A high-speed service area light storage charging pre-scheduling method using upstream traffic flow information

Abstract

The application discloses a high-speed service area light storage and charging pre-scheduling method using upstream traffic flow information and belongs to the technical field of new energy vehicle charging scheduling. In order to solve the problem of response lag of the service area light storage and charging system to the concentrated arrival of vehicles, the application comprises the following steps: obtaining the traffic flow information within a preset distance range upstream of a target high-speed service area and the operation information of energy facilities in the target high-speed service area; calculating the predicted arrival time of a new energy vehicle to the target high-speed service area; forming a vehicle arrival set in each time slice; calculating the predicted charging power demand of the vehicle arrival set in each time slice; calculating the gap power in each future time slice; calculating the target state of charge of a storage system according to the gap power in each future time window; comparing the target state of charge of the storage system with the current state of charge of the storage system; and generating a light storage and charging pre-scheduling scheme of the storage system. The application is suitable for the speed change of a high-speed section, the congestion change and the queuing change at the entrance of a service area.

Description

Technical Field

[0001] This invention belongs to the field of new energy vehicle charging scheduling technology, specifically involving a pre-scheduling method for photovoltaic, energy storage and charging in highway service areas using upstream traffic flow information. Background Technology

[0002] As the proportion of new energy vehicles used on highways gradually increases, the demand for charging at highway service areas shows obvious time-concentration and randomness. During holidays, peak commuting hours, inclement weather, or traffic congestion recovery phases, new energy vehicles may enter service areas in a concentrated manner within a short period of time, leading to increased queuing time for charging piles and creating a significant instantaneous load impact on the service area's power distribution system.

[0003] Existing highway service area photovoltaic-storage-charging systems typically operate based on the usage status of charging piles, photovoltaic output, or electricity prices within the service area. Power adjustments are only made after new energy vehicles have entered the service area or connected to charging piles, resulting in a response lag. On the other hand, numerous roadside sensing devices, ETC gantries, and video detection equipment have been deployed along highways. These devices can obtain upstream traffic flow information in advance, but current technologies have not fully utilized this traffic-side information for pre-emptive scheduling of service area energy systems. Summary of the Invention

[0004] The problem this invention aims to solve is that existing service area photovoltaic-storage-charging systems suffer from delayed response to concentrated vehicle arrivals, insufficient energy storage system preparation, inadequate utilization of power distribution capacity, and low photovoltaic absorption efficiency. The invention proposes a pre-scheduling method for photovoltaic-storage-charging systems in high-speed service areas that utilizes upstream traffic flow information.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for pre-scheduling optical storage and charging in high-speed service areas using upstream traffic flow information includes the following steps:

[0007] S1. Obtain traffic flow information within a preset distance range upstream of the target highway service area, and energy facility operation information within the target highway service area;

[0008] S2. Identify new energy vehicles based on the traffic flow information obtained in step S1, and calculate the estimated arrival time of new energy vehicles to the target highway service area based on vehicle distance, average speed, road condition correction coefficient, and service area entrance congestion correction time. The specific implementation method of step S2 includes the following steps: S2.1. Identify new energy vehicles by means of one or more of the following methods: license plate recognition, vehicle type recognition, ETC vehicle information, and vehicle network information; The confidence level for new energy vehicle identification is constructed by weighting the license plate recognition result, vehicle model recognition result, ETC vehicle type information, and vehicle network information, and the expression is: ; in, For the first i The comprehensive confidence level of the vehicle's identification as a new energy vehicle; , , and The results obtained are based on license plate recognition, vehicle model recognition, ETC vehicle information, and vehicle network information, respectively. i Vehicle identification confidence level; 、 、 and These are the weights corresponding to license plate recognition results, vehicle model recognition results, ETC vehicle information, and vehicle network information, respectively; when If the value exceeds a preset threshold, it is determined to be a new energy vehicle; S2.2. Calculate the estimated arrival time of the new energy vehicle to the target highway service area. The calculation formula is as follows: ; in, For the first The estimated arrival time of the vehicle. For the current moment, For the first The distance of the vehicle from the service area entrance. For the first Average speed of the vehicle on the road segment This is the road condition correction factor. Adjusted time for congestion at service area entrances; Based on the average queue length at the service area entrance and entrance access capacity Sure, ;

