Abstract
Recent advances in electric vehicle (EV) charging capability have seen a wide growth in the consumer market, which will continue to increase in future years with favourable policy incentives. However, the uncontrolled connection and charging of EV may have an adverse effect on three-phase distribution grids operation. This paper presents the impact of EV integration in a real LV Portuguese urban network. It analyses the network loading, energy losses, and voltage imbalances, under different scenarios of EV charging location and phase connection. The DIgSILENT Power Factory software is used in the voltage imbalance studies. Preliminary results show that the voltage drop in the analysed network is significantly affected by the location of the EV. Furthermore, as expected, the unbalanced EV loading leads to an increase of voltage unbalance between phases which is more pronounced when higher levels of EV are considered. © 2021 The Institution of Engineering and Technology.

Abstract
New energy technologies such as Distributed Energy Resources (DER) will affect the spatial and temporal patterns of electricity consumption. Models that mimic technology diffusion processes over time are fundamental to support decisions in power system planning and policymaking. This paper shows that spatiotemporal technology diffusion forecasts consist typically of three main modules: 1) a global technology diffusion forecast, 2) the cellular module that is a spatial data substrate with cell states and transition rules, and 3) a spatial mapping module, commonly based on Geographic Information Systems. This work provides a review of previous spatiotemporal DER diffusion models and details their common building blocks. Analyzing 16 model variants of an exemplary spatial simulation model used to predict electric vehicle adoption patterns in Portugal, the analysis suggests that model performance is strongly affected by careful tuning of spatial and temporal granularities and chosen inference techniques. In general, model validation remains challenging, as early diffusion stages have typically few observations for model calibration.

Abstract
Recent studies found that the adoption of distributed energy resources (DER) tends to cluster spatially and temporally which has significant implications for distribution network planning. Currently, residential DER adoption is mostly driven by public support schemes, also called incentive designs. Therefore, changes in those incentive designs will result in alternative spatiotemporal DER adoption patterns that affect distribution networks differently. Consequently, distribution network operators urgently need to understand the effects of energy policy changes on the spatial distribution of DER to guide network expansion based on realistic scenarios. The presented work and tool allow network operators to plan network expansion with robustness under future incentive design changes.

Abstract
This paper tackles the efficient Power-Flow solution of ill-conditioned cases. In that sense, those methods based on the Continuous Newton's philosophy look very promising, however, these methodologies still present some issues mainly related with the computational efficiency or the robustness properties. In order to overcome these drawbacks, we suggest several modifications about the standard structure of the Continuous Newton's method. Thus, the standard Continuous Newton's paradigm is firstly modified with a frozen Jacobian scheme for reducing its computational burden; secondly, it is extended for being used with High-order Newton-like method for achieving higher convergence rate and, finally, a regularization scheme is introduced for improving its robustness features. On the basis of the suggested improvements, a Power-Flow solution paradigm is developed. As example, a novel Power-Flow solver based on the introduced solution framework and the 4th order Runge-Kutta formula is developed. The novel technique is validated in several realistic large-scale ill-conditioned systems. Results show that the suggested modifications allow to overcome the drawbacks presented by those methodologies based on the Continuous Newton's method. On the light of the results obtained it can be also claimed, that the developed solution paradigm constitutes a promising framework for developing robust and efficient Power-Flow solution techniques.

Abstract
Electrification of society and economy is crucial to fight against climate changes assuming simultaneously a large-scale integration of electricity generation exploiting Renewable Energy Sources (RES). The increasing presence of RES and Electric Vehicles (EV) in Low Voltage (LV) networks, and the emergence of the Smart Grid paradigm will require relevant changes in the operational management of both LV and Medium Voltage (MV) networks. In this paper, two different strategies (separated and coordinated management) for the operational management of MV and LV networks are compared regarding their ability to integrate large amounts of RES and to accept increased electrification of consumption, including EV. Each management strategy is modeled through optimization problems, being then applied to an electrical distribution system consisting of MV and LV networks. Results show that a coordinated operational management outperforms the separated strategy, by allowing the integration of much higher volumes of RES and EV.

Abstract
Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.