Multi-level optical storage and charging resource scheduling method and device and computer equipment

Abstract

The invention relates to a multi-level optical storage and charging resource scheduling method and device and computer equipment. The method comprises the following steps: obtaining a scheduling efficiency correlation factor of a target multi-level optical storage and charging system, transmission parameters between adjacent levels of resource nodes, and real-time load distribution characteristics and real-time energy supply and demand characteristics of current each level of resource nodes, and obtaining a scheduling efficiency correlation factor of the target multi-level optical storage and charging system according to the real-time load distribution characteristics and the real-time energy supply and demand characteristics; and extracting a target scheduling efficiency correlation factor matched with the current operation scene from the scheduling efficiency correlation factors, determining a first resource scheduling value and a second resource scheduling value among the resource nodes of each level in the target time period based on the target scheduling efficiency correlation factor and the transmission parameters, and further predicting the resource scheduling loss rate of the target time period. And under the condition that the resource scheduling loss rate is greater than or equal to a preset scheduling loss threshold value, determining a target optimization scheduling scheme, and scheduling the target multi-level optical storage and charging system based on the target optimization scheduling scheme. By adopting the method, the resources can be efficiently and accurately scheduled.

Description

Technical Field

[0001] This application relates to the field of resource scheduling technology, and in particular to a multi-level optical-storage-charging resource scheduling method, apparatus, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] In the energy sector, multi-level photovoltaic-storage-charging systems are gradually becoming an important form of energy supply and distribution. Because resource nodes at different levels (such as photovoltaic power plants, energy storage stations, and charging piles) are spatially dispersed, the transmission distance and transmission loss coefficients between adjacent levels vary significantly. Failure to grasp these basic transmission parameters will result in substantial energy waste during transmission, reducing overall utilization efficiency. Furthermore, energy supply and demand exhibit distinct temporal characteristics.

[0003] Existing resource scheduling methods often rely on experience or simple time rules, making it difficult to dynamically adjust scheduling strategies based on real-time conditions. This results in insufficient accuracy in resource scheduling, which can easily lead to energy surpluses and idleness, as well as supply shortages that fail to meet demand. Summary of the Invention

[0004] Therefore, it is necessary to provide a multi-level optical storage and charging resource scheduling method, device, computer equipment, computer-readable storage medium, and computer program product that can more accurately schedule resources, addressing the aforementioned technical problems.

[0005] Firstly, this application provides a multi-level optical-storage-charging resource scheduling method, including:

[0006] The scheduling efficiency correlation factor of the target multi-level photovoltaic storage and charging system, the transmission parameters between adjacent level resource nodes, and the real-time load distribution characteristics and real-time energy supply and demand characteristics of each level resource node are obtained. The scheduling efficiency correlation factor is determined based on historical scheduling data and is used to characterize the correlation between resource scheduling quantity and transmission parameters.

[0007] Based on the real-time load distribution characteristics and the real-time energy supply and demand characteristics, a target scheduling efficiency correlation factor that matches the current operating scenario is extracted from the scheduling efficiency correlation factor.

[0008] Based on the target scheduling efficiency correlation factor and the transmission parameters, determine the first resource scheduling value and the second resource scheduling value among resource nodes at each level within the target time period;

[0009] Based on the first resource scheduling value and the second resource scheduling value, predict the resource scheduling loss rate for the target time period;

[0010] If the resource scheduling loss rate is greater than or equal to a preset scheduling loss threshold, a target optimized scheduling scheme is determined, and the target multi-level optical storage and charging system is scheduled based on the target optimized scheduling scheme.

[0011] In one embodiment, obtaining the scheduling efficiency correlation factor of the target multi-level optical storage and charging system includes:

[0012] Acquire historical scheduling data of the target multi-level optical storage and charging system;

[0013] Extract historical load distribution characteristics and historical energy supply and demand characteristics of resource nodes at different levels under different time periods from historical scheduling data;

[0014] Based on the historical load distribution characteristics and historical energy supply and demand characteristics, the historical resource scheduling volume between adjacent resource nodes in each time period is determined.

[0015] Based on the transmission parameters, the scheduling amounts between adjacent resource nodes in the historical resource scheduling amounts are calculated to obtain the scheduling amount difference.

[0016] Determine the ratio of the scheduling difference to the corresponding transmission parameter to obtain multiple candidate ratios;

[0017] The scheduling efficiency correlation factor is obtained by averaging the multiple candidate ratios.

[0018] In one embodiment, the step of extracting a target scheduling efficiency correlation factor that matches the current operating scenario from the scheduling efficiency correlation factors based on the real-time load distribution characteristics and the real-time energy supply and demand characteristics includes:

[0019] The real-time load distribution features are matched with the historical load distribution features to obtain a first matching result;

[0020] The real-time energy supply and demand characteristics are matched with the historical energy supply and demand characteristics to obtain a second matching result;

[0021] Based on the first matching result and the second matching result, a target scheduling efficiency correlation factor that matches the current operating scenario is extracted from the scheduling efficiency correlation factor.

[0022] In one embodiment, determining the first resource scheduling value and the second resource scheduling value among resource nodes at each level within the target time period based on the target scheduling efficiency correlation factor and the transmission parameters includes:

[0023] The energy demand priority of resource nodes at each level is assessed within the target time period to obtain the target scheduling priority;

[0024] Based on the target scheduling priority and the preset resource supply capacity of the energy production nodes, the first scheduling influence parameter is determined;

[0025] Based on the first scheduling impact parameter, the transmission parameter, and the target scheduling efficiency correlation factor, the first resource scheduling value among resource nodes at each level within the target time period is obtained.

[0026] Based on the target scheduling priority and the preset resource consumption requirements of energy consumption nodes, determine the second scheduling influence parameter;

[0027] Based on the second scheduling impact parameter, the transmission parameter, and the target scheduling efficiency correlation factor, the second resource scheduling value among resource nodes at each level within the target time period is obtained.

[0028] In one embodiment, predicting the resource scheduling loss rate for a target time period based on the first resource scheduling value and the second resource scheduling value includes:

[0029] The frequency of energy production fluctuations at energy production nodes within a preset time window is detected to obtain a baseline fluctuation frequency.

[0030] Multiply the first resource scheduling value by the benchmark fluctuation frequency to obtain the first scheduling optimization factor, and multiply the second resource scheduling value by the benchmark fluctuation frequency to obtain the second scheduling optimization factor;

[0031] The first scheduling optimization factor and the second scheduling optimization factor are summed to obtain the comprehensive scheduling optimization factor;

[0032] The comprehensive scheduling optimization factor is matched with the resource scheduling loss rate in the pre-built reference loss database to obtain the resource scheduling loss rate for the target time period.

