Volt–VAR control (VVC) is essential in maintaining voltage stability and operational efficiency in distribution networks, particularly with the increasing integration of distributed energy resources. Traditional methods often struggle to manage real-time fluctuations in demand and generation. First, various resources such as static VAR compensators, photovoltaic systems, and demand response strategies are incorporated into the VVC scheme to enhance voltage regulation. Then, the VVC scheme is formulated as a constrained Markov decision process. Next, a safe deep reinforcement learning (SDRL) algorithm is proposed, incorporating a novel Lagrange multiplier update mechanism to ensure that the control policies adhere to safety constraints during the learning process. Finally, extensive simulations with the IEEE-33 test feeder demonstrate that the proposed SDRL-based VVC approach effectively improves voltage regulation and reduces power losses.

The rapid adoption of electric vehicles (EVs) and the increasing reliance on renewable energy sources necessitate innovative charging infrastructure solutions to address key challenges in energy efficiency, grid stability, and sustainable transportation. Dynamic wireless charging systems, which enable EVs to charge while in motion, offer a transformative approach to mitigating range anxiety and optimizing energy utilization. However, these systems face significant operational challenges, including dynamic traffic conditions, uncertain EV arrival patterns, energy transfer efficiency variations, and renewable energy intermittency. This paper proposes a novel quantum computing-assisted optimization framework for the modeling, operation, and control of wireless dynamic charging infrastructure in urban highway networks. Specifically, we leverage Variational Quantum Algorithms (VQAs) to address the high-dimensional, multi-objective optimization problem associated with real-time energy dispatch, charging pad utilization, and traffic flow coordination. The mathematical modeling framework captures critical aspects of the system, including power balance constraints, state-of-charge (SOC) dynamics, stochastic vehicle arrivals, and charging efficiency degradation due to vehicle misalignment and speed variations. The proposed methodology integrates quantum-inspired optimization techniques with classical distributionally robust optimization (DRO) principles, ensuring adaptability to system uncertainties while maintaining computational efficiency. A comprehensive case study is conducted on a 50 km urban highway network equipped with 20 charging pad segments, supporting an average traffic flow of 10,000 EVs per day. The results demonstrate that the proposed quantum-assisted approach significantly enhances energy efficiency, reducing energy losses by up to 18% compared to classical optimization methods. Moreover, traffic-aware adaptive charging strategies improve SOC recovery by 25% during peak congestion periods while ensuring equitable energy allocation among different vehicle types. The framework also facilitates a 30% increase in renewable energy utilization, aligning energy dispatch with periods of high solar and wind generation. Key insights from the case study highlight the critical impact of vehicle alignment, speed variations, and congestion on wireless charging performance, emphasizing the need for intelligent scheduling and real-time optimization. The findings contribute to advancing the integration of quantum computing into sustainable transportation planning, offering a scalable and robust solution for next-generation EV charging infrastructure.

The growing integration of renewable energy sources and hydrogen technologies into the power grid has introduced significant cyber‐physical vulnerabilities, particularly to false data injection attacks (FDIA). This paper presents a novel three‐stage FDIA optimization model targeting integrated power‐hydrogen systems. The model leverages the interdependencies between power grids and hydrogen infrastructures, aiming to maximize system disruption while minimizing detection likelihood. Using a multi‐objective optimization framework based on the non‐dominated sorting genetic algorithm III (NSGA‐III), the model identifies Pareto‐optimal attack strategies by balancing conflicting objectives. The case study uses synthesized data to represent realistic conditions in the IEEE 118‐bus system, incorporating hydrogen storage facilities and production infrastructure. Simulations demonstrate how coordinated attacks can exacerbate imbalances in power load and hydrogen storage, resulting in cascading failures across both systems. The results show that the power system's recovery time increases exponentially with the attack intensity, while the hydrogen system suffers from slower, more complex recovery dynamics. This work highlights the critical need for advanced cybersecurity frameworks to protect integrated power‐hydrogen infrastructures from sophisticated cyber‐physical threats, emphasizing the importance of monitoring and securing multiple layers of the system.

