A joint model of infrastructure planning and smart charging strategies for shared electric vehicles

This paper presents a data-driven joint model designed to simultaneously deploy and operate infrastructure for shared electric vehicles(SEVs).The model takes into account two prevalent smart charging strategies:the Time-of-Use(TOU)tariff and Vehicle-to-Grid(V2G)technology.We specifically quantify infrastructural demand and simulate the travel and charging behaviors of SEV users,utilizing spatiotemporal and behavioral data extracted from a SEV trajectory dataset.Our findings indicate that the most cost-effective strategy is to deploy slow chargers exclusively at rental stations.For SEV operators,the use of TOU and V2G strategies could potentially reduce charging costs by 17.93%and 34.97%respectively.In the scenarios with V2G applied,the average discharging demand is 2.15kWh per day per SEV,which accounts for 42.02%of the actual average charging demand of SEVs.These findings are anticipated to provide valuable insights for SEV operators and electricity companies in their infrastructure investment decisions and policy formulation.

Assessment of Technical and Economic Impacts of EV User Behavior on EV Aggregator Smart Charging

The increase in global electricity consumption has made energy efficiency a priority for governments.Consequently,there has been a focus on the efficient integration of a massive penetration of electric vehicles(EVs)into energy markets.This study presents an assessment of various strategies for EV aggregators.In this analysis,the smart charging methodology proposed in a previous study is considered.The smart charging technique employs charging power rate modulation and considers user preferences.To adopt several strategies,this study simulates the effect of these actions in a case study of a distribution system from the city of Quito,Ecuador.Different actions are simulated,and the EV aggregator costs and technical conditions are evaluated.

A Smart Charging Algorithm Based on a Fast Charging Station Without Energy Storage System

With the growing popularity of electric vehicles(EV),there is an urgent demand to solve the stress placed on grids caused by the irregular and frequent access of EVs.The traditional direct current(DC)fast charging station(FCS)based on a photovoltaic(PV)system can effectively alleviate the stress of the grid and carbon emission,but the high cost of the energy storage system(ESS)and the under utilization of the grid-connected interlinking converters(GIC)are not very well addressed.In this paper,the DC FCS architecture based on a PV system and ESS-free is first proposed and employed to reduce the cost.Moreover,the proposed smart charging algorithm(SCA)can fully coordinate the source/load properties of the grid and EVs to achieve the maximum power output of the PV system and high utilization rate of GICs in the absence of ESS support for FCS.SCA contains a self-regulated algorithm(SRA)for EVs and a grid-regulated algorithm(GRA)for GICs.While the DC bus voltage change caused by power fluctuations does not exceed the set threshold,SRA readjusts the charging power of each EV through the status of the charging(SOC)feedback of the EV,which can ensure the power rebalancing of the FCS.The GRA would participate in the adjustment process once the DC bus voltage is beyond the set threshold range.Under the condition of ensuring the charging power of all EVs,a GRA based on adaptive droop control can improve the utilization rate of GICs.At last,the simulation and experimental results are provided to verify the effectiveness of the proposed SCA.

Data-driven smart charging for heterogeneous electric vehicle fleets

The ongoing electrification of mobility comes with the challenge of charging electric vehicles(EVs)sufficiently while charging infrastructure capacities are limited.Smart charging algorithms produce charge plans for indi-vidual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a fleet.In practice,EV charging processes follow nonlinear charge profiles such as constant-current,constant-voltage(CCCV).Smart charging must consider charge profiles in order to avoid gaps between charge plans and actual EV power con-sumption.Generally valid models of charge profiles and their parameters for a diverse set of EVs are not publicly available.In this work we propose a data-driven approach for integrating a machine learning model to pre-dict arbitrary charge profiles into a smart charging algorithm.We train machine learning models with a dataset consisting of charging processes from the workplace gathered in 2016–2018 from a heterogeneous EV fleet of 1001 EVs with 18 unique models.Each charging process includes the time series of charging power.After pre-processing,the dataset contains 10.595 charging processes leading to 1.2 million data points in total.We then compare different machine learning models for charge profile predictions finding that XGBoost yields the most accurate predictions with a mean absolute error(MAE)of 126W and a relative MAE of 0.06.Simulations show that smart charging with the integrated XGBoost model leads to a more effective infrastructure usage with up to 21%more energy charged compared to smart charging without considering charge profiles.Furthermore,an ablation study on regression model features shows the EV’s model is not a necessary attribute for accurate charge profile predictions.However,charging features are required including the number of phases used for charging.

