MuFlex: A Scalable, Physics-based Platform for Multi-Building Flexibility Analysis and Coordination
This addresses a gap in benchmarking tools for building energy management, particularly for researchers and practitioners working on multi-building coordination, though it is incremental as it builds on existing simulation and RL frameworks.
The researchers tackled the lack of scalable testbeds for multi-building flexibility coordination by developing MuFlex, an open-source platform that integrates EnergyPlus models with OpenAI Gym, and demonstrated in a case study that aggregating four buildings reduced total peak demand below a specified threshold while maintaining indoor environmental quality.
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.