Photovoltaic mobile energy storage management methods, devices and equipment for power transfer

Abstract

This invention discloses a photovoltaic mobile energy storage management method, device, and equipment for power transfer, relating to the field of power dispatch management technology. The method includes: constructing a grid map of the energy storage cabinet's radiation area, containing distribution information of demand-side, energy storage cabinets, and photovoltaic power supply terminals; continuously collecting the output power of the photovoltaic power supply terminals and the remaining capacity of the energy storage cabinets, and updating the grid map in real time; predicting the power demand of each demand-side based on the power supply period, performing power transfer scheduling analysis in conjunction with the grid map, constructing and continuously adjusting the energy storage priority queue, and executing rolling power transfer. This invention solves the technical problem of existing technologies lacking dynamic scheduling of photovoltaic power in multi-regional, dynamically distributed scenarios, leading to low energy storage resource utilization and delayed regional power supply response. It achieves the technical effect of realizing intelligent cross-regional transfer of photovoltaic power through a grid map of the energy storage cabinet's radiation area and a rolling scheduling strategy, improving energy storage resource utilization and power supply dispatch response speed.

Description

Technical Field

[0001] This invention relates to the field of power dispatching and management technology, and specifically to a photovoltaic mobile energy storage management method, device, and equipment for power transfer. Background Technology

[0002] In the context of current new energy development, photovoltaic (PV) power generation, as a clean and renewable energy source, is widely used in distributed power systems and mobile energy storage scenarios. However, due to the instability and intermittent fluctuations of solar resources, PV power output exhibits significant nonlinear variations, making it difficult to synchronize with real-time electricity demand. To improve the utilization efficiency of PV energy and ensure continuous power supply to various load areas, mobile energy storage systems are gradually being introduced as intermediate buffer devices. However, existing technologies mostly focus on local energy storage control and static charging scheduling, generally lacking scheduling capabilities for large-scale, dynamically distributed PV power. This results in the inability to achieve intelligent flow of power and flexible allocation of resources between areas with different power demand, leading to problems such as low energy storage utilization, poor transfer efficiency, and weak power supply security. Summary of the Invention

[0003] This application provides a photovoltaic mobile energy storage management method, device, and equipment for power transfer, which solves the technical problem that the existing technology lacks dynamic scheduling of photovoltaic power in multi-regional and dynamic distributed scenarios, resulting in low utilization of energy storage resources and lagging regional power supply response.

[0004] The first aspect of this application provides a photovoltaic mobile energy storage management method for power transfer, the method comprising: constructing a grid map of the energy storage cabinet's radiation area, the grid map including demand-side distribution information, energy storage cabinet distribution information, and photovoltaic power supply terminal distribution information of a target power supply area; continuously collecting real-time output power data of the photovoltaic power supply terminals and real-time remaining capacity information of the mobile energy storage cabinets based on a preset time window, and updating the grid map of the energy storage cabinet's radiation area in real time; predicting the power supply demand of each demand side according to the power supply period, and generating energy storage cabinet scheduling requirements for each demand side based on the predicted power supply demand; performing power transfer scheduling analysis based on the energy storage cabinet scheduling requirements of each demand side and the grid map of the energy storage cabinet's radiation area, generating energy storage priority queues for each demand side, the energy storage priority queues including dynamically changing charging scheduling queues and standby power supply queues; dynamically updating the energy storage priority queues according to the grid map of the energy storage cabinet's radiation area, and performing rolling power transfer according to the updated energy storage priority queues.

[0005] A second aspect of this application provides a photovoltaic mobile energy storage management device for power transfer. The device includes: a regional grid map construction module for constructing a grid map of the energy storage cabinet's radiation area, the grid map including demand-side distribution information, energy storage cabinet distribution information, and photovoltaic power supply terminal distribution information for the target power supply area; a real-time energy status update module for continuously collecting real-time output power data from the photovoltaic power supply terminal and real-time remaining capacity information of the mobile energy storage cabinet based on a preset time window, and updating it in real-time to the grid map of the energy storage cabinet's radiation area; and a scheduling demand prediction module for predicting the demand based on... During the power supply period, power demand is predicted for each demand side, and energy storage cabinet scheduling requirements are generated for each demand side based on the predicted power demand. A power transfer scheduling analysis module is used to perform power transfer scheduling analysis based on the energy storage cabinet scheduling requirements of each demand side, combined with the energy storage cabinet radiation area grid map, to generate energy storage priority queues for each demand side. These priority queues include dynamically changing charging scheduling queues and standby power supply queues. A rolling power transfer module is used to dynamically update the energy storage priority queues according to the energy storage cabinet radiation area grid map, and perform rolling power transfer based on the updated priority queues.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The photovoltaic mobile energy storage management method, device, and equipment for power transfer provided in this application relate to the field of power dispatch management technology. By constructing a grid map of the energy storage cabinet's radiation area, integrating the distribution information of the demand side, energy storage cabinets, and photovoltaic power supply end, and collecting and updating supply and demand data in real time, energy storage dispatch demand is generated based on power supply period prediction, and energy storage priority queues are formed and dynamically adjusted to achieve intelligent transfer and efficient dispatch of power among multiple energy storage cabinets. This solves the technical problem of existing technologies lacking dynamic dispatch of photovoltaic power in multi-regional, dynamically distributed scenarios, resulting in low utilization of energy storage resources and lagging regional power supply response. It achieves the technical effect of realizing cross-regional intelligent transfer of photovoltaic power through the grid map of the energy storage cabinet's radiation area and a rolling dispatch strategy, thereby improving the utilization rate of energy storage resources and the speed of power supply dispatch response. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic diagram of a photovoltaic mobile energy storage management method for power transfer provided in an embodiment of this application;

[0010] Figure 2 This is a schematic diagram of the structure of a photovoltaic mobile energy storage management device for power transfer provided in an embodiment of this application;

[0011] Figure 3 This application provides a schematic diagram of the structure of an electronic device.

[0012] Figure labeling: 11 Regional grid map construction module, 12 Real-time energy status update module, 13 Scheduling demand prediction module, 14 Power transfer scheduling analysis module, 15 Rolling power transfer module, 300 Electronic device, 301 Memory, 302 Processor, 303 Communication interface, 304 Bus architecture. Detailed Implementation

[0013] This application provides a photovoltaic mobile energy storage management method, device, and equipment for power transfer, which solves the technical problem that the existing technology lacks dynamic scheduling of photovoltaic power in multi-regional and dynamic distributed scenarios, resulting in low utilization of energy storage resources and lagging regional power supply response.

[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0015] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, apparatus, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0016] Example 1, as Figure 1 As shown, this application provides a photovoltaic mobile energy storage management method for power transfer, the method comprising:

[0017] P10: Construct a grid map of the radiation area of ​​the energy storage cabinet. The grid map of the radiation area of ​​the energy storage cabinet includes the demand-side distribution information of the target power supply area, the distribution information of the energy storage cabinet, and the distribution information of the photovoltaic power supply end.

[0018] Specifically, firstly, based on the target power supply area, a spatial grid modeling approach is used to construct a grid map of the energy storage cabinet's radiation area. This grid map is used to describe and integrate spatial layout information and the status of key energy nodes during energy storage scheduling. During implementation, the system uses a Geographic Information System (GIS) to divide the target area into several regular grid units, such as square grids with sides of 500 meters or 1 kilometer. Each grid unit is assigned a unique number and geographic coordinate range to carry the corresponding resource node information. After completing the grid division, the demand-side information, energy storage cabinet distribution information, and photovoltaic power supply distribution information within the area are located and mapped to the corresponding grid units, and their attribute parameters are recorded, achieving integrated spatial-attribute modeling.

