Jianhui Wang


Detecting False Data Injection Attacks in Smart Grids Using Quantum Embedding Kernels

We explore the use of quantum computing and machine learning to enhance the security of smart grids by detecting false data injection attacks (FDIAs). Quantum embedding kernels are used as a means of detecting these attacks and we present case studies demonstrating the effectiveness of a quantum support vector machine (SVM) algorithm in detecting FDIAs in smart grids. We show that the quantum SVM algorithm exhibits robustness to noise, performing better than other machine learning algorithms within a range of 0% to 42% probability of depolarizing noise. The computational results highlight the potential of the proposed framework to detect FDIAs more effectively and efficiently than with traditional methods. However, further research is needed to fully explore this potential and develop practical solutions for securing smart grids against cyber threats.

On the Integration of Hydrogen into Integrated Energy Systems

The proliferation of renewable energy (RE) brings tremendous challenges to integrated energy systems (IESs). Converting RE into hydrogen, one of the cleanest energy carriers, provides an appealing alternative for decarbonized IESs. Among the various hydrogen applications, blending hydrogen into natural gas systems is already applicable. However, the disparate hydrogen physical properties trigger concerns about hydrogen integration. We investigate the integration of hydrogen into the IESs, focusing on blending hydrogen into natural gas systems. The literature on hydrogen modeling, control, operation, planning, and markets are first reviewed. Based on the convex combination methods, a power-to- hydrogen-heat-methane (P2HHM) model with unit commitment is proposed. The steady-state and dynamic gas flow models with the explicit consideration of hydrogen effects are then presented. Two applications are given based on the proposed hydrogen modeling. A Wasserstein metric-based distributionally robust optimal operation model is discussed first based on the developed P2HHM and gas flow models. Then a sequential Monte Carlo simulation-based reliability assessment model is formulated to analyze the effects of hydrogen physical properties and hydrogen fractions on the optimal operation and reliability of the IESs. Numerical simulations are conducted to analyze the integration of hydrogen and verify the effectiveness of the proposed model.

Equity-based Grid Resilience

The power system worldwide is facing challenges from a changing climate. Decision-makers are tasked with increasing the smartness and resilience of the energy infrastructure to reduce the negative impacts to customers, with the help of new technologies such as household renewable energy generators and electric vehicles. However, a largely overlooked topic is equity, and how the burdens and benefits of the power system are distributed to different communities. To fill this gap, we provide a thorough review of the implication of equity in the power system, the significance of guaranteeing energy equity both in everyday operation and disaster management, and the ongoing efforts to plan for equable resilience in various fields. Finally, a holistic power grid resilience enhancement framework is proposed that covers different stages of disaster management and different dimensions of energy equity.

© Copyright 2023 IEEE — All rights reserved. A not-for-profit organization, IEEE is the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity.