Optimal dispatch for reversible solid oxide cell-based hydrogen/electric vehicle aggregator via stimuli-responsive charging decision estimation

The ongoing growth of green vehicles had led to an increase in demand of cost-effective and driver-satisfactory hydrogen/electric vehicle aggregators (HEVAs). However, existing approaches for cost minimization of HEVA can lead to poor performance due to the inaccurate modelling of power–gas exchange system and neglection of schedulable characteristics of loads. Furthermore, the behaviour of drivers was rarely considered from a psychological perspective. To resolve these limitations, the optimal dispatch scheme of HEVA, equipped with reversible solid oxide cell (rSOC), is investigated by quantifying drivers’ charging decision response toward pricing stimuli. As the core of the bi-directional energy conversion, rSOC is modelled by considering the climbing power constraints and time-dependent restart-up cost. At the driver side, EVs are aggregated as clusters for efficient computation. Two charging modes are designed for drivers with incentive discounts. To measure the relationship between external factors and charging decision response, the stimuli-responsive charging decision estimation is proposed by introducing Weber–Fechner law (W–F Law). To minimum operation cost, a mixed integer nonlinear programming (MINP) method is presented. The results validate that the operation cost of HEVA can be decreased by 19.37%, and the maximum utilization of energy is realised in the proposed scheme. Additionally, the impacts of sizes of power–gas exchange devices are investigated for practical reference. Under a given charging demand, the proposed dispatch scheme can realise installation of smaller devices, and thereby, resulting in lower construction cost.     

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.     

基于点估计法的配电网电动汽车停车场选址定容方法

文章提出了一种基于点估计法的概率方法来确定配电网中电动汽车停车场的最佳容量和位置。该方法考虑了车主驾驶模式的不确定性参数,并研究了这些不确定性参数对停车场选址定容的影响,同时考虑了技术和经济两个方面。另外,考虑到优化调度的必要性,文章还提出了一种电动汽车充电调度的新方法,以实现经济效益最优。文章方法具有原理简单且可以实现全局优化的优点。最后,对测试配电网进行了概率分析,并对所得结果进行了讨论。     

Off-grid solar powered charging station for electric and hydrogen vehicles including fuel cell and hydrogen storage

This paper designs an off-grid charging station for electric and hydrogen vehicles. Both the electric and hydrogen vehicles are charged at the same time. They appear as two electrical and hydrogen load demand on the charging station and the charging station is powered by solar panels. The output power of solar system is separated into two parts. On part of solar power is used to supply the electrical load demand (to charge the electric vehicles) and rest runs water electrolyzer and it will be converted to the hydrogen. The hydrogen is stored and it supplies the hydrogen load demand (to charge the hydrogen-burning vehicles). The uncertainty of parameters (solar energy, consumed power by electrical vehicles, and consumed power by hydrogen vehicles) is included and modeled. The fuel cell is added to the charging station to deal with such uncertainty. The fuel cell runs on hydrogen and produces electrical energy to supply electrical loading under uncertainties. The diesel generator is also added to the charging station as a supplementary generation. The problem is modeled as stochastic optimization programming and minimizes the investment and operational costs of solar and diesel systems. The introduced planning finds optimal rated powers of solar system and diesel generator, operation pattern for diesel generator and fuel cell, and the stored hydrogen. The results confirm that the cost of changing station is covered by investment cost of solar system (95%), operational cost of diesel generator (4.5%), and investment cost of diesel generator (0.5%). The fuel cell and diesel generator supply the load demand when the solar energy is zero. About 97% of solar energy will be converted to hydrogen and stored. The optimal operation of diesel generator reduces the cost approximately 15%.     

Dynamic planning and energy management strategy of integrated charging and hydrogen refueling at highway energy supply stations considering on-site green hydrogen production

The layout of electric vehicles charging stations and hydrogen refueling stations (HRSs) is more and more necessary with the development of electric vehicles (EVs) and progress in hydrogen energy storage technology. Due to the high costs of HRSs and the low demand for hydrogen, it is difficult for independent HRSs to make a profit. This study focuses on the dynamic planning of energy supply stations on highways in the medium and long term, considering the growth of EV charging demand and the change in the proportion of hydrogen fuel cell vehicles (HFCVs). Based on the perspective of renewable energy generators (REGs), this study seeks the dynamic optimal configuration and comprehensive benefits of adding HRS and battery to existing EVCS considering the travel rules of new energy vehicles (NEVs). The results show that (1) It is profitable for REGs to invest in HRSs; (2) The economy of investment in batteries by REGs depends on the source-load matching. It is feasible only when the output of renewable energy is difficult to meet the demand. (3) The business model of REGs producing hydrogen on-site and supplying both electricity and hydrogen is feasible.     

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.     

