Technology Category
Multiagent Systems: Multiagent LearningMachine Learning: Reinforcement LearningHumans and AI: Human-Aware Planning and Behavior Prediction
Control of Renewable Energy Communities using AI and Real-World Data
🤖 AI Summary
Addressing the challenges of coordinated control among electric vehicle charging, HVAC systems, photovoltaics, and energy storage in renewable energy communities (RECs), as well as deployment bottlenecks—including sensor noise, heterogeneous system integration, uncertain user behavior, and unobserved battery state-of-charge (SoC)—this paper proposes EnergAIze, the first end-to-end multi-agent reinforcement learning (MARL) framework tailored for real-world RECs. EnergAIze innovatively integrates noise-robust data preprocessing, a unified synchronization interface for heterogeneous systems, dynamic occupancy modeling, and SoC inference. It employs the MADDPG algorithm enhanced with physics-informed reward shaping and behavior cloning–assisted training, implemented within an edge-cloud cooperative control architecture. Evaluated across a four-residence REC testbed, EnergAIze reduces daily peak load by 9% and overall energy cost by 5%, significantly improving the practicality and economic viability of RL-based control in complex, real-world energy systems.
Technology Category
Application Category
📝 Abstract
The electrification of transportation and the increased adoption of decentralized renewable energy generation have added complexity to managing Renewable Energy Communities (RECs). Integrating Electric Vehicle (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), particularly MultiAgent Deep Deterministic Policy Gradient (MADDPG) algorithms, have 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-reality 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 behaviors. These outcomes underscore the framework's effectiveness, advancing the practical deployment of intelligent energy management solutions in RECs.
Problem
Research questions and friction points this paper is trying to address.
Managing Renewable Energy Communities with AI and real-world data
Integrating EV charging with building energy systems effectively
Bridging simulation-to-reality gap in multi-agent RL control
Innovation
Methods, ideas, or system contributions that make the work stand out.
MADDPG-based multi-agent control strategy
Real-world data and system integration
Optimized load and EV charging scheduling
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