Abstract
3D Virtual Worlds are being used for education and training purposes in a cross-disciplinary way. However, its widespread adoption, particularly in formal learning contexts, is far from being a reality due a broad range of technological challenges. In this reflection paper, our main goal is to argue why and how identity federation should be discussed and adopted as a solution to several barriers that educators and institutions face when using Virtual Worlds. By presenting a clear set of scenarios within different dimensions of the educational process, as classroom management, content reuse, learning analytics, accessibility, and research, we consider identity, traceability, privacy, accountability, and interoperability as main concerns in order to support our argument. Finally, we conclude the paper by presenting paths to a proposal for a workable solution, through the analysis and reflection of different and current efforts that has been made by other teams, towards future technological developments.

Abstract
This paper describes the work done in the 3PHASE project, regarding the development of a state estimator for distribution networks handling substantial integration of DER (Distributed Energy Resources), AMI (Advanced Metering Infrastructure) data and unbalanced and asymmetrical configurations. The load and DER power allocation presented here, as part of a DMS system, constitutes a first estimation of the network, assuming an extreme importance for other studies as it helps solve the lack of measurements problem.

Abstract
The increasing use of renewable energy sources and distributed generation brought several changes to the power system operation, with huge implications to the competitive electricity markets. With the eminent implementation of microgrids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a new type of player, which allows aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players benefits. The contribution of this paper is a clustering methodology regarding the remuneration and tariff of VPP. It proposes a model to implement fair and strategic remuneration and tariff methodologies, using a clustering algorithm, which creates sub-groups of data according to their correlations. The clustering process is evaluated so that the number of data subgroups that brings the most added value for the decision making process is found, according to the players characteristics. The proposed clustering methodology has been tested in a real distribution network with 16 bus, including residential and commercial consumers, PV generation and storage units.

Abstract
Reject option is a technique used to improve classifier's reliability in decision support systems. It consists in withholding the automatic classification of an item, if the decision is considered not sufficiently reliable. The rejected item is then handled by a different classifier or by a human expert. The vast majority of the works on this issue has been concerned with the development of reject option mechanisms to be used by supervised learning architectures (e.g., MLP, LVQ or SVM). In this paper, however, we aim at proposing alternatives to this view, which are based on the self-organizing map (SOM), originally an unsupervised learning scheme, but that has also been successfully used in the design of prototype-based classifiers. The basic hypothesis we defend is that it is possible to design SOM-based classifiers endowed with reject option mechanisms whose performances are comparable to or better than those achieved by standard supervised classifiers. For this purpose, we carried out a comprehensively evaluation of the proposed SOM-based classifiers on two synthetic and three real-world datasets. The obtained results suggest that the proposed SOM-based classifiers consistently outperform standard supervised classifiers.

Abstract
This paper presents the problem of redesigning a supply network of large scale by considering variability of the demand. The central problematic takes root in determining strategic decisions of closing and adjusting of capacity of some network echelons and the tactical decisions concerning to the distribution channels used for transporting products. We have formulated a deterministic Mixed Integer Linear Programming Model (MILP) and a stochastic MILP model (SMILP) whose objective functions are the maximization of the EBITDA (Earnings before Interest, Taxes, Depreciation and Amortization). The decisions of Network Design on stochastic model as capacities, number of warehouses in operation, material and product flows between echelons, are determined in a single stage by defining an objective function that penalizes unsatisfied demand and surplus of demand due to demand changes. The solution strategy adopted for the stochastic model is a scheme denominated as Sample Average Approximation (SAA). The model is based on the case of a Colombian company dedicated to production and marketing of foodstuffs and supplies for the bakery industry. The results show that the proposed methodology was a solid reference for decision support regarding to the supply networks redesign by considering the expected economic contribution of products and variability of the demand.

Abstract
The EU megagrid is foreseen to exploit the very large resource of solar energy in southern Europe and of wind power in northern Europe. Microgrids with enhanced control capabilities can integrate and coordinate local distributed resources enhancing the resilience of the EU megagrid and providing local restoration capabilities. The future modeling challenges of microgrids and in particular the shift to distributed control, enhancing further the microgrids resilience, are highlighted. © 2015 IEEE.