Industrial and commercial energy storage system and energy storage scheduling method thereof

Abstract

The invention relates to the technical field of energy storage scheduling, in particular to an industrial and commercial energy storage system and an energy storage scheduling method thereof. The method comprises the following steps: obtaining the position of an industrial and commercial power plant; constructing a topological network based on the position of the industrial and commercial power plant to generate an industrial and commercial energy storage topological network; analyzing geographic altitude and energy transmission blocking characteristics of the industrial and commercial energy storage topology network, and performing energy storage mode division on the industrial and commercial energy storage topology network to generate a direct energy supply mode, a relay buffer mode and an isolated storage mode; acquiring node states of corresponding nodes in the industrial and commercial energy storage topology network based on a direct energy supply mode; and according to the node state, carrying out peak-valley price difference driven instant charging and discharging scheduling on the corresponding node, and generating a direct energy supply scheduling strategy. According to the industrial and commercial energy storage scheduling method, the flexibility and stability of industrial and commercial energy storage scheduling are improved by considering geographical factors, energy storage mode differentiation, instant scheduling, multi-stage buffering, electric energy quality adjustment and dynamic optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage scheduling, and particularly to an industrial and commercial energy storage system and an energy storage scheduling method thereof. Background Art

[0002] In the early stage, power system scheduling mainly relied on traditional power generation and load regulation methods, and energy storage devices played a limited role in this process. However, with the large-scale access of renewable energy such as wind energy and solar energy, the traditional power scheduling methods have gradually revealed their deficiencies in effectively coping with the fluctuating and intermittent energy supply. Therefore, energy storage technology has gradually become an important part of power scheduling, especially in the industrial and commercial fields, where energy storage devices are used to balance loads and optimize energy utilization efficiency. With the breakthrough of new energy storage technologies such as lithium batteries and compressed air energy storage (CAES), the cost of energy storage devices has gradually decreased, and their performance has been improved. This has greatly enhanced the flexibility and reliability of industrial and commercial energy storage scheduling. Especially the combination of smart grid and big data technologies enables industrial and commercial users to monitor and optimize the operation of energy storage devices in real time, thereby achieving more accurate demand response and load regulation. However, most of the existing energy storage modes in the current art adopt a unified scheduling strategy, lacking segmented management for different demands and scenarios, which in turn leads to low flexibility and stability of industrial and commercial energy storage scheduling. Summary of the Invention

[0003] Based on this, it is necessary to provide an industrial and commercial energy storage system and an energy storage scheduling method thereof to solve at least one of the above technical problems.

[0004] To achieve the above object, an industrial and commercial energy storage scheduling method, the method includes the following steps: Step S1: Obtain the location of industrial and commercial power plants; construct a topological network based on the location of industrial and commercial power plants to generate an industrial and commercial energy storage topological network; analyze the geographical altitude and energy transmission blocking characteristics of the industrial and commercial energy storage topological network, and divide the energy storage mode of the industrial and commercial energy storage topological network to generate a direct energy supply mode, a relay buffer mode, and an isolated storage mode; Step S2: Collect the node status of the corresponding nodes in the industrial and commercial energy storage topological network based on the direct energy supply mode; perform instant charge and discharge scheduling driven by peak-valley price differences on the corresponding nodes according to the node status to generate a direct energy supply scheduling strategy; Step S3: Perform multi-level power buffering and power quality regulation on the corresponding nodes in the industrial and commercial energy storage topological network through the relay buffer mode to generate a relay buffer scheduling strategy; perform island operation and black protection on the industrial and commercial energy storage topological network according to the isolated storage mode to generate an isolated storage scheduling mode; Step S4: Dynamically optimize the impedance matching of the industrial and commercial energy storage topology network according to the direct power supply scheduling strategy, relay buffer scheduling strategy, and isolated storage scheduling mode, and generate an optimal scheduling instruction set to execute the industrial and commercial energy storage scheduling optimization method.

[0005] Through the construction of the topology network of the industrial and commercial power plants and the division of the energy storage mode in combination with the geographical altitude and energy transmission blockage characteristics, the scheduling system not only considers the spatial layout, but also integrates the geographical environment and power transmission characteristics, realizing the refined classification management of energy storage resources from the source. By dividing the direct power supply mode, relay buffer mode, and isolated storage mode, the energy storage scheduling strategies in different scenarios can be generated respectively, enabling the scheduling system to have the dynamic adaptation ability of multiple strategies and multiple paths, and being able to cope with the complex and changeable industrial and commercial power supply and demand environment. In step S2, an instant charge and discharge scheduling mechanism driven by the peak-valley electricity price difference is introduced, enabling the system to instantaneously respond to the energy storage nodes according to the electricity price fluctuation, effectively improving the electricity economy and bringing significant electricity cost optimization benefits to industrial and commercial users. Through the relay buffer mode in step S3, the multi-level power buffer mechanism can flexibly adjust the electric energy in case of load mutation, grid oscillation, etc., playing the role of voltage stabilization and frequency balancing, and significantly improving the overall power quality and operation robustness of the system. The isolated storage mode supports island operation and black start protection, ensuring the stable operation of local power supply in extreme cases of external power supply interruption or grid failure, enhancing the system's independent survival and rapid post-disaster recovery capabilities, and improving the reliability of industrial and commercial energy use. Through the dynamic impedance matching optimization with the comprehensive scheduling strategy, the power loss and reflection in the power transmission process can be effectively reduced, ensuring the stability and efficiency of energy transmission, and automatically adjusting the role of the energy storage nodes in the network to achieve the dynamic optimal configuration of the system. The finally output "optimal scheduling instruction set" has the operability to directly guide the operation of industrial and commercial energy storage facilities, making the whole method not only stay in the theoretical model stage, but also have good engineering application value and being convenient for deployment and implementation. Therefore, by considering geographical factors, energy storage mode differentiation, instant scheduling, multi-level buffering, power quality regulation, and dynamic optimization, the present invention improves the flexibility and stability of industrial and commercial energy storage scheduling.

[0006] Preferably, step S1 includes the following steps: Step S11: Obtain the locations of industrial and commercial power plants; Step S12: Analyze the geographical coordinates of the locations of industrial and commercial power plants, and perform node marking on the geographical coordinates to generate power plant topology node data; Step S13: Extract the geographical connection paths of the power plant topology node data, and construct a topology network through the power plant topology node data and the geographical connection paths to generate an industrial and commercial energy storage topology network; Step S14: Conduct a geographical elevation analysis on the industrial and commercial energy storage topology network, and extract the elevation characteristics of the energy storage paths of the nodes in the industrial and commercial energy storage topology network and identify the energy storage transmission obstacle factors of the edges according to the analysis results, so as to generate energy storage path elevation characteristic data and energy storage energy transmission blocking characteristic data; Step S15: Divide the working elevation intervals of the nodes in the industrial and commercial energy storage topology network based on the energy storage path elevation characteristic data, and calculate the energy transmission loss rate of each node in combination with the energy storage energy transmission blocking characteristic data; Step S16: Divide the energy storage mode of the industrial and commercial energy storage topology network through the energy transmission loss rate, and generate a direct power supply mode, a relay buffer mode, and an isolation storage mode.

[0007] The present invention obtains and marks the geographical coordinates of the power plant through steps S11 and S12, realizes the precise positioning of the industrial and commercial power plant in the geographical information system, and generates standardized topology node data, providing a reliable basis for subsequent network construction. In step S13, a geographical connection path is introduced as a true reflection of the connection between nodes. The constructed industrial and commercial energy storage topology network not only has structural connectivity, but also has geographical rationality and engineering deployability, improving the accuracy and practicality of network modeling. By extracting the energy storage path elevation characteristics and energy transmission blocking factors in step S14, the physical resistance and geographical obstacles in the energy storage path can be revealed, thus avoiding the errors caused by ignoring terrain factors in traditional topology modeling, and contributing to the optimization and rational layout of the energy storage path. In step S15, by combining the elevation characteristics and transmission blocking factors, the energy transmission loss between nodes is comprehensively evaluated, and the consumption degree of energy during the transmission process in the network is quantified, significantly improving the authenticity and quantifiability of the energy storage system efficiency evaluation. In step S16, the energy storage mode is automatically divided through the energy transmission loss rate, realizing the accurate classification of energy storage nodes into three categories: direct power supply, relay buffer, or isolation storage, providing a clear target and reasonable deployment basis for subsequent scheduling strategies, thus avoiding a "one-size-fits-all" energy management. This method has significant advantages in areas with complex landforms or geographical isolation conditions (such as mountains, hills, urban dense areas, etc.), can identify high-loss nodes and transmission blind spots, so as to actively avoid inefficient or infeasible power supply paths, and improve the regional adaptability of the energy storage network.

[0008] Preferably, step S14 includes the following steps: Step S141: Extract the elevation coordinates of the nodes in the industrial and commercial energy storage topology network data to obtain the original node elevation data; perform interpolation fitting and error correction on the original node elevation data to generate standardized node elevation data; Step S142: Conduct a path-level difference analysis on the standardized node elevation data to generate energy storage path elevation characteristic data; Step S143: Conduct a terrain factor scan on the connection paths of the edges in the industrial and commercial energy storage topology network to generate path geographical structure feature data; extract blocking factors from the path geographical structure feature data to generate potential energy storage transmission obstacle data; Step S144: Conduct type annotation and spatial distribution assessment on the potential energy storage transmission obstacle data to generate energy storage energy transmission blocking feature data.

[0009] In the present invention, the original altitude data of node elevations is obtained through step S141, and interpolation fitting and error correction are performed, which can effectively eliminate the elevation data deviation caused by insufficient sampling density or measurement errors; in step S142, by performing path-level difference analysis on the standardized altitude data, the elevation fluctuations and drops of the energy storage paths are extracted; it can effectively identify the gravitational potential energy differences or transmission resistance changes caused by terrain changes in the paths, providing key references for efficient path selection and energy consumption assessment. Step S143 starts from the edge connection paths for terrain factor scanning, combines DEM (Digital Elevation Model) or GIS layer information to generate path geographical structure features; the extracted potential obstacles (such as mountains, building complexes, rivers, etc.) not only consider horizontal obstacles but also can identify interferences at the spatial level, improving the comprehensiveness and accuracy of obstacle detection. Step S144 performs type annotation (such as "terrain height difference obstacle", "construction density obstacle", "ecological reserve restriction", etc.) and spatial distribution assessment on the scanned obstacles; it can classify and analyze the influence weights of different types of obstacle factors, thereby generating energy storage energy transmission blocking feature data to provide a decision-making basis for path selection and scheduling optimization. Through the joint assessment of altitude differences and geographical structure resistances, the system can identify high-altitude transition paths or areas with severe terrain blockages, avoiding energy losses or operation failures caused by incorrect path planning; enhancing the adaptability and stability of the energy storage system in complex geographical environments.

