The approach to planning,design and operation of distribution networks have significantly changed due to the proliferation of distributed energy resources(DERs)together with load growth,energy storage technology advan...The approach to planning,design and operation of distribution networks have significantly changed due to the proliferation of distributed energy resources(DERs)together with load growth,energy storage technology advancements and increased consumer expectations.Planning of active distribution systems(ADS)has been a very hot topic in the 21st Century.A large number of studies have been done on ADS planning.This paper reviews the state of the art of current ADS planning.Firstly,the influences of DERs on the ADS planning are addressed.Secondly,the characteristics and objectives of ADS planning are summarized.Then,up to date planning model and some related research are highlighted in different areas such as forecasting load and distributed generation,mathematical model of ADS planning and solution algorithms.Finally,the paper explores some directions of future research on ADS planning including planning collaboratively with all elements combined in ADS,taking into account of joint planning in secondary system,coordinating goals among different layers,integrating detailed operation simulations and regular performance based reviews into planning,and developing advanced planning tools.展开更多
With the increasing integration of wind farms and electric vehicles(EVs)in power systems,voltage stability is becoming more and more serious.Based on vehicle-to-grid(V2G),an efficient power plant model of EVs(E-EPP)wa...With the increasing integration of wind farms and electric vehicles(EVs)in power systems,voltage stability is becoming more and more serious.Based on vehicle-to-grid(V2G),an efficient power plant model of EVs(E-EPP)was developed to estimate EV charging load with available corresponding response capacity under different charging strategies.A preventive control strategy based on E-EPP was proposed to maintain the static voltage stability margin(VSM)of power system above a predefined security level.Two control modes were used including the disconnection of EV charging load(‘V1G’mode)and the discharge of stored battery energy back to power grid(‘V2G’mode).A modified IEEE 14-bus system with high penetration of wind power and EVs was used to verify the effectiveness of preventive control strategy.Simulation results showed that the proposed strategy can not only improve the static voltage stability of power system with considerable wind generation,but also guarantee the travelling comfort for EV owners.展开更多
The rapidly increasing penetration of electric vehicles(EVs) in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environm...The rapidly increasing penetration of electric vehicles(EVs) in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment pro-tection. Integrating charging facilities, especially highpower chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced,especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is highlighted.Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.展开更多
Renewable energy based distributed generation(DG) has the potential to reach high penetration levels in the residential region. However, its integration at the demand side will cause rapid power fluctuations of the ti...Renewable energy based distributed generation(DG) has the potential to reach high penetration levels in the residential region. However, its integration at the demand side will cause rapid power fluctuations of the tieline in the residential region. The traditional generators are generally difficult to manage rapid power fluctuations due to their insufficient efficiency requirements and low responding speed. With an effective control strategy, the demand side resources(DSRs) including DGs, electric vehicles and thermostatically-controlled loads at thedemand side, are able to serve as the energy storage system to smooth the load fluctuations. However, it is a challenge to properly model different types of DSRs. To solve this problem, a unified state model is first developed to describe the characteristics of different DSRs. Then a load curve smoothing strategy is proposed to offset the load fluctuations of the tie-line of the residential region, where a control matrix deduced from the unified state model is introduced to manage the power outputs of different DSRs,considering the response order and the comfort levels.Finally, a residential region with households is used to validate the load curve smoothing strategy based on the unified state model, and the results show that the power fluctuation rate of the tie-line is significantly decreased.Meanwhile, comparative study results are shown to demonstrate the advantages of the unified state model based load curve smoothing strategy.展开更多
This paper focuses on the optimal scheduling of the district energy system with multiple energy supply modes and flexible loads.For multi-energy system(MES),the energy hub(EH)model including energy storage system and ...This paper focuses on the optimal scheduling of the district energy system with multiple energy supply modes and flexible loads.For multi-energy system(MES),the energy hub(EH)model including energy storage system and integrated electric vehicle(EV)is established.Based on the model,the influence of pollutant trading market on total operation cost is analyzed,and the optimal scheduling strategy is further put forward to realize the minimum purchase cost and emission tax cost of the MES.Finally,this paper compares the economic benefit of the fixed mode and the response mode,and discusses the contribution of the energy storage device and the multi-energy complementary mode to energy utilization efficiency.The simulation results indicate that optimal scheduling strategy of the EH can coordinate various energy complementary modes reasonably.Meanwhile,the proposed strategy is able to improve the operation economy of the EH,and ensure the better response effect of the demand side.The sensitivity analysis demonstrates the impact of pollutant emission price change on emission reduction.展开更多
