Vehicle-network interaction light-storage-charge collaborative optimization method, device, medium and equipment

Abstract

The application relates to a kind of light-storage-charging synergic optimization method, device, medium and equipment of vehicle-network interaction, and charging pile technical field, wherein the method comprises: obtaining the power generation state information of photovoltaic power generation system in park;When the power generation state information is not power generation state, according to the multiple historical charging periods of each charging pile in park and the charging load in the historical charging period, whether there is a vehicle charging in the charging pile in target period is predicted, the target period is the future period after current time;If it is predicted that there is a vehicle charging in the charging pile in the target period, the charging pile is determined as target charging pile, and the charging load demand of the target charging pile in the target period is determined;According to the charging load demand of each target charging pile, the energy storage system in the park supplies power to the corresponding target charging pile.The application has the effect of improving the problem of waste of electrical energy in park.

Description

Technical Field

[0001] This application relates to the field of charging pile technology, specifically to a method, device, medium, and equipment for coordinated optimization of light-storage-charging interaction between vehicles and the grid. Background Technology

[0002] The "photovoltaic-storage-charging" synergistic optimization, through intelligent scheduling, deeply integrates photovoltaic power generation systems (photovoltaics), energy storage systems (storage), and charging piles (charging), dynamically balancing the spatiotemporal differences in "power generation, energy storage, and power consumption." Ultimately, it aims to maximize the utilization of green energy, reduce charging costs, ensure stable power supply, and reduce dependence on the power grid, making it the core model for current park energy management. In short, it doesn't allow "photovoltaics, storage, and charging" to work independently, but rather enables seamless coordination among them through intelligent scheduling—ensuring no waste of photovoltaic power, using stored energy efficiently, and minimizing charging costs. This ultimately achieves a win-win situation of "economic savings, environmental friendliness, and system stability," representing the core development direction for future electric vehicle charging infrastructure.

[0003] Currently, the charging stations within the park typically operate in the following manner: during the day, they primarily utilize electricity generated by the park's photovoltaic power generation system to power all charging stations, while at night, the park's energy storage system provides power to all charging stations. This approach maintains power to all charging stations both day and night. However, if a large number of charging stations are not currently charging, these stations will experience significant standby power consumption, leading to substantial energy waste. Summary of the Invention

[0004] To address the issue of energy waste within the industrial park, this application provides a method, apparatus, medium, and equipment for the coordinated optimization of light, energy storage, and charging in vehicle-grid interaction.

[0005] The first aspect of this application provides a method for coordinated optimization of light-storage-charging in vehicle-to-grid interaction, specifically including:

[0006] Obtain power generation status information of the photovoltaic power generation system in the park;

[0007] When the power generation status information is in the non-power generation state, based on multiple historical charging periods for each charging pile in the park and the charging load during the historical charging periods, it is predicted whether there will be vehicles charging at the charging pile during the target period, where the target period is a future period after the current time.

[0008] If it is predicted that a vehicle will be charging at the charging station during the target time period, the charging station will be identified as the target charging station, and the charging load demand of the target charging station during the target time period will be determined.

[0009] According to the charging load requirements of each target charging pile, power is supplied to the corresponding target charging pile through the energy storage system in the park.

[0010] By adopting the above technical solution, the power generation status information of the photovoltaic power generation system is obtained. If the power generation status information is "no power generation," it means that the photovoltaic power generation system is basically not generating electricity. To ensure that vehicles in the park can charge normally through the charging piles, a power storage system is needed. Simultaneously, to avoid standby energy consumption from supplying power to all charging piles in the park and to improve the problem of energy waste, targeted power supply to charging piles within the park is required. Furthermore, based on multiple historical charging periods and the charging load during those periods, the probability of a vehicle charging at a single charging pile in a target period after the current time is analyzed, thus accurately determining whether a vehicle is charging at the charging pile during the target period. Further, if a vehicle is determined to be charging at a charging pile during the target period, it is designated as the target charging pile. The charging load demand of this target charging pile during the target period is also determined. Finally, based on the charging load demand, targeted power is supplied only to the corresponding target charging pile, and power is not supplied to charging piles other than the target charging pile, thereby improving the problem of energy waste within the park to some extent.

[0011] In one implementation, predicting whether a vehicle will be charging at a charging pile during a target time period based on multiple historical charging periods for each charging pile within the park and the charging load during those historical charging periods specifically includes:

[0012] Based on multiple historical charging periods for each charging station within the park, at least one target charging period is determined.

[0013] Based on multiple historical charging load values ​​within the target charging period, at least one target load range is determined;

[0014] A first weight is determined for the target charging period, and a second weight is determined for each target load range. The first weight represents the probability that a vehicle will be charged at the charging pile during the target charging period, and the second weight represents the probability that the vehicle's charging load is within the corresponding target load range during the target charging period.

[0015] Based on the first weight and each of the second weights, it is predicted whether there will be vehicles charging at the charging station during the target time period.

