Abstract
Planning Energy communities (ECs) requires engaging members, designing business models and governance rules, and sizing distributed energy resources (DERs) for a cost-effective investment. Meanwhile, the growing share of non-dispatchable renewable generation demands more flexible energy systems. Local flexibility markets (LFMs) are emerging as effective mechanisms to procure this flexibility, granting ECs a new revenue stream. Since sizing with flexibility becomes a highly complex problem, we propose a 2-stage methodology for estimating DERs size in an EC with collective self-consumption, flexibility provision and cross-sector (CS) assets such as thermal loads and electric vehicles (EVs). The first stage computes the optimal DER capacities to be installed for each member without flexibility provision. The second stage departs from the first stage capacities to assess how to modify the initial capacities to profit from providing flexibility. The impact of data clustering and flexibility provision are assessed through a case study.

Abstract
Ensuring reliable and high-quality electricity service is critical for consumers and Distribution System Operators (DSO). The DSO's Plan for Development and Investment in the Distribution Network (PDIDN) plays a pivotal role in enhancing network reliability and resilience while balancing technical and financial aspects. This study proposes a novel probabilistic approach for quality-of-service (QoS) estimation in distribution systems, addressing the limitations of traditional deterministic methods. Leveraging Bayesian regression, specifically the Spike and Slab technique, the model incorporates prior knowledge to improve the prediction of key QoS indicators such as SAIDI, SAIFI, and TIEPI. Using historical network data, the model demonstrates superior predictive accuracy and robustness, offering realistic confidence intervals for strategic planning. This method enables informed investments, enhances regulatory compliance, and supports renewable integration. The findings underline the potential of probabilistic modeling in advancing QoS forecasting, encouraging its application in other areas of electric network management.

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
This article proposes a methodology for high loss detection in LV network, based on a very small set of commonly available data/metadata from networks connected to an MV/LV substation. The approach is based on a combination of predictors from several distinct categories, including network data, metadata, and measured smart meter data. Several independent groups of unranked real networks were simulated, and it was possible to find the top ten networks with the highest level of losses with a very satisfactory success rate (76% to 98%), depending on selected groupings folds. Due to the impracticability of analyzing all LV networks, the identification of the highest loss ones is essential for the definition of loss reduction planning since, with this list filtering, it is possible to determine with a good degree of certainty which networks require maintenance or upgrade.