[0019] For demand-side distribution information, all facilities with electricity loads within the region can be identified and modeled, including industrial electricity points, temporary charging stations, dispatch stations, and critical load areas. Data tags are then created for each demand point, including its spatial coordinates, average power demand, daily load fluctuation characteristics, and response level. For uncertain or intermittent load points (such as temporary electric vehicle charging stations), the average power within a sliding time window can be used as the estimation benchmark. These demand points are located to corresponding grids according to their spatial coordinates, and their electricity demand attributes are recorded.

[0020] For energy storage cabinet distribution information, all dispatchable mobile energy storage cabinets within the current area are spatially located, and the system collects each cabinet's number, real-time remaining capacity (in kWh), charging / discharging status, dispatchability status (whether it is currently used for power supply or available for dispatch), estimated service time, and service radius. The service radius can be calculated and determined based on transportation routes, continuous power supply capacity, and geographical obstacles. The system combines the location and service radius of each energy storage cabinet to form a buffer zone coverage area in the grid map and establishes connection records for the grid cells it covers, thus identifying which grids the energy storage cabinet can provide energy support to. Simultaneously, to improve dispatch accuracy, the system models the overlap between the service areas of different energy storage cabinets, identifying service overlap areas for subsequent borrowing or complementary dispatch decisions.

[0021] For photovoltaic power supply distribution information, the system calibrates the actual deployment coordinates of photovoltaic modules, incorporates them into a grid map, and collects real-time power generation data (such as current, voltage, inverter output power, etc.). Simultaneously, combining historical solar irradiance and future weather forecasts, the system assigns a power prediction curve to each photovoltaic unit, serving as a crucial input parameter for dynamic power supply capability.

[0022] After the above information is loaded, the system establishes an energy storage cabinet-demand side mapping matrix to identify the "reachability" between the energy storage cabinet and the serviceable demand side at any given time. The calculation method can use Euclidean distance constraints + state validity judgment, that is, only when the distance is within the service radius and the energy storage cabinet is in a callable state is it recorded as a valid service link. The final generated mesh graph not only includes spatial location relationships, but also embeds the dynamic attributes of each node (power, load, forecast, etc.), and organizes the data through a multi-dimensional array structure or graph structure (such as an adjacency list).

[0023] The completed grid map of the energy storage cabinet's radiation area will serve as input for subsequent scheduling models, supporting key processes such as supply and demand forecasting, priority determination, path planning, and rolling scheduling. Through this implementation, the system can obtain comprehensive information about "who needs electricity, who has electricity, and who is nearby" at any given time, significantly improving the real-time performance of scheduling and the accuracy of resource response.

[0024] P20: Based on a preset time window, continuously collect real-time output power data of the photovoltaic power supply terminal and real-time remaining capacity information of the mobile energy storage cabinet, and update the grid map of the energy storage cabinet's radiation area in real time.

[0025] It should be understood that, in order to ensure that the photovoltaic mobile energy storage management system can respond in real time to the fluctuations in photovoltaic power generation and changes in the status of the energy storage cabinet, the system needs to continuously collect real-time output power data from the photovoltaic power supply end and real-time remaining capacity information of the mobile energy storage cabinet based on a preset time window, and update this data in real time to the grid map of the energy storage cabinet's radiation area. This process is a key link in realizing dynamic scheduling and optimized management, and can provide accurate data support for subsequent power transfer scheduling.

[0026] In practical implementation, the first step is to set a reasonable time window, which represents the cycle in which the system collects and updates data. The length of the time window can be adjusted according to the system's accuracy requirements and data processing capabilities; for example, it can be set every 5 minutes, every 10 minutes, or every 15 minutes. A shorter time window provides more real-time data but increases the data processing burden; a longer time window can reduce data processing pressure but may slow down the system's response speed. Therefore, the length of the time window needs to be selected reasonably based on the actual application scenario and system design goals.

[0027] Within each preset time window, the system samples key parameters of the photovoltaic power generation unit through power monitoring devices (such as RS485 acquisition modules, photovoltaic data acquisition devices, or smart meters) deployed at the output of the photovoltaic module inverter. These parameters include current power (in kW), voltage, current, peak power generation, and daily cumulative power generation. Data is recorded at time intervals (e.g., every 30 seconds or every 5 minutes). This sampling period is flexibly set by the system scheduling module based on the frequency of supply and demand fluctuations and network bandwidth load, and is a configurable parameter.

[0028] Meanwhile, at the energy storage cabinet level, the system collects parameters such as the current state of charge (SOC, in %), discharge capacity (kWh), current power output / input, temperature status, voltage, and current of each cabinet through the BMS (Battery Management System) or PCS (Power Conversion System) integrated into the energy storage system to determine its availability. For mobile energy storage cabinets, their physical location information (e.g., via GPS module) and motion status also need to be collected to ensure that their displacement changes are considered in the scheduling decisions. All collected data will be uploaded to the central dispatch and control system via wireless communication links (such as 4G, LoRa, or industrial Wi-Fi).

[0029] After verification, the collected data will be synchronized in real time to the grid map of the energy storage cabinet's radiation area, covering the data fields of the corresponding photovoltaic power supply nodes and energy storage cabinet nodes. The update process adopts an overwrite or incremental update mechanism to ensure that the grid map reflects the current state of system operation. For example, if the power of a photovoltaic unit decreases due to cloud cover, this change will be collected in the next time window, and the power supply capacity field of that node in the grid map will be refreshed through a write operation. Similarly, if an energy storage cabinet has completed its discharge task in the previous cycle, its SOC will decrease and its capacity will be reduced. This change will also be updated and marked as unavailable for scheduling or low priority for scheduling.

[0030] Furthermore, to ensure data accuracy and reliability, the system also needs to have data verification and anomaly handling mechanisms. For example, if a sensor node fails to collect or transmit data normally within multiple consecutive time windows, the system will automatically mark the node as abnormal and activate backup data collection schemes, such as estimating data using data from adjacent nodes or supplementing with historical data. Simultaneously, the collected data is verified in real time, and obviously abnormal data points are removed to ensure that the data updated to the grid graph is accurate and reliable.

[0031] Through this real-time update mechanism, the grid map of the energy storage cabinet's radiation area has dynamic response capabilities, continuously reflecting the energy status linkage between the "power source, energy storage, and demand side" within the area at the current moment. This provides crucial data support for subsequent power supply forecasting, scheduling analysis, and path planning, ensuring the timeliness, accuracy, and reliability of the system's scheduling strategy.

[0032] P30: Based on the power supply period, predict the power supply demand of each demand side, and generate the energy storage cabinet scheduling demand of each demand side based on the predicted power supply demand.

[0033] Optionally, using the current and future power supply periods as a time reference, and combining historical load data, electricity consumption patterns, and environmental variables of each demand side, the power supply demand of each demand side during the stated period is predicted, and corresponding energy storage cabinet scheduling requirements are generated accordingly to support subsequent power transfer and scheduling decisions.

[0034] Specifically, firstly, based on the historical load curves, average power values, peak-valley load ratios, and other data recorded in the grid map of the energy storage cabinet's radiation area, a load forecasting model divided by time segments is constructed. This forecasting model can employ algorithms such as multi-factor regression, exponential smoothing, or long short-term memory neural networks (LSTM) to comprehensively consider influencing factors such as time (e.g., day / night), weather (e.g., temperature, humidity), and holidays, thereby achieving modeling and prediction of load change trends within each grid.

[0035] During the forecasting process, electricity demand curves for each demand side are calculated within the forecast period, using hourly, half-hourly, or shorter timeframes. These curves include indicators such as maximum load, average power demand, and duration of continuous power supply. For demand sides with significant load fluctuations or high response levels (e.g., critical power supply nodes, medical or transportation facilities), higher forecast resolutions can be set, and safety margin coefficients can be introduced to ensure the dispatching strategy has fault tolerance capabilities. The forecast results form a structured demand dataset and are linked to spatial locations on the grid diagram.

