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.     

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.     

Optimising residential electric vehicle charging under renewable energy: Multi‐agent learning in software simulation and hardware‐in‐the‐loop evaluation

The integration of intermittent renewable energy sources coupled with the increasing demand of electric vehicles (EVs) poses new challenges to the electrical grid. To address this, many solutions based on demand response have been presented. These solutions are typically tested only in software‐based simulations. In this paper, we present the application in hardware‐in‐the‐loop (HIL) of a recently proposed algorithm for decentralised EV charging, prediction‐based multi‐agent reinforcement learning (P‐MARL), to the problem of optimal EV residential charging under intermittent wind power and variable household baseload demands. P‐MARL is an approach that can address EV charging objectives in a demand response aware manner, to avoid peak power usage while maximising the exploitation of renewable energy sources. We first train and test our algorithm in a residential neighbourhood scenario using GridLAB‐D, a software power network simulator. Once agents learn optimal behaviour for EV charging while avoiding peak power demand in the software simulator, we port our solution to HIL while emulating the same scenario, in order to decrease the effects of agent learning on power networks. Experimental results carried out in a laboratory microgrid show that our approach makes full use of the available wind power, and smooths grid demand while charging EVs for their next day's trip, achieving a peak‐to‐average ration of 1.67, down from 2.24 in the baseline case. We also provide an analysis of the additional demand response effects observed in HIL, such as voltage drops and transients, which can impact the grid and are not observable in the GridLAB‐D software simulation.     

Least‐squares based coulomb counting method and its application for state‐of‐charge (SOC) estimation in electric vehicles

Coulomb counting method is a convenient and straightforward approach for estimating the state‐of‐charge (SOC) of lithium‐ion batteries. Without interrupting the power supply, the remaining capacities of them in an electric vehicle (EV) can be calculated by integrating the current leaving and entering the batteries. The main drawbacks of this method are the cumulative errors and the time‐varying coulombic efficiency, which always lead to inaccurate estimations. To deal with this problem, a least‐squares based coulomb counting method is proposed. With the proposed method, the coulombic losses can be compensated by charging/discharging coulombic efficiency η and the measurement drift can be amended with a morbid efficiency matrix. The experimental results demonstrated that the proposed method is effective and convenient. Copyright © 2016 John Wiley & Sons, Ltd.     

Assessment of electric vehicles concerning impacts,charging infrastructure with unidirectional and bidirectional chargers,and power flow comparisons

The advancement and deployment of electric vehicle (EV) technologies are considered as an emergent solution to meet the current and future energy crises. The electrification of transportation systems is a promising approach to green the transportation systems and to reduce the issues of climate change. This paper investigates the present status, latest deployment, and challenging issues in the implementation of EV infrastructure, charging power levels, in conjunction with several charging power topologies, and analyzes EV impacts and prospects in society. In this study, the on‐board and off‐board categories of charging systems with unidirectional and bidirectional power flow comparison are addressed. Moreover, an extensive analysis of unidirectional and bidirectional chargers is presented. Unidirectional charging offers hardware limitation and reduces the interconnection issues. Bidirectional charging provides the fundamental feature of vehicle‐to‐grid technology. Furthermore, the beneficial and harmful impacts of EVs are categorized with remedial measures for harmful impacts and prolific benefits for beneficial impacts.     

A critical review of electric vehicle charging using solar photovoltaic

The application of renewable sources such as solar photovoltaic (PV) to charge electric vehicle (EV) is an interesting option that offers numerous technical and economic opportunities. By combining the emission‐free EV with the low carbon PV power generation, the problems related to the greenhouse gases due to the internal combustion engines can be reduced. Over the years, numerous papers, including several review work, have been published on EV charging using the grid electricity. However, there seems to be an absence of a review paper on EV charging using the PV as one of the energy sources. With growing interest in this topic, this review summarizes and updates some of the important aspects of the PV‐EV charging. For the benefit of a wider audience, it provides the background on the EV fundamentals, batteries and a brief overview on the PV systems. Two types of PV‐EV charging, namely the PV‐grid and the PV‐standalone, are comprehensively covered. Moreover, a case study is carried out in comparison to the grid‐only charging to critically analyse the technical and the economical feasibilities of both types using Matlab simulation. At the end, recommendations and future directions are presented. It is envisaged that the material gathered in this paper will be a valuable source of information for the researchers working on this topic. Copyright © 2015 John Wiley & Sons, Ltd.     