[0009] S3. Based on the estimated arrival time of the new energy vehicles obtained in step S2, the new energy vehicles are allocated to multiple future time slots to form a vehicle arrival set within each time slot;

[0010] S4. Based on the vehicle battery capacity, estimated state of charge at arrival and target state of charge, calculate the expected charging energy demand for each new energy vehicle, then calculate the predicted charging power demand for the vehicle arrival set in each time slice, and generate a charging demand curve.

[0011] S5. Based on the predicted charging power demand obtained in step S4 and the energy facility operation information in the high-speed service area obtained in step S1, calculate the power gap for each future time slot.

[0012] S6. Based on the power gap in each future time window, calculate the target state of charge of the energy storage system, and then compare the target state of charge of the energy storage system with the current state of charge of the energy storage system to generate a pre-scheduling scheme for photovoltaic-storage-charging of the energy storage system.

[0013] S7. During the rolling optimization cycle, repeat steps S1-S6 to reacquire upstream traffic flow information and service area energy facility operation information, update the vehicle's estimated arrival time, charging demand curve, and energy storage target state of charge, and generate a new energy storage system photovoltaic-storage-charging pre-schedule scheme.

[0014] Furthermore, in step S1, traffic flow information includes vehicle distance, vehicle speed, vehicle acceleration, vehicle type, lane position, vehicle passage time, and confidence level of new energy vehicle identification; energy facility operation information includes charging pile occupancy status, charging pile available power, energy storage system state of charge, energy storage system capacity, maximum energy storage charging and discharging power, photovoltaic predicted output, and service area power distribution capacity.

[0015] Furthermore, step S3 forms the expression for the vehicle arrival set within each time slice as follows:

[0016]

[0017] in, This is the set of vehicles arriving in the k-th time slice. This represents the time slice length.

[0018] Furthermore, the specific implementation method of step S4 includes the following steps:

[0019] S4.1. Calculate the estimated charging energy demand for new energy vehicles as follows:

[0020]

[0021] in, For the first The vehicle's projected charging energy requirements. For the first The vehicle's battery capacity, For the first The vehicle's target state of charge. For the first The estimated state of charge of the vehicle upon arrival. For the first Vehicle model correction factor;

[0022]

[0023] in, The most recently available number The vehicle's electrical charge status. For the first The distance the vehicle traveled from the state of charge recording point to the service area. For the first Electricity consumption per unit mileage for each vehicle model; if the vehicle currently has no [specific data / data]... data, Default 20%;

[0024] S4.2. Calculate the predicted charging power demand for the vehicle arrival set within each time slice.

[0025]

[0026] in, For the first Predicted charging power demand for each time slice For the first The simultaneous charging coefficient for each time slice is used to reflect the degree of synchronization between the vehicle and the charging station upon arrival.

[0027] Furthermore, the expression for calculating the gap power in each future time slice in step S5 is as follows:

[0028]

[0029] in, For the first The power gap of each time slice, For the first Photovoltaic power output forecast for a given time period The maximum power that the service area's power distribution system can provide.

[0030] Furthermore, the specific implementation method of step S6 includes the following steps:

[0031] S6.1. Calculate the discharge energy that the energy storage system should reserve within the future time window, using the following expression:

[0032]

[0033] in, The energy to be reserved for the energy storage system within the future time window; K is the total number of time slots;

[0034] Then calculate the target state of charge of the energy storage system. The expression is:

[0035]

[0036] in, For energy storage, a safe standby state of charge, This is the upper limit of the allowable state of charge for energy storage. For the rated capacity of the energy storage system, For energy storage discharge efficiency;

[0037] S6.2. Compare the target state of charge (SOC) of the energy storage system with the current SOC of the energy storage system to generate a pre-scheduling plan for photovoltaic-storage-charging of the energy storage system. This plan includes controlling the energy storage system to perform pre-charging before the vehicle arrives when the current SOC of the energy storage system is less than the target SOC; controlling the energy storage system to remain in standby mode when the current SOC of the energy storage system meets the target SOC and the peak charging period is expected to arrive; and controlling the energy storage system to discharge to support the charging pile group when the actual charging load exceeds the sum of photovoltaic output and distribution capacity after the vehicle arrives.

