Abstract

Abstract
In this paper, a methodology for solving the ED (economic dispatch) problem considering the uncertainty of wind power generation and generators reliability is presented. The corresponding PDF (probability distribution function) of available wind power generation is discretized and introduced in the optimization problem in order to probabilistically describe the power generation of each thermal unit, wind power curtailment, ENS (energy not supplied), excess of power generation, and total generation cost. The reliability of each unit is incorporated by estimating the joint PDF of power generation and failure events, while the PDF of ENS is incorporated by convoluting the PDF of ENS due to the forecasting error and any failure event. The performance of the proposed approach is analyzed by studying two power systems of 5 and 10 units. The proposed method is compared to MCS (Monte Carlo Simulation) approach, being able to reproduce the PDF in a reasonable manner, specifically when system reliability is not taken into account.

Abstract
There is a need to attract students to science and engineering courses. Robotic contests are one of the most promising ways to attract students to the field of robotics and thereby to science and technology. Micromouse contest is one of promising contests where a small autonomous robot has to navigate its way through an unknown 16 x 16 cells maze. Since the design and construction of robots is interesting but difficult, this paper presents a high performance, low-cost robot kit for high school and university students participate on micromouse robot contests. This micromouse kit, developed at University of Tras-os-Montes and Alto Douro (UTAD), fits on a 10x8 cm rectangle and uses very small stepper motors allowing a maximum speed of about 4 m/s thus comparing to state-of-the-art micromice. The kit also incorporates a popular microcontroller hardware module (Arduino Leonardo) which facilitates all programming tasks.

Abstract
Natural hazards, such as big earthquakes, affect the lives of thousands of people at all levels. Extreme-value analysis is an area of statistical analysis particularly concerned with the systematic study of extremes, providing an useful insight to fields where extreme values are probable to occur. The characterization of the extreme seismic activity is a fundamental basis for risk investigation and safety evaluation. Here we study large earthquakes in the scope of the Extreme Value Theory. We focus on the tails of the seismic moment distributions and we propose to estimate relevant parameters, like the tail index and high order quantiles using the geometric-type estimators. In this work we combine two approaches, namely an exploratory oriented analysis and an inferential study. The validity of the assumptions required are verified, and both geometric-type and Hill estimators are applied for the tail index and quantile estimation. A comparison between the estimators is performed, and their application to the considered problem is illustrated and discussed in the corresponding context.

Abstract
The concept of Carrier-Based Demand Response (CBDR) programs in Smart Multi-Energy Systems (MES) is proposed in this paper. It is discussed that by establishing the bi-directional relation between multi-energy demand and MES through the penetration of multi-carrier device technologies, the opportunity of demand-side participation in system operation can be activated. In this paper, the external dependency caused by multi-carrier devices is employed as a demand response. The CBDR is introduced as the flexibility of end-use to change the conversion pattern of input carriers into required demand. As the CBDR program is influenced by energy carrier prices, upstream network obligations and also the customer's behavior, its uncertainty is effectively modeled in this paper. The results compare the difference between the stochastic and deterministic approaches to the problem and show the improved accuracy through the stochastic modeling. The role of those customers that are not taking part in CBDR program is also investigated.

Abstract
The increment in generation costs is one of the most important factors that characterizes the operation of insular power systems, and is related to the location of these systems and the type of fuel used to provide electricity. This situation motivates the integration of renewable generation at high rates, as well as energy storage systems (ESSs), to improve the utilization of these resources. In this paper, a new control strategy is presented for the day-ahead scheduling of insular power systems with a battery energy storage system. The method presented here incorporates the effects of the most relevant components such as thermal generators, wind power generation, power converter, charge controller and ESS, being integrated into the scheduling process of insular power systems as a new contribution to earlier studies. The results provided show a fuel saving of 2% and an improvement in the wind power use of 20%, which is significant.