Road side charging station service validity evaluation method

Abstract

The invention discloses a method for evaluating the service validity of a road-side charging station. The service validity of the charging station is evaluated by analyzing the utilization efficiency and power supply and demand characteristics of the road-side charging station. Determining a charging facility utilization efficiency index by calculating the peak period average queuing charging waiting time of the electric vehicles and the peak hour average idle time of the charging piles in the peak period; calculating the daily average charging demand quantity of the electric vehicles passing on the road by analyzing the daily charging demand quantity of the electric vehicles; determining a power supply and demand index of the light storage and charging integrated road side charging station by calculating the light storage daily average power supply quantity of the charging station; by calculating the service ratio evaluation index of the charging station, considering the influence of power supply and demand of the charging station and determining the service validity evaluation index of the road-side charging station, the service validity of the charging station can be comprehensively evaluated, and decision support is provided for optimizing the layout and operation efficiency of the charging station.

Description

Technical Field

[0001] This invention relates to a method for evaluating the service effectiveness of roadside charging stations, belonging to the field of traffic planning and control technology for energy integration. Background Technology

[0002] With the rapid development and widespread adoption of electric vehicles, the construction of charging infrastructure has become an urgent need for social development. Integrated photovoltaic-energy storage-charging stations, as a green and sustainable charging solution, have received widespread attention. Roadside charging stations, as crucial hubs connecting transportation and energy, directly impact the user experience of electric vehicles and the operational efficiency of road traffic. However, the current lack of scientific and effective methods for evaluating service effectiveness makes it impossible to accurately assess the actual operational performance of roadside charging facilities. Traditional evaluation methods struggle to comprehensively consider the matching of power supply and demand with service availability, and cannot accurately calculate the utilization efficiency of charging stations during peak hours, thus affecting the optimization and layout of charging stations.

[0003] Therefore, it is necessary to fully consider multiple factors such as power supply and demand and utilization efficiency of charging piles, accurately calculate the service effectiveness evaluation index of roadside charging stations, and then evaluate the service capacity and operational efficiency of roadside charging stations integrating photovoltaic, energy storage and charging during peak hours, thereby improving the operational efficiency and resource utilization of charging stations and providing scientific decision support for the high-quality development of energy and transportation integration. Summary of the Invention

[0004] This invention provides a method for evaluating the service effectiveness of roadside charging stations. It can accurately calculate the service effectiveness evaluation index of roadside charging stations, thereby assessing the service capacity and operational efficiency of integrated photovoltaic-energy storage-charging roadside charging stations during peak hours. This improves the operational efficiency and resource utilization of charging stations and provides scientific decision-making support for the high-quality development of energy and transportation integration. It solves the problems disclosed in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A method for evaluating the service validity of roadside charging stations:

[0007] Acquire charging station utilization efficiency characteristic data and power supply and demand characteristic data;

[0008] Calculate the charging station utilization efficiency index based on the aforementioned utilization efficiency characteristic data;

[0009] Calculate the power supply and demand index of the charging station based on the power supply and demand characteristic data;

[0010] The service effectiveness evaluation index of the charging station is calculated based on the utilization efficiency index and the power supply and demand index.

[0011] Furthermore, utilizing efficiency characteristic data ,in, They represent the first During peak hours, the first person charging at the roadside charging station... The arrival time and charging time of each electric vehicle. express The average number of vehicles charging per day during peak hours;

[0012] Electricity supply and demand characteristics data ,in, They represent the first The charging demand of electric vehicles at the roadside charging station and the photovoltaic power generation of the charging station.

[0013] Furthermore, the charging station utilization efficiency index ;

[0014] in, Indicates the number of charging stations. This represents the average idle time of charging stations during peak hours. express Average number of vehicles charging per day during peak hours This indicates the average waiting time for electric vehicles to charge during peak hours.

[0015] Furthermore, ;

[0016] in, This indicates the probability that all charging stations at a charging station will be fully charged during peak hours. This represents the average charging time for electric vehicles during peak hours. This indicates the average percentage of peak hours for charging stations' charging piles.

[0017] ;

[0018] ;

[0019] in, This indicates the probability that the charging piles at a charging station are completely idle during peak hours. This represents the average interval between electric vehicles arriving at charging stations during peak hours.

[0020] ;

[0021] Where n represents the number of busy charging piles in the charging station during peak hours.

[0022] Furthermore, .

[0023] Furthermore, the power supply and demand index of charging stations ;

[0024] in, This indicates the average daily charging demand for electric vehicles on the road. This indicates the average daily power supply from photovoltaic and energy storage at the charging station.

