Abstract
This paper presents a control technique that enhances microgrids stability during the grid-connected and islanded modes. The proposed technique is compared with several existing control strategies in the context of microgrids integration into smart grids. The Lyapunov control theory is utilized in this paper to investigate the operation stability of DG units operating along with the utility grid. As the main contribution, the proposed technique compensates for the instantaneous variations of the reference current components of DG units in the ac-side of the converters. The presented method also considers and properly addresses the dc-voltage variations in the dc-side of the interfacing system. Under the proposed control strategy, DG units are able to deliver active and reactive power to the local loads and/or the main grid in fundamental and harmonic frequencies, with a fast dynamic response and without any interruption. Several simulation scenarios are carried out to demonstrate effectiveness of the proposed control strategy in microgrids during the transient and steady-state operation.

Abstract
Virtual environments for driving simulation aimed to scientific purposes require realistic three-dimensional models of roads. The creation of road models for these purposes, is usually preceded by the design of road paths which fulfill all the desired specific characteristics. Traditionally, the design of road paths is performed by road engineering specialists, resulting in a very time-consuming task. This paper presents a method that allows the procedural generation of road paths aimed to driving simulation experiments (e.g., ergonomics, psychology and traffic engineering). This method is inspired in methods used in roadways engineering, producing roads according to the design standards and similar to those found in the real world. This significantly reduces the need of specialists to prepare the road paths and generate road models suitable for conducting scientific work in driving simulators.

Abstract
This short paper presents a game for collecting metadata to describe video content. Tags, introduced by registered players on a given timecode of the video, are collected and validated based on a collaborative scoring mechanism that excludes erratic annotations. The system follows a gamification approach for motivating users and includes processes for semantically relating concepts.

Abstract

Abstract
This paper investigates the expansion of the network of a monopolist firm that produces a durable good and is also involved in the corresponding aftermarket. We characterize the Markov Perfect Equilibrium of the continuous time dynamic game played by the monopolist and the forward-looking consumers, under the assumption that consumers benefit from the subsequent expansion of the network. The paper contributes to the theoretical discussion on the validity of the Coase conjecture, analyzing whether Coase's prediction that the monopolist serves the market in a "twinkling of an eye" remains valid in our setup. We conclude that the equilibrium network development may actually be gradual, contradicting Coase's conjecture. We find that a necessary condition for such a result is the existence of aftermarket network effects that accrue (at least partly) to the monopolist firm.

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.