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.

Abstract
Portugal is seen worldwide as a case of success in the large-scale integration of renewables in its power system, especially for wind power. Consistent policies and sound management decisions are fundamental, but a sustainable process is not possible without the development of endogenous knowledge. This paper summarizes a set of models, both applied by the industry and representing actual technologic advancement, denoting the context of research and innovation in the country that helps to explain such success. Novelties arise in reliability assessment for systems with renewables, active and reactive power control, integration of wind farms, storage, electric vehicle integration, wind and solar power forecasting and distribution operation and state estimation taking advantage of smart grid structures. In all cases, one relevant trait is evident: the pervasive use of computational intelligence tools.

Abstract
This paper presents a new methodology to define a priority scale for maintenance actions in substations, based on the development of a Composite Risk Index (CRI) associated with each device. Two auxiliary indices are built: Basic Condition (BC) and Operating Condition (OC), representing the physical and functional characteristics of the equipment that can compromise their performance and contribute to the occurrence of failures. Their evaluation is helped by a Technical Capacity Index (TCI), which evaluates how much the equipment has been affected by wear and tear, in the assessment of the Basic Condition, and the classification of the equipment defects by degrees of severity, in the assessment of the Operating Condition. Two cascading Fuzzy Inference Systems of the Mandani type are used, the first in defining the BC, and the second to obtain the equipment CRI denoting maintenance priority, which may then be used in planning maintenance actions. The methodology is verified through an SF6 circuit breaker CRI assessment, and its priority scale for maintenance planning. The method for evaluating the SF6 circuit breakers reliability is validated through a comparison with a statistical approach, using real data collected from equipment installed in Eletrobras Eletronorte Transmission System, in Rondonia, Amazon region of Brazil. (C) 2016 Published by Elsevier B.V.

Abstract
This paper presents a new proposal for sensor fusion in power system state estimation, analyzing the case of data sets composed of conventional measurements and phasor measurements from PMUs. The approach is based on multiple criteria decision-making concepts. The equivalence of an L-1 metric in the attribute space to the results from a Bar-Shalom-Campo fusion model is established. The paper shows that the new fusion proposal allows understanding the consequences of attributing different levels of confidence or trust to both systems. A case study provides insight into the new model.

Abstract
Strong adoption dynamics of private passenger electric vehicles (EV) will require adjustments in the operation and planning of electrical distribution grids. This work proposes a novel approach to assess the impact of electric vehicle charging while considering EV adoption dynamics and commuting patterns. The proposed model uses Geographic Information Systems (GIS) and is applied to a real-world case study. Results suggest that clustering of EV charging will occur and underline the relevance of accurate spatial and temporal charging pattern estimations for distribution grid planning. Overloading of distribution network elements was observed even under light EV penetration rates.

Abstract
The system operator is responsible for maintaining a constant balance between generation and load to keep frequency at the nominal value. This fundamental objective is achieved with upward (e.g., synchronized and nonsynchronized generation units) and downward (e.g., demand response, storage) reserve capacity. The system operator needs to define, in advance, the reserve capacity requirements that mitigate the risk of imbalances due to forecast errors and unplanned outages of generation units. The research trend is to apply probabilistic methodologies for setting the reserve requirements based on uncertainty forecasts for renewable generation and load, as well as a probabilistic modeling of units' outages. This chapter describes two probabilistic methods, which share a common modeling framework, for quantifying risk and reserve requirements in two types of electricity markets: (1) sequential markets with the reserves market after the energy market clearing and (2) cooptimization (or joint market clearing) of energy and reserves. Two case studies with real data are presented to illustrate the application of both methodologies.