Optimal control system for power supply area source network load storage cooperation based on intelligent fusion terminal

Abstract

The present application relates to source network load storage collaborative control technical field, especially to a kind of based on intelligent fusion terminal's table area source network load storage collaborative optimization control system, the present application is collected by acquisition module, collects the energy characteristic parameter and collaborative response characteristic parameter of target table area historical period;Through analysis module, based on the energy characteristic parameter analysis energy characteristic representation value, based on the collaborative response characteristic parameter calculates collaborative response characteristic representation value;Through control module, based on the energy characteristic representation value determines corresponding control strategy;Through verification module, based on the collaborative response characteristic representation value determines whether the stability of table area source network load storage meets standard;Through feedback module, based on the difference between energy characteristic representation value and predetermined energy characteristic representation threshold determines the adjustment range of collaborative response characteristic representation threshold, and reacquire verification.The present application improves the response rate of source network load storage each link collaborative control by verification module and feedback module.

Description

Technical Field

[0001] This invention relates to the field of source-grid-load-storage coordinated control technology, and in particular to a source-grid-load-storage coordinated optimization control system for transformer substations based on an intelligent fusion terminal. Background Technology

[0002] With the high proportion of distributed photovoltaic (PV) grid connections in urban and rural distribution substations and the widespread adoption of fluctuating loads such as electric vehicle charging stations, traditional distribution networks face severe challenges. Existing distribution substation control systems mostly adopt an independent monitoring model for power sources, grids, loads, and energy storage. Inconsistent data dimensions and protocols create data silos, and their control strategies often rely on centralized calculations at remote master stations, resulting in high response delays and difficulty in handling power fluctuations and voltage exceedances within distribution substations at the second or minute level. Furthermore, existing methods lack a unified mechanism for coordinating safety and economic efficiency in decision-making, often leading to conflicting objectives: sacrificing grid safety for economic gains or restricting renewable energy consumption to ensure safety. This hinders the simultaneous improvement of power supply reliability and economic efficiency in distribution substations.

[0003] Chinese Patent Publication No. CN118074226A discloses a method for coordinated control of power generation, grid, load, and storage in a distribution network, comprising the following steps: First, among all power sources within a target range, identify the power sources that have experienced a fault; Second, collect the load parameters and energy storage parameters of each power source within the target range during standard time periods, as well as the supply areas of each power source and the distance between each power source and its supply area; Third, calculate the average value of the load parameters corresponding to the faulty power source during multiple specified standard time periods using variance and / or average value calculation formulas; Fourth, normal power supply... The invention utilizes a source-grid-load-storage coordinated control method to promptly identify alternative power sources when a power source fails. The pre-trained logical screening model enables intelligent scheduling and matching of power sources, improving power supply efficiency and flexibility. Furthermore, load and energy storage analysis allows for rational planning of power source operation, improving power quality and meeting user needs.

[0004] Therefore, the invention has the following problems:

[0005] This invention does not take into account the problem of delayed coordinated control response of various links in the power distribution control system due to data fragmentation between the subsystems of power generation, grid, load and storage and the ambiguity of the decision-making level of the central control system. Summary of the Invention

[0006] To address this issue, the present invention provides a coordinated optimization control system for power generation, grid, load, and storage in a distribution area based on an intelligent fusion terminal. This system overcomes the problem in the prior art where the coordinated control response of each link in the power generation, grid, load, and storage system is delayed due to data fragmentation between the subsystems of power generation, grid, load, and storage and the ambiguity of the decision-making hierarchy of the central control system.

[0007] To achieve the above objectives, the present invention provides a coordinated optimization control system for power generation, grid, load, and energy storage in a distribution area based on an intelligent fusion terminal, comprising:

[0008] The data acquisition module is used to collect historical periodic energy characteristic parameters and collaborative response characteristic parameters of the target transformer area;

[0009] An analysis module, which is connected to the acquisition module, is used to analyze energy characteristic values ​​based on the energy characteristic parameters and calculate collaborative response characteristic values ​​based on the collaborative response characteristic parameters.

[0010] A control module, which is connected to the acquisition module and the analysis module, is used to determine the corresponding control strategy based on the energy characteristic values;

[0011] The verification module, which is connected to the control module, is used to determine whether the stability of the source-grid-load-storage area meets the standard based on the collaborative response characteristic characterization value.

[0012] The feedback module, which is connected to the acquisition module and the verification module, is used to determine the adjustment range of the collaborative response characteristic threshold based on the difference between the energy characteristic characterization value and the predetermined energy characteristic characterization threshold when the source-grid-load-storage of the distribution area does not meet the standard, and to re-acquire and verify.

[0013] The energy characteristic parameters include the voltage loss rate of the transformer area bus, the current loss rate of the transformer area line, and the deep discharge frequency of the energy storage battery. The collaborative response characteristic parameters include the total power of the distributed power source and the total power loss of the transformer area power source.

[0014] Furthermore, the analysis module is used to determine the sum of the first energy characteristic factor, the second energy characteristic factor, and the third energy characteristic factor as the energy characteristic representation value, wherein,

[0015] The first energy characteristic factor is the ratio of the voltage loss rate of the transformer area bus to a predetermined threshold value for the voltage loss rate of the transformer area bus.

