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This study presents a performance comparison of metaheuristics to solve transmission expansion planning (TEP) problems in power systems. The proposed methodology includes the search for the least cost solution, bearing in mind investments and operational costs related to ohmic transmission losses. The multi-stage nature of the TEP is also taken into consideration. Case studies on a small system and on a real sub-transmission network are presented and discussed.

This paper presents an application of probabilistic methodologies to evaluate the reserve requirements of generating systems with large amounts of renewable energy sources. The idea is to investigate the behavior of reliability indices, including those from the well-being analysis, when the major portion of the renewable sources comes from the wind power. Renewable in this work mainly comprises hydroelectric, minihydroelectric and wind power sources. Case studies on configurations of the Portuguese and Spanish generating systems are presented and discussed.

This paper presents a new methodology for reliability evaluation of composite generation and transmission systems, based on non-sequential Monte-Carlo simulation and artificial neural network concepts. Artificial neural network (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, providing significant 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.