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

Abstract

The present invention relates to the field of energy storage scheduling technology, and 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 location of an industrial and commercial power plant; constructing a topological network based on the location of the industrial and commercial power plant to generate an industrial and commercial energy storage topological network; analyzing the geographical altitude and energy transmission barrier characteristics of the industrial and commercial energy storage topological network, and dividing the industrial and commercial energy storage topological network into energy storage modes to generate a direct energy supply mode, a relay buffer mode, and an isolated storage mode; collecting the node status of the corresponding node in the industrial and commercial energy storage topological network based on the direct energy supply mode; performing peak-valley price difference-driven instant charge and discharge scheduling on the corresponding node according to the node status to generate a direct energy supply scheduling strategy. The present invention improves the flexibility and stability of industrial and commercial energy storage scheduling by considering geographical factors, energy storage mode differentiation, instant scheduling, multi-level buffering, power quality regulation, and dynamic optimization.

Description

Technical Field

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

[0002] In the early days, power system dispatch relied primarily on traditional generation and load regulation methods, with energy storage playing a limited role. However, with the large-scale integration of renewable energy sources such as wind and solar power, traditional power dispatch methods have gradually exposed their inability to effectively address fluctuating and intermittent energy supply. Consequently, energy storage technology has become a crucial component of power dispatch, particularly in the industrial and commercial sectors, where it is used to balance loads and optimize energy efficiency. Breakthroughs in new energy storage technologies, such as lithium batteries and compressed air energy storage (CAES), have led to decreasing costs and improved performance. This has significantly enhanced the flexibility and reliability of industrial and commercial energy storage dispatch. In particular, the integration of smart grids and big data technologies has enabled industrial and commercial users to monitor and optimize the operation of energy storage equipment in real time, enabling more precise demand response and load regulation. However, existing energy storage models often employ unified dispatch strategies, lacking specialized management tailored to specific needs and scenarios. This results in limited flexibility and stability in industrial and commercial energy storage dispatch. 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 objectives, a method for dispatching industrial and commercial energy storage is provided, the method comprising the following steps:

[0005] Step S1: 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 barrier 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 direct energy supply mode, relay buffer mode, and isolated storage mode;

[0006] Step S2: Based on the direct energy supply mode, the node status of the corresponding nodes in the industrial and commercial energy storage topology network is collected; according to the node status, the corresponding nodes are subjected to real-time charge and discharge scheduling driven by the peak-valley price difference to generate a direct energy supply scheduling strategy;

[0007] Step S3: Perform multi-level power buffering and power quality regulation on corresponding nodes in the industrial and commercial energy storage topology 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 topology network according to the isolated storage mode to generate an isolated storage scheduling mode;

[0008] Step S4: Dynamic impedance matching optimization is performed 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, and an optimal scheduling instruction set is generated to execute the industrial and commercial energy storage scheduling optimization method.

[0009] The present invention constructs a topological network of the locations of industrial and commercial power plants, and divides energy storage modes in combination with geographical altitude and energy transmission barrier characteristics, so that the scheduling system not only considers the spatial layout, but also integrates the geographical environment and transmission characteristics, and realizes refined classification management of energy storage resources from the source. By dividing the direct energy supply mode, relay buffer mode and isolated storage mode, energy storage scheduling strategies for different scenarios can be generated separately, so that the scheduling system has multi-strategy, multi-path dynamic adaptability, and can 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 peak-valley electricity price difference is introduced, so that the system can respond to energy storage nodes in real time according to electricity price fluctuations, effectively improve the economy of electricity, and bring 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 electricity in the case of load mutation, grid oscillation, etc., play a role in voltage stability and frequency balancing, and significantly improve the overall power quality and operational robustness of the system. The isolated storage mode supports island operation and black start protection, and can ensure stable operation of local power supply in extreme cases of external power supply interruption or power grid failure, enhance the system's ability to survive independently and recover quickly after a disaster, and improve the reliability of industrial and commercial energy use. Dynamic impedance matching optimization through a comprehensive scheduling strategy can effectively reduce power loss and reflection during the transmission process, ensure the stability and efficiency of energy transmission, and automatically adjust the role of energy storage nodes in the network to achieve dynamic optimization configuration of the system. The final output "optimal scheduling instruction set" has the operability to directly guide the operation of industrial and commercial energy storage facilities, so that the entire method not only remains in the theoretical model stage, but has good engineering application value and is easy to deploy and implement. Therefore, the present invention improves the flexibility and stability of industrial and commercial energy storage scheduling by taking into account geographical factors, energy storage mode differentiation, real-time scheduling, multi-level buffering, power quality regulation and dynamic optimization.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain the location of the industrial and commercial power plant;

[0012] Step S12: analyzing the geographic coordinates of the location of the industrial and commercial power plant, and marking the geographic coordinates as nodes to generate power plant topology node data;

[0013] Step S13: extracting the geographical connection paths of the power plant topology node data, and constructing a topology network using the power plant topology node data and the geographical connection paths to generate an industrial and commercial energy storage topology network;

[0014] Step S14: Performing a geographic elevation analysis on the industrial and commercial energy storage topology network, and extracting the energy storage path elevation characteristics of the nodes in the industrial and commercial energy storage topology network and identifying the energy storage transmission barrier factors of the edges based on the analysis results, thereby generating energy storage path elevation characteristic data and energy storage transmission barrier characteristic data;

[0015] Step S15: dividing the working altitude ranges of nodes in the industrial and commercial energy storage topology network based on the energy storage path altitude characteristic data, and calculating the energy transmission loss rate of each node in combination with the energy storage transmission barrier characteristic data;

[0016] Step S16: The industrial and commercial energy storage topology network is divided into energy storage modes according to the energy transmission loss rate, and a direct energy supply mode, a relay buffer mode, and an isolated storage mode are generated.

[0017] The present invention obtains and marks the geographical coordinates of the power plant through steps S11 and S12, realizes the precise positioning of industrial and commercial power plants in the geographic information system, and generates standardized topological node data, providing a reliable foundation for subsequent network construction. In step S13, the 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, thereby improving the accuracy and practicality of network modeling. By extracting the altitude characteristics and energy transmission barrier factors of the energy storage path through step S14, the physical resistance and geographical obstacles in the energy storage path can be revealed, thereby avoiding the errors caused by traditional topological modeling that ignores terrain factors, and contributing to the optimization and rationalization of the layout of the energy storage path. In step S15, the altitude characteristics and transmission barrier factors are combined to comprehensively evaluate the energy transmission loss between nodes, quantify the degree of energy consumption during the transmission process in the network, and significantly improve the authenticity and quantification of the energy storage system efficiency evaluation. Step S16 automatically categorizes energy storage modes based on energy transmission loss rates, accurately classifying energy storage nodes into three categories: direct energy supply, relay buffering, or isolated storage. This provides the foundation for clear objectives and rational deployment of subsequent scheduling strategies, thus avoiding a "one-size-fits-all" approach to energy management. This method has significant advantages in areas with complex topography or geographical isolation (such as mountains, hills, and densely populated urban areas). It can identify high-loss nodes and transmission blind spots, proactively avoiding inefficient or unfeasible energy supply paths, and improving the regional adaptability of energy storage networks.

[0018] Preferably, step S14 includes the following steps:

[0019] Step S141: extracting node elevation coordinates of the industrial and commercial energy storage topology network data to obtain original node elevation data; performing interpolation fitting and error correction on the original node elevation data to generate standardized node elevation data;

[0020] Step S142: performing path level difference analysis on the node standardized altitude data to generate energy storage path altitude characteristic data;

[0021] Step S143: Scan the connection paths of the edges in the industrial and commercial energy storage topology network for terrain factors to generate path geographic structure feature data; extract blocking factors from the path geographic structure feature data to generate potential energy storage transmission obstacle data;

[0022] Step S144: perform type labeling and spatial distribution evaluation on potential energy storage transmission obstacle data to generate energy storage transmission blocking feature data.

[0023] In step S141, the present invention obtains raw node elevation data and performs interpolation fitting and error correction, effectively eliminating elevation data deviations caused by insufficient sampling density or measurement errors. Step S142 extracts the elevation fluctuations and drop of the energy storage path by performing path-level differential analysis on the standardized elevation data. This effectively identifies gravitational potential energy differences or transmission resistance changes caused by terrain changes along the path, providing a key reference for efficient path selection and energy consumption assessment. Step S143 scans terrain factors starting with edge-connected paths and, combined with DEM (digital elevation model) or GIS layer information, generates path geographic structural features. The extracted potential obstacles (such as mountains, buildings, and rivers) not only consider horizontal obstacles but also identify interference at the spatial level, improving the comprehensiveness and accuracy of obstacle detection. Step S144 labels the scanned obstacles by type (e.g., "terrain elevation barrier," "construction density barrier," "ecological protection zone restriction," etc.) and assesses their spatial distribution. The impact weights of different types of obstacles are categorized and analyzed to generate energy storage transmission barrier characteristic data, providing a decision-making basis for path selection and scheduling optimization. By jointly evaluating altitude differences and geographical structural resistance, the system can identify high-altitude transition paths or areas with severe terrain obstruction, avoiding energy loss or operational failures caused by path planning errors and enhancing the adaptability and stability of the energy storage system in complex geographical environments.

