Forecast of electric vehicle charging demand based on traffic flow model and optimal path planning

Su, Shu; Hong, Zhao; Zhang, Hongzhi; Lin, Xin; Yang, Feipeng; Li, Zhengtian

doi:10.1109/isap.2017.8071382

Cited by 16 publications

(15 citation statements)

References 12 publications

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“…In this regard, an energy management system using driving pattern prediction is proposed in [6]. Accordingly, several researchers focus on modelling and forecasting EVs using the driving behaviour [7][8][9][10][11][12][13][14] to mitigate the adverse influences of EVs on power systems. A Monte Carlo-based method combined with the national household travel survey is used in [7,8], while a modified Monte Carlo is proposed in [9] by removing less likely scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…However, this research [6][7][8][9][10][11][12][13][14] uses the driving behaviour of conventional vehicles to estimate the EV driving model, which may lead to some errors due to differences between EVs and conventional cars. On the other hand, some researchers use real data of existing EV charging to model their behaviour [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Stochastic bidding strategy for electrical vehicle charging stations to participate in frequency containment reserves markets

Divshali

Evens

2020

IET Generation Trans & Dist

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This study presents a stochastic bidding strategy for electrical vehicle charging stations (EVCSs) to participate in frequency containment reserves (FCRs) markets. To achieve this, the study starts by developing deterministic models to calculate the maximum FCR that could be provided by each charging event (cycle) of an electric vehicle. These models are established based on the technical requirements of FCR in the Nordic flexibility market, namely the frequency containment reserve for normal operation and frequency containment reserve for disturbances. These deterministic models will be combined with historical data of charging records in EVCS to develop a methodology to calculate the probability density functions of the FCR profiles. Finally, the optimum FCR profiles, which maximise the expected profit of EVCS from participating in the day‐ahead flexibility market, are estimated by performing a stochastic optimisation. The proposed methodology is evaluated by using empirical charging data of public EVCS in the Helsinki area.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic bidding strategy for electrical vehicle charging stations to participate in frequency containment reserves markets

Divshali

Evens

2020

IET Generation Trans & Dist

View full text Add to dashboard Cite

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“…In terms of the statistical results [18], we assumed that State of Charge (SOC) at the first trip time obeyed a normal distribution N (0.8, 0.1), so the initial battery capacity C O (i) of each introduced vehicle can be generated combining with the battery capacity C p (i).…”

Section: Battery Parameter Settingmentioning

confidence: 99%

“…Several studies [15][16][17] introduced Traffic Trip Chain and Markov Decision Chain to simulate the dynamic driving behavior and random charging behavior of EVs, established a dynamic EV charging demand prediction model and evaluated the congestion degree on the distribution network and traffic network caused by large-scale aggregation charging. In addition, researchers [18,19] adopted a microscopic traffic model to depict the dynamic characteristics and electrical state of EVs in the joint simulation system of transportation electrification, and they predicted the evolution trend of vehicle traffic demand and charging demand. Further, traffic nodes and electricity nodes in heterogeneous physical space were mapped to networks in studies [20,21].…”

