In this paper, a vehicle–grid integration (VGI) control strategy for radial power distribution networks is presented. The control schemes are designed at both microgrid level and distribution level. At the VGI microgrid level, the available power capacity for electric vehicle (EV) charging is optimally allocated for charging electric vehicles to meet charging requirements. At the distribution grid level, a distributed voltage compensation algorithm is designed to recover voltage violation when it happens at a distribution node. The voltage compensation is achieved through a negotiation between the grid-level agent and VGI microgrid agents using the alternating direction method of multipliers. In each negotiation round, individual agents pursue their own objectives. The computation can be carried out in parallel for each agent. The presented VGI control schemes are simulated and verified in a modified IEEE 37 bus distribution system. The simulation results are presented to show the effectiveness of the VGI control algorithms and the effect of algorithm parameters on the convergence of agent negotiation.
A distributed electric vehicle (EV) charging scheduling strategy with transactive energy (TE) management is presented in this paper to deal with technical issues in distribution network operation and discuss the economic benefits of EV charging. At an individual EV level, EV owners propose bids to actively participate in the distribution system operation. At the node level, an electric vehicle aggregator (EVA) optimally allocates the available charging power to meet EV charging requirements and cost benefits. At the distribution network level, a distribution system operator (DSO) integrates an electricity price market clearing mechanism with the optimal power flow (OPF) technique to ensure the reliability of the distribution network. Moreover, a distributed algorithm is discussed for solving the EV charging problem with transactive energy management (TEM). The clearing electricity price is achieved through a negotiation process between the DSO and EVAs using the alternating direction method of multipliers (ADMM). The presented EV charging scheduling with TEM is tested on a modified IEEE 33-bus distribution network scenario with 230 EV charging loads. The simulation results demonstrate the effectiveness of the TE-based EV charging scheduling system.
Extreme fast charging (XFC) for electric vehicles (EVs) has emerged recently because of the short charging period. However, the extreme high charging power of EVs at XFC stations may severely impact distribution networks. This paper addresses the estimation of the charging power demand of XFC stations and the design of multiple XFC stations with renewable energy resources in current distribution networks. First, a Monte Carlo (MC) simulation tool was created utilizing the EV arrival time and state-of-charge (SOC) distributions obtained from the dataset of vehicle travel surveys. Various impact factors are considered to obtain a realistic estimation of the charging power demand of XFC stations. Then, a method for determining the optimal energy capacity of the energy storage system (ESS), ESS rated power, and size of photovoltaic (PV) panels for multiple XFC stations in a distribution network is presented, with the goal of achieving an optimal configuration. The optimal power flow technique is applied to this optimization so that the optimal solutions meet not only the charging demand but also the operational constraints related to XFC, ESS, PV panels, and distribution networks. Simulation results of a use case indicate that the presented MC simulation can estimate approximate real-world XFC charging demand, and the optimized ESS and PV units in multiple XFC stations in the distribution network can reduce the annual total cost of XFC stations and improve the performance of the distribution network.
<div class="section abstract"><div class="htmlview paragraph">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.</div></div>
<div class="section abstract"><div class="htmlview paragraph">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.</div></div>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.