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.     

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.     

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.     

Methodology for modelling plug-in electric vehicles in the power system and cost estimates for a system with either smart or dumb electric vehicles

The article estimates the costs of plug-in electric vehicles (EVs) in a future power system as well as the benefits from smart charging and discharging EVs (smart EVs). To arrive in a good estimate, a generation planning model was used to create power plant portfolios, which were operated in a more detailed unit commitment and dispatch model. In both models the charging and discharging of EVs is optimised together with the rest of the power system. Neither the system cost nor the market price of electricity for EVs turned out to be high (36-263 €/vehicle/year in the analysed scenarios). Most of the benefits of smart EVs come from smart timing of charging although benefits are also accrued from provision of reserves and lower power plant portfolio cost. The benefits of smart EVs are 227 €/vehicle/year. This amount has to cover all expenses related to enabling smart EVs and need to be divided between different actors. Additional benefits could come from the avoidance of grid related costs of immediate charging, but these were not part of the analysis.     

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.     

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.     

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 CO₂-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.     

A field study of human factors and vehicle performance associated with PHEV adaptation

Smart-grid and electric-vehicle technologies are rapidly diffusing, yet important policy implications remain to be fully analyzed. This multi-year field study sought to fill part of this gap by exploring human adaptation to plug-in hybrid electric vehicle (PHEV) performance and vehicle charging in smart-grid environments. Homes were equipped with smart meters in a smart-grid experiment conducted by the local utility. Study households were organized by either standard or time-of-use electricity pricing, and randomly assigned to “managed” or “unmanaged” charging scenarios. Using a mixed-methods approach, study data were collected through vehicle data loggers, smart-plugs interviews, and questionnaires. The paper describes vehicle operations and performance; the ways in which households managed PHEV charging; and the manner in which they responded to smart-grid, smart-plug, and dashboard feedback. Findings indicate that households actively managed PHEV charging; however, they preferred flexible charging scenarios. Charging-management decisions were influenced by electricity-pricing. Online feedback on household- and vehicle-electricity consumption was generally ignored, but drivers responded to dashboard feedback as they drove. These results provide empirical bases for government and corporate policymakers to improve policy decisions relative to PHEV impacts on electricity loads, design of smart-grid feedback, and design of charging infrastructures.     

Emissions impacts of plug-in hybrid electric vehicle deployment on the U.S. western grid

The constantly evolving western grid of the United States is characterized by complex generation dispatch based on economics, contractual agreements, and regulations. The future electrification of transportation via plug-in electric vehicles calls for an energy and emissions analysis of electric vehicle (EV) penetration scenarios based on realistic resource dispatch. A resource dispatch and emissions model for the western grid is developed and a baseline case is modeled. Results are compared with recorded data to validate the model and provide confidence in the analysis of EV-grid interaction outlooks. A modeled dispatch approach, based on a correlation between actual historical dispatch and system load data, is exercised to show the impacts (emission intensity, temporally resolved load demand) associated with EV penetration on the western grid. The plug-in hybrid electric vehicle (PHEV) and selected charging scenarios are the focus for the analysis. The results reveal that (1) a correlation between system load and resource group capacity factor can be utilized in dispatch modeling, (2) the hourly emissions intensity of the grid depends upon PHEV fleet charge scenario, (3) emissions can be reduced for some species depending on the PHEV fleet charge scenario, and (4) the hourly model resolution of changes in grid emissions intensity can be used to decide on preferred fleet-wide charge profiles.     

Plug-in hybrid electric vehicles can be clean and economical in dirty power systems

Plug-in hybrid electric vehicles (PHEVs) that are driven and charged in ‘dirty’ power systems, with high penetrations of coal and other polluting generation fuels, may yield higher net emissions than conventional vehicles (CVs). We examine the implications of imposing a constraint on PHEV recharging that forces emissions from PHEVs to be no greater than those from a comparable CV. We use the Texas power system, which has a mix of coal- and natural gas-fired generation and has been shown to yield higher emissions from PHEVs than CVs, as a case study. Our results show that imposing the emissions constraint results in most of the PHEV charging loads being shifted from coal- to cleaner natural gas-fired generators. There is, however, virtually no increase in generation or PHEV driving costs due to efficiency benefits that are possible through coordination of unit commitment and PHEV charging decisions.     

