# Power System Reliability Assessment Considering Coal-Fired Unit Peaking Characteristics
## 1 Introduction  
With the global transformation of the energy structure and the promotion of low-carbon goals, the proportion of renewable energy in power systems is gradually increasing. However, the intermittency and uncertainty of renewable energy pose significant challenges to the stability and economy of power systems [1]. Coal-fired units, as conventional base-load power sources, possess strong regulation capabilities. They can quickly respond to load changes, thus becoming an important resource for peak regulation in power systems [2]. However, coal-fired units operate in off-design conditions during peak regulation, leading to frequent fluctuations in unit parameters. The heat surfaces in the furnace are often subjected to thermal stress, making them more prone to failure [3]. This results in unplanned outages and the loss of load resources and has a significant impact on the reliability of power system operation. Furthermore, the proportion of adjustable capacityin conventional thermal power units is too low, and the space for renewable energy absorption is severely insufficient, leading to phenomena such as wind and solar curtailment. Therefore, it is of great significance to effectively assess the impact of coal-fired units' peak regulation characteristics on power system reliability after the integration of renewable energy. This helps to reasonably arrange generation plans and promote the integration of renewable energy. PPS is a crucial tool for generation planning and unit operation scheduling in power systems. This method enables the calculation of unit output across different operational modes, along with system production costs and reliability indices. It is particularly useful for studies focusing on generation unit output characteristics and system load curves. PPS methods are generally classified into two main categories: simulation-based approaches [4; 5] and analytical methods [6; 7; 8]. The simulation methods, such as Monte Carlo simulation, have the advantages of strong adaptability and simple models, but they suffer from issues related to computational efficiency and accuracy. Analytical methods, with clear physical concepts, high mathematical model accuracy, and fast computation speeds, are suitable when many related factors need to be considered. However, they involve complex mathematical modeling, and it is difficult to obtain the probability distribution and characteristic parameters of various technical and economic indices. Currently, analytical methods remain the core of research on PPS in power systems. Scholars worldwide have conducted extensive work to improve the computational efficiency and accuracy of PPS algorithms. For instance, study [9] integrates transition frequency analysis into the PPS framework, refining the equivalent electrical energy frequency method to assess the impact of wind power integration on dynamic costs. Another study [10] introduces a PPS method based on the UGF theory, effectively capturing the reliability implications of wind power integration. Furthermore, study [11] presents a structural expansion method that models state variable distributions in power systems, incorporating clean energy penetration and low-carbon benefit analysis. While extensive research has addressed the impact of renewable energy intermittency [12] on system reliability, fewer studies have examined the degradation and operational dynamics of coal-fired units during peak regulation and frequency control. A study [13] proposes a PPS-based evaluation framework for deep peak regulation of thermal power to facilitate renewable energy integration. However, it does not account for failure rates or state transition characteristics during operation. Another study [14] develops four multi-state unit models considering various operational conditions, base-load constraints, and segmented unit capacities. Nonetheless, its complex model structure limits computational efficiency and fails to accurately capture the degradation effects of peak regulation on system reliability.