Resilient Control for Demand Response in Smart Grid Against Cyber-Attacks

To accommodate power fluctuations caused by renewable energies and maintain the power balance in smart grid, flexible load resources on the demand side have been widely employed to provide flexibility services, a process known as named demand response (DR). To effectively dispatch such distributed flexible load resources, advanced communication, information, and distributed control techniques are developed in the field of DR, transforming DR into a cyber-physical system (CPS). While deep cyber-physical coupling improves the performance of DR, it also introduces cyber-security threats, such as cyber-attacks, which can cause DR system to go out of control, thereby threatening the smart grid’s safe operation. To this end, this talk will focus on analyzing the impact of cyber-attacks on the DR system in smart grid and developing control-based defense schemes to safeguard the flexibility capability of the DR system in harsh cyber environments.

Improving Model Generalization for Short-Term Customer Load Forecasting with Causal Inference

Short-term customer load forecasting is vital for the normal operation of power systems. Unfortunately, conventional machine learning-based forecasting methods are susceptible to generalization issues, manifested in model performance degradation. In recent years, some studies have employed advanced deep learning technology to overcome the aforesaid problems. However, these methods can only alleviate the adverse impacts of generalization, because they are inherently built on unstable relationships. In this talk, we present a causal inference-based method to improve the generalization for customer load forecasting models. Specifically, we first investigate the causal relations in existing methods, and inject the designed load characteristics as an extra model input. Then, we closely inspect the causality in models by using the causal graph, followed by employing the causal intervention with do-calculus to eliminate the spurious correlations. In addition, we present a novel load forecasting framework to realize the causal intervention. Finally, the effectiveness and superiority of our proposed method are validated on a public dataset.

Coordinated Optimization of Power-Communication Coupling Networks for Dispatching Large-Scale Flexible Resources

The growth of renewable energies elevates the significance of maintaining system balance and imposes more demands on regulation resources. Flexible loads have been extensively regarded as prospective regulation resources for providing ancillary services within power networks, including frequency regulation, primary reserve, and synchronized reserve. However, the dispatch of flexible loads presents the challenges of frequent data transmission and explosive data volume, leading to substantial pressure on communication networks. To improve the communication performance for providing effective ancillary services, the work focuses on the coordinated optimization of power-communication coupling networks for dispatching large-scale flexible resources. Firstly, this talk will introduce the comprehensive framework of model-operation-planning-engineering derived from our ongoing and upcoming works. Then, this talk will share the recent progress in the equivalent modeling of communication networks and the coordinated operation of coupling networks considering dynamic communication prices. Finally, this talk will provide a concise overview of the future works.

Hierarchical Coordinated Control Strategies for Flexible Interconnected Power Grids

The integration of renewable energy sources (RESs) into active distribution networks (ADNs) is essential for reducing carbon emissions but presents significant challenges to the traditional power grid’s structure and regulation capability. Employing soft open point for flexible interconnection can enhance power flow regulation capabilty and mitigate the power imbalance among different regions. In modern power grids, power electronic equipment coexists with conventional regulation devices, each with different response times and operational frequencies. Coordinating these heterogeneous controllable devices efficiently, considering their varied characteristics, is a complex challenge. This work addresses these challenges by leveraging the fast response of power electronics to enhance system robustness and stability. This presentation will detail the completed work on hierarchical coordinated volt/var and volt/watt control for ADNs with SOPs considering voltage stability. The future study plan, which emphasizes millisecond-level coordination control considering power electronics dynamics based on a real-time digital system (RTDS), will also be included.