[0016] In one implementation, predicting whether a vehicle will be charging at the charging station during a target time period based on the first weight and each of the second weights specifically includes:

[0017] At least one target charging period included in the target time period is identified as an important charging period, and the first weight of a single important charging period is multiplied by the second weight of each corresponding target load range to obtain each first multiplication result corresponding to a single important charging period;

[0018] The first multiplication result is compared with a preset first threshold. If the first multiplication result is greater than the first threshold, the first multiplication result is determined as the result to be analyzed.

[0019] The results to be analyzed corresponding to each of the important charging periods are summed to obtain the first summation result;

[0020] If the first summation result is greater than the preset second threshold, it is predicted that there are vehicles charging at the charging pile during the target time period;

[0021] If the first summation result is not greater than the preset second threshold, it is predicted that no vehicle will be charging at the charging pile during the target time period.

[0022] If the target charging period is not included in the target time period, it is predicted that no vehicle will be charging at the charging station during the target time period.

[0023] In one implementation, determining the charging load demand of the target charging pile during the target time period specifically includes:

[0024] The maximum value is selected from each of the first multiplication results corresponding to a single important charging period, and the maximum value is determined as the target multiplication result for a single important charging period;

[0025] Select the largest target multiplication result from the target multiplication results corresponding to each of the important charging periods;

[0026] Based on the target load range corresponding to the product of the maximum target, the charging load demand of the target charging pile within the target time period is determined.

[0027] In one embodiment, the method further includes:

[0028] The summation of the product of the maximum target values ​​corresponding to each of the target charging piles is obtained to get the second summation result;

[0029] The second summation result is compared with a preset third threshold;

[0030] If the second summation result is greater than the third threshold, then the verification is deemed successful;

[0031] The step of supplying power to the corresponding target charging piles through the energy storage system in the park according to the charging load demand of each target charging pile specifically includes:

[0032] After the verification is passed, power is supplied to the corresponding target charging piles through the energy storage system in the park according to the charging load requirements of each target charging pile.

[0033] In one implementation, determining the charging load demand of the target charging pile during the target time period specifically includes:

[0034] When there are two important charging periods, the intersection of the target load ranges corresponding to the two important charging periods is performed to obtain the load intersection range, and the target load range containing the load intersection range is determined as the important load range;

[0035] The first weight of each important charging period is multiplied by the second weight of the corresponding important load range to obtain the second multiplication result;

[0036] The results of the second multiplications are summed to obtain a third summation result. If the third summation result is greater than a preset fourth threshold, the charging load demand of the target charging pile in the target time period is determined according to the load intersection range.

[0037] In one implementation, determining the charging load demand of the target charging pile during the target time period specifically includes:

[0038] Select the maximum value from each of the first multiplication results, and determine the maximum value as the target multiplication result;

[0039] The target multiplication result is compared with a preset first threshold. If the target multiplication result is greater than the first threshold, the charging load demand of the target charging pile in the target time period is determined according to the target load range corresponding to the target multiplication result.

[0040] A second aspect of this application provides a vehicle-to-grid (V2G) light-storage-charging collaborative optimization device, specifically comprising:

[0041] The information acquisition module is used to acquire power generation status information of the photovoltaic power generation system in the park.

[0042] The charging determination module is used to predict whether there will be a vehicle charging at the charging pile during a target time period when the power generation status information is in the non-power generation state, based on multiple historical charging periods of each charging pile in the park and the charging load during the historical charging periods. The target time period is a future time period after the current time.

[0043] The demand determination module is used to determine the charging pile as a target charging pile if it is predicted that there will be vehicles charging at the charging pile during the target time period, and to determine the charging load demand of the target charging pile during the target time period.

[0044] The discharge control module is used to supply power to the corresponding target charging piles through the energy storage system in the park according to the charging load requirements of each target charging pile.

[0045] By adopting the above technical solution, the information acquisition module obtains the power generation status information of the photovoltaic power generation system in the park. Then, when the power generation status information is not in the power generation state, the charging judgment module determines whether there is a vehicle charging at the charging pile during the target time period based on multiple historical charging periods for each charging pile and the charging load during the historical charging periods. Then, when the demand determination module determines that there is a vehicle charging at the charging pile during the target time period, it identifies this charging pile as the target charging pile and determines the charging load demand of the target charging pile during the target time period. Finally, based on the charging load demand of each target charging pile, the energy storage system supplies power to the corresponding target charging pile.

[0046] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when loaded and executed by a processor, performs the steps of the method described in any one of the first aspects.

