The increasingly large number of electric vehicles (EVs) has resulted in a growing concern for EV charging station load prediction for the purpose of comprehensively evaluating the influence of the charging load on distribution networks. To address this issue, an EV charging station load prediction method is proposed in coupledurban transportation and distribution networks. Firstly, a finer dynamic urban transportation network model is f o r m u l a t e dc o n s i d e r i n gb o t hn o d a la n dp a t hr e s i s t a n c e .T h e n ,af i n e rE Vp o w e rc o n s u m p t i o nm o d e li sp r o p o s e d by considering the influence of traffic congestion and ambient temperature. Thirdly, the Monte Carlo method isapplied to predict the distribution of EV charging station load based on the proposed dynamic urban transportation network model and finer EV power consumption model. Moreover, a dynamic charging pricing scheme for EVs is d e v i s e db a s e do nt h eE Vc h a r g i n gs t a t i o nl o a dr e q u i r e m ents and the maximum thresholds to ensure the security operation of distribution networks. Finally, the validity of the proposed dynamic urban transportation model was verified by accurately estimating five sets of test data on travel time by contrast with the BPR model. The five groups of travel time prediction results showed that the average absolute percentage errors could be improved from32.87% to 37.21% compared to the BPR model. Additionally, the effectiveness of the proposed EV charging station load prediction method was demonstrated by four case studies in which the prediction of EV charging load was improved from 27.2 to 31.49 MWh by considering the influence of ambient temperature and speed on power energyconsumption. KEYWORDS Electric vehicle; dynamic traffic information; charging stations; charging load forecasting; dynamic electricity pricing Nomenclature G Transportation network N, L Set of nodes and links in the transportation network W Set of weights for links H Set of time intervals Ci(t),Rij(t) Nodal and link resistance in the transportation network t0 ij Free-flow travel time for link nijwithout congestion Sij Saturation of link nij α,β The coefficient of resistance model c Signal period of each intersection in the transportation networkPublished Online: 03 June 2024 2 EE, 2024 λ Green time rate of each intersection in the transportation network q Vehicles arrival rate pn i,t Charging load of ithvehicle at node n rk Number of EVs at node k Pch k,t Charging load for the kthcharging station at time t v0 Free-flow speed of vehicles Cij Capacity of the link nij qij(t) Transportation flow of link nijat time t χ Empirical coefficient a,b,γ The coefficients for different link levels Tmin p,Tmax p Heating and cooling temperature threshold to commit the air conditioner WR,WL Heating and cooling power of the air conditioner E Power consumption per kilometer FTp Power consumption per kilometer at ambient temperature of Tp Lij Length of link nij Ltd Distance from the current location to the destination Ct p(i) Battery capacity of the ithEV αt,βt Gamma distribution parameters to(i),td(i) Departure and return time of the ithvehicle O(i),D(i) Origin and destination of the ithvehicle LOD Length of O-D pairs Ct(i),Cl(i),C0(i) Remaining, capacity and initial battery charge of the ithvehicle Tf(i) End time of charging for ithvehicle ψ,μ Number of EVs receiving charging service and customers per unit time g Number of chargers in the charging station ρ Service intensity of the charger ε Number of EVs going to the charging station for service per unit time Pch k,t,max The threshold of charging station kat time t n(k) Distribution network node coupled with charging station k ζn Normal charging price η The penalty coefficient of charging price 1Introduction With the large integration of wind and photovoltaic power generations into power systems, the adoption of EVs is growing substantially to reduce environmental pollution and carbon dioxideemissions in transportation systems. By the end of 2023, the stock of EVs in China has reached20.4 million [ 1], and the gradual replacement of traditional gasoline vehicles by EVs will become an irreversible trend [ 2]. As a mobile power load [ 3,4], the widespread promotion of EVs will have a significant influence on the operation of the distribution network along with the urban transportationnetwork [ 5–7]. If EVs are charging at fast charging stations without coordination, it will not only invoke security problems such as voltage deviation or line thermal stability damage