Power Converter’s IGBT Multi-State Reliability Analysis for Low Failure Rate Operation

The reliability of power electronics is a cornerstone of modern technology, ensuring the efficient conversion and control of electrical power in applications ranging from renewable energy systems to electric vehicles and industrial automation. As the demand for reliable power electronics continues to grow, the focus on enhancing the durability and performance of switching devices becomes paramount. Switching devices, the most vulnerable component within converters, underscore the critical need to improve their reliability and prolong the power converter’s lifetime. They are subject to various stresses, including thermal, electrical, and environmental factors, which can lead to premature failure and compromise system performance. This talk will discuss the importance of power electronic reliability, emphasizing the vulnerabilities of IGBTs. Sharing our ongoing work utilizes multistate reliability analysis to assess the reliability of these critical components rigorously. Moreover, this talk will show our proposed model for analyzing IGBT reliability, showcasing how multistate reliability analysis offers insights into their performance under different voltage conditions. Finally, this talk will outline future research directions aimed at further improving the reliability of power electronics.

A Quasi-Z-Source Based Fault-Tolerant PV Micro-Inverter: Design and Control

PV generators are a critical means to decarbonize urban energy systems and alleviate the energy crisis. However, the system reliability is easily compromised by the DC-link capacitor failure and PV module malfunction. To address these problems, a quasi-z-source-based fault-tolerant PV micro-inverter is proposed. The proposed topology features high circuit reliability and fault tolerance with concurrent functions of active power decoupling and differential PV module current manipulation. In this talk, the circuit design will be elaborated. The operation modes of the topology as well as its modulation scheme will be introduced. Based on the circuit models, the control strategy of the proposed converter will be discussed. Finally, simulation results will be analyzed to demonstrate the concurrent functions of APD and fault-resilient operation.

Expeditious MicroVM SnapStart in PM via Augmented Hypervisor

In the era of cloud computing, the industry has embraced snapshotting as a technique to tackle cold starts and efficiently manage numerous short-lived functions. Traditional methods often stumble on ‘page faults,’ disruptions that occur when the memory required is not immediately accessible. This talk introduces PASS, a cutting-edge system that leverages byte-addressable persistent memory (PMEM) for cost-effective and highly concurrent execution of MicroVM SnapStart. PASS functions as a PMEM-aware augmented hypervisor in the user space, revolutionizing MicroVM memory restoration. In this talk, we will discuss how PASS stands as a beacon of innovation, promising to revolutionize cloud function execution. Attendees will gain insights into the challenges faced by existing solutions and how PASS addresses them through its novel approach, ultimately paving the way for more efficient and scalable cloud computing environments.

Data Augmentation Algorithm for Class-Imbalanced Node Classification

Graph neural networks (GNNs) have achieved great success in node classification tasks. However, existing GNNs naturally bias towards the majority classes with more labelled data and ignore those minority classes with relatively few labelled ones. The traditional techniques often resort over sampling methods, but they may cause overfitting problem. More recently, some works propose to synthesize additional nodes for minority classes from the labelled nodes, however, there is no any guarantee if those generated nodes really stand for the corresponding minority classes. In fact, improperly synthesized nodes may result in insufficient generalization of the algorithm. To resolve the problem, in this paper we seek to automatically augment the minority classes from the massive unlabelled nodes of the graph. Specifically, we propose \textit{GraphSR}, a novel self-training strategy to augment the minority classes with significant diversity of unlabelled nodes, which is based on a Similarity-based selection module and a Reinforcement Learning (RL) selection module. The first module finds a subset of unlabelled nodes which are most similar to those labelled minority nodes, and the second one further determines the representative and reliable nodes from the subset via RL technique. Furthermore, the RL-based module can adaptively determine the sampling scale according to current training data. This strategy is general and can be easily combined with different GNNs models. Our experiments demonstrate the proposed approach outperforms the state-of-the-art baselines on various class-imbalanced datasets.