[0025] Furthermore, ;

[0026] in, Indicates the first The charging demand of electric vehicles at charging stations. Indicates the total number of days.

[0027] Furthermore, ;

[0028] in, Indicates the charging power of the energy storage device, Indicates the off-peak period for electricity prices. Indicates the first The photovoltaic power generation of Tian Electric Vehicles at charging stations. Indicates the total number of days.

[0029] Furthermore, the service effectiveness evaluation index of charging stations ;

[0030] in, This represents the service-to-price ratio evaluation index for charging stations. This indicates the power supply and demand index for charging stations;

[0031] The validity of charging station services is evaluated using the charging station service validity evaluation index. The closer the value is to 1, the better the validity.

[0032] Furthermore, the charging station service ratio evaluation index ;

[0033] in, This indicates the utilization efficiency index of the charging station;

[0034] when The closer it is to 1, the more reasonable the service allocation. This indicates that the photovoltaic and energy storage supply at the charging station is insufficient, and there are too many charging piles installed. This indicates that the charging station has an excessive supply of photovoltaic and energy storage power and too few charging piles.

[0035] The beneficial effects achieved by this invention are as follows:

[0036] This invention evaluates the service effectiveness of roadside charging stations by analyzing their utilization efficiency and power supply and demand characteristics. It determines the charging facility utilization efficiency index by calculating the average queuing time for electric vehicles during peak hours and the average idle time of charging piles during peak hours; it calculates the average daily charging demand of electric vehicles on the road by analyzing their daily charging demand; it determines the power supply and demand index of integrated photovoltaic-storage-charging roadside charging stations by calculating the average daily power supply of photovoltaic and energy storage at charging stations; and it determines the service effectiveness evaluation index of roadside charging stations by calculating the charging station service ratio evaluation index and considering the impact of power supply and demand. This comprehensive approach allows for the evaluation of charging station service effectiveness and provides decision support for optimizing charging station layout and operational efficiency. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the process of the present invention;

[0038] Figure 2 This is a schematic diagram illustrating an application scenario of the present invention. Detailed Implementation

[0039] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0040] like Figure 1 As shown, taking a roadside charging station integrating photovoltaic, energy storage, and charging as an example, this invention provides a method for evaluating the service effectiveness of roadside charging stations, including the following steps:

[0041] 1) Construct a feature dataset for roadside charging stations integrating photovoltaic, energy storage, and charging;

[0042] For roadside charging stations that integrate photovoltaic, energy storage, and charging, a feature dataset of roadside charging stations integrating photovoltaic, energy storage, and charging is constructed, specifically including a subset of charging station utilization efficiency feature data and a subset of charging station power supply and demand feature data;

[0043] 2) Determine the utilization efficiency index of roadside charging stations integrating photovoltaic, energy storage, and charging;

[0044] Based on the collected relevant data, the probability distribution of the arrival interval and charging time of electric vehicles during peak hours is fitted and its mean is calculated. Based on the calculated probability of the charging piles at the charging station being completely idle and the probability of all charging during peak hours, the average queuing time for electric vehicles during peak hours is calculated, and the average idle time of the charging piles at the charging station during peak hours is calculated. Then, the utilization efficiency index of the roadside charging facility integrating photovoltaic, energy storage and charging is determined.

[0045] 3) Determine the power supply and demand index of roadside charging stations integrating photovoltaic, energy storage, and charging;

[0046] Based on the daily charging demand of electric vehicles, the average daily charging demand of electric vehicles on the road is calculated. Based on data such as the daily photovoltaic power generation of the charging station, the charging power of the energy storage device, and the off-peak electricity price period, the average daily power supply of photovoltaic and energy storage at the roadside charging station is calculated. On this basis, the power supply and demand index of the integrated photovoltaic, energy storage and charging roadside charging station is determined.

[0047] 4) Determine the evaluation index for roadside charging stations that integrate photovoltaic, energy storage, and charging;

[0048] Based on the utilization efficiency index and power supply and demand index of roadside charging stations integrating photovoltaic, energy storage and charging, the service ratio evaluation index of roadside charging stations is determined. Further considering the impact of power supply and demand of charging stations, the service effectiveness evaluation index of roadside charging stations is determined.

