This article examines the impact of meteorological conditions represented by ambient temperature, ambient humidity, wind speed, and daily precipitation sum on the reliability of low-voltage cable lines. Cable line reliability is crucial to the stability and safety of power systems. Failure of cable lines can lead to power outages. This can cause serious economic and social consequences, as well as threaten human safety, especially in the public sector and critical infrastructure. In addition, any interruption of cable lines generates costs related to repairs, operational losses, and possible contractual penalties. This is why it is so important to investigate the causes of power equipment failures. Many power system failures are caused by weather factors. The main purpose of this article is to quantify the actual impact of weather conditions on the performance and reliability of power equipment in distribution networks. Reliability indicators (failure rate, failure duration, restoration rate, and failure coefficient) for low-voltage cable lines were calculated as a function of weather conditions. Empirical values of the indicators were determined based on many years of observations of power lines operating in the Polish power system. An analysis of the conformity of their empirical distribution with the assumed theoretical model was also conducted. By quantifying the impact of specific weather factors on the operation of power equipment, it becomes possible to identify the ranges in which failures are most likely.

This study investigates strategies regarding energy management in two selected manufacturing companies that focus on researching the load profile under the capacity market conditions. A case study of selected industrial plants is conducted to determine the adequate power and energy capacity of electrical energy storage systems to reduce load variability. The objective of this study is to manage industrial electricity demand by using energy storage to reduce the capacity fee, determined by the discrepancy between average energy consumption during peak and off-peak hours, and to improve the load profile in industrial facilities. The proposed approach combines high-resolution consumption data analysis with parametric evaluation of storage configurations. The impact of storage implementation is assessed by quantifying changes in peak and off-peak demand levels and the resulting capacity-related charges. The results indicate that properly sized energy storage systems effectively flatten load profiles and reduce capacity costs. The study provides practical insights into the integration of energy storage with industrial energy management and highlights its role in enhancing demand-side flexibility under capacity market mechanisms.