Plug-in Hybrid Electric Vehicles Owners Research Articles

Optimal allocation of cloud energy storage system in low-voltage distribution network

In low-voltage distribution networks, an uncontrolled charging of plug-in hybrid electric vehicles (PHEVs) brings about intensive peak loads in distribution transformers’ daily load curves. Due to insulation degradations, this issue ends in transformers’ loss of life (LOL) and early aging. The notion of cloud energy storage system (CESS) with larger power and energy capacities enables consumers to have access to cheaper energy storage facilities. Thanks to CESS installation, semi-smart, controlled, and low-cost charging of PHEVs could be realized to relieve the transformer’s peak loads and reduce the peak-to-average (PAR) ratio of load curve. To do so, this paper develops an optimal planning model for siting and sizing of a CESS to accommodate the charge/discharge requirements of consumers who are willing to collaborate with CESS and hence, implementing a controlled charging of PHEVs. The proposed model considers the investment and operation costs of both CESS and distribution network. It also models the transformer LOL costs. The PHEVs, in private garages, are supposed to be connected to the grid through a smart module whose connection time and status is optimally scheduled and controlled through the operator of the CESS. The CESS charging/discharging is regulated such that the previously determined time targets of PHEV owners are satisfied. To assess performance of the proposed model, extensive numerical studies are carried out in terms of techno-economic indices say as PAR reduction, conducting energy arbitrage, power losses reduction, and etc. Results are discussed in depth.

Energy management of Plug-In Hybrid Electric Vehicles in renewable-based energy hubs

Proliferation of Plug-in Hybrid Electric Vehicles (PHEVs) and integration of various distributed generation (DG) technologies have been recognized to play an undeniable role in modern power systems of the future. In order to effectively model the interactions of these two technologies, this paper develops a multi-criteria framework to coordinate the charging behaviors of PHEVs within an energy hub platform. In this regard, the desirable charging profiles from the viewpoint of both PHEV owners and hub manager are first captured and reported to the PHEVs Coordinator Entity (PCE). The PCE, then, runs an optimization framework in which several criteria including the PHEV owners’ convenience, energy hub’s profit, and the technical performance of the distribution grid are all taken into account as a multi- criteria optimization framework resulting in the PHEVs’ optimal charging patterns. The proposed strategy is applied to the modified IEEE 34-node test system and the results demonstrate the applicability and efficiency of the proposed framework. • Proposing a new framework for energy management of electric vehicles (EVs) based on energy hub platforms. • Modeling different requirements of EVs’ owners, energy hub operator and energy system operator using a multi-criteria optimization algorithm. • Developing different programs for motivating EVs’ owners in adopting charging control mechanisms. • Proposing an iterative algorithm for optimizing charging profiles of EVs considering main features of the energy resources.

Empirical charging behavior of plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEV) offer greenhouse gas emission reduction in car usage if charged frequently and driven mainly on electricity. However, little is known about the actual charging behavior of PHEV owners. Here, we investigate the daily charging of 10,488 Chevrolet Volt PHEV driven on a total of 4.3 million total driving days in the US and Canada. We propose a new method to detect the frequency of individual charging behavior from the daily utility factor and daily distance travelled. Our results show that no charging overnight occurs typically on 3–7% of the driving days per user and additional charging happens on 20–26% of the driving days. We also analyze the relation between charging frequency and utility factor for different user groups and days. Our results show that the utility factor should not be used as the only measure of environmental performance of PHEVs.

Understanding discontinuance among California’s electric vehicle owners

For the market share of plug-in electric vehicles (PEVs) to continue to grow and reach 100% of new vehicle sales, adopters of the technology, who initially buy PEVs, will need to continue choosing them in subsequent purchases. Although much research has focused on the reasons for, and barriers to, initial PEV purchase, less has been devoted to the reasons for discontinuance—abandoning a new technology after first purchasing it. Here, on the basis of results from five questionnaire surveys, we find that PEV discontinuance in California occurs at a rate of 20% for plug-in hybrid electric vehicle owners and 18% for battery electric vehicle owners. We show that discontinuance is related to dissatisfaction with the convenience of charging, having other vehicles in the household that are less efficient, not having level 2 (240-volt) charging at home, having fewer household vehicles and not being male.

