Abstract
With the rising popularity of affective computing techniques, there have been several advances in the field of emotion recognition systems. However, despite the several advances in the field, these systems still face scenario adaptability and practical implementation issues. In light of these issues, we developed a nonspecific method for emotional state classification in interactive environments. The proposed method employs a two-layer classification process to detect Arousal and Valence (the emotion's hedonic component), based on four psychophysiological metrics: Skin Conductance, Heart Rate and Electromyography measured at the corrugator supercilii and zygomaticus major muscles. The first classification layer applies multiple regression models to correctly scale the aforementioned metrics across participants and experimental conditions, while also correlating them to the Arousal or Valence dimensions. The second layer then explores several machine learning techniques to merge the regression outputs into one final rating. The obtained results indicate we are able to classify Arousal and Valence independently from participant and experimental conditions with satisfactory accuracy (97% for Arousal and 91% for Valence).

Abstract
This work presents a methodology to manage Electric Vehicles (EV) charging in quasi-real-time, considering the participation of EV aggregators in electricity markets and the technical restrictions of the electricity grid components, controlled through the distribution system operator. Two methodologies are presented to manage EV charging, one to be used by the EV aggregators and the other by the Distribution System Operators (DSO). The methodology developed for the aggregator has as main objective minimizing the deviation between the energy bought in the market and the energy consumed by EV. The methodology developed for the DSO allows it to manage the grid and solve operational problems that may appear by controlling EV charging. A method to generate a synthetic EV data set is used in this work, which provides information about the EV movement, periods when EV are parked, as well as their energy requirements. This data set is used afterwards to assess the performance of the algorithms developed to manage the EV charging in quasi-real-time.

Abstract
This paper presents a new load flow formulation to solve active and passive electric distribution networks. The fundamental idea discussed here is how to obtain the power flow solution by using the elements of a unique quasi-symmetric matrix called TRX in the iterative process. The method is formulated for single-phase balanced and three-phase unbalanced radially operated networks. It works with real variables as opposed to complex variables used in previous backward/forward sweep algorithms discussed in literature. The proposed TRX matrix constitutes a complete database by including information of network topology structure as well as branch impedances of the distribution feeder. Data arrangement is suitable to be exchanged under standard Common Information Model (CIM) under Distribution Management Systems (DMS) environment allowing an efficient computation of the state of the system for on-line and off-line study applications. The proposed methodology was applied on a group of IEEE test systems and a real distribution system of 49,000 nodes.

Abstract

Abstract

Abstract
Within the Smart Grid paradigm, the MicroGrid concept (MG) presents an adequate framework to monitor and manage the low voltage network and coordinate the resources connected to it, including the smart grid new players, namely the consumers, prosumers and the Electric Vehicles (EV). The coordinated management and control of the MG resources, enables the operation both connected to the main power network or autonomously, due to planned or unplanned outages. In order to operate autonomously, the MG relies in its storage capacity to provide some form of energy buffering capabilities to balance load and generation. This paper presents innovative methodology to coordinate the microgrid storage capacity with EV smart charging strategies and demand response schemes, in order to improve microgrid resilience in the moments subsequent to islanding and reduce the non-served load. The effectiveness of the proposed algorithms are validated though extensive numerical simulations.