[0016] The second energy characteristic factor is the ratio of the line current loss rate of the transformer area to a predetermined threshold for the line current loss rate of the transformer area.

[0017] The third energy characteristic factor is the ratio of the deep discharge frequency of the energy storage battery to a predetermined deep discharge frequency threshold for the energy storage battery.

[0018] Furthermore, the control module is used to determine the corresponding control strategy based on the energy characteristic representation value, including inputting the energy characteristic representation value into a pre-trained multi-objective hierarchical decision model, performing security checks sequentially, and determining the corresponding control strategy, wherein the security checks include voltage security checks, current security checks, and energy storage security checks.

[0019] Furthermore, the voltage safety verification and its corresponding control strategy include:

[0020] If the voltage loss rate of the transformer bus is greater than the predetermined first voltage loss threshold, the energy storage converter is determined to discharge; if the voltage loss rate of the transformer bus is still greater than the predetermined first voltage loss threshold, the new energy equipment is determined to output power.

[0021] If the voltage loss rate of the transformer bus is less than or equal to the predetermined first voltage loss threshold and greater than the predetermined second voltage loss threshold, then the energy storage converter is determined to continue to be powered.

[0022] If the voltage loss rate of the transformer bus is less than or equal to the predetermined second voltage loss threshold, the energy storage converter is charged. If the voltage loss rate of the transformer bus is still less than the predetermined second voltage loss threshold, the reduction in the power of the flexible load is determined.

[0023] Furthermore, the current safety verification and its corresponding control strategy include:

[0024] If the current loss rate of the transformer area line is greater than the predetermined current overload threshold, the reduction range of the flexible load power is determined. If the current loss rate of the transformer area line is still greater than the predetermined current overload threshold, the energy storage converter is shut down.

[0025] If the current loss rate of the transformer area line is less than or equal to the predetermined current overload threshold, the equipment current is determined to be safe, and power continues to be supplied.

[0026] Furthermore, the energy storage safety verification and its corresponding control strategy include:

[0027] If the deep discharge frequency of the energy storage battery is greater than the predetermined first deep discharge frequency threshold of the energy storage battery, the device will stop charging and only discharge or shutdown operations will be allowed.

[0028] If the deep discharge frequency of the energy storage battery is less than or equal to the predetermined first deep discharge frequency threshold and greater than the predetermined second deep discharge frequency threshold, then the energy storage of the device is determined to be safe and power is continued.

[0029] If the deep discharge frequency of the energy storage battery is less than or equal to the predetermined second deep discharge frequency threshold of the energy storage battery, then the device is determined to stop discharging and only charging or shutdown operations are allowed.

[0030] Furthermore, the analysis module is used to calculate the collaborative response characteristic value based on the collaborative response characteristic parameters, wherein the collaborative response characteristic value is determined based on the difference between the total power of the distributed power source and the total power loss of the distribution area power source.

[0031] Furthermore, the verification module is used to determine whether the stability of the source-grid-load-storage system in the distribution area meets the standard based on the collaborative response characteristic characterization value, including:

[0032] If the value of the coordinated response characteristic is less than or equal to the predetermined threshold for coordinated response characteristic, then the stability of the source-grid-load-storage system in the distribution area is determined to meet the standard.

[0033] If the value of the coordinated response characteristic is greater than the predetermined threshold for coordinated response characteristic, then the stability of the source-grid-load-storage system in the distribution area is determined to be non-compliant with the standard.

[0034] Furthermore, the feedback module is used to determine the adjustment range of the collaborative response feature characterization threshold based on the difference between the energy feature characterization value and a predetermined energy feature characterization threshold, including:

[0035] If the difference is less than or equal to a predetermined difference threshold, then the adjustment range of the collaborative response feature characterization threshold is determined to be the first adjustment range;

[0036] If the difference is greater than a predetermined difference threshold, then the adjustment range of the collaborative response feature characterization threshold is determined to be the second adjustment range.

[0037] Furthermore, the acquisition module includes a sensing unit built into the distributed power controller, a smart meter, a flexible load controller, an energy storage converter, a battery management system, and voltage and current transformers in the distribution box of the transformer substation.

[0038] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a coordinated optimization control system for power generation, grid, load, and storage in a distribution area based on an intelligent fusion terminal. The system collects historical energy characteristic parameters and coordinated response characteristic parameters of the target distribution area through a data acquisition module; analyzes energy characteristic values ​​based on the energy characteristic parameters and calculates coordinated response characteristic values ​​based on the coordinated response characteristic parameters through an analysis module; determines corresponding control strategies based on the energy characteristic values ​​through a control module; verifies whether the stability of the power generation, grid, load, and storage system in the distribution area meets the standards based on the coordinated response characteristic values ​​through a verification module; and determines the adjustment range of the coordinated response characteristic threshold based on the difference between the energy characteristic values ​​and a predetermined energy characteristic threshold through a feedback module, and re-collects and verifies the data. This invention improves the response rate of coordinated control across all aspects of power generation, grid, load, and storage through the verification and feedback modules.