[0033] In one embodiment, determining the target optimized scheduling scheme when the resource scheduling loss rate is greater than or equal to a preset scheduling loss threshold includes:

[0034] Multiple candidate resource scheduling schemes are obtained, and the first scheme performance data of each candidate resource scheduling scheme is obtained based on the resource transmission stability and energy utilization rate of each candidate resource scheduling scheme.

[0035] Obtain the current execution scheduling scheme, and based on the resource transmission stability and energy utilization rate of the current execution scheduling scheme, obtain the performance data of the second scheme;

[0036] The performance data of each of the first schemes are compared with the performance data of the second scheme, and the candidate resource scheduling scheme whose performance data of the first scheme is higher than that of the second scheme is determined as the target optimized scheduling scheme.

[0037] Secondly, this application also provides a multi-level photovoltaic-storage-charging resource scheduling device, the device comprising:

[0038] The data acquisition module is used to acquire the scheduling efficiency correlation factor of the target multi-level photovoltaic storage and charging system, the transmission parameters between adjacent level resource nodes, and the real-time load distribution characteristics and real-time energy supply and demand characteristics of the current level resource nodes. The scheduling efficiency correlation factor is determined based on historical scheduling data and is used to characterize the correlation between resource scheduling quantity and transmission parameters.

[0039] The data extraction module is used to extract a target scheduling efficiency correlation factor that matches the current operating scenario from the scheduling efficiency correlation factor based on the real-time load distribution characteristics and the real-time energy supply and demand characteristics.

[0040] The data determination module is used to determine the first resource scheduling value and the second resource scheduling value among resource nodes at each level within the target time period based on the target scheduling efficiency correlation factor and the transmission parameters.

[0041] The data prediction module is used to predict the resource scheduling loss rate for a target time period based on the first resource scheduling value and the second resource scheduling value.

[0042] The scheduling module is used to determine a target optimized scheduling scheme when the resource scheduling loss rate is greater than or equal to a preset scheduling loss threshold, and to schedule the target multi-level optical storage and charging system based on the target optimized scheduling scheme.

[0043] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in any of the above embodiments of the multi-level optical storage and charging resource scheduling method.

[0044] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps in any of the above embodiments of the multi-level optical storage and charging resource scheduling method.

[0045] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the multi-level optical storage and charging resource scheduling method.

[0046] The aforementioned multi-level photovoltaic-storage-charging resource scheduling method, device, computer equipment, computer-readable storage medium, and computer program products acquire transmission parameters between adjacent resource nodes of the target multi-level photovoltaic-storage-charging system. This allows schedulers to clearly understand the location relationships and energy transmission losses of each node. Based on historical scheduling data, a scheduling efficiency correlation factor is statistically derived to determine the relationship between resource scheduling quantity and transmission parameters. Combining the real-time load distribution characteristics and real-time energy supply and demand characteristics of each resource node, a target scheduling efficiency correlation factor suitable for the current operating scenario is extracted from the scheduling efficiency correlation factor. Based on the target scheduling efficiency correlation factor and transmission parameters, the first and second resource scheduling values ​​for the target time period are determined. Furthermore, the corresponding scheduling loss rate is deduced, and a scheduling optimization mechanism is triggered using a scheduling loss threshold as a dynamic criterion. This generates and executes the target optimized scheduling scheme in advance, effectively reducing the waste caused by ineffective or inefficient energy transmission between multiple levels, and ultimately significantly improving the overall energy utilization efficiency and economic operation level of the entire photovoltaic-storage-charging system. The entire solution, by combining historical and real-time data, can grasp the energy supply and demand situation at different times and nodes, making resource scheduling values ​​more aligned with actual needs. This avoids scheduling deviations caused by missing or delayed data, greatly improving the accuracy of resource scheduling and ensuring that energy can be allocated efficiently and on demand among nodes at all levels. Furthermore, resource scheduling and loss optimization ensure that the multi-level photovoltaic-storage-charging system is always in a highly efficient operating state, allowing electricity from energy production nodes to be transmitted more efficiently to consumption nodes, reducing idle or over-allocated energy during transmission and distribution. On the other hand, loss prediction and optimization measures prevent system instability caused by excessive losses, ensuring the stable operation of the photovoltaic-storage-charging system and improving the overall energy utilization efficiency and operational reliability of the system. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is an application environment diagram of a multi-level optical storage and charging resource scheduling method in one embodiment;

[0049] Figure 2 This is a flowchart illustrating a multi-level optical storage and charging resource scheduling method in one embodiment;

[0050] Figure 3 This is a flowchart illustrating the steps for obtaining scheduling efficiency correlation factors in one embodiment;

[0051] Figure 4 This is a flowchart illustrating the steps for extracting target scheduling efficiency correlation factors in one embodiment;

[0052] Figure 5 This is a flowchart illustrating the steps for determining resource scheduling values ​​in one embodiment;

[0053] Figure 6 This is a flowchart illustrating the steps for predicting resource scheduling loss rate in one embodiment.

[0054] Figure 7 This is a flowchart illustrating the steps for determining a target optimized scheduling scheme in one embodiment;

[0055] Figure 8 This is a structural block diagram of a multi-level optical storage and charging resource scheduling device in one embodiment;

[0056] Figure 9 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0059] The multi-level optical storage and charging resource scheduling method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server.

[0060] Specifically, the dispatcher can send a resource scheduling message to the server 104 via terminal 102. The server 104 obtains the scheduling efficiency correlation factor of the target multi-level photovoltaic energy storage and charging system, the transmission parameters between adjacent resource nodes, and the real-time load distribution characteristics and real-time energy supply and demand characteristics of each resource node. The scheduling efficiency correlation factor is determined based on historical scheduling data and is used to characterize the correlation between resource scheduling quantity and transmission parameters. Then, based on the real-time load distribution characteristics and real-time energy supply and demand characteristics, the target scheduling efficiency correlation factor matching the current operating scenario is extracted from the scheduling efficiency correlation factor. Based on the target scheduling efficiency correlation factor and transmission parameters, the first resource scheduling value and the second resource scheduling value between resource nodes at each level within the target time period are determined. Further, based on the first resource scheduling value and the second resource scheduling value, the resource scheduling loss rate for the target time period is predicted. Finally, if the resource scheduling loss rate is greater than or equal to a preset scheduling loss threshold, the target optimized scheduling scheme is determined, and the target multi-level photovoltaic energy storage and charging system is scheduled based on the target optimized scheduling scheme.