[0038] The beneficial effects of this invention are:

[0039] The present invention discloses a pre-scheduling method for photovoltaic-storage-charging systems in highway service areas using upstream traffic flow information. This method can identify potential charging needs in advance before new energy vehicles arrive at the service area, thereby improving the response speed of the photovoltaic-storage-charging system; it can increase the state of charge of energy storage in advance based on future charging peaks, reducing the impact of centralized charging on the power distribution network; it can prioritize energy storage charging when photovoltaic output is high and future charging demand is low, thereby improving the local photovoltaic absorption rate; it can dynamically adjust the power allocation of charging piles according to predicted demand, reducing vehicle queuing time; and it can adapt to changes in highway speed, congestion, and queuing at service area entrances by continuously updating vehicle arrival predictions and predicted charging power demand. Attached Figure Description

[0040] Figure 1 This is a flowchart of a high-speed service area optical storage and charging pre-scheduling method utilizing upstream traffic flow information, as described in this invention.

[0041] Figure 2 This is a schematic diagram illustrating the relationship between predicted charging demand, photovoltaic output, power distribution capacity, and target SOC of energy storage according to the present invention. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described specific embodiments are merely a part of the embodiments of the invention, and not all of them. The components of the specific embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations, and the invention may also have other embodiments.

[0043] Therefore, the following detailed description of specific embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected specific embodiments of the invention. All other specific embodiments obtained by those skilled in the art based on these specific embodiments without inventive effort are within the scope of protection of this invention.

[0044] To further understand the invention's content, features, and effects, the following specific embodiments are provided, along with accompanying drawings. Figure 1 -Appendix Figure 2 Detailed explanation is as follows:

[0045] Example 1:

[0046] A method for pre-scheduling optical storage and charging in high-speed service areas using upstream traffic flow information includes the following steps:

[0047] S1. Obtain traffic flow information within a preset distance range upstream of the target highway service area, and energy facility operation information within the target highway service area;

[0048] Furthermore, in step S1, traffic flow information includes vehicle distance, vehicle speed, vehicle acceleration, vehicle type, lane position, vehicle passage time, and confidence level of new energy vehicle identification; energy facility operation information includes charging pile occupancy status, charging pile available power, energy storage system state of charge, energy storage system capacity, maximum energy storage charging and discharging power, photovoltaic predicted output, and service area power distribution capacity.

[0049] Furthermore, roadside radar, video recognition equipment, ETC gantry equipment, or vehicle-to-infrastructure (V2I) communication equipment are installed within a range of 5 to 30 kilometers upstream of the target highway service area. This allows for real-time acquisition of vehicle passage information, including vehicle speed, vehicle type, lane, passage time, and distance from the service area entrance. The inspection information for upstream new energy vehicles is represented as follows:

[0050]

[0051] in, This refers to the distance between the vehicle and the service area entrance. For vehicle speed, For acceleration, For vehicle type, For the detection time, Confidence level for identifying new energy vehicles.

[0052] S2. Identify new energy vehicles based on the traffic flow information obtained in step S1, and calculate the estimated arrival time of new energy vehicles to the target highway service area based on vehicle distance, average speed, road condition correction coefficient, and service area entrance congestion correction time.

