Abstract
In this study, a two-stage stochastic programming joint day-ahead energy and reserve scheduling model to address uncertainty in wind power generation is developed. Apart from the generation side, the demand side is also eligible as a reserve resource and is modeled through responsive load aggregations, as well as large industrial loads that directly participate in the scheduling procedure. The main contribution of this paper is the inclusion of a risk metric, namely the conditional value-at-risk, which renders a conceptually different resource scheduling framework. The proposed model is employed in order to analyze the behavior of energy and reserve scheduling by both generation and demand for a risk-averse independent system operator. To reach practical conclusions, the proposed methodology is tested on the real non-interconnected insular power system of Crete, Greece, which is characterized by a significant penetration of wind power generation.

Abstract
The rising costs of energy in organizations of various sectors of activity have been demonstrating the need for a correct evaluation of processes and technologies from the point of view of energy. In recent years there has been a major concern in the dissemination and adoption of more efficient technologies to the detriment of a more comprehensive and inclusive of various aspects related to energy consumption. In this context it is very important to develop computational tools that allow the characterization and evaluation of the efficiency of processes in real operating conditions, as well as its monitoring and control, being of great importance in the rationalization of energy costs. Considering the current global economic situation and the rising energy costs, all contributions to the reduction in energy consumption are relevant, allowing the reduction of the energy bill of industry and domestic users. Hence, this paper deals with the reduction of losses in cables of an electrical installation, depending on the equipment used, presenting both simulation and experimental results. The economic choice of the cables section will allow improving the efficiency of the machines in general, providing also an optimal cost/benefit relationship.

Abstract
With the constant increment of wind power generation driven by economic and environmental factors, the optimal utilization of generation resources has become a critical problem discussed by many authors. Within this topic, determination of optimal spinning reserve (SR) requirements is a key and complex issue due to the variable and unpredictable nature of renewable generation besides of generation unit reliability. Cost/benefit relationship has been suggested as a way to determine the optimal amount of power generation to be committed by taking into account renewable power forecasting error and system reliability. In this paper, a technique that combines an analytical convolution process with Monte Carlo Simulation (MCS) approach is proposed to efficiently build cost/benefit relationship. The proposed method uses discrete probability theory and identifies those cases at which convolution analysis can be used by recognizing those situations at which SR does not have any effect; while in the other cases MCS is applied. This approach allows improving significantly the computational efficiency. The proposed technique is illustrated by means of two case studies of 10 and 140 units, demonstrating the capabilities and flexibility of the proposed methodology.

Abstract
In this paper, a methodology to solve Unit Commitment (UC) problem from a probabilistic perspective is developed and illustrated. The method presented is based on solving the Economic Dispatch (ED) problem describing the Probability Distribution Function (PDF) of the output power of thermal generators, energy not supplied, excess of electricity, Generation Cost (GC), and Spinning Reserve (SR). The obtained ED solution is combined with Priority List (PL) method in order to solve UC problem probabilistically, giving especial attention to the probability of providing a determined amount of SR at each time step. Three case studies are analysed; the first case study explains how PDF of SR can be used as a metric to decide the amount of power that should be committed; while in the second and third case studies, two systems of 10-units and 110-units are analysed in order to evaluate the quality of the obtained solution from the proposed approach. Results are thoroughly compared to those offered by a stochastic programming approach based on mixed-integer linear programming formulation, observing a difference on GCs between 1.41% and 1.43% for the 10-units system, and between 3.75% and 4.5% for the 110-units system, depending on the chosen significance level of the probabilistic analysis.

Abstract
Photoplethysmography (PPG) sensors are an inexpensive yet cost-effective way to track data correlated to the heart pulsation. The information is acquired with light signals generated by means of a photodiode and by detecting the amount of reflected or transmitted light through the tissue. In this paper, a novel methodology that enables a systematic approach for evaluating high compact signal processing designs, which are now a trend among several semiconductor manufacturers, is proposed and discussed. In this context, an integrated pulse oximeter sensor is embedded in a custom board and used to test the methodology concept proposed by the authors. Conclusions are duly drawn.

Abstract
Due to communication and technology developments, residential consumers are enabled to participate in Demand Response Programs (DRPs), control their consumption and decrease their cost by using Household Energy Management (HEM) systems. On the other hand, capability of energy storage systems to improve the energy efficiency causes that employing Phase Change Materials (PCM) as thermal storage systems to be widely addressed in the building applications. In this paper, an operational model of HEM system considering the incorporation of more than one type of PCM in plastering mortars (hybrid PCM) is proposed not only to minimize the customer's cost in different DRPs but also to guaranty the habitants' satisfaction. Moreover, the proposed model ensures the technical and economic limits of batteries and electrical appliances. Different case studies indicate that implementation of hybrid PCM in the buildings can meaningfully affect the operational pattern of HEM systems in different DRPs. The results reveal that the customer's electricity cost can be reduced up to 48% by utilizing the proposed model.