电动汽车峰谷分时充放电电价研究

在插电式混合动力汽车有望规模化应用的背景下,供电公司与PHEV用户市场力的博弈越来越频繁和紧密,以电价作为杠杆,计及供电侧平滑电网等效负荷波动和用户侧效益,采用电价综合反应曲线和用电弹性矩阵来分别表示PHEV用户充电和放电弹性特性建立数学模型,应用粒子群优化算法对模型进行了求解。最后,通过算例验证了合理的充放电峰谷分时电价机制调控PHEV用户接入电网行为对平滑系统负荷的有效性,同时PHEV用户经济效益也得到满足。     

Demand response modeling considering Interruptible/Curtailable loads and capacity market programs

Recently, a massive focus has been made on demand response (DR) programs, aimed to electricity price reduction, transmission lines congestion resolving, security enhancement and improvement of market liquidity. Basically, demand response programs are divided into two main categories namely, incentive-based programs and time-based programs. The focus of this paper is on Interruptible/Curtailable service (I/C) and capacity market programs (CAP), which are incentive-based demand response programs including penalties for customers in case of no responding to load reduction. First, by using the concept of price elasticity of demand and customer benefit function, economic model of above mentioned programs is developed. The proposed model helps the independent system operator (ISO) to identify and employ relevant DR program which both improves the characteristics of the load curve and also be welcome by customers. To evaluate the performance of the model, simulation study has been conducted using the load curve of the peak day of the Iranian power system grid in 2007. In the numerical study section, the impact of these programs on load shape and load level, and benefit of customers as well as reduction of energy consumption are shown. In addition, by using strategy success indices the results of simulation studies for different scenarios are analyzed and investigated for determination of the scenarios priority.     

Evaluation of combined heat and power (CHP) systems performance with dual power generation units for different building configurations

This paper evaluates the economic, energetic, and environmental feasibility of using two power generation units (PGUs) to operate a combined heat and power (CHP) system. Several benchmark buildings developed by the Department of Energy simulated using the weather data for Chicago, IL, are used to analyze the proposed configuration. This location has been selected because it usually provides favorable CHP system conditions in terms of cost and emission reduction. For the proposed configuration, one PGU is operated at base load to satisfy part of the electricity building requirements, whereas the other is used to satisfy the remaining electricity requirement operating following the electric load. The dual‐PGU CHP configuration (D‐CHP) is modeled for four different scenarios to determine the optimum operating range for the selected benchmark buildings. The dual‐PGU scenario is compared with the reference building using conventional technology to determine the benefits of this proposed system in terms of operational cost, primary energy reduction, and carbon dioxide emissions. The D‐CHP system results are also compared with a CHP system operating following the electric load (FEL) and base‐loaded CHP system. For three of the selected buildings, the proposed D‐CHP system provides comparable or greater savings in operating cost, primary energy consumption, and carbon dioxide emissions than the optimized conditions for base loading and FEL. In addition, the effect of operating the D‐CHP system only during certain months of the year on the overall operational cost is also evaluated. Results indicate that not operating the D‐CHP system for the months where the thermal load is too low is beneficial for the overall system performance. Copyright © 2012 John Wiley & Sons, Ltd.     

A centralized vehicle‐to‐grid scheme with distributed computing capacity engaging internet of smart charging points: Case study

In order to accommodate additional plug‐in electric vehicle (PEV) charging loads for existing distribution power grids, the vehicle‐to‐grid (V2G) technology has been regarded as a cost‐effective solution. Nevertheless, it can hardly scale up to large PEVs fleet coordination due to the computational complexity issue. In this paper, a centralized V2G scheme with distributed computing capability engaging internet of smart charging points (ISCP) is proposed. Within ISCP, each smart charging point equips a computing unit and does not upload PEV sensitive information to the energy coordinator, to protect PEV users’ privacy. Particularly, the computational complexity can be decreased dramatically by employing distributed computing, viz., by decomposing the overall scheduling problem into many manageable sub‐problems. Moreover, six typical V2G scenarios are analyzed deliberately, and based on that, a load peak‐shaving and valley‐filling scheduling algorithm is built up. The proposed algorithm can be conducted in real‐time to mitigate the uncertainties in arrival time, departure time, and energy demand. Finally, the proposed scheme and its algorithm are verified under the distribution grid of the SUSTech campus (China). Compared with uncoordinated charging, the proposed scheme realizes load peak‐shaving and valley‐filling by 11.98% and 12.68%, respectively. The voltage values are ensured within the limitation range by engaging power flow calculation, in which the minimum voltage values are increasing and the maximum voltage values are decreasing with the expansion of PEV penetration. What is more, the computational complexity of peak‐shaving and valley‐filling strategy is near‐linear, which verifies the proposed scheme can be carried out very efficiently.     

Combined heat and power systems with dual power generation units and thermal storage

The objective of this paper is to study the performance of a combined heat and power (CHP) system that uses two power generation units (PGU). In addition, the effect of thermal energy storage is evaluated for the proposed dual‐PGU CHP configuration (D‐CHP). Two scenarios are evaluated in this paper. In the first scenario, one PGU operates at base‐loading condition, while the second PGU operates following the electric load. In the second scenario, one PGU operates at base‐loading condition, while the second PGU operates following the thermal load. The D‐CHP system is modeled for the same building in four different locations to account for variation of the electric and thermal loads due to weather data. The D‐CHP system results are compared with the reference building by using conventional technology to determine the benefits of this proposed system in terms of operational cost and carbon dioxide emissions. The D‐CHP system results, with and without thermal storage, are also compared with that of single‐PGU CHP systems operating following the electric load (FEL), following the thermal load (FTL), and base‐loaded (BL). Results indicate that the D‐CHP system operating either FEL or FTL in general provides better results than a single‐PGU CHP system operating FEL, FTL, or BL. The addition of thermal storage enhances the potential benefits from D‐CHP system operation in terms of operational cost savings and emissions savings. Copyright © 2013 John Wiley & Sons, Ltd.     