This study presents a comprehensive analysis of system resilience and recovery in the face of cyber attacks on a hydrogen energy infrastructure, utilizing advanced modelling techniques and state‐of‐the‐art cybersecurity strategies. We developed a robust optimization framework integrated with graph neural networks (GNNs) to detect and mitigate sophisticated cyber threats. The GNNs were trained on a dataset that included both normal operational data and simulated attack scenarios, enabling them to identify subtle patterns indicative of intrusions. Once an attack was detected, the system employed a stochastic dual dynamic programming (SDDP) approach to reconfigure operations dynamically, optimizing system performance while minimizing disruption. This dual‐layer defence mechanism–comprising initial detection by the GNN and subsequent mitigation through robust optimization–was tested against various cyber threat scenarios, including data injection, denial of service (DoS) and control system hijacking. Our findings reveal that the automated adaptive recovery (AAR) Strategy, which integrates real‐time monitoring and AI‐driven adaptive response, significantly outperforms traditional methods. Specifically, the AAR Strategy restored system performance to 90 percent within 60 minutes post‐attack, compared to only 70 percent recovery under conventional approaches. A 3D surface plot analysis further demonstrated that system performance declines sharply under prolonged high‐load conditions, with potential performance drops to below 20 percent when the load exceeds 80 percent over a 100‐min period. These results underscore the critical need for integrating adaptive and automated resilience strategies, like the AAR Strategy, into energy infrastructures. Our research contributes to the optimization of cybersecurity measures, offering a robust foundation for future advancements in the resilience of critical energy systems against evolving cyber threats.

This paper presents an innovative optimization framework for the co-management of dynamic electric vehicle (EV) charging lanes and power distribution networks, addressing grid stability amidst fluctuating EV charging demands. Integrating generative adversarial networks (GANs) and distributionally robust optimization (DRO), the framework models uncertainties in traffic flow and renewable energy generation, optimizing system performance under worst-case conditions to mitigate risks of grid instability. Applied to a highway with eight dynamic charging lanes (500 kW per lane), serving up to 50 EVs simultaneously, the framework balances energy contributions from 15 renewable generators (60% of the mix) and 10 non-renewable generators. Simulation results highlight its effectiveness, maintaining grid stability with voltage deviations within 0.02 p.u., reducing energy losses to under 0.8 MW during peak traffic (1500 vehicles per hour), and achieving 95% lane utilization. Dynamic charging enabled EV users to save USD 0.08 per kilometer through reduced stationary charging downtime, optimized travel efficiency, and lower energy costs. Additionally, the system minimizes maintenance costs by optimizing lane and grid reliability. This study underscores the potential of GAN-based DRO methodologies to enhance the efficiency of power grids supporting dynamic EV charging, offering scalable solutions for diverse regions and traffic scenarios.

Due to long-term outdoor working, High-Voltage Disconnectors (HVDs) are prone to potential faults. Currently, most studies on HVD state diagnosis methods have tested only one type of HVD, and the generalization capability of these methods for other HVDs has not been verified. In this paper, we propose an HVD state diagnosis method featuring adaptive recognition capabilities based on Fault Difference Signals, Adjusted Relative Position Matrix and Convolutional Neural Networks (FDS-ARPM-CNN). First, we align the measured operational power signal of the HVD drive motor with the recorded normal operational power signal, deriving the FDS through subtraction. Next, to address the issue of traditional Relative Position Matrix (RPM) conversion processes that lose sample amplitude information, we introduce a targeted improvement to the relative position matrix calculation method, converting the one-dimensional FDS into a two-dimensional image. Finally, we achieve high-accuracy diagnosis and classification of HVD states using a CNN that incorporates Batch Normalization (BN) and GELU activation functions. Experimental validation demonstrates that the neural network model, trained on one model of HVD, maintains strong generalization capabilities on data from other HVD models. This method effectively alleviates the challenges of acquiring fault samples in data-driven approaches for HVD state diagnosis, showcasing significant practical value.