Abstract
In recent years, the level of variable Renewable Energy Sources (vRESs) integrated in power systems has been increasing steadily. This is driven by a multitude of global and local concerns related to energy security and dependence, climate change, etc. The integration of such energy sources is expected to continue growing in the coming years. Despite their multifaceted benefits, variable energy sources introduce technical challenges mainly because of their intermittent nature, particularly at distribution levels. The flexibility of existing distribution systems should be significantly enhanced to partially reduce the side effects of vRESs. One way to do this is using a dynamic network reconfiguration. Framed in this context, this work presents an optimization problem to investigate the impacts of grid reconfiguration on the level of integration and utilization of vRES power in the system. The developed combinatorial model is solved using a genetic algorithm. A standard IEEE 33-node distribution system is employed in the analysis. Simulation results show the capability of network switching in supporting large-scale integration of vRESs in the system while alleviating their side effects. Moreover, the simultaneous consideration of vRES integration and network reconfiguration lead to a better voltage profile, reduced costs and losses in the system.

Abstract
We study a classic international trade model consisting of a strategic game in the tariffs of the governments. The model is a two-stage game where, at the first stage, governments of each country use their welfare functions to choose their tariffs either (i) competitively (Nash equilibrium) or (ii) cooperatively (social optimum). In the second stage, firms choose competitively (Nash) their home and export quantities. We compare the competitive (Nash) tariffs with the cooperative (social) tariffs and we classify the game type according to the coincidence or not of these equilibria as a social equilibrium, a prisoner's dilemma or a lose-win dilemma.

Abstract
In time series analysis, Autoregressive Moving Average (ARMA) models play a central role. Because of the importance of parameter estimation in ARMA modeling and since it is based on aggregate time series so often, we analyze the effect of temporal aggregation on estimation accuracy. We derive the relationships between the aggregate and the basic parameters and compute the actual values of the former from those of the latter in order to measure and compare their estimation accuracy. We run a simulation experiment that shows that aggregation seriously worsens estimation accuracy and that the impact increases with the order of aggregation.

Abstract
The article describes the Impact of using Gamification Elements like Badges, Status Bar and a Rating System on the Interaction, Collaboration, Cooperation and the Presences of the Community of Inquiry Framework, inside an Academic Social Platform. We used a Design Base Research Methodology with Mixed Methods. We started by collecting opinions of users using semistructured interviews. The results from coding, informed on the construction of a gamified prototype, made with Elgg. Then Usability tests were conducted and the data helped refine the subsequent implementation. A Survey was deployed, Observations were made, and we gathered some Analytics. Results are presented congruent to the iterations and discussed. Some indications for the use of Badges and the implementation of Gamification are considered.

Abstract
Power systems worldwide are complex and challenging environments. The increasing necessity for an adequate integration of renewable energy sources is resulting in a rising complexity in power systems operation. Multi-agent based simulation platforms have proven to be a good option to study the several issues related to these systems. In a smaller scale, a home energy management system would be effective for the both sides of the network. It can reduce the electricity costs of the demand side, and it can assist to relieve the grid congestion in peak times. This paper represents a domestic energy management system as part of a multi-agent system that models the smart home energy system. Our proposed system consists of energy management and predictor systems. This way, homes are able to transact with the local electricity market according to the energy flexibility that is provided by the electric vehicle, and it can manage produced electrical energy of the photovoltaic system inside of the home.

Abstract
Increasing penetration of distributed generation (DG), may be interesting from several points of view, but it raises important challenges about distribution system operation and planning practices. To optimal allocation of DG, which play an important role in construction of microgrids, the benefits and risks should be qualified and quantified. This paper introduces several probabilistic indices to evaluate the potential operational effects of increasing penetration of renewable DG units such as wind power and photovoltaic on rural distribution network with the aid of evaluating technical benefits and risks tradeoffs. A probabilistic generation-load model is suggested to calculate these indices which combine a large number of possible operating conditions of renewable DG units with their probabilities. Temporal and annual indices of voltage profile and line flow-related attributes such as interest voltage rise, risky voltage rise, risky voltage down, line loss reduction, line loss increment, and line overload flow are introduced using probability and expected values of their occurrence. Also, to measure the overall interests and risks of installing DG, composite indices are presented. The implementation of the proposed framework in a 4-bus and IEEE 33-bus radial distribution systems shows the effectiveness of the benefits and risks assessment technique with the proposed metrics.