[0010] Preferably, step S2 includes the following steps: Step S21: Perform node screening processing on the industrial and commercial energy storage topology network data in the direct energy supply mode to generate direct energy supply node set data; Step S22: Collect the real-time power status of the direct energy supply node set data to generate direct energy storage node status data; Step S23: Conduct segmented analysis of the energy consumption cycle on the node energy storage status data to generate node time period response feature data; compare the node time period response feature data with the load dynamic range to generate switchable charge and discharge node data; Step S24: Sort the switchable charge and discharge node data in sequence scheduling order to generate timing scheduling parameter data; jointly regulate the timing scheduling parameter data and the direct energy storage node status data to generate direct energy supply scheduling strategy data.

[0011] Through conditional filtering and pattern adaptation screening of nodes in the industrial and commercial energy storage topology network, the present invention can quickly identify energy storage nodes suitable for the direct energy supply mode; after forming the data of the direct energy supply node set, the scheduling scope can be narrowed, the calculation amount and data processing overhead can be reduced, and the system response speed and resource utilization efficiency can be improved. By collecting the power state of the direct energy supply node set, parameters such as its charging level, discharging capacity, remaining capacity, etc. are obtained and dynamically recorded as state data; it provides real-time basic data support for the subsequent formulation of scheduling strategies, and enhances the system's immediate perception and adjustment ability of the node operation status. Through segmented analysis of the energy consumption cycle, the response characteristics of nodes during peak / low valley periods can be identified, and dynamic adaptation to electricity prices and load fluctuations can be achieved; combined with the comparison of the dynamic load range, nodes with flexible switching capabilities are screened out to generate switchable charge-discharge node data, laying a foundation for building a flexible energy supply scheduling mechanism. Based on the switchable charge-discharge nodes, timing scheduling is sorted according to rules such as response ability, electricity margin, and time period priority to generate scheduling parameters; by jointly regulating the sorted parameters and the node state data, "direct energy supply scheduling strategy data" can be accurately generated, realizing efficient energy coordination among nodes and avoiding energy supply conflicts and scheduling imbalances. This method supports dynamic judgment based on peak-valley electricity prices and load fluctuations, realizes an economic driving strategy for charge and discharge, and improves the efficiency of the energy storage system participating in electricity price arbitrage.

[0012] Preferably, step S24 includes the following steps: Step S241: Evaluate the charge-discharge capabilities of the switchable charge-discharge node data to generate node energy storage response ability data; sort the node energy storage response ability data by priority to generate node scheduling priority sequence data; Step S242: Map the node scheduling priority sequence data to a periodic regulation window to generate time period scheduling sorting parameter data; perform cross-node consistency analysis on the time period scheduling sorting parameter data to generate coordinated timing scheduling parameter data; Step S243: Synchronously regulate the coordinated timing scheduling parameter data and the direct energy storage node state data to generate joint regulation strategy data; perform continuity inspection and time period continuous complement processing on the joint regulation strategy data to generate direct energy supply scheduling strategy data.

[0013] By evaluating the energy storage response capabilities of switchable charge-discharge nodes (S241), the present invention can accurately grasp the scheduling potential of each node in different states, and form a scheduling priority sequence based on this, avoiding inefficient or conflicting situations in traditional scheduling, thereby improving the overall energy response efficiency. Through cycle regulation window mapping and cross-node consistency analysis (S242), it is possible to achieve coordination of scheduling strategies between different time periods and different nodes, solve problems such as time series misalignment and response conflicts in multi-node scheduling, and enhance the synergy and stability of the system. The generation and continuous verification of the joint regulation strategy (S243) ensure the integrity and time period continuity of the energy storage node scheduling strategy, avoid discontinuous regulation or redundant switching, and improve the continuous energy supply capacity and intelligent level of the system. The finally generated "direct energy supply scheduling strategy data" has both time continuity and multi-node coordination, can effectively support the real-time energy supply demand in actual energy consumption scenarios, and ensure the stable and sustainable energy supply for critical loads.

[0014] Preferably, in step S3, the multi-level power buffering and power quality regulation of the corresponding nodes in the industrial and commercial energy storage topology network through the relay buffering mode include: Performing node power time series analysis on the corresponding nodes in the industrial and commercial energy storage topology network through the relay buffering mode to generate node power time series data; Performing windowed segmentation processing on the node power time series data, extracting power continuous change sections and mutation points, and generating node power change structure data; Performing slope analysis and mutation frequency statistics on the node power change structure data to identify the rate, density, and duration of power changes, and generating a power disturbance characteristic index set data; Dividing the corresponding nodes in the industrial and commercial energy storage topology network according to the power disturbance characteristic index set data to generate node power fluctuation segments, and performing multi-level power buffering on the node power fluctuation segments to generate node multi-level power buffering data; Using the node multi-level power buffering data to perform power quality regulation based on power balance on the industrial and commercial energy storage topology network to generate a relay buffering scheduling strategy.

[0015] Through the analysis of the node power time series and the extraction of variable structures, the present invention realizes the in-depth identification of the dynamic behavior of the power of industrial and commercial energy storage nodes, effectively captures continuous fluctuations and mutation events, and provides high-resolution data support for subsequent regulation. Through the division of node fluctuation segments and multi-level power buffering processing, hierarchical buffering strategies can be implemented for different fluctuation characteristics, avoiding resource waste or insufficient response under a single strategy regulation, and enhancing the flexible regulation ability of the system. The disturbance characteristic indicators obtained through slope analysis and mutation frequency statistics can accurately reflect the rate and intensity of the node power change, thus supporting the real-time dynamic adjustment of the scheduling strategy and enhancing the ability of the energy storage system to cope with uncertain loads. Finally, through the regulation method based on power balance, it can effectively suppress power quality problems such as voltage fluctuations and frequency offsets, and ensure the stable operation of sensitive loads or key industrial equipment. As a bridge, the relay buffering mode enables the disturbance information of local nodes to be fed back to the whole network scheduling, realizing the coordinated regulation of multiple nodes in the topological network and enhancing the overall stability and robustness of the system. Accurately identifying the power disturbance characteristics and applying multi-level buffering control can effectively improve the load regulation efficiency of the energy storage system, reduce frequent start-stop and unnecessary charge-discharge behaviors, extend the life of the energy storage device, and enhance the economy of the system.

[0016] Preferably, the division of node fluctuation segments for the corresponding nodes in the industrial and commercial energy storage topological network and the multi-level power buffering for the node power fluctuation segments according to the data of the power disturbance characteristic index set include: Dividing the node fluctuation segments for the corresponding nodes in the industrial and commercial energy storage topological network according to the data of the power disturbance characteristic index set to generate node power fluctuation segments, where the node power fluctuation segments include slow-varying transition segments, stable retention segments, multi-segment relay segments, jump response segments, and high-frequency oscillation segments; Extracting the slope and regulating the delayed release for the corresponding nodes in the industrial and commercial energy storage topological network based on the slow-varying transition segments to generate slow-varying slope buffering regulation data; performing intermittent disturbance and virtual load interpolation for the corresponding nodes in the industrial and commercial energy storage topological network based on the stable retention segments to generate stable retention buffering regulation data; Performing relay segment phase locking and leading release window regulation for the corresponding nodes in the industrial and commercial energy storage topological network based on the multi-segment relay segments to generate multi-segment relay buffering regulation data; performing fast amplitude limiting and channel switching buffering for the corresponding nodes in the industrial and commercial energy storage topological network based on the jump response segments to generate jump response buffering regulation data; Performing filtering isolation and inductance absorption smoothing for the corresponding nodes in the industrial and commercial energy storage topological network based on the high-frequency oscillation segments to generate high-frequency oscillation buffering regulation data; integrating the slow-varying slope buffering regulation data, stable retention buffering regulation data, multi-segment relay buffering regulation data, jump response buffering regulation data, and high-frequency oscillation buffering regulation data into node multi-level power buffering data.

[0017] Through the precise division of different power fluctuation segments (such as slow-varying transition segments, stable retention segments, multi-segment relay segments, etc.), the present invention can adopt the most suitable adjustment strategy for each type of fluctuation. This ensures an accurate response to different power change characteristics, avoids the rough application of a single adjustment strategy, and improves the refinement degree of system adjustment. Each fluctuation segment adopts a customized adjustment method. For example, the slow-varying transition segment uses slope extraction and delayed release control, the stable retention segment uses intermittent perturbation and virtual load interpolation, and the jump response segment uses fast amplitude limiting and channel switching buffering, etc. This personalized adjustment can not only specifically suppress different types of power fluctuations but also improve the response speed and stability of the energy storage system. For the jump response segment and high-frequency oscillation segment, the fast amplitude limiting and channel switching buffering, as well as filtering isolation and inductance absorption smoothing, can respond to sudden power fluctuations in an extremely short time to ensure the safety and stability of the power system. This instant response ability improves the system's response efficiency to emergencies. Through the multi-level power buffering technology, the impact of different types of power disturbances on the power grid is effectively eliminated. For example, the filtering isolation and inductance absorption measures for high-frequency oscillation avoid the interference of high-frequency disturbances to the system, ensure the stability of power quality, and reduce the damage caused by frequent fluctuations to equipment. The processing means for various fluctuation segments can accurately adjust the power output of nodes in combination with real-time data. The system has strong intelligence and adaptability, can dynamically select a suitable adjustment strategy according to the power disturbance characteristics, and improve the comprehensive performance of the energy storage system in a complex power environment.

[0018] Preferably, in step S3, the island operation and black protection of the industrial and commercial energy storage topology network according to the isolated storage mode include: Screen the nodes of the industrial and commercial energy storage topology network according to the isolated storage mode to obtain isolated storage nodes; Analyze the location information of the isolated storage nodes and identify the off-grid boundary to generate island operation topology boundary data; Perform breakpoint self-holding switching configuration on the island operation topology boundary data to generate island breakpoint operation configuration data; Reconstruct the load level of the island breakpoint operation configuration data to generate island mode load adaptation data; Collect the node energy storage state data of the isolated storage nodes, divide the priority of energy storage maintenance for the node energy storage state data to generate black start energy storage regulation data; generate a hierarchical black protection response data by generating a wake-up sequence for the black start energy storage regulation data in different time periods; Perform voltage and frequency self-stabilizing regression adjustment on the hierarchical black protection response data to generate isolated operation stability support data; integrate the isolated operation stability support data and the island mode load adaptation data to generate an isolated storage scheduling mode.