[0061] Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, and projection equipment. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted displays. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, and smart glasses. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0062] In one exemplary embodiment, such as Figure 2 As shown, a multi-level optical storage and charging resource scheduling method is provided, which can be applied to... Figure 1 Taking server 104 as an example, the explanation includes steps 100 to 500. Wherein:

[0063] Step 100: Obtain the scheduling efficiency correlation factor of the target multi-level photovoltaic energy storage and charging system, the transmission parameters between adjacent level resource nodes, and the real-time load distribution characteristics and real-time energy supply and demand characteristics of each level resource node.

[0064] Among them, the scheduling efficiency correlation factor is determined based on historical scheduling data and is used to characterize the correlation between resource scheduling quantity and transmission parameters.

[0065] A multi-level photovoltaic-storage-charging system refers to a multi-level photovoltaic-storage-charging system within a target area. This system is a hierarchical energy network composed of photovoltaic power generation units (“photovoltaics”), energy storage devices (“storage”), and electric vehicle charging facilities (“charging”). It is typically divided into multiple levels (such as generation layer, relay layer, and consumption layer) based on voltage level, function, or management boundaries. Resource nodes (hereinafter referred to as nodes) are physical entities in the system that possess energy production, storage, or consumption capabilities, such as photovoltaic power plants, energy storage substations, and public charging pile clusters. Transmission parameters are fundamental parameters describing the physical characteristics of energy transmission between adjacent level nodes, mainly including transmission distance (space or line length) and transmission loss coefficient (reflecting the proportion of energy loss caused by line resistance, dielectric characteristics, etc.). Historical scheduling data refers to time-series data accumulated during the system's past operation, including load curves of each node, photovoltaic output, energy storage charging and discharging status, charging pile utilization rate, and actual scheduling commands and execution results. The scheduling efficiency correlation factor is an indicator derived from historical scheduling data statistics. It is used to quantify the correlation between resource scheduling volume and basic transmission parameters (such as transmission distance and loss coefficient), reflecting the impact of transmission conditions on scheduling efficiency in historical scheduling. Real-time load distribution characteristics describe the temporal distribution pattern, peak and valley values, and other statistical characteristics of the power load at each level of nodes at the current moment, including but not limited to peak load time, slope of change, and fluctuation frequency. Real-time energy supply and demand characteristics reflect the total amount, proportion, and matching status of energy production (such as photovoltaic output) and consumption (such as charging pile demand) at the current moment, including but not limited to current real-time photovoltaic output, energy storage state of charge (SOC), number of charging pile queues, and reservation demand.

[0066] In practical applications, the server can preload the operation logs from the past few months or years from the historical database in batches, and extract the historical load distribution characteristics (such as peak electricity consumption periods) and energy supply and demand characteristics (such as "high photovoltaic production + low charging demand") of each node in each time period at other granularities such as hourly or 15-minute intervals. Based on the historical load distribution characteristics and energy supply and demand characteristics, the scheduling efficiency correlation factors can be determined.

[0067] Next, the hierarchical division rules of the target area (such as a three-level structure: high-voltage photovoltaic → medium-voltage energy storage → low-voltage charging) are read from the configuration database or operation and maintenance management system. The system automatically identifies the primary energy production node (such as a distributed photovoltaic station) as the first baseline node and the secondary energy consumption node (such as a city fast-charging station cluster) as the second baseline node. Then, based on the total number of levels, intermediate energy transfer nodes (such as regional energy storage stations) are marked between the two. Next, Geographic Information System (GIS) or power grid topology data is called to calculate the actual transmission distance between any adjacent nodes. Combined with parameters such as cable type, cross-sectional area, and current, the corresponding transmission loss coefficient is calculated through table lookup or formulas, ultimately forming a structured "adjacent node pair—distance—loss" parameter set as the basic input for subsequent scheduling. Simultaneously, the server collects real-time operating data such as power, voltage, and current of each node in the current target multi-level photovoltaic-storage-charging system. After feature extraction and standardization, real-time load distribution characteristics and energy supply and demand characteristic vectors are obtained.

[0068] Step 200: Based on the real-time load distribution characteristics and real-time energy supply and demand characteristics, extract the target scheduling efficiency correlation factor that matches the current operating scenario from the scheduling efficiency correlation factors.

[0069] Following the previous step, after obtaining the real-time load distribution characteristics and real-time energy supply and demand characteristics, the server can match the current real-time load distribution characteristics and real-time energy supply and demand characteristics with the corresponding entries in the historical feature library to obtain the matching degree. Then, based on the matching degree, the server can extract the correlation factor that best represents the current operating status from the historical scheduling efficiency correlation factors as the target scheduling efficiency correlation factor.

[0070] Step 300: Based on the target scheduling efficiency correlation factor and transmission parameters, determine the first resource scheduling value and the second resource scheduling value among resource nodes at each level within the target time period.

[0071] In this embodiment, the first resource scheduling value refers to the amount of resources scheduled from the primary energy production node (such as a photovoltaic power station) to the intermediate or final nodes, emphasizing forward scheduling under the constraints of supply capacity and priority. The second resource scheduling value refers to the amount of resources allocated to the final energy consumption node (such as a charging pile cluster), emphasizing reverse scheduling driven by demand intensity and priority.

[0072] In practice, the server can first assess the energy demand priority of each node within the target time period, and combine the real-time supply capacity of the primary energy production node with the total consumption demand of the secondary node. It can also integrate the baseline transmission efficiency value (preset by the system topology and equipment characteristics or estimated online) and the target scheduling efficiency correlation factor, and derive the first resource scheduling value and the second resource scheduling value through a product or weighted model to achieve the coordinated determination of bidirectional scheduling quantity.

[0073] Step 400: Based on the first resource scheduling value and the second resource scheduling value, predict the resource scheduling loss rate for the target time period.

[0074] Resource scheduling loss rate refers to the proportion of energy loss caused by factors such as transmission loss, equipment efficiency, and scheduling fluctuations to the total scheduling amount within the target time period.

[0075] In practical applications, the server can pre-construct a three-dimensional mapping table of "scheduling quantity – fluctuation frequency – loss rate" using historical operational data or simulation. It then combines the first resource scheduling value, the second resource scheduling value, and the energy production fluctuation frequency to generate a comprehensive scheduling optimization factor. Based on this factor, it searches for the nearest neighbor entry in the mapping table and directly outputs the resource scheduling loss rate for the target time period. Alternatively, it can use historical data to train a regression model (such as linear regression, multinomial regression, ridge regression, etc.), using the first resource scheduling value, the second resource scheduling value, transmission parameters, fluctuation frequency, etc., as inputs to output the resource scheduling loss rate for the target time period. In other embodiments, it can also input past time periods' load, weather, fluctuation frequency, and the first and second resource scheduling values ​​into a time-series prediction model to output the resource loss rate for the target time period.

