Abstract
With the restructuring of the energy sector in industrialized countries there is an increased complexity in market players' interactions along with emerging problems and new issues to be addressed. Decision support tools that facilitate the study and understanding of these markets are extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent simulator for competitive electricity markets. It is essential to reinforce MASCEM with the ability to recreate electricity markets reality in the fullest possible extent, making it able to simulate as many types of markets models and players as possible. This paper presents the development of the Balancing Market in MASCEM. A key module to the study of competitive electricity markets, as it has well defined and distinct characteristics previously implemented.

Abstract
Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents' behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Abstract
The increasing number of players that operate in power systems leads to a more complex management. In this paper a new multi-agent platform is proposed, which simulates the real operation of power system players. MASGriP - A Multi-Agent Smart Grid Simulation Platform is presented. Several consumer and producer agents are implemented and simulated, considering real characteristics and different goals and actuation strategies. Aggregator entities, such as Virtual Power Players and Curtailment Service Providers are also included. The integration of MASGriP agents in MASCEM (Multi-Agent System for Competitive Electricity Markets) simulator allows the simulation of technical and economical activities of several players. An energy resources management architecture used in microgrids is also explained.

Abstract
The spread and globalization of distributed generation (DG) in recent years has should highly influence the changes that occur in Electricity Markets (EMs). DG has brought a large number of new players to take action in the EMs, therefore increasing the complexity of these markets. Simulation based on multi-agent systems appears as a good way of analyzing players' behavior and interactions, especially in a coalition perspective, and the effects these players have on the markets. MASCEM - Multi-Agent System for Competitive Electricity Markets was created to permit the study of the market operation with several different players and market mechanisms. MASGriP - Multi-Agent Smart Grid Platform is being developed to facilitate the simulation of micro grid (MG) and smart grid (SG) concepts with multiple different scenarios. This paper presents an intelligent management method for MG and SG. The simulation of different methods of control provides an advantage in comparing different possible approaches to respond to market events. Players utilize electric vehicles' batteries and participate in Demand Response (DR) contracts, taking advantage on the best opportunities brought by the use of all resources, to improve their actions in response to MG and/or SG requests.

Abstract
This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network's execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network's integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). © 2012 IEEE.

Abstract
The art of telling stories in digital format is currently widely available due to the popularization of digital cameras, computers and other mobile devices. In this paper we discuss the use of collaborative learning strategies based on Digital Storytelling in corporative training. The text includes a concise review on theoretical and technical foundations about educational communication through the use of audiovisual products based on disciplines such as Communication, Education and Cognitive Sciences. We will also discuss how Digital Storytelling may be integrated in traditional contents oriented self-training systems, used by many corporations. However the main focus will be the discussion of the potential of the use of Digital Storytelling methodology in corporative training context which will be detailed based on data collected in scenarios from Portugal and Brazil. The paper is based on qualitative research and it will argue that digital storytelling may contribute to the improvement of the effectiveness of collaborative learning processes in corporative training, because it provides means for the swift delivery of highly contextualized learning materials and the sharing of relevant personal trainers and trainees’ experiences. Furthermore, Digital Storytelling methodology provides an opportunity to value, respect and promote the multiple and different cultural and social interactions in the corporative knowledge construction process. The research methodology adopted on this study involved three phases: 1) literature review; 2) detailed analysis of the digital Storytelling methodology and its implications in corporative training context; 3) collecting data from and discussing about some corporative cases in Portugal and Brazil. The result of the research aims at suggesting a critical analysis and creative attitude in the production of audiovisual teaching materials for the corporative training context.