Plug-in Hybrid Electric Vehicles Load Research Articles

Stochastic power allocation of distributed tri-generation plants and energy storage units in a zero bus microgrid with electric vehicles and demand response

This work proposes a sequential stochastic coordinated energy management scheme (SCEMS) for a multi-energy carrier zero bus microgrid (ZBMG) in the presence of distributed tri-generation plants (TGPs), renewable sources, energy storage systems (ESSs), plug-in hybrid electric vehicles (PHEVs), auxiliary boiler and chiller units, and controllable loads. The first stage of the proposed architecture deals with implementing an integrated price-based demand response program to maximize consumers’ benefits and boost demand-side participation. The impacts of grid-to-vehicle and vehicle-to-grid modes of PHEV operation are investigated in this study. The second stage of this formulated problem considers the optimal power allocation of TGPs, ESS devices and auxiliary units aiming to satisfy economic, environmental, flexible, and reliability-driven objectives. Furthermore, to ensure optimal serving of heat and cooling load, novel heat and cool-following strategies are suggested. The uncertainties associated with the renewable generators, PHEV load, grid energy price, and load demands are modelled and incorporated in the SCEMS following “Hong’s 2m+1 point estimate method”. This stage also optimizes the distribution network structure for efficient microgrid operation. Moreover, a novel P−PQVδ−zero+ bus triplet approach of controlling power factor of dispatchable distributed generator is proposed. The efficacy of the framed problem is corroborated through examples. The simulation studies show that the proposed coordinated energy management strategy can: (1) economically accomplish peak shaving and valley filling for multi-energy demands; (2) reduce operation cost and electric energy loss by 8.19%∼10.38% and 41.63%∼47.91%, respectively; (3) enhance average flexibility index by 4.06%∼5.62% but with a marginal rise in emission by 0.39%∼1.68%; and (4) improve node voltage profile of the system.

Compilation of Load Spectrum of PHEV Transmission Assembly and Its Simulation Application

This paper presents a method for compiling the load spectrum of the transmission assembly of plug-in hybrid electric vehicles (PHEVs). Based on the analysis of the control strategy of the test vehicle, the power flow transmission route in the transmission assembly is different under different operation modes, so it is necessary to divide different load spectrum blocks according to the operation mode. Based on the big data survey of China’s national standard, it is determined that the typical working conditions are urban road working conditions, high-speed road working conditions, provincial road working conditions and poor road conditions. The mileage proportion of the various working conditions is 55:30:10:5, and the mileage of one cycle is 300 km. A total of three cycles are collected. After data processing and time-domain verification, based on the principle of maximum damage, the cycle with the largest pseudo damage is selected as the sample load data for load spectrum extrapolation. The rain flow counting method is used to count the sample load, and a two-dimensional kernel density estimation mathematical model with adaptive bandwidth is established to estimate the probability density function of the data. The extrapolated rain flow matrix is obtained through Monte Carlo simulation. The load spectrum of the two-dimensional rain flow matrix is transformed into a one-dimensional eight-stage program load spectrum by using a variable mean method, Goodman equation and equal damage principle theory. Finally, the fatigue life of the transmission assembly is simulated and calculated under the environment of Romax Designer simulation software. The two-dimensional kernel density estimation model with adaptive bandwidth is used to fit and extrapolate the load rain flow matrix of each hybrid mode of the PHEV, which solves the problem wherein the shape of the rain flow matrix of each hybrid mode of the hybrid electric vehicle is complex and difficult to fit. Finally, taking the after-sales maintenance data of this model from 2020 to the present as auxiliary proof, the failure components and the failure mileage life of the simulation test results are consistent with the results used by the actual users. This shows that the kernel density estimation model proposed in this paper can well fit the rain flow matrix of the PHEV load spectrum. The extrapolated load spectrum based on this model has high accuracy and authenticity. The method of compiling the load spectrum of the transmission assembly of a hybrid electric vehicle in this paper is effective.

Demand Side Management for Charging Plug-In Hybrid Electric Vehicles

In future the usage of Plug-in hybrid electric vehicles (PHEV) will be in wide range, which will impose huge burden to the distributive system. The peak load at the distribution system can be controlled by Demand Side Management (DSM) strategy. In the proposed study, the load curve of Low-voltage Transformers (LVTs) is made to be flatten, on satisfying the requirement of charging PHEV at given time to the required level. The proposed problem statement is formulated as convex optimization problem, and then the random arrival of PHEV is handled by introducing the moving horizon strategy. Based on this, the PHEV are being disconnected from the LVTs beyond their respective exit times. Such that the demand curve of the LVTs is flattened. This problem is solved using MATLAB and the power demand curves of the LVTs, power curves of the PHEVs and non- PHEV load are compared over a time of 24 hours to show that the power curve is flattened with the penetration of PHEV.