Why do some consumers not charge their plug-in hybrid vehicles? Evidence from Californian plug-in hybrid owners

The environmental benefits of plug-in hybrid electric vehicles (PHEVs) are closely related to the driving and charging behavior of vehicle owners. It is often wrongly assumed that PHEV drivers plug-in once per day. Using data from drivers of the vehicles we show this is not the case and that some drivers rarely charge their PHEV. If the vehicle is not plugged-in regularly, the vehicle will drive fewer electric miles and more gasoline miles, thereby losing out on potential emission savings. Analyzing 30-day charging behavior of 5418 PHEV owners using a logistic regression model, we explore the factors that influence driver’s decisions to not charge their vehicle. Several factors play a role in drivers’ decision to plug-in their PHEV or not, including vehicle characteristics and the availability and cost of charging at various locations. Higher home electricity prices, lower electric driving range, lower electric motor power to vehicle weight ratios, lower potential cost savings from charging, and living in an apartment or condo, among other factors are related to not plugging in a PHEV. The findings have important implications in terms of future policy and vehicle design including which PHEVs policymakers should incentivize and what measures can encourage PHEV owners to plug-in their vehicles to help realize the environmental benefits of the technology.

Impact of Battery Size and Energy Cost on the Market Acceptance of Blended Plug-in Hybrid Electric Vehicles

The blended plug-in hybrid electric vehicles (PHEV) have sprung up in the private car market. However, many consumers are not clear about whether PHEVs will save cost for their ownership and the government and the automakers need to take effective measures to reduce the total cost of ownership (TCO) of PHEVs to promote and popularize PHEVs. This paper investigates the usage data collected from PHEV owners using the same vehicle model of BYD Qin in Shanghai, China, and estimates the TCO of PHEVs considering the acquisition and operation costs. By comparing the TCO with ICE vehicles, it is found that the TCO of ICE vehicles is lower than PHEVs. Optimizing the battery capacity is a feasible way to reduce the TCO of PHEVs, e.g., if the battery capacity is optimized as 20 kWh, the TCO would be reduced by about 6.2%. Besides, the TCO of PHEVs is more sensitive to the fluctuation of the fuel and electricity price.

Gas anxiety and the charging choices of plug-in hybrid electric vehicle drivers

Plug-in hybrid electric vehicles (PHEVs) can provide many of the benefits of battery electric vehicles (BEVs), such as reduced petroleum consumption and greenhouse gas emissions, without the “range anxiety” that can accompany driving a vehicle with limited range when there are few charging opportunities. However, evidence indicates that PHEVs are often plugged in more frequently than BEVs in practice. This is somewhat paradoxical: drivers for whom plugging in is optional tend to do so more frequently than those for whom it is necessary. This has led to the coining of a new term – “gas anxiety” – to describe the apparent desire of PHEV drivers to avoid using gasoline. In this paper, we analyze the variables influencing the charging choices of PHEV owners, testing whether drivers express preferences consistent with the concept of gas anxiety. We analyze data collected in a web-based stated preference survey using a latent class logit model. The results reveal two classes of decision-making patterns among the survey respondents: (1) those who weight the cost of gasoline and the cost of recharging approximately equally (the cost-minimizing class), and (2) those who weight the cost gasoline more heavily than the cost of recharging (the gas anxiety class). Respondents in the gas anxiety class expressed a willingness to recharge at a charging station even when doing so would cost approximately four times as much as the cost of the gasoline avoided. While the gas anxiety class represents the majority of our sample, more recent PHEV adopters are more likely to be in the cost-minimizing class. Looking forward, this suggests that public charging station operators may need to price charging competitively with gasoline on a per-mile basis to attract PHEV owners.

Energy Management Strategy for Plug-In Hybrid Electric Vehicles via Bidirectional Vehicle-to-Grid