[0039] In particular, this invention integrates the sensing unit built into the distributed power controller, smart meter, flexible load controller, energy storage converter, battery management system, and voltage and current transformers in the distribution box of the transformer substation through the acquisition module, constructing a complete distribution substation operation data acquisition network. The hardware integration method solves the problems of scattered data sources and inconsistent communication protocols in traditional systems, enabling the synchronous acquisition of energy characteristic parameters such as the voltage loss rate of the transformer substation bus, line current, and deep discharge frequency of the energy storage battery, as well as coordinated response characteristic parameters such as the total power of the distributed power source and the total power loss of the transformer substation. This ensures the comprehensiveness and real-time nature of data acquisition, provides a complete data foundation for subsequent analysis and decision-making, and reduces control deviations caused by missing or delayed data.

[0040] In particular, this invention standardizes the collected multi-source heterogeneous data by analyzing the central processing unit, memory, and embedded operating system built into the intelligent fusion terminal of the analysis module. This module converts the raw data with different communication protocols and different sampling frequencies into a unified energy characteristic dataset and calculates the collaborative response characteristic value, eliminating the analysis obstacles caused by data format differences. This enables subsequent modules to make accurate judgments based on standardized data, improving the accuracy and reliability of the system's description of the operating status of the transformer area and providing high-quality pre-processed data input for the generation of control strategies.

[0041] In particular, this invention executes a pre-trained multi-objective hierarchical decision model through a control module, sequentially performing voltage safety verification, current safety verification, and energy storage safety verification. Based on a real-time energy characteristic dataset, this module generates corresponding control strategies according to preset safety thresholds, including charging and discharging commands for energy storage converters, output limiting commands for new energy equipment, and power adjustment commands for flexible loads. This hierarchical safety verification mechanism ensures that the power grid operation is always within safe boundaries, reducing the probability of voltage exceeding limits, line overload, and equipment damage, and establishing a safety-first collaborative control foundation.

[0042] In particular, this invention uses a verification module to determine whether the stability of the power generation, grid, load, and storage system in a distribution area meets the standards based on the collaborative response characteristic characterization value. A feedback module responds when the system fails to meet the standards by determining the adjustment range of the collaborative response characteristic characterization threshold based on the difference between the energy characteristic characterization value and a predetermined energy characteristic characterization threshold, and then re-collects and verifies the data. This achieves further optimization of resource allocation and operational economy while ensuring grid security. The flexible adjustment of energy storage devices promotes the local consumption of new energy sources, reduces power losses during grid connection and disconnection, improves the overall economic efficiency of the distribution area, and simultaneously ensures the stable operation of the power grid. Attached Figure Description

[0043] Figure 1 This is a structural block diagram of the source-grid-load-storage collaborative optimization control system for transformer substations based on an embodiment of the present invention;

[0044] Figure 2 The embodiment of the present invention performs a logic decision diagram for voltage safety verification based on the energy feature dataset;

[0045] Figure 3 The present invention provides a logic decision diagram for performing current safety verification based on the energy feature dataset.

[0046] Figure 4 The embodiment of the present invention performs a logic decision diagram for energy storage safety verification based on the energy feature dataset. Detailed Implementation

[0047] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0048] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0049] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connected" should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral connection; it can refer to a mechanical connection or an electrical connection; it can refer to a direct connection or an indirect connection through an intermediate medium; it can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0050] Please see Figure 1 The diagram shown is a structural block diagram of a power distribution area source-grid-load-storage coordinated optimization control system based on an intelligent fusion terminal, according to an embodiment of the present invention. The present invention provides a power distribution area source-grid-load-storage coordinated optimization control system based on an intelligent fusion terminal, comprising:

[0051] The data acquisition module is used to collect historical periodic energy characteristic parameters and collaborative response characteristic parameters of the target transformer area;

[0052] An analysis module, which is connected to the acquisition module, is used to analyze energy characteristic values ​​based on the energy characteristic parameters and calculate collaborative response characteristic values ​​based on the collaborative response characteristic parameters.

[0053] A control module, which is connected to the acquisition module and the analysis module, is used to determine the corresponding control strategy based on the energy characteristic values;

[0054] The verification module, which is connected to the control module, is used to determine whether the stability of the source-grid-load-storage area meets the standard based on the collaborative response characteristic characterization value.

[0055] The feedback module, which is connected to the acquisition module and the verification module, is used to determine the adjustment range of the collaborative response characteristic threshold based on the difference between the energy characteristic characterization value and the predetermined energy characteristic characterization threshold when the source-grid-load-storage of the distribution area does not meet the standard, and to re-acquire and verify.

[0056] The energy characteristic parameters include the voltage loss rate of the transformer area bus, the current loss rate of the transformer area line, and the deep discharge frequency of the energy storage battery. The collaborative response characteristic parameters include the total power of the distributed power source and the total power loss of the transformer area power source.

[0057] It is understandable that the source-grid-load-storage collaborative optimization control system based on intelligent fusion terminals includes a four-layer architecture: perception layer, intelligent fusion terminal layer, communication layer, and dispatch center layer.

[0058] It is understood that the perception layer provides data input to the system, which is manifested as a data acquisition module in this embodiment of the invention. This acquisition module collects data from all devices in real time via local communication protocols such as RS485, LoRa, and dual-mode (HPLC+HRF). The system deploys 20 10kW residential photovoltaic inverters, 1 50kWh energy storage converter, 50 smart meters, 5 electric vehicle charging piles, and 1 set of transformer area bus voltage / current monitoring device.

