The electrification of the transport sector together with large renewable energy deployment requires powerful tools to efficiently use energy assets and infrastructure. In this framework, the forecast of electric vehicle demand and solar photovoltaic (PV) generation plays a fundamental role. This paper studies the impact of forecast accuracy on total electric cost of a simulated electric vehicles (EVs) charging station coupled with true solar PV and stationary battery energy storage. The optimal energy management system is based on the rolling horizon approach implemented in with a mixed integer linear program which takes as input the EV load forecast using long short-term memory (LSTM) neural network and persistence approaches and PV production forecast using a physical hybrid artificial neural network. The energy management system is firstly deployed and validated on an existing multi-good microgrid by achieving a discrepancy of state variables below 10% with respect to offline simulations. Then, eight weeks of simulations from each of the four seasons show that the accuracy of the forecast can increase operational costs by 10% equally distributed between the PV and EV forecasts. Finally, the accuracy of the combined PV and EV forecast matters more than single accuracies: LSTM outperforms persistence to predict the EV load (−30% root mean squared error), though when combined with PV forecast it has higher error (+15%) with corresponding higher operational costs (up to 5%).

Transports is one of the sectors that produce the highest emissions of CO $_2 $ ; in the last ten years, there has been a process of decarbonization which has led to a considerable increase in Electric Vehicles (EVs). However, the sudden introduction of a large number of Electric vehicle supply equipment (EVSE) supplying electrical energy to EVs could cause problems in the management of the electric grid which must cope with the consequent increase in the electrical load demand. In this context, the 24 hour ahead forecast of the power curve associated with the recharge of EVs becomes of vital importance to ensure the reliability of the electric grid. In this paper, different Machine Learning models based on Recurrent Neural Networks (LSTM, GRU) and with different architectures, are compared based on their capability to accurately predict the power curve of an EV charging station one day in advance. A Sequence to Sequence model has been implemented and a thorough analysis of an Attention layer has been detailed. The models are tested on a real world open dataset.

Effective microgrid management necessitates sophisticated strategies to optimally balance grid components and minimize power exchanges with the main grid. Central to this challenge is the energy storage system, typically comprised of lithium-ion batteries, which must operate within specific safety thresholds. Among the different approaches used for battery management in microgrids, model predictive control appears particularly suitable due to its ability to deal with nonlinear systems and constraints. However, the practical deployment of predictive control is often constrained by its substantial computational demands. Notably, achieving high performance typically requires a long prediction horizon, which exacerbates the computational complexity that increases superlinearly with the horizon length. To overcome these limitations, this paper exploits a neural network to approximate the predictive control law, thereby maintaining constant online time complexity regardless of the prediction horizon and facilitating real-time application. This innovative deep learning-based strategy is applied and specifically adapted for the first time to microgrid battery management, incorporating a comparative analysis of several machine learning models to identify the most efficient solution for this application. The results demonstrate that this approach can achieve performance comparable to traditional controllers while ensuring scalability and efficiency. Specifically, the proposed methodology is able to approximate the predictive control action with a mean error of 0.24A and a standard deviation of 2.11A, while reducing the required computational cost by over 200 times when considering a two-day ahead prediction horizon.

The fast increase in Electric Vehicles usage in the last 10 years has raised the need to forecast their energy consumption during charge properly. Lithium-ion batteries have become the major storage component for electric vehicles, avoiding their overcharge can preserve their health and prolong their lifetime. This paper proposes a Machine Learning model based on the K-nearest neighbors classification algorithm for electric vehicle charging session duration forecast. The model forecasts the duration of the charge by assigning the event to its correct class. Each class contains the charging events whose duration is comprised of a certain interval. The only information used by the algorithm is the one available at the beginning of the charging event (arrival time, starting SOC, calendar data). The model is validated on a real-world dataset containing records of charging sessions from more than 100 users, a sensitivity analysis is performed to assess the impact of different information given as input. The effectiveness of the model with respect to the benchmark models is demonstrated with an increase in performance.