Abstract
The energy sector transition to more decentralized and renewable structures requires greater participation by local consumers, which may be enabled by innovative models such as the setup of renewable energy communities (RECs). To maximize the self-consumption of local renewable energy generated by assets normally connected to the low voltage distribution grid, these RECs typically involve jointly owned assets such as collective photovoltaic solar panels (CPVs) and collective energy storage systems (CESS). This work proposes a novel mathematical model for a REC, accounting for three distinct economic approaches to the redistribution of collective benefits among community members. The main objective of this study is to understand how the participation of community members in collective assets (CAs) can help increase the fairness and equity of RECs. An illustrative REC case comprising members with individual and collective ownership of the assets is used to assess the proposed economic approaches. Extracting several answers, among them that the most advantageous configuration comes from agents with quotas in the CESS and CPV. An important conclusion is that depending on the selected economic approach, the social welfare and agent's revenue vary significantly. In any case, CESSs increase equity among REC members.

Abstract
The Brazilian Power System is mainly composed of renewable generation from hydroelectric and wind. Hence, spot and forward electricity prices tend to represent the inherently stochastic nature of these resources, while risk management is a measure taken by agents, especially hydro power plants (HPPs) to hedge against deep financial losses. A HPP goal is to maximize its profit considering uncertainties in forward electricity prices, spot prices, and generation scaling factor (GSF) for years ahead. Therefore, the objective of this work is to simulate the real decision-making process of a HPP, where they need to have a perspective of the forward market and future spot price assessment to negotiate forward electricity contracts. To do so, the present work models the uncertainty in electricity forward prices via two-stage stochastic programming, assessing the benefits of the stochastic solution in comparison to the deterministic one. In addition, different risk aversion levels are assessed using conditional value at risk (CVaR). An important conclusion is that the results show that the greater the HPP risk aversion is, the greater the energy selling via electricity forward contracts. Moreover, the proposed model has benefits in comparison to a deterministic approach.

Abstract
The decarbonisation of the building sector is crucial for Portugal's goal of achieving economy-wide carbon neutrality by 2050. To mobilize communities towards energy efficiency measures, it is important to understand the primary drivers and barriers that must be overcome through policymaking. This paper aims to review existing Energy Policies and Actions (EPA) in Portugal and assess their effectiveness in improving Energy Efficiency (EE) and reducing CO2 emissions in the building sector. The Local Energy Planning Assistant (LEPA) tool was used to model, test, validate and compare the implementation of current and alternative EPAs in the North of Portugal, including the national EE plan. The results indicate that electrification of heating and cooling, EE measures, and the proliferation of Renewable Energy Sources (RES) are crucial for achieving climate neutrality. The study found that the modelling of alternative EPAs can be improved to reduce investment costs and increase Greenhouse Gas (GHG) emissions reduction. Among the alternatives assessed, the proposed one (Alternative 4) presents the best returns on investment in terms of cost savings and emissions reduction. It allows for 52% investment cost savings in the residential sector and 13% in the service sector when compared to the current national roadmap to carbon neutrality (Alternative 2). The estimated emission reduction in 2050 for Alternative 4 is 0.64% for the residential sector and 3.2% for the service sector when compared to Alternative 2.

Abstract
Energy communities have been designed to empower consumers while maximizing the self-consumption of local renewable energy sources (RESs). Their presence in distribution systems can result in strong modifications in the operation and management of such systems, moving from a centralized operation to a distributed one. In this scope, this work proposes a distributed community-based local energy market that aims at minimizing the costs of each community member, accounting for the technical network constraints. The alternating direction method of multipliers (ADMM) is adopted to distribute the market, and preserve, as much as possible, the privacy of the prosumers' assets, production, and demand. The proposed method is tested on a 10-bus medium voltage radial distribution network, in which each node contains a large prosumer, and the relaxed branch flow model is adopted to model the optimization problem. The market framework is proposed and modeled in a centralized and distributed fashion. Market clearing on a day-ahead basis is carried out taking into account actual energy exchanges, as generation from renewable sources is uncertain. The comparison between the centralized and distributed ADMM approach shows an 0.098% error for the nodes' voltages. The integrated OPF in the community-based market is a computational burden that increases the resolution of the market dispatch problem by about eight times the computation time, from 200.7 s (without OPF) to 1670.2 s. An important conclusion is that the proposed market structure guarantees that P2P exchanges avoid the violation of the network constraints, and ensures that community agents' can still benefit from the community-based architecture advantages.

Abstract
Electric vehicle (EV) sales and shared mobility are increasing worldwide. Despite its challenges, e-carsharing has an opportunity to still profit in periods of low rental demand compared to traditional carsharing. The purpose of this paper is to assess the profitability of an e-carsharing company based on distribution local marginal price (DLMP) and vehicle-to-grid (V2G) that cooperates with the distribution system operator (DSO) through a two -stage stochastic model. The AC optimal power flow (ACOPF) is modeled using second-order cone program-ming (SOCP) linearized by the global polyhedral approximation. The IEEE 33 bus test system and a real Kernel distribution for the EV rental demands are used in four planning cases in the GAMS environment. The results indicate that the proposed methodology does not affect EV user satisfaction. Moreover, the planning disregarding the power grid perspective is the most profitable, but the operation may not be possible in real applications due to the high-power flows via V2G. Finally, the e-carsharing planning considering the DSO perspective increased the charging cost by 1.66 % but also reduced the DLMP peak, losses, and peak demand by 2.5 %, 1.5 %, and 5.1 %, respectively. One important conclusion is that the technical benefits brought to the DSO by the e-carsharing company could be turned into services and advantages for both agents, increasing profit and mitigating negative impacts, such as higher operational costs.