[0049] In step 1), constructing a feature dataset for roadside charging stations integrating photovoltaic, energy storage, and charging specifically includes:

[0050] Subset of charging station utilization efficiency feature data ,in, They represent the first During peak hours, the first person charging at the roadside charging station... The arrival time and charging time of each electric vehicle. express The average number of vehicles charging per day during peak hours;

[0051] A subset of power supply and demand characteristics data for charging stations ,in, They represent the first The charging demand of electric vehicles at the roadside charging station and the photovoltaic power generation of the charging station;

[0052] In step 2), determining the utilization efficiency index of the integrated photovoltaic, energy storage, and charging roadside charging station specifically includes:

[0053] 2-1) Calculate the average arrival interval and charging time of electric vehicles during peak hours;

[0054] Based on the collected data The arrival time of electric vehicles charging at the roadside charging station on that day is used to calculate the first... Peak hours Vehicle and the first The interval between electric vehicles arriving at the charging station.

[0055]

[0056] The probability distribution of the interval between electric vehicles arriving at charging stations during peak hours is fitted, and the mean of the interval between electric vehicles arriving at charging stations during peak hours is determined based on the characteristics of the probability distribution. ,

[0057] By combining the collected data on charging times of electric vehicles charging at roadside charging stations during peak hours, a probability distribution is fitted, and the average charging time of electric vehicles charging during peak hours is determined based on the characteristics of this probability distribution. ;

[0058] 2-2) Calculate the average queuing time for charging electric vehicles during peak hours;

[0059] Based on the average arrival interval and charging time of electric vehicles during peak hours, and the number of charging stations Calculate the average percentage of peak hours for charging stations' charging piles.

[0060]

[0061] Calculate the probability that the charging piles at the charging station are completely idle (no vehicles are charging) during peak hours.

[0062]

[0063] The Erlang-C formula is used to calculate the probability that all charging piles at a charging station will be fully charged during peak hours.

[0064]

[0065] Electric vehicle charging queues Figure 2 As shown, using a queuing theory model, the average queuing time for electric vehicles to charge during peak hours is calculated.

[0066]

[0067] 2-3) Calculate the average idle time of charging piles during peak hours at the charging station;

[0068] The average idle time of charging piles during peak hours is calculated based on the average busy time ratio of charging piles at the charging station.

[0069]

[0070] 2-4) Determine the utilization efficiency index of roadside charging facilities that integrate photovoltaic, energy storage, and charging;

[0071] The utilization efficiency index of roadside charging facilities integrating photovoltaic, energy storage, and charging is determined based on the average queuing time for electric vehicles during peak hours, the average idle time of charging piles at charging stations during peak hours, the average number of vehicles charging per day during peak hours, and the number of charging piles.

[0072]

[0073] In step 3), determining the power supply and demand index of the integrated photovoltaic-storage-charging roadside charging station specifically includes:

[0074] 3-1) Calculate the average daily charging demand of electric vehicles on the road;

[0075] Based on the collected data The daily charging demand of electric vehicles in the city is calculated to determine the average daily charging demand of electric vehicles on the road.

[0076]

[0077] 3-2) Calculate the average daily power supply of photovoltaic and energy storage at roadside charging stations;

[0078] Based on the collected data The daily photovoltaic power generation and energy storage device charging power of the Tian charging station Electricity price off-peak period Data is used to calculate the average daily power supply from photovoltaic and energy storage systems at roadside charging stations.

[0079]

[0080] 3-3) Determine the power supply and demand index of roadside charging stations integrating photovoltaic, energy storage, and charging;

[0081] Based on the average daily charging demand of electric vehicles on the road and the average daily power supply of photovoltaic and energy storage at roadside charging stations, a power supply and demand index for integrated photovoltaic, energy storage, and charging roadside charging stations is determined.

[0082]

[0083] In step 4), the evaluation index of the integrated photovoltaic, energy storage, and charging roadside charging station is determined, specifically including:

[0084] 4-1) Determine the service ratio evaluation index for roadside charging stations;

[0085] Based on the utilization efficiency index and power supply and demand index of roadside charging stations integrating photovoltaic, energy storage, and charging, the service ratio evaluation index of roadside charging stations is determined.

[0086]

[0087] when The closer it is to 1, the better the effect. This indicates that the photovoltaic and energy storage supply at the charging station is insufficient, and there are too many charging piles installed. This indicates that the charging station has too much photovoltaic energy and too few charging piles.

[0088] 4-2) Determine the service effectiveness evaluation index for roadside charging stations;

[0089] By combining the service-to-price ratio evaluation index of roadside charging stations with the impact of power supply and demand at charging stations, a service effectiveness evaluation index for roadside charging stations is determined.

[0090]

[0091] The service validity evaluation index of the charging station is used to evaluate the service validity of roadside charging stations that integrate photovoltaic, energy storage and charging. The closer the value is to 1, the better the validity.