[0053] Furthermore, the specific implementation method of step S2 includes the following steps:

[0054] S2.1. Identify new energy vehicles by means of one or more of the following methods: license plate recognition, vehicle type recognition, ETC vehicle information, and vehicle network information;

[0055] The confidence level for new energy vehicle identification is constructed by weighting the license plate recognition result, vehicle model recognition result, ETC vehicle type information, and vehicle network information, and the expression is:

[0056]

[0057] in, The comprehensive confidence level for identifying the new energy vehicle of the i-th vehicle; , , and These are the recognition confidence scores of the i-th vehicle, obtained based on license plate recognition results, vehicle model recognition results, ETC vehicle information, and vehicle network information, respectively. , , and These are the weights corresponding to license plate recognition results, vehicle model recognition results, ETC vehicle information, and vehicle network information, respectively; when If the value exceeds a preset threshold, it is determined to be a new energy vehicle;

[0058] S2.2. Calculate the estimated arrival time of the new energy vehicle to the target highway service area. The calculation formula is as follows:

[0059]

[0060] in, For the first The estimated arrival time of the vehicle. For the current moment, For the first The distance of the vehicle from the service area entrance. For the first Average speed of the vehicle on the road segment This is the road condition correction factor. Adjusted time for congestion at service area entrances; Based on the average queue length at the service area entrance and entrance access capacity Sure, .

[0061] S3. Based on the estimated arrival time of the new energy vehicles obtained in step S2, the new energy vehicles are allocated to multiple future time slots to form a vehicle arrival set within each time slot;

[0062] Furthermore, step S3 forms the expression for the vehicle arrival set within each time slice as follows:

[0063]

[0064] in, This is the set of vehicles arriving in the k-th time slice. This represents the time slice length.

[0065] Furthermore, let the current time be... The prediction time window is Divide future time into There are 1 time slice, each time slice having a length of 1. .

[0066] S4. Based on the vehicle battery capacity, estimated state of charge at arrival and target state of charge, calculate the expected charging energy demand for each new energy vehicle, then calculate the predicted charging power demand for the vehicle arrival set in each time slice, and generate a charging demand curve.

[0067] Furthermore, the specific implementation method of step S4 includes the following steps:

[0068] S4.1. Calculate the estimated charging energy demand for new energy vehicles as follows:

[0069]

[0070] in, For the first The vehicle's projected charging energy requirements. For the first The vehicle's battery capacity, For the first The vehicle's target state of charge. For the first The estimated state of charge of the vehicle upon arrival. For the first Vehicle model correction factor;

[0071]

[0072] in, The most recently available number The vehicle's electrical charge status. For the first The distance the vehicle traveled from the state of charge recording point to the service area. For the first Electricity consumption per unit mileage for each vehicle model; if the vehicle currently has no [specific data / data]... data, Default 20%;

[0073] S4.2. Calculate the predicted charging power demand for the vehicle arrival set within each time slice.

[0074]

[0075] in, For the first Predicted charging power demand for each time slice For the first The simultaneous charging coefficient for each time slice is used to reflect the degree of synchronization between the vehicle and the charging station upon arrival.

[0076] S5. Based on the predicted charging power demand obtained in step S4 and the energy facility operation information in the high-speed service area obtained in step S1, calculate the power gap for each future time slot.

[0077] Furthermore, the expression for calculating the gap power in each future time slice in step S5 is as follows:

[0078]

[0079] in, For the first The power gap of each time slice, For the first Photovoltaic power output forecast for a given time period The maximum power that the service area's power distribution system can provide.

[0080] S6. Based on the power gap in each future time window, calculate the target state of charge of the energy storage system, and then compare the target state of charge of the energy storage system with the current state of charge of the energy storage system to generate a pre-scheduling scheme for photovoltaic-storage-charging of the energy storage system.

[0081] Furthermore, the specific implementation method of step S6 includes the following steps:

[0082] S6.1. Calculate the discharge energy that the energy storage system should reserve within the future time window, using the following expression:

[0083]

[0084] in, The energy to be reserved for the energy storage system within the future time window; K is the total number of time slots;

[0085] Then calculate the target state of charge of the energy storage system. The expression is:

[0086]

[0087] in, For energy storage, a safe standby state of charge, This is the upper limit of the allowable state of charge for energy storage. For the rated capacity of the energy storage system, For energy storage discharge efficiency;

[0088] S6.2. Compare the target state of charge (SOC) of the energy storage system with the current SOC of the energy storage system to generate a pre-scheduling plan for photovoltaic-storage-charging of the energy storage system. This plan includes controlling the energy storage system to perform pre-charging before the vehicle arrives when the current SOC of the energy storage system is less than the target SOC; controlling the energy storage system to remain in standby mode when the current SOC of the energy storage system meets the target SOC and the peak charging period is expected to arrive; and controlling the energy storage system to discharge to support the charging pile group when the actual charging load exceeds the sum of photovoltaic output and distribution capacity after the vehicle arrives.