基于智能电网的电动汽车充放电分时电价及引导策略

为了以绿色、环保能源满足全球可持续发展的需求,可再生能源和电动汽车在全球范围内受到广泛推崇.在此情形下,高比例可再生能源发电和大规模电动汽车无序分散接入电网必将导致供求曲线的不稳定.为此,借助云存储技术和智能电网,提出了一种基于供求曲线的电动汽车充放电分时电价,并在制定充放电价格时考虑充电站的空闲率.以实现充电站和用户...     

Robust design of off-grid solar-powered charging station for hydrogen and electric vehicles via robust optimization approach

In this article, a robust optimization approach for designing an off-grid solar-powered charging station is proposed to provide electric vehicles (EVs) with electricity and hydrogen vehicles (HV) with hydrogen. A water electrolyzer (WE) is installed in the system to produce and store hydrogen, which is used by the HVs and fuel cell (FC). During the inaccessibility of the photovoltaic (PV) system to feed the EVs, the FC runs on hydrogen to regenerate electricity. Besides, in case the PV system and FC have power shortage to meet the demand of EVs, a diesel generator contributes to electricity production. There are uncertainties involved in the power profile of the PV system as well as the hydrogen and electric demands of the charging station. The novelty of this paper is to integrate robust optimization as a powerful nonstochastic framework into the mixed-integer linear programming (MILP) of the deterministic model to deal with the uncertainties. The technical and economic results prove that the construction of the charging station by considering the highs level of robustness against the negative impacts of uncertainties leads to higher capacities of the PV system and diesel generator. Consequently, the total annualized cost increases from $ 287,256 in deterministic mode to $ 326,757 in robust mode, by 13.75%.     

Research on influence and resolution of the relay protections with electric vehicle charging station integrating into distribution network

Electric vehicles have been widely used because of its significant environmental effect, study the influence of the relay protection when electric vehicle charging station integrated into network is important. Three section current protections are configured in distribution network. In this paper, the equivalent model of the charging station is access to distribution network, different fault locations are set up, and the setting value of the corresponding protection are compared with the fault current, finally the impact of the three section current protection is analyzed. A model is built in PSCAD to verify the correctness of the analysis.     

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.     

Smart management of distinct plug‐in hybrid electric vehicle charging stations considering mobility pattern and site characteristics

Management of plug‐in hybrid electric vehicles (PHEVs) is an important alternative energy solution to accord the prevailing environmental depletion. However, adding PHEVs to the existing distribution network may stimulate issues such as increase in peak load, power loss, and voltage deviation. Addressing the aforementioned issues by incorporating distinct mobility patterns together will develop an attractive energy management. In this paper, suitable location of the charging station is presented for a novel 2‐area distribution system following distinct mobility patterns. A comprehensive study by considering the optimal, midst, and unfit site for placing the charging station is incorporated. For managing the charging sequence of PHEVs, a meta‐heuristic solving tool is developed. The main contribution of this programming model is its ability to schedule the vehicles simultaneously in both the areas. The efficiency of the proposed energy management framework is evaluated on the IEEE 33‐bus and IEEE 69‐bus distribution systems. The test system is subjected to different scenarios for demonstrating the superior performance of the proposed solving tool in satisfying the convenience of vehicle owner along with reducing the peak demand. The results show that charging at low electricity price period and discharging at high electricity price period enables the minimum operational cost.     

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.     

Risk-constrained design of autonomous hybrid refueling station for hydrogen and electric vehicles using information gap decision theory

The proposed autonomous hybrid charging station in this paper is energized by a photovoltaic (PV) system, which should provide electric vehicles (EVs), and water electrolyzer (WE) with electricity. The WE operates by using electricity to produce and store hydrogen to feed hydrogen vehicles (HVs). Moreover, a fuel cell (FC) is allocated to the system, which uses the stored hydrogen to regenerate electricity the PV system is beyond reach. A supplementary diesel generator is also installed in the charging station to avoid power shortage as a conservative measurement. The hydrogen and electric demand of the station is accompanied by uncertainties, which should be taken into account in designing the charging station. Therefore, information-gap decision theory (IGDT) is employed to deal with the uncertainties. This approach provides the investor with three different strategies of risk-averse strategy (RAS), risk-neutral strategy (RNS), and risk-seeker strategy (RSS), which can help the investor with making a better decision. The outcome of the simulation proved that in RAS if the investor decides to invest 13.9% more capital, based on the robustness function, the charging station withstands the 9.6% deviation of uncertain parameters’ fraction error. However, should the investor decide to take risks in the construction of the charging station, by paying 13.9% less, the system is 10.7% fragile to the information-gap of uncertainties. Besides, the rated power of the PV system reaches from 1612 kW in RNS to 1731 kW in RAS while it decreased to 1479 kW in RSS.     

