Control of Renewable Energy Communities using AI and Real-World Data

-- The electrification of transportation and the increased adoption of decentralized renewable energy generation have added complexity to managing Renewable Energy Communities (RECs). I ntegrating E lectric V ehicle (EV) charging with building energy systems like heating, ventilation, air conditioning (HVAC), photovoltaic (PV) generation, and battery storage presents significant opportunities but also practical challenges. Reinforcement learning (RL), particula rly Multi - Agent Deep Deterministic Policy Gradient (M ADDPG) algorithms, ha ve shown promising results in simulation, outperforming heuristic control strategies. However, translating these successes into real - world deployments faces substantial challenges, including incomplete and noisy data, integration of heterogeneous subsystems, synchronization issues, unpredictable occupant behavior, and missing critical EV state - of - charge (SoC) information. This paper introduces a framework designed explicitly to handle these complexities and bridge the simulation - to - real ity gap. The framework incorporates EnergAIze, a MADDPG - based multi - agent control strategy, and specifically addresses challenges related to real - world data collection, system integration, and user behavior modeling. Preliminary results collected from a real - world operational REC with four residential buildings demonstrate the practical feasibility of our approach, achieving an average 9 % reduction in daily peak demand and a 5% decrease in energy costs through optimized load scheduling and EV charging behav iors. These outcomes underscore the framework's effectiveness, advancing the practical deployment of intelligent energy management solutions in RECs. Modern smart buildings and energy communities are increasingly integrating distributed energy resources (DERs) such as solar photovoltaics (PV), battery storage, and electric vehicle (EV) charging infrastructure. Collectively, buildings account for approxi mately 32% of global energy consumption and 34% of energy - related CO emissions, underscoring their pivotal role in climate mitigation efforts [1] .