[0019] Through node screening, island operation topology boundary identification, and breakpoint self-holding switching configuration according to the isolated storage mode, the system can quickly achieve island operation when the power grid fails or is externally disturbed, maintain power supply in a local area, enhance the self-healing ability and emergency recovery ability of the power grid. Through load adaptation in the island mode and load level reconstruction, it ensures that the power matching between the load demand and energy storage nodes is optimized during island operation. Effectively avoid overload or voltage fluctuations, thus ensuring the stability of the power grid within the island. The energy storage priority division of the isolated storage node and the generation of black start energy storage regulation data ensure that during the black start process, the energy storage device provides sufficient energy support first, reduces the start-up time, and guarantees the power supply of critical loads. In addition, the generation of the wake-up sequence in different time periods further improves the smoothness and efficiency of the start-up process. Through hierarchical black protection response data and voltage-frequency self-stabilizing regression regulation, the system can adjust the voltage and frequency in real time to prevent voltage dips or frequency instability during island operation, ensuring the operation quality of the power grid. The integration of the scheduling strategy of the isolated operation stability support data and the island mode load adaptation data further optimizes the scheduling strategy of energy storage resources, enabling the energy storage device to provide the required power as needed at critical moments, reducing resource waste, and improving the overall scheduling efficiency of the system.

[0020] Preferably, step S4 includes the following steps: Step S41: Perform scheduling coupling analysis on the industrial and commercial energy storage topology network according to the direct energy supply scheduling strategy, relay buffer scheduling strategy, and isolated storage scheduling mode to generate multi-source node impedance variation data; Step S42: Perform dynamic impedance matching optimization on the multi-source node impedance variation data to generate energy storage scheduling impedance optimization data; Step S43: Generate scheduling instructions based on the energy storage scheduling impedance optimization data to obtain the optimal scheduling instruction set to execute the industrial and commercial energy storage scheduling optimization method.

[0021] Through the coupling analysis of direct energy supply scheduling strategy, relay buffer scheduling strategy and isolated storage scheduling mode, the present invention generates multi-source node impedance variation data, and comprehensively analyzes the impedance characteristics of energy storage nodes from multiple dimensions. This comprehensive analysis can accurately identify uneven or unreasonable resource allocation in the energy storage system, optimize the scheduling strategy, and then improve the overall scheduling efficiency of the energy storage system. By performing dynamic impedance matching optimization on the multi-source node impedance variation data, efficient adaptation of the energy storage device to the power grid system can be achieved. In this process, the energy storage node can automatically adjust its output or charge-discharge behavior according to real-time requirements and network status, ensuring impedance balance between nodes, avoiding system overload or instability, and improving the flexibility and responsiveness of energy storage scheduling. Through dynamic impedance matching optimization, the scheduling of energy storage devices can more precisely adapt to power grid load fluctuations, reducing energy losses caused by mismatches. This not only improves the power utilization efficiency but also helps extend the service life of energy storage devices. The optimal scheduling instruction set generated based on the energy storage scheduling impedance optimization data can provide precise scheduling instructions for the load requirements and status of a specific industrial and commercial energy storage topology network. This instruction set is flexible and efficient, and can optimize the allocation and utilization of energy storage resources under different load conditions and power grid states. During the energy storage scheduling process, by comprehensively analyzing the coupling of multiple scheduling strategies, the stability and reliability of the system can be improved. The optimized scheduling instruction set helps maintain the stable operation of the industrial and commercial energy storage system under different power grid operation modes, reducing fluctuations and sudden failures, and ensuring high reliability during system operation. Dynamic impedance matching optimization provides better ability to cope with sudden load changes, enabling the energy storage system to adjust the scheduling strategy in real time, thus better adapting to complex load changes. This adaptability is particularly important in industrial and commercial environments with large load fluctuations.

[0022] In this specification, an industrial and commercial energy storage system is provided for implementing the above-mentioned industrial and commercial energy storage method. The industrial and commercial energy storage system includes: An energy storage mode division module, configured to obtain the location of the industrial and commercial power plant; construct a topology network based on the location of the industrial and commercial power plant to generate an industrial and commercial energy storage topology network; analyze the geographical altitude and energy transmission blocking characteristics of the industrial and commercial energy storage topology network, and divide the industrial and commercial energy storage topology network into energy storage modes to generate a direct energy supply mode, a relay buffer mode, and an isolated storage mode; A direct energy storage scheduling module, configured to collect the node status of the corresponding nodes in the industrial and commercial energy storage topology network based on the direct energy supply mode; perform peak-valley price difference-driven instant charge-discharge scheduling on the corresponding nodes according to the node status to generate a direct energy supply scheduling strategy; A multi-level buffer scheduling module, which is used to perform multi-level power buffering and power quality regulation on corresponding nodes in the industrial and commercial energy storage topology network through a relay buffer mode, and generate a relay buffer scheduling strategy; perform island operation and black protection on the industrial and commercial energy storage topology network according to the isolation storage mode, and generate an isolation storage scheduling mode; A transmission impedance optimization module, which is used to perform dynamic impedance matching optimization on the industrial and commercial energy storage topology network according to the direct energy supply scheduling strategy, the relay buffer scheduling strategy, and the isolation storage scheduling mode, and generate an optimal scheduling instruction set to execute the industrial and commercial energy storage scheduling optimization method.

[0023] The beneficial effects of the present invention are as follows: By constructing a topology network based on the location of industrial and commercial power plants, it can effectively and accurately reflect the geographical distribution of the energy storage system and the characteristics of the power transmission network, providing a scientific basis for subsequent energy storage scheduling. According to geographical altitude and energy transmission blocking characteristics, energy storage modes are divided, and a direct energy supply mode, a relay buffer mode, and an isolation storage mode are generated respectively. This diverse mode division helps to flexibly select appropriate energy storage scheduling strategies according to different geographical and grid conditions, thereby optimizing the overall system performance. By analyzing the geographical characteristics and energy transmission blocking of the energy storage topology network, it can effectively respond to power demands and energy storage system operating conditions in different environments, improving the adaptability and flexibility of the system to environmental changes. Based on the direct energy supply mode, node states are collected and real-time charge and discharge scheduling driven by peak-valley price differences is performed, which can optimize the charge and discharge behavior of the energy storage system during different electricity price fluctuations, improve the power utilization efficiency, and reduce operating costs. Through the peak-valley price difference scheduling strategy, the energy storage system can charge when the electricity price is low and discharge when the electricity price is high, not only achieving balanced power scheduling, but also bringing significant economic benefits to users. Real-time charge and discharge scheduling can effectively balance the grid load, relieve the pressure during peak grid loads, and improve grid stability. By performing multi-level power buffering and power quality regulation on nodes through the relay buffer mode, it can effectively reduce the impact of grid fluctuations and load mutations on the energy storage system, ensure stable power quality, and improve the stability of the energy storage system. According to the isolation storage mode, island operation and black protection are performed, which can ensure that the energy storage system can automatically switch to the island mode in special situations (such as sudden grid failures), and ensure the continuous power supply of critical loads, improving the emergency response ability of the system. Based on multiple scheduling modes, dynamic impedance matching optimization is performed on the industrial and commercial energy storage topology network, which can accurately adjust the power transmission efficiency between the energy storage system and the grid, ensuring the accuracy and stability of power scheduling. By generating an optimal scheduling instruction set through dynamic impedance optimization, it can automatically optimize the energy storage scheduling strategy under different load conditions, achieve optimal energy allocation and scheduling, and improve the overall performance of the system. Description of the Drawings

[0024] Figure 1 It is a schematic diagram of the step flow of an industrial and commercial energy storage scheduling method; Figure 2 is Figure 1 a detailed implementation step flowchart of step S2 in Figure 3 is Figure 1 a detailed implementation step flowchart of step S4 in The realization, functional features and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed implementation manners

[0025] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be called the second unit, and similarly the second unit may be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0028] To achieve the above object, please refer to Figures 1 to 3 , an industrial and commercial energy storage scheduling method, the method includes the following steps: Step S1: Obtain the location of industrial and commercial power plants; construct a topological network based on the location of industrial and commercial power plants to generate an industrial and commercial energy storage topological network; analyze the geographical altitude and energy transmission blocking characteristics of the industrial and commercial energy storage topological network, and divide the energy storage mode of the industrial and commercial energy storage topological network to generate a direct energy supply mode, a relay buffer mode and an isolation storage mode; Step S2: Based on the direct power supply mode, collect the node status of the corresponding nodes in the industrial and commercial energy storage topology network; perform immediate charge and discharge scheduling driven by peak-valley price differences on the corresponding nodes according to the node status, and generate a direct power supply scheduling strategy; Step S3: Perform multi-level power buffering and power quality regulation on the corresponding nodes in the industrial and commercial energy storage topology network through the relay buffering mode, and generate a relay buffering scheduling strategy; perform island operation and black protection on the industrial and commercial energy storage topology network according to the isolated storage mode, and generate an isolated storage scheduling mode; Step S4: Perform dynamic impedance matching optimization on the industrial and commercial energy storage topology network according to the direct power supply scheduling strategy, the relay buffering scheduling strategy, and the isolated storage scheduling mode, and generate an optimal scheduling instruction set to execute the industrial and commercial energy storage scheduling optimization method.

[0029] In the present invention, through the construction of the topology network of the industrial and commercial power plant location and the division of the energy storage mode in combination with the geographical altitude and energy transmission blocking characteristics, the scheduling system not only considers the spatial layout, but also integrates the geographical environment and power transmission characteristics, realizing the refined classification management of energy storage resources from the source. By dividing the direct power supply mode, the relay buffering mode, and the isolated storage mode, energy storage scheduling strategies in different scenarios can be generated respectively, enabling the scheduling system to have the dynamic adaptation ability of multiple strategies and multiple paths, and being able to cope with the complex and changeable industrial and commercial power supply and demand environment. In Step S2, an immediate charge and discharge scheduling mechanism driven by the peak-valley electricity price difference is introduced, enabling the system to respond immediately to the energy storage nodes according to the electricity price fluctuation, effectively improving the electricity economy, and bringing significant electricity cost optimization benefits to industrial and commercial users. Through the relay buffering mode in Step S3, the multi-level power buffering mechanism can flexibly regulate the electric energy in the case of load mutation, grid oscillation, etc., playing the role of voltage stabilization and frequency balancing, and significantly improving the overall power quality and operation robustness of the system. The isolated storage mode supports island operation and black start protection, and can ensure the stable operation of local power supply in the extreme case of external power supply interruption or grid failure, enhancing the system's independent survival and rapid post-disaster recovery capabilities, and improving the reliability of industrial and commercial energy use. Through the dynamic impedance matching optimization by the comprehensive scheduling strategy, the power loss and reflection in the power transmission process can be effectively reduced, the stability and efficiency of energy transmission can be guaranteed, and the role of the energy storage nodes in the network can be automatically adjusted to realize the dynamic optimal configuration of the system. The finally output "optimal scheduling instruction set" has the operability to directly guide the operation of industrial and commercial energy storage facilities, making the whole method not only stay in the theoretical model stage, but also have good engineering application value and be convenient for deployment and implementation. Therefore, the present invention improves the flexibility and stability of industrial and commercial energy storage scheduling by considering geographical factors, energy storage mode differentiation, immediate scheduling, multi-level buffering, power quality regulation, and dynamic optimization.