[0076] Step 500: If the resource scheduling loss rate is greater than or equal to the preset scheduling loss threshold, determine the target optimized scheduling scheme and schedule the target multi-level optical storage and charging system based on the target optimized scheduling scheme.

[0077] The scheduling loss threshold refers to the upper limit of the acceptable maximum loss rate set by the system, which is used to determine whether the optimization mechanism needs to be activated.

[0078] After obtaining the resource scheduling loss rate, the server can compare the resource scheduling loss rate with a preset scheduling loss threshold. If the resource scheduling loss rate exceeds the preset scheduling loss threshold (e.g., 2.5%), it can retrieve multiple candidate scheduling strategies from a pre-built solution knowledge base. Then, it can evaluate the transmission stability and energy utilization of each solution through simulation or historical backtracking, and then compare it with the current running solution to select the solution with better overall performance as the target optimized scheduling solution. Control commands are then issued to each level of nodes to perform dynamic adjustments, thereby achieving efficient and accurate resource scheduling.

[0079] In the aforementioned multi-level photovoltaic-storage-charging resource scheduling method, obtaining the transmission parameters between adjacent resource nodes of the target multi-level photovoltaic-storage-charging system allows schedulers to clearly understand the location relationships and energy transmission losses of each node. Based on historical scheduling data, a scheduling efficiency correlation factor is calculated to determine the relationship between resource scheduling quantity and transmission parameters. Combining the real-time load distribution characteristics and real-time energy supply and demand characteristics of each resource node, a target scheduling efficiency correlation factor suitable for the current operating scenario is extracted from the scheduling efficiency correlation factor. Based on the target scheduling efficiency correlation factor and transmission parameters, the first and second resource scheduling values ​​for the target time period are determined, and the corresponding scheduling loss rate is further deduced. Using the scheduling loss threshold as a dynamic criterion, a scheduling optimization mechanism is triggered to generate and execute the target optimized scheduling scheme in advance. This effectively reduces the waste caused by ineffective or inefficient energy transmission between multiple levels, ultimately significantly improving the overall energy utilization efficiency and economic operation level of the entire photovoltaic-storage-charging system. The entire solution, by combining historical and real-time data, can grasp the energy supply and demand situation at different times and nodes, making resource scheduling values ​​more aligned with actual needs. This avoids scheduling deviations caused by missing or delayed data, greatly improving the accuracy of resource scheduling and ensuring that energy can be allocated efficiently and on demand among nodes at all levels. Furthermore, resource scheduling and loss optimization ensure that the multi-level photovoltaic-storage-charging system is always in a highly efficient operating state, allowing electricity from energy production nodes to be transmitted more efficiently to consumption nodes, reducing idle or over-allocated energy during transmission and distribution. On the other hand, loss prediction and optimization measures prevent system instability caused by excessive losses, ensuring the stable operation of the photovoltaic-storage-charging system and improving the overall energy utilization efficiency and operational reliability of the system.

[0080] In one exemplary embodiment, such as Figure 3 As shown, step 100 includes steps 102 to 112. Wherein:

[0081] Step 102: Obtain historical scheduling data of the target multi-level optical storage and charging system.

[0082] Step 104: Extract the historical load distribution characteristics and historical energy supply and demand characteristics of resource nodes at different levels under different time periods from historical scheduling data.

[0083] Step 106: Based on historical load distribution characteristics and historical energy supply and demand characteristics, determine the historical resource scheduling volume between adjacent resource nodes in each time period.

[0084] Step 108: Based on the transmission parameters, calculate the scheduling amount between adjacent resource nodes in the historical resource scheduling amount to obtain the scheduling amount difference.

[0085] Step 110: Determine the ratio of the scheduling difference to the corresponding transmission parameter to obtain multiple candidate ratios.

[0086] Step 112: Average the multiple candidate ratios to obtain the scheduling efficiency correlation factor.

[0087] The scheduling difference refers to the deviation between the theoretical scheduling capacity (effective scheduling capacity after considering losses) and the actual historical scheduling capacity under the same transmission conditions.

[0088] In this embodiment, the determination of the scheduling efficiency correlation factor can be achieved by extracting historical load distribution data and historical energy supply and demand data for the first, second, and intermediate benchmark nodes at different time periods from historical scheduling data, thereby obtaining historical load distribution characteristics and historical energy supply and demand characteristics. For example, during past weekday daytime office hours, historical load distribution data for the first benchmark node showed that its energy production was relatively stable and at a high level, and historical energy supply and demand data indicated that energy supply was relatively sufficient at this time; however, historical load distribution data for the second benchmark node showed a sharp increase in charging demand during the peak residential charging period on weekday evenings, indicating a slightly tight energy supply situation.

[0089] Next, based on these extracted historical load distribution characteristics and historical energy supply and demand characteristics, the historical resource scheduling volume between resource nodes at each level within each time period is extracted from the historical scheduling data. For example, during the daytime office hours on weekdays, due to the sufficient energy supply of the first reference node, a large amount of electricity is scheduled from the first reference node to the intermediate reference node for storage; while during the evening peak charging period, the intermediate reference node schedules a large amount of electricity to the second reference node to meet charging demand.

[0090] Furthermore, the scheduling difference is calculated by differentiating the scheduling amounts between adjacent resource nodes in the historical resource scheduling data based on the transmission parameters. Specifically, the transmission parameters of each pair of adjacent resource nodes are first determined, then the historical resource scheduling amounts corresponding to these two nodes are extracted. Next, the theoretical scheduling amount (i.e., the scheduling amount that should be achieved based on the transmission parameters) is obtained by combining the transmission parameters. Finally, the theoretical scheduling amount is compared with the actual historical scheduling amount, and the difference between the two is the scheduling difference. For example, if the basic transmission parameters indicate that 100 kWh of electricity can be theoretically scheduled from the first reference node to the intermediate reference node during daytime office hours, but the actual historical scheduling amount is 80 kWh, then the scheduling difference is 20 kWh. In this way, the scheduling difference between each pair of adjacent resource nodes can be obtained.