Design scheme for fast charging station for electric vehicles with distributed photovoltaic power generation

The demand for fast charging is increasing owing to the rapid expansion of the market for electric vehicles. In addition, the power generation technology for distributed photovoltaic has matured. This paper presents a design scheme for a fast charging station for electric vehicles equipped with distributed photovoltaic power generation system taking the area with certain conditions in Beijing as an example construction site. The technical indexes and equipment lectotype covering the general framework and subsystems of the charging station are determined by analyzing the charging service demand of fast charging stations. In this study, the layout of the station is developed and the operation benefits of the station is analyzed. The design scheme realizes the design objective of “rationalization, modularization and intelligentization” of the fast charging station and can be used as reference for the construction of a fast charging network in urban area.     

Neural network based optimization for cascade filling process of on-board hydrogen tank

Compressed hydrogen storage is widely used in hydrogen fuel cell vehicles (HFCVs). Cascade filling systems can provide different pressure levels associated with various source tanks allowing for a variable mass flow rate. To meet refueling performance objectives, safe and fast filling processes must be available to HFCVs. The main objective of this paper is to establish an optimization methodology to determine the initial thermodynamic conditions of the filling system that leads to the lowest final temperature of hydrogen in the on-board storage tank with minimal energy consumption. First, a zero-dimensional lumped parameter model is established. This simplified model, implemented in Matlab/Simulink, is then used to simulate the flow of hydrogen from cascade pressure tanks to an on-board hydrogen storage tank. A neural network is then trained with model calculation results and experimental data for multi-objective optimization. It is found to have good prediction, allowing the determination of optimal filling parameters. The study shows that a cascade filling system can well refuel the on-board storage tank with constant average pressure ramp rate (APRR). Furthermore, a strong pre-cooling system can effectively lower the final temperature at a cost of larger energy consumption. By using the proposed neural network, for charging times less than 183s, the optimization procedure predicts that the inlet temperature is 259.99–266.58 K, which can effectively reduce energy consumption by about 2.5%.     

Optimal operational strategy for a future electricity and hydrogen supply system in a residential area

This study introduces a novel framework of an electricity and hydrogen supply system integrating with a photovoltaic power station for a residential area. The non-residential parts including the power grid and non-residential vehicles are added to ensure power balance and bring benefits, respectively. The optimal operational strategy of the proposed framework with considering uncertainties is proposed. The objective function minimizes the expected operational cost (EOC) by reducing the imported electricity from the power grid and increasing exported electricity/hydrogen to non-residential vehicles. Additionally, the demand response program (DRP) is applied in the residential load to achieve operational cost reduction. The uncertainties are modeled via various scenarios by using scenario-based stochastic optimization method. Notably, existing research for similar frameworks both lacks the consideration of uncertainties and DRP, and fails to distinguish the residential and non-residential vehicles with different charging behaviors. The results indicate that 1) The feasibility of the proposed framework is validated which can ensure the power balance of the residential area and reduce the operational cost. 2) The EOC is reduced when considering DRP.     

电动汽车与风电协同入网的双层优化策略

文章提出了一种电动汽车(EV)与风电协同入网的双层优化模型。该模型上层以电动汽车与风电协同入网时负荷方差与充电站运营商购电成本最小为目标,得到电动汽车与风电联合的负荷指导曲线。模型下层以电动汽车和风电实际等效负荷与负荷指导曲线偏差最小为目标,进行实时负荷跟随。为保证电动汽车进行有序的充放电,提出了用户综合评价系数和放电影响因子等电动汽车参数,对电动汽车进行队列划分和充放电优先级排序。仿真结果表明,电动汽车与风电协同优化调度,不仅能够减小风电预测误差的影响,促进风电消纳,还能增加充电站运营商的收益,同时实现削峰填谷的目的。     

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.     

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.     

A novel multiobjective charging optimization method of power lithium‐ion batteries based on charging time and temperature rise

Power lithium‐ion batteries have been widely utilized in energy storage system and electric vehicles, because these batteries are characterized by high energy density and power density, long cycle life, and low self‐discharge rate. However, battery charging always takes a long time, and the high current rate inevitably causes great temperature rises, which is the bottleneck for practical applications. This paper presents a multiobjective charging optimization strategy for power lithium‐ion battery multistage charging. The Pareto front is obtained using multiobjective particle swarm optimization (MOPSO) method, and the optimal solution is selected using technique for order of preference by similarity to ideal solution (TOPSIS) method. This strategy aims to achieve fast charging with a relatively low temperature rise. The MOPSO algorithm searches the potential feasible solutions that satisfy two objectives, and the TOPSIS method determines the optimal solution. The one‐order resistor‐capacitor (RC) equivalent circuit model is utilized to describe the model parameter variation with different current rates and state of charges (SOCs) as well as temperature rises during charging. And battery temperature variations are estimated using thermal model. Then a PSO‐based multiobjective optimization method for power lithium‐ion battery multistage charging is proposed to balance charging speed and temperature rise, and the best charging stage currents are obtained using the TOPSIS method. Finally, the optimal results are experimentally verified with a power lithium‐ion battery, and fast charging is achieved within 1534 s with a 4.1°C temperature rise.