[0047] A fourth aspect of this application provides an electronic device, specifically comprising:

[0048] A processor, a memory, and a computer program stored in the memory and capable of running on the processor, the processor being configured to load and execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0049] In summary, this application includes at least one of the following beneficial technical effects:

[0050] The system obtains the power generation status information of the photovoltaic power generation system. If the power generation status is "not generating power," it means that the photovoltaic power generation system is basically not generating power. To ensure that vehicles within the park can charge normally through the charging piles, a power storage system is needed. Simultaneously, to avoid standby energy consumption from powering all charging piles in the park and to mitigate energy waste, targeted power supply to charging piles within the park is required. Furthermore, based on multiple historical charging periods and the charging load during those periods, the likelihood of a vehicle charging at a single charging pile during a target period after the current time is analyzed, thus accurately determining whether a vehicle is charging at the charging pile during the target period. Further, if a vehicle is determined to be charging at a charging pile during the target period, it is designated as a target charging pile. The charging load demand of this target charging pile during the target period is also determined. Finally, based on the charging load demand, targeted power is supplied only to the corresponding target charging piles, excluding other charging piles, thereby mitigating energy waste within the park to some extent. Attached Figure Description

[0051] Figure 1 This is a flowchart illustrating a vehicle-to-grid (V2G) collaborative optimization method for light-storage-charging interaction provided in an embodiment of this application.

[0052] Figure 2 This is a schematic diagram illustrating the relationship between a target charging period and a target load range, provided in an embodiment of this application.

[0053] Figure 3 This is a schematic diagram of the structure of a vehicle-to-grid (V2G) optical-storage-charging collaborative optimization device provided in an embodiment of this application;

[0054] Figure 4 This is a schematic diagram of another vehicle-to-grid (V2G) light-storage-charging collaborative optimization device provided in an embodiment of this application.

[0055] Explanation of reference numerals in the attached diagram: 11. Information acquisition module; 12. Charging judgment module; 13. Demand determination module; 14. Discharge control module; 15. Result verification module. Detailed Implementation

[0056] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0057] In the description of the embodiments of this application, words such as "exemplarily," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0058] In the description of the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, B existing alone, or A and B existing simultaneously. Furthermore, unless otherwise stated, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0059] See Figure 1 This application discloses a flowchart illustrating a method for coordinated optimization of light-storage-charging in vehicle-to-grid (V2G) interaction. This method can be implemented using a computer program or run on a Von Neumann-based V2G-storage-charging coordinated optimization device. The computer program can be integrated into an application or run as a standalone utility application, specifically including:

[0060] S101: Obtain the power generation status information of the photovoltaic power generation system in the park.

[0061] Specifically, in this embodiment, the park is an area with multiple parking spaces equipped with charging piles. The park also includes a photovoltaic power generation system and an energy storage system. The photovoltaic power generation system is a photovoltaic array composed of multiple photovoltaic modules, used to directly convert solar energy into electrical energy to meet the park's electricity demand (mainly powering the charging piles). The energy storage system consists of multiple energy storage cabinets. In the park setting, multiple energy storage cabinets can be connected in parallel or series to match different capacity and power requirements, while improving system reliability and scalability. When there is surplus photovoltaic power generation, i.e., enough to meet the charging needs of the charging piles in the park, to avoid energy waste, it can be stored through the energy storage system. This allows for timely response when the photovoltaic power generation system's output is poor (e.g., when sunlight is weak and the photovoltaic power generation is poor), by discharging through the energy storage system, thus preventing power outages or voltage instability for the charging piles in the park.

[0062] This application embodiment provides a method for coordinated optimization of photovoltaic-storage-charging systems in vehicle-to-grid (V2G) interaction. The executing entity is a server, which is wirelessly connected to a terminal (a personal computer or tablet computer) with a client installed. The server serves as the client's backend server, which can be a standalone physical server or a cluster of multiple physical servers. Personnel send an activation command for coordinated optimization to the server via the client on the terminal. Based on the activation command, the server begins automatic coordinated optimization of photovoltaic-storage-charging systems within the park, and obtains the power generation status information of the photovoltaic power generation systems in the park (either not generating power or generating power). Finally, based on the power generation status information, the server performs coordinated optimization of the scheduling among the photovoltaic power generation systems, energy storage systems, and charging piles within the park. Furthermore, a feasible method for obtaining power generation status information is as follows: The output current of the photovoltaic power generation system is obtained through a current sensor, and the output voltage of the photovoltaic power generation system is obtained through a voltage sensor. Finally, the output current and output voltage are multiplied to obtain the power generation output, i.e., the current output of the photovoltaic power generation system. If the power generation output is not 0, it indicates that the system is still in a photovoltaic power generation state, and the power generation status information of the photovoltaic power generation system is determined to be a power generation state. Conversely, if the power generation output is 0, it indicates that it may be nighttime, and the photovoltaic power generation system is basically not generating power, and the power generation status information is determined to be a non-power generation state.

[0063] S102: When the power generation status information is "not generating power", predict whether there will be vehicles charging at the charging pile during the target period based on multiple historical charging periods for each charging pile in the park and the charging load during the historical charging periods.

[0064] Specifically, in this embodiment, the target time period is a future time period after the current time. It is necessary to predict the charging load demand of charging piles in the park after the current time, so as to provide targeted power supply to the charging piles. For example, if the current time is 10:00, the target time period can be from 10:00 to 11:00. In other embodiments, the target time period can also be from 11:00 to 12:00.

