Abstract
It is widely known and accepted that Nash equilibrium suitably models agents' behavior in electricity markets, since it is coherent with the common sense of their simultaneous profits maximisation. In the literature, these approaches are usually addressed using deterministic representations, despite the fact that electricity markets are highly conditioned by the uncertainty in demand or in agents' bidding strategies. Only some equilibrium-modelling approaches under uncertainty can be found in the literature, most of them using probability distributions. However, probability approaches may lead to very complex formulations and generally require restrictive assumptions (such as normality or independence) that can hardly be verified in real complex problems. A conjectured-price-response equilibrium model that uses LR-possibility distributions to represent the uncertainty of the residual demand curves faced by the participant agents is proposed. Modelling the risk-aversion attitudes of the agents, the resulting possibilistic equilibrium is transformed into a simplified deterministic one, which is solved with a new globally convergent algorithm for variational inequalities problems. Some interesting results for a real-size electricity system show the robustness of this new approach when compared with other risk-neutral approaches.

Abstract
Evolutionary Particle Swarm Optimization (EPSO) is a robust optimization algorithm belonging to evolutionary methods. EPSO borrows the movement rules from Particle Swarm Optimization (PSO) and uses it as a recombination operator that evolves under selection. This paper presents a reactive power planning approach taking advantage of EPSO robustness, in a model that considers simultaneously multiple contingencies and multiple load levels. Results for selected problems are summarized including a trade-off analysis of results.

Abstract
Evolutionary Particle Swarm Optimization (EPSO) is a robust optimization algorithm belonging to evolutionary methods. EPSO borrows the movement rules from Particle Swarm Optimization (PSO) and uses it as a recombination operator that evolves under selection. This paper presents a reactive power planning approach taking advantage of EPSO robustness, in a model that considers simultaneously multiple contingencies and multiple load levels. Results for selected problems are summarized including a trade-off analysis of results.

Abstract
This paper describes the concept of a voltage/NAR controller based on an interaction of fuzzy Mamdani controllers, with the main objective of keeping voltages at all busbars inside an admissible band while avoiding line flows to exceed admissible limits. Estimation of sensitivities via fuzzy clustering of load profiles is proposed. A complex rule base interacts with a Newton-Raphson power flow routine in iterative steps until a terminating criterion is met, following a basic min-max approach. Tests to the method reveal it as one order of magnitude faster than a competing simulated annealing routine.

Abstract
This paper applies the concept of fuzzy controller to voltage/VAR control. Cascading Mamdani controllers are used together with sensitivities to keep voltages in an admissible band while avoiding line flows to exceed admissible limits. A small number of iteration steps with a Newton-Raphson power flow routine are enough to recover voltages into admissible bands.

Abstract
This paper presents an application of Monte Carlo chronological simulation to evaluate the reserve requirements of generating systems, considering 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 energy sources is renewable. Renewable in this work comprises hydroelectric, mini-hydroelectric, and wind power sources. Case studies on a configuration of the Portuguese Generating System are presented and discussed. Copyright (c) 2007 John Wiley & Sons, Ltd.