Smart residential energy management system for demand response in buildings with energy storage devices

In the present scenario, the utilities are focusing on smart grid technologies to achieve reliable and profitable grid operation. Demand side management (DSM) is one of such smart grid technologies which motivate end users to actively participate in the electricity market by providing incentives. Consumers are expected to respond (demand response (DR)) in various ways to attain these benefits. Nowadays, residential consumers are interested in energy storage devices such as battery to reduce power consumption from the utility during peak intervals. In this paper, the use of a smart residential energy management system (SREMS) is demonstrated at the consumer premises to reduce the total electricity bill by optimally time scheduling the operation of household appliances. Further, the SREMS effectively utilizes the battery by scheduling the mode of operation of the battery (charging/floating/discharging) and the amount of power exchange from the battery while considering the variations in consumer demand and utility parameters such as electricity price and consumer consumption limit (CCL). The SREMS framework is implemented in Matlab and the case study results show significant yields for the end user.     

增程式燃料电池混合动力有轨电车电源系统优化配置

为实现燃料电池混合动力有轨电车的经济运行,提出以燃料电池为增程式动力源的运行模式,并通过步进式枚举法对电源系统进行优化配置.首先,定义燃料电池混合动力有轨电车的运行模式及电源系统混合度,构建各电源系统模型;然后,建立电源系统全寿命周期综合成本函数,并考虑约束条件对电源系统进行优化配置;最后,以全寿命周期经济性、充电桩容...     

Aggregator-based demand response mechanism for electric vehicles participating in peak regulation in valley time of receiving-end power grid

With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles (EVs), an aggregator-based demand response (DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator (ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.     

Multicriterion optimal electric drive vehicle selection based on lifecycle emission and lifecycle cost

This research paper examines the optimal choice among conventional gasoline vehicles, hybrid electric vehicles (HEVs), plug‐in HEVs (PHEV), and full‐battery EVs taking into account the different characteristics of these vehicles, such as cost, emissions per mile, and vehicle miles to be traveled between refueling and acceleration time. The existing challenges for wide‐spread deployment of EVs are availability of charging infrastructure, higher cost, long time for charging, and lower travel millage compared with conventional vehicles. Statistical data are considered for determining the spatially varying average daily vehicle miles traveled (VMT) across the United States, which, together with charging behavior, can influence the optimal choice among EV with different travel ranges. Two alternative cases for charging are examined: (1) home‐only charging and (2) home plus work charging. The motivation of this work is to select the optimal EV among their types when lifecycle cost and lifecycle emission are considered. The optimization model seeks to minimize total lifecycle cost and emissions for each level of VMT per day. It is found that when lifecycle cost is the sole objective, HEV is usually the best choice, especially for higher VMT levels. When lifecycle greenhouse gas emission is the sole objective, PHEV1 (PHEV with 1 charging station) is the optimal solution over a wide range of VMTs. The outcome of this provides a roadmap for the selection of EVs based on their annual VMT to reduce both lifecycle emission and lifecycle cost.     

Stochastic security-constrained operation of wind and hydrogen energy storage systems integrated with price-based demand response

Because of highly increasing energy consumption, environmental issues and lack of common energy sources, the use of renewable energy sources especially wind power generation technology is increasing with significant growth in the world. But due to the variable nature of these sources, new challenges have been created in the balance between production and consumption of power system. The hydrogen energy storage (HES) system by storing excess wind power through the technology of power to hydrogen (P2H) and delivering it to the electricity network through hydrogen-based gas turbine at the required hours reduces not only wind alternation but can play an important role in balancing power production and consumption. On the other hand, power consumers by participating in demand response (DR) programs can reduce their consumption at peak load or wind power shortage hours, and increase their consumption at low-load or excess wind power hours to reduce wind power spillage and system energy cost. This paper proposes a stochastic security constrained unit commitment (SCUC) with wind energy considering coordinated operation of price-based DR and HES system. Price-based DR has been formulated as a price responsive shiftable demand bidding mechanism. The proposed model has been tested on modified 6-bus and 24-bus systems. The numerical results show the effect of simultaneous consideration of HES system and price-based DR integrated with wind energy on hourly generation scheduling of thermal units. As a result there is some reduction in wind generation power spillage and daily operation cost.