Abstract
Standalone rural microgrid (MG) systems are considered as a sustainable and economical solutions towards rural area electrifications. Specific control schemes are necessary to adopt for reliable and economic performance of these rural MGs. This study focuses on the optimal utilization of biomass potential considering specific applications of bio generator (BG) with BG-PV-WT-BSS and BG-PV-SMES based standalone rural MG systems. In the first case of the BG-PV-WT-BSS, the optimal sizing/selection of DGs of a rural MG has been proposed using the improved-MILP (I-MILP) approach. The objectives of this study were to minimize total net present cost (TNPC), the levelized cost of energy (LCOE) and GHG emissions. In the second case of BG-PV-SMES, the simulation model of the rural microgrid consisting of a variable speed bio generator (VSBG) and photovoltaic (PV) has been developed. Afterwards, a simplified EMS has been designed for the coordinated operation control of the distributed energy resources (DERs) in the rural MGs using MATLAB/Simulink®^R environment. For the DGs connected via power converter, FOSMC and FCS-MPC based coordinated control has been proposed in the simplified EMS. The purpose of the FOSMC and FCS-MPC based power converter is the improvement of the system performance (for instance power quality, regulated voltage and THD) under external disturbances. Simulation analysis shows the better operation of FOSMC and FCS-MPC under less THD and improved power quality.

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.

Abstract
This paper introduces an Information Theoretic approach for Generalized State Estimation, aiming at achieving reliable topology and state variables co-estimation results, even in the presence of both topology errors and gross measurements. Attention is focused on the final bad data processing stage in which only relevant parts of the power network are represented at the bus-section level. The proposed generalized strategy applied at physical level relies on the superior outlier rejection properties of state estimators based on Maximum Correntropy, a concept borrowed from Information Theoretical Learning. A single objective function unifies the treatment of analog measurements and topology data, leading to an algorithm that does not require re-estimation runs for bad data suppression, and is simpler and more efficient than previously proposed co-estimation methods. Case studies conducted for distinct test-systems are presented, including various types of topology errors and simultaneous occurrence of topology and gross measurement errors. The results suggest that the proposed information-theoretic co-estimation algorithm is able to successfully provide bad data-free real-time network models even in the presence of multiple topology errors, simultaneous gross measurements and inaccurate topology information. Finally, additional tests confirm its superior computational performance as compared with other co-estimation algorithms.