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confidence: 99%

Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior

et al. 2020

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Electric vehicles (EVs) have attracted growing attention in recent years. However, most existing research has not utilized actual traffic data and has not considered real psychological decision-making of owners in analyzing the charging demand. On this basis, an urban EV fast-charging demand forecasting model based on a data-driven approach and human decision-making behavior is presented in this paper. In this methodology, Didi ride-hailing order trajectory data are firstly taken as the original dataset. Through data mining and fusion technology, the regenerated data and rules of traffic operation are obtained. Then, the single EV model with driving and charging behavior parameters is established. Furthermore, a human behavior decision-making model based on Regret Theory is introduced, which comprises the utility of time consumption and charging cost to plan driving paths and recommend fast-charging stations for vehicles. The rules obtained from data mining together with established models are combined to construct the 'Electric Vehicles-Power Grid-Traffic Network' fusion architecture. At last, the actual urban traffic network in Nanjing is selected as an example to design the fast-charging demand load experiments in different scenarios. The results demonstrate that this proposed model is able to effectively predict the spatio-temporal distribution characteristics of urban fast-charging demands, and it more realistically simulates the decision-making psychology of owners' charging behavior.Energies 2020, 13, 1412 2 of 32 331,000 public charging piles and 477,000 private charging piles in 2018, an increase of 74.2% over the same period in 2017 [6].However, the driving and charging behaviors of numerous EVs in urban internal networks are bound to interact with the energy and information generated by the traffic network and power grid [7,8]. The residents' trip rule, urban road network structure and charging facility distribution affect the vehicle driving distribution and charging selection, whereas the vehicle battery parameters, driving path plan and human decision-making behavior also affect the degree of traffic network obstruction and the power grid operation state [9][10][11][12]. Therefore, accurate prediction of EV charging demand and reasonable fast-charging station (FCS) recommendations are the premise of realizing compatibility between EVs and the power grid along with the transportation network.At present, various studies have developed EV charging demand models from the aspect of cooperation between EVs, the transportation system and power system all together. Hence, in [13,14], an origin destination (OD) matrix analysis method was utilized to track the all-weather driving trajectory of EVs and to predict the charging load distribution of the regional electricity grid as well as the flow status of road networks through vehicle traffic trip demands. Several studies [15][16][17] introduced Traffic Trip Chain and Markov Decision Chain to simulate the dynamic driving behavior and random charging behavior o...

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“…Taking into account the fact that EVs are coupled with traffic network and distribution network simultaneously, Luo et al [9] and Shao et al [10] have constructed a fusion system integrated with "vehicle-traffic-distribution" and solved the spatial-temporal distribution of EV charging load by origin-destination matrix. In [11], a dynamic evolution model with EV spatial-temporal distribution has been proposed on the basis of cell agent theory and Dijkstra method. It aimed to plan EV trip path and predict EV charging load.…”

Section: Introductionmentioning

confidence: 99%

Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness

et al. 2020

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Integration of large-scale cluster electric vehicles (EVs) and their spatial-temporal transfer randomness are likely to affect the safety and economic operation of the distribution network. This paper investigates the spatial-temporal distribution prediction of EVs' charging load and then evaluates the reliability of the distribution network penetrated with large-scale cluster EVs. To effectively predict the charging load, trip chain technology, Monte Carlo method, and Markov decision process (MDP) theory are employed. Moreover, a spatial-temporal transfer model of EVs is established, and based on which, an EV energy consumption model and a charging load prediction model are constructed with consideration of temperature, traffic condition and EV owner's subjective willingness in different scenarios. With the application of sequential Monte Carlo method, the paper further evaluates distribution network reliability in various charging scenarios. In the evaluation, indices including per unit value (PUV), fast voltage stability index (FVSI), loss of load probability (LOLP), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and expected energy not supplied (EENS) are incorporated. To validate the proposed prediction model and evaluation method, a series of numerical simulations are conducted on the basis of taking the traffic-distribution system of a typical city as an example. The result demonstrates that the proposed spatial-temporal transfer model is more practical in charging load prediction than the popularly used Dijkstra's shortest path algorithm. Moreover, high temperature, congestion and the increment of EV penetration rate will further weaken distribution network reliability. INDEX TERMS Electric vehicle, reliability evaluation, trip chain technology, Markov decision process, sequential Monte Carlo method, spatial-temporal transfer randomness NOMENCLATURE ABBREVIATIONS EV Electric vehicle MDP Markov decision process SOC State of charge TNN Traffic network node DNN Distribution network node DCP Dispersion of charging power PARAMETERS μ Mean of the beginning time of each trip  Variance of the beginning time of each trip v i,j max Maximum speed allowed at the road (i, j) x EV's position in the travel path D x Distance between the origin of the trip and x C EV Capacity of EV battery

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Forecast of electric vehicle charging demand based on traffic flow model and optimal path planning

Cited by 16 publications

References 12 publications

Stochastic bidding strategy for electrical vehicle charging stations to participate in frequency containment reserves markets

Stochastic bidding strategy for electrical vehicle charging stations to participate in frequency containment reserves markets

Urban Electric Vehicle Fast-Charging Demand Forecasting Model Based on Data-Driven Approach and Human Decision-Making Behavior

Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness

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