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.

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 DOS 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 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.

The paper proposes the use of auxiliary equipment in the low voltage network: an on-load tap changer and a static synchronous compensator (STATCOM) to improve the quality of energy supply to end users. As part of the research, a section of medium and low voltage power grid was modelled using Matlab & Simulink software, which was tested in three scenarios. The first scenario presents the operation of the power grid with the on-load tap changer installed in the transformer block. The second scenario uses the STATCOM for local reactive power compensation. Additionally, the third scenario is the combined work of the on-load tap-changer along with the STATCOM. According to the authors, the method discussed does not bring the expected results in the area of voltage quality improvement, indicating that further research is required, including tests with energy storage.