Abstract
This paper explores, with numerical case studies, the performance of an optimization algorithm that is a variant of EPSO, the Evolutionary Particle Swarm Optimization method. EPSO is already a hybrid approach that may be seen as a PSO with self-adaptive weights or an Evolutionary Programming approach with a self-adaptive recombination operator. The new hybrid DEEPSO retains the self-adaptive properties of EPSO but borrows the concept of rough gradient from Differential Evolution algorithms. The performance of DEEPSO is compared to a well-performing EPSO algorithm in the optimization of problems of the fixed cost type, showing consistently better results in the cases presented.

Abstract
The increasing integration of distributed renewable energy sources, such as photovoltaic (PV) systems, requires adequate regulatory schemes in order to reach economic sustainability. Incentives such as Feed-in Tariffs and Net Metering are seen as key policies to achieve this objective. While the Feed-in Tariff scheme has been widely applied in the past, it has now become less justified mainly due to the sharp decline of the PV system costs. Consequently, the Net Metering scheme is being adopted in several countries, such as Brazil, where it has is in force since 2012. In this context, this paper aims to estimate the minimum monthly residential demand for prosumers located in the different distribution concession areas in the interconnected Brazilian system that ensures the economic viability of the installation of PV systems. In addition, the potential penetration of PV based distributed generation (DG) in residential buildings is also estimated. This study was conducted for the entire Brazilian interconnected system and it demonstrates that the integration of distributed PV systems is technical-economic feasible in several regions of the country reinforcing the role of the distributed solar energy in the diversification of Brazilian electricity matrix.

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 stochastic programming model for PAR/PST definition and location in a network with a high penetration of wind power, with probabilistic representation, to maximize wind power penetration. It also presents a new optimization meta-heuristic, denoted DEEPSO, which is a variant of EPSO, the Evolutionary Particle Swarm Optimization method, borrowing the concept of rough gradient from Differential Evolution algorithms. A test case is solved in an IEEE test system. The performance of DEEPSO is shown to be superior to EPSO in this complex problem.