[0065] If the power generation status information indicates no power generation, then for vehicles charging within the park, the energy storage system needs to charge the charging stations. Therefore, to determine whether any vehicles are charging at the charging stations within the park during the target time period, one feasible implementation method is as follows:

[0066] For a single charging pile within the park, based on the park's historical charging records, multiple historical charging periods for that charging pile are obtained. These historical charging periods include the times when vehicles charged at each charging pile and the charging load during those times. The frequency of recurrence of a single historical charging period is calculated and compared with a preset frequency threshold. If the frequency exceeds the threshold, it indicates that vehicles charged frequently during that historical charging period, and this period is then designated as the target charging period—that is, the time when vehicles are likely to charge at that charging pile. Furthermore, based on the aforementioned historical charging records, multiple charging load values ​​that occurred during the charging of historical vehicles within a single target charging period are obtained, i.e., historical charging load values. Cluster analysis is performed on the multiple historical charging load values ​​to divide them into multiple load ranges that can cover the historical charging load values. For example, if there are historical charging load values ​​of 10kW, 11kW, 15kW, 16kW, 20kW, etc., then the divided load ranges are 10kW~15kW and 15kW~20kW, respectively.

[0067] Furthermore, the number of historical charging load values ​​existing within each load range is counted. The more historical values, the more likely the vehicle's charging load is to fall within the corresponding load range during the target charging period. If the number exceeds a preset threshold, the corresponding load range is determined as the target load range for that target charging period. In other words, the target load range is the range within which the charging load is likely to be when the vehicle is charging during the target charging period. Then, a first weight is determined for each target charging period. The first weight is the ratio of the frequency of a single target charging period's recurrence to the sum of the frequencies of all target charging periods' recurrences. This first weight represents the probability that the vehicle will be charging at a charging station during the target charging period. Finally, a second weight is determined for each target load range. The second weight is the ratio of the number corresponding to a single target load range to the sum of the numbers corresponding to all target load ranges. This second weight represents the probability that the vehicle's charging load is within the corresponding target load range during the target charging period.

[0068] For example, for charging pile 1 in the park, there are target charging time periods A, B, and C. Target charging time period A recurs 60 times, target charging time period B recurs 20 times, and target charging time period C recurs 20 times. Therefore, the first weight of target charging time period A is: 60 times / (60 times + 20 times + 20 times) = 0.6. Furthermore, target charging time period A corresponds to target load ranges a1, a2, and a3. The number of target load ranges a1 is 50, a2 is 30, and a3 is 20. Therefore, the second weight of target load range a1 is: 50 / (50 + 30 + 20) = 0.5. See details in [link to relevant documentation]. Figure 2 . Figure 2 In the text, target load range a1, target load range a2, etc., are all target load ranges corresponding to target charging period A.

[0069] Finally, based on the first and second weights corresponding to each charging pile, it is determined whether a vehicle is charging at that charging pile during the target time period. One feasible method is to identify at least one target charging period within the target time period as an important charging period. The first weight of a single important charging period is multiplied by the second weight of each corresponding target load range to obtain a first multiplication result. This first multiplication result represents the probability that a vehicle is charging during the important charging period and that the actual charging load is within the corresponding target load range. If the first multiplication result is greater than a first threshold, it indicates a high probability that a vehicle is charging during the important charging period and that the actual charging load is within the corresponding target load range; therefore, the first multiplication result is determined as the result to be analyzed. The results to be analyzed for each important charging period are summed to obtain a first summation result. This first summation result represents the overall probability that a vehicle is charging at a single charging pile during the target time period. If the first summation result is greater than a preset second threshold, it is determined that a vehicle is charging at that charging pile during the target time period; otherwise, it is determined that no vehicle is charging at that charging pile during the target time period.

[0070] In other embodiments, the first weights of each important charging period are summed to obtain a total weight sum, which represents the overall probability that a vehicle is charging at the charging station during the entire target period. If the total weight sum exceeds a preset weight threshold, it indicates that the overall probability of a vehicle charging at the charging station during the target period is relatively high, and thus it is determined that a vehicle is charging at the charging station during the target period. Conversely, if the total weight sum is not greater than the weight threshold, it is determined that no vehicle is charging at the charging station during the target period, which also verifies the determination result of whether a vehicle is charging during the target period.

[0071] Furthermore, if the target charging period of the charging station is not included in the target time period, then it is highly likely that no vehicle will be charging at the charging station during the target time period.

[0072] S103: If it is predicted that there are vehicles charging at charging stations during the target time period, then the charging station is identified as the target charging station, and the charging load demand of the target charging station during the target time period is determined.