issues but also easily causetransportation network congestion. In addition, EVs can be dispatched and guided through chargingprice signals as flexible resources [ 8,9]. To mitigate the negative impact of EVs on the operation of urban distribution networks and transportation networks, it is urgently necessary to accurately predictthe spatiotemporal distribution of EV charging demand for designing fast charging stations [ 10]a n d optimizing charging price strategy to ensure the safe operation of the distribution network. EE, 2024 3 The increasing adoption of EVs has attracted a growing concern for EV charging load prediction. In [11], the load of the EV charging station is calculated based on the probability distribution with statistical data. In [ 12], the EV charging station load is predicted based on the copula function. In [ 13], the EV charging load prediction method is developed to coexist with slow charging mode, fast chargingmode, and battery exchange mode. However, the above literatures leave the coupled transportation and distribution network out of consideration, and it is difficult to obtain the spatial distribution of the EV charging load. To describe the dynamic characteristic of EVs in transportation networks,the Markov chain is utilized to estimate EV travel time on routings in [ 14]. In [ 15], the dynamic moving behavior of EVs is characterized based on random trip chains and Markov decision processes,and the spatiotemporal distribution of EV charging load is simulated using a coupled transportationand distribution network model. Literature [ 16] applies the BPR speed-flow model to formulate the dynamic characteristics of transportation networks. However, it fails to consider the delay effectscaused by urban nodal resistance in traffic light intersections. Literature [ 17] predicts EV charging station load distribution based on information such as trip distance, vehicle speed, charging waitingtime, nodal load deviation, etc. However, it does not consider the effect of vehicle speed on the powerconsumption. To address this issue, literature [ 18] estimates the vehicle speed and power consumption under a given trip based on Monte Carlo simulation and mathematical statistical analysis methods.In [19], the influences of EV charging willingness and moving characteristics on power consumption and charging load are considered to predict the spatiotemporal distribution of EV charging load.Literature [ 20] proposes a spatiotemporal distribution model for EV charging loads considering urban functional areas and the effects of temperature. The above literatures mainly focus on private EVs andlack a comprehensive study of the charging behaviors of other groups, such as taxis and other publicutility EVs. To assess the impact of EV type and the dynamic nature of transportation networks on charging station loads, this paper proposes an EV charging station load prediction method in coupled urbantransportation and distribution networks. Firstly, a finer dynamic urban transportation networkmodel is formulated by considering nodal and path resistance. Then, the O-D pairs and Dijkstraalgorithm are introduced to allocate initial distribution and optimal routing policy for EVs. Moreover,the finer EV power energy consumption model is formulated by taking the influence of trafficcongestion and ambient temperature into consideration. The Monte Carlo method is applied to predict the distribution of EV charging demands, together with EV charging station load, through the coupled transportation and distribution networks. Finally, a dynamic charging pricing scheme for EVs isdevised based on the charging station demand requirements and the maximum thresholds to ensurethe security operation of distribution networks. The major contributions of this paper are given as follows: 1) An EV charging station load prediction method is proposed with the finer coupled dynamic transportation network and distribution network. 2) The influence of traffic congestion and environment temperature on EV power consumption is comprehensively formulated which significantly improves the accuracy of the charging station load prediction in urban transportation and distribution networks. 3) A dynamic pricing strategy for EVs is developed by comparing charging demand requirements and the maximum thresholds to ensure the security operation of the distribution network andguide the orderly charging of EVs. The rest of the paper is organized as follows. Section 2 describes the coupling model of vehicles, transportation and distribution networks. Section 3 proposes the model for nodal charging demand 4 EE, 2024 estimation. A dynamic pricing strategy is provided in Section 4 .Section 5 illustrates the validity and accuracy of the proposed method through case studies. The conclusion drawn from the study isprovided in Section 6 . 