## 1 Introduction  
The timely and accurate acquisition of disaster information is crucial for disaster emergency responses and decision-making assistance. Remote sensing technology can dynamically monitor and adapt to various harsh conditions, remaining unaffected by atmospheric conditions [1]. It offers a substantial amount of information, operates with high efficiency, and enables rapid and effective large-scale disaster monitoring and assessment. The electricity system and building infrastructure are often influenced by disasters, which can result in widespread power outages [2; 3; 4], and a noticeable reduction in nighttime light (NTL) intensity. Daytime imagery, used primarily for assessing surface structural failures, and NTL images are more dominant in capturing the dynamics of human activity and interruptions [5]. Specifically, NTL data are more suitable for distinguishing between damage and non-damage classes, while SAR textures features allow for us to better distinguish between different classes of damages at a block scale, such as low and heavy damage [6]. Nevertheless, the development of NTL sensors lags behind day-time sensors [7], and among the numerous studies of earthquake damage, the attenuation of light at night is often overlooked despite its significance. Larger light attenuation values indicate more severe lighting system damage. As a distinctive subset of remote sensing, NTL addresses this limitation by providing essential information during nighttime hours [8].  
The research on power system performance can be roughly divided into the characterization based on network topological characteristics and the power flow analysis [9; 10; 11; 12].  
The former loses the connectivity [13] of part of the grid structure after the earthquake disaster, which will cause great errors in the evaluation. The latter are more rigorous but also more complex, involve higher-level tools, and the inputs they require are often not readily available.  
Under this circumstance, as a secondary disaster of large-scale disasters, power damages cause severe outcomes and thus need to be monitored efficiently and without being costly [14]. Moreover, the correlation between city electricity power system and NTL has been verified in many studies [15; 16; 17] and used in disaster research [4; 18; 14]. For instance, NTL data have been used to model the earthquake effects on the local economies [19; 20], access economic loss and recovery after natural disaster [21], detect real-time flood [22] and identify fires and power outages [23]. Therefore, predicting potential light attenuation values based on PSVE holds practical significance. Especially in disaster-stricken areas, the loss of communication with the external environment during an event underscores the significance of NTL as a proxy for human activity [24].  
Therefore, we developed the PSVE model based on the following principles; the light radiation value in the same area is relatively stable and unchanged. However, the light significantly weakens when natural disasters occur. The change in the light radiation value results in a noticeable disparity in NTL levels before and after the disaster [8]. The disparity in NTL levels can serve as a quantitative measure of the impact of natural disasters on light intensity. The fundamental premise of this paper is that the calculated difference value in light brightness before and after earthquake, subject to reasonable constraints, predominantly reflects the impact of the disaster.