[0073] Specifically, if it is determined that a vehicle is charging at a single charging station during a target time period, then that charging station is designated as a target charging station, i.e., a charging station where vehicles are highly likely to be charging in the future. Further, the charging load demand of the target charging station during the target time period is determined, thus facilitating the targeted provision of charging power to that target charging station subsequently. In this embodiment, a feasible method for determining the charging load demand is as follows: A first weight of a single important charging time period is multiplied by the second weight of each corresponding target load range to obtain a first multiplication result. The first multiplication result characterizes the probability that a vehicle is charging during the important charging time period and that the actual charging load is within the corresponding target load range. Then, the maximum value is selected from each of the first multiplication results, and this maximum value is determined as the target multiplication result corresponding to the important charging time period. The target load range corresponding to the target multiplication result is the load range within which the charging load is highly likely to be when the vehicle is charging during the important charging time period. It should be noted that the charging load demand can be understood as: the charging load required by the target charging station to meet the charging needs of vehicles during the target time period.

[0074] The maximum target multiplication result is selected from the target multiplication results corresponding to each important charging period. The target load range corresponding to the maximum target multiplication result is the load range in which the vehicle is most likely to be charged during the target period. Finally, the charging load demand of the target charging pile during the target period is determined based on the target load range corresponding to the maximum target multiplication result. One feasible method is to determine the maximum value in the target load range corresponding to the maximum target multiplication result as the charging load demand of the target charging pile.

[0075] In one embodiment, the target load range corresponding to the product of the maximum targets is determined as the final load range. The product of the maximum targets corresponding to each target charging pile is summed to obtain a second summation result. The second summation result is compared with a preset third threshold. If the second summation result is greater than the third threshold, it indicates that the overall probability of vehicles charging at each target charging pile within the corresponding final load range during the target time period is relatively high. This verifies that the charging load demand of each target charging pile is relatively reasonable. It also indicates that the overall probability of vehicles charging at charging piles within the park during the target time period is relatively high. This further verifies that there are indeed vehicles charging at charging piles during the target time period, and thus the verification is confirmed to be successful.

[0076] In other embodiments, a feasible way to determine the charging load demand of a target charging pile is as follows: When there are two important charging periods that intersect with the target time period, the target load ranges corresponding to the two important charging periods are intersected. If there is an intersection, the load intersection range is determined, and the target load range containing the load intersection range is determined as the important load range. For example, an important charging period M corresponds to a target load range M1: 11kW-20kW and a target load range M2: 25kW-30kW; another important charging period N corresponds to a target load range N1: 15kW-23kW and a target load range N2: 5kW-10kW. The target load ranges corresponding to the two important charging periods are intersected. Target load range N1 and target load range M1 intersect, and the load intersection range is 15kW-20kW. Therefore, both the intersecting target load ranges N1 and M1 are determined as important load ranges.

[0077] Furthermore, the first weight of each important charging period is multiplied by the second weight of the corresponding important load range to obtain a second multiplication result. The second multiplication result represents the probability that the vehicle is charging during the important charging period and the charging load is within the corresponding important load range. Then, the various second multiplication results are summed to obtain a third summation result. The third summation result represents the probability that the vehicle is charging during the target period and the charging load is within the load intersection range. Finally, the third summation result is compared with a preset fourth threshold. If the third summation result is greater than the fourth threshold, it indicates that the probability that the vehicle is charging during the target period and the charging load is within the load intersection range is high. Therefore, based on the load intersection range, the charging load demand of the target charging pile during the target period is determined. Specifically, the maximum value in the load intersection range is determined as the charging load demand of the target charging pile.

[0078] In another embodiment, a feasible way to determine the charging load demand of a target charging pile is as follows: select the maximum value from each first multiplication result, determine the maximum value as the target multiplication result, compare the target multiplication result with a preset first threshold, if the target multiplication result is greater than the first threshold, it indicates that the vehicle is likely to be charging during an important charging period and the overall charging load is within the corresponding target load range, then the maximum value of the target load range corresponding to the target multiplication result is determined as the charging load demand of the target charging pile.

[0079] In another embodiment, the result of multiplying the target values ​​is compared with a preset first threshold. If the result of multiplying the target values ​​is greater than the first threshold, it indicates that the overall probability of the vehicle charging during a critical charging period and the charging load being within the corresponding target load range is relatively high. Therefore, the critical charging period corresponding to the result of multiplying the target values ​​is determined as a reference charging period, and the target load range corresponding to the result of multiplying the target values ​​is determined as a reference load range. The duration corresponding to at least one reference charging period is calculated, and the duration is multiplied by the minimum value of the reference load range to obtain the energy demand. Finally, the energy demands corresponding to each target charging station are summed to obtain the total energy demand for charging stations within the target period. If the total energy stored in the energy storage system is less than the total energy demand, it indicates that the current energy stored in the energy storage system is insufficient to meet the demand for charging stations within the target period. Therefore, charging of the energy storage system continues during off-peak electricity price periods, drawing energy from the grid.

[0080] S104: Based on the charging load requirements of each target charging pile, power is supplied to the corresponding target charging pile through the energy storage system in the park.

