Abstract
Negative effects of massive industrialization, high rates of fossil-fuel consumption, fast economic growing and technological development have positioned renewable energies as a promising manner to reach an environmentally sustainable society. Detailed knowledge about renewable resources is an important factor, but it is difficult to obtain in most places; in the case of solar and wind resources, energetic potential could vary significantly according to the local conditions. Implementation of Measure Correlate-Predict (MCP) methodology offers a partial solution to this problem; however, the associated error related to the extrapolation process could be in some cases significant. Hence, this paper presents an analytical method to incorporate MCP extrapolation error on the simulation of smart residential energy systems. Beta probability distribution function (PDF) is used to model the extrapolation error and it is combined with a simulation model to estimate PDF of renewable power generation, battery state of charge, and power imported from the distribution system, which allows obtaining a complete perspective of energy system performance.

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
This paper presents a new technique for providing the analysis and comparison of wiretap codes in the small blocklength regime over the binary erasure wiretap channel. A major result is the development of Monte Carlo strategies for quantifying a code's equivocation, which mirrors techniques used to analyze forward error correcting codes. For this paper, we limit our analysis to coset-based wiretap codes, and give preferred strategies for calculating and/or estimating the equivocation in order of preference. We also make several comparisons of different code families. Our results indicate that there are security advantages to using algebraic codes for applications that require small to medium blocklengths.

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
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.