The increasing number of photovoltaic(PV)generation and electric vehicles(EVs)on the load side has necessitated an aggregator(Agg)in power system operation.In this paper,an Agg is used to manage the energy profiles of...The increasing number of photovoltaic(PV)generation and electric vehicles(EVs)on the load side has necessitated an aggregator(Agg)in power system operation.In this paper,an Agg is used to manage the energy profiles of PV generation and EVs.However,the daily management of the Agg is challenged by uncertain PV fluctuations.To address this problem,a robust multi-time scale energy management strategy for the Agg is proposed.In a day-ahead phase,robust optimization is developed to determine the power schedule.In a real-time phase,a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs.A case study indicates a good scheduling performance under an uncertain PV output.Through the convexification,the solving efficiency of the real-time operation model is improved,and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent.Moreover,the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient.The strategy can ensure the flexibility of the Agg for real-time operation.展开更多
This paper focuses on the development of electric vehicle(EV)charging infrastructure in the UK,which is a vital part of the delivering ultra-low-emission vehicle(ULEV)and will transition into low emission energy syste...This paper focuses on the development of electric vehicle(EV)charging infrastructure in the UK,which is a vital part of the delivering ultra-low-emission vehicle(ULEV)and will transition into low emission energy systems in the near future.Following a brief introduction to global landscape of EV and its infrastructure,this paper presents the EV development in the UK.It then unveils the government policy in recent years,charging equipment protocols or standards,and existing EV charging facilities.Circuit topologies of charging infrastructure are reviewed.Next,three important factors to be considered in a typical site,i.e.,design,location and cost,are discussed in detail.Furthermore,the management and operation of charging infrastructure including different types of business models are summarized.Last but not least,challenges and future trends are discussed.展开更多
Fast and accurate forecasting of schedulable capacity of electric vehicles(EVs)plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems.Traditional metho...Fast and accurate forecasting of schedulable capacity of electric vehicles(EVs)plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems.Traditional methods are insufficient to deal with large-scale actual schedulable capacity data.This paper proposes forecasting models for schedulable capacity of EVs through the parallel gradient boosting decision tree algorithm and big data analysis for multi-time scales.The time scale of these data analysis comprises the real time of one minute,ultra-short-term of one hour and one-day-ahead scale of 24 hours.The predicted results for different time scales can be used for various ancillary services.The proposed algorithm is validated using operation data of 521 EVs in the field.The results show that compared with other machine learning methods such as the parallel random forest algorithm and parallel k-nearest neighbor algorithm,the proposed algorithm requires less training time with better forecasting accuracy and analytical processing ability in big data environment.展开更多
Microgrid as an important part of smart grid comprises distributed generators(DGs),adjustable loads,energy storage systems(ESSs)and control units.It can be operated either connected with the external system or islande...Microgrid as an important part of smart grid comprises distributed generators(DGs),adjustable loads,energy storage systems(ESSs)and control units.It can be operated either connected with the external system or islanded with the support of ESSs.While the daily output of DGs strongly depends on the temporal distribution of natural resources such as wind and solar,unregulated electric vehicle(EV)charging demand will deteriorate the unbalance between the daily load curve and generation curve.In this paper,a statistic model is presented to describe daily EV charging/discharging behaviors considering the randomness of the initial state of charge(SOC)of EV batteries.The optimization problem is proposed to obtain the economic operation for the microgrid based on this model.In dayahead scheduling,with the estimated power generation and load demand,the optimal charging/discharging scheduling of EVs during 24 h is achieved by serial quadratic programming.With the optimal charging/discharging scheduling of EVs,the daily load curve can better track the generation curve.The network loss in grid-connected operation mode and required ESS capacity in islanded operation mode are both decreased.展开更多
This paper describes a concept for an independent and redundant safety concept for Lithium batteries in Electric and Hybrid Electric Vehicles. This concept includes an emergency cooling system based on pressurized car...This paper describes a concept for an independent and redundant safety concept for Lithium batteries in Electric and Hybrid Electric Vehicles. This concept includes an emergency cooling system based on pressurized carbon dioxide (CO2). Since carbon dioxide (CO2) is a possible medium of future mobile air conditioning (MAC) systems, the MAC system can be utilized for the one-time emergency cooling described in this paper. In the first part of the paper, some major safety aspects of automotive Li batteries are highlighted. In the second section, the paper describes a technical approach, how these batteries can be made safer. Pressurized CO2, which is a promising candidate for cooling liquids used in future mobile air conditioning (MAC) systems, is used to effectively cool down an overheating or up-heating battery in a critical state. The safety system thereby is not based on an electrical effect, but on a direct and fast-reacting thermal conduction, avoiding a thermal runaway of individual cells. The application of the proposed system is to act preventively just before the thermal runaway gets uncontrollable. In this case, the limited amount of CO2, which is available in the MAC system, fulfils the emergency cooling requirements. The combination of standard car components for the concept leads to an only moderate increase of the total weight and the additional system costs. Therefore, the described system might be of interest for car, battery and air conditioning system producers. This paper explains that the synergetic combination of CO2-based MAC systems and Li-based batteries is an innovative approach to improve environmental compatibility in future vehicles. The concept is proven experimentally on a lab scale with battery cells and battery packs consisting of four serially connected cells, respectively.展开更多
