The paper proposes using a predictive model to optimize the use of electricity in the V2G (vehicle to grid) service. The novelty of the mechanism as a kind of model predictive control (MPC) is that it seeks an effective way of managing electric energy in an Electric Vehicle (EV). Additionally, it proposes a new method of predicting the electricity consumption which allows the battery of an electric vehicle to reconcile two sides: both the system’s and the user’s demand will be met at the same time. The model allows for very precise determination of the vehicle's demand for the energy related to the progressive movement, taking into account the parameters characteristic of a given vehicle model, its suspension structure and aerodynamics. In addition, the machine learning algorithm was proposed for the prediction model as a hybrid (offline and online) of supervised learning. As the first part of the research, by using Matlab/Simulink/dSpace software, a prediction of EV energy consumption was created on a selected route at different times of the day (offline data matrix). At the same time, the simulated route was travelled by a BMW i3 EV (online data matrix). Based on the developed machine learning algorithm the results of the electric energy consumption were compared. The research confirms that if the correct mechanism for prediction of energy consumption by the EV is used, it is possible to define the amount of energy needed for a V2G service. The measurement error was obtained at 0.5%. The added value is setting up the EV energy security of customers after the V2G service and a correct WIN-WIN relation between the Low Voltage grid and EV customers' needs.

The article is a continuation of the authors’ ongoing research related to power flow and voltage control in LV grids. It outlines how the Distribution System Operator (DSO) can use Machine Learning (ML) technology in a future grid. Based on supervised learning, a Selectively Coherent Model of Converter System Control for an LV grid (SCM_CSC) is proposed. This represents a fresh, new approach to combining off and on-line computing for DSOs, in line with the decarbonisation process. The main kernel of the model is a neural network developed from the initial prediction results generated by regression analysis. For selected PV system operation scenarios, the LV grid of the future dynamically controls the power flow using AC/DC converter circuits for Battery Energy Storage Systems (BESS). The objective function is to maintain the required voltage conditions for high PV generation in an LV grid line area and to minimise power flows to the MV grid. Based on the training and validation data prepared for artificial neural networks (ANN), a Mean Absolute Percentage Error (MAPE) of 0.15% BESS and 0.51–0.55% BESS 1 and BESS 2 were achieved, which represents a prediction error level of 170–300 VA in the specification of the BESS power control. The results are presented for the dynamic control of BESS 1 and BESS 2 using an ANN output and closed-loop PID control including a 2nd order filter. The research work represents a further step in the digital transformation of the energy sector.

A significant challenge for the DSO (Distribution System Operator) will be to choose the optimum strategy for flexibility service in the LV area with high RES (renewable energy sources) penetration. To this end, a representative LV grid operated in Poland was selected for analysis. Three research scenarios with RES generation were presented in the range of 1–8 kW for the power factor from 0.9 to 1. The grid PV capacity was determined for four load profiles. Based on this factor, optimum RES volume management service types were determined. Under the flexibility service, the proposed power conversion services and active RES operations for DSO were proposed. The research was conducted using the Matlab and PowerWorld Simulator environment. Optimum active power values were obtained for the RES generation function for single and dual operation systems of the power conversion system. In future, the knowledge in the field of grid capacity will enable the DSO to increase the operating efficiency of the LV grid. It will enable the optimum use of the RES generation maximisation function and proper strategy selection. It will improve the energy efficiency of the power input through the MV/LV node.

The possibility of charging and possibly discharging electric cars can influence not only the balancing of power demand profiles in the grid and the stabilization of voltage profiles but also the appropriate management of electricity within the grid of an industrial plant equipped with its own RES resources. For this purpose, the concept of “power supply modes” can be introduced, which involves intelligent demand-side management. Each technological process in an industrial plant should be assigned a specific level of importance and priority. These priorities can be numbered according to their importance (weights) and marked with appropriate colors. One thus obtains a qualitative assessment of energy consumption within the plant (demand side) through the lens of power modes. With respect to the ability to charge electric vehicles within the plant grid, such priorities can also be assigned to individual charging options. If a given RES has sufficient generation capacity during a particular time period, the cost of charging is low. However, if the RESs are not operational during a given period (e.g., nighttime in the case of photovoltaics or during calm weather in the case of wind turbines), vehicles can still be charged but according to a different priority, which, of course, involves higher costs. By having access to data on the generation capacity of distributed RESs and knowing the preferences of employees, including the number of electric cars and the expected periods of vehicle charging, it is possible to predict the degree of use of available green energy and manage it efficiently. The analyses presented in the article represent an original approach to the flexibility of operation not only of the electricity grid but also of the internal energy system of industrial plants. It offers a novel perspective aimed at maximizing the share of RESs in the overall energy balance and minimizing the costs associated with the operation of RESs. The theoretical opportunity of sustainable sharing with employees a dedicated charging mode named “free charging”, powered by RESs, could represent an appropriate solution for CO₂ emission reduction within Scope 3, Category 3, “employee commuting”, according to the GHG Protocol requirements. The original methodology proposed in the article aligns with activities related to the energy transition.

The increasing integration of renewable energy sources (RESs) reduces dependence on conventional generators, thereby minimizing the negative environmental impact of fossil fuels. The distributed location of RESs also affects the voltage profiles (voltage values in network nodes) and reduces power losses. The growing number of RESs connected to the network increases the total installed power in the sources in the power system. This contributes to the periodic excess of generated power. It creates the need to limit generation in conventional power plants and to switch off some RESs. This article proposes an original methodology for optimally managing overloads of high-voltage power lines. The combination of the power flow tracking method and metaheuristic optimization allows for the effective elimination of line overloads. The aim of the calculations is to find the optimal power distribution in the selected sources, which provide minimal power limitation. As a result, this means a minimal reduction in the total generation in RESs. In this way, the effect of eliminating line overloads is achieved at the lowest possible cost of power redispatching. On the basis of the IEEE 118 bus test network, computational cases are considered, which are examples of emergency states.