Modeling the Impact of Electric Vehicle Charging Infrastructure on Regional Energy Systems: Fields of Action for an Improved e-Mobility Integration

Since e-Mobility is on the rise worldwide, large charging infrastructure networks are required to satisfy the upcoming charging demand. Planning these networks not only involves different objectives from grid operators, drivers and Charging Station (CS) operators alike but it also underlies spatial and temporal uncertainties of the upcoming charging demand. Here, we aim at showing these uncertainties and assess different levers to enable the integration of e-Mobility. Therefore, we introduce an Agent-based model assessing regional charging demand and infrastructure networks with the interactions between charging infrastructure and electric vehicles. A global sensitivity analysis is applied to derive general guidelines for integrating e-Mobility effectively within a region by considering the grid impact, the economic viability and the Service Quality of the deployed Charging Infrastructure (SQCI). We show that an improved macro-economic framework should enable infrastructure investments across different types of locations such as public, highway and work to utilize cross-locational charging peak reduction effects. Since the height of the residential charging peak depends up to 18% on public charger availability, supporting public charging infrastructure investments especially in highly utilized power grid regions is recommended.

Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review

As a result of environmental pollution and the ever-growing demand for energy, there has been a shift from conventional vehicles towards electric vehicles (EVs). Public acceptance of EVs and their large-scale deployment raises requires a fully operational charging infrastructure. Charging infrastructure planning is an intricate process involving various activities, such as charging station placement, charging demand prediction, and charging scheduling. This planning process involves interactions between power distribution and the road network. The advent of machine learning has made data-driven approaches a viable means for solving charging infrastructure planning problems. Consequently, researchers have started using machine learning techniques to solve the aforementioned problems associated with charging infrastructure planning. This work aims to provide a comprehensive review of the machine learning applications used to solve charging infrastructure planning problems. Furthermore, three case studies on charging station placement and charging demand prediction are presented. This paper is an extension of: Deb, S. (2021, June). Machine Learning for Solving Charging Infrastructure Planning: A Comprehensive Review. In the 2021 5th International Conference on Smart Grid and Smart Cities (ICSGSC) (pp. 16–22). IEEE. I would like to confirm that the paper has been extended by more than 50%.

Electric vehicle charging station planning with dynamic prediction of elastic charging demand: a hybrid particle swarm optimization algorithm

This paper is concerned with the electric vehicle (EV) charging station planning problem based on the dynamic charging demand. Considering the dynamic charging behavior of EV users, a dynamic prediction method of EV charging demand is proposed by analyzing EV users’ travel law via the trip chain approach. In addition, a multi-objective charging station planing problem is formulated to achieve three objectives: (1) maximize the captured charging demands; (2) minimize the total cost of electricity and the time consumed for charging; and (3) minimize the load variance of the power grid. To solve such a problem, a novel method is proposed by combining the hybrid particle swarm optimization (HPSO) algorithm with the entropy-based technique for order preference by similarity to ideal solution (ETOPSIS) method. Specifically, the HPSO algorithm is used to obtain the Pareto solutions, and the ETOPSIS method is employed to determine the optimal scheme. Based on the proposed method, the siting and sizing of the EV charging station can be planned in an optimal way. Finally, the effectiveness of the proposed method is verified via the case study based on a test system composed of an IEEE 33-node distribution system and a 33-node traffic network system.

Electric Vehicles Charging Management System for Optimal Exploitation of Photovoltaic Energy Sources Considering Vehicle-to-Vehicle Mode

The growing penetration of distributed renewable energy sources (RES) together with the increasing number of new electric vehicle (EV) model registrations is playing a significant role in zero-carbon energy communities’ development. However, the ever-larger share of intermittent renewable power plants, combined with the high and uncontrolled aggregate EV charging demand, requires an evolution toward new planning and management paradigms of energy districts. Thus, in this context, this paper proposes novel smart charging (SC) techniques that aim to integrate as much as possible RES generation and EV charging demand at the local level, synergically acting on power flows and avoiding detrimental effects on the electrical power system. To make this possible, a centralized charging management system (CMS) capable of individually modulating each charging power of plugged EVs is presented in this paper. The CMS aims to maximize the charging self-consumption from local RES, flattening the peak power required to the external grid. Moreover, the CMS guarantees an overall good state of charge (SOC) at departure time for all the vehicles without requiring additional energy from the grid even under low RES power availability conditions. Two methods that differ as a function of the EV power flow direction are proposed. The first SC only involves unidirectional power flow, while the second one also considers bidirectional power flow among vehicles, operating in vehicle-to-vehicle (V2V) mode. Finally, simulations, which are presented considering an actual case study, validate the SC effects on a reference scenario consisting of an industrial area having a photovoltaic (PV) plant, non-modulable electrical loads, and EV charging stations (CS). Results are collected and performance improvements by operating the different SC methods are compared and described in detail in this paper.

Comparison and Analysis of GPS Measured Electric Vehicle Charging Demand: The Case of Western Sweden and Seattle

Electrification of transportation using electric vehicles has a large potential to reduce transport related emissions but could potentially cause issues in generation and distribution of electricity. This study uses GPS measured driving patterns from conventional gasoline and diesel cars in western Sweden and Seattle, United States, to estimate and analyze expected charging coincidence assuming these driving patterns were the same for electric vehicles. The results show that the electric vehicle charging power demand in western Sweden and Seattle is 50–183% higher compared to studies that were relying on national household travel surveys in Sweden and United States. The after-coincidence charging power demand from GPS measured driving behavior converges at 1.8 kW or lower for Sweden and at 2.1 kW or lower for the United States The results show that nominal charging power has the largest impact on after-coincidence charging power demand, followed by the vehicle’s electricity consumption and lastly the charging location. We also find that the reduction in charging demand, when charging is moved in time, is largest for few vehicles and reduces as the number of vehicles increase. Our results are important when analyzing the impact from large scale introduction of electric vehicles on electricity distribution and generation.

Day-Ahead Forecast of Electric Vehicle Charging Demand with Deep Neural Networks

The increasing penetration rate of electric vehicles, associated with a growing charging demand, could induce a negative impact on the electric grid, such as higher peak power demand. To support the electric grid, and to anticipate those peaks, a growing interest exists for forecasting the day-ahead charging demand of electric vehicles. This paper proposes the enhancement of a state-of-the-art deep neural network to forecast the day-ahead charging demand of electric vehicles with a time resolution of 15 min. In particular, new features have been added on the neural network in order to improve the forecasting. The forecaster is applied on an important use case of a local charging site of a hospital. The results show that the mean-absolute error (MAE) and root-mean-square error (RMSE) are respectively reduced by 28.8% and 19.22% thanks to the use of calendar and weather features. The main achievement of this research is the possibility to forecast a high stochastic aggregated EV charging demand on a day-ahead horizon with a MAE lower than 1 kW.