Smart utilization of renewable energy sources in a microgrid system integrated with plug‐in hybrid electric vehicles

Mass roll‐out of plug‐in hybrid electric vehicles (PHEVs) and significant penetration of renewable energy sources in distribution system play a major role in delivering low carbon environment. However, placing and utilizing these units randomly result in overloading, increased power loss, and reduced voltage profile. This paper responds to these technical challenges by using a strategic placement method for locating the distributed generation (DG) and the charging station (CS) of PHEVs in a multi‐zone distribution system. For simultaneously scheduling of these units in each zone, the smart energy management framework is proposed in this paper. Apart from usual energy management constraints, this paper also incorporates the real‐time constraints involving the capacity of PHEV batteries, the mobility pattern, and the power level of the charging infrastructure. The simulation studies are carried out for each hour of a day. To cope with this time constraint execution, particle swarm optimization algorithm‐based approach is used. The proposed framework is tested in IEEE 33 and IEEE 69 bus radial distribution system. The obtained results imply that the presented energy management framework provides maximum profits for the vehicle owner, and meanwhile it fulfills preferences of the user in each zone simultaneously.     

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.     

Simultaneous planning of plug-in hybrid electric vehicle charging stations and wind power generation in distribution networks considering uncertainties

Recently, plug-in hybrid electric vehicles (PHEV) are becoming more attractive than internal combustion engine vehicles (ICEV). Hence, design and modeling of charging stations (CSs) has vital importance in distribution system level. In this paper, a new formulation for PHEV charging stations is presented with the strategic presence of wind power generation (WPG). This study considers constraints of the system losses, the regulatory voltage limits, and the charge/discharge schedule of PHEV based on the social behavior of drivers for appropriate placement of PHEV charging stations in electricity grid. The role of CSs and WPG units must be correctly assessed to optimize the investment and operation cost for the whole system. However, the wind generation owners (WGOs) have different objective functions which might be contrary to the objectives of distribution system manager (DSM). It is assumed that aggregating and management of charge/discharge program of PHEVs are smartly carried out by DSM. This paper presents a long-term bi-objective model for optimal planning of PHEV charging stations and WPG units in distribution systems which simultaneously optimize two objectives, namely the benefits of DSM and WGO. It also considers the uncertainty of load growth, electricity price and PHEV access to the charging station using Mont-Carlo simulation (MCS) method. Initial state of charge uncertainty is also modeled based on scenario approach in PHEV batteries and wind turbine power generation using weibull distribution. Non dominated sorting genetic algorithm (NSGA-II) is used to solve the optimization problem. The simulation has been conducted on the nine-bus system.     

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 6.00 a.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.     

A price‐regulated electric vehicle charge‐discharge strategy for G2V,V2H,and V2G

Electric vehicles (EVs) and smart grids are gradually revolutionising the transportation sector and electricity sector respectively. In contrast to unplanned charging/discharging, smart use of EV in home energy management system (HEMS) can ensure economic benefit to the EV owner. Therefore, this paper has proposed a new energy pricing controlled EV charging/discharging strategy in HEMS to acquire maximum financial benefit. EV is scheduled to be charged/discharged according to the price of electricity during peak and off‐peak hours. In addition, two different types of EV operation modes, ie, grid‐to‐vehicle (G2V) in off‐peak time and vehicle‐to‐home (V2H) in on‐peak time are considered to determine comparative economic benefit of planned EV charging/discharging. The real load profile of a house in Melbourne and associated electricity pricing is selected for the case study to determine the economic gain. The simulation results illustrate that EV participating in V2H contributes approximately 11.6% reduction in monthly electricity costs compared with G2V operation mode. Although the facility of selling EV energy to the grid is not available currently, the pricing controlled EV charging/discharging presented in the paper can be used if such facility becomes available in the future.     

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.     

Impact of electric vehicles on a future renewable energy‐based power system in Europe with a focus on Germany

Electric mobility is expected to play a key role in the decarbonisation of the energy system. Continued development of battery electric vehicles is fundamental to achieving major reductions in the consumption of fossil fuels and of CO₂ emissions in the transport sector. Hydrogen can become an important complementary synthetic fuel providing electric vehicles with longer ranges. However, the environmental benefit of electric vehicles is significant only if their additional electricity consumption is covered by power production from renewable energy sources. Analysing the implications of different scenarios of electric vehicles and renewable power generation considering their spatial and temporal characteristics, we investigate possible effects of electric mobility on the future power system in Germany and Europe. The time horizon of the scenario study is 2050. The approach is based on power system modelling that includes interchange of electricity between European regions, which allows assessing long‐term structural effects in energy systems with over 80% of renewable power generation. The study exhibits strong potential of controlled charging and flexible hydrogen production infrastructure to avoid peak demand increases and to reduce the curtailment of renewable power resulting in reduced system operation, generation, and network expansion costs. A charging strategy that is optimised from a systems perspective avoids in our scenarios 3.5 to 4.5 GW of the residual peak load in Germany and leads to efficiency gains of 10% of the electricity demand of plug‐in electric vehicles compared with uncontrolled loading.     

