Method for calculating charging load based on electric vehicle charging behavior simulation

Abstract

The application discloses a kind of based on electric vehicle charging behavior simulation's charging load calculation method, comprising:1 establish the charging time selection model based on user charging willingness time set, judge the selection of user to charging time;2 establish the user charging place selection model based on comprehensive charging consumption, judge the selection of user to charging place;3 establish the automobile charging state judgment model based on charging duration, judge whether electric vehicle is in charging state;4 calculate the total charging load of each electric vehicle in each charging station at each time, obtain the space-time distribution of load.The application fully considers the charging willingness of each electric vehicle itself and the space-time distribution of load when calculating electric vehicle charging load, both in line with the charging habits of car owner, and prevent the emergence of charging load fragmentation.

Description

Technical Field

[0001] This invention proposes a charging load calculation method based on the simulation of electric vehicle charging behavior, taking into account the differences in charging time and location choices among different electric vehicle users. Background Technology

[0002] The high proportion of renewable energy connected to the grid, characterized by uncertainty, places higher demands on the flexibility of grid operation. Electric vehicles, as a typical flexible resource, exhibit similar characteristics in both time and space, allowing their charging loads to shift flexibly. By managing electric vehicles in an orderly manner and scheduling them to charge at different times and locations, it is possible to reduce system network losses, balance fluctuations in renewable energy output, and contribute positively to the power flow balance of the grid.

[0003] Current research on electric vehicle charging load distribution mainly focuses on analyzing the entire electric vehicle population, neglecting the differences in charging intentions among individual electric vehicle owners. This research method struggles to comprehensively characterize the impact of factors such as charging station scale, traffic conditions, and vehicle state of charge on the charging choices of different vehicle owners.

[0004] Some studies focus solely on the load level, calculating only the charging load of electric vehicles after applying different load control methods. This approach ignores the integrity of the electric vehicle charging process, leading to a situation where an electric vehicle is charged in multiple non-adjacent time periods, thus fragmenting its charging load. This fragmented charging load situation cannot accurately reflect the actual charging demand of electric vehicles. Summary of the Invention

[0005] The purpose of this invention is to address the problems existing in the prior art, specifically the difficulty in accurately calculating the spatiotemporal distribution of electric vehicle charging load. This invention proposes a charging load calculation method based on electric vehicle charging behavior simulation, aiming to fully consider the charging intentions of different car owners and the charging status of electric vehicles when calculating electric vehicle load. This will allow for an accurate description of the spatiotemporal distribution of electric vehicle load, providing a reference for the site selection and capacity planning of charging stations, thereby further improving energy utilization.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] The charging load calculation method based on electric vehicle charging behavior simulation of this invention is characterized by the following steps:

[0008] Step 1: Establish a charging time selection model based on the user's charging intention time set to determine the user's choice of charging time:

[0009] Step 1.1: Use equation (1) to obtain the state of charge (soc) of the i-th electric vehicle at time t. i (t):

[0010]

[0011] In equation (1), C i Let soc be the battery capacity of the i-th electric vehicle. i (0) represents the initial state of charge of the i-th electric vehicle, V i Let R be the speed of the i-th electric vehicle. i Let be the driving energy consumption of the i-th electric vehicle;

[0012] Step 1.2: Use equation (2) to obtain the charging intention time set Ω of the i-th electric vehicle. i :

[0013] Ω i ={t|soc i,down <soc i (t)<soc i,up} (2)

[0014] In equation (2), soc i,down State of charge limit for charging the i-th electric vehicle, soc i,up The upper limit of the state of charge when charging the i-th electric vehicle;

[0015] Step 1.3: Use equations (3) and (4) to obtain the charging time T selected for the i-th electric vehicle. i :

[0016]

[0017]

[0018] In equations (3)-(4), P en,t P contributes to the new energy source at time t. en,i The new energy output for the selected charging time of the i-th electric vehicle, v i,t Indicates whether the i-th electric vehicle chooses to charge at time t, when v i,t When v = 0, it means that the i-th electric vehicle did not choose to charge at time t. i,t =1, which means that the i-th electric vehicle chooses to charge at time t, and M1 is the linearization coefficient of the charging time judgment model;

[0019] Step 2: Establish a user charging location selection model based on comprehensive charging consumption to determine the user's choice of charging location:

[0020] Step 2.1: Set each intersection in the traffic road as a node, and use the speed-flow practical model shown in equations (5)-(6) to characterize the equivalent length of the road:

[0021]

[0022]

[0023] In equations (5)-(6), l p,q Let p be the actual length of the road segment between nodes p and q. Let be the equivalent road segment length between nodes p and q at time t, and let a, b, and n be adaptive coefficients for different road grades. Let be the road segment saturation between node p and node q at time t. Let CL be the actual road traffic flow between node p and node q at time t. p,q Let be the capacity of the road segment between node p and node q;

[0024] Step 2.2: Set the i-th electric vehicle to depart from node p at time t and head to the j-th charging station located at node q for charging. Then, use equation (7) to establish a comprehensive charging consumption model that considers the expected waiting time at the charging station and the shortest distance from the electric vehicle to the charging station.