Review on Optimization of Forecasting and Coordination Strategies for Electric Vehicle Charging

The rapid development of electric vehicles(EVs)has benefited from the fact that more and more countries or regions have begun to attach importance to clean energy and environmental protection.This paper focuses on the optimization of EV charging,which cannot be ignored in the rapid development of EVs.The increase in the penetration of EVs will generate new electrical loads during the charging process,which will bring new challenges to local power systems.Moreover,the uncoordinated charging of EVs may increase the peak-to-valley difference in the load,aggravate harmonic distortions,and affect auxiliary services.To stabilize the operations of power grids,many studies have been carried out to optimize EV charging.This paper reviews these studies from two aspects:EV charging forecasting and coordinated EV charging strategies.Comparative analyses are carried out to identify the advantages and disadvantages of different methods or models.At the end of this paper,recommendations are given to address the challenges of EV charging and associated charging strategies.

A novel forecasting approach to schedule aggregated electric vehicle charging

To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging sessions,their charging demand and arrival and departure times.The use of forecasting techniques can reduce the uncertainty about these charging session characteristics,but since these characteristics are interrelated,this is not straightforward.Remarkably,forecasting frameworks that cover all required characteristics to schedule the charging of an electric vehicle fleet are absent in scientific literature.To cover this gap,this study proposes a novel approach for forecasting the charging requirements of an electric vehicle fleet,which can be used as input to schedule their aggregated charging demand.In the first step of this approach,the charging session characteristics of an electric vehicle fleet are translated to three parameter values that describe a virtual battery.Subsequently,optimal predictor variable and hyperparameter sets are determined.These serve as input for the last step,in which the virtual battery parameter values are forecasted.The approach has been tested on a real-world case study of public charging stations,considering a high number of predictor variables and different forecasting models(Multivariate Linear Regression,Random Forest,Artificial Neural Network and k-Nearest Neighbors).The results show that the different virtual battery parameters can be forecasted with high accuracy,reaching R^(2) scores up to 0.98 when considering 400 charging stations.In addition,the results indicate that the forecasting performance of all considered models is somehow similar and that only a low number of predictor variables are required to adequately forecast aggregated electric vehicle charging characteristics.

A Review on Electric Vehicle Charging Infrastructure Development in the UK

This paper focuses on the development of electric vehicle(EV)charging infrastructure in the UK,which is a vital part of the delivering ultra-low-emission vehicle(ULEV)and will transition into low emission energy systems in the near future.Following a brief introduction to global landscape of EV and its infrastructure,this paper presents the EV development in the UK.It then unveils the government policy in recent years,charging equipment protocols or standards,and existing EV charging facilities.Circuit topologies of charging infrastructure are reviewed.Next,three important factors to be considered in a typical site,i.e.,design,location and cost,are discussed in detail.Furthermore,the management and operation of charging infrastructure including different types of business models are summarized.Last but not least,challenges and future trends are discussed.

A hybrid policy gradient and rule-based control framework for electric vehicle charging

Recent years have seen a significant increase in the adoption of electric vehicles,and investments in electric vehicle charging infrastructure and rooftop photo-voltaic installations.The ability to delay electric vehicle charging provides inherent flexibility that can be used to compensate for the intermittency of photo-voltaic generation and optimize against fluctuating electricity prices.Exploiting this flexibility,however,requires smart control algorithms capable of handling uncertainties from photo-voltaic generation,electric vehicle energy demand and user’s behaviour.This paper proposes a control framework combining the advantages of reinforcement learning and rule-based control to coordinate the charging of a fleet of electric vehicles in an office building.The control objective is to maximize self-consumption of locally generated electricity and consequently,minimize the electricity cost of electric vehicle charging.The performance of the proposed framework is evaluated on a real-world data set from EnergyVille,a Belgian research institute.Simulation results show that the proposed control framework achieves a 62.5%electricity cost reduction compared to a business-as-usual or passive charging strategy.In addition,only a 5%performance gap is achieved in comparison to a theoretical near-optimal strategy that assumes perfect knowledge on the required energy and user behaviour of each electric vehicle.

Microgrid economic operation considering plug-in hybrid electric vehicles integration

In this paper,the microgrid economic scheduling mathematical model considering the integration of plug-in hybrid electric vehicles(PHEVs)is presented and the influence of different charging and discharging modes on microgrid economic operation is analyzed.The generic algorithm is used to find an economically optimal solution for the microgrid and PHEV owners.The scheduling of PHEVs and the microgrid are optimized to reduce daily electricity cost and the potential benefits of controlled charging/discharging are explored systematically.Constraints caused by vehicle utilization as well as technical limitations of distributed generation and energy storage system are taken into account.The proposed economic scheduling is evaluated through a simulation by using a typical grid-connected microgrid model.