This paper develops a P-HIL model to analyze the real-world impact of XFC charging profiles on the grid frequency. Using charging profiles captured for different EVs and E-trucks at different start SoC and temperature conditions, a database of 93 profiles was used to develop a MW-level charging site scenario. The scenario envisions a charging site with EVSE usage that is mixed for fleet charging at a small business with public charging availability when fleet charging is undesired. A statistical study of 1446 DCFC and XFC charging sessions was conducted to identify start and end SoCs and develop the expected load profiles. Two real-world XFCs and BESS have been connected to a real-time digitally simulated modified IEEE 37-bus distribution grid and run on an Opal-RT real-time digital simulator. Several communication protocols, including UDP, Modbus, OCPP, and TCP have been employed in the associated MQTT IoT environment. This model can be used to design the optimal control strategy that could determine the amount of injected or absorbed active power from the co-located BESS to mitigate the effect of running a large number of XFCs clusters on the grid frequency and rate of change of frequency.

Extreme Fast Charging (XFC) infrastructure is crucial for an increase in electric vehicle (EV) adoption. However, an unmanaged implementation may lead to negative grid impacts and huge power costs. This paper presents an optimal energy management strategy to utilize grid-connected Energy Storage Systems (ESS) integrated with XFC stations to mitigate these grid impacts and peak demand charges. To achieve this goal, an algorithm that controls the charge and discharge of ESS based on an optimal power threshold is developed. The optimal power threshold is determined to carry out maximum peak shaving for given battery size and SOC constraints. To validate the effectiveness of the developed strategies and algorithms at the distribution network level, real-time power simulations are performed with a modified IEEE 37-bus test feeder model and loads, including a real-world energy plaza at Argonne National Laboratory (ANL), 4 XFC-ESS sets, 4 commercial and 6 workplace nodes with both level 2 and XFC charging. 3 commercial, 2 workplace, and 8 residential nodes with only level 2 charging. The grid simulates a total of 83 XFC and 300 level 2 stations. To realistically estimate the charging power demand of ANL XFC stations a statistical approach using the probability distribution model of the ANL historical dataset is employed. Unlike other studies, the scope of the paper is not just limited to the simulation study, but it discusses and compares the results with the experimental testing performed using real-time communication with two sets of XFC and ESS at ANL. Furthermore, the peak shaving threshold determination discussed in this paper works irrespective of the generic profile, it considers both charging and discharging of the ESS. The simulation results demonstrate the effectiveness of the presented algorithms to improve voltage distortion by carrying out maximum peak shaving for given battery size and SOC constraints.

The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems. The subsystems can communicate with each other via message queuing telemetry transport communication (MQTT) protocol. The OPAL-RT is used to perform grid simulation and optimal EV charging energy management at the distribution grid level. It communicates with node level EVCSS and the SEP to collect real-time charging data and send charging power commands. The OPAL-RT can also communicate with transmission level controllers to provide grid services, such as frequency regulation. The EVCSS manages regional EV charging to limit the effects of clustered EV charging on the distribution grid. It uses standardized communication protocols: Open Charge Point Protocol 2.0 for charging station networks and ISO 15118 between EVs and charging stations. The modular open systems design approach of the platform allows the integration of EV charging control algorithms and hardware charging systems for performance evaluation and interoperability testing. The experimental test results show that the communication links of the platform work properly, and the EV charging control algorithms can respond to transmission level grid service request with minimal impact on local operations.

During fiscal year 2019 (FY 2019), the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) funded early stage research & development (R&D) projects that address Batteries and Electrification of the U.S. transportation sector. The VTO Electrification Sub-Program is composed of Electric Drive Technologies, and Grid Integration activities. The Electric Drive Technologies group conducts R&D projects that advance electric motors and power electronics technologies. The Grid and Charging Infrastructure group conducts R&D projects that advance grid modernization and electric vehicle charging technologies. This document presents a brief overview of the Electrification Sub-Program and progress reports for its R&D projects. Each of the progress reports provide a project overview and highlights of the technical results that were accomplished in FY 2019.

Real-Time simulation and Hardware-in-the-Loop (HIL) testing are increasingly adopted by industry for the development and validation of complex systems. This paper presents the real-time modeling and power management of a Vehicle-Grid Integration (VGI) system. The VGI system consists of six AC level 2 Plug-in Electric Vehicle (PEV) charging stations, a Photovoltaics (PV) farm, a commercial building load, and a switch connecting to 240V single phase power grid. PEV charging activities follow the SAE J1772 standard. An energy management algorithm is designed for the VGI system to coordinate the PEV charging with the building load and PV renewable generation. The coordination maintains the power consumption of the VGI system below utility’s demand charge pricing threshold. A real-time power system simulator, Opal-RT, is used in this study. The OPAL-RT system allows users to build detailed power system models using Matlab Simulink/SimPowerSystems and RT-LAB library, and run the models in real-time. The model-based approach enables the integration of power system models seamlessly with the power management algorithm and power electronics-level controllers. The simulation results show that the VGI model emulates the real system well and the coordinated PEV charging helps to balance the power generation and consumption of the VGI system to meet power management requirement.

l reference architecture that relates five interoperability layers with the two-dimensional smart grid plane. The interoperability dimension defines five abstract interoperability layers: Business, Function, Information, Communication and Component. The two­ dimensional smart grid plane defines smart grid components and subsystems from electrical process and information management viewpoints. One dimension covers the complete electrical energy conversion chain: Bulk Generation, Transmission, Distribution, DER and Customer's Premises, while the other dimension represents the hierarchical levels of power system management: Process, Field, Station, Operation, Enterprise and Market.

This paper studies what are needed to enable the standardization of Vehicle Grid Integration (VGI). The requirements of interoperable VGI are examined at multiple interoperability layers defined by reference architecture models, including European Commission's Mandate 490 (EU-M490), National Institute of Standards and Technology (NIST) Smart Grid Architectural Methodology (SGAM), and the Institute of Electrical and Electronic Engineers (IEEE) 2030 Smart Grid Interoperability Reference Model (SGIRM). The current status of standards and technology development is reviewed and VGI demonstrations are discussed. The paper identifies barriers for the implementation of an interoperable VGI and provides recommendations to address these challenges.

The purpose of this paper is to outline the development and implementation of SAE J2953. SAE J2953 contains the requirements and procedures of interoperability testing. Within SAE J2953 interoperability test articles are defined as an Electric Vehicle Supply Equipment (EVSE) paired with a Plug-in Electric Vehicle (PEV). SAE J2953 requires the development and application of test fixtures with the ability to monitor mechanical forces and electrical signals of a charge system without modification or disassembly of the EVSE and PEV under test. Electrical signal monitoring includes pilot, proximity, and line conductors of the SAE J1772 TM AC coupler. This paper will outline the requirements of the fixtures as well as a specific build. Data will be presented showing full implementation of the SAE J2953 procedures including root-cause analysis and standards gap discovery.