This paper considers the problem of electric drive of shearers simulation to assess the indicators of power supply system (PSS) energy efficiency in the context of the introduction of modern devices for controlling the flow of electricity and power. The block diagram of the shearer electric drive simulation model is presented. To take into account fluctuations in the level of consumption of active and reactive power, a model of the executive body of the shearer was used in the work, including a model of the moment of resistance on the auger when cutting. As a result, in the MATLAB Simulink environment, a simulation model of the electric drive of the UKD300 shearer was developed, suitable for assessing the energy efficiency of the electrical complex of mining areas and the feasibility of using modern devices for controlling the flow of electricity and power. As a result of the simulation, it was found that a significant irregularity in the graph of reactive power consumption, caused by repeated short-term operation, makes the use of capacitor units ineffective to compensate for reactive power.

This paper investigates the factors influencing the power consumption of electric vehicle (EV) charging infrastructure and develops a methodology for determining the design electrical loads of EV charging stations (EVCSs). A comprehensive review of existing research on demand factor (DF) calculations for EVCSs is presented, highlighting discrepancies in current approaches and identifying key influencing factors. To address these gaps, a simulation model was developed in Python 3.11.9, generating minute-by-minute power consumption profiles based on EVCS parameters, EV fleet characteristics, and charging behavior patterns. In contrast with state-of-the-art methods that often provide limited reference values or scenario-specific analyses, this study quantifies the influence of key factors and demonstrates that the average number of daily charging sessions, EVCS power rating, and the number of charging ports are the most significant determinants of DF. For instance, increasing the number of sessions from 0.5 to 4 per day raises DF by 2.4 times, while higher EVCS power ratings reduce DF by 32–56%. This study proposes a practical generalized algorithm for calculating DF homogeneous and heterogeneous EVCS groups. The proposed model demonstrates superior accuracy (MAPE = 6.01%, R² = 0.987) compared with existing SOTA approaches, which, when applied to our dataset, yielded significantly higher errors (MAPE of 50.36–67.72%). The derived expressions enable efficient planning of distribution networks, minimizing overestimation of design loads and associated infrastructure costs. This work contributes to the field by quantifying the impact of behavioral and technical factors on EVCS power consumption, offering a robust tool for grid planners and policymakers to optimize EV charging infrastructure deployment.

The paper considers the problem of reducing greenhouse gas emissions in the process of coal mining during the coal mine methane utilization in power supply systems. An algorithm to form recommendations for the implementation of CMM generation is presented. A simulation model for one of the Kuzbass coal mines was developed in the PowerFactory software application. The simulation model considers the uneven nature of the power consumption of mining equipment. As a result of modeling, daily power consumption profiles and voltage levels in the coal mine power supply system were determined before and after the implementation of the proposed measures. Based on the results, the technical and economic effects was estimated, which consisted in reducing the direct and indirect carbon footprint, electricity and capacity fees. It has been established that the cost of carbon dioxide emission quotas significantly affects the investment attractiveness of cogeneration projects. Based on the results, recommendations are given to stimulate the development of small generation in coal mines.

In this paper, a method for determining the parameters of the Volt/Var characteristics of inverters of electric vehicle charging stations to regulate voltage in distribution networks is proposed, which differs from the existing ones by taking into account the possibility of the joint control of active and reactive power and the impedance of the power distribution line. The method proposed in this paper allows researchers to determine the slope and width of the dead band of the Volt/Var characteristics according to the criterion of limiting the maximum voltage deviations to an acceptable value or maximizing the reactive power of the inverter upon reaching a specified voltage. To test this method, a quasi-dynamic modeling of the distribution network with electric vehicle charging stations regulating voltage using the Volt/Var characteristics was performed. Based on the modeling results, it is shown that fast electric vehicle charging stations can be used to regulate voltage in the distribution network with relatively minor constraints on the charging active power.

In this work, we investigate the effect of home charging stations for electric vehicles on voltage deviations in a 0.4 kV suburban distribution network. A 10/0.4 kV transformer substation and a 0.4 kV distribution network, supplying electricity to 114 private residential buildings, were selected as the research objects. In order to assess the effect of home charging stations on voltage deviations, a stochastic quasi-dynamic model of the electrical network was developed in the Python programming language using the Pandapower library. This model allows daily profiles of power consumption and voltage to be simulated at various numbers and connection points of home charging stations, taking the random behavior of electric vehicle owners into account. For maintaining the voltage level within the permissible limits, inverters for on-board chargers of electric vehicles in terms of reactive power sources and the shift of the charging start time to the night hours are considered. According to the simulation results, when 30% of 0.4 kV suburban distribution network consumers use a home charging station, the load on the main section of the supply line can briefly approach 100% and the depth of negative voltage deviations can exceed 20%. The Volt-Var control by on-board chargers of electric vehicles was established to reduce significantly voltage deviations in the distribution network (reducing the duration of voltage deviations below −5%, i.e., from 27.3 to 12.9%) with an insignificant effect on the charge duration of electric vehicles. The results obtained can be used in the long-term planning of distribution electric networks in the context of a widespread use of electric vehicles.

THE PURPOSE . The purpose of this study is to conduct a comprehensive analysis of the impact of key parameters of the electric vehicle (EV) fleet and electric vehicle charging infrastructure (EVCI) on electricity consumption and peak power demand in Russia's power system in the medium and long term. In contrast to existing research, the focus is not only on forecasting the number of EVs but also on a scenario-based analysis of a wide range of influencing factors. METHODS . A mathematical model was developed to calculate the electricity consumption and maximum power demand of EVCI for all Russian cities with a population exceeding 50,000. The model accounts for the regional distribution of the EV fleet, its structure and model composition, climatic conditions, EVCI specifications, and behavioral factors. The significance of the factors was assessed using multiple linear regression. RESULTS . It was found that for an EV fleet of 1 million units, the variation in influencing factors can lead to changes in EVCI electricity consumption ranging from 0.14% to 0.62%, and in peak power demand from 0.09% to 2.26% of the 2024 levels of Russia's Unified Energy System (UES). The EVCI parameters have the greatest impact on peak power demand: the number of EVs per charging station (37.1%) and the share of fast chargers (20.8%), along with the share of plug-in hybrid electric vehicles (23.7%). Electricity consumption is most significantly influenced by the share of plug-in hybrids (32.3%), their utilization rate (26.3%), and the share of electric buses in the fleet (21.6%). The forecast up to 2050 shows that EVCI electricity consumption could reach 8.13-11.85%, and peak power demand could reach 11.54- 16.81% of the 2024 levels.