The impact of PHEVs charging and network topology optimization on bulk power system reliability

Exhausting fossil fuels and increasing environmental pollutions call for the wider deployment of plug-in hybrid electric vehicles (PHEVs), which enable the interactions of transportation and electric sectors. The extra loads for charging massive PHEVs could compromise the power system reliability and impose considerable stress on the power system. Transmission line congestions and generation capacity inadequacy could be caused under this circumstance. However, there is little literature which comprehensively studies the impact of large-scale PHEVs on the bulk power system reliability when smart grid technologies are incorporated including smart charging and flexible transmission topology control technologies. This work studies the impacts of different charging strategies and transmission network operation strategies on the bulk power system reliability with high PHEVs integration. An evolution strategy particle swarm optimization (ESPSO) algorithm is applied to optimize the charging load of PHEVs and shave the peak load. A network topology optimization (NTO) operation strategy is employed to lessen the transmission congestions and reduce the load curtailment. Based on the sequential Monte Carlo simulation method, these technologies are integrated into the reliability evaluation process. Numerical studies and sensitivity analysis are conducted on modified IEEE RTS-79 systems. The results verify the effectiveness of the technologies to realize the potential of existing power system infrastructures so as to relieve the stress caused by the PHEVs load, increase the available penetration of PHEVs and promote the bulk power system reliability which is meaningful for power system planning and risk management.

Optimal operating strategy of virtual power plant considering plug-in hybrid electric vehicles load

Summary Virtual power plant (VPP) is the owner and/or manager of a group of distributed generations as well as some end consumers, which may have random behavior. In addition to the uncertainties associated with generation and demand, the market price is also a source of uncertainty in VPP scheduling. In this paper, a novel stochastic operating strategy is proposed for VPPs to participate in day-ahead market, considering different uncertainties in generation, consumption, and electricity market price. Highly stochastic consumption of plug-in hybrid electric vehicles (PHEVs) is also considered in the present study, as an emerging source of uncertainty. To mitigate the loading effect of PHEVs on the network, a smart charging strategy is proposed, which encourages the owners to charge their PHEVs in off-peak hours, and the results of this strategy are used in estimating the load. To achieve the maximum profit for the VPP in an electricity market, different methods based on point estimation method and Monte Carlo simulation are analyzed to handle the uncertainties. Moreover, three strategies are proposed for charging PHEVs, and their effect on VPP's total cost is compared. The proposed stochastic programming is solved by a modified version of teaching–learning-based optimization algorithm. Numerical simulations on modified 18-bus distribution system corroborate the efficacy of the proposed methodology. Copyright © 2015 John Wiley & Sons, Ltd.

An Event-Based Simulation Framework to Examine the Response of Power Grid to the Charging Demand of Plug-In Hybrid Electric Vehicles

This paper describes the development of a discrete-event simulation framework that emulates the interactions between the power grid and plug-in hybrid electric vehicles (PHEVs) and examines whether the capacity of the existing power system can meet the PHEV load demand. The probability distribution functions for the arrival time and energy demand of each vehicle are extracted from real-world statistical transportation data. The power grid's limited generation and transmission capacities are considered to be the major constraints. Therefore, vehicles may have to wait to receive any charge. The proposed simulation framework is justified and described in some detail in applying it to two real cases in the United States to determine certain regions' grid potential to support PHEVs. Both Level-1 and -2 charging are considered.

A residential PHEV load coordination mechanism with renewable sources in smart grids

A high penetration of Plug-in Hybrid Electric Vehicles (PHEVs) can lead to stress and overload in distribution systems when uncoordinated charging is considered. Thus, coordination mechanisms to manage PHEV charging during peak periods are needed. This paper evaluates the impact of PHEVs in the residential distribution grid and proposes a coordination mechanism based on heuristic rules as a load shaping tool. The impact of the mechanism on user convenience is also studied, and the benefits of having a renewable generation system is examined. Results show that our coordination mechanism is able to prevent the overloading of the distribution transformer. Further, it has been found that the performance of the renewable system (e.g., renewable fraction and renewable cost ratio) depend on many factors such as weather, region, equipment specification, and user preference.