2Formulation of Coupled Transportation and Distribution Networks EVs can be used as transportation means in transportation network and mobile electric loads in distribution network. The optimal routing policy between Origin-Destination (O-D) pairs depends ontransportation network topology and traffic congestion, which in turn affects the selection of chargingstations for EVs. Similarly, the selection of charging stations will affect the power load distribution,which could invoke security issues such as the bus voltage and line power flow exceeding the allowablevalues, security, and the economic operation of the distribution network. To accurately predict the spatial-temporal distribution of EV charging load, the coupling model of vehicles, transportation and distribution networks is formulated. The schematic diagram of the vehicles-transportation-distribution coupling network is illustrated in Fig. 1 . 1 8 13 21 221423 15 10 3 424 25 5 1218 277 29 30 3132 33 123 4 5 6 78 91 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 18 25 24 23 22 21 20 1926 27 2830 3129 2 2628 17 920 1611 196 32 Transportation Node Distribution Network Node Charging Station Coupling Between Transportation and Distribution Figure 1: Schematic diagram of vehicles-transportation-distribution coupling network 2.1Dynamic Transportation Network Model Fig. 2 illustrates the schematic diagram of the transportation network topology. With the assump- tion that all links are bidirectional, the model of transportation network topology can be formulatedasEq. (1) . EE, 2024 5 ⎧ ⎪⎪⎪⎪⎪⎨ ⎪⎪⎪⎪⎪⎩G=(N,L,H,W) N={1, 2, 3, ···,n} L=/braceleftbig n ij|i∈N,j∈N,i/negationslash=j/bracerightbig H={1, 2, 3, ···,h} W=/braceleftbig wk ij|nij∈L/bracerightbig(1) For purpose of accurately quantifying the trip cost of EVs between O-D pairs in urban trans- portation network, we use the nodal resistance and link resistance to formulate dynamic urbantransportation network model. n1 n2 n3 n4n5w12 w13 w14 w34 w45w35w15w25 w24 Figure 2: Schematic diagram of the transportation network topology As shown in Fig. 3 , the trip time of EVs is influenced both by the link congestion (generally represented as link resistance) and by the time delays caused by traffic light controls at intersections(typically represented as nodal resistance). Therefore, the trip resistance composed of link impedanceand nodal resistance can be formulated as Eq. (2) . w k ij(t)=Ci(t)+Rij(t) (2) CiRijCj Figure 3: Schematic diagram of urban route resistance Referring to the literature [ 21], we divide the condition of links into four levels based on the link saturation degree: unimpeded (0 <Sij≤0.6), slow-moving (0.6 <Sij≤0.8), congested (0.8 <Sij≤1), and severely congested (1 <Sij≤2). The values of nodal resistance and link resistance corresponding 6 EE, 2024 to different saturation levels are shown in Eqs. (3) and(4): Rij(t)=⎧ ⎨ ⎩R1 ij(t):t0 ij(1+α/parenleftbig Sij/parenrightbigβ),0 ≤Sij≤1 R2 ij(t):t0 ij(1+α/parenleftbig 2−Sij/parenrightbigβ),1<Sij≤2(3) Ci(t)=⎧ ⎪⎪⎪⎪⎨ ⎪⎪⎪⎪⎩C1 i(t):9 10/bracketleftBigg c(1−λ)2 2/parenleftbig 1−λSij/parenrightbig+Sij2 2q/parenleftbig 1−Sij/parenrightbig/bracketrightBigg ,0<Sij≤0.6 C2 i(t):c(1−λ)2 2/parenleftbig 1−λSij/parenrightbig+1.5/parenleftbig Sij−0.6/parenrightbig 1−SijSij,Sij>0.6(4) Combining the Eqs. (3) and(4), we can get the trip resistance as Eq. (5) : wk ij(t)=⎧ ⎪⎪⎪⎪⎨ ⎪⎪⎪⎪⎩R1 ij(t)+C1 i(t),0<Sij≤0.6 R1 ij(t)+C2 i(t),0 . 6 <Sij≤0.8 R1 ij(t)+C2 i(t),0 . 8 <Sij≤1 R2 ij(t)+C2 i(t),1<Sij≤2(5) 2.2EV Charging Station Model Coupled the Transportation and Distribution Networks The urban distribution network and transportation network are physically coupled through EV charging stations. For EV charging station kwhich is coupled with node n(k) in distribution network, its charging load can be expressed as Eq. (6) : Pch n(k),t=rk/summationdisplay i=1Pn i,t (6) 3Methodology of EV Charging Station Load Prediction To accurately predict the charging demand requirements of EVs, this section firstly constructs a power consumption model accounting for the influences of ambient temperature and vehicle speed, and then formulates the EV moving and charging models. Moreover, the charging waiting time is alsocalculated based on the M/M/C queuing theory. 