[0092] Example:

[0093] The present invention provides a further illustration of the service validity evaluation method for a roadside charging station integrating photovoltaic, energy storage, and charging through an example. The following describes the specific steps of the service validity evaluation method for a roadside charging station integrating photovoltaic, energy storage, and charging, and determines the service validity evaluation index of a certain roadside charging station integrating photovoltaic, energy storage, and charging.

[0094] The charging station investigated in this embodiment is located on the side of a main road in a city and is equipped with integrated photovoltaic, energy storage and charging facilities, with a total of 8 charging piles. The evaluation period is 30 days.

[0095] S1: Construct a feature dataset for roadside charging stations integrating photovoltaic, energy storage, and charging;

[0096] Based on field investigation, a characteristic dataset of roadside charging stations integrating photovoltaic, energy storage, and charging was constructed, specifically including a subset of charging station utilization efficiency characteristic data and a subset of charging station power supply and demand characteristic data. Table 1 shows the arrival times of electric vehicles and the charging duration of electric vehicles during peak hours at this charging station (only partial data is listed).

[0097]

[0098] Table 1

[0099] Table 2 shows the daily photovoltaic power generation, energy storage device charging power, off-peak electricity prices, and daily charging demand of electric vehicles at this roadside charging station (only partial data is listed):

[0100]

[0101] Table 2

[0102] S2: Determine the utilization efficiency index of roadside charging stations that integrate photovoltaic, energy storage and charging;

[0103] S21: Calculate the average of the arrival interval and charging time of electric vehicles during peak hours.

[0104] Based on the arrival times of electric vehicles at the roadside charging station collected over 30 days, the interval time between electric vehicles arriving at the charging station during peak hours was calculated and its probability distribution was fitted. Based on the characteristics of its probability distribution, the mean interval time between electric vehicles arriving at the charging station during peak hours was determined to be 4 minutes. Combined with the charging time of electric vehicles charging at the roadside charging station during peak hours collected, its probability distribution was fitted and the mean charging time of electric vehicles during peak hours was determined to be 27 minutes.

[0105] S22: Calculate the average queuing time for electric vehicles to charge during peak hours.

[0106] Based on the average arrival interval and charging time of electric vehicles during peak hours, and the number of charging piles, the average busy time ratio of charging piles at charging stations during peak hours is calculated to be 0.84. The probability that charging piles at charging stations are completely idle (no vehicles charging) during peak hours is calculated to be 0.08%. Using the Erlang-C formula, the probability that all charging piles at charging stations are charging during peak hours is calculated to be 55.7%. Using a queuing theory model, the average queuing time for electric vehicles to charge during peak hours is calculated to be 12 minutes.

[0107] S23: Calculate the average idle time of charging piles at charging stations during peak hours.

[0108] Based on the average busy time ratio of charging piles at the charging station during peak hours, the average idle time of charging piles at the charging station during peak hours is calculated to be 10 minutes.

[0109] S24: Determine the utilization efficiency index of roadside charging facilities integrating photovoltaic, energy storage, and charging.

[0110] Based on the average queuing time for electric vehicles to charge during peak hours, the average idle time of charging piles at charging stations during peak hours, the average number of vehicles charging per day during peak hours, and the number of charging piles, the utilization efficiency index of roadside charging facilities integrating photovoltaic, energy storage, and charging is determined to be 0.56.

[0111] S3: Determine the power supply and demand index of roadside charging stations integrating photovoltaic, energy storage and charging;

[0112] S31: Calculate the average daily charging demand of electric vehicles on the road.

[0113] Based on the daily charging demand of electric vehicles collected over K days, the average daily charging demand of electric vehicles on the road is calculated to be 500 kWh.

[0114] S32: Calculate the average daily power supply of photovoltaic and energy storage at roadside charging stations.

[0115] Based on the data collected over 30 days, the daily photovoltaic power generation of the charging station is 200 kWh, and the energy storage device charging power is 150 kW. Combined with the data from the off-peak electricity price period, the average daily power supply of the charging station is calculated to be 560 kWh.

[0116] S33: Determine the power supply and demand index for roadside charging stations integrating photovoltaic, energy storage, and charging.

[0117] Based on the average daily charging demand of electric vehicles on the road and the average daily power supply of photovoltaic and energy storage at roadside charging stations, the power supply and demand index of integrated photovoltaic, energy storage and charging roadside charging stations is determined to be 0.89.