[0081] Specifically, after the charging load demand of each target charging pile within the target time period is determined and verified, it is necessary to determine the discharge period for the energy storage system to supply power to each target charging pile. In this embodiment, the specific process is as follows: for a single target charging pile, the important charging period corresponding to the maximum target multiplication result is determined as the discharge period. Finally, through the energy storage system, the matching charging load demand is allocated to the corresponding target charging pile before the discharge period, thereby realizing the advance and targeted allocation of charging load for charging piles in the park, avoiding the energy waste problem that would occur if all charging piles were supplied with power uniformly. In other embodiments, for a single target charging pile, if its target multiplication result is greater than a first threshold, then the important charging period corresponding to the target multiplication result is determined as the discharge period, and the maximum value in the target load range corresponding to the target multiplication result is taken as the charging load demand corresponding to the discharge period. Before each discharge period, power is supplied to the target charging pile in advance according to the corresponding charging load demand through the energy storage system.

[0082] In one embodiment, if the power generation status information is "power generation status", then the actual charging load demand of charging piles in the park within a preset time period is determined. The preset time period is a future time period after the current time. One feasible way to determine the actual charging load demand is as follows: at least one target charging period included in the preset time period is identified as a key charging period, and the first weight of each key charging period is summed to obtain a weight summation result. If the weight summation result is greater than a weight threshold, then it is determined that there are vehicles charging at charging piles within the preset time period, and the charging pile is identified as a key charging pile. Further, the first weight of a single key charging period is multiplied by the second weight of each corresponding target load range to obtain a product result. The largest product result is selected from each product result. If the largest product result is greater than the first threshold, then the maximum value of the target load range corresponding to the largest product result is determined as the actual charging load demand corresponding to the key charging pile. The maximum charging load demand is selected from at least one actual charging load demand corresponding to each key charging pile. Then, the sum of these maximum charging load demands is calculated. If the sum of demands exceeds the actual output of the photovoltaic power generation system, it indicates a supply shortage. In this case, the energy storage system discharges to provide some of the charging load. If the sum of demands is less than the actual output of the photovoltaic power generation system, there is a photovoltaic surplus, and the photovoltaic power generation system charges the energy storage system. It should be noted that the "output" of the photovoltaic power generation system, simply put, is the actual electrical power output of the system at a given moment, usually measured in kilowatts (kW) or megawatts (MW). It essentially reflects "how much electricity the photovoltaic system can generate at that moment" and is a key indicator for measuring its real-time power generation capacity.

[0083] The implementation principle of the photovoltaic-storage-charging collaborative optimization method for vehicle-to-grid interaction in this application embodiment is as follows: The power generation status information of the photovoltaic power generation system is obtained. If the power generation status information is "not generating power," it indicates that the photovoltaic power generation system is basically not generating power. Subsequently, to ensure that vehicles within the park can charge normally through charging piles, power needs to be supplied through an energy storage system. Simultaneously, to avoid standby energy consumption caused by supplying power to all charging piles within the park and to improve the problem of energy waste, targeted power supply needs to be provided to charging piles within the park. Furthermore, based on multiple historical charging periods of the charging piles and the charging load during those historical charging periods, the probability of a vehicle charging at a single charging pile during a target period after the current time is analyzed, thereby accurately determining whether a vehicle is charging at a charging pile during the target period. Further, if a vehicle is determined to be charging at a charging pile during the target period, it is identified as a target charging pile. Simultaneously, the charging load demand of this target charging pile during the target period is determined. Finally, based on the charging load demand, targeted power is supplied only to the corresponding target charging pile, and power is not supplied to charging piles other than the target charging pile, thereby improving the problem of energy waste within the park to a certain extent.

[0084] The following are embodiments of the apparatus described in this application, which can be used to execute the embodiments of the method described in this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method described in this application.

[0085] Please see Figure 3 This is a schematic diagram of the structure of the vehicle-to-grid (V2G) optical-storage-charging collaborative optimization device provided in this embodiment of the application. This V2G optical-storage-charging collaborative optimization device can be implemented as all or part of a device through software, hardware, or a combination of both. The device includes an information acquisition module 11, a charging judgment module 12, a demand determination module 13, and a discharge control module 14.

[0086] Information acquisition module 11 is used to acquire power generation status information of photovoltaic power generation systems in the park;

[0087] The charging judgment module 12 is used to predict whether there are vehicles charging at the charging piles in the target time period when the power generation status information is not generating power, based on multiple historical charging periods of each charging pile in the park and the charging load in the historical charging periods. The target time period is the future time period after the current time.

[0088] The demand determination module 13 is used to determine the charging pile as the target charging pile if it is predicted that there are vehicles charging at the charging pile during the target time period, and to determine the charging load demand of the target charging pile during the target time period.

[0089] The discharge control module 14 is used to supply power to the corresponding target charging piles through the energy storage system in the park according to the charging load requirements of each target charging pile.

[0090] Optionally, the charging detection module 12 is specifically used for:

[0091] Based on multiple historical charging periods for each charging station within the park, at least one target charging period is determined.