[0025]

[0026] In equation (7), Let's consider the total energy consumption of the i-th electric vehicle at time t, starting from node p and charging at the j-th charging station located at node q. Let ω1 and ω2 be the estimated waiting time at the j-th charging station located at node q at time t, and let ω1 and ω2 be two weighting coefficients.

[0027] Step 2.3: Use equation (8) to determine whether the i-th electric vehicle selects the j-th charging station. i,j :

[0028]

[0029] In equation (8), Let N be the minimum total energy consumption of the i-th electric vehicle at time t. CS The number of charging stations, when u i,j When u = 0, it means that the i-th electric vehicle did not select the j-th charging station. i,j =1, representing that the i-th electric vehicle selects the j-th charging station, and M2 is the linearization coefficient of the charging location judgment model;

[0030] Step 3: Establish a vehicle charging status judgment model based on charging time to determine whether the electric vehicle is in a charging state:

[0031] Step 3.1: Use equation (9) to obtain the time required for the i-th electric vehicle to travel from its current location to the j-th charging station at time t.

[0032]

[0033] Step 3.2: Use equation (10) to obtain the starting charging time of the i-th electric vehicle after it travels from its current position to the j-th charging station at time t.

[0034]

[0035] Step 3.3: Use equation (11) to obtain the charging time T required for the i-th electric vehicle to fully charge at the j-th charging station. ch,i,j :

[0036]

[0037] In equation (12), η is the charging efficiency, and P ch,j Let be the charging power of the charging pile in the j-th charging station;

[0038] Step 3.4: Use equation (12) to obtain the charging status of the i-th electric vehicle at time t when it goes to the j-th charging station for charging.

[0039]

[0040] In equation (12), This indicates that at time t, the i-th electric vehicle is in a charging state when it goes to the j-th charging station for charging. This indicates that at time t, the i-th electric vehicle is not charging when it goes to the j-th charging station. day It represents the number of hours in a calendar day;

[0041] Step 4: Calculate the total charging load of electric vehicles in the j-th charging station at time t using equation (13). This yields the total charging load of electric vehicles at each charging station at each time point, which serves as the spatiotemporal distribution of the load.

[0042]

[0043] In equation (13), N EV This represents the total number of electric vehicles.

[0044] The present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the charging load calculation method, and the processor is configured to execute the program stored in the memory.

[0045] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program is executed by a processor to perform the steps of the charging load calculation method.

[0046] Compared with existing technologies, the beneficial effects of this invention are reflected in:

[0047] 1. This invention proposes a user charging intention time set and comprehensive charging consumption, which can accurately characterize the electric vehicle user's preference for charging time and charging location, solve the problem that the charging behavior of electric vehicle users is difficult to describe intuitively, and provide a technical means for the power grid to participate in the regulation of electric vehicle load.

[0048] 2. This invention calculates the charging load within a charging station based on the charging behavior simulation of individual electric vehicles, ensuring the accuracy of the charging load calculation results and preventing the charging load from being fragmented. Attached Figure Description

[0049] Figure 1 This is a flowchart of the charging load calculation based on the simulation of electric vehicle charging behavior. Detailed Implementation

[0050] In this embodiment, as Figure 1 As shown, a method for calculating charging load based on electric vehicle charging behavior simulation is performed according to the following steps:

[0051] Step 1: Establish a charging time selection model based on the user's charging intention time set to determine the user's choice of charging time:

[0052] Step 1.1: Use equation (1) to obtain the state of charge (soc) of the i-th electric vehicle at time t. i (t):

[0053]

[0054] In equation (1), C i Let the battery capacity of the i-th electric vehicle be 82kWh, and soc i (0) represents the initial state of charge of the i-th electric vehicle, generated using Monte Carlo sampling based on historical statistical data. i Let R be the speed of the i-th electric vehicle, denoted as 40 km / h. iLet be the driving energy consumption of the i-th electric vehicle, assuming that the electric vehicle consumes 20.5 kWh of electricity per 100 kilometers;

[0055] Step 1.2: Use equation (2) to obtain the charging intention time set Ω of the i-th electric vehicle. i :