[0091] Finally, the scheduling difference is ratioed to its corresponding transmission parameter (scheduling difference divided by transmission parameter) to obtain multiple candidate ratios. For example, if the scheduling difference is 20 kWh and the calculated value related to transmission between adjacent nodes (such as the product of transmission distance and loss coefficient) is 10, then the candidate ratio is 2. All such candidate ratios are averaged to obtain the scheduling efficiency correlation factor.

[0092] In this embodiment, by systematically extracting load and supply-demand characteristics, reconstructing historical scheduling behavior, introducing physical transmission constraints, and quantifying scheduling deviations, a scheduling efficiency correlation factor that characterizes the correlation between "scheduling quantity and transmission parameters" is ultimately formed. This mechanism enables accurate matching of similar historical states and dynamic adjustment of scheduling strategies when facing real-time operating scenarios, significantly improving the energy utilization efficiency of multi-level photovoltaic energy storage and charging systems and reducing transmission losses.

[0093] like Figure 4 As shown, in an exemplary embodiment, step 200 includes steps 202 to 206, wherein:

[0094] Step 202: Perform similarity matching between the real-time load distribution characteristics and the historical load distribution characteristics to obtain the first matching result.

[0095] Step 204: Perform similarity matching between real-time energy supply and demand characteristics and historical energy supply and demand characteristics to obtain the second matching result.

[0096] Step 206: Based on the first matching result and the second matching result, extract the target scheduling efficiency correlation factor that matches the current running scenario from the scheduling efficiency correlation factors.

[0097] In practice, the server can first obtain the real-time load distribution characteristics and real-time energy supply and demand characteristics of resource nodes at each level. For example, on a sunny weekday at noon, the real-time load distribution characteristics of the first benchmark node (such as an energy production node like a solar photovoltaic power station) are that the photovoltaic output reaches its peak, generating a large amount of electricity per hour; while the real-time energy supply and demand characteristics of the second benchmark node (such as an energy consumption node like an electric vehicle charging pile cluster in a city's commercial area) are that there are many charging vehicles at this time, and the energy demand is strong.

[0098] Next, algorithms such as cosine similarity and Euclidean distance can be used to match the real-time load distribution characteristics with historical load distribution characteristics, quantifying the similarity between feature vectors to obtain the first matching result. Then, the real-time energy supply and demand characteristics are matched with historical energy supply and demand characteristics to obtain the second matching result. For example, suppose that in historical data there exists a period of weekday midday with sunny weather, where the photovoltaic output of the first reference node is at its peak, and the charging demand of the charging pile cluster at the second reference node is also strong. In this case, the real-time load distribution characteristics can match well with the historical load distribution characteristics of that period, and the first matching result will show a high degree of matching. Similarly, the real-time energy supply and demand characteristics can also match well with the historical energy supply and demand characteristics of that period, and the second matching result will also show a high degree of matching.

[0099] Next, the first and second matching results are correlated with scheduling efficiency correlation factors. Historically, periods with high matching degrees have corresponding scheduling efficiency correlation factors that reflect resource scheduling efficiency patterns under similar load and supply-demand conditions. By quantifying the values ​​of the first and second matching results (e.g., using similarity scoring, 0-10 points, with higher scores indicating higher matching degrees), the scheduling efficiency correlation factor that best matches the current real-time situation (i.e., the historical scenario corresponding to high matching degrees) is selected. This factor is the target scheduling efficiency correlation factor adapted to the current real-time situation. For example, if the scenario of "weekday noon, sunny weather, peak photovoltaic output, and strong demand for charging piles" in a certain historical period has a high degree of matching with the current real-time load and supply-demand characteristics (first and second matching results), then the scheduling efficiency correlation factor corresponding to that historical period will be determined to be highly adapted to the current real-time situation and thus extracted as an adaptation correlation factor for subsequent resource scheduling value judgments, thereby improving the accuracy and rationality of scheduling.

[0100] In this embodiment, the logical chain of "feature extraction - similarity matching - correlation factor screening" effectively bridges the gap between historical experience and real-time decision-making. While ensuring the safe and stable operation of the system, it maximizes the efficiency of renewable energy consumption and the benefits of energy storage charging and discharging, providing key technical support for the intelligent and refined scheduling of multi-level photovoltaic-storage-charging systems.

[0101] like Figure 5 As shown, in an exemplary embodiment, step 300 includes steps 302 to 310, wherein:

[0102] Step 302: Evaluate the energy demand priority of resource nodes at each level within the target time period to obtain the target scheduling priority.

[0103] Step 304: Determine the first scheduling impact parameter based on the target scheduling priority and the preset resource supply capacity of the energy production nodes.

[0104] Step 306: Based on the first scheduling influence parameter, transmission parameter and target scheduling efficiency correlation factor, obtain the first resource scheduling value between resource nodes at each level within the target time period.

[0105] Step 308: Determine the second scheduling influence parameter based on the target scheduling priority and the preset resource consumption requirements of the energy consumption nodes.

[0106] Step 310: Based on the second scheduling influence parameter, transmission parameter and target scheduling efficiency correlation factor, obtain the second resource scheduling value between resource nodes at each level within the target time period.

[0107] Energy demand priority refers to the degree of importance of different resource nodes to power supply security during a target period. Target scheduling priority is the scheduling ranking basis formed by quantifying the priority of each node, used to guide the order of resource allocation. The first scheduling impact parameter is a weighted index characterizing the overall supply capacity and downstream demand priority, used to characterize "schedulable potential". The second scheduling impact parameter is a weighted index characterizing the total overall end-user demand and priority weight.

[0108] In this embodiment, the server can assess the energy demand priority of resource nodes at each level within the target time period based on the operational plan for that period, thereby obtaining the target scheduling priority. For example, during peak electricity consumption periods, resource nodes belonging to hospitals and schools will have a significantly higher energy demand priority than resource nodes in ordinary commercial locations. Next, a first scheduling impact parameter is obtained by statistically analyzing the target scheduling priority and the resource supply capacity of the first baseline node. For instance, the predicted output or available capacity data of energy production nodes during the target time period can be read, and combined with the demand proportion and priority weight of each downstream node, the first scheduling impact parameter is determined, achieving a quantified coupling of supply-side capacity and demand-side importance.

[0109] For example, the first baseline node can be an energy production node such as a photovoltaic power plant. Assuming the photovoltaic power plant can provide 1000 kWh of electricity during the target period, while high-priority nodes require a total of 600 kWh of electricity, the first scheduling impact parameter can be calculated by combining priority weighting and other relevant factors. This parameter integrates the supply capacity of the energy production end and the demand priority of each node.

