Beyond batteries: An examination of the benefits and barriers to plug-in hybrid electric vehicles (PHEVs) and a vehicle-to-grid (V2G) transition

This paper explores both the promise and the possible pitfalls of the plug-in hybrid electric vehicles (PHEV) and vehicle-to-grid (V2G) concept, focusing first on its definition and then on its technical state-of-the-art. More originally, the paper assesses significant, though often overlooked, social barriers to the wider use of PHEVs (a likely precursor to V2G) and implementation of a V2G transition. The article disputes the idea that the only important barriers facing the greater use of PHEVs and V2G systems are technical. Instead, it provides a broader assessment situating such “technical” barriers alongside more subtle impediments relating to social and cultural values, business practices, and political interests. The history of other energy transitions, and more specifically the history of renewable energy technologies, implies that these “socio-technical” obstacles may be just as important to any V2G transition—and perhaps even more difficult to overcome. Analogously, the article illuminates the policy implications of such barriers, emphasizing what policymakers need to achieve a transition to a V2G and PHEV world.     

Cost and emissions impacts of plug-in hybrid vehicles on the Ohio power system

Plug-in hybrid electric vehicles (PHEVs) have been promoted as a potential technology that can reduce vehicles’ fuel consumption, decreasing transportation-related emissions and dependence on imported oil. The net emission and cost impacts of PHEV use are intimately connected with the electricity generator mix used for PHEV charging, which will in turn depend on when during the day PHEVs are recharged. This paper analyzes the effects of a PHEV fleet in the state of Ohio. The analysis considers two different charging scenarios—a controlled and an uncontrolled scenario—which offer the grid operator different levels of control over the timing of PHEV charging. The analysis shows that PHEV use could result in major reductions in gasoline consumption of close to 70% per vehicle compared to a conventional vehicle (CV) under both charging scenarios. Moreover, despite the high penetrations of coal in the Ohio power system, net CO₂ emissions from a PHEV could be up to 24% lower than that of a CV in the uncontrolled case, however, CO₂ and NO_x emissions would increase in both scenarios.     

Dynamic competition between plug-in hybrid and hydrogen fuel cell vehicles for personal transportation

This article addresses the issue of the diffusion of hydrogen cars in the market, particularly the competition with electric cars for the replacement of conventional vehicles. Using the multi-technological competition model developed by Le Bas and Baron-Sylvester’s (Diffusion technologique non binaire et schéma épidémiologique. Une reconsidération. Economie Appliquée 1995; tome XLVIII(3):71–101), it is shown that the early deployment of plug-in hybrid vehicles—the only electric technology which can compete with fuel cell cars in the multipurpose vehicle field—risks closing the market for hydrogen in the future. Moreover, the advent of the hydrogen vehicle depends on the rapid advancements in fuel cell technologies, as well as on the existence of an infrastructure with a sufficient coverage.     

Fuel reduction and electricity consumption impact of different charging scenarios for plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) consume both gasoline and grid electricity. The corresponding temporal energy consumption and emission trends are valuable to investigate in order to fully understand the environmental benefits. The 24-h energy consumption and emission profile depends on different vehicle designs, driving, and charging scenarios. This study assesses the potential energy impact of PHEVs by considering different charging scenarios defined by different charging power levels, locations, and charging time. The region selected for the study is the South Coast Air Basin of California. Driving behaviors are derived from the National Household Travel Survey 2009 (NHTS 2009) and vehicle parameters are based on realistic assumptions consistent with projected vehicle deployments. Results show that the reduction in petroleum consumption is significant compared to standard gasoline vehicles and the ability to operate on electricity alone is crucial to cold start emission reduction. The benefit of higher power charging on petroleum consumption is small. Delayed and average charging are better than immediate charging for home, and non-home charging increases peak grid loads.     

Impact of plug-in hybrid electric vehicles on power systems with demand response and wind power

This paper uses a new unit commitment model which can simulate the interactions among plug-in hybrid electric vehicles (PHEVs), wind power, and demand response (DR). Four PHEV charging scenarios are simulated for the Illinois power system: (1) unconstrained charging, (2) 3-hour delayed constrained charging, (3) smart charging, and (4) smart charging with DR. The PHEV charging is assumed to be optimally controlled by the system operator in the latter two scenarios, along with load shifting and shaving enabled by DR programs. The simulation results show that optimally dispatching the PHEV charging load can significantly reduce the total operating cost of the system. With DR programs in place, the operating cost can be further reduced.     