[0036] After completing the power demand forecast, based on the total amount of electricity required by each demand end in the current time period and the duration of guarantee, combined with its spatial location and the available adjustable energy storage cabinet resources within its coverage area, a corresponding energy storage cabinet scheduling requirement is generated. This scheduling requirement includes at least the following parameters: the number of energy storage cabinets required, the minimum capacity requirement per cabinet, the continuous power supply capacity (kWh) and power output requirement (kW), and the response time limit (i.e., how long the allocation must be completed). Simultaneously, for areas where a short-term shortage is predicted but a stable power supply capability is subsequently available, a scheduling buffer strategy can be set up, i.e., allocating some energy storage resources first to guarantee peak periods, and then supplementing the remaining capacity through rolling scheduling.

[0037] All generated energy storage cabinet scheduling requirements will be written into the scheduling request list and passed as input to the next step of the power transfer analysis and energy storage priority queue construction process. Through this step, the logical transformation from predicted demand to resource allocation can be realized, enabling energy storage resources to be efficiently allocated and dynamically coordinated in a goal-oriented manner.

[0038] P40: Based on the energy storage cabinet scheduling requirements of each demand side, and combined with the grid map of the energy storage cabinet radiation area, power transfer scheduling analysis is performed to generate energy storage priority queues for each demand side. The energy storage priority queues include dynamically changing charging scheduling queues and standby power supply queues.

[0039] Furthermore, step P40 in this embodiment of the application also includes:

[0040] P41: All energy storage cabinets in the target area are uniformly numbered, and the numbering information is written into the grid map of the energy storage cabinet radiation area, corresponding one-to-one with the energy storage cabinet distribution information; P42: Based on the energy storage cabinet distribution information, the local energy storage cabinet information of each demand end is obtained, including the online energy storage cabinet number, capacity, and remaining power; P43: The energy storage cabinet scheduling requirements of each demand end are received, and supply and demand are matched with the corresponding local energy storage cabinet information to generate energy storage cabinet matching results; P44: Based on the energy storage cabinet matching results, combined with the grid map of the energy storage cabinet radiation area, power transfer scheduling analysis is performed to generate energy storage priority queues for each demand end. The energy storage priority queues include dynamically changing charging scheduling queues and standby power supply queues.

[0041] It should be understood that after obtaining the energy storage cabinet scheduling requirements from each demand side, the process further combines the existing energy storage cabinet radiation area grid map to perform power transfer scheduling analysis, generate energy storage priority queues for each demand side, and divide each energy storage cabinet into a charging scheduling queue and a standby power supply queue according to its current status and scheduling adaptability. This process not only enables fine-grained resource scheduling but also constructs a task execution infrastructure that meets dynamic response characteristics, providing support for subsequent rolling transfers.

[0042] In practice, firstly, all energy storage cabinets within the target area are uniformly numbered. The numbering follows a one-to-one mapping principle to ensure that each energy storage cabinet has a unique identifier in the spatial grid. This number information is then written into the energy storage cabinet node information of the grid map of the energy storage cabinet's radiation area, and bound to its spatial coordinates, service capacity, service status, and other attribute data to achieve a precise mapping between the number and the entity.

[0043] Next, based on the aforementioned numbering system and energy storage cabinet distribution information, the local energy storage cabinet information within the coverage area of ​​each demand side is extracted from the grid map. This includes the energy storage cabinet number currently online, the current adjustable capacity (unit: kWh), the remaining capacity percentage (SOC), the maximum sustainable discharge power, and whether there are any dispatch tasks. This information reflects the current status of the local energy storage resource pool that the demand side can call upon, and is a prerequisite for performing supply and demand matching judgments.

[0044] Subsequently, the system receives energy storage cabinet scheduling requests generated by various demand sides, including parameters such as the required energy storage quantity, minimum single cabinet capacity, continuous power supply duration, and response time. These requests are then matched one-to-one with local energy storage cabinet information. Local energy storage resources are prioritized for supply-demand balance, and the matching results are output based on the matching success rate. The matching logic can employ greedy matching, linear programming, or heuristic search algorithms to maximize power supply assurance while minimizing scheduling costs. Matching results are categorized into three states: fully satisfied, partially satisfied, and requiring external dispatch for supplementation, clearly indicating the selected energy storage cabinet list and its available power supply duration.

[0045] Next, based on the matching results of energy storage cabinets and combined with the distribution locations of energy storage cabinets in the grid map and their spatial relationships with other demand sides, an overall power transfer and scheduling analysis of energy storage resources within the region is conducted. During the analysis, the system simultaneously evaluates the local inter-cabinet resource coordination capability and the feasibility of cross-regional transfer and scheduling. It also comprehensively considers factors such as the current status of energy storage cabinets, power redundancy, movement paths, photovoltaic power supply capacity, and scheduling latency to comprehensively rank each energy storage cabinet and generate an energy storage priority queue.

[0046] In the final energy storage priority queue, each demand side corresponds to a queue structure, which is internally divided into two sub-queues: one is the charging scheduling queue, which prioritizes energy storage cabinets that currently receive photovoltaic power for replenishment; the other is the standby power supply queue, which consists of energy storage cabinets that currently have sufficient power and need to continue supplying power to local loads. The energy storage cabinets in these two queues can be cross-adjusted at any time according to changes in status, realizing a state-driven dynamic rescheduling mechanism. This queue structure can be implemented using a priority queue or a double-ended queue, possessing high responsiveness and task instruction adaptability, and is the core foundation for subsequent execution of the rolling power transfer strategy.

[0047] Furthermore, step P43 in this embodiment of the application also includes:

[0048] P43-1: The energy storage cabinet scheduling requirements of each demand side include the required number and capacity of energy storage cabinets; P43-2: Based on the required number and capacity of energy storage cabinets, the supply and demand capacity is converted and matched with the local energy storage cabinet information to generate an energy storage cabinet matching result. The energy storage cabinet matching result includes local available energy storage cabinet information and active energy storage cabinet information. Furthermore, the active energy storage cabinet information includes the need to borrow energy storage cabinets and information on surplus energy storage cabinets.

[0049] Optionally, during the implementation of energy storage cabinet supply and demand matching, after receiving the energy storage cabinet scheduling requests from various demand sides, the system first extracts key parameters from the scheduling requests in a structured manner, especially the two core elements: the number of energy storage cabinets required and the required capacity. The number of energy storage cabinets required refers to the minimum number of energy storage cabinets that the demand side needs to call within the current predicted power supply cycle to cover the basic power guarantee during local peak load periods; while the required capacity is the total available energy storage capacity that the demand side hopes to obtain from the scheduling, usually measured in kilowatt-hours (kWh), to meet its minimum power supply demand within a specified period. After obtaining the above requirements, the system enters the supply and demand capacity conversion process.

[0050] In practice, information on all energy storage cabinets within the same grid or service radius as the current demand side is extracted from the energy storage cabinet radiation area grid map. Energy storage cabinets currently online and in a callable state are selected. Information such as the cabinet number, current state of charge (SOC), dischargeable capacity, and scheduling constraints (e.g., whether it is currently serving other tasks) is read sequentially for each cabinet. Based on predefined priority rules (e.g., prioritizing cabinets with larger remaining capacity and higher historical stability), the process of selecting and accumulating adjustable capacity is executed. During the selection process, the number of allocated energy storage cabinets is simultaneously counted, and allocation is terminated when one of two conditions is met: first, the accumulated capacity reaches or exceeds the demand capacity; second, the number of allocated cabinets reaches the maximum limit for the number of callable energy storage cabinets. This conversion and allocation process can be implemented using a greedy algorithm to minimize the number of allocated cabinets and optimize resource utilization efficiency while meeting response time requirements.