[0030] In the embodiment of the present invention, referring to Figure 1As shown in the figure, it is a schematic diagram of the step flow of a method for scheduling industrial and commercial energy storage in the present invention. In this example, the method for scheduling industrial and commercial energy storage includes the following steps: Step S1: Obtain the location of industrial and commercial power plants; construct a topological network based on the location of industrial and commercial power plants to generate an industrial and commercial energy storage topological network; analyze the geographical altitude and energy transmission blocking characteristics of the industrial and commercial energy storage topological network, and divide the energy storage mode of the industrial and commercial energy storage topological network to generate a direct energy supply mode, a relay buffer mode, and an isolated storage mode; Step S2: Collect the node status of the corresponding nodes in the industrial and commercial energy storage topological network based on the direct energy supply mode; perform real-time charge and discharge scheduling driven by peak-valley price differences on the corresponding nodes according to the node status to generate a direct energy supply scheduling strategy; Step S3: Perform multi-level power buffering and power quality regulation on the corresponding nodes in the industrial and commercial energy storage topological network through the relay buffer mode to generate a relay buffer scheduling strategy; perform island operation and black protection on the industrial and commercial energy storage topological network according to the isolated storage mode to generate an isolated storage scheduling mode; Step S4: Perform dynamic impedance matching optimization on the industrial and commercial energy storage topological network according to the direct energy supply scheduling strategy, the relay buffer scheduling strategy, and the isolated storage scheduling mode to generate an optimal scheduling instruction set to execute the industrial and commercial energy storage scheduling optimization method.

[0031] In the embodiments of the present invention, specific geographical location data of industrial and commercial power plants are obtained from a database by using Geographic Information System (GIS) technology. These data can be obtained through satellite remote sensing, UAV aerial photography, or existing ground facilities to ensure the accuracy of the longitude and latitude coordinates of each power plant. Data processing includes cleaning, normalization, and verification to ensure high precision and consistency of all locations. Based on the obtained power plant location data, a topological structure of the industrial and commercial energy storage system is constructed using a graph theory model (such as a graph network algorithm). Each power plant and energy storage node is represented as a node in the network, and the connections between nodes represent the power flow between energy storage systems in the power grid. Calculate the distance between nodes and the power transmission lines, and set the connection edge weights based on these parameters. Use Geographic Information System (GIS) to analyze the geographical elevation of each power plant and identify topographical features that affect power transmission, such as mountains, plateaus, etc. Combine the physical parameters of the transmission lines (such as line length, type, and voltage level) to model the energy transmission blocking characteristics, analyze the impact of different terrains on power transmission, and determine the existing resistance areas. Divide the energy storage modes into direct energy supply mode, relay buffer mode, and isolated storage mode. In the direct energy supply mode, during peak power demand periods, the energy storage device directly supplies power to the surrounding loads to maximize the use of the fast response ability of the energy storage device. In the relay buffer mode, when the power demand is relatively stable, the energy storage device acts as a relay node to buffer electrical energy and adjust the power supply stability to avoid over-supply or under-supply of power. In the isolated storage mode, when a power grid failure occurs or independent operation is required, the energy storage device provides power in island mode to ensure the continuous operation of critical facilities. Install sensors at each energy storage node to collect real-time status information of the node, including battery charge, battery health, voltage, charge status, etc. Use edge computing devices to perform preliminary processing on the collected real-time data and transmit it to the central control system. According to the market electricity price information (i.e., peak-valley electricity price), combined with the real-time status of the energy storage node, make immediate charge and discharge decisions. Through optimization algorithms (such as linear programming or reinforcement learning), choose to charge when the electricity price is low and discharge when the electricity price is high to obtain the maximum economic benefit. Generate a real-time charge and discharge scheduling plan according to the charge and discharge characteristics of the energy storage device (such as maximum power output, efficiency, etc.) and transmit it to the corresponding energy storage node for execution. During high-demand periods, preferentially use the energy storage node to provide direct energy supply. The scheduling strategy ensures that each energy storage node charges and discharges in an optimal manner, thereby providing reliable power support for industrial and commercial loads during peak demand periods. When the power demand is relatively stable, the energy storage node performs multi-level power buffering according to the relay buffer mode scheduling. The system realizes the relay buffer strategy through hierarchical management based on information such as real-time power load prediction and energy storage node status. Adopt a multi-level power regulation model (such as a distributed power dispatching algorithm) to regulate the charge and discharge status of the energy storage node to ensure the stability of power supply and reduce the dependence on the main power grid.In the case of large power fluctuations or poor power quality, the energy storage system optimizes the power quality by regulating the charge and discharge process of the battery. This process includes voltage regulation, frequency regulation, etc., to ensure that industrial and commercial users receive high-quality power supply. When a fault or power outage occurs in the power grid, the energy storage system automatically switches to the isolated storage mode to provide independent operation capabilities. At this time, the energy storage node not only needs to supply power but also has the black start ability, that is, the ability to restore the power supply system. By using real-time fault detection algorithms and automated switching mechanisms, it is ensured that the energy storage node can quickly identify power grid faults and start the island operation mode to guarantee the power supply of critical loads. By analyzing the dynamic impedance of the industrial and commercial energy storage topology network, the system can adjust the power output of the energy storage device in real time to match the load characteristics of the power grid. Impedance matching is achieved by optimizing the relationship between the power output of the energy storage node and electrical parameters such as grid current and voltage. Optimization algorithms (such as genetic algorithms or particle swarm optimization algorithms) can be used to dynamically adjust the charge and discharge schedule of the energy storage node, thereby achieving load balancing and maximizing system stability and efficiency. Based on the optimization results, the system generates an optimal scheduling instruction set. These instructions include scheduling parameters such as the charge and discharge time, power, and frequency of each energy storage node to ensure that the overall operation of the energy storage system meets market demand, grid stability, and economic benefits. The optimal scheduling instruction set is sent to each energy storage node through an automated control system to execute specific energy storage scheduling tasks.

[0032] Preferably, step S1 includes the following steps: Step S11: Obtain the location of industrial and commercial power plants; Step S12: Analyze the geographical coordinates of the location of industrial and commercial power plants, and perform node marking on the geographical coordinates to generate power plant topology node data; Step S13: Extract the geographical connection paths of the power plant topology node data, and construct a topology network through the power plant topology node data and geographical connection paths to generate an industrial and commercial energy storage topology network; Step S14: Perform geographical elevation analysis on the industrial and commercial energy storage topology network, and extract the elevation characteristics of the energy storage paths of the nodes in the industrial and commercial energy storage topology network and identify the energy storage transmission obstacle factors of the edges according to the analysis results to generate energy storage path elevation characteristic data and energy storage energy transmission blocking characteristic data; Step S15: Divide the working elevation intervals of the nodes in the industrial and commercial energy storage topology network based on the energy storage path elevation characteristic data, and calculate the energy transmission loss rate of each node in combination with the energy storage energy transmission blocking characteristic data; Step S16: Divide the energy storage mode of the industrial and commercial energy storage topology network through the energy transmission loss rate to generate a direct energy supply mode, a relay buffer mode, and an isolated storage mode.

[0033] In the embodiments of the present invention, the precise geographical location of industrial and commercial power plants is obtained by using multiple data sources (such as satellite remote sensing, Geographic Information System (GIS), unmanned aerial vehicle aerial photography, etc.). The data sources include publicly available geographical information data, on-site survey data, and facility data within the enterprise. The geographical coordinates (longitude, latitude) of the power plant and other additional information (such as the capacity and type of the power plant, etc.) are collected in a database for subsequent analysis. The collected location data of industrial and commercial power plants is processed. GIS analysis tools (such as ArcGIS, QGIS) are used to precisely analyze the geographical coordinates of each power plant to ensure the accuracy and consistency of the coordinate data. Each power plant location is marked with a node. Each node represents an energy storage node, and the geographical coordinates of the power plant, power generation capacity, and basic information of the energy storage system are associated with the node to form "power plant topological node data". Spatial coordinate data of the nodes is generated through GIS analysis, including the longitude and latitude of each power plant, geographical location description (such as city, county, street, etc.), and relative position relationship with other nodes. Based on the power plant topological node data, the geographical connection paths between different nodes are analyzed. The paths can be extracted based on factors such as terrain, existing power transmission lines, and planned power grids. Shortest path algorithms such as the Dijkstra algorithm are used to calculate the connection paths between nodes, considering factors such as the length of the path and terrain changes. Using topological principles and network construction methods (such as graph theory), the nodes and their connection paths are modeled to form the power grid topology structure of the industrial and commercial energy storage system. Each node and path form a graph, where the nodes represent energy storage devices and the edges represent power transmission lines. This topological structure can reflect the geographical distribution of the energy storage system and the power flow path. Based on Digital Elevation Model (DEM) data, the altitude of each node in the energy storage system is analyzed. GIS tools are used for the extraction and analysis of elevation data to identify the altitude characteristics of each node. 3D modeling tools (such as ArcGIS 3D Analyst) are used to model the geographical elevation of the energy storage path to generate the altitude change characteristics of the energy storage path. By analyzing the terrain data, obstacle factors affecting power transmission are identified, such as natural obstacles like mountains, canyons, rivers, etc., or physical obstacles in power grid construction (such as overly long lines, difficult maintenance, etc.). Quantitative analysis is performed on these obstacle factors to generate "energy storage energy transmission blocking characteristic data", recording the power transmission difficulty and resistance values of each path segment or node. According to the altitude characteristic data of the energy storage path, the nodes in the energy storage system are divided into multiple working altitude intervals. Each interval represents a different altitude level, and power transmission optimization is performed according to different altitude conditions. Using geographical information system analysis tools, such as contour maps, the area is divided into several altitude levels and matched with the actual altitude information of the power plant nodes. According to the altitude characteristics of the energy storage path and the influence of the obstacle factors, the energy transmission loss rate of each node in different altitude intervals is calculated.Calculations can be performed using a transmission loss model (such as Ohm's law or an optimized transmission loss model considering terrain effects). For power transmission at different altitudes, different power losses will be encountered, especially for long-distance power transmission or in high-altitude areas, where the energy loss increases. Combining the obstacle factor data, the energy loss rate of the nodes is obtained. Based on the calculated energy transmission loss rate and the energy storage path characteristic data, the energy storage modes of the entire industrial and commercial energy storage topology network are divided. The appropriate energy storage mode is mainly selected according to the efficiency of energy transmission and the power demand of each node. The direct energy supply mode is applicable to nodes on low-loss paths or high-efficiency energy transmission paths, and the energy storage device directly supplies power to the load. The relay buffer mode is applicable to nodes with high losses during transmission. The energy storage device acts as a relay node to reduce losses by buffering power. The isolated storage mode is applicable to areas with high energy losses or unstable power grids. The energy storage device operates in island mode to ensure the stability and reliability of the system. Based on the energy transmission loss rate and the path characteristic data, decision trees or clustering algorithms (such as the K-means or CART model) are used to divide the energy storage modes of each node in the energy storage system. Each node will be assigned to the most suitable energy storage mode to optimize the overall energy efficiency and stability of the system.

