Neuro-Reachability of Networked Microgrids

Zhou, Yifan; Zhang, Peng

doi:10.1109/tpwrs.2021.3085706

Cited by 16 publications

(8 citation statements)

References 35 publications

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“…For future work, we will investigate other techniques for obtaining BaCs, including synthesis of BaC using NNs [32]. To eliminate the need for a dynamic model of the MG, we plan to explore: (i) learning neural ODEs [4,33] that capture the MG dynamics, and (ii) deriving BaCs and switching conditions from said dynamics. We also plan to extend our approach to networked microgrids [31].…”

Section: Discussionmentioning

confidence: 99%

A Barrier Certificate-based Simplex Architecture with Application to Microgrids

Damare¹,

Roy²,

Smolka³

et al. 2022

Preprint

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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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Section: Discussionmentioning

confidence: 99%

A Barrier Certificate-based Simplex Architecture with Application to Microgrids

Damare¹,

Roy²,

Smolka³

et al. 2022

Preprint

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show abstract

“…2021; Tang et al 2021; Wang et al. 2021; Zhang 2021; Zhou and Zhang 2021a, Zhou and Zhang 2021b, Zhou, Zhang, and Yue 2021). The project is partnering with a $1B microgrid project at the Energy & Innovation Park (EIP) in New Britain, Connecticut, which aims to transform the traditional manufacturing base of that city to a new digital economy supporting the rapidly growing Data Center sector.…”

Section: Civil/built Infrastructurementioning

confidence: 99%

“…Further, to provide runtime safety assurance despite possible flaws and vulnerabilities in virtualized controllers, a Neural Simplex Architecture is being developed. If a safety violation is imminent, the decision module switches control from the AI-based advanced controller to a verified-safe baseline controller and incrementally retrains the AI-based controller (Jiang et al 2021;Tang et al 2021;Wang et al 2021;Zhang 2021;Zhou and Zhang 2021a, Zhou and Zhang 2021b, Zhou, Zhang, and Yue 2021. The project is partnering with a $1B microgrid project at the Energy & Innovation Park (EIP) in New Britain, Connecticut, which aims to transform the traditional manufacturing base of that city to a new digital economy supporting the rapidly growing Data Center sector.…”

Section: Ai-enabled Provably Resilient Networked Microgridsmentioning

confidence: 99%

Enabling AI innovation via data and model sharing: An overview of the NSF Convergence Accelerator Track D

et al. 2022

Self Cite

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This article provides a brief overview of 18 projects funded in Track D—Data and Model Sharing to Enable AI Innovation—of the 2020 Cohort of the National Science Foundation's (NSF) Convergence Accelerator (CA) program. The NSF CA is focused on transitioning research to practice for societal impact. The projects described here were funded for one year in phase I of the program, beginning September 2020. Their focus is on delivering tools, technologies, and techniques to assist in sharing data as well as data‐driven models to enable AI innovation. A broad range of domain areas is covered by the funded efforts, spanning across healthcare and medicine, to climate change and disaster, and civil/built infrastructure. The projects are addressing sharing of open as well as sensitive/private data. In September 2021, six of the eighteen projects described here were selected for phase II of the program, as noted in this article.

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“…AI-assisted simulation is realized based on the internal APIs. Firstly, data-driven AI models can be used for dynamic component modeling [15], [34]. Although the model may suffer from the problem of interpretability, the measurementbased AI model can also be accurate and adaptive.…”

Section: ) Ai-assisted Simulationmentioning

confidence: 99%

“…Research on artificial intelligence (AI) has achieved a growth spurt in the past few years. AI algorithms such as graph neural networks (GNNs) and reinforcement learning (RL) have been applied to a variety of power system studies such as measurement enhancement [14], dynamic component modeling [15], parameter inference [16], optimization and control [17], and stability assessment [18]. AI models can learn and approximate any functions with enough samples [19].…”

Section: Introductionmentioning

confidence: 99%

Exploration of Artificial-intelligence Oriented Power System Dynamic Simulators

Xiao

Chen

Wang

et al. 2023

Journal of Modern Power Systems and Clean Energy

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With the rapid development of artificial intelligence (AI), it is foreseeable that the accuracy and efficiency of dynamic analysis for future power system will be greatly improved by the integration of dynamic simulators and AI. To explore the interaction mechanism of power system dynamic simulations and AI, a general design for AI-oriented power system dynamic simulators is proposed, which consists of a high-performance simulator with neural network supportability and flexible external and internal application programming interfaces (APIs). With the support of APIs, simulation-assisted AI and AIassisted simulation form a comprehensive interaction mechanism between power system dynamic simulations and AI. A prototype of this design is implemented and made public based on a highly efficient electromechanical simulator. Tests of this prototype are carried out in four scenarios including sample generation, AI-based stability prediction, data-driven dynamic component modeling, and AI-aided stability control, which prove the validity, flexibility, and efficiency of the design and implementation for AI-oriented power system dynamic simulators.

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Neuro-Reachability of Networked Microgrids

Cited by 16 publications

References 35 publications

A Barrier Certificate-based Simplex Architecture with Application to Microgrids

A Barrier Certificate-based Simplex Architecture with Application to Microgrids

Enabling AI innovation via data and model sharing: An overview of the NSF Convergence Accelerator Track D

Exploration of Artificial-intelligence Oriented Power System Dynamic Simulators

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