As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management method.However,the conventional optimization-model-based ...As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management method.However,the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online execution.To address this issue,a long short-term memory(LSTM)recurrent neural network(RNN)based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building prosumers.Under the proposed system control structure,the LSTM algorithm can be separated into offline and online stages.At the offline stage,the LSTM is used to map states(inputs)to decisions(outputs)based on the network training.At the online stage,once the current state is input,the LSTM can quickly generate a solution without any additional prediction.A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM network.The simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.展开更多
Smart parking lots are smart places capable of supporting both parking and charging services for electric vehicles(EVs).In order to manage EV charging,the parking lot local controller(PLLC)requires data exchange with ...Smart parking lots are smart places capable of supporting both parking and charging services for electric vehicles(EVs).In order to manage EV charging,the parking lot local controller(PLLC)requires data exchange with EV charging stations(EVCSs)through communication infrastructures.However,data losses and communication delays are unavoidable and may significantly degrade the system performance.This work aims to investigate the underlying communication networks for remote monitoring of EVCSs in a smart campus parking lot.The communication network consists of two subnetworks:parking area network(PAN)and campus area network(CAN).PAN covers communication among EVs,charging stations and PLLC,while CAN enables dedicated communication between PLLCs and a global controller of the university.As one of the major obstacles in EV system is the lack of unified communication architecture to integrate EVCS in the power grid,we develop communication models for the in-vehicle system and EVCSs based on logical node concept of IEC 61850 standard.Furthermore,we implement network models for EVCSs using OPNET modeler.Different communication technologies and configurations are considered in modeling and simulations,and end-toend delay is evaluated and discussed.展开更多
There is a general concern that the increasing penetration of electric vehicles(EVs)will result in higher aging failure probability of equipment and reduced network reliability.The electricity costs may also increase,...There is a general concern that the increasing penetration of electric vehicles(EVs)will result in higher aging failure probability of equipment and reduced network reliability.The electricity costs may also increase,due to the exacerbation of peak load led by uncontrolled EV charging.This paper proposes a linear optimization model for the assessment of the benefits of EV smart charging on both network reliability improvement and electricity cost reduction.The objective of the proposed model is the cost minimization,including the loss of load,repair costs due to aging failures,and EV charging expenses.The proposed model incorporates a piecewise linear model representation for the failure probability distributions and utilizes a machine learning approach to represent the EV charging load.Considering two different test systems(a 5-bus network and the IEEE 33-bus network),this paper compares aging failure probabilities,service unavailability,expected energy not supplied,and total costs in various scenarios with and without the implementation of EV smart charging.展开更多
The aim of this paper is to develop a simple EV model and predict its energy consumption with a variable and fixed ratio gearbox over a standard driving cycle in order to understand whether this could offer significan...The aim of this paper is to develop a simple EV model and predict its energy consumption with a variable and fixed ratio gearbox over a standard driving cycle in order to understand whether this could offer significant efficiency gains. The powertrain of a generic electric vehicle was modelled in Matlab / Simulink using the QSS Toolkit. The electric vehicle was then fitted with different transmissions with different levels of complexity. Simulations were done to investigate the energy consumptions across 6 standard driving cycles. The emerging conclusions are that it is possible to improve overall energy consumption levels by around 5 to 12 % with a variable ratio gearbox depending on the driving cycle used. However, there are many other practical considerations which must be weighed against this positive result - and the paper discusses the impact of several of these such as, gearbox efficiency, additional weight, cost and complexity, effect on drivability and potential for motor downsizing.展开更多
In this paper,a particular standard MicroGrid(MG)is accurately simulated in the presence of the Electric Vehicles(EVs)participating in decentralized primary frequency control service.It examines effect of number of th...In this paper,a particular standard MicroGrid(MG)is accurately simulated in the presence of the Electric Vehicles(EVs)participating in decentralized primary frequency control service.It examines effect of number of the participating EVs on the short-term dynamic behaviour.The simulation results confirm that frequency deviation will not definitely become zero even though an unlimited number of the EVs participate.The output power of each EV is determined according to the frequency deviation.On the other hand,the output power of each EV affects the value of the frequency deviation,especially in small-scale MGs and MGs with predominant inductance behaviour.Eventually,an equilibrium point is reached after a new EV is added that depends on the characteristics of the MG and the functions executed in the MG central controller during such a service.Additionally,effect of Reflex method,an advanced charging technique for EVs,on the frequency deviation is examined.展开更多
The vehicle-to-grid(V2G)technology enables the bidirectional power flow between electric vehicle(EV)batteries and the power grid,making EV-based mobile energy storage an appealing supplement to stationary energy stora...The vehicle-to-grid(V2G)technology enables the bidirectional power flow between electric vehicle(EV)batteries and the power grid,making EV-based mobile energy storage an appealing supplement to stationary energy storage systems.However,the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation.To unlock the scheduling potential of EVs,this paper proposes a source-load-storage cooperative low-carbon scheduling strategy considering V2G aggregators.The uncertainty of EV charging patterns is managed through a rolling-horizon control framework,where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs.Moreover,a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon.This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs.Subsequently,a Nash bargaining based cooperative scheduling model involving a distribution system operator(DSO),an EV aggregator(EVA),and a load aggregator(LA)is established to maximize the social welfare and improve the low-carbon performance of the system.This model is solved by the alternating direction method of multipliers(ADMM)algorithm in a distributed manner,with privacy of participants fully preserved.The proposed strategy is proven to achieve the objective of low-carbon economic operation.展开更多