[0034] Preferably, step S14 includes the following steps: Step S141: Extract the node elevation coordinates of the industrial and commercial energy storage topology network data to obtain the original node altitude data; perform interpolation fitting and error correction on the original node altitude data to generate the normalized node altitude data; Step S142: Perform path-level difference analysis on the normalized node altitude data to generate the altitude characteristic data of the energy storage path; Step S143: Scan the terrain factors of the connection paths of the edges in the industrial and commercial energy storage topology network to generate the path geographical structure characteristic data; extract the blocking factors from the path geographical structure characteristic data to generate the potential energy storage transmission obstacle data; Step S144: Perform type annotation and spatial distribution evaluation on the potential energy storage transmission obstacle data to generate the energy storage energy transmission blocking characteristic data.

[0035] In the embodiments of the present invention, elevation information of each node in the industrial and commercial energy storage topology network is extracted by using Digital Elevation Model (DEM) or Light Detection and Ranging (LiDAR) data. Elevation data can be extracted by using GIS tools (such as ArcGIS, QGIS) or remote sensing image analysis techniques. According to the geographical coordinates of the nodes, the elevation value of each node is accurately extracted from the elevation dataset. Interpolation processing is performed on the original elevation data, and methods such as Kriging interpolation, spline interpolation, or Inverse Distance Weighting (IDW) interpolation are used to smooth and fit the elevation data. By comparing with other data sources (such as field measurement data or elevation data with higher accuracy), the errors in the interpolated data are corrected and standardized to ensure the accuracy and consistency of the elevation data. Finally, "node standardized elevation data" is generated to make the elevation information of each node consistent in the same reference system. The path-level difference method (such as linear interpolation method) is adopted to analyze the elevation changes between nodes in the industrial and commercial energy storage topology network. By analyzing the elevation differences between nodes, the elevation changes of each node on the path are calculated. Difference analysis is performed on each path (i.e., the connection between nodes), the elevation increase and decrease values in the path are calculated, and the relationship between elevation changes and power transmission is analyzed. Polynomial fitting or local linear interpolation methods can be used to smooth and process these elevation change data to ensure that the elevation characteristics on the path are continuous and reasonable. According to the path-level difference analysis results, "energy storage path elevation characteristic data" is generated, including the starting and ending elevations of each path, the elevation changes of the intermediate nodes on the path, and the overall slope and inclination angle of the path. Terrain factor scanning is performed on each connection path (i.e., network edge) in the energy storage topology network. Using the Digital Elevation Model (DEM) and terrain data analysis tools, the terrain type of each path (such as mountains, canyons, rivers, etc.) and its potential impact on power transmission are analyzed. Information such as geology, vegetation cover, and land use can also be integrated to analyze the geographical structure characteristics on the path, such as terrain undulation, slope, and curvature. "Path geographical structure characteristic data" is generated, which contains the geographical type, slope, curvature, etc. characteristics of each path. The characteristic data of each path will support the feasibility, efficiency, and impedance calculation of power transmission. According to the path geographical structure characteristic data, blocking factors are extracted, such as natural obstacles (mountains, rivers) or obstacles caused by human activities (roads, buildings, farmlands, etc.). Obstacles with a greater impact on power transmission are identified and marked as potential energy storage transmission obstacles. Based on the path geographical structure characteristic data and the blocking factor extraction results, "potential energy storage transmission obstacle data" is generated, recording the potential obstacles on each path and their impact degree on power transmission. Type annotation is performed on the potential energy storage transmission obstacle data to classify different obstacles, such as natural obstacles (mountains, forests, lakes) and man-made obstacles (buildings, power facilities, roads).Classify the obstacles according to their types and degrees of influence, and assign a priority or degree of obstruction to each obstacle. Conduct a spatial distribution analysis of potential energy storage transmission obstacles. Use spatial analysis tools (such as spatial statistics, heat maps, etc.) to analyze the distribution of obstacles in the industrial and commercial energy storage topology network. Evaluate the influence range of each obstacle and its potential risks in power transmission, so as to provide decision-making support for path selection and energy optimization. Generate "energy storage energy transmission blocking feature data" based on the evaluation of obstacle types and spatial distributions. This data will include detailed information, types, degrees of influence of each potential obstacle on the path, and its actual impact on power transmission.

[0036] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes: Step S21: Screen the node data of the industrial and commercial energy storage topology network in the direct energy supply mode to generate direct energy supply node set data; Step S22: Collect the real-time power state of the direct energy supply node set data to generate direct energy storage node state data; Step S23: Conduct a segmented analysis of the energy consumption cycle of the node energy storage state data to generate node time period response characteristic data; Compare the load dynamic range of the node time period response characteristic data to generate switchable charge and discharge node data; Step S24: Sort the sequence scheduling order of the switchable charge and discharge node data to generate time sequence scheduling parameter data; Jointly regulate the time sequence scheduling parameter data and the direct energy storage node state data to generate direct energy supply scheduling strategy data.

[0037] In the embodiments of the present invention, based on the node information in the industrial and commercial energy storage topology network, nodes that meet the requirements of the direct energy supply mode are first screened out. These nodes are usually those that are relatively close to the power plant and have a high load demand. A distance-based screening method or a power demand-based screening method is used to determine the eligible nodes. For example, nodes within a certain range from the energy storage center and with a load demand greater than a certain threshold can be screened out as direct energy supply nodes. The data of the screened node set is recorded, including information such as the unique identifier, location coordinates, and current load demand of the nodes. An intelligent electricity meter or a real-time data acquisition system is used to collect the real-time electricity status of each node in the direct energy supply node set. These devices can monitor key information such as the battery power, charge and discharge rate, current, and voltage of each node. Through wireless sensor network (WSN) or Internet of Things (IoT) technology, the data is collected and transmitted to the central control system in real time. Based on the collected real-time data, the energy storage status of each node is calculated (for example, the remaining battery power, energy storage efficiency, charge and discharge history, etc.). These real-time status data are summarized and "direct energy storage node status data" is generated, providing a basis for scheduling decisions. According to the historical electricity consumption data and load demand of the nodes, the electricity consumption cycle of the nodes is identified. Usually, these cycles can be divided into peak periods, valley periods, and normal periods. Combining time series analysis techniques, the load change pattern of each node is automatically identified. The load curve of the node is segmented, for example, the electricity consumption data within a day is divided by hour, and its load peak and valley changes are analyzed to obtain the power demand characteristics in each time period. Based on the segmented analysis of the energy consumption cycle, "node time period response characteristic data" is generated, which reflects the response characteristics of the node in different time periods, such as power demand fluctuations, energy storage capacity changes, charge and discharge response times, etc. This data helps to judge which nodes can perform charge and discharge scheduling in specific time periods to maximize energy storage utilization and power system balance. The node time period response characteristic data is compared to analyze the load dynamic range in different time periods (for example, low load range, peak load range, etc.), and judge which nodes have large load fluctuations and which nodes have higher regulation potential. Based on the comparison results of the load dynamic range, nodes suitable for charge and discharge switching, that is, "switchable charge and discharge nodes", are screened out. The information such as the screened switchable charge and discharge nodes, their adjustment timing, energy requirements, and response characteristics is summarized to form "switchable charge and discharge node data", which provides specific charge and discharge operation objects for scheduling. According to the switchable charge and discharge node data, combined with the electricity status, load demand, and energy storage response ability of the nodes, a scheduling optimization algorithm (such as genetic algorithm, particle swarm optimization algorithm, etc.) is used to sort the nodes to generate the scheduling order of the nodes.Determine the scheduling priority. Based on factors such as the battery capacity, power fluctuation, and load demand of the nodes, arrange the nodes that most need to charge or discharge to execute first. According to the scheduling order, generate "sequential scheduling parameter data", which includes information such as the scheduling time, scheduling order, charge and discharge amount, and priority of each node, providing specific scheduling instructions for further execution. Through the dynamic adjustment of the scheduling parameters, ensure that the energy balance of each node and the power supply demand are maximally satisfied. Conduct a joint analysis of the sequential scheduling parameter data and the "direct energy storage node status data", and use data such as the real-time power status, energy storage system efficiency, and system load to dynamically adjust the scheduling strategy. Use a multi-objective optimization model (such as minimizing energy loss, maximizing system efficiency, minimizing latency, etc.) to generate the optimal "direct energy supply scheduling strategy data" to ensure the efficient operation of the energy storage system.

[0038] Preferably, step S24 includes the following steps: Step S241: Evaluate the charge and discharge capabilities of the switchable charge and discharge node data to generate node energy storage response capability data; sort the node energy storage response capability data by priority to generate node scheduling priority sequence data; Step S242: Map the node scheduling priority sequence data to the periodic regulation window to generate time period scheduling sorting parameter data; conduct cross-node consistency analysis on the time period scheduling sorting parameter data to generate coordinated sequential scheduling parameter data; Step S243: Synchronously regulate the coordinated sequential scheduling parameter data and the direct energy storage node status data to generate joint regulation strategy data; conduct continuity inspection and time period continuous complement processing on the joint regulation strategy data to generate direct energy supply scheduling strategy data.