[0024] Preferably, step S2 includes the following steps:

[0025] Step S21: performing 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;

[0026] Step S22: collecting real-time power status data of the direct energy supply node set to generate direct energy storage node status data;

[0027] Step S23: Performing energy usage cycle segmentation analysis on the node energy storage status data to generate node time period response characteristic data; performing load dynamic interval comparison on the node time period response characteristic data to generate switchable charge and discharge node data;

[0028] Step S24: sorting the switchable charging and discharging node data in a sequence scheduling order to generate timing scheduling parameter data; jointly regulating the timing scheduling parameter data and the direct energy storage node status data to generate direct energy supply scheduling strategy data.

[0029] The present invention can quickly identify energy storage nodes suitable for direct energy supply mode by performing conditional filtering and mode adaptation screening on the nodes in the industrial and commercial energy storage topology network; after forming the data of the direct energy supply node set, the scheduling scope can be narrowed, the amount of calculation and data processing overhead can be reduced, and the system response speed and resource utilization efficiency can be improved. The power status of the direct energy supply node set is collected to obtain its charging level, discharge capacity, remaining capacity and other parameters, and dynamically record them as status data; provide real-time basic data support for the subsequent scheduling strategy formulation, and enhance the system's ability to immediately perceive and adjust the node operation status. Through segmented analysis of the energy consumption cycle, the response characteristics of the node during peak / valley periods can be identified, and dynamic adaptation to electricity prices and load fluctuations can be achieved; combined with the load dynamic interval comparison, nodes with flexible switching capabilities are screened out, and switchable charging and discharging node data is generated, laying the foundation for building a flexible energy supply scheduling mechanism. Based on switchable charging and discharging nodes, scheduling is performed sequentially according to rules such as response capability, remaining power, and time period priority to generate scheduling parameters. By combining these scheduling parameters with node status data, "direct energy supply scheduling strategy data" can be accurately generated, achieving efficient energy coordination between nodes and avoiding energy supply conflicts and scheduling imbalances. This method supports dynamic judgment based on peak and valley electricity prices and load fluctuations, implementing an economically driven charging and discharging strategy and improving the efficiency of energy storage systems in participating in electricity price arbitrage.

[0030] Preferably, step S24 includes the following steps:

[0031] Step S241: Evaluate the charge and discharge capabilities of the switchable charge and discharge node data to generate node energy storage response capability data; prioritize the node energy storage response capability data to generate node scheduling priority sequence data;

[0032] Step S242: performing period control window mapping on the node scheduling priority sequence data to generate time period scheduling sorting parameter data; performing cross-node consistency analysis on the time period scheduling sorting parameter data to generate coordinated time series scheduling parameter data;

[0033] Step S243: Synchronously regulate the coordinated time sequence scheduling parameter data and the direct energy storage node status data to generate joint regulation strategy data; perform continuity verification and time period continuous completion processing on the joint regulation strategy data to generate direct energy supply scheduling strategy data.

[0034] By evaluating the energy storage response capabilities of switchable charging and discharging nodes (S241), the present invention accurately determines the scheduling potential of each node under different conditions and, based on this, forms a scheduling priority sequence, avoiding the inefficiencies and conflicts inherent in traditional scheduling and thus improving overall energy response efficiency. Through periodic control window mapping and cross-node consistency analysis (S242), scheduling strategies can be coordinated across different time periods and nodes, resolving issues such as timing misalignment and response conflicts in multi-node scheduling and enhancing the system's coordination and stability. The generation and continuity verification of the joint control strategy (S243) ensures the integrity and time-period continuity of the energy storage node scheduling strategy, avoids control interruptions or redundant switching, and improves the system's continuous energy supply capability and intelligence level. The resulting "direct energy supply scheduling strategy data" combines temporal continuity with multi-node coordination, effectively supporting real-time energy supply demands in actual energy usage scenarios and ensuring stable and sustainable energy supply for critical loads.

[0035] Preferably, in step S3, performing multi-level power buffering and power quality regulation on corresponding nodes in the industrial and commercial energy storage topology network through the relay buffer mode includes:

[0036] Perform 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;

[0037] Perform window segmentation processing on the node power time series data, extract the power continuous change segments and mutation points, and generate the node power change structure data;

[0038] Perform slope analysis and mutation frequency statistics on node power change structure data to identify the rate, density, and duration of power changes, and generate power disturbance characteristic indicator set data;

[0039] According to the power disturbance characteristic index set data, the corresponding nodes in the industrial and commercial energy storage topology network are divided into node fluctuation segments to generate node power fluctuation segments, and multi-level power buffering is performed on the node power fluctuation segments to generate node multi-level power buffering data;

[0040] The multi-level power buffer data of nodes is used to adjust the power quality of industrial and commercial energy storage topology networks based on power balance, and a relay buffer scheduling strategy is generated.

[0041] The present invention achieves in-depth identification of the dynamic behavior of industrial and commercial energy storage node power through node power time series analysis and change structure extraction, effectively captures continuous fluctuations and mutation events, and provides high-resolution data support for subsequent adjustments. Through node fluctuation segment division and multi-level power buffering processing, a hierarchical buffering strategy can be implemented for different fluctuation characteristics to avoid resource waste or insufficient response under single strategy regulation, and enhance the flexible regulation capability of the system. The disturbance characteristic indicators obtained through slope analysis and mutation frequency statistics can accurately reflect the rate and intensity of node power changes, thereby supporting real-time dynamic adjustment of scheduling strategies and improving the ability of energy storage systems to cope with uncertain loads. Ultimately, through a power balance-based regulation method, it is possible to effectively suppress power quality problems such as voltage fluctuations and frequency offsets, and ensure the stable operation of sensitive loads or key industrial equipment. The relay buffer mode acts as a bridge, so that the disturbance information of local nodes can be fed back to the whole network scheduling, realizing the coordinated regulation of multiple nodes in the topology network, and enhancing the overall stability and robustness of the system. Accurately identifying power disturbance characteristics and applying multi-level buffering control can effectively improve the load regulation efficiency of the energy storage system, reduce frequent starts and stops and unnecessary charging and discharging behaviors, extend the life of energy storage equipment and improve system economy.

[0042] Preferably, the dividing of corresponding nodes in the industrial and commercial energy storage topology network into node fluctuation segments according to the power disturbance characteristic index set data and performing multi-level power buffering on the node power fluctuation segments includes:

[0043] Based on the power disturbance characteristic index set data, the corresponding nodes in the industrial and commercial energy storage topology network are divided into node fluctuation segments to generate node power fluctuation segments, where the node power fluctuation segments include slow transition segment, stable retention segment, multi-segment relay segment, jump response segment and high-frequency oscillation segment;

[0044] Based on the slow transition section, the slope of the corresponding nodes in the industrial and commercial energy storage topology network is extracted and delayed release is regulated to generate slow slope buffer adjustment data; based on the stable retention section, the intermittent disturbance and virtual load interpolation of the corresponding nodes in the industrial and commercial energy storage topology network are performed to generate stable retention buffer adjustment data;

[0045] Based on the multi-segment relay segment, the relay segment phase locking and leading release window adjustment are performed on the corresponding nodes in the industrial and commercial energy storage topology network to generate multi-segment relay buffer adjustment data; based on the jump response segment, the corresponding nodes in the industrial and commercial energy storage topology network are fast limited and channel switching buffered to generate jump response buffer adjustment data;

[0046] Based on the high-frequency oscillation segment, the corresponding nodes in the industrial and commercial energy storage topology network are filtered, isolated, and the inductive absorption is stabilized to generate high-frequency oscillation buffer adjustment data; the slowly varying slope buffer adjustment data, stable retention buffer adjustment data, multi-segment relay buffer adjustment data, jump response buffer adjustment data, and high-frequency oscillation buffer adjustment data are integrated into the node multi-level power buffer data.

[0047] By precisely categorizing different power fluctuation segments (such as the gradual transition segment, the stable retention segment, and the multi-segment relay segment), the present invention can adopt the most appropriate regulation strategy for each type of fluctuation. This ensures precise response to diverse power variation characteristics, avoids the crude application of a single regulation strategy, and improves the refinement of system regulation. Each fluctuation segment employs a customized regulation method. For example, the gradual transition segment employs slope extraction and delayed release control, the stable retention segment utilizes intermittent disturbances and virtual load interpolation, and the jump response segment utilizes rapid limiting and channel switching buffering. This personalized regulation not only effectively suppresses different types of power fluctuations but also improves the response speed and stability of the energy storage system. For the jump response and high-frequency oscillation segments, rapid limiting and channel switching buffering, as well as filtering isolation and inductive absorption stabilization, enable extremely rapid response to sudden power fluctuations, ensuring the safety and stability of the power system. This immediate response capability improves the system's response efficiency to emergencies. Multi-stage power buffering technology effectively mitigates the impact of different types of power disturbances on the power grid. For example, filtering and isolation of high-frequency oscillations and inductive absorption measures prevent high-frequency disturbances from interfering with the system, ensuring stable power quality and reducing equipment damage caused by frequent fluctuations. Processing methods for various fluctuation ranges combine real-time data to precisely adjust node power output. The system possesses strong intelligent and adaptive capabilities, dynamically selecting appropriate regulation strategies based on power disturbance characteristics, improving the overall performance of the energy storage system in complex power environments.

