Abstract
The deployment of energy communities (EC) will foster new business models contributing to the decentralization and democratization of energy access and a reduction in the energy bill of final consumers. This decentralization is only possible if investments are made in production and storage technologies, that must be installed near the locals of consumption, according to common rules of the regulatory frameworks of EC. In this paper we propose a methodology for the optimal sizing of production and shared storage assets, and we assess the cost reduction of considering shared storage assets. We then formulate seven business models (BM) that dictate how to share this benefit among the EC members, and we propose two indicators to assess them. Results show the difficulty in choosing a BM as well as the limitations of the BM and of the indicators.

Abstract
This article presents a methodology to estimate the evolution of QoS indices, based on investments and maintenance costs carried out in the DN. The indices were estimated at various disaggregated levels, including the global index, 3 different QoS zones (urban, semi-urban and rural) and 278 municipalities, thereby facilitating the mitigation of QoS asymmetries by allocating investments and maintenance actions to specific regions. To achieve this objective, an optimization problem was formulated to allocate investments and maintenance costs to municipalities with higher improvement benefit-cost ratios, potentially exhibiting lower levels of QoS. This methodology was adopted by the Portuguese DSO to establish the future investments plan from 2023 to 2027. The results demonstrate estimations of good performance, considering the stochastic nature of the phenomena affecting QoS (e.g. atmospheric conditions), which are included in this study, thus developing confidence levels for the global indices.

Abstract
A common problem in distribution planning is the scarcity of historic data (training examples) relative to the number of variables, meaning that most data-driven techniques cannot be applied in such situations, due to the risk of overfitting. Thus, the suitable regression techniques are restrained to efficient models, preferably with embedded regularization features. This article compares three of these techniques: LASSO, Bayesian and CMLR (Conditioned multi-linear regression - a new approach developed within the scope of a project with a distribution company). The results showed that each technique has its own advantages and limitations. The Bayesian regression has the main advantage of providing inherent confidence intervals. The LASSO is a very economic and efficient regression tool. The CMLR is versatile and provided the best performance.A common problem in distribution planning is the scarcity of historic data (training examples) relative to the number of variables, meaning that most data-driven techniques cannot be applied in such situations, due to the risk of overfitting. Thus, the suitable regression techniques are restrained to efficient models, preferably with embedded regularization features. This article compares three of these techniques: LASSO, Bayesian and CMLR (Conditioned multi-linear regression - a new approach developed within the scope of a project with a distribution company). The results showed that each technique has its own advantages and limitations. The Bayesian regression has the main advantage of providing inherent confidence intervals. The LASSO is a very economic and efficient regression tool. The CMLR is versatile and provided the best performance.

Abstract
Nontechnical losses in electricity distribution networks are often associated with a countries' socioeconomic situation. Although the amount of global losses is usually known, the separation between technical and commercial (nontechnical) losses will remain one of the main challenges for DSO until smart grids become fully implemented and operational. The most common origins of commercial losses are energy theft and deliberate or accidental failures of energy measuring equipment. In any case, the consequences can be regarded as consumption anomalies. The work described in this paper aims to answer a request from a DSO, for the development of tools to detect consumption anomalies at end-customer facilities (HV, MV and LV), invoking two types of assessment. The first consists of the identification of typical patterns in the set of consumption profiles of a given group or zone and the detection of atypical consumers (outliers) within it. The second assessment involves the exploration of the load diagram evolution of each specific consumer to detect changes in the consumption pattern that could represent situations of probable irregularities. After a representative period, typically 12 months, these assessments are repeated, and the results are compared to the initial ones. The eventual changes in the typical classes or consumption scales are used to build a classifier indicating the risk of anomaly.

Abstract
This article analyzes the effects of the current policy trends - high levels of distributed generation (DG) and grid load/capacity ratio - on network efficiency. It starts by illustrating the network losses performance under different DG and load/capacity conditions. The second part concerns the simulation of network investments with the purpose of loss reduction for diverse system circumstances, including the impact of DG levels, energy cost, and discount rate. The attained results showed that DG, particularly large parks, have a negative impact on network efficiency: network losses tend to intensify with DG growth, under the current regulation. Furthermore, network investments in loss reduction would have a small global impact on network efficiency if the DG parks' connection lines are not included in the grid concession (not subjected to upgrade). Finally, the study determines that it is preferable to invest sooner, rather than to postpone the grid reinforcement for certain conditions, namely for low discount rates. © 2022 IEEE.