programming branch and bound mathematical technique was applied for the solution of the test case. Two different simulation approaches for the calculation of the behavior of the fine subject to lightning phenomena were evaluated without loss of generality: the approach proposed in [1], selected as an initial test due to its simplicity, and the improved version presented in [2]. Results are presented and compared to the design obtained through conventional tower design approaches with important reductions in the investment costs, encouraging the use and further development of the methodology.

Abstract

Abstract
This paper presents a new Fuzzy Dynamic Programming model that calculates the optimum solution of problems with uncertainties in data defined by fuzzy sets. The result includes the determination of an Intrinsic Risk Threshold of the solution. Extrinsic Risk Thresholds may also be set by a Decision Maker, in order to obtain more robust solutions. The technique is applied to the calculation of Distribution System expansion costs to serve the objectives of a Regulatory Authority (Regulator) in fixing levels of efficiency, targets and penalties to a regulated market.

Abstract
This paper describes a research where the main goal was to predict the future values of a time series of the hourly demand of Portugal global electricity consumption in the following day. In a preliminary phase several regression techniques were experimented: K Nearest Neighbors, Multiple Linear Regression, Projection Pursuit Regression, Regression Trees, Multivariate Adaptive Regression Splines and Artificial Neural Networks (ANN). Having the best results been achieved with ANN, this technique was selected as the primary tool for the load forecasting process. The prediction for holidays and days following holidays is analyzed and dealt with. Temperature significance on consumption level is also studied. Results attained support the adopted 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.