Abstract
This chapter presents EPSO (Evolutionary Particle Swarm Optimization), as an evolutionary meta-heuristic that implements a scheme of self-adaptive recombination, borrowing the movement rule from PSO (Particle Swarm Optimization). Besides the basic model, it discusses a Stochastic Star topology for the communication among particles and presents a variant called differential EPSO or dEPSO. The chapter presents results in a didactic Unit Commitment/Generator Scheduling Power System problem and results of a competition among algorithms in an intelligent agent platform for Energy Retail Market simulation where EPSO comes out as the winner algorithm. © 2007 Springer-Verlag Berlin Heidelberg.

Abstract

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.