The large-scale development of electric vehicles(EVs)requires numerous charging stations to serve them,and the charging stations should be reasonably laid out and planned according to the charging demand of electric v...The large-scale development of electric vehicles(EVs)requires numerous charging stations to serve them,and the charging stations should be reasonably laid out and planned according to the charging demand of electric vehicles.Considering the costs of both operators and users,a site selection model for optimal layout planning of charging stations is constructed,and a queuing theory approach is used to determine the charging pile configuration to meet the charging demand in the planning area.To solve the difficulties of particle swarm global optimization search,the improved random drift particle swarm optimization(IRDPSO)and Voronoi diagram are used to jointly solve for the optimal layout of electric vehicles.The final arithmetic analysis verifies the feasibility and practicality of the model and algorithm,and the results show that the total social cost is minimized when the charging station is 9,the location of the charging station is close to the center of gravity and the layout is reasonable.展开更多
To reduce the difficulty and enhance the enthusiasm of private-owned electric vehicles(EVs) in participating in frequency regulation ancillary service market(FRASM), a decision aid model(DAM) is proposed. This paper p...To reduce the difficulty and enhance the enthusiasm of private-owned electric vehicles(EVs) in participating in frequency regulation ancillary service market(FRASM), a decision aid model(DAM) is proposed. This paper presents three options for EV participating in FRASM, i. e., the base mode(BM), unidirectional charging mode(UCM), and bidirectional charging/discharging mode(BCDM), based on a reasonable simplification of users' participating willingness. In BM, individual EVs will not be involved in FRASM, and DAM will assist users to set the optimal charging schemes based on travel plans under the time-of-use(TOU) price. UCM and BCDM are two modes in which EVs can take part in FRASM. DAM can assist EV users to create their quotation plan, which includes hourly upper and lower reserve capabilities and regulation market mileage prices. In UCM and BCDM, the difference is that only the charging rate can be adjusted in the UCM, and the EVs in BCDM can not only charge but also discharge if necessary. DAM can estimate the expected revenue of all three modes, and EV users can make the final decision based on their preferences. Simulation results indicate that all the three modes of DAM can reduce the cost, while BCDM can get the maximum expected revenue.展开更多
Speed forecasting has numerous applications in intelligent transport systems’design and control,especially for safety and road efficiency applications.In the field of electromobility,it represents the most dynamic pa...Speed forecasting has numerous applications in intelligent transport systems’design and control,especially for safety and road efficiency applications.In the field of electromobility,it represents the most dynamic parameter for efficient online in-vehicle energy management.However,vehicles’speed forecasting is a challenging task,because its estimation is closely related to various features,which can be classified into two categories,endogenous and exogenous features.Endogenous features represent electric vehicles’characteristics,whereas exogenous ones represent its surrounding context,such as traffic,weather,and road conditions.In this paper,a speed forecasting method based on the Long Short-Term Memory(LSTM)is introduced.The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries.The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting.Simulation results show that the multivariate model outperforms the univariate model for short-and long-term forecasting.展开更多
As the adoption of Electric Vehicles(EVs)intensifies,two primary challenges emerge:limited range due to battery constraints and extended charging times.The traditional charging stations,particularly those near highway...As the adoption of Electric Vehicles(EVs)intensifies,two primary challenges emerge:limited range due to battery constraints and extended charging times.The traditional charging stations,particularly those near highways,exacerbate these issues with necessary detours,inconsistent service levels,and unpredictable waiting durations.The emerging technology of dynamic wireless charging lanes(DWCLs)may alleviate range anxiety and eliminate long charging stops;however,the driving speed on DWCL significantly affects charging efficiency and effective charging time.Meanwhile,the existing research has addressed load balancing optimization on Dynamic Wireless Charging(DWC)systems to a limited extent.To address this critical issue,this study introduces an innovative eco-driving speed control strategy,providing a novel solution to the multi-objective optimization problem of speed control on DWCL.We utilize mathematical programming methods and incorporate the longitudinal dynamics of vehicles to provide an accurate physical model of EVs.Three objective functions are formulated to tackle the challenges at hand:reducing travel time,increasing charging efficiency,and achieving load balancing on DWCL,which corresponds to four control strategies.The results of numerical tests indicate that a comprehensive control strategy,which considers all objectives,achieves a minor sacrifice in travel time reduction while significantly improving energy efficiency and load balancing.Furthermore,by defining the energy demand and speed range through an upper operation limit,a relatively superior speed control strategy can be selected.This work contributes to the discourse on DWCL integration into modern transportation systems,enhancing the EV driving experience on major roads.展开更多
基金This work was supported by National High Technology Research and Development Program of China under Grant 2014AA051901(Key Technology Research and Demonstration for Active Distribution Grid).
