The advantages of asphalt pavement in terms of driving comfort, construction efficiency, and ease of maintenance have established it as the predominant choice for high-grade pavements at present. However, being highly sensitive to temperature and stress, asphalt performance is significantly influenced by external environmental conditions and loading, making it susceptible to various distress phenomena. Particularly in high-latitude regions, asphalt pavement cracking severely limits asphalt pavement’s functional performance and service lifespan under cold climatic conditions. To enhance the low-temperature cracking resistance of asphalt pavement in cold regions, tools such as VOS viewer 1.6.20 and Connected Papers were utilized to systematically organize, analyze, and summarize relevant research from the past 40 years. The results reveal that temperature shrinkage cracks and thermal fatigue cracks represent the primary forms of asphalt pavement distress in these regions. Cracking in asphalt pavement in cold regions is primarily influenced by structural design, pavement materials, construction technology, and climatic conditions. Among these factors, surface layer stiffness, base layer type, and the rate of temperature decrease exert the most significant impact on cracking resistance, collectively accounting for approximately 45.4% of all cracking-related factors. The low-temperature performance of asphalt pavement can be effectively improved through several strategies, including adopting full-thickness asphalt pavement with a skeleton-dense structure or reduced average particle size, incorporating functional layers, appropriately increasing the thickness of the upper layer and the compaction temperature of the lower layer, utilizing continuous surface layer construction techniques, and applying advanced materials. High-performance modifiers such as SBR and SBS, nanomaterials with good low-temperature performance, and warm mixing processes designed for cold regions have proven particularly effective. Among various improvement methods, asphalt modification has demonstrated superior effectiveness in enhancing the deformation capacity of asphalt and its mixtures, significantly boosting the low-temperature performance of asphalt pavements. Asphalt modification accounts for approximately 50% of the improvement methods evaluated in this study, with an average improvement in low-temperature performance reaching up to 143%. This paper provides valuable insights into the underlying causes of cracking distress in asphalt pavements in cold regions and offers essential guidance for improving the service quality of such pavements in these challenging environments.

The rapid development of Internet-of-Things (IoT) in smart grid has enabled millions of grid-connected Distributed Controllable Resources (DCR, e.g., electric vehicles, controllable loads) to provide service to the grid, such as frequency regulation and demand response. The integration of these DCRs may become a large virtual power plant (VPP) network with various characteristics. This poses great challenges from both control and management perspectives, e.g., computation/communication burden, optimization complexity, scalability limitation, prosumer privacy, etc. This paper proposes an effective autonomous incentive-based DCR control and management framework to integrate a large amount of DCRs to provide grid services, which simultaneously provides accurate active power adjustment to the grid, optimizes DCR allocations, and maximizes the profits for all prosumers and system operators. A model-free Deep Deterministic Policy Gradient (DDPG) based method is designed to find the optimal incentives in a continuous action space to encourage prosumers to adjust their power consumption. The method is implemented in a consortium open-source blockchain platform, Hyperledger Fabric, which facilitates controls and transaction management. To demonstrate the effectiveness of the framework, extensive experimental studies are conducted using real-world data.

Transient event identification is essential for power system operation and situational awareness. The increased penetration of the high sampling frequency phasor measurement units (PMUs) enables using PMU data to analyze power system events, and thus enhances power system visualization, monitoring and control. At the same time, the risks associated with cyberattacks on power systems increase. A malicious cyberattack on PMUs, aiming at generating fake transient data, may lead to incorrect actions that jeopardize system reliability. Therefore, it is critical to distinguish between fake data and real data when analyzing transient events. Utilizing PMU measurements, this article develops a data-driven approach, based on text-mining methodologies, for classifying transient events and identifying fake events caused by false data attacks. The developed methodology provides credible information regarding the cause of various events, and facilitates postevent decision-making to prevent potential cascading failures. Case studies, performed on the IEEE 30-bus and IEEE 118-bus systems, show that the developed approach is efficient in classifying false data and identifying different transient events regardless of the system topology, loading conditions, or the placement of PMUs.

Effective mitigation of thermal overloads is crucial in preventing power grid outages. With large-scale adoption of phasor measurement units, new and more effective mitigation opportunities have emerged. However, the possibility of false data attacks on the measured data, or the relay status information, threatens the promise of utilizing measurements for timely mitigation of thermal overloads. False data can lead to wrong estimation of the system states and the power flow model, which may eventually lead to wrong mitigations. To address these newly introduced challenges, in this article, a data-driven model-predictive control method is introduced for mitigation of the thermal overloads, which is resilient to false data injection attacks. This is achieved by constructing a data-driven power flow model from the trustworthy system measurements, which is independent from system topology and model parameters. Hence, the power flow model becomes immune to wrong system model information. To eliminate the adverse impacts of false data on the measurements, the actual system states are recovered from the historical trustworthy data if an attack is detected. Case studies demonstrate that the developed model predictive control methodology effectively identifies the optimal mitigations in a finite prediction horizon.

Controlled islanding is a last-resort recovery scheme for preventing cascading failures. Although effective, high levels of uncertainty from renewable energy resources challenge the promise of controlled islanding to prevent additional failures. Such uncertainties can lead to subsequent failures in individual islands, render the overall recovery approach ineffective, and reduce the grid resilience. This paper develops a stochastic cascading failure model to assess the resilience of islands against cascading failures under high penetration of renewables. The stochastic model incorporates physical system constraints and system data to predict the failures. Hence, the most probable cascades in islands with high uncertainties are efficiently identified, which enables evaluating the islands' resilience to cascades. The developed model significantly improves the efficiency and accuracy of the resilience study in the presence of uncertainties. Case studies indicate that the increased penetration of intermittent renewables can improve or degrade the system resilience depending on the level of renewable generation. The case studies also reveal that an islanding strategy to minimize the imbalance of load and generation can lead to less resilient islands. These findings substantially enhance the understanding of controlled islanding under high generation uncertainties and improve resilience-oriented grid planning.