The pricing of charging for electric vehicles in China—Dilemma and solution

The number of electric vehicles in China is expected to grow rapidly, triggering the nationwide large-scale construction of charging stations. At the same time, a reasonable charging price has not been established. This article records the views of station operators and EV users and calculates a charging pricing range. The price should not only ensure the profit of operators, but also help reduce EV users’ expenditure compared to using internal-combustion-engine vehicles. Based on current energy prices and battery costs, charging stations are unable to make profit, and the pricing shortfall is up to 0.78 RMB yuan(kWh)⁻¹. Only with a 25% increase in energy price or 25% reduction in battery cost can charging stations become profitable. Several suggestions are proposed to improve station profits. First, ensuring a high station load is helpful to increase profits, and it is estimated the reasonable number of chargers in Beijing is approximately 6000, distributed among 672 stations. Second, the use of storage batteries for on/off-peak electricity self-management can also increase the annual profit by 600,000 RMB yuan. In addition, other methods like a jointed station alliance, multiple energy supplement approaches, vehicle-to-grid technology and state subsidy can also accelerate the development of the charging service industry.     

PV powered smart charging station for PHEVs

A huge inrush of PHEVs is envisioned in the future. There is a growing risk that, this proliferation in the number of PHEVs will trigger extreme surges in demand while charging them during rush hours. To mitigate this impact, a smart charging station is proposed in which the charging of the PHEVs is controlled in such a way that the impact of charging during peak load period is not felt on the grid. The power needed to charge the plug in hybrids comes from grid-connected photovoltaic generation or the utility or both. The three way interaction between the PV, PHEVs and the grid ensures optimal usage of available power, charging time and grid stability. The system designed to achieve the desired objective consists of a photovoltaic system, DC/DC boost converter, DC/AC bi-directional converter and DC/DC buck converter. The output of DC/DC boost converter and input of DC/AC bi-directional converter share a common DC link. A unique control strategy based on DC link voltage sensing is proposed for the above system for efficient transfer of energy.     

Grid integration of intermittent renewable energy sources using price-responsive plug-in electric vehicles

Plug-in electric vehicles (PEVs) are expected to balance the fluctuation of renewable energy sources (RES). To investigate the contribution of PEVs, the availability of mobile battery storage and the control mechanism for load management are crucial. This study therefore combined the following: a stochastic model to determine mobility behavior, an optimization model to minimize vehicle charging costs and an agent-based electricity market equilibrium model to estimate variable electricity prices. The variable electricity prices are calculated based on marginal generation costs. Hence, because of the merit order effect, the electricity prices provide incentives to consume electricity when the supply of renewable generation is high. Depending on the price signals and mobility behavior, PEVs calculate a cost minimizing charging schedule and therefore balance the fluctuation of RES. The analysis shows that it is possible to limit the peak load using the applied control mechanism. The contribution of PEVs to improving the integration of intermittent renewable power generation into the grid depends on the characteristic of the RES generation profile. For the German 2030 scenario used here, the negative residual load was reduced by 15–22% and the additional consumption of negative residual load was between 34 and 52%.     

Impact of battery weight and charging patterns on the economic and environmental benefits of plug-in hybrid vehicles

Plug-in hybrid electric vehicle (PHEV) technology is receiving attention as an approach to reducing US dependency on foreign oil and greenhouse gas (GHG) emissions from the transportation sector. PHEVs require large batteries for energy storage, which affect vehicle cost, weight, and performance. We construct PHEV simulation models to account for the effects of additional batteries on fuel consumption, cost, and GHG emissions over a range of charging frequencies (distance traveled between charges). We find that when charged frequently, every 20 miles or less, using average US electricity, small-capacity PHEVs are less expensive and release fewer GHGs than hybrid electric vehicles (HEVs) or conventional vehicles. For moderate charging intervals of 20–100 miles, PHEVs release fewer GHGs, but HEVs have lower lifetime costs. High fuel prices, low-cost batteries, or high carbon taxes combined with low-carbon electricity generation would make small-capacity PHEVs cost competitive for a wide range of drivers. In contrast, increased battery specific energy or carbon taxes without decarbonization of the electricity grid would have limited impact. Large-capacity PHEVs sized for 40 or more miles of electric-only travel do not offer the lowest lifetime cost in any scenario, although they could minimize GHG emissions for some drivers and provide potential to shift air pollutant emissions away from population centers. The tradeoffs identified in this analysis can provide a space for vehicle manufacturers, policymakers, and the public to identify optimal decisions for PHEV design, policy and use. Given the alignment of economic, environmental, and national security objectives, policies aimed at putting PHEVs on the road will likely be most effective if they focus on adoption of small-capacity PHEVs by urban drivers who can charge frequently.     

