Distributed energy storage collaborative regulation method and system based on smart grid

Abstract

This invention discloses a method and system for coordinated regulation of distributed energy storage based on smart grids, relating to the fields of smart grids and distributed energy storage technology. The specific steps of this method are as follows: a multi-physics data acquisition network is constructed by deploying high-precision sensors to collect data across all dimensions; cross-field information entropy flow and overall coordinated entropy are quantitatively modeled and calculated; a health assessment system is constructed to define safety thresholds; multi-objective dynamic weight decision-making is performed to generate regulation commands; finally, the commands are executed, real-time data is collected and processed throughout the entire process, the operating status is verified, a closed-loop regulation is formed, and the safe and stable operation of the power grid is ensured. This invention, by constructing a multi-physics data acquisition network, achieves multi-dimensional and full-scenario assessment of equipment health status, breaking through limitations; it establishes multi-dimensional comprehensive optimization objectives, dynamically adapts to decisions, and achieves coordinated linkage. This invention improves the adaptability, operational efficiency, and reliability of distributed energy storage systems in smart grids, providing a guarantee for the consumption of new energy and the stable operation of the power grid.

Description

Technical Field

[0001] This invention relates to the fields of smart grid and distributed energy storage technology, specifically to a method and system for coordinated control of distributed energy storage based on smart grid. Background Technology

[0002] As the energy transition continues and new energy power generation technologies develop rapidly, the importance of smart grids, as the core carrier for integrating various energy resources and optimizing energy allocation, is becoming increasingly prominent. Distributed energy storage, with its flexible charging and discharging regulation capabilities, has become a key support for smoothing fluctuations in new energy output and improving the stability and flexibility of grid operation. During operation, core equipment such as distributed energy storage batteries, grid lines, and transformers do not exist in isolation under a single physical field, but rather form a complex multi-physical field environment in which electrical, thermal, and mechanical fields intertwine and influence each other. The operating states of these physical fields are closely related, and changes in parameters in one field may trigger chain reactions in other fields, directly affecting the overall safe operation and comprehensive efficiency of the system. As the scale of grid operation continues to expand and the proportion of new energy grid connection continues to increase, the operating conditions of the system are becoming increasingly complex and variable, placing higher demands on the coordinated control of distributed energy storage and grid equipment. It is urgent to establish a control system that can comprehensively capture multi-physical field information and accurately characterize the interaction laws between fields to meet the needs of high-quality development of smart grids.

[0003] Traditional distributed energy storage control methods have significant limitations, making it difficult to meet the collaborative control needs in complex multi-physics environments. At the data acquisition level, they often focus on monitoring key parameters of a single physical field, lacking systematic acquisition of data from all dimensions of electrical, thermal, and mechanical fields. This results in an incomplete perception of the system's operating status and an inability to fully capture the interrelationships between different fields. In the modeling and evaluation stages, they fail to effectively quantify the interactions between different physical fields, relying heavily on threshold judgments of single physical parameters for state assessment. This ignores the ordered nature of multi-field information coupling, leading to one-sided health status assessments that fail to reflect the true state of system operation. In the decision-making and control process, optimization target weights are often fixed and cannot be adaptively adjusted according to dynamic changes in the system's operating status. This results in control commands lacking specificity and flexibility. Furthermore, traditional control models lack a complete closed-loop feedback mechanism, making it difficult to verify and optimize control effects in real time. This can easily lead to control lag or over-control, affecting control efficiency, increasing equipment operating losses, and even causing safety hazards. Consequently, they are ill-suited to the complex and ever-changing operating scenarios of smart grids. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method and system for coordinated regulation of distributed energy storage based on smart grids. By constructing a multi-physics field data acquisition network, it comprehensively acquires operational data from electrical, thermal, and mechanical fields. Through cross-field information quantification modeling and dual-dimensional health assessment, it accurately depicts the system's operating status. With multiple objectives as optimization directions, it dynamically adjusts weights based on comprehensive health to generate targeted regulation commands. Relying on a closed-loop verification mechanism, it provides real-time feedback optimization to ensure the effectiveness of regulation. The system achieves full-process coordination of data acquisition, modeling and evaluation, and decision execution through the linkage of five major functional modules, effectively improving the operational safety, stability, and adaptability of smart grids and distributed energy storage systems, and providing strong support for efficient energy utilization.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: On the one hand, a distributed energy storage collaborative control method based on smart grids, the specific steps of which are as follows:

[0006] S1, Multi-physics data acquisition: High-precision sensors are deployed at key monitoring locations of distributed energy storage batteries, power grid lines, and transformers to build a multi-physics data acquisition network, which collects real-time operational data of electrical, thermal, and mechanical fields to form a multi-physics raw dataset.