Moving from assumption to observation: Implications for energy and emissions impacts of plug-in hybrid electric vehicles

Plug-in hybrid electric vehicles (PHEVs) are currently for sale in most parts of the United States, Canada, Europe and Japan. These vehicles are promoted as providing distinct consumer and public benefits at the expense of grid electricity. However, the specific benefits or impacts of PHEVs ultimately relies on consumers purchase and vehicle use patterns. While considerable effort has been dedicated to understanding PHEV impacts on a per mile basis few studies have assessed the impacts of PHEV given actual consumer use patterns or operating conditions. Instead, simplifying assumptions have been made about the types of cars individual consumers will choose to purchase and how they will drive and charge them. Here, we highlight some of these consumer purchase and use assumptions, studies which have employed these assumptions and compare these assumptions to actual consumer data recorded in a PHEV demonstration project. Using simulation and hypothetical scenarios we discuss the implication for PHEV impact analyses and policy if assumptions about key PHEV consumer use variables such as vehicle choice, home charging frequency, distribution of driving distances, and access to workplace charging were to change.     

II. A combined model for determining capacity usage and battery size for hybrid and plug-in hybrid electric vehicles

We combine a detailed battery model with a simple vehicle model to examine the battery size and capacity usage of a Li_xC₆/Li_y+0.16Mn_1.84O₄ cell (with a normal and artificially flat equilibrium potential) and a Li_4+3xTi₅O₁₂/Li_yFePO₄ cell. The features of cell chemistry we are concerned with are the magnitude and shape of the cell equilibrium potential and internal resistance. Our key findings include that a battery for a hybrid electric vehicle application has a capacity usage from 15 to 25% (for a minimum separator area size), and as one moves from a HEV battery to a plug-in hybrid electric vehicle battery there is a change in the slope of the separator area vs. equivalent-electric range curve due to the shape of the pulse-power capability. We also find that defining the resistance using the HPPC protocol has limitations because in general the pulse resistance depends on the applied current and pulse duration. Our detailed, combined model also shows that the benefits of a flat-potential system may be limited because of the relative positions of a flat and sloped equilibrium potential, and the lack of a driving force for the relaxation of solid-phase concentration gradients throughout the electrode. That latter effect is shown to be more significant for electrodes with a non-uniform current distribution.     

I. A simplified model for determining capacity usage and battery size for hybrid and plug-in hybrid electric vehicles

We develop a simplified model to examine the effect of the shape and magnitude of the battery pulse-power capability on capacity usage and battery size. The simplified model expresses the capacity usage and a dimensionless battery area in terms of a dimensionless energy-to-power ratio and a parameter that characterizes the shape of the pulse-power capability. We also present dimensional results that show how the capacity usage depends on the equivalent-electric range and separator area, and how the battery area depends on the equivalent-electric range. Key results include the presence of a Langmuir-like relationship between the capacity usage and the dimensionless energy-to-power ratio, and a linear relationship between the dimensionless energy-to-power ratio and a dimensionless area, with a slope and offset that depend on the shape of the pulse-power capability. We also found that a flat pulse-power capability curve increases capacity usage and decreases battery size, and that two important parameters for battery design are (U − V_min)V_min/R, which reflects the maximum power capability, and Q〈V〉, which reflects the battery energy. The results and analysis contained herein are used to help interpret the results from a combined battery and vehicle model, presented in a companion paper.     

Electric and plug-in hybrid vehicles influence on CO2 and water vapour emissions

The main objective of this research is to quantify the impact of introducing electric vehicles and plug-in hybrid vehicles, including fuel cell on conventional fleets. The impact is estimated in terms of local pollutants, HC, CO, NO_x, PM, and in terms of CO₂ and water vapour global emissions. The specific fleet of Portugal, roughly 6 million light-duty vehicles (30% diesel, 70% gasoline) is considered, and the mobility indicator of the fleet, 90 thousand million p × km, is kept constant throughout the analysis. Probability density functions for energy consumption and emissions are derived for conventional, electric and plug-in hybrid vehicles, in charge depleting and charge sustaining modes. The Monte Carlo method is used to obtain average distribution estimates for discounting values of “old vehicles” that are removed from the fleet, and to add average distribution estimates for the “new vehicles” entering the fleet. Considering the actual Portuguese fleet as the reference case, local pollutant emissions decrease by a factor of 10-53%, for 50% fleet replacement. A potential 23% decrease of CO₂ is foreseen, and a potential 31% increase of H₂O emissions is forecasted. Life cycle water vapour emissions tend to rise and are, typically, 2-4 times higher than CO₂ values at the upstream stage, due to its release in the cooling towers of thermal power plants. It is interesting to note that considering 1 MJ of energy required at vehicle wheels, in an overall life cycle context, both fuel cell and electric modes have nearly twice as much H₂O emissions than internal combustion vehicles. CO₂ emissions tend to decrease with electric drive vehicles penetration due to the higher fleet life cycle efficiency.     

