Adaptive data-driven secondary control and cyberattack-resilient secondary control for AC microgrids

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More than 100 remote communities in Canada are not connected to the national electrical grid. They rely mainly on diesel for electricity, suffering from high costs and pollutions. Meanwhile, damage from snowstorms and wildfires becomes a leading cause of power outages in Canada. To increase the renewable energy supply and enhance the resiliency of electric power grids under extreme weather, microgrids, small-scale and self-sufficient energy systems, can play a crucial role. Compared to the bulk power systems, microgrids that can operate in both grid-connected and islanded modes have the advantages of low-carbon consumption and high self-healing capability. Despite many benefits, microgrids also bring new challenges. Microgrids dominated by the converter-interfaced distributed energy resources (DERs) are characterized with low inertia, meaning that microgrid voltage and frequency tend to experience large deviations subject to real-time power imbalance. Such deviations without timely correction may cause a severer power imbalance or even catastrophic system collapse. Secondary control is conceived as an effective means to correct the voltage and frequency deviations, which is conventionally carried out based on an apriori accurate physical model and intact sensor data. Nevertheless, an accurate physical model may not always be available due to diverse operating modes of converters and varying network topologies, while sensor data is susceptible to cyberattacks because of the vulnerability of information infrastructure. The plausibility of cyberattacks against the electric power sector is evident from the recent intrusion events, e.g., the penetration of a U.S. nuclear power plant near Burlington, Kansas, in 2017.To address these challenges, in this research project, we intend to develop novel adaptive secondary control and cyberattack-resilient secondary control for AC microgrids. Leveraging on Koopman operator theory, distributed control, and reinforcement learning, we will: 1). develop a novel online adaptive secondary control framework for microgrids to correct frequency and voltage deviations in real-time, without any prior knowledge of the grid model and warm-up training; 2). study the impacts of cross-layer cyberattacks on the developed secondary control methodologies; 3) design cyberattack-resilient secondary control methods using deep reinforcment learning techniques.Widespread power outages due to extreme weather or malicious cyberattacks are not only costly but also wreak havoc on millions of people's daily lives and profoundly disrupt the delivery of essential services (e.g., food, health care). The proposed research will yield novel cyberattack-resilient secondary control algorithms to ensure the stable operation of microgrids under varying grid topologies, working modes, and even under cyberattacks. The research outcomes will contribute to the advancement of developing secure and stable microgrids, the enhancement of reliable electric power, and the development of secure, clean, and resilient communities.