3.1EV Power Consumption Model Considering Ambient Temperature and Vehicle Speed The EV power consumption per kilometer which depends on the vehicle speed and ambient temperature has a direct impact on the battery’s state of charge and the magnitude of the EV chargingload distribution. In general, researchers assume that the vehicle speed and ambient temperature are invariable during the trip. This assumption neglects the impact of vehicle speed and air conditioning status onpower consumption per kilometer and charging demand of EVs. As a result, a significant deviation ofthe EV’s power consumption from real values occurs. For instance, an EV power consumption couldincrease by more than 25% in idle state, the commitment of air conditioning could cause increasepower energy consumption by more than 20%. EE, 2024 7 To assess EV power consumption accurately, referring to [ 22], a vehicle speed-flow practical model is firstly constructed based on real time traffic flow as Eq. (7) : ⎧ ⎪⎪⎪⎨ ⎪⎪⎪⎩v ij(t)=v0 1+/parenleftBig qij(t) Cij/parenrightBigχ χ=a+b·/parenleftbiggqij(t) Cij/parenrightbiggγ(7) Based on Eq. (7) , the EV power consumption model considering environmental temperature and speed can be represented as Eqs. (8) –(10): FTp=KTp+E (8) KTp=⎧ ⎪⎪⎪⎪⎪⎨ ⎪⎪⎪⎪⎪⎩W LL vij,Tp>Tmax p WRL vij,Tp<Tmin p 0,Tmin p<Tp<Tmax p(9) E=0.21−0.001 vij+1.531 vij(10) 3.2EV Moving and Charging Characteristics To accurately depict the moving and charging characteristics of EVs, this paper categorizes EVs into three types based on their travel and charging characteristics: commuter private cars, taxis, andother public utility vehicles. The specific charging and travel characteristics of each type are as follows: 1) Commuting private EV: These vehicles mainly travel between residential and work locations with relatively fixed paths; they have a relatively long charging durations and fixed chargingstations. 2) Electric taxis: The O-D pairs of this type EVs are stochastic. The trip time and the number of O-D pairs are more than other type EVs. As a result, taxis need fast charging and have flexiblecharging station selections. 3) Public utility EV: This category includes official private vehicles, business vehicles, and functional vehicles (such as logistics and sanitation vehicles). The O-D pairs of these EVs arevariable with multiple trips. The charging duration is long and the choice of charging stationis not fixed. It should be noted that the charging characteristic of urban buses are almost unaffected by traffic flow for urban buses have specific O-D pairs and charging stations. Therefore, urban buses are notconsidered in this paper. 3.2.1 Driving Route Planning for EVs For purpose of depicting the optimal trajectory for ithEV, this paper initially assigns O-D pairs. Then the Dijkstra algorithm is utilized to search the optimal routing policy. The objective is to selectthe shortest trajectory distance between O-D pairs as depicted in Eq. (11) . As illustrated in Eq. (12) , when the link n ijis included in the optimal trajectory di(i), the value of xiji ss e tt o1a n d0o t h e r w i s e . 8 EE, 2024 LOD=min/summationdisplay nij∈Lxijwk ij(t) (11) xij=/braceleftbigg 1,nij∈di(i) 0,nij/∈di(i)(12) 3.2.2 EVs Charging Characteristics According to the statistical results of different type of EVs, the battery capacity of different types of EVs conform to a gamma distribution as depicted in Eq. (13) : f/bracketleftbig Ct p(i);αt;βt/bracketrightbig =1 βαt tΓ(αt)Ct p(i)αt−1e−Ctp(i) βt (13) Referring to [ 23], the initial state of charge (SOC) of EVs follows a normal distribution N(0.5, 0.1). To prevent negative influence of overcharging, the value of SOC after charging is set to be 80%to 90% of the battery capacity. The specific charging characteristics of each type are as follows: 1) Commuting private EV: These vehicles typically travel between residential and work locations. The charging demand is triggered when the remaining SOC is insufficient for the next trip. Thetrigger condition for charging can be described as Eq. (14) : C t(i)/lessorequalslantLtd(i)FTp (14) 2) Electric taxis: This category of vehicles tends to have random trip patterns and primarily relies on fast charging. Therefore, the action of charging is triggered when Ct(i) falls below a specific threshold value Cl(i) as described as Eq. (15) , in which the value of τranges from 0.15 to 0.35. For taxis, when the charging action is triggered, the Dijkstra algorithm is used to search nearestEV charging station immediately. C t(i)/lessorequalslantτCl(i) (15) 3) Public utility EV: Considering the frequent trips of these vehicles, if the remaining SOC is insufficient