[0048] Preferably, in step S3, performing island operation and black protection on the industrial and commercial energy storage topology network according to the isolated storage mode includes:

[0049] Node screening is performed on the industrial and commercial energy storage topology network according to the isolated storage mode to obtain isolated storage nodes;

[0050] Analyze the location information of isolated storage nodes and identify off-grid boundaries to generate island operation topology boundary data;

[0051] Perform breakpoint self-sustaining switching configuration on the island operation topology boundary data to generate island breakpoint operation configuration data;

[0052] Reconstruct the load level of the island breakpoint operation configuration data to generate island mode load adaptation data;

[0053] Collect node energy storage status data of isolated storage nodes, prioritize the node energy storage status data, and generate black start energy storage control data; generate time-based wake-up sequences for the black start energy storage control data, and generate hierarchical black protection response data;

[0054] The voltage and frequency self-stabilization regression adjustment is performed on the hierarchical black protection response data to generate the isolated operation stability support data; the isolated operation stability support data and the island mode load adaptation data are integrated into the scheduling strategy to generate the isolated storage scheduling mode.

[0055] The present invention performs node screening, island operation topology boundary identification, and breakpoint self-sustaining switching configuration according to the isolated storage mode. The system can quickly achieve island operation and maintain power supply in the local area when a power grid failure or external interference occurs, thereby enhancing the self-healing and emergency recovery capabilities of the power grid. Through island mode load adaptation and load level reconstruction, it is ensured that the power matching between load demand and energy storage nodes is optimized during island operation. Overload or voltage fluctuations are effectively avoided, thereby ensuring the stability of the power grid in the island. The energy storage maintenance priority division of the isolated storage node and the generation of black start energy storage control data ensure that during the black start process, the energy storage equipment gives priority to providing sufficient energy support, reducing the startup time and ensuring the power supply of critical loads. In addition, the generation of time-divided wake-up sequences further improves the smoothness and efficiency of the startup process. Through hierarchical black protection response data and voltage and frequency self-stabilization regression adjustment, the system can adjust the voltage and frequency in real time to prevent voltage drops or frequency instability during island operation, thereby ensuring the operation quality of the power grid. The scheduling strategy integration of isolated operation stability support data and island mode load adaptation data further optimizes the scheduling strategy of energy storage resources, enabling energy storage equipment to provide the required power on demand at critical moments, reducing resource waste and improving the overall scheduling efficiency of the system.

[0056] Preferably, step S4 includes the following steps:

[0057] Step S41: performing 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 variation data;

[0058] Step S42: Perform dynamic impedance matching optimization on the impedance variation data of multiple source nodes to generate energy storage scheduling impedance optimization data;

[0059] 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.

[0060] The present invention performs scheduling coupling analysis on the direct energy supply scheduling strategy, relay buffer scheduling strategy and isolated storage scheduling mode to generate multi-source node impedance variation data, and comprehensively analyzes the impedance characteristics of the energy storage node from multiple dimensions. This comprehensive analysis can accurately identify the uneven or unreasonable resource allocation in the energy storage system, optimize the scheduling strategy, and thus improve the overall scheduling efficiency of the energy storage system. Dynamic impedance matching optimization of the multi-source node impedance variation data can achieve efficient adaptation of the energy storage equipment and the power grid system. This process enables the energy storage node to automatically adjust its output or charge and discharge behavior according to real-time demand and network status, ensure impedance balance between each node, avoid system overload or instability, and improve the flexibility and responsiveness of energy storage scheduling. Through dynamic impedance matching optimization, the scheduling of energy storage equipment can more accurately adapt to grid load fluctuations and reduce energy loss caused by mismatch. This not only improves the efficiency of electric energy use, but also helps to extend the service life of energy storage equipment. The optimal scheduling instruction set generated based on the energy storage scheduling impedance optimization data can provide accurate scheduling instructions for the load demand and status of specific industrial and commercial energy storage topology networks. This instruction set offers flexibility and efficiency, optimizing the allocation and utilization of energy storage resources under varying load conditions and grid states. During the energy storage scheduling process, comprehensive analysis of the coupling of multiple scheduling strategies improves system stability and reliability. The optimized scheduling instruction set helps maintain the smooth operation of industrial and commercial energy storage systems under different grid operating modes, reduces fluctuations and sudden failures, and ensures high reliability during system operation. Dynamic impedance matching optimization provides enhanced capabilities for responding to sudden load changes, enabling the energy storage system to adjust its scheduling strategy in real time to better adapt to complex load fluctuations. This adaptability is particularly important in industrial and commercial environments where large load fluctuations are common.

[0061] In this specification, an industrial and commercial energy storage system is provided for executing the above-mentioned industrial and commercial energy storage method. The industrial and commercial energy storage system includes:

[0062] The energy storage mode classification module is used to 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 barrier characteristics of the industrial and commercial energy storage topological network, and classify the industrial and commercial energy storage topological network into energy storage modes, generating direct energy supply mode, relay buffer mode, and isolated storage mode;

[0063] The direct energy storage scheduling module is used to collect the node status of corresponding nodes in the industrial and commercial energy storage topology network based on the direct energy supply mode; based on the node status, it performs real-time charge and discharge scheduling driven by peak and valley price differences for the corresponding nodes to generate a direct energy supply scheduling strategy;

[0064] The multi-level buffer scheduling module 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 the relay buffer mode, generating a relay buffer scheduling strategy; and performs island operation and blackout protection on the industrial and commercial energy storage topology network according to the isolated storage mode, generating an isolated storage scheduling mode;

[0065] The transmission impedance optimization module is used to dynamically optimize the impedance matching of industrial and commercial energy storage topology networks based on direct energy supply scheduling strategies, relay buffer scheduling strategies, and isolated storage scheduling modes, and generate the optimal scheduling instruction set to execute the industrial and commercial energy storage scheduling optimization method.

[0066] The beneficial effect of the present invention is that by constructing a topological network based on the locations 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. Energy storage modes are divided according to geographical altitude and transmission barrier characteristics, generating direct energy supply mode, relay buffer mode, and isolated storage mode. This diversified mode division helps flexibly select appropriate energy storage scheduling strategies based on different geographical and grid conditions, thereby optimizing overall system performance. By analyzing the geographical characteristics and transmission barrier characteristics of the energy storage topological network, it can effectively respond to power demand and energy storage system operating conditions in different environments, improving the system's adaptability and flexibility to environmental changes. Based on the direct energy supply mode, node status is collected and peak-valley price difference-driven real-time charge and discharge scheduling can optimize the charge and discharge behavior of the energy storage system during different electricity price fluctuations, improve energy utilization efficiency, and reduce operating costs. Through the peak-valley price difference scheduling strategy, the energy storage system can charge when electricity prices are low and discharge when electricity prices are 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, alleviate the pressure on the grid during peak load periods, and improve grid stability. Multi-level power buffering and power quality regulation of nodes through relay buffer mode can effectively reduce the impact of grid fluctuations and sudden load changes on the energy storage system, ensure stable power quality, and improve the stability of the energy storage system. Island operation and black protection based on the isolated storage mode can ensure that the energy storage system can automatically switch to island mode under special circumstances (such as sudden grid failures) and guarantee continuous power supply to critical loads, improving the system's emergency response capabilities. Dynamic impedance matching optimization of industrial and commercial energy storage topology networks based on multiple scheduling modes can accurately adjust the power transmission efficiency between the energy storage system and the grid, ensuring the accuracy and stability of power scheduling. Generating the optimal scheduling instruction set through dynamic impedance optimization can automatically optimize the energy storage scheduling strategy under different load conditions, achieve optimal energy distribution and scheduling, and improve the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A schematic flow chart of steps for an industrial and commercial energy storage scheduling method;

[0068] Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0069] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0070] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0071] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0072] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0073] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0074] To achieve this, please refer to Figures 1 to 3 , a method for dispatching industrial and commercial energy storage, the method comprising the following steps:

[0075] Step S1: 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 barrier 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 direct energy supply mode, relay buffer mode, and isolated storage mode;

[0076] Step S2: Based on the direct energy supply mode, the node status of the corresponding nodes in the industrial and commercial energy storage topology network is collected; according to the node status, the corresponding nodes are subjected to real-time charge and discharge scheduling driven by the peak-valley price difference to generate a direct energy supply scheduling strategy;

[0077] Step S3: Perform multi-level power buffering and power quality regulation on corresponding nodes in the industrial and commercial energy storage topology 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 topology network according to the isolated storage mode to generate an isolated storage scheduling mode;

[0078] Step S4: Dynamic impedance matching optimization is performed 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, and an optimal scheduling instruction set is generated to execute the industrial and commercial energy storage scheduling optimization method.

[0079] The present invention constructs a topological network of the locations of industrial and commercial power plants, and divides energy storage modes in combination with geographical altitude and energy transmission barrier characteristics, so that the scheduling system not only considers the spatial layout, but also integrates the geographical environment and transmission characteristics, and realizes refined classification management of energy storage resources from the source. By dividing the direct energy supply mode, relay buffer mode and isolated storage mode, energy storage scheduling strategies for different scenarios can be generated separately, so that the scheduling system has multi-strategy, multi-path dynamic adaptability, and can 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 peak-valley electricity price difference is introduced, so that the system can respond to energy storage nodes in real time according to electricity price fluctuations, effectively improve the economy of electricity, and bring 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 electricity in the case of load mutation, grid oscillation, etc., play a role in voltage stability and frequency balancing, and significantly improve the overall power quality and operational robustness of the system. The isolated storage mode supports island operation and black start protection, and can ensure stable operation of local power supply in extreme cases of external power supply interruption or power grid failure, enhance the system's ability to survive independently and recover quickly after a disaster, and improve the reliability of industrial and commercial energy use. Dynamic impedance matching optimization through a comprehensive scheduling strategy can effectively reduce power loss and reflection during the transmission process, ensure the stability and efficiency of energy transmission, and automatically adjust the role of energy storage nodes in the network to achieve dynamic optimization configuration of the system. The final output "optimal scheduling instruction set" has the operability to directly guide the operation of industrial and commercial energy storage facilities, so that the entire method not only remains in the theoretical model stage, but has good engineering application value and is easy to deploy and implement. Therefore, the present invention improves the flexibility and stability of industrial and commercial energy storage scheduling by taking into account geographical factors, energy storage mode differentiation, real-time scheduling, multi-level buffering, power quality regulation and dynamic optimization.

