Abstract
Energy communities (ECs) are emerging as a promising step to mitigate energy poverty and climate changes, since their main objective is to obtain environmental, economic, and social benefits for the participants, namely in terms of increasing local production using primary renewable resources. In the European Union (EU), Directives D2018 and D944 established a common regime for the promotion of ECs. Given the relevance of the topic, comparing regulations in force in Brazil, Germany, Portugal, and Spain, can contribute to mitigate risks, as well as save time and energy resources. Among the assessed aspects, this work analyzes requirements to access to the activity and measurement issues, which are already well and clearly defined. As for business models and remuneration, focus is given to energy cooperatives and feed-in payments. In turn, the main barriers include financing, end of incentives, need to develop new business models, and issues related to peer-to-peer (P2P) transactions. © 2022 IEEE.

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
This paper explores the application of Deep Learning techniques to forecast electricity market prices. Three Deep Learning (DL) techniques are tested: Dense Neural Networks (DNN), Long Short-Term Memory Networks (LSTM) and Convolutional Neural Networks (CNN); and two non-DL techniques: Multiple Linear Regression and Gradient Boosting (GB). First, this work compares the forecast skill of all techniques for electricity price forecasting. The results analysis showed that CNN consistently remained among the best performers when predicting the most unusual periods such as the Covid19 pandemic one. The second study evaluates the potential application of CNN for automatic feature extraction over a dataset composed by multiple explanatory variables of different types, overcoming part of the feature selection challenges. The results showed that CNNs can be used to reduce the need for a variable selection phase.

Abstract
Energy policies in the European Union (EU) and its 27 member states respond to three main concerns namely energy security, economic development, and environmental sustainability. All the three "Es'' are pursued simultaneously with some slight differences in emphasizing the mutual importance of these, in particular the cost factors. The legislation of the EU (e. g., ETS-Emission Trading Scheme, directives) increasingly guides the member states' energy policies. However, energy policy directions are still made domestically, for example, on the support on renewable energy technologies. In this work, we look into distributed generation (DG), since it has been grown considerable in the past few years and can be used to partially fulfill renewable energy targets. The policy makers have to make decisions about regulation directives, more specifically they have to change the current regulation in order to incentive the increase in DG. However, these decisions have not only economic impacts but also technical impacts that must be accounted for. In this regard, a decision aid tool would help the policy makers in estimating producer economic impacts, as well as power network technical impacts, of various possible regulation directives. Here, we propose an interactive decision aid tool that models the aforementioned impacts and thus, can be used by policy makers to experiment with different regulation directives before deciding on the ones to set.

Abstract

Abstract
The large-scale integration of microgeneration (MG) can bring several technical benefits, such as: improving the voltage profile, reducing power losses and allowing for network capacity investment deferral. Furthermore, it is now widely accepted that introducing new renewable MG, such as wind turbines, photovoltaic panels or biomass can help control carbon emissions, reduce our dependence on oil and contribute to a sustainable energy growth. This paper presents an empirical analysis of the benefits of MG on avoided losses, voltage profiles and branch congestion. The main goal is to clarify whether the current regulatory framework allows for obtaining all the MG potential gains. The main conclusion is that some legal constraints should be removed, or at least relaxed, in order to promote the growth of distributed power generation, particularly, for domestic MG.