Abstract
Community-based electricity markets, which are defined as groups of members that share common interests in renewable distributed generation, allow prosumers to embrace more active roles by opening up several opportunities for trading electricity. At the same time, such markets may favor conventional consumers by allowing them to choose cheaper electricity providers. Due to trends in power sector modernization, community-based electricity markets are of great research interest, and there are already some associated models. However, there is a research gap in searching for integrated and holistic approaches that go beyond economic aspects, consider social and environmental aspects, and assume the balanced co-existence of community-based and conventional markets. This work fills this critical research gap by adapting/applying the optimized tariff model, Bass diffusion model, life cycle assessment, and multi-objective optimization to the context of community-based markets. Results indicate that favoring conventional markets in the short term and community-based markets in the medium term is beneficial. Moreover, regulated tariffs should increase slightly in the short/medium-term to accommodate DG growth. Additionally, community-based markets can decrease electricity expenses by around 13.6 % considering the market participants. Thus, such markets can be significantly beneficial in mitigating energy poverty.

Abstract
Distributed energy resources (DERs) offer untapped potential to meet the flexibility needs of power systems with a high share of non-dispatchable renewable generation, and local flexibility markets (LFMs) can be effective mechanisms for procuring it. In LFMs, energy communities (ECs) can aggregate and offer flexibility from their members' DERs to other parties. However, since flexibility prices are only known after markets clear, flexibility bidding curves can be used to deal with this price uncertainty. Building on previous work by the authors, this paper employs a two-stage methodology to calculate flexibility bids for an EC participating in an LFM, including not only batteries and photovoltaic panels, but also cross-sector (CS) flexible assets like thermal loads and electric vehicles (EVs) to assess their impact. In Stage 1, the EC manager minimizes the energy bill without flexibility to define its baseline. In Stage 2, it computes the optimal flexibility to be offered for each flexibility price to build the flexibility bidding curve. Case examples allow to assess the impact of CS flexible assets on the final flexibility offered.

Abstract
Planning Energy communities (ECs) requires engaging members, designing business models and governance rules, and sizing distributed energy resources (DERs) for a costeffective investment. Meanwhile, the growing share of nondispatchable 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
Agriculture and energy use are increasingly linked, especially as farms' energy needs grow. Renewable Energy Communities (RECs) help farmers, particularly in remote areas, access affordable surplus energy from other producers, while sellers gain extra revenue. This study focuses on the creation of RECs as a sustainable and economically viable solution for small and medium-sized agribusinesses to address their energy challenges. We explore the complementarities and potential benefits of RECs from the experience learned in the Tools4AgriEnergy project, using RECreation digital platform for the management of RECs. A case study is used, based on the Alqueva region in Portugal with six members that develop different agri-food sector activities. Using tariffs compliant with Portuguese regulations, results indicate that the development of self-consumption activities can achieve significant energy cost savings annually.

Abstract
Renewable energy resources are crucial for addressing global economic and environmental challenges. Energy communities, which unite consumers to pursue shared energy goals, present a promising solution for reducing energy costs and enhancing sustainability. This study analyzes the optimal sizing and operation of energy community resources, formulating the problem as mixed-integer linear programming (MILP) models. Two tools are employed: one for daily operation, calculating energy setpoints for community assets such as battery energy storage systems (BESS) and electric vehicles (EVs), and another for sizing photovoltaic (PV) panels and BESS capacities to minimize costs while optimizing local energy trades. Due to the high computational demands of MILP, three optimization methods are compared: deterministic, hybrid particle swarm optimization (PSO), and evolutionary PSO (EPSO). The hybrid PSO method handles binary and continuous variables efficiently, while EPSO introduces diversity to improve solution quality in complex scenarios. These metaheuristic approaches address the trade-off between solution accuracy and computational effort, providing reliable tools for decision-makers in energy communities.