[0051] After initial scheduling, energy storage cabinet matching results are generated and categorized. First, the set of energy storage cabinets that fully meet local power supply needs is identified and written into the current grid status record as locally available energy storage cabinet information. Then, the system assesses whether there are resource gaps or redundancies during the matching process. If the total adjustable capacity of local energy storage cabinets is insufficient to meet demand, or the number of allocated cabinets cannot meet the required quantity, the system records this gap information and generates a borrowing request entry, indicating the required supplementary power, the minimum required number of energy storage cabinets, and the allocation response time limit, serving as the scheduling input for subsequent cross-regional resource coordination. Conversely, if the number of locally available energy storage cabinets exceeds the current actual allocation demand, and some energy storage cabinets are in a surplus state (e.g., power exceeding a certain set percentage, or not currently undertaking local tasks), they are registered as surplus energy storage cabinet information and added to the available energy storage resource pool for responding to borrowing requests from other regions.

[0052] Ultimately, the matching results will be synchronized back to the corresponding energy storage cabinet node data structure in the energy storage cabinet radiation area grid map and registered as active scheduling objects in the scheduling engine. Through this processing mechanism based on refined capacity matching and dynamic classification management, the automatic classification and task binding of energy storage resources within the region across three states—"local use—redundancy recovery—external transfer and sharing"—is achieved. This provides accurate and real-time scheduling basis for subsequent energy storage priority calculation, power transfer path generation, and rolling scheduling strategies. This process is highly executable and can be periodically and automatically executed by the scheduling controller in the regional energy storage management platform.

[0053] Furthermore, step P44 in this embodiment of the application also includes:

[0054] P44-1: Based on the energy storage cabinet matching results, extract the borrowing energy storage cabinet demand and surplus energy storage cabinet information for each demand side; P44-2: According to the energy storage cabinet radiation area grid map, obtain the distribution location of each energy storage cabinet; P44-3: Based on the borrowing energy storage cabinet demand and surplus energy storage cabinet information, combined with the distribution location of each active energy storage cabinet and the distribution information of photovoltaic power supply terminals, perform power transfer scheduling analysis and generate an energy storage cabinet interaction scheme; P44-4: Perform energy storage cabinet reallocation according to the energy storage cabinet interaction scheme, and perform energy storage priority analysis on the energy storage cabinets of each demand side after reallocation to generate an energy storage priority queue for each demand side.

[0055] Specifically, after completing the initial matching of energy storage cabinets and generating local available and active energy storage cabinet information, the process proceeds to the analysis of inter-regional energy storage resource coordination and transfer scheduling. First, based on the energy storage cabinet matching results from the previous step, the borrowing energy storage cabinet requirements corresponding to the unmet power supply needs of each demand side are extracted, and information on surplus energy storage cabinets with transfer potential is identified. The borrowing requirements clearly mark the capacity gap, cabinet quantity gap, and allocation time limit for each demand side, while surplus energy storage cabinets record their cabinet number, available release capacity, and current location status, serving as the basis for dispatchable resources.

[0056] Subsequently, based on the grid map of the energy storage cabinet's radiation area, the spatial distribution information of all energy storage cabinets participating in the scheduling analysis was extracted, including their current grid cell number, absolute geographic coordinates (GPS positioning or GIS projection coordinates), and the network distance, reachability time, and path status (such as whether they are in an obstacle area or subject to traffic restrictions) between the energy storage cabinet and adjacent demand ends, providing basic spatial support for subsequent path calculation and task redistribution.

[0057] After acquiring the spatial location information of all participating entities, the system, combining the borrowing request, surplus resources, energy storage cabinet distribution locations, and the current power supply status of the photovoltaic power supply terminals, enters the power transfer scheduling analysis stage. During the analysis, an optimization model is constructed with "shortest supply-demand path + minimum energy consumption + supply-demand priority" as the scheduling objectives. By calculating parameters such as the path distance between the borrowing party and the surplus party, unit transfer cost, power transmission efficiency, and photovoltaic charging conditions, possible transfer routes are scored and ranked, prioritizing the matching of allocation paths that meet response time limits, have low path energy consumption, and high target capacity matching. For example, graph search algorithms (such as Dijkstra's algorithm or...) can be used. A multi-objective heuristic scheduling model or mixed integer programming method can be used to form the optimal energy storage cabinet interaction scheme, which determines how much electricity to transfer from which surplus node, to which borrowing adjustment point, and when it will arrive.

[0058] Next, based on the aforementioned energy storage cabinet interaction scheme, an energy storage cabinet reallocation operation is performed. This involves releasing some surplus energy storage cabinets from their original bound demand sides and dispatching them to the target demand side to complete the energy replenishment task, without affecting the current power supply stability. Before dispatching, the system automatically assesses whether there is a risk of local load rebound, and the system automatically registers location changes and task status updates upon arrival at the target location. After all reallocation operations are completed, the system recalculates the actual allocation of energy storage cabinets at each demand side and performs energy storage priority analysis on the energy storage cabinets under each demand side based on factors such as the current task objective, remaining power capacity, and power supply importance level, forming a reconstructed energy storage priority queue. This queue still uses the "Standby Power Supply Queue" and the "Charging Scheduling Queue" as its core structures, dynamically sorting them according to the remaining capacity of the energy storage cabinets, the adaptability of photovoltaic power supply, and the expected load for the next scheduling cycle.

[0059] The entire scheduling-redistribution-priority generation process is periodically executable and can support multi-cycle dynamic updates under the rolling optimization mechanism. It is a key control link to achieve optimal power transfer and efficient resource allocation within a region.

[0060] Furthermore, step P44-4 of the embodiments of this application also includes:

[0061] P44-41: The energy storage cabinets at each demand end after redistribution include local energy storage cabinets and transfer energy storage cabinets; P44-42: When the local energy storage cabinet at any demand end meets the power supply demand, and the transfer energy storage cabinet is a removed energy storage cabinet, the energy storage information of the local energy storage cabinet is obtained, and the energy storage is arranged in order according to the size of the energy storage to generate an energy storage sequence, which is identified by the energy storage cabinet number; P44-43: Based on the energy storage sequence, energy storage cabinets are extracted in descending order of energy storage until the total extracted energy storage meets the local preset power supply threshold. The extracted energy storage cabinets are used as the standby power supply queue, and the remaining energy storage cabinets are used as the charging scheduling queue; P44-44: The standby power supply queue and the charging scheduling queue form the energy storage priority queue.

[0062] It should be understood that the process of prioritizing energy storage resources can be further refined to provide a clear basis for task scheduling in subsequent rolling power transfer. First, the status of the energy storage cabinets currently bound to each demand side should be uniformly summarized, clarifying that the energy storage resources currently actually possessed by that demand side consist of two parts: locally deployed energy storage cabinets and newly introduced transfer energy storage cabinets through transfer scheduling. The status identifiers of both types of energy storage cabinets should be refreshed, and the latest location information, remaining power, and transfer timestamp, among other attribute parameters, should be written into the system.

[0063] Next, under the scenario where the power supply conditions are met—that is, when the total adjustable capacity of the local energy storage cabinets is sufficient to meet the minimum power supply threshold set by the demand side—the transfer energy storage cabinet is marked as "removable" and temporarily included in the candidate release range. At this time, the system collects the energy inventory information of all local energy storage cabinets at the demand side, including the current SOC, battery health factor, discharge limit, and scheduling freeze status of each cabinet, forming a complete dataset. Subsequently, the system sorts the local energy storage cabinets according to the order of energy inventory from high to low, generating an energy inventory sequence. This sequence not only records the remaining power of each energy storage cabinet but also includes the cabinet number, allocation time, and last scheduling record, among other related information for subsequent judgment.

[0064] Next, the system extracts energy from the aforementioned energy stock sequence, selecting energy storage cabinets one by one in order from high to low energy levels and adding them to the standby power supply candidate set. After each round of selection, the system updates the total available energy value of the candidate set and determines in real time whether the locally set power supply threshold has been met. This threshold is typically set based on the load level and the power supply continuity requirements for the target period (e.g., "continuously guaranteeing a 5kW load for the next 3 hours"), and can be user-defined or automatically generated by the system strategy module based on historical curves. Once the cumulative energy of the selected energy storage cabinet meets the threshold, the system stops selecting and classifies the selected energy storage cabinet into the standby power supply queue. The remaining unselected local energy storage cabinets, due to their relatively low energy levels or being in a redundant state, are automatically assigned to the charging scheduling queue, ready to receive photovoltaic charging or be allocated to other load areas in subsequent periods.