文摘The approach to planning,design and operation of distribution networks have significantly changed due to the proliferation of distributed energy resources(DERs)together with load growth,energy storage technology advancements and increased consumer expectations.Planning of active distribution systems(ADS)has been a very hot topic in the 21st Century.A large number of studies have been done on ADS planning.This paper reviews the state of the art of current ADS planning.Firstly,the influences of DERs on the ADS planning are addressed.Secondly,the characteristics and objectives of ADS planning are summarized.Then,up to date planning model and some related research are highlighted in different areas such as forecasting load and distributed generation,mathematical model of ADS planning and solution algorithms.Finally,the paper explores some directions of future research on ADS planning including planning collaboratively with all elements combined in ADS,taking into account of joint planning in secondary system,coordinating goals among different layers,integrating detailed operation simulations and regular performance based reviews into planning,and developing advanced planning tools.
基金This work was supported in part by the National Natural Science Foundation of China(collaborating with EPSRC of UK)(Nos.51361130152 and EP/L001039/1)the National Science and Technology Support Program of China(No.2013BAA01B03)Research on Reactive Power Control and Comprehensive Evaluation Technique of Large Scale Integration of Wind/Photovoltaic Power Generation(No.NY71-14-035).
文摘With the increasing integration of wind farms and electric vehicles(EVs)in power systems,voltage stability is becoming more and more serious.Based on vehicle-to-grid(V2G),an efficient power plant model of EVs(E-EPP)was developed to estimate EV charging load with available corresponding response capacity under different charging strategies.A preventive control strategy based on E-EPP was proposed to maintain the static voltage stability margin(VSM)of power system above a predefined security level.Two control modes were used including the disconnection of EV charging load(‘V1G’mode)and the discharge of stored battery energy back to power grid(‘V2G’mode).A modified IEEE 14-bus system with high penetration of wind power and EVs was used to verify the effectiveness of preventive control strategy.Simulation results showed that the proposed strategy can not only improve the static voltage stability of power system with considerable wind generation,but also guarantee the travelling comfort for EV owners.
基金support by the Young Elite Scientists Program of CSEE (No. JLB-2018-95)the National Natural Science Foundation of China (No. 51621065, No. U1766203)+1 种基金the support by FEDER funds through COMPETE 2020by Portuguese funds through FCT, under SAICT-PAC/0004/2015 (No. POCI-01-0145-FEDER-016434), 02/SAICT/2017 (No. POCI-01-0145-FEDER-029803) and UID/EEA/50014/2019 (No. POCI-01-0145-FEDER-006961)
文摘The rapidly increasing penetration of electric vehicles(EVs) in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment pro-tection. Integrating charging facilities, especially highpower chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced,especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is highlighted.Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.
基金supported by National High Technology Research and Development Program of China(863Program)(No.2015AA050403)National Natural Science Foundation of China(No.51677124,No.51607033,No.51607034)Research and Demonstration on Combined Optimal Operation and Testing Technology for New Distributed Energy,Energy Storage and Active Load of State Grid Corporation of China
文摘Renewable energy based distributed generation(DG) has the potential to reach high penetration levels in the residential region. However, its integration at the demand side will cause rapid power fluctuations of the tieline in the residential region. The traditional generators are generally difficult to manage rapid power fluctuations due to their insufficient efficiency requirements and low responding speed. With an effective control strategy, the demand side resources(DSRs) including DGs, electric vehicles and thermostatically-controlled loads at thedemand side, are able to serve as the energy storage system to smooth the load fluctuations. However, it is a challenge to properly model different types of DSRs. To solve this problem, a unified state model is first developed to describe the characteristics of different DSRs. Then a load curve smoothing strategy is proposed to offset the load fluctuations of the tie-line of the residential region, where a control matrix deduced from the unified state model is introduced to manage the power outputs of different DSRs,considering the response order and the comfort levels.Finally, a residential region with households is used to validate the load curve smoothing strategy based on the unified state model, and the results show that the power fluctuation rate of the tie-line is significantly decreased.Meanwhile, comparative study results are shown to demonstrate the advantages of the unified state model based load curve smoothing strategy.
基金supported in part by the National Natural Science Foundation of China(No.61433004,No.61703289)。
文摘This paper focuses on the optimal scheduling of the district energy system with multiple energy supply modes and flexible loads.For multi-energy system(MES),the energy hub(EH)model including energy storage system and integrated electric vehicle(EV)is established.Based on the model,the influence of pollutant trading market on total operation cost is analyzed,and the optimal scheduling strategy is further put forward to realize the minimum purchase cost and emission tax cost of the MES.Finally,this paper compares the economic benefit of the fixed mode and the response mode,and discusses the contribution of the energy storage device and the multi-energy complementary mode to energy utilization efficiency.The simulation results indicate that optimal scheduling strategy of the EH can coordinate various energy complementary modes reasonably.Meanwhile,the proposed strategy is able to improve the operation economy of the EH,and ensure the better response effect of the demand side.The sensitivity analysis demonstrates the impact of pollutant emission price change on emission reduction.