考虑需求响应和V2G的低碳能源网控制策略

为平抑大量电动汽车(EV)入网所致的负荷波动,实现能源网低碳电力运行,通过结合V2G和P2G技术,将电力系统与天然气系统之间的能量耦合。先引入需求响应策略建立负荷侧分时电价,引导大规模EV参与V2G系统,改变用户的用能时段;其次考虑碳排放环境因素建立低碳能源网模型,以经济性最优为目标利用禁忌-粒子群算法进行求解;最后利用算例对比分析了低碳能源网的能量调度和运行成本情况,验证了所提方法的有效性。     

Master‐slave game optimization method of smart energy systems considering the uncertainty of renewable energy

As the uncertainty of renewable energy output brings more and more risks to the day‐ahead dispatch of the power grid, an optimization scheduling strategy of a smart energy system based on improved master‐slave game model is proposed. Risk factors related to the uncertainties of renewable energy are introduced into the master‐slave game model. Taking the smart energy system as the leader and the end users as the follower, an optimized operation model of the smart energy system based on the improved master‐slave game model is established, which is transformed into a single‐layer linear programming model according to the Karush‐Kuhn‐Tucher conditions and the duality theorem. The benefits of the system and electric vehicle users in four application scenarios are obtained by the YALMIP algorithm and the sensitivity affecting the economics of the smart energy system is analyzed. The validity of the model is verified by a simulation analysis of actual operation data from the smart energy system in China. The simulation results show that the method proposed in this paper can increase the revenue of the smart energy system by 7%, reduce the risk cost and charging cost of electric vehicle users by 63.92% and 48.34%.     

Optimal operation of an all-in-one EV station with photovoltaic system including charging,battery swapping and hydrogen refueling

The issue of electrification of transportation is discussed due to the possibility of depletion of conventional resources in the near future and environmental problems caused by carbon emissions. For this purpose, different options have been proposed for the electrification of electric vehicles (EVs). Each potential EV user can choose a different EV type according to his desire, so different EV types can be seen in the environment. However, one of the most important reasons why the prevalence of EVs has not increased is the scarcity of EV charging, swapping, or refueling stations. In this respect, there is a need for an all-in-one EV station (AiOEVS) that can serve all types of EVs around and that all users know to be able to meet their energy needs easily and in line with their wishes. In this study, the economically optimum energy management model via mixed-integer linear programming (MILP) approach of an AiOEVS including a photovoltaic (PV) system as well electrolyzer and consisting of three different parts (charging for plug-in EVs, swapping for swappable EVs, and refueling for hydrogen fuel-cell EVs (HFCEVs)) is proposed. Besides, energy is purchased from the grid with time-of-use electricity prices. The proposed optimum operating framework is beneficial for each party. Furthermore, the hydrogen tank, swappable batteries, and long-parking plug-in EVs provide operational flexibility. The AiOEVS owner obtains a net profit of 33.12% at the end of the day. Furthermore, when the capacity of the PV is doubled or tripled, the gain increases by 11.69% or 23.41%, respectively.     

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.     

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.     

Performance analysis of series/parallel and dual side LCC compensation topologies of inductive power transfer for EV battery charging system

In an inductive battery charging system, for better power transfer capability and attaining required power level, compensation is necessary. This paper analyzes series/parallel (S/P) and dual side inductor-capacitor-capacitor (LCC) compensation topologies for inductive power transfer of electric vehicle (EV) battery charging system. The design and modeling steps of inductive power transfer for electric vehicle battery charging system are presented. Besides, the equivalent electrical circuits are used to describe the circuit compensation topologies. The results convey that the efficiency of dual side LCC compensation is higher than that of S/P compensation at variable mutual inductance (misalignment).     

Optimal decentralized charging control algorithm for electrified vehicles connected to smart grid

Electrified vehicles (EV) and renewable power sources are two important technologies for sustainable ground transportation. If left unmitigated, the additional electric load could over-burden the electric grid. Meanwhile, a challenge for integrating renewable power sources into the grid lies in the fact their intermittency requires more regulation services which makes them expensive to deploy. Fortunately, EVs are controllable loads and the charging process can be interrupted. This flexibility makes it possible to manipulate EV charging to reduce the additional electric load and accommodate the intermittency of renewable power sources. To illustrate this potential, a two-level optimal charging algorithm is designed, which achieves both load shifting and frequency regulation. Load shifting can be realized through coordination of power generation and vehicle charging while reducing power generation cost and carbon dioxide emissions. To ensure practicality, a decentralized charging algorithm for load shifting is formulated by emulating the charging pattern identified through linear programming optimization solutions. The frequency regulation is also designed based on frequency droop that can be implemented in a decentralized way. The two control objectives can be integrated because they are functionally separated by time scale. Simulation results are presented to demonstrate the performance of the proposed decentralized algorithm.     