[0080] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart of steps of an industrial and commercial energy storage scheduling method according to the present invention. In this example, the industrial and commercial energy storage scheduling method includes the following steps:

[0081] Step S1: 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 barrier 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 direct energy supply mode, relay buffer mode, and isolated storage mode;

[0082] Step S2: Based on the direct energy supply mode, the node status of the corresponding nodes in the industrial and commercial energy storage topology network is collected; according to the node status, the corresponding nodes are subjected to real-time charge and discharge scheduling driven by the peak-valley price difference to generate a direct energy supply scheduling strategy;

[0083] Step S3: Perform multi-level power buffering and power quality regulation on corresponding nodes in the industrial and commercial energy storage topology 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 topology network according to the isolated storage mode to generate an isolated storage scheduling mode;

[0084] Step S4: Dynamic impedance matching optimization is performed 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, and an optimal scheduling instruction set is generated to execute the industrial and commercial energy storage scheduling optimization method.

[0085] In this embodiment of the present invention, geographic information system (GIS) technology is used to obtain the specific geographic location data of industrial and commercial power plants from a database. This data can be obtained through satellite remote sensing, drone aerial photography, or existing ground-based facilities, ensuring the accuracy of the longitude and latitude coordinates of each power plant. Data processing includes cleaning, normalization, and verification to ensure high accuracy and consistency for all locations. Based on the acquired power plant location data, a graph theory model (such as a graph network algorithm) is used to construct the topology of the industrial and commercial energy storage system. 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. The distances between nodes and power transmission lines are calculated, and these parameters are used to set the edge weights of the connections. The geographic altitude of each power plant is analyzed using a geographic information system (GIS) to identify terrain features that affect power transmission, such as mountains and plateaus. Energy transmission barriers are modeled based on the physical parameters of the transmission lines (such as line length, type, and voltage level). The impact of different terrain types on power transmission is analyzed to identify areas of resistance. Energy storage modes are categorized into direct supply, relay buffering, and isolated storage. In the direct supply mode, energy storage devices directly supply power to surrounding loads during peak demand periods, maximizing their rapid response capabilities. In the relay buffering mode, energy storage devices act as relay nodes to buffer power during periods of relatively stable demand, adjusting power supply stability and preventing oversupply or undersupply. In the isolated storage mode, energy storage devices provide island-mode power when grid failures occur or when independent operation is required, ensuring the continuous operation of critical facilities. Sensors are installed at each energy storage node to collect real-time status information, including battery charge level, battery health, voltage, and charge status. Edge computing devices perform preliminary processing on this collected real-time data and transmit it to a central control system. Real-time charging and discharging decisions are made based on market electricity price information (i.e., peak and off-peak prices) and the real-time status of the energy storage nodes. Optimization algorithms (such as linear programming or reinforcement learning) are used to select when charging is low and discharging is high to maximize economic benefits. Based on the charging and discharging characteristics of the energy storage equipment (such as maximum power output and efficiency), a real-time charging and discharging scheduling plan is generated and transmitted to the corresponding energy storage node for execution. During periods of high demand, energy storage nodes are prioritized for 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 power demand is relatively stable, the energy storage node performs multi-level power buffering according to the relay buffer mode scheduling. The system implements the relay buffer strategy through hierarchical management based on real-time power load forecasts, energy storage node status, and other information. A multi-level power regulation model (such as a distributed power scheduling algorithm) is used to regulate the charging and discharging status of energy storage nodes to ensure the stability of power supply and reduce dependence on the main power grid.In situations of large power fluctuations or poor power quality, energy storage systems optimize power quality by regulating the battery's charge and discharge processes. This process includes voltage and frequency regulation, ensuring high-quality power supply for industrial and commercial users. In the event of a grid fault or outage, the energy storage system automatically switches to isolated storage mode, providing independent operation. In this situation, the energy storage nodes must not only provide power but also possess black start capabilities, meaning the ability to restore the power supply system. Real-time fault detection algorithms and automated switching mechanisms ensure that energy storage nodes can quickly identify grid faults and initiate island operation to ensure power supply to 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 devices in real time to match the grid load characteristics. Impedance matching is achieved by optimizing the relationship between the energy storage node's power output and grid electrical parameters such as current and voltage. Optimization algorithms (such as genetic algorithms or particle swarm optimization) can be used to dynamically adjust the charge and discharge schedules of the energy storage nodes to achieve load balancing and maximize system stability and efficiency. Based on the optimization results, the system generates an optimal scheduling instruction set. These instructions include scheduling parameters such as charge and discharge time, power, and frequency for each energy storage node, ensuring 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 the automated control system to execute specific energy storage scheduling tasks.

[0086] Preferably, step S1 includes the following steps:

[0087] Step S11: Obtain the location of the industrial and commercial power plant;

[0088] Step S12: analyzing the geographic coordinates of the location of the industrial and commercial power plant, and marking the geographic coordinates as nodes to generate power plant topology node data;

[0089] Step S13: extracting the geographical connection paths of the power plant topology node data, and constructing a topology network using the power plant topology node data and the geographical connection paths to generate an industrial and commercial energy storage topology network;

[0090] Step S14: Performing a geographic elevation analysis on the industrial and commercial energy storage topology network, and extracting the energy storage path elevation characteristics of the nodes in the industrial and commercial energy storage topology network and identifying the energy storage transmission barrier factors of the edges based on the analysis results, thereby generating energy storage path elevation characteristic data and energy storage transmission barrier characteristic data;

[0091] Step S15: dividing the working altitude ranges of nodes in the industrial and commercial energy storage topology network based on the energy storage path altitude characteristic data, and calculating the energy transmission loss rate of each node in combination with the energy storage transmission barrier characteristic data;

[0092] Step S16: The industrial and commercial energy storage topology network is divided into energy storage modes according to the energy transmission loss rate, and a direct energy supply mode, a relay buffer mode, and an isolated storage mode are generated.

[0093] In this embodiment of the present invention, the precise geographic location of industrial and commercial power plants is obtained using a variety of data sources (such as satellite remote sensing, geographic information systems (GIS), and drone aerial photography). These data sources include publicly available geographic information data, field survey data, and internal enterprise facility data. The power plant's geographic coordinates (latitude and longitude) and other additional information (such as the plant's capacity and type) are collected in a database for subsequent analysis. The collected industrial and commercial power plant location data is processed. GIS analysis tools (such as ArcGIS and QGIS) are used to accurately analyze the geographic coordinates of each power plant to ensure the accuracy and consistency of the coordinate data. Nodes are marked for each power plant location. Each node represents an energy storage node. The power plant's geographic coordinates, power generation capacity, and basic energy storage system information are associated with the node to form "power plant topology node data." GIS analysis generates spatial coordinate data for each node, including each power plant's latitude and longitude, geographic location description (such as city, county, or street), and relative positional relationships with other nodes. Based on the power plant topology node data, the geographic connection paths between different nodes are analyzed. 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, taking into account factors such as path length and terrain variations. Using topological principles and network construction methods (such as graph theory), nodes and their connection paths are modeled to form the grid topology of the industrial and commercial energy storage system. Each node and path forms a graph, where nodes represent energy storage devices and edges represent power transmission lines. This topology reflects the geographic distribution of the energy storage system and the power flow paths. The elevation of each node in the energy storage system is analyzed based on digital elevation model (DEM) data. GIS tools are used to extract and analyze elevation data to identify the elevation characteristics of each node. 3D modeling tools (such as ArcGIS 3D Analyst) are used to model the geographic elevation of the energy storage paths and generate elevation variation characteristics of the paths. By analyzing terrain data, obstacles to power transmission are identified, such as natural obstacles like mountains, canyons, and rivers, as well as physical obstacles in grid construction (such as long lines and difficult maintenance). These obstacles are quantitatively analyzed to generate "energy storage transmission barrier characteristic data," which records the power transmission difficulty and resistance for each path segment or node. Based on the energy storage path's altitude characteristics, the nodes in the energy storage system are divided into multiple operating altitude intervals according to their altitude. Each interval represents a different altitude level, and power transmission is optimized based on these altitude conditions. Geographic Information System analysis tools, such as contour maps, are used to divide the region into several altitude levels and match them with the actual altitude information of the power plant nodes. Based on the altitude characteristics of the energy storage path and the influence of obstruction factors, the energy transmission loss rate of each node in different altitude intervals is calculated.Calculations can be performed using transmission loss models (such as Ohm's law or optimized transmission loss models that account for terrain effects). Power transmission at different altitudes encounters different power losses, especially over long distances or at high altitudes, where energy losses increase. Combined with barrier factor data, the node energy loss rate is calculated. Based on the calculated energy transmission loss rate and energy storage path characteristics, the entire industrial and commercial energy storage topology network is divided into energy storage modes. The appropriate energy storage mode is selected primarily based on energy transmission efficiency and the power requirements of each node. The direct power supply mode is suitable for nodes with low-loss or high-efficiency energy transmission paths, where the energy storage device directly supplies power to the load. The relay buffer mode is suitable for nodes with high transmission losses, where the energy storage device acts as a relay node to buffer power and reduce losses. The isolated storage mode is suitable for areas with high energy losses or unstable power grids, where the energy storage device operates in island mode to ensure system stability and reliability. Based on the energy transmission loss rate and path characteristics, a decision tree or clustering algorithm (such as the K-means or CART model) is used to divide the energy storage mode for each node in the energy storage system. Each node will be assigned to the most appropriate energy storage mode to optimize the overall energy efficiency and stability of the system.