[0056] Ω i ={t|soc i,down <soc i (t)<soc i,up} (2)

[0057] In equation (2), soc i,down State of charge limit for charging the i-th electric vehicle, soc i,up The upper limit of the state of charge when charging the i-th electric vehicle;

[0058] Step 1.3: Use equations (3) and (4) to obtain the charging time T selected for the i-th electric vehicle. i :

[0059]

[0060]

[0061] In equations (3)-(4), P en,t P is generated by Monte Carlo sampling to measure the output of new energy sources at time t, combined with historical statistical data. en,i The new energy output for the selected charging time of the i-th electric vehicle, v i,t Indicates whether the i-th electric vehicle chooses to charge at time t, when v i,t When v = 0, it means that the i-th electric vehicle did not choose to charge at time t. i,t =1, which means that the i-th electric vehicle chooses to charge at time t. M1 is the linearization coefficient of the charging time judgment model and is set to 10000.

[0062] Step 2: Establish a user charging location selection model based on comprehensive charging consumption to determine the user's choice of charging location:

[0063] Step 2.1: Set each intersection in the traffic road as a node, and use the speed-flow practical model shown in equations (5)-(6) to characterize the equivalent length of the road:

[0064]

[0065]

[0066] In equations (5)-(6), l p,qLet p be the actual length of the road segment between nodes p and q. Let be the equivalent road segment length between nodes p and q at time t, and let a, b, and n be adaptive coefficients for different road grades, with a, b, and n being 1.726, 3.15, and 3, respectively. Let be the road segment saturation between node p and node q at time t. Let CL be the actual road traffic flow between node p and node q at time t. p,q Let be the capacity of the road segment between node p and node q;

[0067] Step 2.2: Set the i-th electric vehicle to depart from node p at time t and head to the j-th charging station located at node q for charging. Then, use equation (7) to establish a comprehensive charging consumption model that considers the expected waiting time at the charging station and the shortest distance from the electric vehicle to the charging station.

[0068]

[0069] In equation (7), Let's consider the total energy consumption of the i-th electric vehicle at time t, starting from node p and charging at the j-th charging station located at node q. Let ω1 and ω2 be the estimated waiting time at the j-th charging station located at node q at time t, and let ω1 and ω2 be two weighting coefficients, set to 0.6 and 0.4 respectively.

[0070] Step 2.3: Use equation (8) to determine whether the i-th electric vehicle selects the j-th charging station. i,j :

[0071]

[0072] In equation (8), Let N be the minimum total energy consumption of the i-th electric vehicle at time t. CS The number of charging stations, when u i,j When u = 0, it means that the i-th electric vehicle did not select the j-th charging station. i,j =1, which means that the i-th electric vehicle selects the j-th charging station, and M2 is the linearization coefficient of the charging location judgment model, which is set to 10000;

[0073] Step 3: Establish a vehicle charging status judgment model based on charging time to determine whether the electric vehicle is in a charging state:

[0074] Step 3.1: Use equation (9) to obtain the time required for the i-th electric vehicle to travel from its current location to the j-th charging station at time t.

[0075]

[0076] Step 3.2: Use equation (10) to obtain the starting charging time of the i-th electric vehicle after it travels from its current position to the j-th charging station at time t.

[0077]

[0078] Step 3.3: Use equation (11) to obtain the charging time T required for the i-th electric vehicle to fully charge at the j-th charging station. ch,i,j :

[0079]

[0080] In equation (11), η is the charging efficiency, which is set to 0.85, and P ch,j Let the charging power of the charging pile in the j-th charging station be 42kW;

[0081] Step 3.4: Use equation (12) to obtain the charging status of the i-th electric vehicle at time t when it goes to the j-th charging station for charging.

[0082]

[0083] In equation (12), This indicates that at time t, the i-th electric vehicle is in a charging state when it goes to the j-th charging station for charging. This indicates that at time t, the i-th electric vehicle is not charging when it goes to the j-th charging station. day This represents the number of hours in a calendar day, and is set to 24.

[0084] Step 4: Calculate the total charging load of electric vehicles in the j-th charging station at time t using equation (13). This yields the total charging load of electric vehicles at each charging station at each time point, which serves as the spatiotemporal distribution of the load.

[0085]

[0086] In equation (13), N EV This represents the total number of electric vehicles.

[0087] In summary, the charging load calculation method based on electric vehicle charging behavior simulation proposed in this invention first simulates the choices of charging time and charging location by different car owners and judges the charging status of electric vehicles at each time. Then, it calculates the charging load of each charging station at each time, thereby accurately describing the spatiotemporal distribution of electric vehicle load.