[0065] Finally, based on the results of this round of energy analysis, a formal energy storage priority queue was established for this demand side. The standby power supply queue undertakes current or upcoming actual power supply tasks and has a high scheduling lock-in weight, while the charging scheduling queue serves as a backup and charging task object, accepting energy replenishment or transfer tasks in subsequent time windows. This priority queue structure can be stored and managed through a state machine or priority queue model, supporting fast lookup, state switching, and scheduling sorting, providing a key logical foundation for subsequent rolling scheduling and batch energy transfer. Through the implementation of this step, classified management and hierarchical scheduling of multi-source energy storage cabinets under dynamic task allocation can be achieved, ensuring supply and demand matching while maximizing the utilization of photovoltaic energy and improving power transfer efficiency.

[0066] Furthermore, steps P44-42 in the embodiments of this application also include:

[0067] P44-42a: When the local energy storage cabinet at any demand end does not meet the power supply demand, and the transfer energy storage cabinet is a moved-in energy storage cabinet, the energy storage information of the local energy storage cabinet is obtained, and the energy storage cabinets are arranged in order according to the size of the energy storage to generate an energy storage sequence. The energy storage sequence is marked with the energy storage cabinet number. P44-43a: Based on the energy storage sequence, energy storage cabinets with energy storage greater than the storage threshold are selected to form a standby power supply queue, and the remaining energy storage cabinets are used as a charging scheduling queue.

[0068] In one possible embodiment of this application, for another typical scenario, namely when the local energy storage cabinet cannot independently meet the power supply demand, an energy storage priority adjustment process under different logics is executed. When the system determines that the total adjustable capacity of the local energy storage cabinet at a certain demand end is lower than the power supply threshold set for that area, it indicates that the current local energy storage resources are insufficient and need to be supplemented by a transfer energy storage cabinet introduced by external scheduling. At this time, the transfer energy storage cabinet is marked as a moved-in energy storage cabinet.

[0069] First, obtain the energy status information of the existing local energy storage cabinets on the demand side, including the remaining available power (in kWh), current operating status (online / offline), maximum discharge capacity, and whether it is in a low-power lock-in state for each cabinet. Sort all local energy storage cabinets according to their remaining power to generate an energy inventory sequence. This sequence includes each cabinet's unique ID, location information, historical dispatch count, response latency, and other key attributes, providing multi-dimensional support for subsequent selection strategies.

[0070] Next, a screening operation is performed on the energy storage cabinets in this sequence. The screening logic is based on a set parameter: the energy storage threshold. This threshold is the minimum available energy standard set by the system to determine whether the energy storage cabinet has the ability to provide standby power. For example, if the energy storage threshold is set to 3kWh in a certain scenario, then all energy storage cabinets with remaining energy higher than this value are considered capable of independently handling local loads and are included in the standby power supply queue. Conversely, energy storage cabinets with insufficient remaining energy will be assigned to the charging scheduling queue, meaning they will not undertake the current power supply task for the time being, and will be transferred to other areas later when they receive photovoltaic charging.

[0071] This screening mechanism, even in resource-scarce or locally insufficient scenarios, can effectively ensure local power supply continuity and avoid energy waste by prioritizing power supply from high-energy surplus cabinets and buffering low-energy cabinets. It also creates clear boundaries for the transfer of energy storage cabinets, improving the scheduling accuracy and response efficiency of subsequent power transfer. This strategy complements the high-energy-priority extraction logic in steps P44-43 in the execution path, enabling the energy storage priority management logic of this invention to simultaneously cover both surplus and shortage scheduling scenarios, demonstrating strong executability and engineering applicability.

[0072] P50: According to the grid diagram of the energy storage cabinet's radiation area, the energy storage priority queue is dynamically updated, and rolling power transfer is performed based on the updated energy storage priority queue.

[0073] Furthermore, step P50 in this embodiment of the application also includes:

[0074] P51: Based on the grid map of the energy storage cabinet's radiation area, obtain real-time data on the energy storage changes and predicted power demand of local energy storage cabinets at each demand end; P52: Based on the energy storage change data and predicted power demand, perform rolling adjustments to the energy storage priority queue to generate an updated energy storage priority queue and energy storage cabinet transfer path; P53: Based on the updated energy storage priority queue, dynamically allocate photovoltaic power generation to efficiently charge multiple energy storage cabinets in batches, and schedule and transfer energy storage cabinets according to the energy storage cabinet transfer path.

[0075] Optionally, using the grid map of the energy storage cabinet's radiation area as the spatial and data foundation, the aforementioned generated energy storage priority queue is dynamically monitored and continuously optimized to ensure that the scheduling of energy storage resources can adapt to load changes and photovoltaic power fluctuations, thus realizing a rolling power transfer and scheduling mechanism. This process is centered on multi-cycle, multi-regional rolling optimization, and updates are performed based on the latest data in each scheduling cycle to support continuous and efficient energy storage allocation and power supply and demand matching.

[0076] Specifically, firstly, based on the node status in the grid map of the energy storage cabinet's radiation area, real-time data on the energy storage changes of the local energy storage cabinets within the corresponding grid for each demand side is acquired. This includes dynamic indicators such as changes in the state of charge (SOC) of the energy storage cabinets, real-time discharge / charge power, and the remaining percentage of available capacity. Simultaneously, combined with historical load data, environmental factors, and equipment operating status, the predicted power demand for that demand side is updated, including power demand trends within a unit time period, power supply duration, and reliability level. All of the above data serve as inputs for the rolling adjustment logic, triggering calculations within the system's set periodic windows (e.g., every 5 minutes, every 15 minutes).

[0077] Next, the current energy storage priority queue is adjusted on a rolling basis. Specifically, based on the changes in energy inventory at each demand end and the predicted power supply demand, the suitability of energy storage cabinets in the existing standby power supply queue and charging dispatch queue is reassessed. If an energy storage cabinet is found to be below the low power threshold or its corresponding demand end is expected to experience a surge in load, the energy storage cabinet will be removed from the power supply queue and transferred to the charging dispatch queue, or moved to other areas with lower load pressure. Conversely, for energy storage cabinets in the charging queue, if their power recovers to a dischargeable level and there is a power shortage at the demand end, they will be promoted to the power supply queue. At the same time, based on the power shortage and surplus status between regions, energy storage cabinet transfer paths are generated. This path calculation comprehensively considers parameters such as transfer distance, path energy consumption, response time, and task priority, ultimately forming an executable cross-grid energy storage transfer instruction set for the current cycle.

[0078] Finally, photovoltaic resources are optimally allocated based on the updated energy storage priority queue. The scheduling engine monitors the output power of the photovoltaic power generation end in real time, and dynamically calculates the photovoltaic power allocation ratio based on the energy redundancy of the energy storage cabinets, expected load, and priority level. This enables batch charging control among multiple energy storage cabinets and issues corresponding power control commands through inverters or DC-DC power modules. Simultaneously, based on the aforementioned generated energy storage cabinet transfer path, the engine drives the relevant energy storage cabinets to perform path scheduling execution, including the entire process of energy storage cabinets being moved from their original areas, transferred to the target demand end via a designated path, location change records, and status synchronization updates.

[0079] Through the coordinated execution of the above steps, a closed-loop rolling optimization mechanism of "prediction-adjustment-transfer-feedback" for energy storage resource scheduling is achieved. This effectively improves the energy storage response capability and power security level under the background of unstable photovoltaic power output, and adapts to the comprehensive requirements of timeliness, flexibility, and energy efficiency of energy storage systems under high dynamic load environments. This method has high engineering feasibility and is applicable to intelligent energy management scenarios such as distributed photovoltaic microgrids, mobile energy storage scheduling platforms, and multi-site power supply systems.