[0059] Understandably, the intelligent converged terminal layer is deployed in or near the distribution box in the transformer substation, possessing four main functions: data acquisition, edge computing, local control, and communication forwarding. An edge computing terminal equipped with four RS485 channels, one dual-mode (HPLC+HRF) channel, and one 4G / 5G module is selected and installed inside the distribution box in the transformer substation, connected to a 220V AC power supply.

[0060] Understandably, the communication layer is divided into a local communication sublayer and a remote communication sublayer. The local communication sublayer uses RS485, LoRa, and dual-mode (HPLC+HRF) protocols to connect the intelligent fusion terminal and the sensing layer equipment, and is suitable for short-distance data transmission within the distribution area. The remote communication sublayer uses 4G / 5G and fiber optic protocols to connect the intelligent fusion terminal and the dispatch center layer, and realizes the remote transmission of massive amounts of data.

[0061] Understandably, the dispatch center layer is deployed within the regional power company, possessing data storage and visualization capabilities, global optimization dispatching functions, and fault early warning and tracing functions. Specifically, the data storage and visualization function receives and stores data uploaded from the intelligent integrated terminals of each distribution area, displaying the real-time operating status of the distribution area's power generation, grid, load, and storage systems on a large screen, such as renewable energy output curves, load curves, and changes in energy storage SOC. The global optimization dispatching function performs global optimization based on data from each distribution area when multiple distribution areas have collaborative needs, issuing unified dispatching instructions to the relevant intelligent integrated terminals. The fault early warning and tracing function analyzes historical and real-time data to identify potential equipment faults in the distribution area and generates fault tracing reports. A data server and visualization platform are deployed to receive operational data from this distribution area and five surrounding distribution areas.

[0062] Understandably, the acquisition module collects data from the sensing layer device every 10 seconds as an acquisition cycle.

[0063] This invention provides a coordinated optimization control system for power generation, grid, load, and storage in a distribution area based on an intelligent fusion terminal. The system utilizes a data acquisition module to collect historical energy characteristic parameters and coordinated response characteristic parameters of the target distribution area. An analysis module analyzes energy characteristic values ​​based on the energy characteristic parameters and calculates coordinated response characteristic values ​​based on the coordinated response characteristic parameters. A control module determines corresponding control strategies based on the energy characteristic values. A verification module determines whether the stability of the power generation, grid, load, and storage system in the distribution area meets the standards based on the coordinated response characteristic values. A feedback module determines the adjustment range of the coordinated response characteristic threshold based on the difference between the energy characteristic values ​​and a predetermined energy characteristic threshold, and then re-acquires and verifies the data. This invention improves the response rate of coordinated control across all stages of the power generation, grid, load, and storage system through the verification and feedback modules.

[0064] Specifically, the analysis module is used to determine the sum of the first energy characteristic factor, the second energy characteristic factor, and the third energy characteristic factor as the energy characteristic representation value, wherein,

[0065] The first energy characteristic factor is the ratio of the voltage loss rate of the transformer area bus to a predetermined threshold value for the voltage loss rate of the transformer area bus.

[0066] The second energy characteristic factor is the ratio of the line current loss rate of the transformer area to a predetermined threshold for the line current loss rate of the transformer area.

[0067] The third energy characteristic factor is the ratio of the deep discharge frequency of the energy storage battery to a predetermined deep discharge frequency threshold for the energy storage battery.

[0068] Understandably, the analysis module standardizes the energy characteristic parameters by converting them into JSON format.

[0069] This invention improves the accuracy of system energy efficiency assessment and data processing efficiency by standardizing the collected energy characteristic parameters and converting them into JSON format, and by summing multiple key energy characteristic parameters using normalization factors and converting them into a standardized data format. The scheme calculates the ratio factors of voltage loss rate, current loss rate, and battery deep discharge frequency to their respective thresholds, transforming energy parameters of different dimensions into dimensionless evaluation indicators. This enables comprehensive quantitative analysis of multi-dimensional energy characteristics. Combined with the standardized processing of JSON format, a unified data interaction standard is established, reducing the computational complexity in the multi-source data fusion process, providing a reliable quantitative basis for the formulation of subsequent control strategies, and enhancing the system's ability to perceive the overall energy efficiency status of the distribution area.

[0070] Specifically, the control module is used to determine the corresponding control strategy based on the energy characteristic representation value, including inputting the energy characteristic representation value into a pre-trained multi-objective hierarchical decision model, performing security checks in sequence, and determining the corresponding control strategy. The security checks include voltage security checks, current security checks, and energy storage security checks.

[0071] Understandably, the pre-trained multi-objective hierarchical decision model uses a built-in collaborative optimization algorithm to dynamically verify and analyze the optimal control strategy based on real-time collected energy characteristic data.

[0072] Understandably, when communication is interrupted or remote dispatch commands are delayed, the control module automatically activates the local control function and sends control commands directly to the energy storage device and flexible load controller based on the edge computing results to ensure the stable operation of the distribution area.

