Abstract
The prospect of distributed generation investment planning (DGIP) is especially relevant in insular networks because of a number of reasons such as energy security, emissions and renewable integration targets. In this context, this paper presents a DGIP model that considers various DG types, including renewables. The planning process involves an economic analysis considering the costs of emissions, reliability and other relevant cost components. In addition, a comprehensive sensitivity analysis is carried out in order to investigate the effect of variability and uncertainty of model parameters on DG investment decisions. The ultimate goal is to identify the parameters that significantly influence the decision-making process and to quantify their degree of influence. The results show that uncertainty has a meaningful impact on DG investment decisions. In fact, the degree of influence varies from one parameter to another. However, in general, ignoring or inadequately considering uncertainty and variability in model parameters has a quantifiable cost. The analyses made in this paper can be very useful to identify the most relevant model parameters that need special attention in planning practices. © 2015 IEEE.

Abstract
A problem faced by oil companies is the maintenance of the location register of pipelines that cross the surf zone, the regular survey of their location, and also their inspection. A survey of the state of art did not allow identifying operating systems capable of executing such tasks. Commercial technologies available on the market also do not address this problem and/or do not satisfy the presented requirements. A possible solution is to use robotic systems which have the ability to walk on the shore and in the surf zone, subject to existing currents and ripples, and being able to withstand these ambient conditions. In this sense, the authors propose the development of a spider crab biologically inspired robot to achieve those tasks. Based on these ideas, this work presents a biomechanical study of the spider crab, its modeling and simulation using the SimMechanics toolbox of Matlab/Simulink, which is the first phase of this more vast project. Results show a robot model that is moving in an "animal like" manner, the locomotion, the algorithm presented in this paper allows the crab to walk sideways, in the desired direction.

Abstract
Optimization problems in electric power systems under high levels of uncertainty have been solved using stochastic programming methods for years. This is especially the case for medium-term problems and systems with a large share of hydro storages. The increased uncertainty in power system operation coming from volatile renewables has made the stochastic techniques interesting in shorter time frames as well. In the stochastic models the uncertainty is typically included by a discretized set of scenarios. This increases the computational burden significantly so a common approach is to preprocess and reduce the number of scenarios. Scenario reduction methods have been shown to function relatively well in expected value stochastic optimization. However, such setting of stochastic optimization is often criticized as being risk-prone so other risk-averse models exist. The evolutionary computation algorithms' flexibility permits the implementation of these risk models with relative simplicity. In this paper, a population-based evolutionary computation algorithm is applied to unit commitment problem under uncertainty and the paper illustrates several approaches to including risk policies in an evolutionary algorithm fitness function and illustrates its flexibility along with the link between scenario reduction similarity metric and risk policy.

Abstract
This paper presents a new stochastic multi-layer agent-based model to study the behavior of electricity market participants. The wholesale market players including renewable power producers are modeled in the first layer of the proposed multi-agent environment. The players optimize bidding/offering strategies to participate in the electricity markets. In the second layer, responsive customers including plug-in electric vehicle (PEV) owners and consumers who participate in demand response (DR) programs are modeled as independent agents. The objective of the responsive customers is to increase their benefit while retaining welfare. The interaction between market players in day-ahead and real-time markets is modeled using an incomplete information game theory algorithm. Due to the uncertainties of resources and customers' behavior, the model is developed using a stochastic framework. A case study containing wind power producers (WPPs), PEV aggregators and retailers providing DR is considered to demonstrate the usefulness and proficiency of the proposed multi-layer agent-based model.

Abstract
This paper presents a novel nonparametric methodology to evaluate convergence in an industry, considering a multi-input multi-output setting for the assessment of total factor productivity. In particular, we develop two new indexes to evaluate sigma-convergence and beta-convergence that can be computed using nonparametric techniques such as Data Envelopment Analysis. The methodology developed is particularly useful to enhance productivity assessments based on the Malmquist index. The methodology is applied to a real world context, consisting of a sample of Portuguese construction companies that operated in the sector between 2008 and 2010. The empirical results show that Portuguese companies tended to converge, both in the sense of a and beta, in all construction activity segments in the aftermath of the financial crisis.

Abstract
In this paper, we analyze numerically and experimentally a remote optical sensor system based on Raman Amplification, composed by one long period grating operating as a sensor head separated by 50 km from the optical source and the interrogation unit composed by two fiber Bragg gratings. Since the active components of the system and the sensor head are separated over such a large distance, it is necessary to consider optical amplification to strengthen the optical signal. The use of Raman amplification allows reaching the desirable gain bandwidth by changing the pump lasers parameters such as the power, number of pumps and spectral position. We present the obtained measurement results of the environmental temperature for two different setups that was analyzed a priori by the numerical model. We show that the power ratio between the two central wavelengths of the FBG has a linear relation with the change of LPG resonance with temperature.