[0080] Furthermore, step P52 in this embodiment of the application also includes:

[0081] P52-1: The energy storage priority queue is structurally adjusted. Based on the updated energy inventory order and transfer task execution status, the standby power supply queue and charging scheduling queue are re-divided to form an updated energy storage priority queue. P52-2: Based on a preset rolling window, the energy inventory change trend of each energy storage cabinet in the energy storage priority queue is periodically evaluated, and a matching analysis is performed in conjunction with the predicted change in power supply demand to generate a supply-demand matching result. P52-3: Based on the supply-demand matching result, it is determined whether there is a supply-demand imbalance at each demand end within the current time window. If so, the number of transfer energy storage cabinets to be supplemented and the location of the target demand end are determined based on the energy gap calculation model. P52-4: Based on the number of transfer energy storage cabinets to be supplemented and the location of the target demand end, combined with the geographical location information of each energy storage cabinet in the grid map of the energy storage cabinet radiation area and transfer delay constraints, a transfer path for energy storage cabinets prioritizing the minimum transfer distance is generated.

[0082] Specifically, based on the completion of photovoltaic power allocation and transfer instruction preparation, we can further deepen the structural dynamic adjustment of the energy storage priority queue and the optimization of transfer scheduling paths to ensure that rolling power transfer maintains optimal performance in terms of timeliness, matching accuracy and energy efficiency.

[0083] First, the current energy storage priority queue is structurally reorganized. Specifically, based on the order of energy storage cabinet changes collected in the previous rolling cycle and the execution status of the current transfer task, the system re-identifies the scheduling role of each energy storage cabinet. Energy storage cabinets in the high-energy range, already connected to local power supply tasks, or currently transferred but not yet completed their charging tasks will be prioritized as standby power supply targets; while energy storage cabinets in the low-energy state, having completed their power supply tasks, or about to receive the next round of charging plans will be transferred to the charging scheduling queue. When reorganizing the queue structure, the system also considers whether the energy storage cabinets in the transfer state are currently moving or about to be transferred in or out, avoiding duplicate or conflicting allocations. After the queue structure adjustment is completed, an updated energy storage priority queue is formed and registered in the regional scheduling engine for use in the next cycle.

[0084] Next, using a set rolling window (e.g., 10 minutes, 30 minutes) as the period, data fitting and evaluation are performed on the energy storage change trend of each energy storage cabinet in the energy storage priority queue. For example, by statistically analyzing the slope of the SOC change of each energy storage cabinet through sliding time series, its discharge rate or charging rate is determined, and combined with the demand-side load trend of the area where the energy storage cabinet is located, trend matching analysis is performed. If the trend is a rapid decline and the regional load continues to increase, it may indicate a risk of supply and demand imbalance; if the trend is a recovery in power generation and the load stabilizes, it is judged as a good supply and demand match. At this stage, a refined supply and demand matching result data table is generated, including indicators such as the degree of consistency of power generation trends between each energy storage cabinet and its power supply object, task pressure index, and power supply risk coefficient.

[0085] Furthermore, based on the aforementioned supply and demand matching results, it is determined whether a supply-demand imbalance exists within the current time window. Once it is identified that the expected load on a certain demand side will exceed the sustainable discharge capacity of its current energy storage cabinet in the next cycle (i.e., the current total energy is less than the predicted power × duration), it is determined that there is an energy gap in the region, and the built-in energy gap calculation model is invoked. This model will consider factors such as the gap energy (kWh), required power (kW), and duration (min) to calculate the number of transfer energy storage cabinets that need to be supplemented, and specify the geographical location of the target demand side and the allocation time limit, providing task target input for subsequent path calculation.

[0086] Finally, based on the required number of energy storage cabinets and the location of the target demand side, and combining the current distribution coordinates, mobility status, and expected response time of all candidate energy storage cabinets in the grid map of the energy storage cabinet radiation area, all candidate transfer paths from the schedulable source point to the target demand side are calculated. A minimum transfer distance priority principle is introduced, along with a time delay constraint judgment mechanism, to eliminate all paths that are expected to be unable to complete the transfer within the allocation time limit. Subsequently, a graph search algorithm (such as Dijkstra's algorithm or...) is used. The system generates a path score by taking into account multiple factors such as distance, energy consumption, and response time, and outputs the optimal path set as the energy storage transfer scheduling path for the current cycle.

[0087] By executing the above steps in a coordinated manner, not only can the continuous and dynamic maintenance of the energy storage priority queue be achieved, but a multi-level rolling power dispatch system covering spatial, power, and time dimensions can also be constructed, significantly improving the operating efficiency, response speed, and power supply stability of the photovoltaic energy storage system.

[0088] In summary, the embodiments of this application have at least the following technical effects:

[0089] This application constructs a grid map of the energy storage cabinet's radiation area, integrates the distribution information of the demand side, energy storage cabinets, and photovoltaic power supply, collects and dynamically updates power and capacity data in real time, generates energy storage scheduling requirements based on demand predictions during power supply periods, performs power transfer scheduling in conjunction with spatial information, generates and continuously updates the energy storage priority queue, and realizes efficient allocation and intelligent transfer of photovoltaic power among multiple energy storage cabinets.

[0090] The technology achieves the goal of enabling intelligent cross-regional transfer of photovoltaic power through a grid map of the energy storage cabinet's radiation area and a rolling dispatch strategy, thereby improving the utilization rate of energy storage resources and the response speed of power supply dispatch.

[0091] Example 2, based on the same inventive concept as the photovoltaic mobile energy storage management method for power transfer in the foregoing examples, such as... Figure 2As shown, this application provides a photovoltaic mobile energy storage management device for power transfer. The device and method embodiments in this application are based on the same inventive concept. The device includes:

[0092] The regional grid map construction module 11 is used to construct a grid map of the radiation area of ​​the energy storage cabinet. The grid map of the radiation area of ​​the energy storage cabinet includes demand-side distribution information of the target power supply area, distribution information of the energy storage cabinet, and distribution information of the photovoltaic power supply end.

[0093] The real-time energy status update module 12 is used to continuously collect real-time output power data of the photovoltaic power supply terminal and real-time remaining capacity information of the mobile energy storage cabinet based on a preset time window, and update the grid map of the radiation area of ​​the energy storage cabinet in real time.

[0094] The scheduling demand prediction module 13 is used to predict the power supply demand of each demand end according to the power supply period, and generate the energy storage cabinet scheduling demand of each demand end according to the predicted power supply demand.

[0095] The power transfer scheduling analysis module 14 is used to perform power transfer scheduling analysis based on the energy storage cabinet scheduling requirements of each demand end and in combination with the grid map of the energy storage cabinet radiation area, and generate energy storage priority queues for each demand end. The energy storage priority queues include dynamically changing charging scheduling queues and standby power supply queues.

[0096] The rolling power transfer module 15 is used to dynamically update the energy storage priority queue according to the grid map of the energy storage cabinet's radiation area, and to perform rolling power transfer according to the updated energy storage priority queue.

[0097] Furthermore, the power transfer scheduling and analysis module 14 is also used to perform the following steps:

[0098] All energy storage cabinets in the target area are uniformly numbered, and the numbering information is written into the grid map of the energy storage cabinet radiation area, corresponding one-to-one with the energy storage cabinet distribution information; based on the energy storage cabinet distribution information, the local energy storage cabinet information of each demand end is obtained, including the online energy storage cabinet number, capacity, and remaining power; the energy storage cabinet scheduling requirements of each demand end are received, and supply and demand are matched with the corresponding local energy storage cabinet information to generate energy storage cabinet matching results; based on the energy storage cabinet matching results, combined with the grid map of the energy storage cabinet radiation area, power transfer scheduling analysis is performed to generate energy storage priority queues for each demand end, and the energy storage priority queues include dynamically changing charging scheduling queues and standby power supply queues.