to arrive at the destination, if Eq. (14) is satisfied, these vehicles would go to the nearest EV charging station for charging. Otherwise, destination slow charging would betriggered. Finally, the charging duration and the charging end time for EV can be represented by Eqs. (16) and(17), respectively. T c(i)=0.9Cp(i)−Ct(i) ηcPc(16) Tf(i)=Ta(i)+Tw(i)+Tc(i) (17) 3.3M/M/C Queuing Theory For the number of piles at charging station is limited, EVs may need to wait for charging. Therefore, the M/M/C queuing theory is applied to quantify the waiting time for EVs. On the assumption that the number of EVs arriving at EV charging stations follows Poisson distribution, the average system queue waiting time Twcan be formulated as Eqs. (18) and(19)based on the M/M/C queuing theory. EE, 2024 9 Tw=(gρ)gρ g!(1−ρ)2εΘ (18) Θ=/bracketleftBiggg−1/summationdisplay k=01 k!/parenleftbiggψ μ/parenrightbiggk +1 g!·1 1−ρ·/parenleftbiggψ μ/parenrightbiggg/bracketrightBigg−1 (19) 3.4Procedures of EV Charging Station Load Prediction The schematic diagram of EV charging station load prediction in this paper is illustrated in Fig. 4 . The detailed procedures of EV charging station load prediction are described as follows: 1) Construct the coupled urban transportation and distribution network model and allocate different types of EVs at transportation network nodes based on the given proportion. 2) Calculate corresponding moving and charging parameters for EVs based on the Monte Carlo random sampling method. 3) Search for the shortest paths for O-D pairs based on dynamic Dijkstra’s algorithm.4) Calculate the power consumption of different types of EVs according to the speed-flow model, temperature-speed based power consumption model, and M/M/C queuing theory. 5) Determine whether EVs charging demand is trigged. When the conditions for charge is satisfied, EVs would go to the nearest charging station to plug in distribution network. Finally, EV charging station load is obtained by aggregating all the EV charging load at station. `Number of EVs at Transportation Network Nodes Commuting Private EVElectric TaxisPublic Utility EV `Starting Nodes O(i)Destination Nodes D(i) Departure Times t0(i)Return Times td(i)`EV Charging Characteristics Battery Capacity Cp(i)Initial Charge Capacity C0(i) Remaining Battery Charge Ct(i)EV Energy Consumption EMonte Carlo Random Sampling ` Power Consumption Models of EVs Speed ModelsTemperature ModelsM/M/C Queuing TheoryDriving Route Planning Residential Areas Commercial Areas Industrial Areas Charging Triggered `Total Load at Each Node of the Distribution NetworkVoltage at Each Node of the Distribution NetworkEV Moving Characteristics OD Matrix (Travel needs O(i)and D(i))Dijkstra Algorithm (Shortest Distance) Figure 4: Schematic diagram of EV charging station load prediction 10 EE, 2024 4Dynamic Pricing Policy for Optimizing EV Charging Load Distribution To obtain the dynamic pricing strategy for EV charging stations based on the charging demand requirement and threshold, this section firstly calculates the maximum charging load threshold of eachcharging station based on the security-constrained optimal power flow as depicted in Eq. (20) : maxT/summationdisplay t=1K/summationdisplay k=1Pch k,t,maxsubject to/braceleftbig Pch k,t,max∀k,∀t/bracerightbig ∈Z (20) where Zis the feasible region set constrained by DistFlow equation [ 24] and security constraints such as nodal voltage constraints, line thermal stability constraints, charging station capacity constraints,etc. IfP ch k,t>Pch k,t,maxis satisfied, it means the charging load requirement exceeds the maximum threshold and will result in security issues for the power distribution network. In this case, a penalty chargingprice is needed during overload periods to guide the EVs choose a new EV charging station to reducethe charging cost and ensure the security of distribution network. The dynamic charging price modeladopted in this paper is represented as Eq. (21) : ζ k,t=⎧ ⎪⎨ ⎪⎩ζn Pch k,t≤Pch k,t,max ζn/parenleftBigg 1+η/radicalBigg −lgPch k,t,max Pch k,t/parenrightBigg Pch k,t>Pch k,t,max(21) The proposed dynamic pricing model not only improve the economy and efficiency of charging station operation, but also encourage EVs to adjust the charging time based on the pricing signal, thuspromoting the safe operation of the distribution network. 5Case Study In this section, the validity of the proposed method is verified by using a coupled system of the IEEE 33 bus power distribution network and a 32 node transportation network. 