Abstract
In this paper is presented a Game Theory based methodology to allocate transmission costs, considering cooperation and competition between producers. As original contribution, it finds the degree of participation on the additional costs according to the demand behavior. A comparative study was carried out between the obtained results using Nucleolus balance and Shapley Value, with other techniques such as Averages Allocation method and the Generalized Generation Distribution Factors method (GGDF). As example, a six nodes network was used for the simulations. The results demonstrate the ability to find adequate solutions on open access environment to the networks.

Abstract
This paper proposes a swarm intelligence long-term hedging tool to support electricity producers in competitive electricity markets. This tool investigates the long-term hedging opportunities available to electric power producers through the use of contracts with physical (spot and forward) and financial (options) settlement. To find the optimal portfolio the producer risk preference is stated by a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance estimation and the expected return are based on a forecasted scenario interval determined by a long-term price range forecast model, developed by the authors, whose explanation is outside the scope of this paper. The proposed tool makes use of Particle Swarm Optimization (PSO) and its performance has been evaluated by comparing it with a Genetic Algorithm (GA) based approach. To validate the risk management tool a case study, using real price historical data for mainland Spanish market, is presented to demonstrate the effectiveness of the proposed methodology.

Abstract
The development of automated individualized drug dosage regimens, namely in general anaesthesia environment, has been a subject of interest in the last decades. The use of continuous intravenous drug administration aims at, accurately, maintaining the system at a desired target effect concentration level. Different methods have been proposed for the design of individualized dosage regimens. In this study individual drug dose design is achieved through the characterization of transient initial response induced by a bolus administration of drug. This approach is based on the statistical analysis of the data using Walsh-Fourier spectral analysis which provides information about patient dynamics, allowing the on-line drug dose design using multiple linear least squares and quantile regression technics. The proposed methodology is illustrated in the case where the effect measured on the patient corresponds to the neuromuscular blockade (NMB) level and the drug to the muscle relaxant atracurium.

Abstract
This paper presents the participation of distributed generation and interruptible loads for Demand Side Management (DSM) in market environment. This contribution has two aspects, first, the technical requirements that should be implemented on these applications. Then the way DGs and ILs are offered in market and what should be included in their contracts. In this paper the above mentioned issues will be discussed and a database system will be presented by which a Disco can deal with interruptible loads and distributed generations simultaneously in its contracts.

Abstract
According to the conclusions of the study "Smart Portugal 2020", the Information and Communication Technologies (ICT) can contribute to a reduction of 15% in our ecological footprint. The role of innovation and the early adoption of solutions that could reduce consumption in all economical sectors will be vital to the 20/20/20 European Strategy [1]. Active network management for generation, storage and demand is one of the basis for the SmartGrid concept, where the actions of all agents connected to the electricity system (generators, consumers and Prosumers) can be intelligently integrated aiming for a sustainable, efficient and secure energy supply system [2]. The implementation of this type of system requires an intelligent control and a management system based on advanced communication and monitoring solutions, as well as on self-healing and pre-fault detection technologies. SmartGrids are, therefore, the most efficient approach to integrate Distributed Generation (DG) and renewable energy sources in a coordinated way with demand management in a sustainable system. This paper describes EDP approach to the EnergyCom infrastructure, with improvement of Quality of Service (QoS) and operational efficiency as main target, and presents the importance of SmartGrids as an answer to the challenges of the Electric Energy Distribution, and its role in the corporate strategy. The 3-phase implementation program of the 3rd generation electric grid in EDP Distribuição is also presented.

Abstract
Loads estimation is becoming each time more fundamental for an efficient management and planning of electric distribution systems. Among the factors that contribute to this need of more efficiency are the increasing complexity of these networks, the deregulation process and the competition in an open energy market, and environment preservation requirements. However, the only information generally available at MV and LV levels is essentially of commercial nature, i.e., monthly energy consumption, hired power contracts and activity codes. In consequence, distribution utilities face the problem of estimating load diagrams to be used in planning and operation studies. The typical procedure uses measurements in typical classes of consumers defined by experts to construct inference engines that, most of the times, only estimate peak loads. In this paper, the definition of classes was performed by clustering the collected load diagrams. Artificial Neural Networks (ANN) were then used for load Curve estimation. This article describes the adopted methodology and presents some representative results. Performance attained is discussed as well as a method to achieve confidence intervals of the main predicted diagrams.