[0039] In the embodiments of the present invention, the charging and discharging capabilities of each node are evaluated by using the historical energy storage data of the node (such as battery capacity, battery health status, charge and discharge efficiency) and real-time status data (such as current remaining power, current load demand). A physical model-based method can be adopted, such as evaluating the capabilities according to the discharge characteristics, charging characteristics of the battery and the response speed of the energy storage system; or a data-driven method can be adopted, such as predicting the charging and discharging capabilities of the node through historical data regression analysis. The evaluation results will be used as the "energy storage response capability data" of the node, and this data includes the maximum charging capability, discharging capability and charge and discharge efficiency of each node. Based on the energy storage response capability data of the nodes, all nodes are sorted by priority. The priority sorting is based on multiple factors, such as: the charging and discharging capabilities of the nodes (the stronger the response capability of the node, the higher the priority); the load demand of the nodes (nodes with large load demands need to be scheduled first); the health status of the battery (batteries in good health have higher priorities); the scheduling timeliness of the nodes (nodes that need to respond quickly are prioritized). The sorted results generate the "node scheduling priority sequence data" for subsequent scheduling decisions. Combining the historical power consumption data of the nodes, the peak, trough and regular load cycles in different time periods are analyzed. Based on these time periods, a "cycle regulation window" is defined, that is, the scheduling status of the nodes within a certain time period. The node scheduling priority sequence data is mapped into each cycle regulation window to ensure that the scheduling strategy meets the load demands in different time periods. According to the changes in the load demands, the scheduling order of the nodes can be adjusted to adapt to different power supply situations. The "time period scheduling sorting parameter data" is generated, and this data includes information such as the scheduling order, scheduling priority, required charge and discharge amount of each node within each time period. Analyze the scheduling strategies of different nodes within the same time period to ensure cross-node scheduling consistency. For example, ensure that there are no power supply conflicts or network blockages when scheduling multiple nodes simultaneously. Conduct a consistency analysis on the time period scheduling sorting parameter data to determine whether there are resource conflicts, transmission bottlenecks, etc. among different nodes within the same time period, and adjust the node scheduling order or the scheduling amount according to the analysis results. Generate the "coordinated timing scheduling parameter data" to ensure that the charging and discharging operations of each node can be coordinated within the entire scheduling cycle, avoid conflicts and optimize resource allocation. Synchronously regulate the "coordinated timing scheduling parameter data" and the "direct energy storage node status data" to ensure that the scheduling of each node not only conforms to the scheduling priority sequence, but also needs to be dynamically adjusted according to its current energy storage status. Use a real-time data feedback mechanism to adjust the scheduling order or the charge and discharge amount of the nodes. For example, if the battery power of a certain node is lower than expected, then schedule this node to charge first, and vice versa for discharging. According to the synchronous regulation results, generate the "joint regulation strategy data", which combines the information of the priority scheduling order, energy storage status and cycle regulation window of the nodes. Through joint regulation, accurate power allocation and energy storage optimization can be achieved.Perform a continuity test on the combined regulation strategy data to ensure a smooth transition of the charge and discharge states between each time period and the next during the scheduling process, and avoid excessive power fluctuations or scheduling conflicts. When a scheduling gap or discontinuity is detected, use time period continuous completion processing to fill in the missing scheduling information and ensure the continuity of power supply. Generate the final "direct energy supply scheduling strategy data", which contains information such as the charge and discharge strategies, priorities, and scheduling orders of all nodes at different time periods, providing complete instructions for the scheduling of the entire industrial and commercial energy storage system.

[0040] Preferably, in step S3, the multi-level power buffering and power quality regulation of the corresponding nodes in the industrial and commercial energy storage topology network through the relay buffering mode includes: Perform node power time series analysis on the corresponding nodes in the industrial and commercial energy storage topology network through the relay buffering mode to generate node power time series data; Perform windowed segmentation processing on the node power time series data, extract the power continuous change sections and mutation points, and generate node power change structure data; Perform slope analysis and mutation frequency statistics on the node power change structure data to identify the rate, density, and duration of power changes, and generate power disturbance characteristic index set data; According to the power disturbance characteristic index set data, divide the corresponding nodes in the industrial and commercial energy storage topology network into node fluctuation segments, generate node power fluctuation segments, and perform multi-level power buffering on the node power fluctuation segments to generate node multi-level power buffering data; Use the node multi-level power buffering data to perform power quality regulation based on power balance on the industrial and commercial energy storage topology network to generate a relay buffering scheduling strategy.

[0041] In the embodiments of the present invention, for each node in the industrial and commercial energy storage topology network, sensors and data acquisition devices are used to collect the power output data of the node. The power time series data includes the changes in voltage, current, and power of the node over time. The original power data is formatted and standardized to ensure data consistency. For example, the power values are aggregated according to time periods such as hours and minutes to generate regular time series data. The power data of the node at different time periods is organized into a time series, which reflects the power demand and supply of the node at different time points. The power time series data of the node is divided according to time windows, and the time windows can be set to several minutes, several hours, etc. according to actual needs. Within each time window, the power changes are analyzed to identify continuous sections with relatively stable power value changes, and these sections are extracted. Within each time window, the points with large power value changes are analyzed, and the mutation points whose changes exceed the preset threshold are marked. These mutation points represent drastic changes in power output or demand and need to be adjusted by the energy storage system. Through segmented extraction and mutation point identification, a data set reflecting the power change structure of the node is generated, including continuous sections of power changes and mutation point information. The slope analysis is performed on each power change section to calculate the rate of power change. A larger slope change indicates a rapid change in power demand, which will cause instability in the power grid. The frequency of mutation points appearing in the time series of each node is statistically analyzed, which helps to evaluate the frequency of power fluctuations of the node. Frequent power fluctuations require more buffer energy to maintain system stability. Through slope analysis and mutation frequency statistics, a set of power disturbance characteristic index data is generated, including: the change rate (slope) of each section; the frequency of mutation points of each node; the duration of power fluctuations of each node; the density of power fluctuations (the number of fluctuations per unit time). Based on the power disturbance characteristic index data, the power time series of each node is divided into fluctuation segments. Each fluctuation segment corresponds to an obvious power fluctuation interval, which contains multiple mutation points. For each fluctuation segment, a multi-level power buffer mechanism is applied for mitigation. The specific method is as follows: The power fluctuations are initially buffered by a single energy storage device. For example, the low-frequency fluctuations of the node can be simply absorbed or released by the battery. For larger power fluctuations, multiple energy storage nodes can be enabled to cooperate together to achieve a greater degree of power fluctuation balance. At this time, multiple energy storage units discharge or charge in coordination to mitigate load fluctuations. In the face of drastic power changes, cross-node energy storage scheduling can be started, and higher-capacity energy storage devices are used to adjust the power on a large scale, so as to ensure the stability of the entire industrial and commercial energy storage topology network. Different levels of power buffering are performed in multiple fluctuation segments to generate "node multi-level power buffer data", including the energy storage usage and adjustment strategies of each node in different buffer stages. The multi-level power buffer data of the node is used to adjust the power balance of the entire energy storage system.By adjusting the charge-discharge strategies of each node, the balance between the power demand and supply of all nodes in the system is ensured. Through the regulation of energy storage devices, the power transmission quality between nodes is improved, power disturbances, harmonics, and frequency fluctuations in the power grid are reduced, and the power quality of the system is enhanced. According to the multi-level power buffer data of the nodes and the power balance regulation results, a "relay buffer scheduling strategy" is generated, which provides specific scheduling instructions for each node in the industrial and commercial energy storage topology network, including: the charge-discharge timing of each node; the power fluctuation mitigation strategy of each node; the scheduling parameters for the collaborative work of each node.

[0042] Preferably, the node fluctuation segment division of the corresponding nodes in the industrial and commercial energy storage topology network and the multi-level power buffering of the node power fluctuation segments according to the power disturbance characteristic index set data include: The corresponding nodes in the industrial and commercial energy storage topology network are divided into node fluctuation segments according to the power disturbance characteristic index set data, and node power fluctuation segments are generated, where the node power fluctuation segments include slow-varying transition segments, stable retention segments, multi-segment relay segments, jump response segments, and high-frequency oscillation segments; Based on the slow-varying transition segments, the slope extraction and delay release regulation are performed on the corresponding nodes in the industrial and commercial energy storage topology network to generate slow-varying slope buffer regulation data; based on the stable retention segments, the intermittent disturbance and virtual load interpolation are performed on the corresponding nodes in the industrial and commercial energy storage topology network to generate stable retention buffer regulation data; Based on the multi-segment relay segments, the relay segment phase locking and leading release window regulation are performed on the corresponding nodes in the industrial and commercial energy storage topology network to generate multi-segment relay buffer regulation data; based on the jump response segments, the fast amplitude limiting and channel switching buffer are performed on the corresponding nodes in the industrial and commercial energy storage topology network to generate jump response buffer regulation data; Based on the high-frequency oscillation segments, the filtering isolation and inductance absorption smoothing are performed on the corresponding nodes in the industrial and commercial energy storage topology network to generate high-frequency oscillation buffer regulation data; the slow-varying slope buffer regulation data, stable retention buffer regulation data, multi-segment relay buffer regulation data, jump response buffer regulation data, and high-frequency oscillation buffer regulation data are integrated into node multi-level power buffer data.