[0089] Furthermore, when photovoltaic output exceeds the predicted charging demand and energy storage is not fully utilized, the surplus photovoltaic power is:

[0090] ;

[0091] The energy storage pre-charge power is:

[0092] ;

[0093] When the predicted charging demand exceeds the sum of the photovoltaic and distribution capacities, the energy storage discharge support power is:

[0094] ;

[0095] The pre-scheduling controller generates instructions according to the following rules:

[0096] When satisfied And there is a future window within Then the energy storage system is controlled to perform pre-charging.

[0097] When satisfied If the peak is expected to arrive soon, the energy storage system will be kept in standby mode.

[0098] When the vehicle actually arrives at and connects to the charging station, and meets the requirements... Then the energy storage system discharges to support the charging pile group.

[0099] After a vehicle arrives at a service area and connects to a charging station, the charging power distribution module dynamically allocates the output power of each charging station based on the vehicle's waiting time, battery state of charge, expected dwell time, charging station rated power, and energy storage system discharge capacity. When power distribution capacity is limited, priority is given to vehicles with longer waiting times, lower remaining battery power, or shorter expected dwell times.

[0100] S7. During the rolling optimization cycle, repeat steps S1-S6 to reacquire upstream traffic flow information and service area energy facility operation information, update the vehicle's estimated arrival time, charging demand curve, and energy storage target state of charge, and generate a new energy storage system photovoltaic-storage-charging pre-schedule scheme.

[0101] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0102] Although this application has been described above with reference to specific embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of this application. In particular, as long as there is no structural conflict, the features in the specific embodiments disclosed in this application can be combined with each other in any way. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, this application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for pre-scheduling optical storage and charging in high-speed service areas using upstream traffic flow information, characterized in that, Includes the following steps: S1. Obtain traffic flow information within a preset distance range upstream of the target highway service area, and energy facility operation information within the target highway service area; S2. Identify new energy vehicles based on the traffic flow information obtained in step S1, and calculate the estimated arrival time of new energy vehicles to the target highway service area based on vehicle distance, average speed, road condition correction coefficient, and service area entrance congestion correction time. The specific implementation method of step S2 includes the following steps: S2.

1. Identify new energy vehicles by means of one or more of the following methods: license plate recognition, vehicle type recognition, ETC vehicle information, and vehicle network information; The confidence level for new energy vehicle identification is constructed by weighting the license plate recognition result, vehicle model recognition result, ETC vehicle type information, and vehicle network information, and the expression is: ； in, For the first i The comprehensive confidence level of the vehicle's identification as a new energy vehicle; , , and The results obtained are based on license plate recognition, vehicle model recognition, ETC vehicle information, and vehicle network information, respectively. i Vehicle identification confidence level; 、 、 and These are the weights corresponding to license plate recognition results, vehicle model recognition results, ETC vehicle information, and vehicle network information, respectively; when If the value exceeds a preset threshold, it is determined to be a new energy vehicle; S2.

2. Calculate the estimated arrival time of the new energy vehicle to the target highway service area. The calculation formula is as follows: ； in, For the first The estimated arrival time of the vehicle. For the current moment, For the first The distance of the vehicle from the service area entrance. For the first Average speed of the vehicle on the road segment This is the road condition correction factor. Adjusted time for congestion at service area entrances; Based on the average queue length at the service area entrance and entrance access capacity Sure, ; S3. Based on the estimated arrival time of the new energy vehicles obtained in step S2, the new energy vehicles are allocated to multiple future time slots to form a vehicle arrival set within each time slot; S4. Based on the vehicle battery capacity, estimated state of charge at arrival and target state of charge, calculate the expected charging energy demand for each new energy vehicle, then calculate the predicted charging power demand for the vehicle arrival set in each time slice, and generate a charging demand curve. S5. Based on the predicted charging power demand obtained in step S4 and the energy facility operation information in the high-speed service area obtained in step S1, calculate the power gap for each future time slot. S6. Based on the power gap in each future time window, calculate the target state of charge of the energy storage system, and then compare the target state of charge of the energy storage system with the current state of charge of the energy storage system to generate a pre-scheduling scheme for photovoltaic-storage-charging of the energy storage system. S7. During the rolling optimization cycle, repeat steps S1-S6 to reacquire upstream traffic flow information and service area energy facility operation information, update the vehicle's estimated arrival time, charging demand curve, and energy storage target state of charge, and generate a new energy storage system photovoltaic-storage-charging pre-schedule scheme.

