Abstract
This paper is concerned with design considerations and tradeoffs involved in the power electronics development for light electric vehicles. A review of propulsion system design, power conversion structure and control is presented. A three-phase squirrel-cage induction motor is used as propulsion system for an electric scooter. The motor is controlled at different operating conditions by means of a simple scalar control using a low cost controller board developed for light electric vehicles used in local areas. Experimental results show that the proposed digital controller is able to follow the reference speed with a suitable dynamics for the electric scooter.

Abstract
This paper presents a digital controller suitable for an instructional laboratory in electric drives. A prototype has been designed specifically to meet the requirements of low cost and motivating education tool, and it contains all of the active functions required to implement the open loop control of induction motors. This approach develops the concept of "grey box" module where the details are built for convenience, but not hidden inside as a "black box". Introductory laboratory experiments are presented to demonstrate how this hardware can be integrated in laboratory classes to present the most important concepts as regards adjustable speed drives.

Abstract
This paper presents a low cost, modular, configurable and fully protected education tool based on a generic electronic converter to be used by students, providing them with skills regarding power electronics and converters, and enabling them to learn from experience the most important issues concerning DC and AC electric drives.

Abstract
This paper provides a different approach for electricity price forecast from risk management point of view. Making use of neural networks, the methodology presented here has as main concern finding the maximum and the minimum System Marginal Price (SMP) for a specific programming period, with a certain confidence level. To train the neural network, probabilistic information from past years is used. This approach was developed with the objective of integrating a decision-support system that uses Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data are presented and discussed in detail. © 2005 ISAP.

Abstract
In a liberalized electricity market, participants have several types of contracts to sell or buy electrical energy. Increasing electricity markets liquidity and, simultaneously, providing to market participants tools for hedging against spot electricity price were the two main reasons for the appearance of those types of contracts. However, due to the payoff nonlinearity characteristic of those contracts, deciding the optimal portfolio that best adjusts to their necessities becomes a hard task. This paper presents an optimization model applied to optimal contract allocation using Particle Swarm Optimization (PSO). This optimization model consists on finding the portfolio that maximizes the electricity producer results and simultaneously allows the practice of the hedge against the volatility of the System Marginal Price (SMP). Risk management is considered through the consideration of a mean-variance optimization function. An example for a programming period is presented using spot, forward and options contracts. PSO performance in such type of problems is evaluated by comparing it with the Genetic Algorithms (GA).

Abstract
It is known that Cournot game theory has been one of the theoretical approaches used more often to model electricity market behavior. Nevertheless, this approach is highly influenced by the residual demand curves of the market agents, which are usually not precisely known. This imperfect information has normally been studied with probability theory, but possibility theory might sometimes be more helpful in modeling not only uncertainty but also imprecision and vagueness. In this paper, two dual approaches are proposed to compute a robust Cournot equilibrium, when the residual demand uncertainty is modeled with possibility distributions. Additionally, it is shown that these two approaches can be combined into a bicriteria programming model, which can be solved with an iterative algorithm. Some interesting results for a real-size electricity system show the robustness of the proposed methodology. © 2005 Cambridge University Press.