Abstract
The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.

Abstract
The liberalization of energy markets has imposed several modifications in the electricity market environment. The paradigm of monopoly market ceased to exist, and new models have been put into practice. The new models have increased the incentive on competitiveness, making market players struggle to achieve the best outcomes out of market participation. Producers aim at reaching the maximum profit on the sale of energy, while consumers try to minimize their spending on electrical energy. The proposed methodology considers the optimization of players' participation in multiple market opportunities. Reference prices that are expected in each market type at each moment are achieved through the application of neural networks. Using the forecasted prices, the proposed portfolio optimization method allocates the sale and purchase of electrical energy to different markets throughout the time, with the aim at achieving the most advantageous participation profile. A particle swarm approach is used to reduce the execution time while guaranteeing the minimum degradation of the results. Results of the swarm methodology are compared to those of a deterministic approach, using real data from the Iberian electricity market - MIBEL.

Abstract
This paper presents a decision support methodology for electricity market players' bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method's adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts' negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems' technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players' decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operatorMIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts' negotiations.

Abstract
The continuous changes in electricity markets' mechanisms and operations turn this environment into a challenging domain for the participating entities. Simulation tools are increasingly being used for decision support purposes of such entities. In particular, multi-agent based simulation, which facilitates the modeling of different types of mechanisms and players, is being fruitfully applied to the study of worldwide electricity markets. An effective decision support to market players' negotiations is, however, still not properly reached due to the uncertainty that results from the increasing penetration of renewable generation and the complexity of market mechanisms themselves. In this scope, this paper proposes a novel metalearner that provides decision support to market players in their negotiations. The proposed metalearner uses as input the output of several other market negotiation strategies, which are used to create a new, enhanced response. The final result is achieved through the combination and evolution of the strategies' learning results by applying a genetic algorithm. © 2015 IEEE.

Abstract
In this paper the first steps for the derivation of a mathematical model to describe the mechanical behaviour of a cylindrical electromagnetic vibration energy harvester, designed to extract energy from human gait to power biomedical implantable devices, are provided. As it is usual, in the modelling of such devices, the proposed mechanical model is also based on the solution of Newton's second law, but here a nonlinear closed-form expression is used for the resulting magnetic force of the system, unlike what has been done in previous works where, traditionally, that expression is a linear or is a nonlinear approximation of the real one. The main feature of this mechanical model is that it depends on several parameters which are related to the main characteristics of this kind of devices, which constitutes a major advantage with respect to the usual models available in the literature since these characteristics can always be changed in order to optimize the device.

Abstract
Prior knowledge of possible routes is undoubtedly an added value for autonomous navigation in irregular agricultural terrains. This information is particularly important when it involves the navigation of a monitoring robot, which necessarily carries a wide range of expensive sensors and when the vineyard presents a non-uniform configuration and extends over a very highly uneven terrain. In such case, a small navigation positional error can result in a large vertical deviation and consequently, a serious fall, which may damage or even destroy the robot. This article presents an automated way of deriving possible routes in this kind of terrain using three curve-skeleton algorithms for the 3D surfaces of the vineyard where the robot may navigate. The skeleton curves and real trajectory were represented in a graphical user OpenGL application developed for this purpose. A thinning, a geometric and a distance field algorithm were used for this study. The skeleton curves were compared with a real navigation path made by an expert when driving a tractor while spraying of the vineyard. In order to meet expert recommendations, the thinning algorithm was validated as the most suitable to achieve the aim of the study as it minimizes the quadratic average distance function applied to the skeleton points and the real trajectory. The limits of the most suitable curve-skeleton will be used as decision making points to establish navigation criteria for next step path planning.