[0073] This invention improves the safety and reliability of transformer substation operation by employing a pre-trained multi-objective hierarchical decision model for localized edge computing and establishing an autonomous control mechanism in the event of communication interruption. Through sequential voltage, current, and energy storage safety checks, a systematic safety protection system is formed, which can promptly respond to operational risks such as voltage overruns and line overloads. Dynamic strategy calculation based on real-time data ensures the matching degree between control commands and operating status, while the activation of local control functions reduces communication latency, maintains stable operation of the transformer substation under abnormal conditions, and reduces the probability of systemic operational risks.

[0074] Please see Figure 2 As shown, this is a logic decision diagram for performing voltage safety verification based on the energy feature dataset in an embodiment of the present invention. The voltage safety verification and its corresponding control strategy of the present invention include:

[0075] If the voltage loss rate of the transformer bus is greater than the predetermined first voltage loss threshold, the energy storage converter is determined to discharge; if the voltage loss rate of the transformer bus is still greater than the predetermined first voltage loss threshold, the new energy equipment is determined to output power.

[0076] If the voltage loss rate of the transformer bus is less than or equal to the predetermined first voltage loss threshold and greater than the predetermined second voltage loss threshold, then the energy storage converter is determined to continue to be powered.

[0077] If the voltage loss rate of the transformer bus is less than or equal to the predetermined second voltage loss threshold, the energy storage converter is charged. If the voltage loss rate of the transformer bus is still less than the predetermined second voltage loss threshold, the reduction in the power of the flexible load is determined.

[0078] In this embodiment, the predetermined first voltage loss threshold is preset, wherein the ideal voltage is predetermined to be 220V, the safe voltage amplitude is predetermined to be 5%, and the predetermined first voltage loss threshold is determined based on the sum of the ideal voltage and the safe voltage amplitude. In this embodiment, the predetermined first voltage loss threshold is preferably 231V.

[0079] In this embodiment, the initial discharge power of the energy storage converter is determined by multiplying 50% of the rated maximum discharge power of the energy storage converter, the minimum difference between the voltage loss rate of the transformer bus and a predetermined first voltage loss threshold, and the voltage amplification factor, wherein the voltage amplification factor is preset, and the preferred voltage amplification factor in this embodiment is 10.

[0080] In this embodiment, if the voltage loss rate of the distribution bus is still greater than the predetermined first voltage loss threshold after a single acquisition cycle of 10 seconds, the output of the new energy equipment is determined. The output limit value of the new energy equipment is determined based on the ratio of the product of the total power of the distributed power source and the predetermined first voltage loss threshold to the voltage loss rate of the distribution bus.

[0081] In this embodiment, the predetermined second voltage loss threshold is preset, wherein the ideal voltage is predetermined to be 220V, the safe voltage amplitude is predetermined to be 5%, and the predetermined second voltage loss threshold is determined based on the difference between the ideal voltage and the safe voltage amplitude. In this embodiment, the predetermined second voltage loss threshold is preferably 209V.

[0082] In this embodiment, the initial charging power of the energy storage converter is determined by multiplying 50% of the rated maximum charging power of the energy storage converter, the minimum difference between a predetermined second voltage loss threshold and the voltage loss rate of the transformer bus, and the voltage amplification factor. The voltage amplification factor is preset, and in this embodiment, the preferred voltage amplification factor is 10.

[0083] In this embodiment, if the voltage loss rate of the background bus is still less than the predetermined second voltage loss threshold after a single acquisition cycle of 10 seconds, the reduction range of the flexible load power is determined. The reduction range of the flexible load power is preset, and the preferred reduction range in this embodiment is 30%.

[0084] This invention improves the stability of transformer substation voltage and the safety of equipment operation by establishing a hierarchical and quantitative voltage safety verification and control strategy. The scheme is based on a graded response mechanism with a clear voltage loss threshold. First, it intervenes quickly in voltage by adjusting the charging and discharging power of energy storage. If the expected effect is not achieved within a single control cycle, it then sequentially initiates the restriction of new energy output or the adjustment of flexible load power. This progressive control method avoids excessive or insufficient control measures. The correlation calculation between charging and discharging power and voltage deviation enhances the accuracy of control, while the fixed acquisition cycle ensures the timeliness of system response. Thus, while maintaining voltage stability, it reduces the losses caused by frequent equipment operation.

[0085] Please see Figure 3 As shown, this is a logic decision diagram for performing current safety verification based on the energy feature dataset in an embodiment of the present invention. The current safety verification and its corresponding control strategy of the present invention include:

[0086] If the current loss rate of the transformer area line is greater than the predetermined current overload threshold, the reduction range of the flexible load power is determined. If the current loss rate of the transformer area line is still greater than the predetermined current overload threshold, the energy storage converter is shut down.

[0087] If the current loss rate of the transformer area line is less than or equal to the predetermined current overload threshold, the equipment current is determined to be safe, and power continues to be supplied.

[0088] In this embodiment, the predetermined current overload threshold is preset, wherein the rated current of the line is predetermined, and the predetermined current overload threshold is determined based on the product of the rated current of the line and the current overload coefficient. The current overload coefficient is selected in the range of [0.98, 1.12], and the preferred current overload coefficient in this embodiment is 1.05.

