  This paper investigates the increasing roles of Renewable Energy Sources
(RES) and Electric Vehicles (EVs). While indicating a new era of sustainable
energy, these also introduce complex challenges, including the need to balance
supply and demand and smooth peak consumptions amidst rising EV adoption rates.
Addressing these challenges requires innovative solutions such as Demand
Response (DR), energy flexibility management, Renewable Energy Communities
(RECs), and more specifically for EVs, Vehicle-to-Grid (V2G). However, existing
V2G approaches often fall short in real-world adaptability, global REC
optimization with other flexible assets, scalability, and user engagement. To
bridge this gap, this paper introduces EnergAIze, a Multi-Agent Reinforcement
Learning (MARL) energy management framework, leveraging the Multi-Agent Deep
Deterministic Policy Gradient (MADDPG) algorithm. EnergAIze enables
user-centric and multi-objective energy management by allowing each prosumer to
select from a range of personal management objectives, thus encouraging
engagement. Additionally, it architects' data protection and ownership through
decentralized computing, where each prosumer can situate an energy management
optimization node directly at their own dwelling. The local node not only
manages local energy assets but also fosters REC wide optimization. The
efficacy of EnergAIze was evaluated through case studies employing the
CityLearn simulation framework. These simulations were instrumental in
demonstrating EnergAIze's adeptness at implementing V2G technology within a REC
and other energy assets. The results show reduction in peak loads, ramping,
carbon emissions, and electricity costs at the REC level while optimizing for
individual prosumers objectives.