As a paradigm of the incoming smart grid, vehicle-to-grid (V2G) has been proposed as a promising solution to increase the adoption rate of plug-in hybrid electric vehicles (PHEVs). In this paper, we investigate the energy management strategies for PHEVs via bidirectional V2G. We first follow a cost-conscious approach from the PHEV owner point of view. To minimize the daily energy cost, we formulate the energy management problem via dynamic programming (DP). However, the “well-known” complexity in solving DP poses a computational challenge even for a small number of iterations. Therefore, we propose a state-independent four-threshold $(s,S,s^{\prime},S^{\prime})$ battery charging/discharging policy and theoretically prove the optimality of the proposed energy management strategy based on stochastic inventory theory. A backward iteration algorithm is further developed to practically implement the $(s,S,s^{\prime},S^{\prime})$ feedback policy. Second, from the distribution system operator's perspective, we aim to shave the peak load and flatten the overall load profile. To this end, we propose an optimal PHEV charging scheme and further derive a reminiscent “water-filling” solution for this scenario. Realistic PHEV battery models, time-of-use electricity pricing rate, and real data of household demand are integrated into our formulated V2G system model. The theoretical analysis and proofs are instrumental to the future large-scale PHEV adoption in smart grids.

Optimal Sizing of Storage System in a Fast Charging Station for Plug-in Hybrid Electric Vehicles

Fast charging station is indispensable for widespread use of plug-in hybrid electric vehicle (PHEV), as it provides a mean to fully charge a PHEV in a short period of time. Application of electrical storage systems (ESSs) in fast charging stations is considered as a way to reduce operational costs of the station and to alleviate negative impacts of station operation on the power grid. This paper proposes an approach to determine the optimal size of the storage system for a fast charging station. In the first step, PHEV charging demand in a station is calculated considering PHEV characteristics as well as driving patterns of PHEV owners. The optimal size of ESS in the charging station is then determined, such that the station energy cost and the storage cost are minimized. The proposed approach considers various technical and economic issues, such as energy loss and life cycle cost of storage system. Using the proposed approach, a set of studies is conducted to verify the effectiveness and applicability of the proposed algorithm.

Electricity-price arbitrage with plug-in hybrid electric vehicle: Gain or loss?

Customers, utilities, and society can gain many benefits from distributed energy resources (DERs), including plug-in hybrid electric vehicles (PHEVs). Using battery on PHEV to arbitrage electricity price is one of the potential benefits to PHEV owners. There is, however, disagreement on the magnitude of such profit. This study uses a stochastic optimization model to estimate the potential profit from electricity price arbitrage of two types of PHEVs (PHEV-10, and PHEV-40) under three scenarios with variant electricity tariff and PHEV owners over a five-year period.The simulation results indicate that under current market structure, even with significant improvement in battery technologies (e.g., higher efficiency, lower cost), the PHEV owners can't achieve a positive arbitrage profit. This finding implies that expected arbitrage profit solely is not a viable option to engage PHEVs larger adoption. Subsidy and combining PHEV arbitraging with alternative PHEV services are required.

Two-Stage Optimization Method for Energy Loss Minimization in Microgrid Based on Smart Power Management Scheme of PHEVs

Utilizing plug-in hybrid electric vehicles (PHEVs) is growing fast and booming nowadays. These vehicles, as portable loads and energy sources, may be connected to standard sockets at home. As a result, those extra electrical loads, grid to vehicle, generations, and vehicle to grid, have several impacts on distribution networks, e.g., network energy loss. This paper presents a two-stage optimization method to minimize the energy loss of microgrid with different penetration levels of PHEVs. In the first stage, a novel convex quadratic objective function for active power management of PHEVs is proposed, and daily required energy of PHEVs is calculated based on stochastic model of PHEV owners' behavior. It is supposed that PHEVs can be employed as distributed capacitors. Therefore, reactive power of PHEVs is specified in the second stage. Afterward, the proposed methodology is applied to a realistic distribution network. It will be illustrated that network energy loss is likely to rise considerably in the case of increasing penetration level of PHEVs without smart charging strategy; in order to minimize network energy loss, a smart management scheme will have to be considered. Also, the significant impact of PHEVs' active and reactive power management on energy loss reduction will be demonstrated.

Game-theoretic control of PHEV charging with power flow analysis

Due to an ever-increasing market penetration of plug-in hybrid electric vehicles (PHEVs), the charging demand is expected to become a main determinant of the load in future distribution systems. In this paper, we investigate the problem of controlling in-home charging of PHEVs to accomplish peak load shifting while maximizing the revenue of the distribution service provider (DSP) and PHEV owners. A leader-follower game model is proposed to characterize the preference and revenue expectation of PHEV owners and DSP, respectively. The follower (PHEV owner) decides when to start charging based on the pricing schedule provided by the leader (DSP). The DSP can incentivize the charging of PHEV owners to avoid system peak load. The costs associated with power distribution, line loss, and voltage regulation are incorporated in the game model via power flow analysis. Based on a linear approximation of the power flow equations, the solution of sub-game perfect Nash equilibrium (SPNE) is obtained. A case study is performed based on the IEEE 13-bus test feeder and realistic PHEV charging statistics, and the results demonstrate that our proposed PHEV charging control scheme can significantly improve the power quality in distribution systems by reducing the peak load and voltage fluctuations.