A review on the economic dispatch and risk management of the large-scale plug-in electric vehicles (PHEVs)-penetrated power systems

Nowadays, the deterioration of ecological environment and the ever rising gas price make green transportation our relentless pursuit. Energy-saving, low-emission even zero-emission electric vehicles (EVs) have been considered as one solution to the problem. With the rapid development of plug-in electric vehicle (PHEV) and forceful support and incentives from the government, PHEV and its supporting facilities are being gradually popularized. When randomly being connected to the power grid in large scale, PHEVs will bring new challenges to power grid in operation and management. This paper presents an overall review on historical research on power system integrated with electric vehicles and especially focuses on economic dispatch of PHEV in the electricity market. The paper also discusses the joint scheduling problem considering other renewable energy resources and risk management of PHEV-penetrated power systems.     

Regulatory framework and business models for charging plug-in electric vehicles: Infrastructure,agents, and commercial relationships

Electric vehicles (EVs) present efficiency and environmental advantages over conventional transportation. It is expected that in the next decade this technology will progressively penetrate the market. The integration of plug-in electric vehicles in electric power systems poses new challenges in terms of regulation and business models. This paper proposes a conceptual regulatory framework for charging EVs. Two new electricity market agents, the EV charging manager and the EV aggregator, in charge of developing charging infrastructure and providing charging services are introduced. According to that, several charging modes such as EV home charging, public charging on streets, and dedicated charging stations are formulated. Involved market agents and their commercial relationships are analysed in detail. The paper elaborates the opportunities to formulate more sophisticated business models for vehicle-to-grid applications under which the storage capability of EV batteries is used for providing peak power or frequency regulation to support the power system operation. Finally penetration phase dependent policy and regulatory recommendations are given concerning time-of-use pricing, smart meter deployment, stable and simple regulation for reselling energy on private property, roll-out of public charging infrastructure as well as reviewing of grid codes and operational system procedures for interactions between network operators and vehicle aggregators.     

A comprehensive optimized model for on‐board solar photovoltaic system for plug‐in electric vehicles: energy and economic impacts

Environmental concerns along with high energy demand in transportation are leading to major development in sustainable transportation technologies, not the least of which is the utilization of clean energy sources. Solar energy as an auxiliary power source of on‐board fuel has not been extensively investigated. This study focuses on the energy and economic aspects of optimizing and hybridizing, the conventional energy path of plug‐in electric vehicles (EVs) using solar energy by means of on‐board photovoltaic (PV) system as an auxiliary fuel source. This study is novel in that the authors (i) modeled the comprehensive on‐board PV system for plug‐in EV; (ii) optimized various design parameters for optimum well‐to‐tank efficiency (solar energy to battery bank); (iii) estimated hybrid solar plug‐in EVs energy generation and consumption, as well as pure solar PV daily range extender; and (iv) estimated the economic return of investment (ROI) value of adding on‐board PVs for plug‐in EVs under different cost scenarios, driving locations, and vehicle specifications. For this study, two months in two US cities were selected, which represent the extremities in terms of available solar energy; June in Phoenix, Arizona and December in Boston, Massachusetts to represent the driving conditions in all the US states at any time followed by assessment of the results worldwide. The results show that, by adding on‐board PVs to cover less than 50% (around 3.2 m²) of the projected horizontal surface area of a typical passenger EV, the daily driving range could be extended from 3.0 miles to 62.5 miles by solar energy based on vehicle specifications, locations, season, and total time the EV remains at Sun. In addition, the ROI of adding PVs on‐board with EV over its lifetime shows only small negative values (larger than ?45%) when the price of electricity remains below Environmental concerns along with high energy demand in transportation are leading to major development in sustainable transportation technologies, not the least of which is the utilization of clean energy sources. Solar energy as an auxiliary power source of on‐board fuel has not been extensively investigated. This study focuses on the energy and economic aspects of optimizing and hybridizing, the conventional energy path of plug‐in electric vehicles (EVs) using solar energy by means of on‐board photovoltaic (PV) system as an auxiliary fuel source. This study is novel in that the authors (i) modeled the comprehensive on‐board PV system for plug‐in EV; (ii) optimized various design parameters for optimum well‐to‐tank efficiency (solar energy to battery bank); (iii) estimated hybrid solar plug‐in EVs energy generation and consumption, as well as pure solar PV daily range extender; and (iv) estimated the economic return of investment (ROI) value of adding on‐board PVs for plug‐in EVs under different cost scenarios, driving locations, and vehicle specifications. For this study, two months in two US cities were selected, which represent the extremities in terms of available solar energy; June in Phoenix, Arizona and December in Boston, Massachusetts to represent the driving conditions in all the US states at any time followed by assessment of the results worldwide. The results show that, by adding on‐board PVs to cover less than 50% (around 3.2 m²) of the projected horizontal surface area of a typical passenger EV, the daily driving range could be extended from 3.0 miles to 62.5 miles by solar energy based on vehicle specifications, locations, season, and total time the EV remains at Sun. In addition, the ROI of adding PVs on‐board with EV over its lifetime shows only small negative values (larger than ?45%) when the price of electricity remains below $0.18/kWh and the vehicle is driven in low‐solar energy area (e.g. Massachusetts in the US and majority of Europe countries). The ROI is more than 148% if the vehicle is driven in high‐solar energy area (e.g. Arizona in the US, most Africa countries, Middle East, and Mumbai in India), even if the electricity price remains low. For high electricity price regions ($0.35/kWh), the ROI is positive and high under all driving scenarios (above 560%). Also, the reported system has the potential to reduce electricity consumption from grid by around 4.5 to 21.0 MWh per EV lifetime. A sensitivity analysis has been carried out, in order to study the impacts of the car parked in the shade on the results. Copyright © 2016 John Wiley & Sons, Ltd.     