[0089] In this embodiment, the reduction in the power of the flexible load is preset, and the preferred reduction in this embodiment is 50%.

[0090] In this embodiment, if the current loss rate of the transformer area line is still greater than the predetermined current overload threshold for half a collection cycle, i.e., 5 seconds, then the energy storage converter is shut down until the current loss rate of the transformer area line is less than or equal to the rated current of the line.

[0091] This invention improves the safe operation level of transformer substations and the reliability of protection equipment by establishing a phased and object-specific overcurrent collaborative control strategy. The scheme adopts a progressive processing logic of first adjusting the load and then shutting down the energy storage. When the line current is detected to exceed the preset threshold, the power of the flexible load is reduced first to alleviate the line pressure. If the overcurrent situation is not eliminated within the shortened monitoring period, the energy storage system is further shut down. This hierarchical control method maintains the continuity of power supply and ensures the determinism of protection actions. Through reasonable response timing and equipment action coordination, the risk of long-term overload operation of the line is reduced and the service life of power equipment is extended.

[0092] Please see Figure 4 As shown, this is a logic decision diagram for performing energy storage safety verification based on the energy feature dataset in an embodiment of the present invention. The energy storage safety verification and its corresponding control strategy of the present invention include:

[0093] If the deep discharge frequency of the energy storage battery is greater than the predetermined first deep discharge frequency threshold of the energy storage battery, the device will stop charging and only discharge or shutdown operations will be allowed.

[0094] If the deep discharge frequency of the energy storage battery is less than or equal to the predetermined first deep discharge frequency threshold and greater than the predetermined second deep discharge frequency threshold, then the energy storage of the device is determined to be safe and power is continued.

[0095] If the deep discharge frequency of the energy storage battery is less than or equal to the predetermined second deep discharge frequency threshold of the energy storage battery, then the device is determined to stop discharging and only charging or shutdown operations are allowed.

[0096] In the embodiment, the predetermined first energy storage battery deep discharge frequency threshold is preset, and the preferred embodiment is the predetermined first energy storage battery deep discharge frequency threshold of 90%.

[0097] In this embodiment, the predetermined deep discharge frequency threshold of the second energy storage battery is preset, and preferably, the predetermined deep discharge frequency threshold of the second energy storage battery is 10%.

[0098] This invention extends the lifespan of energy storage devices and maintains the system's regulation capability by setting a safe operating range for the deep discharge frequency of energy storage batteries and establishing corresponding interlocking logic. The scheme adopts a dual-threshold management mechanism: when the state of charge exceeds the upper threshold, a charging operation is performed; when it falls below the lower threshold, a discharging operation is performed. This ensures that the battery operates within a safe state of charge range. This control method prevents overcharging and over-discharging of the battery, reduces irreversible damage to battery materials, maintains the health of the energy storage system, and ensures that the system still has the necessary regulation capability at critical moments by maintaining a reasonable charge and discharge margin, thus supporting the stability of the power distribution area operation.

[0099] Specifically, the analysis module is used to calculate the collaborative response characteristic value based on the collaborative response characteristic parameters, wherein the collaborative response characteristic value is determined based on the difference between the total power of the distributed power source and the total power loss of the distribution area power source.

[0100] It is understandable that the total power of distributed power sources refers to the sum of the active power actually generated by all distributed power sources in the distribution area within a single data acquisition cycle.

[0101] It is understandable that the total power loss of a distribution area refers to the sum of the active power lost by all electrical equipment in the distribution area within a single data collection cycle.

[0102] This invention uses the real-time difference between the total power of distributed power sources and the total power loss of the distribution area as a characteristic value for coordinated response, thereby improving the local consumption capacity of new energy and optimizing the economic operation of the system. This calculation method directly reflects the balance of power supply and demand within the distribution area, providing a clear criterion for subsequent control strategies. Based on this characteristic value, the system can accurately identify operating scenarios where new energy output is excessive or insufficient, and thus initiate targeted adjustment measures such as energy storage charging or discharging. This power balance-based calculation model simplifies the decision-making logic, improves the response speed of the control system to new energy fluctuations, reduces unnecessary power grid switching, lowers network losses, and improves the overall energy self-sufficiency rate and economic operation level of the distribution area.

[0103] Specifically, the verification module is used to determine whether the stability of the power grid-source-load-storage distribution area meets the standard based on the collaborative response characteristic value, including:

[0104] If the value of the coordinated response characteristic is less than or equal to the predetermined threshold for coordinated response characteristic, then the stability of the source-grid-load-storage system in the distribution area is determined to meet the standard.

[0105] If the value of the coordinated response characteristic is greater than the predetermined threshold for coordinated response characteristic, then the stability of the source-grid-load-storage system in the distribution area is determined to be non-compliant with the standard.

[0106] In this embodiment, the predetermined collaborative response characteristic characterization threshold is preset, wherein the rated maximum charging power of the energy storage converter is predetermined, and the predetermined collaborative response characteristic characterization threshold is determined based on the rated maximum charging power of the energy storage converter.

