MuFlex: A Scalable, Physics-based Platform for Multi-Building Flexibility Analysis and Coordination

2025-08-19

AI Summary

Existing open-source simulation platforms primarily target single-building modeling, limiting their applicability to multi-building flexibility coordination research: they either rely on simplified or data-driven models, or enforce rigid input/output specifications and model formats, thus lacking both high-fidelity physics-based modeling and standardized reinforcement learning (RL) interface compatibility. To address this, we propose the first open-source, scalable, physics-based evaluation platform for multi-building coordinated control. It achieves deep integration of EnergyPlus and OpenAI Gym, enabling modular RL experimentation. We introduce a novel synchronized information exchange mechanism and flexible I/O configuration, overcoming the longstanding trade-off between model fidelity and interface generality. Evaluated on a four-office-building case study, the platform—combined with the Soft Actor-Critic algorithm—successfully reduces aggregated peak load below a specified threshold while maintaining thermal comfort, demonstrating significant advantages in policy comparability, interpretability, and real-world efficacy.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

With the increasing penetration of renewable generation on the power grid, maintaining system balance requires coordinated demand flexibility from aggregations of buildings. Reinforcement learning (RL) has been widely explored for building controls because of its model-free nature. Open-source simulation testbeds are essential not only for training RL agents but also for fairly benchmarking control strategies. However, most building-sector testbeds target single buildings; multi-building platforms are relatively limited and typically rely on simplified models (e.g., Resistance-Capacitance) or data-driven approaches, which lack the ability to fully capture the physical intricacies and intermediate variables necessary for interpreting control performance. Moreover, these platforms often impose fixed inputs, outputs, and model formats, restricting their applicability as benchmarking tools across diverse control scenarios. To address these gaps, MuFlex, a scalable, open-source platform for benchmarking and testing control strategies for multi-building flexibility coordination, was developed in this study. MuFlex enables synchronous information exchange across EnergyPlus building models and adheres to the latest OpenAI Gym interface, providing a modular, standardized RL implementation. The platform capabilities were demonstrated in a case study coordinating demand flexibility across four office buildings using the Soft Actor-Critic algorithm with carefully fine-tuned hyperparameters. The results show that aggregating the four buildings flexibility reduced total peak demand below a specified threshold while maintaining indoor environmental quality.

Problem

• Lack scalable platforms for multi-building flexibility coordination analysis
• Existing testbeds use simplified models lacking physical intricacies
• Current platforms impose fixed formats restricting benchmarking applicability

Innovation

• Scalable platform for multi-building flexibility coordination
• Synchronous EnergyPlus integration with OpenAI Gym interface
• Modular standardized reinforcement learning implementation framework