Abstract
This paper shows the conceptual differences between adopting a probabilistic weighting of the futures and a risk averse strategy, in power system planning under uncertain scenarios. It is illustrated with a distribution planning problem, where optimal solutions in both cases are determined by a Genetic Algorithm. It shows that the probabilistic approach is less safe and cannot detect some interesting solutions.

Abstract
This paper demonstrates that a classical stochastic optimization is, in many cases, not convenient for power system planning. Instead, a risk analysis approach is proposed. In a comparison of both planning paradigms, the probabilistic approach is in occasions not adequate, is half blind to compromise solutions and leads, in numerous, cases to riskier decisions. The technical discussion is illustrated with a distribution planning example.

Abstract
This paper provides an overview and a list of references on the use of Evolutionary Algorithms (EA) in Power Systems and related fields. As didactic examples, the paper presents two applications of EA for two different problems in Power Systems. (C) 1997 Elsevier Science Ltd.

Abstract
This paper presents a methodology developed within the SOLARGIS project - a Joule project - aiming at evaluating the potential of integrating renewable forms of energy for dispersed electricity production. With this project we also wanted to demonstrate the efficiency of GIS - Geographical Information Systems - as a tool to analyse the integration of renewable forms of energy. In this paper we present the methodologies developed to identify renewable resources in a given geographic region, to detect high potential areas for wind farm siting and to evaluate the efficiency and market of isolated systems to be used for dispersed rural electrification. In this last methodology we used fuzzy models to describe the uncertainties in demand and cost values.

Abstract
The electric distribution planning problem consists on deciding which facilities (injection points and feeders) to construct to meet future injections. Uncertainty on future demands and independent producers' injections, in the distribution network introduce additional difficulties to the problem. This paper presents a framework to cope with uncertainties, resulting from natural resources producers (mini-hydro and wind generation), based on fuzzy descriptions of the dispersed generation injections, leading to detailed models to be used in planning tools. The models include basic uncertainty and linguistic modifiers when extra information, from experts, is available. The proposed approach is useful both for operation and planning purposes.

Abstract
In this paper several concepts related to the application of Monte Carlo simulation to evaluate reliability indices of composite power systems are reviewed. In the sequence of previous papers, one integrates in the Monte Carlo simulation a DC Fuzzy Optimal Power Flow model to cope with uncertainties in peak loads. This general framework is now enhanced as one can specify fuzzy load duration curves, uncertainties in repair and failure rates and can adopt either non-chronological or chronological sampling strategies. This package can thus be used to evaluate the impact in the expected values of Power and Energy Not Supplied of uncertainties affecting several variables and parameters.