Regime-switching based vehicle-to-building operation against electricity price spikes

Electricity price may present very large spikes due to imbalance between generation and demand, especially during heavily loaded periods. Such peak price may incur significant cost to building operation. With the vehicle-to-building (V2B) technology, electric vehicle battery can be used as temporal energy source for the building load for a short period, which leads to a possible solution for reducing the energy cost during peak-price periods. In this paper, the problem of reducing the energy cost due to the peak price is approached from the prospective of risk management. A regime-switching based risk management scheme is proposed for the V2B operation based on the availability of electric vehicles (EV) plugged in the parking lots attached to the building. In the low risk regime, the objective is to minimize the EV charging cost. While in the high risk regime, the objective is to reduce the potentially high energy cost due the peak price via the power stored in EV batteries. Based on Markov regime-switching model, the operation minimizes the conditional value at risk involved. Simulation results show that the proposed framework can greatly reduce the energy cost against the electricity peak prices.     

Effectiveness of smart charging of electric vehicles under power limitations

This article investigates charging strategies for plug‐in hybrid electric vehicles (PHEV) as part of the energy system. The objective was to increase the combined all‐electric mileage (total distance driven using only the traction batteries in each PHEV) when the total charging power at each workplace is subject to severe limitations imposed by the energy system. In order to allocate this power optimally, different input variables, such as state‐of‐charge, battery size, travel distance, and parking time, were considered. The required vehicle mobility was generated using a novel agent‐based model that describes the spatiotemporal movement of individual PHEVs. The results show that, in the case of Helsinki (Finland), smart control strategies could lead to an increase of over 5% in the all‐electric mileage compared to a no‐control strategy. With a high prediction error, or with a particularly small or large battery, the benefits of smart charging fade off. Smart PHEV charging strategies, when applied to the optimal allocation of limited charging power between the cars of a vehicle fleet, seem counterintuitively to provide only a modest increase in the all‐electric mileage. A simple charging strategy based on allocating power to PHEVs equally could thus perform sufficiently well. This finding may be important for the future planning of smart grids as limiting the charging power of larger PHEV fleets will sometimes be necessary as a result of grid restrictions. Copyright © 2013 John Wiley & Sons, Ltd.     

Optimal charging of electric drive vehicles in a market environment

With a potential to facilitate the integration of renewable energy into the electricity system, electric drive vehicles may offer a considerable flexibility by allowing for charging and discharging when desired. This paper takes the perspective of an aggregator that manages the electricity market participation of a vehicle fleet and presents a framework for optimizing charging and discharging of the electric drive vehicles, given the driving patterns of the fleet and the variations in market prices of electricity. When the aggregator is a price-taker the optimization can be stated in terms of linear programming whereas a quadratic programming formulation is required when he/she has market power. A Danish case study illustrates the construction of representative driving patterns through clustering of survey data from Western Denmark and the prediction of electricity price variations through regression on prices from the Nordic market. The results show that electric vehicles provide flexibility almost exclusively through charging. Moreover, the vehicles provide flexibility within the day but only limited flexibility from day to day when driving patterns are fixed.     

Reliability evaluation and analysis for NEV charging station considering the impact of charging experience

Battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (HFCVs) will predominate in near future, and the new energy vehicle (NEV) charging station which provides charging services for aforementioned NEVs could grow rapidly. The reliability of the NEV charging station would be the primary concern for early construction and NEV users. This study investigates the reliability evaluation of NEV charging station considering the impact of charging experience and analyzes the influence of various factors by comparing the evaluation results. The explicit modelling of the station considering power generation system, coupling devices and hydrogen storage is presented and an optimal revenue model is established to coordinate the operation of the station. A reliability index system is established to evaluate the charging reliability of the NEV charging station and reflect the charging experience. In addition, an amount model estimating the number of vehicles accessed in the coming days is proposed to address the impact of driver charging experience on the reliability evaluation. The results show that it is necessary to consider the charging experience in reliability evaluation. The comparison and analysis of reliability evaluation results reveal that the charging reliability and profit of the charging station are influenced by the initial hydrogen in tank, the price of hydrogen/electricity and the sizes of electrolyzer, hydrogen tank and fuel cell. The reliability evaluation provides guidance for determining the parameters of these factors.     