Adequacy Assessment of Power Distribution Network with Large Fleets of PHEVs Considering Condition-Dependent Transformer Faults

As a new form of distributed energy resources, massive plug-in hybrid electric vehicles (PHEVs) could affect the power distribution system adequacy considering their intermittent charging loads and the load recovery ability during system outages. This paper proposes a comprehensive framework for adequacy evaluation of power distribution networks with PHEVs penetration. A condition-dependent outage model is used in this paper to obtain the time sequential failure rate of the transformer. Also, a business model for the PHEVs is developed to encourage the PHEV owners to charge their vehicles in such a way that the distribution system adequacy is enhanced. Based on this model, a smart charging algorithm is proposed for the PHEVs to minimize their charging cost and enhance the adequacy of the distribution network at the same time. Various simulation studies are carried out to verify the effectiveness of the proposed smart charging approach. The simulation results show that the proposed approach is effective in enhancing both the adequacy of the distribution network and economic profits of PHEVs.

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.

English

2 ABSTRACT: Plug in Hybrid Electric Vehicles (PHEVs) charging and discharging, renewable energy resource generation and utilization is most important in future power system control. Proper integration of these energy sources gives solution to the challenges. In this paper Mixed Integer Linear Programming (MILP) was proposed for plug in Hybrid Electric Vehicles charging and discharging in a charging station. Charging station consists of Photo Voltaic (PV) system with practical constraints, power balance constraints, battery charging and discharging constraints are considered. The results proposed that the proposed algorithm minimize the charging cost of PHEVs and optimal power flow for the grid connected PHEVs systems. Likewise, PHEVs owners could yield more profit by discharging their vehicles to the grid in addition to having preferred charge in the departure time.

Coordinated charging of plug-in hybrid electric vehicles in smart hybrid AC/DC distribution systems

This paper introduces an online coordination approach for plug-in hybrid electric vehicles (PHEVs) charging in smart hybrid AC/DC distribution systems. The goal of the proposed method is to optimally charge the PHEVs in order to maximize the PHEV owners' satisfaction without violating the network constraints. The charging costs, which represent the PHEV owners' satisfaction in this work, are based on real-time pricing. The proposed approach includes consideration of PHEV owners' requirements, PHEV batteries' characteristics, and hybrid distribution system limitations. Moreover, a sliding window concept is introduced to facilitate managing the PHEV charging and the system interlinking converters in real-time. A 38-bus test system has been modified to include DC links and used to validate the developed online charging scheme. The test results clearly demonstrate the effectiveness of the proposed method.

Maximizing the revenues of data centers in regulation market by coordinating with electric vehicles

Frequency regulation is a major market service to reduce the undesired imbalance between power supply and demand in the power market. In order to participate in the regulation market, both the supply and demand sides need to be capable of flexibly adjusting their power generation and consumption, respectively. As the scale of Internet data centers is increasing rapidly, their significant power consumption has enabled them to become an important player in the regulation market for maximized profits and thus minimized operating expenses. On the other side, Plug-in Hybrid Electric Vehicles (PHEVs) have also recently been identified as a major participant in the regulation market, due to their large power demand for battery charging.In this paper, we propose a novel power management scheme that jointly leverages a data center and its employees’ PHEVs to (1) maximize the revenues that the data center receives from the regulation market and (2) get the PHEVs charged at no expense to their owners. Our scheme features a two-level hierarchical power control design. At the first level, our scheme interacts with the regulation market to provide information about the data center power consumption on an hourly basis. At the second level, the scheme decides the power budgets for the servers and UPS in the data center, as well as PHEVs, in real time, to follow the given regulation signal. In addition, we show how to leverage the thermal energy storage (TES) tanks available in many data centers to adapt the cooling power consumption for better management of the data center power demand and further increased regulation revenues. We evaluate the proposed scheme with real-world workload and regulation traces. The results show that our scheme performs a high-quality regulation service. As a result, the proposed scheme outperforms several commonly used baselines by having higher regulation revenues, and so lower operating expenses, for the data center. Finally, we analyze the cost savings of the PHEV owners, throughout the lifetime of the PHEVs, by getting their batteries charged at no expense.

