Abstract
This paper describes a Load Allocation model to be used in a DMS environment. A process of rough allocation is initiated, based on information on actual measurements and on data about installed capacity and power and energy consumption at LV substations. This process generates a fuzzy load allocation, which is then corrected by a fuzzy state estimator procedure in order to generate a crisp power flow compatible set of load allocations, coherent with available real time measurements recorded in the SCADA.

Abstract
In this paper, an advanced control system for the optimal operation and management of isolated power systems with increased renewable power integration is presented. The control system minimises the production costs through on-line optimal scheduling of the power units, taking into account short-term forecasts of the load and the renewable resources. The power system security is supervised via on-line security assessment modules, which emulate the power system frequency changes caused by pre-selected disturbances. For each of the above functions, a number of techniques have been applied, both conventional and AI based. The system has been installed in the dispatch center of Crete since June 1999, and is under evaluation. © 2000 IEEE.

Abstract
This paper presents an approach for including nonstatistical uncertainties in engineering economic analysis, particularly utility economic analysis, by modeling uncertain variables with fuzzy numbers. In this case, the mathematical operations are defined by the extension principle and the results obtained are also in a form of fuzzy numbers. This approach can be seen as an extension of a previously proposed one that uses interval numbers and interval analysis for including such uncertainties. However, this paper considers also the dependence which may exist between the fuzzy variables and shows the impact this dependence may have on the results. In this context, a way of modeling partial correlation between the variables of the same kind is proposed.

Abstract
This paper provides a description of a new approach for steady state security evaluation, using fuzzy nearest prototype classifiers, The basic method has an off-line training phase, used to design the fast classifiers for on-line purposes, allowing more than the two traditional security classes. A battery of these fuzzy classifiers, valid for a specific configuration of the network, is adopted to produce a global evaluation for all relevant single Contingencies. An important feature of this approach is that it selects automatically the most appropriate number of security clusters for each selected contingency. Natural language-labeling is also used to produce standardized sentences about the security level of the system, improving in this way the communication process between the system and the operator. The paper is completed by an example on a realistic model of the Hellenic interconnected power system, where seven contingencies were simulated.

Abstract
In this paper we describe a multiobjective formulation for the long term planning of distribution networks considering a number of important features. The model admits fuzzy representations for loads and evaluates nodal long term marginal prices. It integrates a number of criteria related to investment, operational and reliability costs, risk index measuring the ability to accommodate load uncertainties and the remuneration collected using long term marginal prices. After using a Simulated Annealing approach to identify efficient expansion plans, it is finally conducted a decision analysis in order to select the most adequate plan. At a final section, we illustrate the formulation with a case study based on a Portuguese distribution network.

Abstract
In this paper we describe a new approach to identify the combination of tap transformer positions, capacitor bank steps together with the minimum amount of toad to be shed that assures one to obtain a specified security degree of a power system. The basic approach is designed to identify the most adequate actions to be taken for a given contingency. This identification procedure uses Genetic Algorithms given their adequacy to model discrete actions. However, Genetic Algorithms are known for their usually large computation time. In order to address this issue and having in mind the objective of developing a real time tool, we incorporated a classification procedure based on Neural Networks. The paper includes results obtained using the developed approach both to evaluate the quality of the solutions for a number of contingencies and the quality of the overall performance when using the Neural Network tool. Results obtained for a reduced version of the Brazilian Mate Grosso transmission system are presented and discussed.