Abstract
The present work aims to compare several load disaggregation methods. While the supervised alternative was found to be the most competent, the semi-supervised is proved to be close in terms of potential, while the unsupervised alternative seems insufficient. By the same token, the tests with long-lasting data prove beneficial to confirm the long-term performance since no significant loss of performance is noticed with the scalar of the time-horizon. Finally, the patchwork of new parametrization and methodology fine-tuning also proves interesting for improving global performance in several methods.

Abstract
In Europe, clean distributed generation, DG, is perceived as a crucial instrument to build the path towards carbon emission neutrality. DG already reached a large share in the generation mix of several countries and the reduction of technical losses is one of its most mentioned advantages. In this scope, this paper discusses the weaknesses of this postulation using real networks. The adopted methodology involves the power flow simulation of a collection of real networks, using 15 min real measurements of loads and generations for a whole year. The clustering of similar cases allows identifying the situations that cause higher losses. A complementary objective of this research was to define an approach to mitigate this problem in terms of identifying the branches that, if reinforced, most contribute to losses reduction. The results obtained confirm the rationality of the proposed methodology.

Abstract
The financial planning of distribution systems usually includes the prediction of annual mandatory investments, concerning the resources that the DSO is compelled to allocate as a result of new network connections, required by new consumers or new energy producers. This paper presents a methodology to estimate the mandatory investments that the DSO should do in the distribution network. These estimations are based on historical data, load growth expectations and various socioeconomic indices. However, the available database contains very few annual investment examples (one aggregated value per year since 2002) compared to the large number of variables (potential inputs), which is a factor of regression overfitting. Thus, the applicable regression techniques are restrained to simple but efficient models. This paper describes a new methodology to identify the most suitable estimation models. The implemented application automatically builds, selects, and tests estimation models resulting from combinations of input variables. The final forecast is provided by a committee of models. Results obtained so far confirm the feasibility of the adopted methodology.

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.