基金supported in part by the National Natural Science Foundation of China(No.51877078)the Fundamental Research Funds for the Central Universities(No.2018MS012)
文摘The increasing number of photovoltaic(PV)generation and electric vehicles(EVs)on the load side has necessitated an aggregator(Agg)in power system operation.In this paper,an Agg is used to manage the energy profiles of PV generation and EVs.However,the daily management of the Agg is challenged by uncertain PV fluctuations.To address this problem,a robust multi-time scale energy management strategy for the Agg is proposed.In a day-ahead phase,robust optimization is developed to determine the power schedule.In a real-time phase,a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs.A case study indicates a good scheduling performance under an uncertain PV output.Through the convexification,the solving efficiency of the real-time operation model is improved,and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent.Moreover,the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient.The strategy can ensure the flexibility of the Agg for real-time operation.
基金a research project in collaboration with and sponsored by XU JI Power Co.,Ltd.,Xuchang,China。
文摘This paper focuses on the development of electric vehicle(EV)charging infrastructure in the UK,which is a vital part of the delivering ultra-low-emission vehicle(ULEV)and will transition into low emission energy systems in the near future.Following a brief introduction to global landscape of EV and its infrastructure,this paper presents the EV development in the UK.It then unveils the government policy in recent years,charging equipment protocols or standards,and existing EV charging facilities.Circuit topologies of charging infrastructure are reviewed.Next,three important factors to be considered in a typical site,i.e.,design,location and cost,are discussed in detail.Furthermore,the management and operation of charging infrastructure including different types of business models are summarized.Last but not least,challenges and future trends are discussed.
基金supported by National Natural Science Foundation of China(No.51577047)International Collaboration Project supported by Bureau of Science and Technology,Anhui Province(No.1604b0602015).
文摘Fast and accurate forecasting of schedulable capacity of electric vehicles(EVs)plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems.Traditional methods are insufficient to deal with large-scale actual schedulable capacity data.This paper proposes forecasting models for schedulable capacity of EVs through the parallel gradient boosting decision tree algorithm and big data analysis for multi-time scales.The time scale of these data analysis comprises the real time of one minute,ultra-short-term of one hour and one-day-ahead scale of 24 hours.The predicted results for different time scales can be used for various ancillary services.The proposed algorithm is validated using operation data of 521 EVs in the field.The results show that compared with other machine learning methods such as the parallel random forest algorithm and parallel k-nearest neighbor algorithm,the proposed algorithm requires less training time with better forecasting accuracy and analytical processing ability in big data environment.
基金The research of this paper was supported by National Natural Science Foundation of China(No.51577032)Natural Science Foundation of Jiangsu Province(No.BK20160679)+1 种基金EPSRC UK-China joint research consortium(EP/F061242/1)Science bridge award(EP/G042594/1).
文摘Microgrid as an important part of smart grid comprises distributed generators(DGs),adjustable loads,energy storage systems(ESSs)and control units.It can be operated either connected with the external system or islanded with the support of ESSs.While the daily output of DGs strongly depends on the temporal distribution of natural resources such as wind and solar,unregulated electric vehicle(EV)charging demand will deteriorate the unbalance between the daily load curve and generation curve.In this paper,a statistic model is presented to describe daily EV charging/discharging behaviors considering the randomness of the initial state of charge(SOC)of EV batteries.The optimization problem is proposed to obtain the economic operation for the microgrid based on this model.In dayahead scheduling,with the estimated power generation and load demand,the optimal charging/discharging scheduling of EVs during 24 h is achieved by serial quadratic programming.With the optimal charging/discharging scheduling of EVs,the daily load curve can better track the generation curve.The network loss in grid-connected operation mode and required ESS capacity in islanded operation mode are both decreased.
文摘This paper describes a concept for an independent and redundant safety concept for Lithium batteries in Electric and Hybrid Electric Vehicles. This concept includes an emergency cooling system based on pressurized carbon dioxide (CO2). Since carbon dioxide (CO2) is a possible medium of future mobile air conditioning (MAC) systems, the MAC system can be utilized for the one-time emergency cooling described in this paper. In the first part of the paper, some major safety aspects of automotive Li batteries are highlighted. In the second section, the paper describes a technical approach, how these batteries can be made safer. Pressurized CO2, which is a promising candidate for cooling liquids used in future mobile air conditioning (MAC) systems, is used to effectively cool down an overheating or up-heating battery in a critical state. The safety system thereby is not based on an electrical effect, but on a direct and fast-reacting thermal conduction, avoiding a thermal runaway of individual cells. The application of the proposed system is to act preventively just before the thermal runaway gets uncontrollable. In this case, the limited amount of CO2, which is available in the MAC system, fulfils the emergency cooling requirements. The combination of standard car components for the concept leads to an only moderate increase of the total weight and the additional system costs. Therefore, the described system might be of interest for car, battery and air conditioning system producers. This paper explains that the synergetic combination of CO2-based MAC systems and Li-based batteries is an innovative approach to improve environmental compatibility in future vehicles. The concept is proven experimentally on a lab scale with battery cells and battery packs consisting of four serially connected cells, respectively.
