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Chip seal is widely used for preventive maintenance to mitigate pavement deterioration, but it is prone to aggregate loss during pavement service. To further promote the development and application of chip seals in road engineering in China, the research progress of the adhesion behavior of aggregate and binder in chip seals was reviewed in this paper, focusing on the adhesion mechanism of emulsified asphalt and alkaline aggregate. The Influencing factors and evaluation methodology of chip seals' aggregate adhesion behavior were also discussed. The results demonstrate that the adhesion process between emulsified asphalt and alkaline aggregate is divided into three processes including infiltration, demulsification, and cluster, which is more complicated when compared to hot asphalt. When designing a chip seal, not only the characteristics of single material should be paid attention to, but also the combination of binder and aggregate matters a lot. To form good adhesion between aggregate and asphalt binder, various influencing factors such as material selection, design method, and construction technical index should be considered comprehensively in the whole design, construction, and operation process. Three methods for evaluating adhesion behavior are summarized, including macroscopic adhesion performance tests, image analysis technology, and model prediction. It is not objective to evaluate the aggregate adhesion behavior of chip seal only by a single evaluation method. A comprehensive evaluation based on the micro-macro multi-scale method should be considered in the future.

With the increasing penetration of distributed energy in the distribution network, it is urgent to study how to ensure the stable and reliable operation of the power grid under fault conditions. To solve this problem, this study proposes a method for partitioning distribution network islands and reconstructing faults considering the Conditional Value at Risk (CVaR). This method aims to enhance the resilience of the distribution network and the recovery capability of critical loads. Initially, a partitioning model for distribution network islands based on depth-first and breadth-first search algorithms was constructed. Building upon this partitioning, a fault reconstruction method for distribution networks that considers CVaR was developed. This method utilizes CVaR theory to transform costs and quantifies the risk that the uncertainty of distributed energy resources poses to distribution network reconstruction strategies. Finally, the effectiveness of the proposed method is demonstrated using an improved IEEE 33-node system, generating typical fault scenarios.

Optimal power flow (OPF) calculation methods are important for the power system operation and mainly focus on the deterministic power flow calculation, neglecting the impact of demand response on online security calculation of power systems with renewable energy sources. Therefore, this paper proposes an OPF calculation method that considers the uncertainties of wind power, photovoltaic (PV) power generation and demand-side response. Firstly, the research focuses on the renewable energy grid, considering the uncertainties of wind power and PV power generation, and establishes uncertainty models for wind power and PV output. Secondly, based on cloud model theory, an uncertainty model for demand response is established. According to the established models, an efficient OPF model is constructed with a linearized submodels considering multiple uncertainties. By testing on the IEEE 30-bus system as a typical example, we found the effectiveness and superiority of the proposed OPF calculation method can benefit the power system economic operation and demand side resource utilization.

The increasing penetration of renewable energy resources has greatly changed the pattern of the modern power system. In this case, the operation modes of power systems are becoming much more complex and the traditional experience-based method is no longer practical in typical operation modes analysis. In this paper, a clustering and decision tree-based scheme is proposed for the analysis of the typical operation modes of power systems. Specifically, the k-means++ clustering algorithm is adopted to classify the operation data into different groups, which represent the typical operation modes. The group labels and several important operation features are used to construct a decision tree for the quantitative description of different kinds of typical operation modes. In addition, feature importance is analyzed and the decision tree is pruned with a balance between complexity and classification accuracy. Case studies are conducted based on the actual system planning data of the Jiangsu power grid to verify the effectiveness of the proposed scheme. From the results, system operators could get deep insights into system planning with a high share of renewable energy resources.