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

Abstract
This paper addresses a critical analysis of the impact of the wind ramp events with unforeseen magnitude in power systems at the very short term, modeling the response of the operational reserve against this type of phenomenon. A multi-objective approach is adopted, and the properties of the Pareto-optimal fronts are analyzed in cost versus risk, represented by a worst scenario of load curtailment. To complete this critical analysis, a study about the usage of the reserve in the event of wind power ramps is performed. A case study is used to compare the numerical results of the models based on stochastic programming and models that take a risk analysis view in the system with high level of wind power. Wind power uncertainty is represented by scenarios qualified by probabilities. The results show that the reliability reserve may not be adequate to accommodate unforeseen wind ramps and therefore the system may be at risk.

Abstract
Short-term load forecasting plays an important role to the operation of electric systems, as a key parameter for planning maintenances and to support the decision making process on the purchase and sale of electric power. A particular case in this respect is the consumption forecasting on special days, which can be a complex task as it presents unusual load behavior, when compared to regular working days. Moreover, its reduced number of samples makes it hard to properly train and validate more complex and nonlinear prediction algorithms. This paper tackles this problem by proposing a new approach to improve the accuracy of the predictions amidst existing special days, employing an Information Theoretic Learning Mean Shift algorithm for pattern discovery, classifying and densifying the available scarce consumption data. The paper describes how this methodology was applied to an electrical load forecasting problem in the northern region of Brazil, improving the previously obtained accuracy held by the power company.

Abstract
This paper presents an orthogonal implementation for power system state estimators based on the Maximum Correntropy Criterion (MCC). The proposed approach leads to a numerically robust estimator which exhibits self -healing properties, in the sense that gross errors in analog measurements are automatically rejected. As a consequence, robust estimates are produced without the need of running the state estimator again after bad data identification and removal. Numerical robustness is achieved by means of a specialized orthogonal algorithm based on fast Givens Rotations, which is able to handle the dynamic measurement weighting mechanism implied by the Parzen window concept associated to MCC. Results for a 3 -bus test system are presented to properly illustrate the Correntropy principles, and several case studies conducted on the IEEE 30 -bus and 57 -bus benchmark systems are used to validate the proposed methodology.

Abstract
The uptake of electric vehicles (EV) will require important modifications in traditional grid planning and load forecasting techniques. Existing literature suggests that the integration of EVs will be more adversarial to elements of the existing electricity infrastructure in terms of power supply (kW) than energy (kWh) delivery. While several studies analyzed the grid impact of electric vehicle fleets, few consider the adoption process itself which may lead to strong spatial variations of the utilization of charging infrastructure. The presented approach extends spatial load forecasting, introducing diffusion theory elements to analyze spatio-temporal clustering of EV charging demand. Using open-access census and grid data, this work develops a deterministic framework to forecast spatial patterns of EV charging applied to a real-world environment. Outcomes suggest substantial spatial clustering of EV adoption patterns, showing substation overrating for EV penetration rates of 25% and above with 7.4kW charging power.