Abstract
Quality prediction of load evolution at different levels of distribution network is a basic requirement for adequate operation planning of modern power systems. This paper describes the models, based on artificial neural networks, developed for active and reactive power forecasting at primary substations' transformers. The main goal consists on defining a regression process characterized by good quality estimates of those future values, based on historical data. Anticipation interval shall include from the next hour to one week in advance. The implemented forecasting tool is able to deal with noisy data, holidays and special occasions and adapts forecasts in case of power network reconfiguration whenever planned. Used techniques and implementation foundations of selected forecasting models are reported. Finally, the potential of the adopted approach is sustained by illustrative examples. © 2003 IEEE.

Abstract
In this paper, the Unit Commitment (UC) problem is presented and solved, following an innovative approach based on a metaheuristic procedure. The problem consists on deciding which electric generators must be committed, over a given planning horizon, and on defining the production levels that are required for each generator, so that load and spinning reserve requirements are verified, at minimum production costs. Due to its complexity, exact methods proved to be inefficient when real size problems were considered. Therefore, heuristic methods have for long been developed and, in recent years, metaheuristics have also been applied with some success to the problem. Methods like Simulated Annealing, Tabu Search and Evolutionary Programming can be found in several papers, presenting results that are sufficiently interesting to justify further research in the area. In this paper, a resolution framework based on GRASP - Greedy Randomized Adaptive Search Procedure - is presented. To obtain a general optimisation tool, capable of solving different problem variants and of including several objectives, the operations involved in the optimisation process do not consider any particular characteristics of the classical UC problem. Even so, when applied to instances with very particular structures, the computational results show the potential of this approach.

Abstract
The need of fast algorithms for radial distribution networks that take advantage of their particular structure has been increasing, namely due to the use of genetic algorithms and meta-heuristics for optimization in planning and operation. In this paper, a new method for power flow calculation in radial networks is presented. It uses an iterative process along the branches, in a way similar to other methods, but the main idea is very different from previous approaches, since it is based on the exact power flow solution for a single branch and also because it provides a complete solution (not only voltage magnitudes). The method is fast and robust for different types of networks and loads, including heavy loads. The paper includes the theoretical derivation of the method, an illustration example and tests with benchmarking networks. © 2003 IEEE.

Abstract
In this paper, we describe an integrated methodology to compute long-term marginal prices in distribution networks. Long-term marginal prices are considered the most interesting and economically sound way of allocating network costs to users. Additionally, they inherently deal with the revenue reconciliation problem, as they generally do not require other large supplementary tariff terms. The proposed methodology uses fuzzy sets to model uncertainties in load forecasts and considers several criteria to guide the identification of solutions. At the end, there is a final decision-making step to select the most adequate expansion plan according to the preferences of the decision maker.

Abstract
This paper addresses the problem of transmission expansion-planning in the context of the re-regulation and liberalization of power systems. The transmission expansion-planning problem is formulated as an integer problem and it is solved keeping that characteristic using Simulated Annealing. In the scope of the access to the transmission networks and the corresponding payments for Use of Networks, the paper discusses the advantages and drawbacks from adopting several cost methods and, more specifically, short-term and long-term nodal marginal prices. Long-term marginal prices are computed in the framework of the Simulated Annealing algorithm and led to the calculation of the Marginal Based Remuneration to the transmission company. The paper includes case studies based on the Portuguese 400/220/150 kV network and on a didactic 6-bus system. © 2003 IEEE.

Abstract
This paper presents the Simulator of Intelligent Transportation Systems (SITS). The SITS is based on a microscopic simulation approach to reproduce real traffic conditions in an urban or non-urban network and considers different types of vehicles, drivers and roads. A dynamical analysis of several traffic phenomena is then addressed. The results of using classical system theory tools point out that it is possible to study traffic systems, taking advantage on the knowledge gathered with automatic control algorithms. In this line of thought, it was also presented a new modelling formalism based on the embedding of statistics and Fourier transform. © 2003 IEEE.