[0043] In the embodiments of the present invention, the nodes in the industrial and commercial energy storage topology network are divided in detail by using the power disturbance characteristic index set data obtained from step S33. It mainly includes the following five types of fluctuation segments: The slow-varying transition segment is characterized by slow power change, long duration, usually accompanied by slow load or demand changes. The stable retention segment is characterized by small power change and infrequent fluctuations, and the power demand of the node is stable or remains unchanged for a long time. The multi-segment relay segment is characterized by violent fluctuations in the node power, usually caused by alternating large-amplitude power changes multiple times. The jump response segment is characterized by rapid power change in the node, usually accompanied by sudden load or demand changes. The high-frequency oscillation segment is characterized by high frequency of power fluctuations in the node, showing small-amplitude and high-frequency fluctuations, usually caused by rapid load demand or grid instability. Based on the power disturbance characteristic index set data, five types of node power fluctuation segment data are generated, and corresponding adjustment strategies are defined for each fluctuation segment. The slope analysis is carried out on the power change in the slow-varying transition segment to calculate the power change rate. The power fluctuation is adjusted by means of delayed release to avoid grid instability caused by sudden changes. That is, when the power changes gradually, the energy storage device is used to slowly release or absorb energy to smooth the power change. Based on the slope extraction and delayed release control, the slow-varying slope buffer adjustment data is generated to adjust the charge and discharge rate and timing of the energy storage device. For occasional disturbances in the power stable retention segment, the method of virtual load interpolation is used to simulate the impact of the disturbance on the grid, and the energy storage system is used for appropriate compensation. The weak disturbances in the stable retention segment are compensated by the interpolation adjustment of the virtual load to avoid the impact of these disturbances on the overall system. According to the virtual load interpolation result, the stable retention buffer adjustment data is generated to ensure the stability of the grid during the retention segment. For the power fluctuations in the multi-segment relay segment, the phase-locking technology is adopted to enable the charge and discharge operations between multiple fluctuation segments to be coordinated, making the power fluctuations smoother. Between the relay segments, a leading release window is set to predict and release or absorb the energy of the energy storage device in advance to ensure energy balance. The multi-segment relay buffer adjustment data is generated through phase-locking and leading release window adjustment to ensure the coordination and smooth transition between multiple fluctuation segments. When the power change speed is too fast, the fast amplitude limiting technology is used to limit the amplitude of the power change to avoid the impact of excessive fluctuations on the grid. In the jump response segment, the channel switching technology is adopted to connect different energy storage devices to the power fluctuation channel and quickly adjust the power supply according to needs. The jump response buffer adjustment data is generated through the fast amplitude limiting and channel switching buffer technology to cope with sudden power fluctuations. For the high-frequency power fluctuations in the high-frequency oscillation segment, the filtering technology is adopted to isolate these high-frequency noises and reduce their impact on the grid stability. In the high-frequency oscillation segment, the inductance device is used to smooth the fluctuations and reduce the rapid change of the current. The high-frequency oscillation buffer adjustment data is generated through the filtering and inductance absorption smoothing technology to eliminate high-frequency disturbances.Integrate the adjustment strategy data of different fluctuation segments to generate complete "node multi-level power buffer data".

[0044] Preferably, in step S3, the island operation and black protection of the industrial and commercial energy storage topology network according to the isolated storage mode include: Screen the nodes of the industrial and commercial energy storage topology network according to the isolated storage mode to obtain isolated storage nodes; Analyze the location information of the isolated storage nodes and identify the off-grid boundary to generate island operation topology boundary data; Perform breakpoint self-holding switching configuration on the island operation topology boundary data to generate island breakpoint operation configuration data; Reconstruct the load level of the island breakpoint operation configuration data to generate island mode load adaptation data; Collect the node energy storage state data of the isolated storage nodes, and divide the priority of energy storage maintenance for the node energy storage state data to generate black start energy storage regulation data; generate hierarchical black protection response data by generating a wake-up sequence for different time periods for the black start energy storage regulation data; Perform voltage and frequency self-stabilizing regression adjustment on the hierarchical black protection response data to generate isolated operation stability support data; integrate the isolated operation stability support data and the island mode load adaptation data into a scheduling strategy to generate an isolated storage scheduling mode.

[0045] In the embodiments of the present invention, nodes that meet the island operation conditions are selected based on the power demand and energy storage capacity of the energy storage nodes. These nodes are usually located at the edge of the power grid or in areas with relatively independent functions and can continue to supply power after being disconnected from the main power grid. By analyzing the node characteristics of the energy storage system (such as energy storage capacity, discharge capacity, load demand, etc.), the isolated storage node data is generated. According to the location of the isolated storage nodes (such as geographical coordinates, load density, connectivity, etc. information), it is analyzed whether they are suitable for forming an island operation mode. The geographical information system (GIS) and topology analysis algorithms are used to identify the off-grid boundaries of the nodes. By analyzing the power connection relationships between the nodes, it is determined which nodes can still maintain power supply and operate in the island mode after being disconnected. Based on the analysis results, the island operation topology boundary data is generated, which describes which nodes and their connections can maintain stable operation after going off-grid. For the breakpoint positions in the island operation topology boundary, a self-sustaining switching configuration is designed. The breakpoints are usually power transmission nodes connecting different regions. Through intelligent switching control devices, the grid state at the breakpoint is automatically judged, and a decision is made on whether to perform a switch to ensure the continuity and stability of power supply. According to the island topology boundary data and the breakpoint self-sustaining switching configuration, the island breakpoint operation configuration data is generated, including automatic switching schemes, start-stop strategies of energy storage devices, etc. In the island operation mode, the load demands between the nodes will change. To ensure that the load can be smoothly adapted in the island mode, first, the load within the island is reconstructed, and the load levels are divided. The load reconstruction includes load reduction, adjustment, and distribution, etc., to adapt to the available energy storage capacity. The island mode load adaptation data is generated through load reconstruction to ensure that the loads of each node can be effectively managed and controlled in the island operation mode. The energy storage state data of the isolated storage nodes is monitored and collected in real time, including information such as energy storage capacity, power quantity, charge-discharge state, etc. According to the available power quantity of the energy storage devices and the load demands, the energy storage systems are prioritized to ensure that the most critical loads can be guaranteed first. The energy storage devices with high priority will perform charge-discharge scheduling first to maintain power supply. According to the energy storage state data and the priority division, the black start energy storage regulation data is generated, which guides how the energy storage devices can quickly restore power supply in the island mode. According to the black start energy storage regulation data, a wake-up sequence is designed for each energy storage device. The wake-up sequence of the energy storage device is allocated according to its energy storage capacity, load demand, and operating state. The time-segmented wake-up sequence can ensure that after a power outage, the energy storage devices are started step by step according to the plan, avoiding system instability caused by too rapid load increase. According to the time-segmented wake-up sequence data, the hierarchical black protection response data is generated. These data will guide how each energy storage device responds to load changes step by step in the island mode and ensure the stability of the power grid. In the island mode, due to the continuous changes of the energy storage system and the load, voltage and frequency instability will occur.Through the voltage - frequency regulation mechanism, the voltage frequency is self - stably regressed and regulated to ensure that the power grid frequency fluctuates within a reasonable range, avoiding large - scale power outages. Through the voltage - frequency regression regulation, stability - support data is generated. These data provide the necessary stability guarantee for the operation of the island mode. The stability - support data is integrated with the load - adaptation data of the island mode to generate a complete isolated - storage scheduling mode. This mode guides how the energy storage device conducts charge - discharge scheduling in the island mode to ensure the stable operation of the power grid.

[0046] As an example of the present invention, refer to Figure 3 As shown, in this example, step S4 includes: Step S41: Conduct scheduling coupling analysis on the industrial and commercial energy storage topology network according to the direct - energy supply scheduling strategy, the relay - buffer scheduling strategy, and the isolated - storage scheduling mode to generate multi - source node impedance mutation data; Step S42: Perform dynamic impedance matching optimization on the multi - source node impedance mutation data to generate energy - storage scheduling impedance optimization data; Step S43: Generate scheduling instructions based on the energy - storage scheduling impedance optimization data to obtain an optimal scheduling instruction set to execute the industrial and commercial energy - storage scheduling optimization method.

[0047] In the embodiments of the present invention, based on the energy storage nodes and their scheduling strategies in the industrial and commercial energy storage topology network, factors such as the stored energy of each node, load demand, and power balance are considered to analyze the relationship between them and the power load. Through the relay buffering mode, the impact of energy storage nodes on load regulation during the multi-level buffering process is evaluated. The focus is on the power fluctuation situation, buffering capacity, and power quality regulation ability of the nodes. For the island operation mode, the role of the energy storage system in power supply during island operation and its scheduling requirements are analyzed, including the black start response, load adaptation, and voltage and frequency regulation of energy storage devices. The scheduling strategies of direct energy supply, relay buffering, and isolated storage are coupled and analyzed to explore their mutual influence in the same industrial and commercial energy storage topology network. By establishing a scheduling coupling model, the coordination and interaction of different scheduling strategies in the power grid are analyzed. Through scheduling coupling analysis, the power and voltage fluctuations occurring during the scheduling process of each energy storage node are evaluated, and the impedance variation data of the nodes is generated. This data represents the electrical energy fluctuation characteristics of energy storage nodes under different scheduling strategies, including load changes, power transfer, and current impedance changes. During the scheduling process of the energy storage system, impedance matching refers to the matching of power transmission and voltage adjustment between the energy storage device and the power grid, with the goal of keeping the current, voltage, and power of the energy storage system in the best state during the scheduling process. Considering the dynamic characteristics of energy storage nodes in the industrial and commercial energy storage topology network (such as charge and discharge speed, energy storage capacity, power response, etc.), the impedance of multi-source nodes is optimized for real-time matching. This process includes real-time monitoring of the load demand, power fluctuation, and voltage status of the energy storage device, and dynamic adjustment. Optimization methods based on genetic algorithms, particle swarm optimization (PSO), or reinforcement learning can be used to adjust the impedance between energy storage nodes in real time to optimize the power flow and voltage regulation effect of the energy storage system. The impedance is dynamically adjusted through the optimization algorithm to generate energy storage scheduling impedance optimization data. These data include the optimal scheduling schemes for each energy storage node to ensure the maximization of the efficiency of the energy storage system under various scheduling strategies and the best power quality state. According to the energy storage scheduling impedance optimization data, specific energy storage scheduling instructions are generated. These instructions will include information such as the charge and discharge timing, power distribution, and start and stop times of energy storage devices for each node. Each energy storage device will execute the scheduling operation according to the instructions to ensure that the operation of the entire energy storage network conforms to the predetermined scheduling strategy. According to different scheduling strategies (direct energy supply, relay buffering, and isolated storage), an optimal scheduling instruction set is generated. The instruction set will consider the real-time status and load demand of the energy storage device while maintaining the stability of the power grid. The optimized scheduling method is executed and the response of the energy storage nodes is monitored in real time. Through the feedback mechanism, the scheduling instructions are dynamically adjusted to ensure that the power distribution and power quality of the energy storage system are maintained in the optimal state. Through the intelligent monitoring system, various parameters (such as power, frequency, voltage, etc.) during the scheduling execution process are tracked, and timely adjustments are made according to the feedback data to ensure the effectiveness and accuracy of the scheduling instructions.Evaluate the effects of the scheduling optimization implementation from the following aspects: Power quality: Ensure that the voltage and frequency fluctuations are controlled within a reasonable range to avoid excessive or too low voltage. Load response: Evaluate the load adaptation ability of the energy storage device to determine whether it can quickly respond to load changes. Energy storage efficiency: The charge and discharge efficiency of the energy storage device to ensure that the energy storage system can provide power with the lowest loss. Generate a scheduling execution report based on the evaluation results to ensure continuous optimization and improvement in future scheduling processes.