[0107] This invention improves the coordination of system operation and the level of equipment protection by establishing a stability judgment standard based on the correlation between the characteristic value of coordinated response and the capability of the energy storage system. The scheme uses the difference between the total power of distributed power sources and the total power loss of power distribution areas as the stability criterion, and sets a judgment threshold based on its matching relationship with the maximum charging power of energy storage. This makes the system stability assessment results compatible with the actual adjustment capability of the equipment, reducing the probability of misjudgment caused by setting the threshold too high, and reducing the risk of equipment overload caused by setting the threshold too low. By quantifying the correlation mechanism between judgment and equipment capability, the accuracy of system stability judgment under different operating conditions is enhanced, providing a reliable basis for subsequent control decisions.

[0108] Specifically, the feedback module is used to determine the adjustment range of the collaborative response feature representation threshold based on the difference between the energy feature representation value and a predetermined energy feature representation threshold, including:

[0109] If the difference is less than or equal to a predetermined difference threshold, then the adjustment range of the collaborative response feature characterization threshold is determined to be the first adjustment range;

[0110] If the difference is greater than a predetermined difference threshold, then the adjustment range of the collaborative response feature characterization threshold is determined to be the second adjustment range.

[0111] In this embodiment, the predetermined difference threshold is preset, wherein the fluctuation range of the energy characteristic value within the historical period of the target transformer area is predetermined, and the predetermined difference threshold is determined based on the upper limit of the fluctuation range of the energy characteristic value. In this embodiment, the upper limit of the fluctuation range is preferably 15%.

[0112] In this embodiment, the first adjustment range is preset, and preferably the first adjustment range is 10%.

[0113] In this embodiment, the second adjustment range is preset, and preferably, the second adjustment range is 30%.

[0114] This invention improves the long-term adaptability and control accuracy of the system by establishing an adaptive threshold adjustment mechanism based on historical operating data. This scheme dynamically corrects the threshold for the cooperative response characteristic by using differentiated adjustment amplitudes based on the deviation between the energy characteristic representation value and the threshold. When the deviation is within the historical fluctuation range, a smaller adjustment amplitude is used to maintain system stability; when the deviation exceeds the normal range, a larger adjustment amplitude is used to accelerate the system response speed. This tiered adjustment method enables the system to automatically adjust its sensitivity according to different operating conditions, reducing both frequent actions caused by overly sensitive threshold settings and response lag caused by overly conservative threshold settings, thus achieving adaptive matching between control system parameters and operating status.

[0115] Specifically, the acquisition module includes a sensing unit built into the distributed power controller, a smart meter, a flexible load controller, an energy storage converter, a battery management system, and voltage and current transformers in the distribution box of the transformer substation.

[0116] Understandably, distributed power controllers are used to collect output data from photovoltaic inverters and small wind power converters within the distribution area, including voltage, current, power, power generation, and operating status, including grid-connected or off-grid status and fault signals.

[0117] Understandably, smart meters are used to collect real-time power and electricity consumption data for residential and commercial loads.

[0118] Understandably, flexible load controllers are used to acquire and receive status and control signals for adjustable loads such as electric vehicle charging stations and energy storage air conditioners.

[0119] Understandably, the energy storage converter and battery management system are used to collect the state of charge, voltage, current, and charging / discharging power of the energy storage battery, while also receiving charging / discharging commands from the smart fusion terminal.

[0120] Understandably, the voltage transformers and current transformers in the distribution box are used to collect power grid operation data such as bus voltage, line current, and power factor.

[0121] This invention achieves the beneficial effect of complete basic data for realizing panoramic status perception and collaborative control of distribution transformer areas by integrating a multi-source heterogeneous data acquisition network of distributed power sources, loads, energy storage, and grid status. The solution systematically integrates various sensing and metering devices such as photovoltaic inverters, smart meters, flexible load controllers, energy storage systems, and grid transformers, covering the entire process from power generation and consumption to energy storage. This hardware architecture can simultaneously acquire multi-dimensional data such as voltage, current, power, state of charge, and equipment operating status, solving the problem of scattered data sources and inconsistent specifications in traditional systems. It provides comprehensive and consistent input information for subsequent analysis modules, laying the physical foundation for the reliable operation of the entire collaborative optimization control system.