Adversarial Examples and its Applications to Privacy Protection

While Deep Neural Networks (DNNs) have achieved tremendous success in various image processing tasks such as classification, segmentation, denoising, and more, they are vulnerable when applied to Adversarial Examples (AEs). AEs are manipulated images that deceive the model by adding perturbations to normal images, capable of aiding in the detection and enhancement of DNNs’ robustness. Numerous methods for generating AEs have emerged, but in the practical black-box setting where attackers lack access to DNNs’ architecture, parameters or even training datasets, they can only obtain prediction scores or labels of images by querying the DNN. Current state-of-the-art black-box attack methods still face challenges with excessive query counts and relatively low attack success rate (ASR). In this talk, I will introduce our proposed algorithm aimed at enhancing the query efficiency and ASR of black-box attack methods. Additionally, I will discuss two other approaches we have designed for image privacy protection using AEs. One improves the accuracy of privacy-preserving image classification while allowing for ciphertext recovery under authorized access. The other allows human recognition of images while preventing DNNs from extracting image information, regardless of whether images are transmitted over online social networks

Expeditious MicroVM SnapStart in PM via Augmented Hypervisor

In the era of cloud computing, the industry has embraced snapshotting as a technique to tackle cold starts and efficiently manage numerous short-lived functions. Traditional methods often stumble on ‘page faults,’ disruptions that occur when the memory required is not immediately accessible. This talk introduces PASS, a cutting-edge system that leverages byte-addressable persistent memory (PMEM) for cost-effective and highly concurrent execution of MicroVM SnapStart. PASS functions as a PMEM-aware augmented hypervisor in the user space, revolutionizing MicroVM memory restoration. In this talk, we will discuss how PASS stands as a beacon of innovation, promising to revolutionize cloud function execution. Attendees will gain insights into the challenges faced by existing solutions and how PASS addresses them through its novel approach, ultimately paving the way for more efficient and scalable cloud computing environments.

Data Augmentation Algorithm for Class-Imbalanced Node Classification

Graph neural networks (GNNs) have achieved great success in node classification tasks. However, existing GNNs naturally bias towards the majority classes with more labelled data and ignore those minority classes with relatively few labelled ones. The traditional techniques often resort over sampling methods, but they may cause overfitting problem. More recently, some works propose to synthesize additional nodes for minority classes from the labelled nodes, however, there is no any guarantee if those generated nodes really stand for the corresponding minority classes. In fact, improperly synthesized nodes may result in insufficient generalization of the algorithm. To resolve the problem, in this paper we seek to automatically augment the minority classes from the massive unlabelled nodes of the graph. Specifically, we propose \textit{GraphSR}, a novel self-training strategy to augment the minority classes with significant diversity of unlabelled nodes, which is based on a Similarity-based selection module and a Reinforcement Learning (RL) selection module. The first module finds a subset of unlabelled nodes which are most similar to those labelled minority nodes, and the second one further determines the representative and reliable nodes from the subset via RL technique. Furthermore, the RL-based module can adaptively determine the sampling scale according to current training data. This strategy is general and can be easily combined with different GNNs models. Our experiments demonstrate the proposed approach outperforms the state-of-the-art baselines on various class-imbalanced datasets.

Adversarial Examples and its Applications to Privacy Protection

While Deep Neural Networks (DNNs) have achieved tremendous success in various image processing tasks such as classification, segmentation, denoising, and more, they are vulnerable when applied to Adversarial Examples (AEs). AEs are manipulated images that deceive the model by adding perturbations to normal images, capable of aiding in the detection and enhancement of DNNs’ robustness. Numerous methods for generating AEs have emerged, but in the practical black-box setting where attackers lack access to DNNs’ architecture, parameters or even training datasets, they can only obtain prediction scores or labels of images by querying the DNN. Current state-of-the-art black-box attack methods still face challenges with excessive query counts and relatively low attack success rate (ASR). In this talk, I will introduce our proposed algorithm aimed at enhancing the query efficiency and ASR of black-box attack methods. Additionally, I will discuss two other approaches we have designed for image privacy protection using AEs. One improves the accuracy of privacy-preserving image classification while allowing for ciphertext recovery under authorized access. The other allows human recognition of images while preventing DNNs from extracting image information, regardless of whether images are transmitted over online social networks