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.

Abstract
This paper presents a method to identify the status (open or closed) of breakers in network branches, in the absence of status signal or electric measurements on the branch including the breaker. Indirect power measurements from the SCADA are combined to form a 2D image array, which is fed into a Convolutional Neural Network. The image construction is based on ranking measurements with the Cauchy-Schwarz divergence between two signal distributions (for breaker open and closed). The success rate obtained with this technique is close to 100% in the IEEE testbed adopted. © 2019 IEEE.

Abstract
This paper describes an open standards-based information system that can support the democratization and consumer empowerment through flexibility activation in the distribution networks of the near future. The paper outlines a software infrastructure focused on technical issues, closely following the OpenADR standard and the corresponding IEC 62746-10 standard. The infrastructure represents a communication backbone allowing the connection, registering, activation and reporting for different types of granular consumer flexibility. The flexibility sources can be very diverse - from controllable charging set points of electric vehicle chargers and district-level storages such as stationary batteries, towards taking advantage of comparatively large time constants of thermal systems in residential and commercial buildings. In the viewpoint of the proposed system, all these flexibility provisions represent distributed energy resources in a wider sense. The system thus offers interoperable support for consumer-level integration of different energy systems (electricity, heat and gas), and additional flexibility sources are made available to the electric power system, all the time keeping the user comfort and avoiding service disruptions. The paper outlines the technical infrastructure as a backbone activating new sources of flexibility, helping the further proliferation of renewable energy sources and establishing new market actors.

Abstract
Phasor Measurement Units (PMUs) in transmission systems is one of the most promising sources of data to increase situational awareness of network monitoring. However, the inclusion of PMU measurements along with the ones from traditional Supervisory Control and Data Acquisition (SCADA) systems to perform state estimation brings additional challenges, such as the vast difference in sampling rates and precision between these two types of measurements. This paper formally introduces a Bayesian inference approach in the form of a new State Estimator for transmission systems able to deal with the different sampling rates of those measurements. The proposed approach provides accurate state estimates even for buses that are not observable by PMU measurements, and when load variation occurs during the time interval between two SCADA data scans. Several simulation results (with IEEE transmission test systems) are used to illustrate the features of the proposed approach.