[0099] Furthermore, the power transfer scheduling and analysis module 14 is also used to perform the following steps:

[0100] The energy storage cabinet scheduling requirements of each demand side include the required number and capacity of energy storage cabinets; based on the required number and capacity of energy storage cabinets, supply and demand capacity conversion and matching are performed with the local energy storage cabinet information to generate energy storage cabinet matching results. The energy storage cabinet matching results include local available energy storage cabinet information and active energy storage cabinet information. Furthermore, the active energy storage cabinet information includes the need to borrow energy storage cabinets and information on surplus energy storage cabinets.

[0101] Furthermore, the power transfer scheduling and analysis module 14 is also used to perform the following steps:

[0102] Based on the energy storage cabinet matching results, the borrowing demand and surplus energy storage cabinet information of each demand side are extracted; according to the grid map of the energy storage cabinet radiation area, the distribution location of each energy storage cabinet is obtained; based on the borrowing demand and surplus energy storage cabinet information, combined with the distribution location of each active energy storage cabinet and the distribution information of photovoltaic power supply terminals, power transfer scheduling analysis is performed to generate an energy storage cabinet interaction scheme; energy storage cabinets are redistributed according to the energy storage cabinet interaction scheme, and energy storage priority analysis is performed on the energy storage cabinets of each demand side after redistribution to generate an energy storage priority queue for each demand side.

[0103] Furthermore, the power transfer scheduling and analysis module 14 is also used to perform the following steps:

[0104] The redistributed energy storage cabinets at each demand end include local energy storage cabinets and transfer energy storage cabinets. When the local energy storage cabinet at any demand end meets the power supply demand, and the transfer energy storage cabinet is a removed energy storage cabinet, the energy storage information of the local energy storage cabinet is obtained and arranged in order according to the size of the energy storage to generate an energy storage sequence. The energy storage sequence is marked with an energy storage cabinet number. Based on the energy storage sequence, energy storage cabinets are extracted in descending order of energy storage until the total extracted energy storage meets the local preset power supply threshold. The extracted energy storage cabinets are designated as the standby power supply queue, and the remaining energy storage cabinets are designated as the charging scheduling queue. The standby power supply queue and the charging scheduling queue together form the energy storage priority queue.

[0105] Furthermore, the power transfer scheduling and analysis module 14 is also used to perform the following steps:

[0106] When the local energy storage cabinet at any demand end cannot meet the power supply demand, and the transfer energy storage cabinet is a moved-in energy storage cabinet, the energy storage information of the local energy storage cabinet is obtained, and the energy storage cabinets are arranged in order according to the size of the energy storage to generate an energy storage sequence. The energy storage sequence is marked with the energy storage cabinet number. Based on the energy storage sequence, energy storage cabinets with energy storage greater than the storage threshold are selected to form a standby power supply queue, and the remaining energy storage cabinets are used as a charging scheduling queue.

[0107] Furthermore, the rolling power transfer module 15 is also used to perform the following steps:

[0108] Based on the grid map of the energy storage cabinet's radiation area, real-time data on energy inventory changes and predicted power demand of local energy storage cabinets at each demand end are obtained; according to the energy inventory change data and predicted power demand, the energy storage priority queue is adjusted in a rolling manner to generate an updated energy storage priority queue and energy storage cabinet transfer path; based on the updated energy storage priority queue, photovoltaic power generation is dynamically allocated to efficiently charge multiple energy storage cabinets in batches, and energy storage cabinets are scheduled and transferred according to the energy storage cabinet transfer path.

[0109] Furthermore, the rolling power transfer module 15 is also used to perform the following steps:

[0110] The energy storage priority queue is structurally adjusted. Based on the updated energy inventory order and transfer task execution status, the standby power supply queue and charging scheduling queue are re-divided to form an updated energy storage priority queue. Based on a preset rolling window, the energy inventory change trend of each energy storage cabinet in the energy storage priority queue is periodically evaluated, and a matching analysis is performed in conjunction with the predicted change in power supply demand to generate a supply-demand matching result. Based on the supply-demand matching result, it is determined whether there is a supply-demand imbalance at each demand end within the current time window. If so, the number of transfer energy storage cabinets to be supplemented and the location of the target demand end are determined based on the energy gap calculation model. Based on the number of transfer energy storage cabinets to be supplemented and the location of the target demand end, combined with the geographical location information of each energy storage cabinet in the grid map of the energy storage cabinet radiation area and the transfer delay constraint, a transfer path for the energy storage cabinet with the minimum transfer distance priority is generated.

[0111] Example 3, Exemplary Electronic Device, as described below. Figure 3 The present application describes the electronic device according to its embodiments.

[0112] Based on the same inventive concept as the photovoltaic mobile energy storage management method for power transfer in the foregoing embodiments, this application also provides a photovoltaic mobile energy storage management device for power transfer, comprising: a processor coupled to a memory for storing a program, wherein when the program is executed by the processor, the device performs the steps of the method described in Embodiment 1.

[0113] The electronic device 300 includes a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. The communication interface 303, processor 302, and memory 301 can be interconnected via the bus architecture 304; the bus architecture 304 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus architecture 304 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0114] Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits used to control the execution of programs according to the present application.

[0115] Communication interface 303 uses any transceiver-like device for communicating with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), wired access network, etc.

[0116] Memory 301 may be ROM or other types of static storage devices capable of storing static information and instructions, RAM or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact discread-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory may exist independently and be connected to the processor via bus architecture 304. Memory may also be integrated with the processor.

[0117] The memory 301 stores computer execution instructions for implementing the scheme of this application, and the processor 302 controls the execution. The processor 302 executes the computer execution instructions stored in the memory 301, thereby realizing the photovoltaic mobile energy storage management method for power transfer provided in the above embodiments of this application.

[0118] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0119] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0120] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A photovoltaic mobile energy storage management method for power transfer, characterized in that, The method includes: Construct a grid map of the radiation area of ​​the energy storage cabinet, which includes demand-side distribution information, energy storage cabinet distribution information, and photovoltaic power supply distribution information for the target power supply area; Based on a preset time window, the real-time output power data of the photovoltaic power supply terminal and the real-time remaining capacity information of the mobile energy storage cabinet are continuously collected and updated in real time to the grid map of the energy storage cabinet's radiation area. Based on the power supply period, the power supply demand of each demand side is predicted, and the energy storage cabinet scheduling demand of each demand side is generated based on the predicted power supply demand. Based on the energy storage cabinet scheduling requirements of each demand side, and combined with the energy storage cabinet radiation area grid map, power transfer scheduling analysis is performed to generate energy storage priority queues for each demand side. These priority queues include dynamically changing charging scheduling queues and standby power supply queues, including: Receive the energy storage cabinet scheduling requests from each demand side, match them with the corresponding local energy storage cabinet information, and generate energy storage cabinet matching results; Based on the energy storage cabinet matching results, extract the borrowing demand for energy storage cabinets and the information on surplus energy storage cabinets from each demand side. Based on the grid map of the radiation area of ​​the energy storage cabinet, the distribution location of each energy storage cabinet is obtained; Based on the borrowing demand for energy storage cabinets and the information on surplus energy storage cabinets, combined with the distribution location of each active energy storage cabinet and the distribution information of photovoltaic power supply terminals, an energy transfer scheduling analysis is performed to generate an energy storage cabinet interaction scheme. The energy storage cabinets are redistributed according to the energy storage cabinet interaction scheme, and energy storage priority analysis is performed on the energy storage cabinets of each demand side after redistribution to generate energy storage priority queues for each demand side; the energy storage priority queues are dynamically updated according to the grid map of the energy storage cabinet radiation area, and rolling power transfer is performed according to the updated energy storage priority queues, including: Based on the grid map of the energy storage cabinet's radiation area, real-time data on the energy storage changes of local energy storage cabinets at each demand end and prediction of power supply demand are obtained. Based on the energy stock change data and predicted power demand, the energy storage priority queue is adjusted in a rolling manner to generate an updated energy storage priority queue and energy storage cabinet transfer path. Based on the updated energy storage priority queue, photovoltaic power generation is dynamically allocated, multiple energy storage cabinets are efficiently charged in batches, and energy storage cabinets are scheduled and transported according to the energy storage cabinet transport path.