Impact of Dynamic PHEV load on Photovoltaic System

This paper presents the dynamics of photovoltaic (PV) cell with Plug in Hybrid Electric Vehicles (PHEVs) load. It is expected that PHEVs are going to be charged during the day with solar PV energy sources at the parking sites of PHEVs. The present work offers a complete system for charging PHEVs with PV cell where PHEVs load are modelled based on third order battery model. System dynamics are analyzed at maximum power point while Perturb and Observe (P&O) method is used to ensure the tracking of maximum power point from the PV cell. This paper analyses the impact of PHEV loads on the dynamic behaviour of a solar power system under both small disturbance and large change in radiation. Simulation results demonstrate that it is important to consider the dynamics of PHEVs load for charging with PV cell. DOI: http://dx.doi.org/10.11591/ijece.v2i5.1239

Impacts of Charging Choices for Plug-In Hybrid Electric Vehicles in 2030 Scenario

This study systematically examined the potential impacts of recharging scenarios for multiple plug-in hybrid electric vehicles (PHEVs) in the western United States—in particular, the service area of the Western Electricity Coordinating Council (WECC)—in 2030. The goal of the study was twofold: to examine the impact of scenarios for market penetration and charging of PHEVs on the electric utilities and transmission grid and to estimate the potential reductions in petroleum use and greenhouse gas (GHG) emissions attributable to PHEV miles traveled on primarily grid electricity. Three charging scenarios for PHEVS were examined: (a) begin recharging upon arrival at home at the end of the last daily trip, (b) complete recharging of batteries just before the start of the first daily trip, and (c) any additional charging opportunity during the daytime. The three charging scenarios produced distinct hourly electric load profiles, with the opportunity-charging scenario resulting in a significant increase in load during the daytime. However, when the utility dispatch simulations were run for these charging scenarios in the WECC area, they all exhibited similar marginal-generation mixes (dominated by the natural gas combined-cycle technology) to satisfy the PHEV load, and GHG emissions were within 2% of each other. A well-to-wheel analysis revealed that the marginal-generation mixes produced 40% to 45% lower GHG emissions by PHEVs than did conventional gasoline internal combustion engine vehicles.

On the aggregate grid load imposed by battery health-conscious charging of plug-in hybrid electric vehicles

This article examines the problem of estimating the aggregate load imposed on the power grid by the battery health-conscious charging of plug-in hybrid electric vehicles (PHEVs). The article begins by generating a set of representative daily trips using (i) the National Household Travel Survey (NHTS) and (ii) a Markov chain model of both federal and naturalistic drive cycles. A multi-objective optimizer then uses each of these trips, together with PHEV powertrain and battery degradation models, to optimize both PHEV daily energy cost and battery degradation. The optimizer achieves this by varying (i) the amounts of charge obtained from the grid by each PHEV, and (ii) the timing of this charging. The article finally computes aggregate PHEV power demand by accumulating the charge patterns optimized for individual PHEV trips. The results of this aggregation process show a peak PHEV load in the early morning (between 5.00 and 6.00a.m.), with approximately half of all PHEVs charging simultaneously. The ability to charge at work introduces smaller additional peaks in the aggregate load pattern. The article concludes by exploring the sensitivity of these results to the relative weighting of the two optimization objectives (energy cost and battery health), battery size, and electricity price.

Integration of plug-in hybrid electric vehicles in a regional wind-thermal power system

This study investigates consequences of integrating plug-in hybrid electric vehicles (PHEVs) in a wind-thermal power system supplied by one quarter of wind power and three quarters of thermal generation. Four different PHEV integration strategies, with different impacts on the total electric load profile, have been investigated. The study shows that PHEVs can reduce the CO 2-emissions from the power system if actively integrated, whereas a passive approach to PHEV integration (i.e. letting people charge the car at will) is likely to result in an increase in emissions compared to a power system without PHEV load. The reduction in emissions under active PHEV integration strategies is due to a reduction in emissions related to thermal plant start-ups and part load operation. Emissions of the power sector are reduced with up to 4.7% compared to a system without PHEVs, according to the simulations. Allocating this emission reduction to the PHEV electricity consumption only, and assuming that the vehicles in electric mode is about 3 times as energy efficient as standard gasoline operation, total emissions from PHEVs would be less than half the emissions of a standard car, when running in electric mode.