[0094] Preferably, step S14 includes the following steps:

[0095] Step S141: extracting node elevation coordinates of the industrial and commercial energy storage topology network data to obtain original node elevation data; performing interpolation fitting and error correction on the original node elevation data to generate standardized node elevation data;

[0096] Step S142: performing path level difference analysis on the node standardized altitude data to generate energy storage path altitude characteristic data;

[0097] Step S143: Scan the connection paths of the edges in the industrial and commercial energy storage topology network for terrain factors to generate path geographic structure feature data; extract blocking factors from the path geographic structure feature data to generate potential energy storage transmission obstacle data;

[0098] Step S144: perform type labeling and spatial distribution evaluation on potential energy storage transmission obstacle data to generate energy storage transmission blocking feature data.

[0099] In this embodiment of the present invention, digital elevation model (DEM) or laser radar (LiDAR) data is used to extract elevation information for each node in an industrial and commercial energy storage topology network. GIS tools (such as ArcGIS and QGIS) or remote sensing image analysis techniques can be used to extract elevation data. Based on the node's geographic coordinates, the elevation value of each node is accurately extracted from the elevation dataset. The raw elevation data is interpolated, using methods such as kriging, spline interpolation, or inverse distance weighted (IDW) interpolation to smooth and fit the elevation data. Errors in the interpolated data are corrected by comparing it with other data sources (such as field measurements or higher-precision elevation data), and normalization is performed to ensure the accuracy and consistency of the elevation data. Ultimately, "node-standardized elevation data" is generated, ensuring that the elevation information for each node is consistent within the same reference system. A path-level difference method (such as linear interpolation) is used to analyze the elevation variations between nodes in the industrial and commercial energy storage topology network. By analyzing the elevation differences between nodes, the elevation variation of each node along the path is calculated. A difference analysis is performed on each path (i.e., the connection between nodes), calculating the elevation gain and loss along the path and analyzing the relationship between elevation change and power transmission. Polynomial fitting or local linear interpolation methods can be used to smooth and process these elevation change data to ensure that the elevation characteristics along the path are continuous and reasonable. Based on the results of the path-level difference analysis, "storage path elevation characteristic data" is generated, including information such as the elevation of each path's starting and ending points, the elevation change of intermediate nodes along the path, and the path's overall slope and inclination angle. A terrain factor scan is performed on each connecting path (i.e., network edge) in the energy storage topology network. Using a digital elevation model (DEM) and terrain data analysis tools, the terrain type (e.g., mountains, canyons, rivers, etc.) of each path is analyzed, along with its potential impact on power transmission. Geological, vegetation cover, and land use information can also be integrated to analyze the path's geographical structure, such as terrain relief, slope, and curvature. This generates "path geographical structure characteristic data," which contains the geographical type, slope, curvature, and other characteristics of each path. This characteristic data for each path supports calculations of power transmission feasibility, efficiency, and impedance. Based on the geographic structural feature data of the path, blocking factors are extracted, such as natural obstacles (mountains, rivers) or obstacles caused by human activities (roads, buildings, farmland, etc.). Obstacles with significant impacts on power transmission are identified and marked as potential energy storage transmission obstacles. Based on the geographic structural feature 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 on power transmission. The potential energy storage transmission obstacle data is labeled and classified into different types, such as natural obstacles (mountains, forests, lakes) and man-made obstacles (buildings, power facilities, roads).Obstacles are classified according to their type and impact, and each is assigned a priority or level of obstruction. The spatial distribution of potential energy storage transmission obstacles is analyzed. Spatial analysis tools (such as spatial statistics and heat maps) are used to analyze the distribution of obstacles within the industrial and commercial energy storage topology network. The impact range of each obstacle and its potential risk in power transmission are assessed to provide decision support for route selection and energy optimization. Based on the obstacle type and spatial distribution assessment, "energy storage transmission barrier feature data" is generated. This data will include detailed information on each potential obstacle on the path, including its type, impact level, and actual impact on power transmission.

[0100] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0101] Step S21: performing 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;

[0102] Step S22: collecting real-time power status data of the direct energy supply node set to generate direct energy storage node status data;

[0103] Step S23: Performing energy usage cycle segmentation analysis on the node energy storage status data to generate node time period response characteristic data; performing load dynamic interval comparison on the node time period response characteristic data to generate switchable charge and discharge node data;

[0104] Step S24: sorting the switchable charging and discharging node data in a sequence scheduling order to generate timing scheduling parameter data; jointly regulating the timing scheduling parameter data and the direct energy storage node status data to generate direct energy supply scheduling strategy data.

[0105] In this embodiment of the present invention, based on 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. These nodes are typically those that are close to power plants and have high load demands. Eligible nodes are identified using a distance-based or power demand-based screening method. For example, nodes within a certain range from the energy storage center and with load demands greater than a certain threshold can be screened as direct energy supply nodes. Data from the screened node set is recorded, including information such as the node's unique identifier, location coordinates, and current load demand. Smart meters or real-time data acquisition systems are used to collect real-time power status information for each node in the direct energy supply node set. These devices can monitor key information such as battery charge, charge and discharge rates, current, and voltage at each node. Wireless sensor networks (WSNs) or Internet of Things (IoT) technologies are used to collect and transmit this data in real time to a central control system. Based on this collected real-time data, the energy storage status of each node (e.g., remaining battery charge, storage efficiency, charge and discharge history, etc.) is calculated. This real-time status data is aggregated and generated into "direct energy storage node status data," which provides a basis for scheduling decisions. Based on the node's historical electricity usage data and load demand, the node's electricity usage cycle is identified. These cycles can typically be divided into peak, valley, and normal periods. Time series analysis techniques are used to automatically identify the load variation pattern for each node. The node's load curve is segmented, for example, by hourly dividing daily electricity usage data. Load peaks and valleys are analyzed to determine the power demand characteristics for each time period. Based on this segmented analysis of the energy usage cycle, "node time period response characteristic data" is generated. This data reflects the node's response characteristics in different time periods, such as power demand fluctuations, energy storage capacity changes, and charge and discharge response times. This data helps determine which nodes are suitable for charge and discharge scheduling within specific time periods to maximize energy storage utilization and balance the power system. The node time period response characteristic data is compared to analyze the load dynamics within different time periods (e.g., low load periods, peak load periods), identifying nodes with significant load fluctuations and those with greater regulation potential. Based on the load dynamics comparison results, nodes suitable for charge and discharge switching, namely "switchable charge and discharge nodes," are selected. The selected switchable charging and discharging nodes, along with their adjustment timing, energy requirements, and response characteristics, are aggregated to form "switchable charging and discharging node data." This dataset provides specific charging and discharging operation targets for scheduling. Based on this switchable charging and discharging node data, combined with the node's state of charge, load requirements, and energy storage response capabilities, a scheduling optimization algorithm (such as a genetic algorithm or particle swarm optimization) is used to sequence the nodes and generate a scheduling order.Determine scheduling priorities and prioritize nodes that are most in need of charging or discharging based on factors such as their battery capacity, power fluctuations, and load demand. Generate "sequential scheduling parameter data" based on the scheduling order. This data contains information such as the scheduling time, scheduling order, charge and discharge capacity, and priority for each node, providing specific scheduling instructions for further execution. Dynamic adjustment of scheduling parameters ensures that the energy balance and power supply needs of each node are maximized. Combined analysis of sequential scheduling parameter data with "direct energy storage node status data" is performed, dynamically adjusting the scheduling strategy using real-time power status, energy storage system efficiency, and system load data. Multi-objective optimization models (such as minimizing energy loss, maximizing system efficiency, and minimizing latency) are used to generate optimal "direct energy supply scheduling strategy data" to ensure the efficient operation of the energy storage system.

[0106] Preferably, step S24 includes the following steps:

[0107] Step S241: Evaluate the charge and discharge capabilities of the switchable charge and discharge node data to generate node energy storage response capability data; prioritize the node energy storage response capability data to generate node scheduling priority sequence data;

[0108] Step S242: performing period control window mapping on the node scheduling priority sequence data to generate time period scheduling sorting parameter data; performing cross-node consistency analysis on the time period scheduling sorting parameter data to generate coordinated time series scheduling parameter data;

[0109] Step S243: Synchronously regulate the coordinated time sequence scheduling parameter data and the direct energy storage node status data to generate joint regulation strategy data; perform continuity verification and time period continuous completion processing on the joint regulation strategy data to generate direct energy supply scheduling strategy data.

