Abstract
In the beginning of the Iberian Electricity Market (MIBEL), in 2006, the Portuguese regulator created a new tariff scheme, aiming at responding to the new market competition environment. At the same time, the regulator intended to improve consumers' awareness and incentivize renewables generation. After one decade, this policy may be considered successful, as it led to a good level of transparency (all tariff costs are clear and public) and renewables production had increased considerably. However, this strategy has brought other less positive aspects. One of them is the attractiveness of the tariff system in terms of energy savings. In fact, the test cases present in this article demonstrate that the current tariff scheme does not stimulate energy efficiency. Other complementary studies are performed to illustrate the impact of the tariff structure design on the potential energy savings.

Abstract
The recent development of electric vehicles (EVs) has brought a new set of problems regarding their integration in power networks, particularly in terms of the potential growth of peak load. The peak growth leads to the increase of losses and braches charging and to voltage drops. Conversely, optimizing EV charging policy creates new opportunities for both network safety and energy trading through the markets. This paper presents a multi-level framework combining two representations of a medium voltage (MV) network in order to optimize the EV charging policy. A minimizing cost approach is set, modeling day-ahead markets, and taking into account losses. The proposed methodology is tested on a typical MV network.

Abstract
Climate change has been a much-commented subject in the last years. The energy sector is a major responsible for this event and one of the most affected by it. Increasing the participation of renewable is a way to mitigate these effects. However, a system with large share of renewables (like Brazil) is more vulnerable to climate phenomena. This article analyzes the implementation of smart grids as a strategy to mitigate and adapt the electricity sector to climate change. Different climate and energy sector scenarios were simulated using a bottom-up approach with an accounting model. The results show that smart grids can help save energy, increase network resilience to natural hazards and reduce operational, maintenance costs and investments in new utilities. It would also allow tariffs diminution because of generation and losses costs reductions.

Abstract
Over time, the electricity price and energy consumption are increasingly growing their weight as prime foundations of the electrical sector, with their analysis and forecasts being targeted as key elements for the stable maintenance of electricity markets. The advent of smart grids is escalating the importance of forecasting because of the expected ubiquitous monitoring and growing complexity of a data-rich ever-changing milieu. So, the increasing data volatility will require forecasting tools able to rapidly readjust to a dynamic environment. The Generalized Regression Neural Network (GRNN) approach is a solution that has recently re-emerged, emphasizing good performance, fast run-times and ease of parameterization. The merging of this model with more conventional methods allows us to obtain more sturdy solutions with shortened training times, when compared to conventional Artificial Neural Networks (ANN). Overall, the performance of the GRNN, although slightly inferior to that of the ANN, is suitable, but linked to much lower training times. Ultimately, the GRNN would be a proper solution to blend with the latest smart grids features, which may require much reduced forecasting training times.

Abstract
The price evolution in electricity market with large share of renewables often exhibits a deep volatility, triggered by external factors such as wind and water availability, load level and also by business strategies of market agents. Consequently, in many real applications, the performance of electricity price is not appropriate. The goal of this article is to analyze the available market data and characterize circumstances that affect the evolution of prices, in order to allow the identification of states that promote price instability and to confirm that class segmentation allows increasing forecast performance. A regression technique (based on Artificial Neural Networks) was applied first to the whole set and then to each class individually. Performances results showed a clear advantage (above 20%) of the second approach when compared to the first one.

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.