[0110] Next, based on the first scheduling influence parameter, the baseline transmission efficiency value in the transmission parameters, and the target scheduling efficiency correlation factor, the first resource scheduling value among resource nodes at each level during the target time period is obtained. The baseline transmission efficiency value refers to the efficiency of energy transmission from the first baseline node to the next level node; for example, the efficiency of energy transmission from a photovoltaic power station to the next level node is 90%. Combining these three factors, the amount of electrical energy dispatched from the first baseline node to other nodes is obtained, i.e., the first resource scheduling value. For example, multiplying the first scheduling influence parameter by the baseline transmission efficiency value (derived from the transmission parameters), and then multiplying by the target scheduling efficiency correlation factor, yields the first resource scheduling value after considering physical losses and corrections based on historical scheduling patterns.

[0111] Next, similarly, the second scheduling impact parameter is obtained by statistically analyzing the target scheduling priority and the resource consumption demand of the second baseline node. Specifically, this can be achieved by obtaining the total demand of each energy-consuming node during the target time period, and determining the second scheduling impact parameter based on its priority weight and proportion in the total demand, highlighting the guarantee weight of high-priority loads. The second baseline node can be an energy-consuming node such as a charging pile cluster, for example, if this charging pile cluster is expected to consume 800 kWh of electricity during the target time period. The second scheduling impact parameter is then obtained by combining the demand priority of each node. This parameter reflects the combination of energy consumption demand and priority.

[0112] In this embodiment, by introducing target scheduling priority, differentiated protection of critical loads is achieved; through the symmetrical design of the first scheduling influence parameter and the second scheduling influence parameter, both supply-side feasibility and demand-side rationality are taken into account; and by combining the transmission parameter and the target scheduling efficiency correlation factor, the scheduling value is made to conform to both physical constraints and historical operating experience. The final output bidirectional scheduling value is more engineering feasible, significantly reducing scheduling deviation and energy loss, and improving the safety, economy and intelligence level of the multi-level photovoltaic energy storage and charging system in complex operating scenarios.

[0113] There are no restrictions on the methods used to predict resource scheduling loss rates. For example... Figure 6 As shown, in an exemplary embodiment, step 400 includes steps 402 to 408, wherein:

[0114] Step 402: Detect the frequency of energy production fluctuations at energy production nodes within a preset time window to obtain the baseline fluctuation frequency.

[0115] Step 404: Multiply the first resource scheduling value by the benchmark fluctuation frequency to obtain the first scheduling optimization factor; multiply the second resource scheduling value by the benchmark fluctuation frequency to obtain the second scheduling optimization factor.

[0116] Step 406: Sum the first scheduling optimization factor and the second scheduling optimization factor to obtain the comprehensive scheduling optimization factor.

[0117] Step 408: Match the comprehensive scheduling optimization factor with the resource scheduling loss rate in the pre-built reference loss database to obtain the resource scheduling loss rate for the target time period.

[0118] Energy production fluctuation frequency refers to the number of times its output power changes significantly (e.g., exceeding a set threshold) within a specified time window (e.g., 1 hour). The baseline fluctuation frequency is an indicator used by users to quantify the instability of the node's output, reflecting the impact of renewable energy intermittency on dispatch stability. The first and second dispatch optimization factors are composite indicators integrating dispatch scale and production volatility, used to characterize the "potential loss pressure" of dispatch behavior under the current volatile environment.

[0119] In practice, the server can first detect the frequency of energy production fluctuations at the first baseline node (such as an energy production node like a photovoltaic power station) to obtain the baseline fluctuation frequency. For example, if a photovoltaic power station experiences five significant fluctuations in energy production within one hour due to cloud cover, then the baseline fluctuation frequency is five times.

[0120] Next, the first resource scheduling value (the amount of electricity scheduled outward from the first reference node, assumed to be 800 kWh) is multiplied by the reference fluctuation frequency (5 times) to obtain the first scheduling optimization factor, i.e., 800 × 5 = 4000; the second resource scheduling value (the amount of electricity scheduled to the second reference node, such as a charging pile cluster, assumed to be 600 kWh) is multiplied by the reference fluctuation frequency (5 times) to obtain the second scheduling optimization factor, i.e., 600 × 5 = 3000. Then, the first scheduling optimization factor and the second scheduling optimization factor are summed to obtain the comprehensive scheduling optimization factor, which is 4000 + 3000 = 7000. Subsequently, a pre-built reference loss database is called, which stores the scheduling loss rate data corresponding to the multi-level photovoltaic-storage-charging system under different resource scheduling amounts and different energy production fluctuation frequencies. Finally, the comprehensive scheduling optimization factor (7000) is matched with the reference loss database to obtain the current loss prediction value (i.e., resource scheduling loss rate) for the target period. For example, the current resource scheduling loss rate is 3% after matching. This allows us to know the loss situation in the scheduling process in advance, which is convenient for subsequent scheduling optimization.

[0121] For example, the predicted value of the current loss can be calculated using the second calculation formula. (i.e., resource scheduling loss rate), where, For reference loss database and current integrated scheduling optimization factors Closest minimum comprehensive scheduling optimization factor The corresponding loss rate, For reference loss database and current integrated scheduling optimization factors Closest to the minimum comprehensive scheduling optimization factor The corresponding loss rate, This is the comprehensive scheduling optimization factor obtained from the current calculation. Reference loss database and current The closest minimum comprehensive scheduling optimization factor, Reference loss database and current The closest maximum comprehensive scheduling optimization factor.

[0122] In this embodiment, the intermittent nature of renewable energy is incorporated into the scheduling loss prediction system by introducing the dynamic disturbance index of energy production fluctuation frequency. By constructing scheduling quantities and fluctuation frequencies, an intuitive quantification of system operating pressure is achieved, and rapid loss mapping is realized through a reference database, facilitating subsequent scheduling optimization.

[0123] like Figure 7 As shown, in an exemplary embodiment, step 500 includes steps 502 to 506, wherein:

[0124] Step 502: Obtain multiple candidate resource scheduling schemes, and based on the resource transmission stability and energy utilization rate of each candidate resource scheduling scheme, obtain the first scheme performance data of each candidate resource scheduling scheme.

[0125] Step 504: Obtain the current execution scheduling scheme, and based on the resource transmission stability and energy utilization rate of the current execution scheduling scheme, obtain the performance data of the second scheme.

[0126] Step 506: Compare the performance data of each first scheme with the performance data of the second scheme respectively, and determine the candidate resource scheduling scheme whose performance data of the first scheme is higher than that of the second scheme as the target optimized scheduling scheme.