[0110] In this embodiment of the present invention, each node's charge and discharge capabilities are assessed using its historical energy storage data (e.g., battery capacity, battery health status, and charge and discharge efficiency) and real-time status data (e.g., current remaining charge and current load demand). This assessment can be based on physical models, such as battery discharge and charge characteristics, and the energy storage system's response speed. Alternatively, a data-driven approach can be employed, such as predicting node charge and discharge capabilities through historical data regression analysis. The assessment results serve as the node's "energy storage response capability data," which includes each node's maximum charge and discharge capabilities, as well as charge and discharge efficiency. Based on the node's energy storage response capability data, all nodes are prioritized. This prioritization is based on multiple factors, including: node charge and discharge capabilities (nodes with stronger response capabilities receive higher priority); node load demand (nodes with higher load demands receive priority scheduling); battery health (healthy batteries receive higher priority); and node scheduling timeliness (nodes requiring faster response receive priority). The ranking results generate "node scheduling priority sequence data" for subsequent scheduling decisions. Combined with the node's historical electricity usage data, peak, valley, and regular load periods are analyzed for different time periods. Based on these time periods, "periodic control windows" are defined, representing the node scheduling status within a specific time period. Node scheduling priority sequence data is mapped to each periodic control window to ensure that the scheduling strategy aligns with load demand in different time periods. Based on changes in load demand, the node scheduling order can be adjusted to accommodate varying power supply conditions. "Periodic scheduling order parameter data" is generated, which includes information such as the scheduling order, scheduling priority, and required charge and discharge capacity for each node within each time period. The scheduling strategies of different nodes within the same time period are analyzed to ensure cross-node scheduling consistency. For example, this ensures that power supply conflicts or network congestion do not occur when scheduling multiple nodes simultaneously. A consistency analysis of the periodic scheduling order parameter data is performed to determine whether resource conflicts, transmission bottlenecks, or other issues exist among different nodes within the same time period. Based on the analysis results, the node scheduling order or the scheduling capacity are adjusted. "Coordinated timing scheduling parameter data" is generated to ensure that the charging and discharging operations of each node are coordinated throughout the entire scheduling cycle, avoiding conflicts and optimizing resource allocation. The "coordinated timing scheduling parameter data" is synchronized with the "direct energy storage node status data" to ensure that each node's scheduling not only adheres to the scheduling priority sequence but also dynamically adjusts based on its current energy storage status. Real-time data feedback is used to adjust the scheduling order or charge / discharge capacity of nodes. For example, if a node's battery charge is lower than expected, it is prioritized for charging; if it is lower, it is prioritized for discharging. Based on the synchronized control results, "joint control strategy data" is generated. This data combines information about the node's scheduling priority, energy storage status, and the periodic control window. This joint control enables precise power distribution and energy storage optimization.Continuity checks are performed on the joint control strategy data to ensure a smooth transition between the charge and discharge states of each scheduling period and the next, avoiding excessive power fluctuations or scheduling conflicts. When scheduling is missing or discontinuous, continuous time-period completion processing is used to fill in the missing scheduling information and ensure the continuity of power supply. The final "direct energy supply scheduling strategy data" is generated. This dataset contains information such as the charging and discharging strategies, priorities, and scheduling order of all nodes in different time periods, providing complete instructions for the scheduling of the entire industrial and commercial energy storage system.

[0111] Preferably, in step S3, performing multi-level power buffering and power quality regulation on corresponding nodes in the industrial and commercial energy storage topology network through the relay buffer mode includes:

[0112] Perform 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;

[0113] Perform window segmentation processing on the node power time series data, extract the power continuous change segments and mutation points, and generate the node power change structure data;

[0114] Perform slope analysis and mutation frequency statistics on node power change structure data to identify the rate, density, and duration of power changes, and generate power disturbance characteristic indicator set data;

[0115] According to the power disturbance characteristic index set data, the corresponding nodes in the industrial and commercial energy storage topology network are divided into node fluctuation segments to generate node power fluctuation segments, and multi-level power buffering is performed on the node power fluctuation segments to generate node multi-level power buffering data;

[0116] The multi-level power buffer data of nodes is used to adjust the power quality of industrial and commercial energy storage topology networks based on power balance, and a relay buffer scheduling strategy is generated.

[0117] In an embodiment of the present invention, sensors and data acquisition devices are used to collect power output data from each node in an industrial and commercial energy storage topology network. This power time series data includes the time-varying changes in the node's voltage, current, and power. The raw power data is formatted and standardized to ensure data consistency. For example, power values are aggregated by time periods such as hours and minutes to generate regular time series data. The node's power data from different time periods is organized into a time series, which reflects the node's power demand and supply at different points in time. The node's power time series data is divided into time windows, which can be set to minutes, hours, or other time windows based on actual needs. Within each time window, power changes are analyzed to identify continuous segments with relatively stable power value changes and extract these segments. Within each time window, points with significant power value changes are analyzed, and sudden changes exceeding a preset threshold are marked. These sudden changes represent drastic changes in power output or demand, requiring adjustment by the energy storage system. Through segmented extraction and sudden change point identification, a data set is generated that reflects the node's power change structure, including information about the continuous segments and sudden change points of power change. Slope analysis is performed on each power fluctuation segment to calculate the rate of power change. Fluctuations with larger slopes indicate rapid changes in power demand, which can cause grid instability. The frequency of sudden changes in each node's time series is counted to help assess the frequency of power fluctuations at that node. Frequent power fluctuations require more buffering energy to maintain system stability. Slope analysis and sudden change frequency statistics generate a set of power disturbance characteristic indicators, including: the rate of change (slope) for each segment; the frequency of sudden changes at each node; the duration of power fluctuations at each node; and the density of power fluctuations (the number of fluctuations per unit time). Based on this power disturbance characteristic indicator data, the power time series of each node is divided into fluctuation segments. Each fluctuation segment corresponds to a distinct power fluctuation range and contains multiple sudden changes. A multi-stage power buffering mechanism is applied to each fluctuation segment to mitigate the fluctuations. Specifically, a single energy storage device provides initial buffering of power fluctuations. For example, batteries can be used to absorb or release low-frequency fluctuations at the node. For larger power fluctuations, multiple energy storage nodes can be used to collaborate and achieve greater balance. At this point, multiple energy storage units discharge or charge in coordination to mitigate load fluctuations. In the face of drastic power fluctuations, cross-node energy storage scheduling can be initiated, employing higher-capacity energy storage devices to adjust power on a large scale, thereby ensuring the stability of the entire industrial and commercial energy storage network topology. Different levels of power buffering are implemented within multiple fluctuation segments, generating "node multi-level power buffering data" that includes energy storage usage and adjustment strategies for each node during different buffering phases. This multi-level power buffering data is used to balance power across the entire energy storage system.By adjusting the charging and discharging strategies of each node, the power demand and supply balance across all nodes in the system is ensured. Energy storage device adjustments improve the quality of power transmission between nodes, reduce power disturbances, harmonics, and frequency fluctuations in the grid, and enhance the system's power quality. Based on the node's multi-level power buffering data and power balance adjustment results, a "relay buffer scheduling strategy" is generated. This strategy provides specific scheduling instructions for each node in the industrial and commercial energy storage topology network, including: charging and discharging timing for each node; power fluctuation mitigation strategies for each node; and scheduling parameters for coordinated operation of each node.

[0118] Preferably, the dividing of corresponding nodes in the industrial and commercial energy storage topology network into node fluctuation segments according to the power disturbance characteristic index set data and performing multi-level power buffering on the node power fluctuation segments includes:

[0119] Based on the power disturbance characteristic index set data, the corresponding nodes in the industrial and commercial energy storage topology network are divided into node fluctuation segments to generate node power fluctuation segments, where the node power fluctuation segments include slow transition segment, stable retention segment, multi-segment relay segment, jump response segment and high-frequency oscillation segment;

[0120] Based on the slow transition section, the slope of the corresponding nodes in the industrial and commercial energy storage topology network is extracted and delayed release is regulated to generate slow slope buffer adjustment data; based on the stable retention section, the intermittent disturbance and virtual load interpolation of the corresponding nodes in the industrial and commercial energy storage topology network are performed to generate stable retention buffer adjustment data;

[0121] Based on the multi-segment relay segment, the relay segment phase locking and leading release window adjustment are performed on the corresponding nodes in the industrial and commercial energy storage topology network to generate multi-segment relay buffer adjustment data; based on the jump response segment, the corresponding nodes in the industrial and commercial energy storage topology network are fast limited and channel switching buffered to generate jump response buffer adjustment data;

[0122] Based on the high-frequency oscillation segment, the corresponding nodes in the industrial and commercial energy storage topology network are filtered, isolated, and the inductive absorption is stabilized to generate high-frequency oscillation buffer adjustment data; the slowly varying slope buffer adjustment data, stable retention buffer adjustment data, multi-segment relay buffer adjustment data, jump response buffer adjustment data, and high-frequency oscillation buffer adjustment data are integrated into the node multi-level power buffer data.