[0048] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0049] The above are only specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for industrial and commercial energy storage scheduling, characterized in that, It includes the following steps: Step S1: Obtain the location of the industrial and commercial power plant; construct a topological network based on the location of the industrial and commercial power plant to generate an industrial and commercial energy storage topological network; analyze the geographical altitude and energy transmission blocking characteristics of the industrial and commercial energy storage topological network, and divide the energy storage mode of the industrial and commercial energy storage topological network to generate a direct power supply mode, a relay buffer mode, and an isolated storage mode; Step S2: Collect the node status of the corresponding nodes in the industrial and commercial energy storage topological network based on the direct power supply mode; perform instant charge and discharge scheduling driven by peak-valley price difference on the corresponding nodes according to the node status to generate a direct power supply scheduling strategy; Step S3: Perform multi-level power buffering and power quality regulation on the corresponding nodes in the industrial and commercial energy storage topological network through the relay buffer mode to generate a relay buffer scheduling strategy; Perform island operation and black protection on the industrial and commercial energy storage topological network according to the isolated storage mode to generate an isolated storage scheduling mode; Step S4: Optimize the dynamic impedance matching of the industrial and commercial energy storage topological network according to the direct power supply scheduling strategy, the relay buffer scheduling strategy, and the isolated storage scheduling mode to generate an optimal scheduling instruction set to execute the industrial and commercial energy storage scheduling optimization method.

2. The industrial and commercial energy storage scheduling method according to claim 1, wherein Step S1 includes the following steps: Step S11: Obtain the location of the industrial and commercial power plant; Step S12: Analyze the geographical coordinates of the industrial and commercial power plant location, and perform node marking on the geographical coordinates to generate power plant topological node data; Step S13: Extract the geographical connection paths of the power plant topological node data, and construct a topological network through the power plant topological node data and the geographical connection paths to generate an industrial and commercial energy storage topological network; Step S14: Perform geographical elevation analysis on the industrial and commercial energy storage topological network, and extract the energy storage path altitude characteristics of the nodes in the industrial and commercial energy storage topological network and identify the energy storage transmission obstacle factors of the edges according to the analysis results to generate energy storage path altitude characteristic data and energy storage energy transmission blocking characteristic data; Step S15: Divide the working altitude range of the nodes in the industrial and commercial energy storage topological network based on the energy storage path altitude characteristic data, and calculate the energy transmission loss rate of each node in combination with the energy storage energy transmission blocking characteristic data; Step S16: Divide the energy storage mode of the industrial and commercial energy storage topological network through the energy transmission loss rate to generate a direct power supply mode, a relay buffer mode, and an isolated storage mode.

3. The industrial and commercial energy storage scheduling method according to claim 2, wherein Step S14 includes the following steps: Step S141: Extract the node elevation coordinates of the industrial and commercial energy storage topological network data to obtain the original node altitude data; perform interpolation fitting and error correction on the original node altitude data to generate normalized node altitude data; Step S142: Perform path-level difference analysis on the normalized node altitude data to generate energy storage path altitude characteristic data; Step S143: Scan the terrain factors of the connection paths of the edges in the industrial and commercial energy storage topological network to generate path geographical structure characteristic data; extract the blocking factors from the path geographical structure characteristic data to generate potential energy storage transmission obstacle data; Step S144: Perform type annotation and spatial distribution evaluation on the potential energy storage transmission obstacle data to generate energy storage energy transmission blocking characteristic data.

4. The industrial and commercial energy storage scheduling method according to claim 1, wherein Step S2 includes the following steps: Step S21: Screen and process the node data of the industrial and commercial energy storage topology network in the direct power supply mode to generate direct power supply node set data; Step S22: Collect the real-time power status of the direct power supply node set data to generate direct energy storage node status data; Step S23: Conduct a segmented analysis of the energy usage cycle for the node energy storage status data to generate node time period response characteristic data; Compare the load dynamic range of the node time period response characteristic data to generate switchable charge and discharge node data; Step S24: Sort the switchable charge and discharge node data in sequence scheduling order to generate timing scheduling parameter data; Jointly regulate the timing scheduling parameter data and the direct energy storage node status data to generate direct power supply scheduling strategy data.

5. The industrial and commercial energy storage scheduling method according to claim 4, characterized in that, Step S24 includes the following steps: Step S241: Evaluate the charge and discharge capabilities of the switchable charge and discharge node data to generate node energy storage response capability data; Sort the node energy storage response capability data by priority to generate node scheduling priority sequence data; Step S242: Map the node scheduling priority sequence data to a periodic regulation window to generate time period scheduling sorting parameter data; Conduct cross-node consistency analysis on the time period scheduling sorting parameter data to generate coordinated timing scheduling parameter data; Step S243: Synchronously regulate the coordinated timing scheduling parameter data and the direct energy storage node status data to generate joint regulation strategy data; Conduct continuity inspection and time period continuous complementation processing on the joint regulation strategy data to generate direct power supply scheduling strategy data.

6. The industrial and commercial energy storage scheduling method according to claim 1, wherein, In step S3, multi-level power buffering and power quality regulation of the corresponding nodes in the industrial and commercial energy storage topology network through the relay buffer mode includes: Conduct node power time series analysis on the corresponding nodes in the industrial and commercial energy storage topology network through the relay buffer mode to generate node power time series data; Perform windowed segmentation processing on the node power time series data, extract the power continuous change section and mutation points to generate node power change structure data; Conduct slope analysis and mutation frequency statistics on the node power change structure data to identify the rate, density, and duration of power change, and generate power disturbance characteristic index set data; According to the power disturbance characteristic index set data, divide the corresponding nodes in the industrial and commercial energy storage topology network into node power fluctuation segments, generate node power fluctuation segments, and conduct multi-level power buffering on the node power fluctuation segments to generate node multi-level power buffering data; Use the node multi-level power buffering data to conduct power quality regulation based on power balance for the industrial and commercial energy storage topology network to generate relay buffer scheduling strategies.

7. The industrial and commercial energy storage scheduling method according to claim 6, wherein The dividing the corresponding nodes in the industrial and commercial energy storage topology network into node power fluctuation segments and conducting multi-level power buffering on the node power fluctuation segments according to the power disturbance characteristic index set data includes: Dividing the corresponding nodes in the industrial and commercial energy storage topology network into node power fluctuation segments according to the power disturbance characteristic index set data, where the node power fluctuation segments include slow change transition segments, stable retention segments, multi-segment relay segments, jump response segments, and high-frequency oscillation segments; Extract the slope and regulate the delayed release of the corresponding nodes in the industrial and commercial energy storage topology network based on the slow-varying transition section to generate slow-varying slope buffer adjustment data; perform intermittent perturbation and virtual load interpolation on the corresponding nodes in the industrial and commercial energy storage topology network based on the stable retention section to generate stable retention buffer adjustment data; Perform relay section phase locking and leading release window adjustment on the corresponding nodes in the industrial and commercial energy storage topology network based on the multi-section relay section to generate multi-section relay buffer adjustment data; perform fast amplitude limiting and channel switching buffer on the corresponding nodes in the industrial and commercial energy storage topology network based on the jump response section to generate jump response buffer adjustment data; Perform filtering isolation and inductor absorption smoothing on the corresponding nodes in the industrial and commercial energy storage topology network based on the high-frequency oscillation section to generate high-frequency oscillation buffer adjustment data; integrate the slow-varying slope buffer adjustment data, stable retention buffer adjustment data, multi-section relay buffer adjustment data, jump response buffer adjustment data, and high-frequency oscillation buffer adjustment data into node multi-level power buffer data.

8. The industrial and commercial energy storage scheduling method according to claim 1, wherein In step S3, the island operation and black protection of the industrial and commercial energy storage topology network according to the isolated storage mode include: Screen the nodes of the industrial and commercial energy storage topology network according to the isolated storage mode to obtain isolated storage nodes; Analyze the location information of the isolated storage nodes and identify the off-grid boundary to generate island operation topology boundary data; Perform breakpoint self-holding switching configuration on the island operation topology boundary data to generate island breakpoint operation configuration data; Reconstruct the load level of the island breakpoint operation configuration data to generate island mode load adaptation data; Collect the node energy storage state data of the isolated storage nodes, and divide the priority of the energy storage maintenance of the node energy storage state data to generate black start energy storage regulation data; generate hierarchical black protection response data by generating a wake-up sequence for the black start energy storage regulation data in different time periods; Perform voltage and frequency self-stabilization regression adjustment on the hierarchical black protection response data to generate isolated operation stability support data; integrate the isolated operation stability support data and the island mode load adaptation data into a scheduling strategy to generate an isolated storage scheduling mode.

9. The industrial and commercial energy storage scheduling method according to claim 1, characterized in that Step S4 includes the following steps: Step S41: Perform scheduling coupling analysis on the industrial and commercial energy storage topology network according to the direct energy supply scheduling strategy, relay buffer scheduling strategy, and isolated storage scheduling mode to generate multi-source node impedance variation data; Step S42: Perform dynamic impedance matching optimization on the multi-source node impedance variation data to generate energy storage scheduling impedance optimization data; Step S43: Generate scheduling instructions based on the energy storage scheduling impedance optimization data to obtain an optimal scheduling instruction set to execute the industrial and commercial energy storage scheduling optimization method.

10. An industrial and commercial energy storage system, characterized in that, For executing the industrial and commercial energy storage scheduling method as claimed in claim 1, the industrial and commercial energy storage system includes: An energy storage mode division module, configured to obtain the location of the industrial and commercial power plant; construct a topology network based on the location of the industrial and commercial power plant to generate an industrial and commercial energy storage topology network; analyze the geographical altitude and energy transmission blocking characteristics of the industrial and commercial energy storage topology network, and divide the energy storage mode of the industrial and commercial energy storage topology network to generate a direct energy supply mode, a relay buffer mode, and an isolated storage mode; The direct energy storage scheduling module is used to collect the node status of the corresponding nodes in the industrial and commercial energy storage topology network based on the direct energy supply mode; perform immediate charge and discharge scheduling driven by peak-valley price differences on the corresponding nodes according to the node status, and generate a direct energy supply scheduling strategy; The multi-level buffer scheduling module is used to perform multi-level power buffering and power quality regulation on the corresponding nodes in the industrial and commercial energy storage topology network through the relay buffer mode, and generate a relay buffer scheduling strategy; perform island operation and black protection on the industrial and commercial energy storage topology network according to the isolation storage mode, and generate an isolation storage scheduling mode; The transmission impedance optimization module is used to perform dynamic impedance matching optimization on the industrial and commercial energy storage topology network according to the direct energy supply scheduling strategy, the relay buffer scheduling strategy, and the isolation storage scheduling mode, and generate an optimal scheduling instruction set to execute the industrial and commercial energy storage scheduling optimization method.

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