[0118] S4: Determine the evaluation index for roadside charging stations that integrate photovoltaic, energy storage, and charging;

[0119] S41: Determine the service ratio evaluation index for roadside charging stations

[0120] Based on the utilization efficiency index and power supply and demand index of roadside charging stations integrating photovoltaic, energy storage and charging, the service ratio evaluation index of roadside charging stations is determined to be 0.71.

[0121] S42: Determine the service effectiveness evaluation index for roadside charging stations

[0122] Based on the service ratio evaluation index of roadside charging stations and further considering the impact of power supply and demand at charging stations, the service effectiveness evaluation index of roadside charging stations is determined to be 0.8. This value is close to 1, indicating that the charging station can efficiently meet the charging needs of electric vehicles during peak hours, and that the utilization efficiency of power supply and charging facilities has reached a relatively good level.

[0123] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0124] A computer-readable storage medium storing one or more programs, the one or more programs including instructions that, when executed by a computing device, cause the computing device to perform a method for evaluating the effectiveness of roadside charging station services.

[0125] A computing device includes one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, and the one or more programs include instructions for performing a method for evaluating the effectiveness of roadside charging station services.

[0126] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0128] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0129] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0130] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A method for evaluating the service effectiveness of roadside charging stations, characterized in that: Acquire charging station utilization efficiency characteristic data and power supply and demand characteristic data; Calculate the charging station utilization efficiency index based on the aforementioned utilization efficiency characteristic data; Calculate the power supply and demand index of the charging station based on the power supply and demand characteristic data; The service effectiveness evaluation index of the charging station is calculated based on the utilization efficiency index and the power supply and demand index.

2. The method for evaluating the service effectiveness of roadside charging stations according to claim 1, characterized in that: Utilizing efficiency feature data ,in, They represent the first During peak hours, the first person charging at the roadside charging station... The arrival time and charging time of each electric vehicle. express The average number of vehicles charging per day during peak hours; Electricity supply and demand characteristics data ,in, They represent the first The charging demand of electric vehicles at the roadside charging station and the photovoltaic power generation of the charging station.

3. The method for evaluating the service effectiveness of roadside charging stations according to claim 1, characterized in that: Charging station utilization efficiency index ; in, Indicates the number of charging stations. This represents the average idle time of charging stations during peak hours. express Average number of vehicles charging per day during peak hours This indicates the average waiting time for electric vehicles to charge during peak hours.

4. The method for evaluating the service effectiveness of roadside charging stations according to claim 3, characterized in that: ； in, This indicates the probability that all charging stations at a charging station will be fully charged during peak hours. This represents the average charging time for electric vehicles during peak hours. This indicates the average percentage of peak hours for charging stations' charging piles. ； ； in, This indicates the probability that the charging piles at a charging station are completely idle during peak hours. This represents the average interval between electric vehicles arriving at charging stations during peak hours. ； Where n represents the number of busy charging piles in the charging station during peak hours.

5. The method for evaluating the service effectiveness of roadside charging stations according to claim 4, characterized in that: 。 6. The method for evaluating the service effectiveness of roadside charging stations according to claim 1, characterized in that: Electricity supply and demand index for charging stations ; in, This indicates the average daily charging demand for electric vehicles on the road. This indicates the average daily power supply from photovoltaic and energy storage at the charging station.

7. The method for evaluating the service effectiveness of roadside charging stations according to claim 6, characterized in that: ； in, Indicates the first The charging demand of electric vehicles at charging stations. Indicates the total number of days.

8. The method for evaluating the service effectiveness of roadside charging stations according to claim 6, characterized in that: ； in, Indicates the charging power of the energy storage device, Indicates the off-peak period for electricity prices. Indicates the first The photovoltaic power generation of Tian Electric Vehicles at charging stations. Indicates the total number of days.

9. The method for evaluating the service effectiveness of roadside charging stations according to claim 1, characterized in that: Charging station service effectiveness evaluation index ; in, This represents the service-to-price ratio evaluation index for charging stations. This indicates the power supply and demand index of charging stations; The validity of charging station services is evaluated using the charging station service validity evaluation index. The closer the value is to 1, the better the validity.

10. The method for evaluating the service effectiveness of roadside charging stations according to claim 9, characterized in that: Charging station service ratio evaluation index ; in, This indicates the utilization efficiency index of the charging station; when The closer it is to 1, the more reasonable the service allocation. This indicates that the photovoltaic and energy storage supply at the charging station is insufficient, and there are too many charging piles installed. This indicates that the charging station has an excessive supply of photovoltaic and energy storage power and too few charging piles.

Images