Potential impacts assessment of plug-in electric vehicles on the Portuguese energy market

Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs), which obtain their fuel from the grid by charging a battery, are set to be introduced into the mass market and expected to contribute to oil consumption reduction. In this research, scenarios for 2020 EVs penetration and charging profiles are studied integrated with different hypotheses for electricity production mix. The impacts in load profiles, spot electricity prices and emissions are obtained for the Portuguese case study. Simulations for year 2020, in a scenario of low hydro production and high prices, resulted in energy costs for EVs recharge of 20 cents/kWh, with 2 million EVs charging mainly at evening peak hours. On the other hand, in an off-peak recharge, a high hydro production and low wholesale prices' scenario, recharge costs could be reduced to 5.6 cents/kWh. In these extreme cases, EV's energy prices were between 0.9€ to 3.2€ per 100 km. Reductions in primary energy consumption, fossil fuels use and CO₂ emissions of up to 3%, 14% and 10%, respectively, were verified (for a 2 million EVs' penetration and a dry year's off-peak recharge scenario) from the transportation and electricity sectors together when compared with a BAU scenario without EVs.     

Optimized sizing of photovoltaic grid‐connected electric vehicle charging system using particle swarm optimization

In this paper, the particle swarm optimization (PSO) is used to find optimum size of the photovoltaic (PV) array and energy storage unit (ESU) for PV grid‐connected charging system (in office workplace) for electric vehicles (EV). It is designed in such a way that the EVs are charged at a fixed price (rather than time‐of‐use price) without incurring economic losses to the station owner. The simulation is modeled using the single diode model (for PV) and the state of charge of Li‐ion battery (for ESU and EV). The objective function of the PSO is formulated based on a financial model that comprises of the grid tariff, EV demand, and the purchasing as well as selling prices of the energy from PV and ESU. By integrating the financial model with energy management algorithm (EMA), the PSO computes the minimum number of PV modules (N_pv) and ESU batteries (N_bat) for a various number of vehicles and office holidays. The resiliency of the proposed system is validated under different weather conditions, EV fleet, parity levels, energy prices, and operating period. Furthermore, the performance of the proposed system is compared with the standard grid charging system. The results suggest that with the computed N_pv and N_bat, the charging price is decreased by approximately 16%, while the EV charging burden on the grid is reduced by 94% to 99%. It is envisaged that this work provides the guidance for the installers to precisely determine the optimum size of the components prior to the physical construction of the charging station.     

The effects of electricity pricing on PHEV competitiveness

Plug-in hybrid electric vehicles (PHEVs) will soon start to be introduced into the transportation sector, thereby raising a host of issues related to their use, adoption and effects on the electricity sector. Their introduction has the potential to significantly reduce carbon emissions from the transportation sector, which has led to government policies aimed at easing their introduction. If their widespread adoption is set as a target it is imperative to consider the effects of existing policies that may increase or decrease their adoption rate. In this study, we present a micro level electricity demand model that can gauge the effects of PHEVs on household electricity consumption and the subsequent economic attractiveness of the vehicles. We show that the electricity pricing policy available to the consumer is a very significant factor in the economic competitiveness of PHEVs. Further analysis shows that the increasing tier electricity pricing system used in California will substantially blunt adoption of PHEVs in the state; and time of use electricity pricing will render PHEVs more economically attractive in any state.     

Air quality and environmental impacts of alternative vehicle technologies in Ontario,Canada

This study is focused on the province-wide emissions in Ontario, Canada and urban air pollution in the city of Toronto. The life-cycle (LC) impacts of utilizing alternative fuels for transportation purposes is considered in terms of six major stressors for climate change, acidification and urban air quality. The vehicles considered are plug-in hybrid electric vehicles (PHEVs), fuel cell vehicles (FCVs) and fuel cell plug-in hybrid electric vehicles (FCPHEVs). Modeling of the penetration rates for these types of vehicles has been completed based on the maximum base-load capacity of Ontario's electricity grid to accommodate the generation of hydrogen and charging of vehicles using grid electricity. Results show that the reduction in greenhouse gas emissions from adoption of PHEVs or FCVs will exceed 3% of the current emissions from the transportation sector in Ontario while FCPHEVs may achieve almost twice this reduction. All vehicles exhibit similar impacts on the precursors for photochemical smog although the province-wide effects differ significantly.     

Plug-in hybrid electric vehicle impacts on hourly electricity demand in the United States

In this study we explore the effects of different charging behaviors of PHEVs in the United States on electricity demand profiles and energy use, in terms of time of day and location (at home, the workplace, or public areas). Based on driving behavior statistics on vehicle distance traveled and daily trips (US DOT, 2003) in the US, we develop a simulation algorithm to estimate the PHEV charging profiles of electricity demand with plausible plug-in times and depth of discharge of the PHEVs.