2. The method for pre-scheduling of optical storage and charging in high-speed service areas using upstream traffic flow information according to claim 1, characterized in that, The traffic flow information in step S1 includes vehicle distance, vehicle speed, vehicle acceleration, vehicle type, lane position, vehicle passage time, and confidence level of new energy vehicle identification; the energy facility operation information includes charging pile occupancy status, charging pile available power, energy storage system state of charge, energy storage system capacity, maximum energy storage charging and discharging power, photovoltaic predicted output, and service area power distribution capacity.

3. The method for pre-scheduling of optical storage and charging in high-speed service areas using upstream traffic flow information according to claim 2, characterized in that, Step S3 forms the expression for the vehicle arrival set within each time slice: ； in, This is the set of vehicles arriving in the k-th time slice. This represents the time slice length.

4. The method for pre-scheduling of optical storage and charging in high-speed service areas using upstream traffic flow information according to claim 3, characterized in that, The specific implementation method of step S4 includes the following steps: S4.

1. Calculate the estimated charging energy demand for new energy vehicles as follows: ； in, For the first The vehicle's projected charging energy requirements. For the first The vehicle's battery capacity, For the first The vehicle's target state of charge. For the first The estimated state of charge of the vehicle upon arrival. For the first Vehicle model correction factor; ； in, The most recently available number The vehicle's electrical charge status. For the first The distance the vehicle traveled from the state of charge recording point to the service area. For the first Electricity consumption per unit mileage for each vehicle model; if the vehicle currently has no [specific data / data]... data, Default 20%; S4.

2. Calculate the predicted charging power demand for the vehicle arrival set within each time slice. ； in, For the first Predicted charging power demand for each time slice For the first The simultaneous charging coefficient for each time slice is used to reflect the degree of synchronization between the vehicle and the charging station upon arrival.

5. The method for pre-scheduling of optical storage and charging in high-speed service areas using upstream traffic flow information according to claim 4, characterized in that, The expression for calculating the gap power in each future time slice in step S5 is as follows: ； in, For the first The power gap of each time slice, For the first Photovoltaic power output forecast for a given time period The maximum power that the service area's power distribution system can provide.

6. The method for pre-scheduling of optical storage and charging in high-speed service areas using upstream traffic flow information according to claim 5, characterized in that, The specific implementation method of step S6 includes the following steps: S6.

1. Calculate the discharge energy that the energy storage system should reserve within the future time window, using the following expression: ； in, The energy to be reserved for the energy storage system within the future time window; K is the total number of time slots; Then calculate the target state of charge of the energy storage system. The expression is: ； in, For energy storage, a safe standby state of charge, This is the upper limit of the allowable state of charge for energy storage. For the rated capacity of the energy storage system, For energy storage discharge efficiency; S6.

2. Compare the target state of charge (SOC) of the energy storage system with the current SOC of the energy storage system to generate a pre-scheduling plan for photovoltaic-storage-charging of the energy storage system. This plan includes controlling the energy storage system to perform pre-charging before the vehicle arrives when the current SOC of the energy storage system is less than the target SOC; controlling the energy storage system to remain in standby mode when the current SOC of the energy storage system meets the target SOC and the peak charging period is expected to arrive; and controlling the energy storage system to discharge to support the charging pile group when the actual charging load exceeds the sum of photovoltaic output and distribution capacity after the vehicle arrives.

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