5.1Parameter Settings The topology structure of the transportation network is depicted as Fig. 5 , which includes 32 nodes and 53 links, with an average length of 2.18 km. The lengths corresponding to links are showninTable 1 . Six EV charging stations are located at node 1, 23, 16, 31, 25 and 18, respectively. The parameters of transportation network are set as c =30s,λ=0.7,q=0.8, and ζ n=70 $/MWh. Each node is equipped with sufficient AC 220 V slow charging piles with a rated power of 7 kW , and every fast charging station has five 120 kW dual-gun charging piles, which can simultaneously provide charging service for ten EVs. The IEEE 33 bus power distribution network is shown in Fig. 6 . Six charging stations in transportation network are coupled with the node 6, 8, 17, 18, 22, and 28 in distribution network,respectively. EE, 2024 11 2930 31 32 1262728 2345678 9 1112 13 14 1516 24 2517 2318 19 20 212210Charging station 678 9 1112 13 14 1516 17 10 293000 31 323 26272818 19 Charging station 345242333 1 225220222 212222 Commercial Area Residential Area 1 Industrial Area Residential Area 2CS1CS2CS3CS4 CS6 CS5 Figure 5: Structure of transportation network topology Table 1: Lengths corresponding to links in tested transportation network Link Length/km Link Length/km Link Length/km Link Length/km {1,2} 2.0 {13,14}2.1 {23,24}2.3 {10,30}2.6 {2,3} 2.2 {14,15}1.7 {24,25}2.1 {18,29}2.0 {3,4} 1.7 {15,16}1.7 {1,22} 2.4 {19,28}3.3 {3,5} 2.2 {14,16}1.9 {5,23} 1.8 {20,27}2.3 {5,6} 2.3 {16,17}2.2 {4,23} 2.0 {22,26}2.5 {6,7} 2.2 {17,18}2.2 {4,25} 2.3 {28,31}2.2 {7,8} 2.0 {18,19}2.3 {6,14} 1.8 {26,32}2.0 {8,9} 2.1 {19,20}2.3 {14,23}2.2 {26,27}1.8 {9,10} 3.3 {20,21}2.4 {15,24}2.0 {27,28}2.0 {10,11}1.7 {20,22}2.1 {19,24}2.8 {28,29}2.2 {11,12}2.1 {21,25}1.8 {19,25}3.0 {29,30}1.8 {12,13}2.0 {1,21} 2.0 {19,28}2.8 {30,31}2.5 {7,13} 2.1 {3,21} 3.2 {11,29}2.8 {31,32}2.4 {9,13} 2 . 3 – –– –– – 01 2345678910111213141516 17181920 21 222324252627 28 293031 32Charging station CS2 CS1 CS5CS3CS4 CS6 Figure 6: Diagram of IEEE 33 bus system 12 EE, 2024 Some necessary instructions of the case study are given below: 1) Due to data limitations and the complexity of urban transportation network, we just focus on the main links to simplify the modeling of the transportation network and reduce simulationtime, highlighting the impact of traffic network constraints on the spatiotemporal characteris-tics of EV station charging load. 2) The line thermal stability limits have been adjusted appropriately to match the load distribution of the test area based on the standard data of IEEE 33 bus distribution network. 3) The links in transportation network are categorized into two levels. For level I, the values of a, b,a n dγare set to 1.726, 3.15, and 3, respectively. For level II, the corresponding values are set to be 2.076, 2.870, and 3, respectively. The number of total EVs in this paper is set to be 3500, with the proportions of commuting private EV, electric taxis, and public utility EV being 45%, 30%, and 25%, respectively. The initial distributionof these three types of vehicles on a typical workday is illustrated in Fig. 7 . 0 5 10 15 20 25 30020406080100120140160 Commute Private EVElectric Taixs Public Utility EV Transportation nodessVEforebmuN Figure 7: Initial EV distribution in the transportation network 5.2Dynamic Transportation Network Model Testing To evaluate the effectiveness of the dynamic urban transportation network model including nodal resistance in predicting EV charging station load, two cases are presented for detailed comparativeanalysis. Case 1: The Time-flow model in the BPR function model [ 25] is considered without nodal resistance. Case 2: The proposed transportation model in this paper considers the effects of nodal resistance and link resistance. To predict travel time of each trajectory with different number of nodes, 100 testing experiments are conducted, respectively. The average travel time in the two cases is shown in Fig. 8 . The results show that the travel time deviation of case 1 and case 2 expands continuously with the increase of the numberof nodes passed by. In this regard, the deviation gradually increases from 6.05 min when passingthrough the trajectory with 3 nodes to 16.32 min when passing through the trajectory with 7 nodes,and the relative deviation increases from 32.87% at the minimum to 37.21% at the maximum. It can EE, 2024 13 be concluded that due to the large number of intersections in urban transportation network, the nodal resistance has a great impact on the trip time, which will then affect the driving route planning of EVs,and finally have an