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.     

Economic and environmental evaluations of dedicated and residential electric tariffs for plug‐in electric vehicles

Plug‐in electric vehicle (PEV) owners may have multiple different electric tariffs offered by their local utility companies from which to choose. The offered PEV tariffs are designed mainly to shift the electric demand for charging cars to the time when the grid is less strained. This paper investigates both the economic and the environmental impacts of adopting dedicated PEV electric tariffs from the PEV owners' perspective. The overall conclusion is that the dedicated tariffs are well designed for PEVs from the economical perspective but not from the environmental perspective. Case studies of the cost minimization model show that on average the dedicated PEV tariffs will result in approximately half the cost of the electric bill and slightly lower greenhouse gas (GHG) emissions (less than 1%) compared with the standard flat‐rate residential tariffs. Case studies of the emission minimization model show that the GHG emissions can be reduced by 10.47% as compared with the cost minimization model, but this will lead to an increase in the total charging cost that can be as high as 15.44% on average.     

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.     

基于两阶段优化模型的电动汽车充电站放电策略

随着电动汽车的普及,合理制定其充放电策略,实现主动配电网和充电站的双赢成为电动汽车负荷并网的研究重点,为此提出一种充电站充放电计划的两阶段优化模型.该模型综合考虑了主动配电网和充电站双方的利益,并计及了电动汽车实际负荷与预测负荷不符的情况.在日前优化阶段,以成本分析为基础,采用Nash谈判法求解配电网和充电站的多目标优...     

Optimal dispatch of hydrogen/electric vehicle charging station based on charging decision prediction

The hydrogen/electric vehicle charging station (HEVCS) is widely regarded as a highly attractive system for facilitating the popularity of hydrogen and electric vehicles in the future. However, conventional optimal dispatch of HEVCS could lead to poor performance due to the lack of adequate consideration of vehicle charging decision behaviours and neglection of the impacts of different information sources on it. This paper investigates a charging demand prediction method that considers multi-source information and proposes a multi-objective optimal dispatching strategy of HEVCS. First, an information interaction framework of integrated road network, vehicles and HEVCS is introduced. Road network model and HEVCS model are established based on the proposed framework. To improve the flexibility of dispatch, two charging modes are designed, which are intended to guide drivers to adjust their consumption behaviour by electricity price incentives. Furthermore, psychologically based hybrid utility-regret decision model and Weber-Fechner (W–F) stimulus model are developed to reasonably predict drivers' choice of charging stations and charging modes. The daily revenue of HEVCS and the total queuing time of drivers are the objective functions considered in this paper simultaneously. The above multi-objective optimization results that the proposed strategy can effectively improve the benefits of HEVCS and reduce energy waste. Additionally, this paper discusses the results of a sensitivity analysis conducted by varying incentive discount, which reveals the combined benefits of the HEVCS and the vehicles are effectively increased by setting reasonable incentive discounts.     

柔性电动汽车和分布式电源的协同优化调度研究

针对电动汽车(EV)接入电网后,出现的负荷"峰上加峰"的问题,在描述传统机组发电特征及调度分布式电源作为补充的基础上,提出了柔性电动汽车与分布式电源协同优化调度方案。该方案考虑4类电动汽车的出行规律及充电方式,同时以发电投资总成本与柔性EV用能成本之和最小作为优化目标,建立车网交互(V2G)的机组组合模型。在考虑柔性EV行驶特性与机组运行特性基础上,将所提模型转化为一个大规模混合整数线性规划(MILP)问题,利用Ipsolve解算器求得最优解以及最优调度方案。文章选取某城市各类电动汽车为研究对象,分析柔性EV用能成本对系统总成本与CO2排放量的影响。仿真结果验证了采用柔性EV与风电协同优化传统机组出力的有效性和考虑柔性EV用能成本的必要性。     

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.