[0127] Energy utilization rate refers to the proportion of dispatched electricity effectively consumed by terminals. The currently executing scheduling scheme refers to the scheduling strategy being implemented by the system, representing the actual control logic under the current operating state.

[0128] In practice, the server can output scheduling optimization information when the resource scheduling loss rate is greater than or equal to a preset scheduling loss threshold. For example, if the resource scheduling loss rate of a multi-level photovoltaic-storage-charging system is predicted to reach a set warning threshold during a certain period, the scheduling optimization process will be triggered, and relevant scheduling optimization information will be output. Then, based on the output scheduling optimization information, multiple candidate resource scheduling schemes for the multi-level photovoltaic-storage-charging system are identified. These schemes include different energy allocation strategies and transmission path selections. Next, the resource transmission stability and energy utilization rate of each candidate resource scheduling scheme are tested to obtain the performance data of the first scheme. For example, Scheme A has small voltage fluctuations during resource transmission and an energy utilization rate of 90%; Scheme B has slightly worse resource transmission stability, but an energy utilization rate of 92%.

[0129] Next, the resource transmission stability and energy utilization rate of the currently executing resource scheduling scheme are detected to obtain the performance data of the second scheme. Then, the performance data of each of the first schemes are compared with the performance data of the second scheme. From all candidate resource scheduling schemes, the scheme with performance data superior to the current performance data is extracted and used as the target scheduling scheme to replace the currently executing resource scheduling scheme, thereby improving the resource scheduling efficiency and effectiveness of the multi-level photovoltaic energy storage and charging system.

[0130] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0131] Based on the same inventive concept, this application also provides a multi-level optical-storage-charging resource scheduling device for implementing the multi-level optical-storage-charging resource scheduling method described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the multi-level optical-storage-charging resource scheduling device provided below can be found in the limitations of the multi-level optical-storage-charging resource scheduling method described above, and will not be repeated here.

[0132] In one exemplary embodiment, such as Figure 8 As shown, a multi-level photovoltaic storage and charging resource scheduling device 800 is provided, including: a data acquisition module 810, a data extraction module 820, a data determination module 830, a data prediction module 840, and a scheduling module 850, wherein:

[0133] The data acquisition module 810 is used to acquire the scheduling efficiency correlation factor of the target multi-level photovoltaic storage and charging system, the transmission parameters between adjacent level resource nodes, and the real-time load distribution characteristics and real-time energy supply and demand characteristics of the current level resource nodes. The scheduling efficiency correlation factor is determined based on historical scheduling data and is used to characterize the correlation between resource scheduling quantity and transmission parameters.

[0134] The data extraction module 820 is used to extract the target scheduling efficiency correlation factor that matches the current operating scenario from the scheduling efficiency correlation factor based on the real-time load distribution characteristics and real-time energy supply and demand characteristics.

[0135] The data determination module 830 is used to determine the first resource scheduling value and the second resource scheduling value among resource nodes at each level within the target time period based on the target scheduling efficiency correlation factor and transmission parameters.

[0136] The data prediction module 840 is used to predict the resource scheduling loss rate for a target time period based on the first resource scheduling value and the second resource scheduling value.

[0137] The scheduling module 850 is used to determine the target optimized scheduling scheme when the resource scheduling loss rate is greater than or equal to the preset scheduling loss threshold, and to schedule the target multi-level optical storage and charging system based on the target optimized scheduling scheme.

[0138] In one embodiment, the data acquisition module 810 is further configured to acquire historical scheduling data of the target multi-level photovoltaic storage and charging system, extract historical load distribution characteristics and historical energy supply and demand characteristics of resource nodes at each level under different time periods from the historical scheduling data, determine the historical resource scheduling amount between adjacent resource nodes in each time period based on the historical load distribution characteristics and historical energy supply and demand characteristics, calculate the scheduling amount between adjacent resource nodes in the historical resource scheduling amount according to the transmission parameters, obtain the scheduling amount difference, determine the ratio of the scheduling amount difference to the corresponding transmission parameters, obtain multiple candidate ratios, and perform averaging on the multiple candidate ratios to obtain the scheduling efficiency correlation factor.

[0139] In one embodiment, the data extraction module 820 is further configured to perform similarity matching between real-time load distribution features and historical load distribution features to obtain a first matching result, perform similarity matching between real-time energy supply and demand features and historical energy supply and demand features to obtain a second matching result, and extract a target scheduling efficiency correlation factor that matches the current operating scenario from the scheduling efficiency correlation factors based on the first matching result and the second matching result.

[0140] In one embodiment, the data determination module 830 is further configured to assess the energy demand priority of resource nodes at each level within a target time period to obtain a target scheduling priority; determine a first scheduling influence parameter based on the target scheduling priority and the preset resource supply capacity of energy production nodes; obtain a first resource scheduling value among resource nodes at each level within the target time period based on the first scheduling influence parameter, transmission parameters, and a target scheduling efficiency correlation factor; determine a second scheduling influence parameter based on the target scheduling priority and the preset resource consumption demand of energy consumption nodes; and obtain a second resource scheduling value among resource nodes at each level within the target time period based on the second scheduling influence parameter, transmission parameters, and a target scheduling efficiency correlation factor.

[0141] In one embodiment, the data prediction module 840 is further configured to detect the frequency of energy production fluctuations of energy production nodes within a preset time window, obtain a baseline fluctuation frequency, multiply a first resource scheduling value by the baseline fluctuation frequency to obtain a first scheduling optimization factor, multiply a second resource scheduling value by the baseline fluctuation frequency to obtain a second scheduling optimization factor, sum the first scheduling optimization factor and the second scheduling optimization factor to obtain a comprehensive scheduling optimization factor, and match the comprehensive scheduling optimization factor with the resource scheduling loss rate in a pre-built reference loss database to obtain the resource scheduling loss rate for the target time period.

[0142] In one embodiment, the scheduling module 850 is further configured to acquire multiple candidate resource scheduling schemes, obtain first scheme performance data for each candidate resource scheduling scheme based on the resource transmission stability and energy utilization rate of each candidate resource scheduling scheme, acquire the currently executing scheduling scheme, and obtain second scheme performance data based on the resource transmission stability and energy utilization rate of the currently executing scheduling scheme, compare the performance data of each first scheme with the performance data of the second scheme respectively, and determine the candidate resource scheduling scheme whose first scheme performance data is higher than the second scheme performance data as the target optimized scheduling scheme.

[0143] Each module in the aforementioned multi-level optical storage and charging resource scheduling device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0144] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores historical scheduling data, scheduling efficiency correlation factors, and transmission parameters. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a multi-level optical storage and charging resource scheduling method.