基金This work was supported by the National Natural Science Foundation of China(No.51877078)the State Key Laboratory of Smart Grid Protection and Operation Control Open Project(No.SGNR0000KJJS1907535)the Beijing Nova Program(No.Z201100006820106)。
文摘As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management method.However,the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online execution.To address this issue,a long short-term memory(LSTM)recurrent neural network(RNN)based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building prosumers.Under the proposed system control structure,the LSTM algorithm can be separated into offline and online stages.At the offline stage,the LSTM is used to map states(inputs)to decisions(outputs)based on the network training.At the online stage,once the current state is input,the LSTM can quickly generate a solution without any additional prediction.A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM network.The simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.
基金supported by the National Research Foundation of Korea(NRF)grant funded by Korea government(MIST)(No.2017R1A2B4004868).
文摘Smart parking lots are smart places capable of supporting both parking and charging services for electric vehicles(EVs).In order to manage EV charging,the parking lot local controller(PLLC)requires data exchange with EV charging stations(EVCSs)through communication infrastructures.However,data losses and communication delays are unavoidable and may significantly degrade the system performance.This work aims to investigate the underlying communication networks for remote monitoring of EVCSs in a smart campus parking lot.The communication network consists of two subnetworks:parking area network(PAN)and campus area network(CAN).PAN covers communication among EVs,charging stations and PLLC,while CAN enables dedicated communication between PLLCs and a global controller of the university.As one of the major obstacles in EV system is the lack of unified communication architecture to integrate EVCS in the power grid,we develop communication models for the in-vehicle system and EVCSs based on logical node concept of IEC 61850 standard.Furthermore,we implement network models for EVCSs using OPNET modeler.Different communication technologies and configurations are considered in modeling and simulations,and end-toend delay is evaluated and discussed.
文摘There is a general concern that the increasing penetration of electric vehicles(EVs)will result in higher aging failure probability of equipment and reduced network reliability.The electricity costs may also increase,due to the exacerbation of peak load led by uncontrolled EV charging.This paper proposes a linear optimization model for the assessment of the benefits of EV smart charging on both network reliability improvement and electricity cost reduction.The objective of the proposed model is the cost minimization,including the loss of load,repair costs due to aging failures,and EV charging expenses.The proposed model incorporates a piecewise linear model representation for the failure probability distributions and utilizes a machine learning approach to represent the EV charging load.Considering two different test systems(a 5-bus network and the IEEE 33-bus network),this paper compares aging failure probabilities,service unavailability,expected energy not supplied,and total costs in various scenarios with and without the implementation of EV smart charging.
文摘The aim of this paper is to develop a simple EV model and predict its energy consumption with a variable and fixed ratio gearbox over a standard driving cycle in order to understand whether this could offer significant efficiency gains. The powertrain of a generic electric vehicle was modelled in Matlab / Simulink using the QSS Toolkit. The electric vehicle was then fitted with different transmissions with different levels of complexity. Simulations were done to investigate the energy consumptions across 6 standard driving cycles. The emerging conclusions are that it is possible to improve overall energy consumption levels by around 5 to 12 % with a variable ratio gearbox depending on the driving cycle used. However, there are many other practical considerations which must be weighed against this positive result - and the paper discusses the impact of several of these such as, gearbox efficiency, additional weight, cost and complexity, effect on drivability and potential for motor downsizing.
文摘In this paper,a particular standard MicroGrid(MG)is accurately simulated in the presence of the Electric Vehicles(EVs)participating in decentralized primary frequency control service.It examines effect of number of the participating EVs on the short-term dynamic behaviour.The simulation results confirm that frequency deviation will not definitely become zero even though an unlimited number of the EVs participate.The output power of each EV is determined according to the frequency deviation.On the other hand,the output power of each EV affects the value of the frequency deviation,especially in small-scale MGs and MGs with predominant inductance behaviour.Eventually,an equilibrium point is reached after a new EV is added that depends on the characteristics of the MG and the functions executed in the MG central controller during such a service.Additionally,effect of Reflex method,an advanced charging technique for EVs,on the frequency deviation is examined.
基金partially supported by the National Natural Science Foundation of China(General Program)(No.52077107)Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(No.NY220082).
