Abstract
The location of a new substation is a key factor in the expansion of electrical distribution systems. This location is strategic from the point of view of the costs associated with energy supply; therefore, a holistic and integral planning of sub-transmission and primary distribution subsystems requires the development of suitable optimization methods to support the decision process. Although future electric load growth is a critical factor to define capacity and location of new substations, other technical, environmental, soil characteristics, risk, social, and administrative criteria that influence the final location are also crucial. A multicriteria decision analysis based on geographic information system is proposed in this paper to combine those criteria taking into account decision makers' preferences and physical restrictions on land use. The main contributions of this paper are the identification of the criteria and the analysis of service areas in existing substations to impose constraints on the problem. A spatial heat map that facilitates the visual interpretation of the spatial relations of the criteria is produced based on a suitability score. The proposed method was evaluated in the service area of an Ecuadorian distribution energy utility. It was found that the two more important criteria are the electric load density and the distance to subtransmission network with weights of 44% and 23%, respectively. The proposed analysis is able to identify ideal locations for new substations, which can be used by the planner to find the best long-term network expansion alternative.

Abstract
The growing adoption of photovoltaic systems as a result of government incentives and the cost-effectiveness of the technology will bring significant environmental benefits and help countries meeting their international commitments in terms of renewables share. Nevertheless, an unsuitable site location could compromise its production and lead to a poor integration. An optimal location of photovoltaic systems must account for factors such as land use restrictions, orography, environmental, climatic limitations, and proximity to infrastructure. A key aspect that needs to be further researched is the influence of the electric demand requirement and its spatial distribution on the enhancement of photovoltaic integration. This paper proposes a novel approach to define optimal sites for photovoltaic plants, connected to the medium-voltage level, using a geographic information system based multi-criteria decision making and spatial overlay with electric load. The main feature of this work is the use of high-resolution information to spatially characterize the demand and make a density analysis. The performance of the proposed method is assessed in the service area of an Ecuadorian power utility. Scenarios considering solar potential and the massive penetration of a new type of load are assessed to define the photovoltaic sites that enhance the integration of renewable sources in the case study.

Abstract
Este estudio plantea una alternativa de consecución de los parámetros eléctricos en modelos de transformadores trifásicos mediante registros obtenidos con lecturas de aparatos de medición en sistemas de protección y monitoreo asociados al transformador eléctrico. El posible uso de este procedimiento se focaliza en la estimación del modelo eléctrico en transformadores trifásicos durante su operación y funcionamiento en un sistema eléctrico y su respuesta frente a eventos transitorios. Esta propuesta permitirá aminorar recursos y uso de tiempo en comparación con metodologías existentes; puesto que, éstas usualmente necesitan que el transformador este fuera de servicio para la conexión de equipo especializado de alto costo, siendo muchas veces necesario trasladar el transformador a un laboratorio especializado.  Los trabajos existentes proporcionan modelos completos del transformador para simulación de eventos transitorios, estos se usaron como datos de partida para sintonizar los parámetros en MATLAB-SIMULINK. Evaluada la efectividad y confiabilidad del estudio, éste será de gran ayuda en la determinación de parámetros eléctricos transitorios en transformadores nuevos o con un tiempo considerable de funcionamiento, pues debido al uso del equipo y las condiciones de operación, los parámetros eléctricos pueden variar con respecto a sus registros de fábrica.

Abstract
This study introduces a robust orthogonal implementation for a new class of information theory-based state estimation algorithms that rely on the maximum correntropy criterion (MCC). They are attractive due to their capability to suppress bad data. In practice, applying the MCC concept amounts to solving a matrix equation similar to the weighted least-squares normal equation, with difference that measurement weights change as a function of iteratively adjusted observation window widths. Since widely distinct measurement weights are a source of numerical ill-conditioning, the proposed orthogonal implementation is beneficial to impart numerical robustness to the MCC solution. Furthermore, the row-processing nature of the proposed solver greatly facilitates bad data removal as soon as outliers are identified by the MCC algorithm. Another desirable feature of the orthogonal MCC estimator is that it avoids the need of post-processing stages for bad data treatment. The performance of the proposed scheme is assessed through tests conducted on the IEEE 14-bus, 30-bus, 57-bus and 118-bus test systems. Simulation results indicate that the MCC orthogonal implementation exhibits superior bad data suppression capability as compared with conventional methods. It is also advantageous in terms of computational effort, particularly as the number of simultaneous bad data grows.

Abstract
Residential consumers have been adopting distributed energy resources (DER) like photovoltaics (PV), electric vehicles (EV) as well as electric heating, ventilation and air conditioning devices (HVAC) in recent years - thus substantially reshaping power systems. This study is dedicated to the analysis of such adopters in continental Portugal, using both spatial analysis tools and census data with information theoretic criteria. Results suggest that the current uptake of EV, PV, and HVAC is characterised by spatially auto-correlated adoption patterns. The analysis of census variables, on the other hand, reveals that Portuguese EV, PV, and HVAC adopters exhibit a few surprising, unrecorded characteristics compared with previous studies. Comparing different dataset resolutions, EV and HVAC adopters are found to be most similar across all three aggregation levels considered. Results further show that fewer adopter groups tend to own both EV-HVAC and PV-HVAC, reducing per se synergy potentials that may arise behind the metre. One of the main outcomes from this work is that studies describing energy technology adopters using census variables might receive very unstable results across different data aggregation levels. This may lead to adverse effects on studies' conclusiveness and energy policy design choices.

Abstract
The paper provides the theoretical proof of earlier published experimental evidence of the favorable properties of a new method for State Estimation - the Generalized Correntropy Interior Point method (GCIP). The model uses a kernel estimate of the Generalized Correntropy of the error distribution as objective function, adopting Generalized Gaussian kernels. The problem is addressed by solving a constrained non-linear optimization program to maximize the similarity between states and estimated values. Solution space is searched through a special setting of a primal-dual Interior Point Method. This paper offers mathematical proof of key issues: first, that there is a theoretical shape parameter value for the kernel functions such that the feasible solution region is strictly convex, thus guaranteeing that any local solution is global or uniquely defined. Second, that a transformed system of measurement equations assures an even distribution of leverage points in the factor space of multiple regression, allowing the treatment of leverage points in a natural way. In addition, the estimated residual of GCIP model is not necessarily zero for critical (non-redundant) measurements. Finally, that the normalized residuals of critical sets are not necessarily equal in the new model, making the identification of bad data possible in these cases.