2. The photovoltaic mobile energy storage management method for power transfer as described in claim 1, characterized in that, Based on the energy storage cabinet scheduling requirements of each demand side, and combined with the grid map of the energy storage cabinet's radiation area, an energy transfer scheduling analysis is performed to generate an energy storage priority queue for each demand side, including: All energy storage cabinets in the target area are uniformly numbered, and the numbering information is written into the grid map of the radiation area of ​​the energy storage cabinets, corresponding one-to-one with the distribution information of the energy storage cabinets; Based on the energy storage cabinet distribution information, obtain the local energy storage cabinet information of each demand end, including the online energy storage cabinet number, capacity, and remaining power. Based on the energy storage cabinet matching results, and combined with the grid map of the energy storage cabinet's radiation area, an energy transfer scheduling analysis is performed to generate energy storage priority queues for each demand side. The energy storage priority queues include dynamically changing charging scheduling queues and standby power supply queues.

3. The photovoltaic mobile energy storage management method for power transfer as described in claim 2, characterized in that, The system receives energy storage cabinet scheduling requests from each demand side, matches them with corresponding local energy storage cabinet information to generate energy storage cabinet matching results, including: The energy storage cabinet scheduling requirements of each demand side include the required number and capacity of energy storage cabinets; Based on the required quantity and capacity of the energy storage cabinets, a supply and demand capacity conversion and matching is performed with the local energy storage cabinet information to generate an energy storage cabinet matching result. The energy storage cabinet matching result includes local available energy storage cabinet information and active energy storage cabinet information. Furthermore, the active energy storage cabinet information includes the need for borrowed energy storage cabinets and information on surplus energy storage cabinets.

4. The photovoltaic mobile energy storage management method for power transfer as described in claim 1, characterized in that, Energy storage priority analysis is performed on the energy storage cabinets at each demand end after redistribution, generating energy storage priority queues for each demand end, including: The energy storage cabinets at each demand side after redistribution include local energy storage cabinets and transfer energy storage cabinets; When the local energy storage cabinet at any demand end meets the power supply demand, and the transfer energy storage cabinet is a moved-out energy storage cabinet, the energy storage information of the local energy storage cabinet is obtained, and the energy storage information is arranged in order according to the size of the energy storage to generate an energy storage sequence. The energy storage sequence is marked with the energy storage cabinet number. Based on the energy stock sequence, energy storage cabinets are extracted in descending order of energy stock until the total extracted energy stock meets the local preset power supply threshold. The extracted energy storage cabinets are used as the standby power supply queue, and the remaining energy storage cabinets are used as the charging scheduling queue. The energy storage priority queue is composed of the standby power supply queue and the charging scheduling queue.

5. The photovoltaic mobile energy storage management method for power transfer as described in claim 4, characterized in that, The method further includes: When the local energy storage cabinet at any demand end cannot meet the power supply demand, and the transfer energy storage cabinet is a moved-in energy storage cabinet, the energy storage information of the local energy storage cabinet is obtained, and the energy storage is arranged in order according to the size of the energy storage to generate an energy storage sequence. The energy storage sequence is marked with the energy storage cabinet number. Based on the energy stock sequence, energy storage cabinets with energy stock greater than the stock threshold are selected to form a standby power supply queue, while the remaining energy storage cabinets serve as a charging scheduling queue.

6. The photovoltaic mobile energy storage management method for power transfer as described in claim 1, characterized in that, Based on the energy stock change data and predicted power demand, the energy storage priority queue is adjusted in a rolling manner to generate an updated energy storage priority queue and energy storage cabinet transfer path, including: The energy storage priority queue is restructured. Based on the updated energy inventory order and the execution status of the transfer task, the standby power supply queue and the charging scheduling queue are re-divided to form an updated energy storage priority queue. Based on a preset rolling window, the energy storage change trend of each energy storage cabinet in the energy storage priority queue is periodically evaluated, and a matching analysis is performed in combination with the predicted change in power supply demand to generate a supply and demand matching result. Based on the supply and demand matching results, it is determined whether there is a supply and demand imbalance in each demand side within the current time window. If so, the number of transfer storage cabinets to be supplemented and the location of the target demand side are determined based on the energy gap calculation model. Based on the number of energy storage cabinets to be supplemented and the location of the target demand end, combined with the geographical location information of each energy storage cabinet in the grid map of the energy storage cabinet radiation area and the transfer delay constraints, a transfer path for the energy storage cabinet with the minimum transfer distance as the priority is generated.

7. A photovoltaic mobile energy storage management device for power transfer, characterized in that, The device includes: The regional grid map construction module is used to construct a grid map of the radiation area of ​​the energy storage cabinet. The grid map of the radiation area of ​​the energy storage cabinet includes demand-side distribution information, energy storage cabinet distribution information, and photovoltaic power supply distribution information of the target power supply area. The real-time energy status update module is used to continuously collect real-time output power data of the photovoltaic power supply terminal and real-time remaining capacity information of the mobile energy storage cabinet based on a preset time window, and update the grid map of the energy storage cabinet's radiation area in real time. The scheduling demand prediction module is used to predict the power supply demand of each demand end according to the power supply period, and generate the energy storage cabinet scheduling demand of each demand end according to the predicted power supply demand. The power transfer scheduling and analysis module is used to perform power transfer scheduling analysis based on the energy storage cabinet scheduling requirements of each demand end and in combination with the grid map of the energy storage cabinet radiation area, and generate energy storage priority queues for each demand end. The energy storage priority queues include dynamically changing charging scheduling queues and standby power supply queues. A rolling power transfer module is used to dynamically update the energy storage priority queue according to the grid map of the energy storage cabinet's radiation area, and to perform rolling power transfer according to the updated energy storage priority queue. Furthermore, the power transfer scheduling and analysis module is also used to perform the following steps: Receive the energy storage cabinet scheduling requests from each demand side, match them with the corresponding local energy storage cabinet information, and generate energy storage cabinet matching results; Based on the energy storage cabinet matching results, the borrowing demand and surplus energy storage cabinet information of each demand side are extracted; according to the grid map of the energy storage cabinet radiation area, the distribution location of each energy storage cabinet is obtained; based on the borrowing demand and surplus energy storage cabinet information, combined with the distribution location of each active energy storage cabinet and the distribution information of photovoltaic power supply terminals, power transfer scheduling analysis is performed to generate an energy storage cabinet interaction scheme; energy storage cabinets are redistributed according to the energy storage cabinet interaction scheme, and energy storage priority analysis is performed on the energy storage cabinets of each demand side after redistribution to generate an energy storage priority queue for each demand side; Furthermore, the rolling power transfer module is also used to perform the following steps: Based on the grid map of the energy storage cabinet's radiation area, real-time data on energy inventory changes and predicted power demand of local energy storage cabinets at each demand end are obtained; according to the energy inventory change data and predicted power demand, the energy storage priority queue is adjusted in a rolling manner to generate an updated energy storage priority queue and energy storage cabinet transfer path; based on the updated energy storage priority queue, photovoltaic power generation is dynamically allocated to efficiently charge multiple energy storage cabinets in batches, and energy storage cabinets are scheduled and transferred according to the energy storage cabinet transfer path.

8. An electronic device, characterized in that, include: A processor coupled to a memory for storing a program, which, when executed by the processor, causes the apparatus to perform the steps of the method as claimed in any one of claims 1 to 6.

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