[0092] Based on multiple historical charging load values ​​within the target charging period, at least one target load range is determined;

[0093] A first weight is determined for the target charging period, and a second weight is determined for each target load range. The first weight represents the probability that a vehicle will be charged at a charging station during the target charging period, and the second weight represents the probability that the vehicle's charging load will be within the corresponding target load range during the target charging period.

[0094] Based on the first weight and each of the second weights, predict whether there will be vehicles charging at the charging station during the target time period.

[0095] Optionally, the charging detection module 12 is specifically used for:

[0096] At least one target charging period included in the target time period is identified as an important charging period, and the first weight of a single important charging period is multiplied by the second weight of each corresponding target load range to obtain each first multiplication result corresponding to a single important charging period;

[0097] The first multiplication result is compared with a preset first threshold. If the first multiplication result is greater than the first threshold, the first multiplication result is determined as the result to be analyzed.

[0098] The results to be analyzed for each important charging period are summed to obtain the first summation result;

[0099] If the first summation result is greater than the preset second threshold, it is predicted that there are vehicles charging at the charging station during the target time period.

[0100] If the first summation result is not greater than the preset second threshold, it is predicted that no vehicles will be charging at the charging station during the target time period.

[0101] If the target charging period is not included in the target time period, it is predicted that no vehicles will be charging at the charging station during the target time period.

[0102] Optional, requirement determination module 13, specifically used for:

[0103] The maximum value is selected from the first multiplication results corresponding to a single important charging period, and the maximum value is determined as the target multiplication result for the single important charging period.

[0104] Select the maximum target multiplication result from the target multiplication results corresponding to each important charging period;

[0105] Based on the target load range corresponding to the result of multiplying the maximum target, the charging load demand of the target charging pile within the target time period is determined.

[0106] Optional, such as Figure 4 As shown, the device also includes a result verification module 15, specifically used for:

[0107] The summation of the product of the maximum target values ​​for each target charging station is then obtained to get the second summation result.

[0108] The second summation result is compared with a preset third threshold;

[0109] If the second summation result is greater than the third threshold, then the verification is considered successful.

[0110] Optionally, the requirements determination module 13 is also used for:

[0111] When there are two important charging periods, the intersection of the target load ranges corresponding to the two important charging periods is performed to obtain the load intersection range, and the target load range containing the load intersection range is determined as the important load range;

[0112] The first weight of each important charging period is multiplied by the second weight of the corresponding important load range to obtain the second multiplication result;

[0113] The results of the second multiplications are summed to obtain the third sum. If the third sum is greater than the preset fourth threshold, the charging load demand of the target charging pile in the target time period is determined according to the load intersection range.

[0114] Optionally, the requirements determination module 13 is also used for:

[0115] Select the maximum value from each of the first multiplication results and determine the maximum value as the target multiplication result;

[0116] The result of multiplying the targets is compared with a preset first threshold. If the result of multiplying the targets is greater than the first threshold, the charging load demand of the target charging pile in the target time period is determined according to the target load range corresponding to the result of multiplying the targets.

[0117] It should be noted that the above-described vehicle-to-grid (V2G) optical-storage-charging collaborative optimization device, when executing the V2G optical-storage-charging collaborative optimization method, is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the above-described V2G optical-storage-charging collaborative optimization device and the V2G optical-storage-charging collaborative optimization method embodiment belong to the same concept, and their implementation process is detailed in the method embodiment, which will not be repeated here.

[0118] This application also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements a vehicle-to-grid (V2G) optical-storage-charging collaborative optimization method as described in the above embodiments.

[0119] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0120] The above-described vehicle-to-grid (V2G) optical-storage-charging collaborative optimization method is stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the above method.

[0121] This application also discloses an electronic device in which a computer program is stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, it implements the above-mentioned method for coordinated optimization of optical-storage-charging interaction between vehicles and the Internet.

[0122] The electronic device can be a desktop computer, a laptop computer, or a cloud server, and includes, but is not limited to, a processor and a memory. For example, the electronic device may also include input / output devices, network access devices, and buses.

[0123] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it.

[0124] The memory can be an internal storage unit of an electronic device, such as a hard disk or RAM, or an external storage device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) equipped on the electronic device. Furthermore, the memory can be a combination of an internal storage unit and an external storage device. The memory is used to store computer programs and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0125] In this electronic device, the optical-storage-charging collaborative optimization method for vehicle-to-grid interaction described in the above embodiment is stored in the memory of the electronic device and loaded and executed on the processor of the electronic device for convenient use.