文摘The vehicle-to-grid(V2G)technology enables the bidirectional power flow between electric vehicle(EV)batteries and the power grid,making EV-based mobile energy storage an appealing supplement to stationary energy storage systems.However,the stochastic and volatile charging behaviors pose a challenge for EV fleets to engage directly in multi-agent cooperation.To unlock the scheduling potential of EVs,this paper proposes a source-load-storage cooperative low-carbon scheduling strategy considering V2G aggregators.The uncertainty of EV charging patterns is managed through a rolling-horizon control framework,where the scheduling and control horizons are adaptively adjusted according to the availability periods of EVs.Moreover,a Minkowski-sum based aggregation method is employed to evaluate the scheduling potential of aggregated EV fleets within a given scheduling horizon.This method effectively reduces the variable dimension while preserving the charging and discharging constraints of individual EVs.Subsequently,a Nash bargaining based cooperative scheduling model involving a distribution system operator(DSO),an EV aggregator(EVA),and a load aggregator(LA)is established to maximize the social welfare and improve the low-carbon performance of the system.This model is solved by the alternating direction method of multipliers(ADMM)algorithm in a distributed manner,with privacy of participants fully preserved.The proposed strategy is proven to achieve the objective of low-carbon economic operation.
基金the National Social Science Foundation of China(No.18AJL014)。
文摘The large-scale development of electric vehicles(EVs)requires numerous charging stations to serve them,and the charging stations should be reasonably laid out and planned according to the charging demand of electric vehicles.Considering the costs of both operators and users,a site selection model for optimal layout planning of charging stations is constructed,and a queuing theory approach is used to determine the charging pile configuration to meet the charging demand in the planning area.To solve the difficulties of particle swarm global optimization search,the improved random drift particle swarm optimization(IRDPSO)and Voronoi diagram are used to jointly solve for the optimal layout of electric vehicles.The final arithmetic analysis verifies the feasibility and practicality of the model and algorithm,and the results show that the total social cost is minimized when the charging station is 9,the location of the charging station is close to the center of gravity and the layout is reasonable.
基金supported in part by the National Natural Science Foundation of China(No.51777065).
文摘To reduce the difficulty and enhance the enthusiasm of private-owned electric vehicles(EVs) in participating in frequency regulation ancillary service market(FRASM), a decision aid model(DAM) is proposed. This paper presents three options for EV participating in FRASM, i. e., the base mode(BM), unidirectional charging mode(UCM), and bidirectional charging/discharging mode(BCDM), based on a reasonable simplification of users' participating willingness. In BM, individual EVs will not be involved in FRASM, and DAM will assist users to set the optimal charging schemes based on travel plans under the time-of-use(TOU) price. UCM and BCDM are two modes in which EVs can take part in FRASM. DAM can assist EV users to create their quotation plan, which includes hourly upper and lower reserve capabilities and regulation market mileage prices. In UCM and BCDM, the difference is that only the charging rate can be adjusted in the UCM, and the EVs in BCDM can not only charge but also discharge if necessary. DAM can estimate the expected revenue of all three modes, and EV users can make the final decision based on their preferences. Simulation results indicate that all the three modes of DAM can reduce the cost, while BCDM can get the maximum expected revenue.
基金supported by MIGRID project(No.5-398,2017–2019),which was funded by USAID under the PEER program
文摘Speed forecasting has numerous applications in intelligent transport systems’design and control,especially for safety and road efficiency applications.In the field of electromobility,it represents the most dynamic parameter for efficient online in-vehicle energy management.However,vehicles’speed forecasting is a challenging task,because its estimation is closely related to various features,which can be classified into two categories,endogenous and exogenous features.Endogenous features represent electric vehicles’characteristics,whereas exogenous ones represent its surrounding context,such as traffic,weather,and road conditions.In this paper,a speed forecasting method based on the Long Short-Term Memory(LSTM)is introduced.The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries.The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting.Simulation results show that the multivariate model outperforms the univariate model for short-and long-term forecasting.
基金funded by the National Natural Science Foundation of China(72201149)Xinjiang Key Laboratory of Green Mining of Coal resources,Ministry of Education(KLXGY-KB2420)Guangzhou Basic and Applied Basic Research(SL2023A04J00802).
文摘As the adoption of Electric Vehicles(EVs)intensifies,two primary challenges emerge:limited range due to battery constraints and extended charging times.The traditional charging stations,particularly those near highways,exacerbate these issues with necessary detours,inconsistent service levels,and unpredictable waiting durations.The emerging technology of dynamic wireless charging lanes(DWCLs)may alleviate range anxiety and eliminate long charging stops;however,the driving speed on DWCL significantly affects charging efficiency and effective charging time.Meanwhile,the existing research has addressed load balancing optimization on Dynamic Wireless Charging(DWC)systems to a limited extent.To address this critical issue,this study introduces an innovative eco-driving speed control strategy,providing a novel solution to the multi-objective optimization problem of speed control on DWCL.We utilize mathematical programming methods and incorporate the longitudinal dynamics of vehicles to provide an accurate physical model of EVs.Three objective functions are formulated to tackle the challenges at hand:reducing travel time,increasing charging efficiency,and achieving load balancing on DWCL,which corresponds to four control strategies.The results of numerical tests indicate that a comprehensive control strategy,which considers all objectives,achieves a minor sacrifice in travel time reduction while significantly improving energy efficiency and load balancing.Furthermore,by defining the energy demand and speed range through an upper operation limit,a relatively superior speed control strategy can be selected.This work contributes to the discourse on DWCL integration into modern transportation systems,enhancing the EV driving experience on major roads.
