MuFlex: A Scalable, Physics-based Platform for Multi-Building Flexibility Analysis and Coordination Article Swipe
Ziyan Wu
,
Ivan Korolija
,
Rui Tang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2508.13532
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2508.13532
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., Resistanc-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 multi-building flexibility coordination, was developed. 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's capabilities were demonstrated in a case study coordinating demand flexibility across four office buildings using the Soft Actor-Critic (SAC) algorithm. The results show that under four buildings' coordination, SAC effectively reduced the aggregated peak demand by nearly 12% with maintained indoor comfort to ensure the power demand below the threshold. The platform is released open-source on GitHub: https://github.com/BuildNexusX/MuFlex.
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- Type
- preprint
- Language
- en
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- http://arxiv.org/abs/2508.13532
- https://arxiv.org/pdf/2508.13532
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https://doi.org/10.48550/arxiv.2508.13532Digital Object Identifier
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MuFlex: A Scalable, Physics-based Platform for Multi-Building Flexibility Analysis and CoordinationWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-08-19Full publication date if available
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Ziyan Wu, Ivan Korolija, Rui TangList of authors in order
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https://arxiv.org/abs/2508.13532Publisher landing page
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https://arxiv.org/pdf/2508.13532Direct link to full text PDF
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0Total citation count in OpenAlex
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