[0126] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for coordinated optimization of light-storage-charging in vehicle-to-grid interaction, characterized in that, The method includes: Obtain power generation status information of the photovoltaic power generation system in the park; When the power generation status information is "no power generation," based on multiple historical charging periods for each charging pile within the park and the charging load during those historical charging periods, it predicts whether a vehicle will be charging at the charging pile during a target period. This includes: determining at least one target charging period based on multiple historical charging periods for each charging pile within the park; determining at least one target load range based on multiple historical charging load values ​​during the target charging period; determining a first weight for the target charging period and a second weight for each of the target load ranges, where the first weight represents the probability that a vehicle will be charging at the charging pile during the target charging period, and the second weight represents the probability that the vehicle's charging load is within the corresponding target load range during the target charging period; and predicting whether a vehicle will be charging at the charging pile during the target period based on the first weight and each of the second weights, where the target period is a future period after the current time. If it is predicted that a vehicle will be charging at the charging station during the target time period, the charging station will be identified as the target charging station, and the charging load demand of the target charging station during the target time period will be determined. According to the charging load requirements of each target charging pile, power is supplied to the corresponding target charging pile through the energy storage system in the park.

2. The vehicle-to-grid (V2G)-storage-charging collaborative optimization method according to claim 1, characterized in that, The step of predicting whether a vehicle will be charging at the charging station during a target time period based on the first weight and each of the second weights specifically includes: At least one target charging period included in the target time period is identified as an important charging period, and the first weight of a single important charging period is multiplied by the second weight of each corresponding target load range to obtain each first multiplication result corresponding to a single important charging period; The first multiplication result is compared with a preset first threshold. If the first multiplication result is greater than the first threshold, the first multiplication result is determined as the result to be analyzed. The results to be analyzed corresponding to each of the important charging periods are summed to obtain the first summation result; If the first summation result is greater than the preset second threshold, it is predicted that there are vehicles charging at the charging pile during the target time period; If the first summation result is not greater than the preset second threshold, it is predicted that no vehicle will be charging at the charging pile during the target time period. If the target charging period is not included in the target time period, it is predicted that no vehicle will be charging at the charging station during the target time period.

3. The vehicle-to-grid (V2G)-storage-charging collaborative optimization method according to claim 2, characterized in that, Determining the charging load demand of the target charging pile during the target time period specifically includes: The maximum value is selected from each of the first multiplication results corresponding to a single important charging period, and the maximum value is determined as the target multiplication result for a single important charging period; Select the largest target multiplication result from the target multiplication results corresponding to each of the important charging periods; Based on the target load range corresponding to the product of the maximum target, the charging load demand of the target charging pile within the target time period is determined.

4. The vehicle-to-grid (V2G)-storage-charging collaborative optimization method according to claim 3, characterized in that, The method further includes: The summation of the product of the maximum target values ​​corresponding to each of the target charging piles is obtained to get the second summation result; The second summation result is compared with a preset third threshold; If the second summation result is greater than the third threshold, then the verification is deemed successful; The step of supplying power to the corresponding target charging piles through the energy storage system in the park according to the charging load demand of each target charging pile specifically includes: After the verification is passed, power is supplied to the corresponding target charging piles through the energy storage system in the park according to the charging load requirements of each target charging pile.

5. The vehicle-to-grid (V2G) solar-storage-charging collaborative optimization method according to claim 2, characterized in that, Determining the charging load demand of the target charging pile during the target time period specifically includes: When there are two important charging periods, the intersection of the target load ranges corresponding to the two important charging periods is performed to obtain the load intersection range, and the target load range containing the load intersection range is determined as the important load range; The first weight of each important charging period is multiplied by the second weight of the corresponding important load range to obtain the second multiplication result; The results of the second multiplications are summed to obtain a third summation result. If the third summation result is greater than a preset fourth threshold, the charging load demand of the target charging pile in the target time period is determined according to the load intersection range.

6. The vehicle-to-grid (V2G) coordinated optimization method for light-storage-charging as described in claim 2, characterized in that, Determining the charging load demand of the target charging pile during the target time period specifically includes: Select the maximum value from each of the first multiplication results, and determine the maximum value as the target multiplication result; The target multiplication result is compared with a preset first threshold. If the target multiplication result is greater than the first threshold, the charging load demand of the target charging pile in the target time period is determined according to the target load range corresponding to the target multiplication result.

7. A vehicle-to-grid (V2G) optical-storage-charging collaborative optimization device, used to implement the V2G optical-storage-charging collaborative optimization method according to any one of claims 1 to 6, characterized in that, include: The information acquisition module (11) is used to acquire the power generation status information of the photovoltaic power generation system in the park; The charging judgment module (12) is used to predict whether there will be a vehicle charging at the charging pile in the target time period when the power generation status information is in the non-power generation state, based on multiple historical charging periods of each charging pile in the park and the charging load in the historical charging periods. The target time period is a future time period after the current time. The demand determination module (13) is used to determine the charging pile as the target charging pile if it is predicted that there are vehicles charging at the charging pile during the target time period, and to determine the charging load demand of the target charging pile during the target time period. The discharge control module (14) is used to supply power to the corresponding target charging piles through the energy storage system in the park according to the charging load requirements of each target charging pile.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is loaded and executed by the processor, it implements the method of any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor loads and executes the computer program, it implements the method of any one of claims 1-6.

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