Runtime-Assured, Real-Time Neural Control of Microgrids
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We present SimpleMG, a new, provably correct design methodology for runtime assurance of microgrids (MGs) with neural controllers. Our approach is centered around the Neural Simplex Architecture, which in turn is based on Sha et al.'s Simplex Control Architecture. Reinforcement Learning is used to synthesize high-performance neural controllers for MGs. Barrier Certificates are used to establish SimpleMG's runtime-assurance guarantees. We present a novel method to derive the condition for switching from the unverified neural controller to the verified-safe baseline controller, and we prove that the method is correct. We conduct an extensive experimental evaluation of SimpleMG using RTDS, a high-fidelity, real-time simulation environment for power systems, on a realistic model of a microgrid comprising three distributed energy resources (battery, photovoltaic, and diesel generator). Our experiments confirm that SimpleMG can be used to develop high-performance neural controllers for complex microgrids while assuring runtime safety, even in the presence of adversarial input attacks on the neural controller. Our experiments also demonstrate the benefits of online retraining of the neural controller while the baseline controller is in control
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