[0123] In this embodiment of the present invention, the power disturbance characteristic index set data obtained in step S33 is used to perform a detailed classification of nodes in the industrial and commercial energy storage topology network. These five main types of fluctuation segments are as follows: A gradual transition segment is characterized by slow power changes, lasting for a long time and typically accompanied by slow load or demand changes. A stable retention segment is characterized by small and infrequent power changes, with the node's power demand stable or remaining constant for a long time. A multi-segment relay segment is characterized by dramatic fluctuations in node power, typically consisting of multiple, alternating, large power changes. A jump response segment is characterized by rapid node power changes, typically accompanied by sudden load or demand changes. A high-frequency oscillation segment is characterized by high-frequency node power fluctuations, characterized by small, high-frequency fluctuations, typically 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 regulation strategies are defined for each fluctuation segment. The power changes in the gradual transition segment are analyzed for slope, and the power change rate is calculated. Power fluctuations are adjusted through delayed release to avoid grid instability caused by sudden changes. Specifically, when power gradually changes, energy storage devices slowly release or absorb energy to smooth power variations. Based on slope extraction and delayed release control, slowly varying slope buffer adjustment data is generated to adjust the charge and discharge rates and timing of the energy storage devices. For occasional disturbances during the stable power retention period, virtual load interpolation is used to simulate their impact on the grid, and the energy storage system is used to provide appropriate compensation. Virtual load interpolation is used to compensate for minor disturbances during the stable retention period, minimizing their impact on the overall system. Based on the virtual load interpolation results, stable retention buffer adjustment data is generated to ensure grid stability during the retention period. For power fluctuations during multiple relay periods, phase locking technology is used to coordinate charging and discharging operations across the multiple fluctuation periods, smoothing power fluctuations. A leading release window is set between relay periods to predict and preemptively release or absorb energy from the energy storage device, ensuring energy balance. Phase locking and leading release window adjustment are used to generate multi-segment relay buffer adjustment data, ensuring coordination and smooth transitions between the multiple fluctuation periods. When power changes too quickly, rapid limiting technology is used to limit the amplitude of the power change, preventing excessive fluctuations from impacting the power grid. In the jump response phase, channel switching technology is used to connect different energy storage devices to the power fluctuation channel, allowing for rapid adjustment of power supply as needed. Rapid limiting and channel switching buffering technologies are used to generate jump response buffering adjustment data to address sudden power fluctuations. For high-frequency power fluctuations in the high-frequency oscillation phase, filtering technology is used to isolate these high-frequency noises and reduce their impact on grid stability. In the high-frequency oscillation phase, inductive devices are used to smooth out the fluctuations and reduce rapid current changes. Filtering and inductive absorption stabilization technologies are used to generate high-frequency oscillation buffering adjustment data to eliminate high-frequency disturbances.Integrate the regulation strategy data of different fluctuation segments to generate complete "node multi-level power buffer data".

[0124] Preferably, in step S3, performing island operation and black protection on the industrial and commercial energy storage topology network according to the isolated storage mode includes:

[0125] Node screening is performed on the industrial and commercial energy storage topology network according to the isolated storage mode to obtain isolated storage nodes;

[0126] Analyze the location information of isolated storage nodes and identify off-grid boundaries to generate island operation topology boundary data;

[0127] Perform breakpoint self-sustaining switching configuration on the island operation topology boundary data to generate island breakpoint operation configuration data;

[0128] Reconstruct the load level of the island breakpoint operation configuration data to generate island mode load adaptation data;

[0129] Collect node energy storage status data of isolated storage nodes, prioritize the node energy storage status data, and generate black start energy storage control data; generate time-based wake-up sequences for the black start energy storage control data, and generate hierarchical black protection response data;

[0130] The voltage and frequency self-stabilization regression adjustment is performed on the hierarchical black protection response data to generate the isolated operation stability support data; the isolated operation stability support data and the island mode load adaptation data are integrated into the scheduling strategy to generate the isolated storage scheduling mode.

[0131] In this embodiment of the present invention, energy storage nodes that meet island operation conditions are screened based on their power requirements and energy storage capacity. These nodes are typically located at the edge of the grid or in areas with relatively independent functions, and can continue to provide power even after disconnection from the main grid. This screening is performed by analyzing the node characteristics of the energy storage system (such as storage capacity, discharge capacity, and load demand), generating isolated storage node data. Based on the location of the isolated storage nodes (e.g., geographic coordinates, load density, connectivity, and other information), their suitability for island operation is analyzed. A geographic information system (GIS) and topology analysis algorithm are used to identify the node's off-grid boundary. By analyzing the power connections between nodes, it is determined which nodes can maintain power supply and island mode operation after disconnection. Based on the analysis results, island operation topology boundary data is generated, describing which nodes and their connections can maintain stable operation after disconnection. For breakpoints within the island operation topology boundary, a self-sustaining switching configuration is designed. Breakpoints are typically power transmission nodes connecting different areas. Intelligent switching control devices automatically determine the grid status at the breakpoint and decide whether to initiate a switchover, ensuring continuous and stable power supply. Based on the island topology boundary data and the breakpoint self-sustaining switchover configuration, island breakpoint operation configuration data is generated, including the automatic switchover plan and energy storage device start / stop strategies. In island operation mode, load demands between nodes fluctuate. To ensure smooth load adaptation in island mode, loads within the island are first reconfigured and divided into load levels. Load reconfiguration includes load reduction, adjustment, and distribution to adapt to available energy storage capacity. Load adaptation data for island mode is generated through load reconfiguration, ensuring effective load management and control at each node in island operation mode. Real-time monitoring and collection of energy storage status data from isolated storage nodes, including storage capacity, power, and charge / discharge status, are performed. Energy storage systems are prioritized based on the available power of energy storage devices and load demands, ensuring that the most critical loads are prioritized. High-priority energy storage devices are prioritized for charge and discharge scheduling to maintain power supply. Based on energy storage status data and priority classification, black start energy storage control data is generated to guide energy storage devices in quickly restoring power supply in island mode. Based on the black start energy storage control data, a wakeup sequence is designed for each energy storage device. The wake-up sequence for energy storage devices is assigned based on their energy storage capacity, load demand, and operating status. This time-slot wake-up sequence ensures that energy storage devices gradually start up as planned after a power outage, preventing system instability caused by a rapid load increase. Based on the time-slot wake-up sequence data, graded blackout protection response data is generated. This data guides how each energy storage device responds to load changes in island mode and ensures grid stability. In island mode, voltage and frequency instability can occur due to the constant fluctuations in the energy storage system and load.The voltage-frequency regulation mechanism automatically stabilizes and regresses the voltage and frequency, ensuring grid frequency fluctuations remain within a reasonable range and preventing large-scale power outages. This voltage-frequency regression regulation generates stability support data. This data provides the necessary stability assurance for island mode operation. This stability support data is integrated with island mode load adaptation data to generate a complete isolated storage scheduling model. This model guides the charging and discharging scheduling of energy storage devices in island mode, ensuring stable grid operation.

[0132] As an example of the present invention, refer to Figure 3 As shown, in this example, step S4 includes:

[0133] Step S41: performing 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 variation data;

[0134] Step S42: Perform dynamic impedance matching optimization on the impedance variation data of multiple source nodes to generate energy storage scheduling impedance optimization data;

[0135] 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.

[0136] In this embodiment of the present invention, based on energy storage nodes and their scheduling strategies in an industrial and commercial energy storage topology network, the relationship between each node and the power load is analyzed, taking into account factors such as the energy storage capacity, load demand, and power balance of each node. Using a relay buffering model, the impact of energy storage nodes on load regulation during multi-level buffering is evaluated. Key considerations are given to the node's power fluctuation, buffering capacity, and power quality regulation capabilities. For islanded operation, the role of the energy storage system in power supply and its scheduling requirements during islanded operation are analyzed, including the black start response, load adaptation, and voltage and frequency regulation of the energy storage device. A coupled analysis of direct power supply, relay buffering, and isolated storage scheduling strategies is conducted to explore their mutual impact within the same industrial and commercial energy storage topology network. A scheduling coupling model is established to analyze the coordination and interaction of different scheduling strategies within the power grid. This scheduling coupling analysis evaluates the power and voltage fluctuations experienced by each energy storage node during the scheduling process, generating node impedance variation data. This data represents the energy fluctuation characteristics of the energy storage node under different scheduling strategies, including load variations, power transfer, and current impedance changes. During energy storage system scheduling, impedance matching refers to the matching of power transmission and voltage regulation between energy storage devices and the grid. The goal is to maintain optimal current, voltage, and power during the energy storage system's scheduling process. Taking into account the dynamic characteristics of energy storage nodes in commercial and industrial energy storage topologies (such as charging and discharging rates, storage capacity, and power response), real-time impedance matching optimization is performed for multiple source nodes. This process involves real-time monitoring of energy storage device load demand, power fluctuations, and voltage conditions, 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 power flow and voltage regulation within the energy storage system. Dynamic impedance adjustment through optimization algorithms generates energy storage scheduling impedance optimization data. This data includes the optimal scheduling scheme for each energy storage node, ensuring maximum efficiency and optimal power quality for the energy storage system under various scheduling strategies. Based on the energy storage scheduling impedance optimization data, specific energy storage scheduling instructions are generated. These instructions include information such as the charging and discharging timing of each node, power allocation, and the start and stop times of energy storage devices. Each energy storage device executes dispatch operations according to instructions, ensuring that the entire energy storage network operates in accordance with the predetermined dispatch strategy. Based on different dispatch strategies (direct power supply, relay buffering, and isolated storage), an optimal dispatch instruction set is generated. This instruction set takes into account the real-time status and load demands of the energy storage devices while maintaining grid stability. The optimized dispatch method is implemented and the response of the energy storage nodes is monitored in real time. A feedback mechanism dynamically adjusts dispatch instructions to ensure optimal power distribution and power quality across the energy storage system. An intelligent monitoring system tracks various parameters (such as power, frequency, and voltage) during dispatch execution and makes timely adjustments based on feedback data to ensure the effectiveness and accuracy of dispatch instructions.The effectiveness of dispatch optimization execution is evaluated primarily across the following aspects: Power quality: Ensures voltage and frequency fluctuations are within reasonable ranges, avoiding excessively high or low voltages. Load response: Evaluates the load adaptability 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 ensures that the energy storage system can provide power with minimal losses. A dispatch execution report is generated based on the evaluation results to ensure continuous optimization and improvement during future dispatch processes.