[0145] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0146] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in any of the embodiments of the multi-level optical storage and charging resource scheduling method.

[0147] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps in any of the embodiments of the multi-level optical storage and charging resource scheduling method.

[0148] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in any of the embodiments of the multi-level optical storage and charging resource scheduling method.

[0149] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis such as real-time system operation data, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with relevant regulations.

[0150] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0151] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0152] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A multi-level photovoltaic-storage-charging resource scheduling method, characterized in that, The method includes: The scheduling efficiency correlation factor of the target multi-level photovoltaic storage and charging system, the transmission parameters between adjacent level resource nodes, and the real-time load distribution characteristics and real-time energy supply and demand characteristics of each level resource node are obtained. The scheduling efficiency correlation factor is determined based on historical scheduling data and is used to characterize the correlation between resource scheduling quantity and transmission parameters. Based on the real-time load distribution characteristics and the real-time energy supply and demand characteristics, a target scheduling efficiency correlation factor that matches the current operating scenario is extracted from the scheduling efficiency correlation factor. Based on the target scheduling efficiency correlation factor and the transmission parameters, determine the first resource scheduling value and the second resource scheduling value among resource nodes at each level within the target time period; Based on the first resource scheduling value and the second resource scheduling value, predict the resource scheduling loss rate for the target time period; If the resource scheduling loss rate is greater than or equal to a preset scheduling loss threshold, a target optimized scheduling scheme is determined, and the target multi-level optical storage and charging system is scheduled based on the target optimized scheduling scheme.

2. The method according to claim 1, characterized in that, The process of obtaining the scheduling efficiency correlation factor of the target multi-level optical storage and charging system includes: Acquire historical scheduling data of the target multi-level optical storage and charging system; Extract historical load distribution characteristics and historical energy supply and demand characteristics of resource nodes at different levels under different time periods from historical scheduling data; Based on the historical load distribution characteristics and historical energy supply and demand characteristics, the historical resource scheduling volume between adjacent resource nodes in each time period is determined. Based on the transmission parameters, the scheduling amounts between adjacent resource nodes in the historical resource scheduling amounts are calculated to obtain the scheduling amount difference. Determine the ratio of the scheduling difference to the corresponding transmission parameter to obtain multiple candidate ratios; The scheduling efficiency correlation factor is obtained by averaging the multiple candidate ratios.

3. The method according to claim 2, characterized in that, The step of extracting a target scheduling efficiency correlation factor that matches the current operating scenario from the scheduling efficiency correlation factors based on the real-time load distribution characteristics and the real-time energy supply and demand characteristics includes: The real-time load distribution features are matched with the historical load distribution features to obtain a first matching result; The real-time energy supply and demand characteristics are matched with the historical energy supply and demand characteristics to obtain a second matching result; Based on the first matching result and the second matching result, a target scheduling efficiency correlation factor that matches the current operating scenario is extracted from the scheduling efficiency correlation factor.

4. The method according to any one of claims 1 to 3, characterized in that, The step of determining the first resource scheduling value and the second resource scheduling value among resource nodes at each level within the target time period based on the target scheduling efficiency correlation factor and the transmission parameters includes: The energy demand priority of resource nodes at each level is assessed within the target time period to obtain the target scheduling priority; Based on the target scheduling priority and the preset resource supply capacity of the energy production nodes, the first scheduling influence parameter is determined; Based on the first scheduling impact parameter, the transmission parameter, and the target scheduling efficiency correlation factor, the first resource scheduling value among resource nodes at each level within the target time period is obtained. Based on the target scheduling priority and the preset resource consumption requirements of energy consumption nodes, determine the second scheduling influence parameter; Based on the second scheduling impact parameter, the transmission parameter, and the target scheduling efficiency correlation factor, the second resource scheduling value among resource nodes at each level within the target time period is obtained.

5. The method according to any one of claims 1 to 3, characterized in that, The step of predicting the resource scheduling loss rate for a target time period based on the first resource scheduling value and the second resource scheduling value includes: The frequency of energy production fluctuations at energy production nodes within a preset time window is detected to obtain a baseline fluctuation frequency. Multiply the first resource scheduling value by the benchmark fluctuation frequency to obtain the first scheduling optimization factor, and multiply the second resource scheduling value by the benchmark fluctuation frequency to obtain the second scheduling optimization factor; The first scheduling optimization factor and the second scheduling optimization factor are summed to obtain the comprehensive scheduling optimization factor; The comprehensive scheduling optimization factor is matched with the resource scheduling loss rate in the pre-built reference loss database to obtain the resource scheduling loss rate for the target time period.

6. The method according to any one of claims 1 to 3, characterized in that, The step of determining the target optimized scheduling scheme when the resource scheduling loss rate is greater than or equal to a preset scheduling loss threshold includes: Multiple candidate resource scheduling schemes are obtained, and the first scheme performance data of each candidate resource scheduling scheme is obtained based on the resource transmission stability and energy utilization rate of each candidate resource scheduling scheme. Obtain the current execution scheduling scheme, and based on the resource transmission stability and energy utilization rate of the current execution scheduling scheme, obtain the performance data of the second scheme; The performance data of each of the first schemes are compared with the performance data of the second scheme, and the candidate resource scheduling scheme whose performance data of the first scheme is higher than that of the second scheme is determined as the target optimized scheduling scheme.

7. A multi-level photovoltaic-storage-charging resource scheduling device, characterized in that, The device includes: The data acquisition module is used to acquire the scheduling efficiency correlation factor of the target multi-level photovoltaic storage and charging system, the transmission parameters between adjacent level resource nodes, and the real-time load distribution characteristics and real-time energy supply and demand characteristics of the current level resource nodes. The scheduling efficiency correlation factor is determined based on historical scheduling data and is used to characterize the correlation between resource scheduling quantity and transmission parameters. The data extraction module is used to extract a target scheduling efficiency correlation factor that matches the current operating scenario from the scheduling efficiency correlation factor based on the real-time load distribution characteristics and the real-time energy supply and demand characteristics. The data determination module is used to determine the first resource scheduling value and the second resource scheduling value among resource nodes at each level within the target time period based on the target scheduling efficiency correlation factor and the transmission parameters. The data prediction module is used to predict the resource scheduling loss rate for a target time period based on the first resource scheduling value and the second resource scheduling value. The scheduling module is used to determine a target optimized scheduling scheme when the resource scheduling loss rate is greater than or equal to a preset scheduling loss threshold, and to schedule the target multi-level optical storage and charging system based on the target optimized scheduling scheme.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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