impact on the spatiotemporal distribution of charging station load. The dynamictransportation model considering nodal resistance adopted in this paper can accurately simulate thedriving characteristics of EVs, and improve the prediction accuracy of the spatiotemporal distribution of EV charging load by optimizing the routing selection of EVs. 3456701020304050)nim(emitlevarT 0 Number of nodes passedcase1 case2 Figure 8: Comparison of the travel time in different cases 5.3The Validity Analysis of the Proposed EV Charging Station Load Prediction Method To comprehensively evaluate the effectiveness of the EV charging station load prediction model proposed in this paper, four cases are illustrated for comparative analysis. Case 1: EV charging station load prediction model that does not take the influence of ambient temperature and speed on power energy consumption into consideration. Case 2: EV charging station load prediction model that considers the effect of ambient temperature on EV energy consumption but does not account for vehicle speed influence. Case 3: EV charging station load prediction model that considers the influence of vehicle speed on EV energy consumption but does not account for ambient temperature influence. Case 4: The proposed method in this paper.The EV charging station load in four cases is illustrated in Fig. 9 . The total charging load energy of the regional charging stations over 24 h is 27.2 MWh in case 1. When considering the influence of ambient temperature on EV power consumption in case 2, the total charging load energy increases to 28.87 MWh with an increase of approximately 6.14% compared to case 1. The increased charging load energy is attributed to the operation of EV air conditioning in case2 due to ambient temperature influences. By comparing the results of case 1 and case 3, the total charging load of regional charging stations increases from 27.2 to 29.39 MWh with an increase of 2.19 MWh, or about 8.05%. The reason is that when EVs are moving in urban transportation network, EVs may work at idle speed or frequentlystartup and shutdown due to traffic congestion, resulting in an increase in power consumption, whichin turn increases the total charging load demand of EV charging stations. 14 EE, 2024 Case1051015202530CS1 CS2 CS3 CS4 CS5 CS6 Case2 Case3 Case4)hWM(dnamedgnigrahC35 27.228.87 29.3931.49 Figure 9: Comparison of EV charging station load in four cases In case 4, considering the influence of temperature and vehicle speed on the charging load demand, the charging load of the six charging stations in the region becomes 31.49 MWh, which increases by4.29 MWh relative to case 1. It can be observed that the incremental charging load is greater than thesum of that in case 2 and case 3 relative to case 1. The reason is that idle state not only increases thepower consumption per unit mileage, but also increases the travel time, and the power consumption ofair conditioning further increases compared with case 3. Consequently, the charging load incrementpredicted by case 4 relative to case 1 is greater than the sum of the charging load increment consideringthe influence of temperature and speed alone. Fig. 10 shows the spatiotemporal distribution of EV charging load in transportation network. It can be observed that the peak value of overall charging demand occurs from 14:00–18:00. As can beseen from the Fig. 10 , the charging load is mainly concentrated in nodes 4, 13, 17, 28, etc. The above nodes mainly correspond to the residential and commercial centers of the traffic nodes, and presents a“double peak” type charging demand. It should be noted that some electric taxis also choose to chargeat night after finishing their daytime operations, and the charging load during 0:00–8:00 is relativelyflat and are composed of electric taxis and commuting private EVs. 1020 050150gnigrahC demand(kW)100 30 0 Figure 10: Spatiotemporal distribution of EV charging load EE, 2024 15 5.4Analysis of Dynamic Charging Price The charging load, maximum charging load threshold, and dynamic charging price of each EV charging station are shown in Fig. 11 . 