Distributed generation hybrid AC/DC microgrid protection: A critical review on issues,strategies, and future directions

The integration of distributed energy resources (DERs) with conventional systems emerges as an intelligent solution for providing uninterrupted and secure power even at times of high load demand. Better load management with a mature fault handling mechanism makes AC a viable option which has an efficiency of 78.24%. In contrast with less power loss and slightly better efficiency of 84.6%, DC microgrid is a reliable option in a low power environment. In order to accommodate all operating conditions and load types, a hybrid system can be designed with a theoretical efficiency of more than 90%. Bidirectional power flows, low inertia, the transition between different modes of operations are the challenges for the protection of alternating current (AC) and direct current (DC) microgrid systems. Power balance fluctuation, absence of zero-crossing currents, selection of suitable grounding, and coordination between different rating devices restrict the hybrid system to achieve the said efficiency constantly. This paper reviews in detail of existing protection along with grid-connected algorithms for both modes of operation. Finally, the limitation, major hurdles, and future course of action for a reliable, efficient, and secure hybrid grid system are figured out.     

Robust controller design of heat pump and plug-in hybrid electric vehicle for frequency control in a smart microgrid based on specified-structure mixed H2/H∞ control technique

This paper proposes a new robust controller design of heat pump (HP) and plug-in hybrid electric vehicle (PHEV) for frequency control in a smart microgrid (MG) system with wind farm. The intermittent power generation from wind farm causes severe frequency fluctuation in the MG. To alleviate frequency fluctuation, the smart control of power consumption of HP and the power charging of PHEV in the customer side can be performed. The controller structure of HP and PHEV is a proportional integral derivative (PID) with single input. To enhance the performance and robustness against system uncertainties of the designed controller, the particle swarm optimization based-mixed H₂/H_∞ control is applied to design the PID controllers of HP and PHEV. Simulation studies confirm the superior robustness and frequency control effect of the proposed HP and PHEV controllers in comparison to the conventional controller.     

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

The new generation of artificial intelligence (AI), called AI 2.0, has recently become a research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and electric power system (Smart EEPS). In AI 2.0, machine learning (ML) forms a typical representative algorithm category used to achieve predictions and judgments by analyzing and learning from massive amounts of historical and synthetic data to help people make optimal decisions. ML has preliminarily been applied to the Smart Grid (SG) and Energy Internet (EI) fields, which are important Smart EEPS representatives. AI 2.0, especially ML, is undergoing a critical period of rapid development worldwide and will play an essential role in Smart EEPS. In this context, this study, combined with the emerging SG and EI technologies, takes the typical representative of AI 2.0—ML—as the research objective and reviews its research status in the operation, optimization, control, dispatching, and management of SG and EI. The paper focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields. In this survey, we begin with an introduction to these seven types of ML methods and then systematically review their applications in Smart EEPS. Finally, we discuss ML development under the big data thinking and offer a prospect for the future development of AI 2.0 and ML in Smart EEPS. We conduct this survey intended to arouse the interest and excitement of experts and scholars in the EEPS industry and to look ahead to efforts that jointly promote the rapid development of AI 2.0 in the Smart EEPS field.