Distribution System V1G PEV Charging Impacts Report

An empirically-validated methodology to simulate electricity demand for electric vehicle charging

This paper presents a model predictive control (MPC) for off-board plug-in electric vehicle (PEV) chargers with photovoltaic (PV) integration using two-level four-leg inverter topology. The PEV charger is controlled by a unified controller that incorporates direct power and current MPC to dynamically control decoupled active-reactive power flow in a smart grid environment as well as to control PEV battery charging and discharging reliably. PV power generation with maximum power tracking is seamlessly integrated with the power flow control to provide additional power generation. Fast dynamic response and good steady-state performance under all power flow modes and various environmental conditions are evaluated and analyzed. From the results obtained, the charger demonstrates less than 1.5% total harmonic distortion as well as low active and reactive power ripple of less than 7% and 8% respectively on the grid for all power flow modes. The PEV battery also experiences a low charging and discharging current ripple of less than 2.5%. Therefore, the results indicate the successful implementation of the proposed charger and its control for PV integrated off-board PEV chargers.

Real-time charging coordination of plug-in electric vehicles based on hybrid fuzzy discrete particle swarm optimization

Plug-in electric vehicles (PEVs) represent economic and eco-friendly means of transportation. High PEV penetration levels are expected during 2020 – 2050. Consequently, a considerable portion of the total power consumption in the future smart grid will be due to the PEV charging demands. Therefore, PEV charging has drawn significant attention from both academia and industry. In this paper, we shed some light on key research issues related to PEV charging in the future smart grid. Specifically, we discuss the challenges related to charging facility planning and PEV charging coordination for both mobile and parked vehicles. In addition, we summarize our main research results related to the aforementioned topics. Finally, future research directions are provided.

Smart grids have been introduced to replace conventional power distribution systems without real time monitoring for accommodating the future market penetration of plug-in electric vehicles (PEVs). When a large number of PEVs require simultaneous battery charging, charging coordination techniques have become one of the most critical factors to optimize the PEV charging performance and the conventional distribution system. In this case, considerable computational complexity of a central controller and exchange of real time information among PEVs may occur. To alleviate these problems, a novel threshold-based random charging (TBRC) operation for a decentralized charging system is proposed. Using PEV charging thresholds and random access rates, the PEVs themselves can participate in the charging requests. As PEVs with a high battery state do not transmit the charging requests to the central controller, the complexity of the central controller decreases due to the reduction of the charging requests. In addition, both the charging threshold and the random access rate are statistically calculated based on the average of supply power of the PEV charging system that do not require a real time update. By using the proposed TBRC with a tolerable PEV charging degradation, a 51% reduction of the PEV charging requests is achieved.

Charging stations are critical infrastructure for the integration of plug-in electric vehicles (PEVs) in the future distribution systems. With a steadily increasing PEV penetration level, the PEV charging demands of charging stations are expected to constitute a significant portion of the total electric power demands. An accurate estimation of PEV charging demands is crucial for the planning and operation of future distribution systems. However, the estimation remains a challenging issue, as the charging demands of nearby charging stations are closely correlated to each other and depend on vehicle drivers' response to charging prices. The evaluation of charging demands is further complicated by the highly dynamic vehicle mobility, which results in random PEV arrivals and departures. In order to address these challenges, a BCMP queueing network model is presented in this paper, in which each charging station is modeled as a service center with multiple servers (chargers) and PEVs are modeled as the customers in the service centers. Based on the stationary distribution of the number of PEVs in each charging station, the statistics of PEV charging demands can be obtained. The analytical model is validated by a case study based on realistic vehicle statistics extracted from 2009 National Household Travel Survey and New York State Transportation Federation Traffic Data Viewer.

Coordinated charging of Plug-In Electric Vehicles (PEVs) in residential distribution systems is a new concept currently being explored in the wake of smart grids. Utilities are exploring these options as there are concerns about potential stresses and network congestions that may occur with random and uncoordinated multiple domestic PEV charging activities. Such operations may lead to degraded power quality, poor voltage profiles, overloads in transformer and cables, increased power losses and overall a reduction in the reliability and economy of smart grids. Future smart grids communication network will play an important role in PEV operation because the battery chargers can be remotely coordinated by the utility and harnessed for storing surplus grid energy and reused to support the grid during peak times. Based on a recently proposed PEV charging algorithm, this paper focuses on the impact of coordinated charging on distribution transformer loading and performance. Simulation results are presented to explore the ability of the PEV coordination algorithm in reducing the stress on distribution transformers at different PEV penetration levels. The performance of various distribution transformers within the simulated smart grid is examined for a modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs.

The massive and random charging activities by increasing penetration of the plug-in electric vehicles (PEVs) shall bring significant peak load in the near future. And the massive Greenhouse Gas (GHG) emission caused by electricity generation also brings challenges to the environment. And the charging management mechanism therefore is the key to charging PEVs with low GHG emission and help utility operators optimize generation strategy. The paper firstly develops an hourly dispatch model to simulate GHG emissions caused by electricity generations, and an access network structure composing submeters and smart meter mesh network is proposed. Then a novel coordinated charging management mechanism using queuing theory is implemented to minimize the PEV charging load impact to the electricity grid. To assess the mechanism performance, the paper constructs an hourly PEV charging model to simulate the grid impact and GHG emissions. And the two detailed and comprehensive scenarios with incoordinated and coordinated charging mechanism are simulated while key parameters are generated, analyzed, and approved. Simulation results show that the coordinated charging mechanism works efficiently on reducing the grid peak load, improving the utility utilization, and reducing GHG emission. Study findings in this paper will help support informed decision-making regarding PEV development and deployment in support of national energy and environmental policy.

