The methods of artificial intelligence (AI) have been used in the planning and operation of electric power systems for more than 40 years. In recent years, due to the development of microprocessor and data storage technologies, the effectiveness of this use has greatly increased. This paper provides a systematic overview of the application of AI, including the use of machine learning (ML) in the electric power system. The potential application areas are divided into four blocks and the classification matrix has been used for clustering the AI application tasks. Furthermore, the data acquisition methods for setting the parameters of AI and ML algorithms are presented and discussed in a systematic way, considering the supervised and unsupervised learning methods. Based on this, three complex application examples, being wind power generation forecasting, smart grid security assessment (using two methods), and automatic system fault detection are presented and discussed in detail. A summary and outlook conclude the paper.

The number of large energy storage units installed in the power system has increased over the last few years. This fact remains closely linked to the increase in the share of renewable energy in electricity generation. This is necessary to maintain the stability of the grid, which is becoming increasingly difficult to maintain due to the growing number of renewable energy sources (RES). Energy production from these sources is difficult to estimate, and possible unplanned shortages and surpluses in production are the cause of voltage and frequency fluctuations, which is an undesirable state. Consequently, the use of energy storage not only contributes to the regulation of grid operation but can also, under appropriate conditions, constitute an additional load if too much energy is generated by RES, or the source when the generation from RES is insufficient. The main contributions of this paper are as follows: A presentation of practical results achieved by implementing two optimal control strategies for a 1 MW (0.5 MWh) battery energy storage (BES) cooperating with a large 144 MW photovoltaic farm. In the first case, the BES was used to generate curtailment at photovoltaic farm to avoid power grid overload. The second case focuses on maximizing profits from selling the energy produced in periods when the unit price for energy was the highest according to energy market forecasts. In both cases, the storage was used simultaneously to cover the producer’s own demand, which eliminated the costs associated with the purchase of energy from the operator, especially during the night supply. A technical and economic evaluation was prepared for both cases, considering the real profits from the investment. The potential of using the BES to increase the functionality of photovoltaic energy sources was determined and discussed in the paper.

An important aspect of the off-grid utilization of hybrid generation systems is the integration of energy storage facilities into their structures, which allows for improved power supply reliability. However, this results in a significant increase in the cost of such systems. Therefore, it is justified to use optimization resulting in the minimization of the selected economic indicator taking into account the most important technical constraints. For this reason, this work proposes an algorithm to optimize the structure of a hybrid off-grid power distribution system (with electrochemical energy storage), designed to supply a load with known daily energy demand. The authors recommend genetic algorithm utilization as well as a modified criterion for evaluating the quality of solutions based on the Levelized Cost of Energy (LCOE) index. Several technical and economic analyses were presented, including unit costs, power distribution of the wind and solar sections, nominal battery capacity, SSSI index (System Self-Sufficiency Index), etc. The model of the system includes durability of the elements which have a significant impact on the periodic battery replacement. The tests were carried out for two types of loads and two types of electrochemical batteries (NMC—Lithium Nickel Manganese Cobalt Oxide; and PbO₂—Lead-Acid Battery), taking into account the forecast of an increased lifetime of NMC type batteries and decreasing their price within five years. The proposed synthesis method of photovoltaic-wind (PV-wind) hybrid off-line systems leads to limiting the energy capacity of electrochemical storages. Based on the analyses, the authors proposed recommended methods to improve (reduce) the value of the criterion index (LCOE) for PV-wind off-grid systems while maintaining the assumed level of power supply reliability.

Transforming the transport sector to zero emission is an integral part of changes to the energy sector worldwide. This effects not only the electrification of the private sector but also the commercial sector. The aim of this study is to develop methodologies, algorithms and associated requirements for the integration of electric vehicles into a logistics application with a possible reduction in operating costs. The most favorable solution for a company was evaluated using the analytic hierarchy process algorithm considering three main aspects: economic, environmental and technical. An analysis of the environmental impact of the vehicle fleet in terms of atmospheric emissions was also conducted, based on the data available for combustion and electric vehicles, considering the well-to-tank approach. The costs associated with operating an electric vehicle were identified and compared to the current costs associated with operating a standard diesel-based fleet. Incorporating the identified costs of electrifying the vehicle fleet, an algorithm was implemented to reduce the number of vehicles in the company and, thereby, significantly reducing the costs associated with fleet maintenance.

This article describes the analyses of modeling the wear process of lithium-nickel-manganese-cobalt cells operating cyclically under constant load conditions. The main aging processes taking place in cells and the methodology of the modeling are discussed. The process of cell wear is examined, taking into account the influence of cyclic operating parameters (temperature, discharge current, and discharge depth). On the basis of the analyses carried out, a new function reflecting the influence of ambient temperature on the durability of the cell is proposed. A new fuzzy model of the wear process of the NMC 18650 type cell, depending on the parameters of the discharge half cycle, has been developed. The results of the model have been verified by experimental research.

The intensive electrification of the automotive sector means that the energy system must be able to adapt to the current market situation. The increase in energy demand is a major factor associated with electric vehicles. The study analyzed the operation of a grid-connected facility operating a vehicle fleet providing transport services in the region Halle/Saale, Germany. Measurement data were used in the analysis, including global positioning system data of the vehicles and technical data, including average fuel consumption on a given route section, daily load demand of the industrial facility, and energy generation from photovoltaics. This paper shows the impact of using a battery electric vehicles (BEVs) fleet in the load distribution for the industrial facility considered. The NEDC energy consumption profile for the Nissan e-NV200 were used in this study. Furthermore, the paper presented simulation results allowing one to determine the usage potential, energy demand, and consumption of EVs using real data, reliably representing the processes related to EV daily use. The measurement data were captured using available specialized equipment: Dako-Key (GPS data), PV power generation (Siemens 7KM PAC4200), and load (Janitza UMG 604-Pro) in September, 2018. On this basis, it is possible to identify the effects and variations in load on the power grid during the replacement of combustion vehicle fleets used currently by EVs for the provision of transport services. Three models were presented, making it possible to calculate changes in energy demand for each scenario. In the first model, EVs were charged exclusively from the distribution network. In the second, the energy generation from a renewable source was considered and the possibility of compensating the energy demand of the vehicles from this source was demonstrated. In the third model, the daily load profile and the period of maximum load in the electricity grid were considered. The results are presented in graphical and tabular form. Finally, the potential of using an EV fleet to increase the functionality of a modern industry object was determined and discussed. Based on data for the adopted scenarios, electrification of transport can increase demand for energy by 40.9% for individual enterprises. The electrification of the automotive sector will increase the instantaneous energy demand of businesses, forcing the integration of renewable energy sources during designing new invests.