[0122] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A smart fusion terminal-based distribution area source network load storage collaborative optimization control system, characterized in that, include: The data acquisition module is used to collect historical periodic energy characteristic parameters and collaborative response characteristic parameters of the target transformer area; An analysis module, connected to the acquisition module, is used to analyze energy characteristic values ​​based on the energy characteristic parameters and to calculate collaborative response characteristic values ​​based on the collaborative response characteristic parameters. The sum of the first energy characteristic factor, the second energy characteristic factor, and the third energy characteristic factor is determined as the energy characteristic characterization value, where, The first energy characteristic factor is the ratio of the voltage loss rate of the transformer area bus to the predetermined threshold value of the voltage loss rate of the transformer area bus. The second energy characteristic factor is the ratio of the transformer area line current loss rate to the predetermined transformer area line current loss rate threshold. The third energy characteristic factor is the ratio of the deep discharge frequency of the energy storage battery to a predetermined deep discharge frequency threshold for the energy storage battery. The collaborative response characteristic value is determined based on the difference between the total power of the distributed power source and the total power loss of the distribution area power source; A control module, connected to the acquisition module and the analysis module, is used to determine a corresponding control strategy based on the energy characteristic values; wherein, determining the corresponding control strategy based on the energy characteristic values ​​includes inputting the energy characteristic values ​​into a pre-trained multi-objective hierarchical decision model, and sequentially executing safety verification and the corresponding control strategy, wherein the safety verification includes voltage safety verification, current safety verification, and energy storage safety verification. The voltage safety verification and corresponding control strategy include: if the voltage loss rate of the transformer bus is greater than a predetermined first voltage loss threshold, then the energy storage converter is determined to discharge; if the voltage loss rate of the transformer bus is still greater than the predetermined first voltage loss threshold, then the output of the new energy equipment is determined; if the voltage loss rate of the transformer bus is less than or equal to the predetermined first voltage loss threshold and greater than the predetermined second voltage loss threshold, then the energy storage converter is determined to continue to be powered; if the voltage loss rate of the transformer bus is less than or equal to the predetermined second voltage loss threshold, then the energy storage converter is determined to charge; if the voltage loss rate of the transformer bus is still less than the predetermined second voltage loss threshold, then the reduction range of the flexible load power is determined. If the voltage loss rate of the background bus in a single acquisition cycle, i.e., 10 seconds, is still less than the predetermined second voltage loss threshold, then the reduction range of the flexible load power is determined. The reduction range of the flexible load power is preset and is 30%. The verification module, which is connected to the control module, is used to determine whether the stability of the source-grid-load-storage area meets the standard based on the collaborative response characteristic characterization value. If the value of the coordinated response characteristic is less than or equal to the predetermined threshold for coordinated response characteristic, then the stability of the source-grid-load-storage system in the distribution area is determined to meet the standard. If the value of the coordinated response characteristic is greater than the predetermined threshold for the coordinated response characteristic, then the stability of the source-grid-load-storage system in the distribution area is determined to be non-compliant with the standard. The feedback module, which is connected to the acquisition module and the verification module, is used to determine the adjustment range of the collaborative response characteristic threshold based on the difference between the energy characteristic characterization value and the predetermined energy characteristic characterization threshold when the source-grid-load-storage of the distribution area does not meet the standard, and to re-acquire and verify. If the difference is less than or equal to a predetermined difference threshold, then the adjustment range of the collaborative response feature characterization threshold is determined to be the first adjustment range; If the difference is greater than a predetermined difference threshold, then the adjustment range of the collaborative response feature characterization threshold is determined to be the second adjustment range; The predetermined difference threshold is set in advance. The fluctuation range of the energy characteristic value of the target transformer area within the historical period is predetermined. The predetermined difference threshold is determined based on the upper limit of the fluctuation range of the energy characteristic value. The upper limit of the fluctuation range is 15%. The first adjustment range is preset, and the first adjustment range is 10%. The second adjustment range is preset, and the second adjustment range is 30%. The energy characteristic parameters include the voltage loss rate of the transformer area bus, the current loss rate of the transformer area line, and the deep discharge frequency of the energy storage battery. The collaborative response characteristic parameters include the total power of the distributed power source and the total power loss of the transformer area power source.

2. The integrated source-grid-load-storage coordinated optimization control system for transformer substations based on intelligent fusion terminals as described in claim 1, characterized in that, The current safety verification and its corresponding control strategy include: If the current loss rate of the transformer area line is greater than the predetermined current overload threshold, the reduction range of the flexible load power is determined. If the current loss rate of the transformer area line is still greater than the predetermined current overload threshold, the energy storage converter is shut down. If the current loss rate of the transformer area line is less than or equal to the predetermined current overload threshold, the equipment current is determined to be safe, and power continues to be supplied. 3.The smart fusion terminal-based optimization control system for source-payload storage coordination in a transformer area, according to claim 1, characterized in that, The energy storage safety verification and corresponding control strategies include: If the deep discharge frequency of the energy storage battery is greater than the predetermined first deep discharge frequency threshold of the energy storage battery, the device will stop charging and only discharge or shutdown operations will be allowed. If the deep discharge frequency of the energy storage battery is less than or equal to the predetermined first deep discharge frequency threshold and greater than the predetermined second deep discharge frequency threshold, then the energy storage of the device is determined to be safe and power is continued. If the deep discharge frequency of the energy storage battery is less than or equal to the predetermined second deep discharge frequency threshold of the energy storage battery, then the device is determined to stop discharging and only charging or shutdown operations are allowed. 4.The smart fusion terminal-based optimization control system for the source network and payload storage coordination in a transformer area, according to claim 1, wherein, The feedback module is used to determine the adjustment range of the collaborative response characteristic characterization threshold based on the difference between the energy characteristic characterization value and the predetermined energy characteristic characterization threshold, including: If the difference is less than or equal to a predetermined difference threshold, then the adjustment range of the collaborative response feature characterization threshold is determined to be the first adjustment range; If the difference is greater than a predetermined difference threshold, then the adjustment range of the collaborative response feature characterization threshold is determined to be the second adjustment range. 5.The smart fusion terminal-based optimization control system for the source network and payload storage coordination in a transformer area according to claim 1, characterized in that, The data acquisition module includes a sensor unit built into the distributed power controller, a smart meter, a flexible load controller, an energy storage converter, a battery management system, and voltage and current transformers in the distribution box of the transformer substation.

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