Energy communities (ECs) are an important tool towards a fair energy transition. Hence, the European Union (EU) has positioned ECs at the centre of its energy strategy and the foreseen transformation of its energy system. This paper aims to give an overview of key aspects and challenges for the implementation of the EC concept. Firstly, the regulatory framework is examined with a focus on the new definitions for ECs introduced by the EU, Renewable Energy Communities (RECs) and Citizen Energy Communities (CECs). Secondly, examples of established ECs and their main objectives are mentioned. Additionally, based on the identified challenges and requirements of establishing ECs, the key technologies that are implemented or have the potential to be deployed in an EC are examined, as well as innovative cross-cutting services that are optimally suited to be integrated in an EC. Moreover, the data management challenges linked to some of these technologies are considered. Finally, an overview of actual or potential financing schemes to support the EC development is given. Overall, the analysis highlighted the regulatory, technical and financial aspects and challenges that ECs are facing and the need to address them so that the EC concept is effective and successful. The main challenges identified for each of these aspects are the regulatory compliance with the legal framework, the data management dimension when innovative technological concepts are adopted and the financing of new projects.

Urban energy community planning often relies on optimisation models that assume households make purely rational techno-economic decisions. This study develops a behaviour-informed optimisation framework to examine how individual preferences and local electricity market structures shape investment and operation of rooftop photovoltaic (PV) systems and battery storage in a small residential community. Ten heterogeneous buildings are modelled as agents with distinct demand profiles and behavioural traits, including willingness to invest, risk aversion, and environmental preference. Using identical techno-economic inputs, the novelty lies in developing and comparing three multi-objective optimisation problems representing different decision-making approaches: (i) individual end-user optimisation, where households independently decide and size their own PV and battery systems; (ii) centralised community optimisation, where a single coordinating entity manages system sizing and operation for the whole community; and (iii) a multi-agent decentralised market, where households act as independent agents that jointly optimise investment and operation while trading and sharing electricity locally. All cases are evaluated against a baseline optimisation that determines optimal sizing and operation without considering behavioural preferences. Results show that the individual end-user optimisation leads to uneven investment and operational outcomes across households, while the centralised community optimisation achieves a more balanced distribution of PV and battery capacities, energy flows and associated costs and benefits across participating households. Across the behaviour-informed cases, total installed PV capacity ranges from 17 kWp in the centralised community case to 46 kWp in the decentralised market case, compared with 10 kWp in the techno-economic baseline. Battery storage reaches 78 kWh in the decentralised market case, along with significant reductions in total operating costs and emissions (70.5% and 14.9%, respectively, relative to the baseline). The findings highlight a clear trade-off: more balanced outcomes are associated with lower technology uptake levels, while the decentralised market improves overall community performance but concentrates benefits among a subset of households.