Economic Scheduling of Residential Plug-In (Hybrid) Electric Vehicle (PHEV) Charging

In the past decade, plug-in (hybrid) electric vehicles (PHEVs) have been widely proposed as a viable alternative to internal combustion vehicles to reduce fossil fuel emissions and dependence on petroleum. Off-peak vehicle charging is frequently proposed to reduce the stress on the electric power grid by shaping the load curve. Time of use (TOU) rates have been recommended to incentivize PHEV owners to shift their charging patterns. Many utilities are not currently equipped to provide real-time use rates to their customers, but can provide two or three staggered rate levels. To date, an analysis of the optimal number of levels and rate-duration of TOU rates for a given consumer demographic versus utility generation mix has not been performed. In this paper, we propose to use the U.S. National Household Travel Survey (NHTS) database as a basis to analyze typical PHEV energy requirements. We use Monte Carlo methods to model the uncertainty inherent in battery state-of-charge and trip duration. We conclude the paper with an analysis of a different TOU rate schedule proposed by a mix of U.S. utilities. We introduce a centralized scheduling strategy for PHEV charging using a genetic algorithm to accommodate the size and complexity of the optimization.

Charging Behavior Impacts on Electric Vehicle Miles Traveled

The growing market for plug-in electric vehicles (PEVs) features new models of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) with varying battery sizes and electric driving ranges. How are the various models being used in the real world? A common assumption in PEV impact analysis is that PEV owners will maximize their vehicle's utility by appropriately sizing the battery to their driving needs and by charging their vehicle as much as possible to recover the cost of the vehicle purchase. On the basis of these assumptions, a high correlation between PHEV owner use of the vehicle and the number of plug-in events is expected, and drivers of PHEVs with a small battery are expected to plug in more than do owners of vehicles with a larger battery and similar driving patterns. The assumptions presented are examined through a survey of more than 3,500 PEV owners conducted in California in May and June 2013. The online survey included extensive data on driving and charging behavior using web map questions. Owners of all PEV models on the market, including more than 600 Volts and 800 Prius Plug-Ins, were surveyed. The results show that small-battery PHEV electric vehicle miles traveled are lower than longer-range PHEV or BEV electric vehicle miles traveled not only because of battery size but also because of public charging availability and charging behavior. Higher electric-range PHEV and BEV drivers charge more often and report more charging opportunities in areas where smaller-battery PHEVs could not find chargers.

Optimizing the Energy Delivery via V2G Systems Based on Stochastic Inventory Theory

In this paper, we study the optimal energy delivery problem from viewpoints of both the vehicle owner and aggregator, in load shaving services of a vehicle-to-grid (V2G) system. We formulate the optimization problem based on a general plug-in hybrid electric vehicle (PHEV) model, taking into account the randomness in vehicle mobility, time-of-use electricity pricing, and realistic battery modeling. Stochastic inventory theory is applied to analyze the problem. We mathematically prove that a state-dependent <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$(S,S^{\prime})$</tex> </formula> policy is optimal for the daily energy cost minimization of each vehicle, and develop an estimation algorithm to calculate the parameters of the optimal policy for practical applications. Furthermore, we investigate the multi-vehicle aggregator design problem by considering the power system constraints. A policy adjustment scheme is proposed to adjust the values of <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$S$</tex></formula> and <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$S^{\prime}$</tex></formula> with respect to the optimal policy adopted by each PHEV, such that the aggregated recharging and discharging power constraints of the power system can be satisfied, while minimizing the incremental cost (or revenue loss) of PHEV owners. Based on characteristics of the state-dependent <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$(S,S^{\prime})$</tex></formula> policy and our proposed policy adjustment scheme, the optimal aggregator operation problem is transformed into a convex optimization one which can be readily solved by existing algorithms. The performance of our proposed schemes is evaluated via simulations based on real data collected from Canadian utilities, households, and commuters.