[0137] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0138] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for dispatching industrial and commercial energy storage, characterized in that: The following steps are involved: Step S1: Acquire 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 barrier 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; wherein step S1 includes the following steps: Step S11: Obtain the location of the industrial and commercial power plant; Step S12: analyzing the geographic coordinates of the location of the industrial and commercial power plant, and marking the geographic coordinates as nodes to generate power plant topology node data; Step S13: extracting the geographical connection paths of the power plant topology node data, and constructing a topology network using the power plant topology node data and the geographical connection paths to generate an industrial and commercial energy storage topology network; Step S14: Performing a geographic elevation analysis on the industrial and commercial energy storage topology network, and extracting the energy storage path elevation characteristics of the nodes in the industrial and commercial energy storage topology network and identifying the energy storage transmission barrier factors of the edges based on the analysis results, thereby generating energy storage path elevation characteristic data and energy storage transmission barrier characteristic data; Step S15: dividing the working altitude ranges of nodes in the industrial and commercial energy storage topology network based on the energy storage path altitude characteristic data, and calculating the energy transmission loss rate of each node in combination with the energy storage transmission barrier characteristic data; Step S16: dividing the industrial and commercial energy storage topology network into energy storage modes based on the energy transmission loss rate, generating a direct energy supply mode, a relay buffer mode, and an isolated storage mode; wherein step S14 includes the following steps: Step S141: extracting node elevation coordinates of the industrial and commercial energy storage topology network data to obtain original node elevation data; performing interpolation fitting and error correction on the original node elevation data to generate standardized node elevation data; Step S142: performing path level difference analysis on the node standardized altitude data to generate energy storage path altitude characteristic data; Step S143: Scan the connection paths of the edges in the industrial and commercial energy storage topology network for terrain factors to generate path geographic structure feature data; extract blocking factors from the path geographic structure feature data to generate potential energy storage transmission obstacle data; Step S144: performing type labeling and spatial distribution evaluation on potential energy storage transmission obstacle data to generate energy storage transmission barrier feature data; Step S2: Based on the direct energy supply mode, the node status of the corresponding nodes in the industrial and commercial energy storage topology network is collected; according to the node status, the corresponding nodes are subjected to real-time charge and discharge scheduling driven by the peak-valley price difference to generate a direct energy supply scheduling strategy; Step S3: Perform multi-level power buffering and power quality regulation on corresponding nodes in the industrial and commercial energy storage topology 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 topology network according to the isolated storage mode to generate an isolated storage scheduling mode; Step S4: Dynamic impedance matching optimization is performed 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, and an optimal scheduling instruction set is generated to execute the industrial and commercial energy storage scheduling optimization method.

2. The industrial and commercial energy storage scheduling method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing 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: collecting real-time power status data of the direct energy supply node set to generate direct energy storage node status data; Step S23: Performing energy usage cycle segmentation analysis on the node energy storage status data to generate node time period response characteristic data; performing load dynamic interval comparison on the node time period response characteristic data to generate switchable charge and discharge node data; Step S24: sorting the switchable charging and discharging node data in a sequence scheduling order to generate timing scheduling parameter data; jointly regulating the timing scheduling parameter data and the direct energy storage node status data to generate direct energy supply scheduling strategy data.

3. The industrial and commercial energy storage scheduling method according to claim 2, 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; prioritize the node energy storage response capability data to generate node scheduling priority sequence data; Step S242: performing period control window mapping on the node scheduling priority sequence data to generate time period scheduling sorting parameter data; performing cross-node consistency analysis on the time period scheduling sorting parameter data to generate coordinated time series scheduling parameter data; Step S243: Synchronously regulate the coordinated time sequence scheduling parameter data and the direct energy storage node status data to generate joint regulation strategy data; perform continuity verification and time period continuous completion processing on the joint regulation strategy data to generate direct energy supply scheduling strategy data.

4. The industrial and commercial energy storage scheduling method according to claim 1, characterized in that: In step S3, performing multi-level power buffering and power quality regulation on corresponding nodes in the industrial and commercial energy storage topology network through the relay buffer mode includes: Perform 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 window segmentation processing on the node power time series data, extract the power continuous change segments and mutation points, and generate the node power change structure data; Perform slope analysis and mutation frequency statistics on node power change structure data to identify the rate, density, and duration of power changes, and generate power disturbance characteristic indicator set data; According to the power disturbance characteristic index set data, the corresponding nodes in the industrial and commercial energy storage topology network are divided into node fluctuation segments to generate node power fluctuation segments, and multi-level power buffering is performed on the node power fluctuation segments to generate node multi-level power buffering data; The multi-level power buffer data of nodes is used to adjust the power quality of industrial and commercial energy storage topology networks based on power balance, and a relay buffer scheduling strategy is generated.

5. The industrial and commercial energy storage scheduling method according to claim 4, characterized in that: The method of dividing the corresponding nodes in the industrial and commercial energy storage topology network into node fluctuation segments according to the power disturbance characteristic index set data and performing multi-level power buffering on the node power fluctuation segments includes: Based on the power disturbance characteristic index set data, the corresponding nodes in the industrial and commercial energy storage topology network are divided into node fluctuation segments to generate node power fluctuation segments, where the node power fluctuation segments include slow transition segment, stable retention segment, multi-segment relay segment, jump response segment and high-frequency oscillation segment; Based on the slow transition section, the slope of the corresponding nodes in the industrial and commercial energy storage topology network is extracted and delayed release is regulated to generate slow slope buffer adjustment data; based on the stable retention section, the intermittent disturbance and virtual load interpolation of the corresponding nodes in the industrial and commercial energy storage topology network are performed to generate stable retention buffer adjustment data; Based on the multi-segment relay segment, the relay segment phase locking and leading release window adjustment are performed on the corresponding nodes in the industrial and commercial energy storage topology network to generate multi-segment relay buffer adjustment data; based on the jump response segment, the corresponding nodes in the industrial and commercial energy storage topology network are fast limited and channel switching buffered to generate jump response buffer adjustment data; Based on the high-frequency oscillation segment, the corresponding nodes in the industrial and commercial energy storage topology network are filtered, isolated, and the inductive absorption is stabilized to generate high-frequency oscillation buffer adjustment data; the slowly varying slope buffer adjustment data, stable retention buffer adjustment data, multi-segment relay buffer adjustment data, jump response buffer adjustment data, and high-frequency oscillation buffer adjustment data are integrated into the node multi-level power buffer data.

6. The industrial and commercial energy storage scheduling method according to claim 1, characterized in that: In step S3, performing island operation and black protection on the industrial and commercial energy storage topology network according to the isolated storage mode includes: Node screening is performed on the industrial and commercial energy storage topology network according to the isolated storage mode to obtain isolated storage nodes; Analyze the location information of isolated storage nodes and identify off-grid boundaries to generate island operation topology boundary data; Perform breakpoint self-sustaining 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 node energy storage status data of isolated storage nodes, prioritize the node energy storage status data, and generate black start energy storage control data; generate time-based wake-up sequences for the black start energy storage control data, and generate hierarchical black protection response data; The voltage and frequency self-stabilization regression adjustment is performed on the hierarchical black protection response data to generate the isolated operation stability support data; the isolated operation stability support data and the island mode load adaptation data are integrated into the scheduling strategy to generate the isolated storage scheduling mode.

7. The industrial and commercial energy storage scheduling method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing 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 variation data; Step S42: performing dynamic impedance matching optimization on the impedance variation data of multiple source nodes 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.

8. An industrial and commercial energy storage system, characterized in that: For executing the industrial and commercial energy storage scheduling method according to claim 1, the industrial and commercial energy storage system comprises: The energy storage mode classification module is used to 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 barrier characteristics of the industrial and commercial energy storage topological network, and classify the industrial and commercial energy storage topological network into energy storage modes, generating direct energy supply mode, relay buffer mode, and isolated storage mode; The direct energy storage scheduling module is used to collect the node status of corresponding nodes in the industrial and commercial energy storage topology network based on the direct energy supply mode; based on the node status, it performs real-time charge and discharge scheduling driven by peak and valley price differences for the corresponding nodes to 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 corresponding nodes in the industrial and commercial energy storage topology network through the relay buffer mode, generating a relay buffer scheduling strategy; and performs island operation and blackout protection on the industrial and commercial energy storage topology network according to the isolated storage mode, generating an isolated storage scheduling mode; The transmission impedance optimization module is used to dynamically optimize the impedance matching of industrial and commercial energy storage topology networks based on direct energy supply scheduling strategies, relay buffer scheduling strategies, and isolated storage scheduling modes, and generate the optimal scheduling instruction set to execute the industrial and commercial energy storage scheduling optimization method.

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