This study focuses on the problem of the efficient energy management of an independent fleet of freight electric vehicles (EVs) providing service to a city multi-floor manufacturing cluster (CMFMC) within a metropolis while considering the requirements of smart sustainable electromobility and the limitations of the power system. The energy efficiency monitoring system is considered an information support tool for the management process. An object-oriented formalization of monitoring information technology is proposed which has a block structure and contains three categories of classes (information acquisition, calculation algorithms, and control procedures). An example of the implementation of the class “Operation with the electrical grid” of information technology is presented. The planning of the freight EVs charging under power limits of the charging station (CS) was carried out using a situational algorithm based on a Fuzzy expert system. The situational algorithm provides for monitoring the charging of a freight EV at a charging station, taking into account the charge weight index (CWI) assigned to it. The optimization of the CS electrical load is carried out from the standpoint of minimizing electricity costs and ensuring the demand for EV charging without going beyond its limits. A computer simulation of the EV charging mode and the CS load was performed. The results of modeling the electrical grid and CS load using the proposed algorithm were compared with the results of modeling using a controlled charging algorithm with electrical grid limitations and an uncontrolled charging algorithm. The proposed approach provides a reduction in power consumption during peak hours of the electrical grid and charging of connected EVs for an on-demand state of charge (SOC).

The new model of the wholesale electricity market in Ukraine causes appearance the market for the day ahead. In this market, the generating company undertakes to supply a certain amount of electricity. So, it is necessary to carry on the most accurate forecast of possible electricity generation by solar power plant (SPP). Generation value depends on certain factors. A brief summary of different influence of parameters on the PV cell performance has been provided. The article analyzes and identifies the factors that should be included in the forecast mathematical model of electricity generation by a solar power plant for a certain short-term period. According to analyzed data from SPP located in the Kyiv region, such parameters are the intensity of solar radiation, temperature and humidity, wind speed, and atmospheric pressure. The degree of influence of these factors on the initial function of electric energy generation were estimated by analyzing the scatter plot diagrams of relationship between parameters and correlation coefficients. Thus, the analysis of the influence of factors on the magnitude of electricity generation allowed to determine the priority of including each of the parameters in the mathematical model of the SPP power forecast. It was established that the influence of certain climate parameters for target function is different in each season. Therefore, in the mathematical model for forecasting electric power generation, it is necessary to take into account seasonality. In addition, the dynamic value change of factors also affects the current magnitude of electricity generation. Moreover, at different times of the year and with different combination of the corresponding values of climatic parameters, this effect may have different magnitudes. Therefore, the data obtained from the last periods before the forecasting should have a greater impact on obtaining the predicted value than the data from previous periods.

In energy systems, it is crucial to forecast solar energy generation for optimization of operations and to mitigate the impact of uncertainty. Forecasting solar energy involves predicting solar irradiance, for which historical solar irradiance and weather parameter data are typically required. However, such data are often unavailable for residential and commercial solar microgrids. This study proposes an hourly forecasting model for next-day Solar Power Production (SPP) that doesn't rely on historical solar irradiance data. The forecasting is performed using deep learning techniques like Long Short-Term Memory (LSTM), and the results are integrated into the MATLAB&Simulink simulation platform. A graphical user interface (GUI) within the MATLAB&Simulink software complex is presented as a simulation platform for hourly SPP forecasting. This platform serves as a useful tool for researchers studying energy management and forecasting as well as planning renewable energy-based energy system operations. The study involves predicting the amount of energy generated by Solar Power Production (SPP). The developed GUI was employed to forecast the SPP output power over test days. The forecasted data were then evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Experimental results demonstrated reasonable accuracy, with the proposed model achieving an RMSE of 0.835 W and an MAE of 0.353 W in certain datasets.

Purpose. The purpose of this research is to increase the efficiency of the charging station, which is to meet the demand of energy for charging the batteries of electric vehicles without exceeding the limits of the grid restrictions. Methodology. The model of a controlled charging system was considered to study the efficiency of charging stations with the quantitative connection of electric vehicles to the charger under conditions of power limitation. Results. The load graph during the controlled charging method does not exceed the set limit voltage and power consumption settings, but with the uncontrolled method there is an overload of 16% during peak hours. The main disadvantage of the conventional method of charging with restrictions is that EVs that have been connected to the charging slots later than other EVs, were not charged in conditions of limited power supply. The charging stations require a charging system that does not have such a disadvantage – it is necessary that almost all connected electric vehicles fill their required level of charge and meet the limitations of the network at the same time. Investigating such a system is the task of future research. Originality. Every year the number of electric vehicles in Ukraine is growing rapidly. This also determines the development of a network of charging stations, both commercial and for domestic use. According to the Rules for the use of electricity, the contract for electricity consumption specifies the allowable capacity for the household or enterprise. Violation of this limit may cause the facility to become disconnected from the power supply with additional penalties. Practical value. The solutions to the problem of charging a large number of EVs are mainly based on different types of planning and creating a profile of their charging scenarios with technical limitations. These restrictions depend on the situation of the charging station (the number of connected EVs, their charging levels, the possibility of simultaneous charging for a few EVs, and the declared charging time). The charging period convenient for the EV owner and the network load schedule may conflict. The development of a realtime charging control system should provide each EV guaranteed charging even in conditions of limited power consumption. References 13, tables 4, figures 18.

Це дослідження фокусується на проблемі підвищення ефективності використання електричних транспортних засобів за рахунок розробки оптимальної системи керування технічним станом акумулятора електромобіля. Як інструмент оптимального керування технічним станом електромобіля розроблена система з квадратичним регулятором та виконано порівнняня ЛКР-регулятора з ПІ-регулятором. Для розробленої систем управління була використона модель простору станів, що дозволило ефектив­но контролювати батарею, аналізувати важливі параметри та приймати оптимальні рішення щодо її заряджання, розряджання та балансування. Процес моделювання в середовищі MATLAB/Simulink дав змогу встановити параметри батареї та порівняти температурні, струмові й напругові параметри з застосуванням різних регуляторів. Унаслідок порівняльного аналізу досліджень виявлено, що LQR-регулятор показав кращі результати щодо стабільності температури, безпеки струму, контрольованого зростання стану заряду та підтримки стабільної напруги порівняно з ПІ-регулятором. Висновки підтверджують, що використання LQR-регулятора ефективніше контролює параметри батареї, сприяє попередженню перегріву, зменшенню ризику пошкодження батареї та підвищенню загальної безпеки й продуктивності роботи батареї в цілому.