Abstract
The total losses volume represents a substantial amount of energy and, consequently, a large cost that is often included in the tariffs structure. Uneven connection of single-phase loads is a major cause for three-phase unbalance and a fundamental cause for active power losses, particularly in Low Voltage (LV) networks. This paper analyzes the impact of load unbalance on LV network losses. In the first phase, several load scenarios per phase are considered to characterize how losses depend on load unbalance. The second phase examines the data collected per phase on a set of real networks, aiming at illustrating real-world cases. The third phase analyzes the effect that public lighting and microgeneration may have in the load unbalance and on the subsequent energy losses. The results of this work clearly demonstrate that it is possible to reduce three-phase unbalance (and losses) through a judicious distribution of loads and microgeneration.

Abstract
Global climate change is currently a focus issue because of its impacts on the most diverse natural systems and, consequently, the development of humanity. The electricity sector is a major contributor to climate change because of its long-standing dependence on fossil fuels. However, the energy paradigm is changing, and renewable sources tend to play an increasingly important role in the energy mix in Portugal. Due to the strong relationship between renewable energies and climate-related natural resources, the climate change phenomenon could have considerable effects on the electricity sector. This paper analyzes the effects of climate change on the energy mix in Portugal in the medium / long term (up to 2050). The proposed methodology is based on the simulation of climate scenarios and projections of installed power by type and consumption. The combinations of these conditions are inputted to an energy accounting simulation tool, able to combine all information and provide a characterization of the system state for each case. The most favorable forecasted scenarios indicate that a fully renewable electricity system is achievable in the medium term, in line with the objectives of the European Union, as long as investments in renewable sources continue to be stimulated in the coming years.

Abstract
The building sector is responsible for a large share of Europe's energy consumption. Modelling buildings thermal behavior is a key factor for achieving the EU energy efficiency goals. Moreover, it can be used in load forecasting applications, for the prediction of buildings total energy consumption. The first phase of this work is the application of Artificial Immune Systems (AIS) for clustering buildings with similar physical characteristics and similar thermal efficiency. In the second phase, Artificial Neural Networks (ANN) are used to estimate the buildings heating and cooling loads. A final sensitivity test is performed to identify which building features have the most impact on the heating and cooling loads. The results obtained in the first phase revealed very distinct cluster prototypes, which demonstrates the AIS discriminating ability. The good estimation performance obtained in the second phase showed that this approach can be integrated in energy efficiency audits. Finally, the sensitivity analysis provided indications for actions (or legislation directives) in order to promote the design of more efficient buildings. © 2019 IEEE.

Abstract
This paper presents qualitative and quantitative analysis of the traveling waves induced by faults on direct current (DC) transmission lines of line-commutated converter high-voltage direct current (LCC HVDC) systems for detecting the wavefront arrival times using the boundary wavelet coefficients from real-time stationary wavelet transform (RT-SWT). The qualitative analysis takes into account the steady-state operation and the detection of the inception times of both first and second wavefronts at the converter stations. The behavior of the boundary wavelet coefficients in DC transmission lines is examined considering the effects of the main parameters that influence the detection of the traveling waves, such as mother wavelets, sampling frequency, DC transmission line terminations, electrical noises, as well as fault resistance and distance. An algorithm designed to run in real-time and able to minimize the factors that hamper the performance of traveling wave-based protection (TWP) methods is proposed to detect the first and second surge arrival times. Quantitative results are achieved based on the accuracy of one- and two-terminal fault location estimation methods, and indicate the proper operation of the presented algorithm.

Abstract
This paper presents a new method to identify classes of events, by processing phasor measurement units (PMU) frequency data through deep neural networks. Deep tapered Multi-layer Perceptrons of the half-autoencoder type, Deep Belief Networks and Convolutional Neural Networks (CNN) are compared, using real data from Brazil. A sound success is obtained by a transformation of time-domain signals, from dynamic events recorded, into 2D images; these images wee processed with a CNN, taking advantage of the strong dependency existing among neighboring pixels in images. The training, computing and processing was achieved with a GPU (Graphics Processing Unit), allowing speeding-up of the process up to 30 times and rendering the process suitable to increase the online situational awareness of network operators.

Abstract
This paper presents a data-driven spatial net-load forecasting model that is applied to the distribution network expansion problem. The model uses population census data with Information Theory-based Feature Selection to predict spatial adoption patterns of residential electric vehicle chargers and photovoltaic modules. Results are high-resolution maps (0.02 km(2)) that allow distribution network planners to forecast asymmetric changes in load patterns and assess resulting impacts on installed HV/MV substation transformers in distribution systems. A risk analysis routine identifies the investment that minimizes the maximum regret function for a 15-year planning horizon. One of the outcomes from this study shows that traditional approaches to allocate distributed energy resources in distribution networks underestimate the impact of adopting EV and PV on the grid. The comparison of different allocation methods with the presented diffusion model suggests that using conventional approaches might result in strong underinvestment in capacity expansion during early uptake and overinvestment in later diffusion stages.