Plug-in electric vehicles (PEVs) could provide valuable ancillary service for power systems with intermittent renewables. This paper proposes a charging model of a large amount of PEVs to mitigate the high-level wind power fluctuations in a distribution network so as to regulate system frequency. With the prerequisite that all PEV daily driving patterns are completely satisfied, the PEV charging power is economically allocated to counterbalance the wind generation intermittency. Afterward, a center-free control scheme based on the consensus algorithm is designed for PEVs to share the fluctuating wind generation in a fully distributed manner. The scheme is robust and flexible to practical PEV charging behaviors including arrival and departure time, initial and desired SOC, as well as the frequently changed departure time. Comprehensive simulations on a distribution system with coal and diesel generators, several wind farms and 2000 PEVs demonstrate that the proposed PEV charging scheme could effectively regulate the system frequency in the real time while the PEV users’ charging requirements could be flexibly satisfied.

This article presents the performance analysis of future Provincial Electricity Authority of Thailand (PEA) distribution networks under high penetration of plug‐in electric vehicle (PEV) home charging using the Monte Carlo method. Network performance indices considered in this study are the voltage profile, power losses, load factor, and voltage unbalance. The voltage profile and power losses are evaluated based on the PEA criteria. In addition, the voltage unbalance factors (%VUF) are evaluated according to the IEC 61000‐2‐4: 2000‐06 standards. A selected PEA distribution network based on the existing data is used for the simulation study with the DIgSILENT PowerFactory. The penetration of PEV home charging at each node and phase is assigned using the Monte Carlo method, which in particular is used to randomly generate patterns of PEV charging behaviors. The simulation study presents five cases; the average points of charge, max‐to‐min charge, min‐to‐max charge, central feeder charge, and random point of charge. All cases are compared with a base case (without PEVs). Charging scenarios at phases ‘a’, ‘b’, and ‘c’ are assumed having overlapped time duration. The PEV charging duration per phase is assumed 6 h. Simulation results indicate that PEV charging time, charging point, and penetration levels at each phase and node are significant factors impacting the network performance. Voltage profiles slightly drop in all cases, meanwhile the average voltage drop increases by 7% compared with the base case; case 3 has the maximum voltage drop, which is deeper than PEA criteria. The power losses increase in all cases. The average power losses have increased by 103.52%. The %VUF increases gradually with the length of the feeder. The maximum %VUF occurs at the end of the feeder for all cases and is higher than 2.0% of the IEC standard. On the other hand, PEV charging improves the load factor. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

This research tests an implicit assumption about plug-in electric vehicle (PEV) charging infrastructure: people—including people not interested in PEVs—see public PEV charging. Data from a survey of all car-owning households in California are combined with data on public PEV charging and PEV registrations to estimate a structural equation model to test the direct and indirect effects of public charging density on whether participants see charging infrastructure and whether they have considered acquiring a PEV. We also explore the direct and indirect effects of seeing public charging on PEV purchase consideration. Results show the density of public charging is neither directly nor indirectly related to whether people see public charging or their level of PEV purchase consideration. Our results suggests that for public charging to drive PEV sales growth, it may need to be accompanied by strategies to increase consumers’ engagement with PEVs.

Due to the economic and environmental benefits, it is envisaged that many residential customers will increasingly utilize rooftop photovoltaic (PV) systems and plug-in electric vehicles (PEVs). However, the intermittent nature of PVs and the additional loads required for the PEV charging can increase the power quality issues in the low voltage distribution networks (LVDN). In particular, the surplus PV produced power during low load and the random connection of PEVs in addition to the asymmetrical loading levels of residential consumers can aggravate the voltage quality in LVDNs. In this paper, different voltage parameters for the LVDNs with installed rooftop PVs and PEVs are assessed. Further, a novel control strategy is proposed for the domestic management of the PEV charging and the efficacious utilization of reactive power for maintaining the voltage variations within the allowable limits. The simulation results demonstrate the impacts of the integration of PVs and PEVs on the voltage quality and confirm the effectiveness of the proposed strategy to mitigate the voltage variations in LVDNs.

Demand management to mitigate impacts of plug-in electric vehicle fast charge in buildings with renewables

Plug-in hybrid electric vehicles and smart grids: Investigations based on a microsimulation

Plug-in Electric Vehicles (PEV) integration into innovative smart grids has been studied widely. Recently, special attention is paid to the role of the aggregation agent, which might be responsible of controlling the charge schedules to its own and the system's benefit. Furthermore, an increasing awareness of the stochasticity involved in PEV charging, reflected in a number of recent publications, can be observed. Among others, the PEV energy retail problem with interactions in day-ahead and balancing markets has been formulated from the aggregator's perspective, taking into account location dependent network tariffs in the form of capacity prices for active power. However, a numerical quantification of the benefit from accounting for stochasticity has yet to be carried out. Therefore, this paper uses standard methodology to calculate stochastic programming quality metrics such as the value of the stochastic solution (VSS) and the expected value of private information (EVPI) for an established PEV energy retail aggregator model. Applied to a real medium voltage system with urban characteristics and realistic spatial PEV mobility, different levels of mobility forecasts are included as information at the first-stage here and now decisions.