[0088] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0089] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

Claims

1. A method for calculating charging load based on electric vehicle charging behavior simulation, characterized in that, The procedure is as follows: Step 1: Establish a charging time selection model based on the user's charging intention time set to determine the user's choice of charging time: Step 1.1: Use equation (1) to obtain the state of charge (soc) of the i-th electric vehicle at time t. i (t): (1) In equation (1), C i Let soc be the battery capacity of the i-th electric vehicle. i (0) represents the initial state of charge of the i-th electric vehicle, V i Let R be the speed of the i-th electric vehicle. i Let be the driving energy consumption of the i-th electric vehicle; Step 1.2: Use equation (2) to obtain the charging intention time set Ω of the i-th electric vehicle. i : (2) In equation (2), soc i,down State of charge limit for charging the i-th electric vehicle, soc i,up The upper limit of the state of charge when charging the i-th electric vehicle; Step 1.3: Use equations (3) and (4) to obtain the charging time T selected for the i-th electric vehicle. i : (3) (4) In equations (3)-(4), To contribute to the new energy source at time t The new energy output for the selected charging time of the i-th electric vehicle, v i,t Indicates whether the i-th electric vehicle chooses to charge at time t, when v i,t When v = 0, it means that the i-th electric vehicle did not choose to charge at time t. i,t =1, indicating that the i-th electric vehicle chooses to charge at time t. The linearization coefficients of the charging time determination model are used; Step 2: Establish a user charging location selection model based on comprehensive charging consumption to determine the user's choice of charging location: Step 2.1: Set each intersection in the traffic road as a node, and use the speed-flow practical model shown in equations (5)-(6) to characterize the equivalent length of the road: (5) (6) In equations (5)-(6), l p,q Let p be the actual length of the road segment between nodes p and q. Let be the equivalent road segment length between nodes p and q at time t, and let a, b, and n be adaptive coefficients for different road grades. Let be the road segment saturation between node p and node q at time t. Let be the actual road traffic flow between node p and node q at time t. Let be the capacity of the road segment between node p and node q; Step 2.2: Set the i-th electric vehicle to depart from node p at time t and head to the j-th charging station located at node q for charging. Then, use equation (7) to establish a comprehensive charging consumption model that considers the expected waiting time at the charging station and the shortest distance from the electric vehicle to the charging station. (7) In equation (7), ctt i,j represents the total cost of the i-th electric vehicle starting from node p and charging at the j-th charging station located at node q at time t. Let ω1 and ω2 be the estimated waiting time at the j-th charging station located at node q at time t, and let ω1 and ω2 be two weighting coefficients. Step 2.3: Use equation (8) to determine whether the i-th electric vehicle selects the j-th charging station. i,j : (8) In equation (8), ctt i represents the minimum comprehensive consumption of the i-th electric vehicle at time t. The number of charging stations, when u i,j When u = 0, it means that the i-th electric vehicle did not select the j-th charging station. i,j =1 means that the i-th electric vehicle chooses the j-th charging station. Linearization coefficients for the charging location determination model; Step 3: Establish a vehicle charging status judgment model based on charging time to determine whether the electric vehicle is in a charging state: Step 3.1: Use equation (9) to obtain the time required for the i-th electric vehicle to travel from its current location to the j-th charging station at time t. : (9) Step 3.2: Use equation (10) to obtain the starting charging time of the i-th electric vehicle after it travels from its current position to the j-th charging station at time t. : (10) Step 3.3: Use equation (11) to obtain the charging time required for the i-th electric vehicle to fully charge at the j-th charging station. : (11) In equation (12), η is the charging efficiency, and P ch,j Let be the charging power of the charging pile in the j-th charging station; Step 3.4: Use equation (12) to obtain the charging status of the i-th electric vehicle at time t when it goes to the j-th charging station for charging. : (12) In equation (12), =1 indicates that at time t, the i-th electric vehicle is in a charging state when it goes to the j-th charging station to charge. =0 indicates that at time t, the i-th electric vehicle is not charging when it goes to the j-th charging station. It represents the number of hours in a calendar day; Step 4: Calculate the total charging load of electric vehicles in the j-th charging station at time t using equation (13). This allows us to obtain the total charging load of electric vehicles in each charging station at each time point and use it as the spatiotemporal distribution of the load: (13) In equation (13), N EV This represents the total number of electric vehicles.

2. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports the processor in executing the charging load calculation method of claim 1, and the processor is configured to execute the program stored in the memory.

3. A computer-readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to perform the steps of the charging load calculation method of claim 1.

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