00.10.20.30.40.50.60.7 Time (hour)0:00 5:00 10:00 15:00 20:00 24:00)WM(dnamedgnigrahC 0 Charging price ($/MWh) 20120 406080100140Charging demand Load thresholdCharging priceCharging station 1 00.10.20.30.40.50.60.7 Time (hour)0:00 5:00 10:00 15:00 20:00 24:00Charging demand (MW) 0 Charging price ($/MWh) 20120 406080100140Charging demand Load thresholdCharging priceCharging station 2 00.10.20.30.40.50.60.7 Time (hour)0:00 5:00 10:00 15:00 20:00 24:00)WM(dnamedgnigrahC 0 Charging price ($/MWh) 20120 406080100140Charging demand Load threshold Charging priceCharging station 3 00.10.20.30.40.50.60.7 Time (hour)0:00 5:00 10:00 15:00 20:00 24:00Charging demand (MW) 0 Charging price ($/MWh) 20120 406080100140Charging demand Load thresholdCharging priceCharging station 4 00.10.20.30.40.50.60.7 Time (hour)0:00 5:00 10:00 15:00 20:00 24:00)WM(dnamedgnigrahC 0 Charging price ($/MWh) 20120 406080100140Charging demand Load thresholdCharging priceCharging station 5 00.10.20.30.40.50.60.7 Time (hour)0:00 5:00 10:00 15:00 20:00 24:00Charging demand (MW) 0 Char ging price ($/MWh) 20120 406080100140Charging demand Load thresholdCharging priceCharging station 6 Figure 11: Distribution of charging load demand, threshold and price for EV charging stations 16 EE, 2024 It can be found that charging prices are all 70 $/MWh during periods when the charging demand is less than the threshold for EV charging stations. However, during the periods 14:00–19:00, the chargingdemand at EV charging stations are relatively higher than other periods. Consequently, the chargingdemand requirement at hours 15:00 for station 2 surpasses the threshold, and the charging demandrequirement during period 14:00–18:00 for station 3, along with the charging demand requirement during period 14:00–19:00 for station 4 and the charging demand requirement at hours 15:00, 16:00 and 18:00 exceed their threshold, respectively. As can be seen from Fig. 11 , the charging price for hours when the charging load exceed the threshold are adjusted based on the dynamic pricing model and themore excess, the higher the price. With the dynamic charging price strategy, we can guide the EV driversto charge at charging stations 1, 2 and 5 instead of charging at stations 3, 4 and 6 during peak charginghours or charging at off-peak hours to save the cost. 6Conclusion This paper proposes an EV charging station load prediction method in coupled urban transporta- tion and distribution networks. The effectiveness of the proposed model is validated through casestudies on a coupled IEEE 33 bus power distribution network and a 32-node transportation network.The conclusions are as follows: 1) The dynamic transportation network model adopted in this paper takes the influence of path congestion and intersection traffic lights on vehicle movement into consideration. The fivegroups of travel time prediction results for a trajectory with 3, 4, 5, 6, and 7 nodes showed thatthe average absolute percentage errors could be improved from 32.87% to 37.21% comparedto the BRP model. 2) The EV power consumption model applied in this paper takes the influence of traffic con- gestion and environment temperature into consideration. As a result, the prediction of EVscharging load was improved from 27.2 to 31.49 MWh through the comparative analysis of thefour cases, which significantly improves the accuracy of the charging station load predictionand provides reliable support for determining charging price policy in urban transportationand distribution networks. 3) Based on the predicted EV charging station loads and the thresholds, the dynamic charging price function is used to formulate a charging price strategy, which can not only improvethe overall charging efficiency of regional EV charging stations during peak charging hours,but also provide price guidance signals for EV charging station selection, and ensure the safeoperation of the distribution network. The charging stations are key coupling elements with vehicles, transportation and distribution networks, the strategic placement and capacity of charging stations are crucial for urban developmentwith the growing large number of EVs in transportation and distribution networks. Therefore, futureresearch will focus on optimized planning of charging stations based on the proposed EV chargingstation load prediction method. Acknowledgement: The authors acknowledge Northeast Electric Power University for facilitating and supporting the research work. Funding Statement: This project is supported by the National Natural Science Foundation of China (No. U22B20105). EE, 2024 17 Author Contributions: The authors confirm contribution to the paper as follows: study conception and design: Benxin Li, Xuanming Chang; project administration: Xuanming Chang; analysis andinterpretation of results: Benxin Li; draft manuscript preparation: Benxin Li, Xuanming Chang. Allauthors reviewed the results and approved the final version of the manuscript. Availability of Data and Materials: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.