[0007] S2, Cross-field information quantification modeling: The interaction between electrical field, thermal field and mechanical field is abstracted into cross-field information flow. Based on the original dataset of multi-physics field, the cross-field information entropy flow between physical fields is calculated by the cross-field information entropy flow quantification formula. Then, the cross-field information entropy flow is substituted into the multi-physics field collaborative entropy integration formula to calculate the overall collaborative entropy of multi-physics field.

[0008] S3, Construction of a dual-dimensional health assessment: Combining the equipment's factory technical standards, smart grid operation specifications, and the calculation results of cross-field information entropy flow and multi-physics overall collaborative entropy, a multi-physics health assessment system is constructed to define the equipment's physical operation safety threshold and the multi-field information coupling order threshold.

[0009] S4, Multi-objective dynamic weight decision-making: Taking the electrical operation safety of the power grid, the thermal safety of energy storage batteries, the thermal / mechanical safety of power grid equipment, and the orderliness of multi-field information networks as comprehensive optimization objectives, the comprehensive health is calculated using the multi-physics field dual-dimensional health assessment formula, the weight of the comprehensive optimization objectives is dynamically adjusted according to the comprehensive health, and distributed energy storage charging and discharging control commands and grid-side auxiliary equipment linkage commands are generated.

[0010] S5, Regulation Execution and Closed-Loop Verification: Receives and executes regulation commands, adjusts the charging and discharging rate and power of distributed energy storage, starts and stops the grid-side cooling system, optimizes the operating load of grid equipment, and collects data in real time for full-process processing to verify the operating status and form a regulation closed loop.

[0011] Furthermore, the deployment of high-precision sensors at key monitoring locations of distributed energy storage batteries, power grid lines, and transformers specifically includes: deploying temperature, current, internal resistance, and wind speed sensors on the surface of individual battery cells, positive and negative busbars, battery boxes, and heat dissipation channels of distributed energy storage batteries; deploying current, temperature, leakage current, and temperature and humidity sensors on the conductors, line joints, insulator strings, and surrounding environment of power grid lines; and deploying temperature, vibration, oil temperature, oil level, and contact resistance sensors on the core, windings, oil tank, tap changer, and foundation of transformers.

[0012] Furthermore, the real-time collected full-dimensional operational data of electrical, thermal, and mechanical fields specifically includes: electrical field data including the voltage, current, and power of distributed energy storage batteries, the voltage, current, power, and losses of power grid lines, and the primary and secondary voltages, currents, and losses of transformers; thermal field data including the body temperature, ambient temperature, and related medium temperature of distributed energy storage batteries, power grid lines, and transformers; and mechanical field data including the vibration frequency, vibration amplitude, and related vibration angles of distributed energy storage batteries, power grid lines, and transformers.

[0013] Furthermore, the mathematical expression of the cross-field information entropy flow formula is: ;in, Let be the cross-field information entropy flow between field i and field j at time t; Let be the mutual information value between field i and field j at time t; , This is the operating condition correction factor, used to calibrate the calculation results according to the actual operating conditions and adapt to different operating scenarios; Let be the parameter coupling coefficient between field i and field j at time t; The energy transfer difference between field i and field j at time t; Let be the difference in parameter deviation between field i and field j at time t; Let i be the parameter reference deviation between i and j. The calculation steps are as follows: First, collect the time series data of the state parameters of the power equipment's multi-physics field, fit the probability density distribution of the parameter sequence of each physics field, and calculate the information entropy value of a single physics field; second, calculate the information transfer between any two physics fields based on the mutual information algorithm, and then combine the inter-field coupling direction coefficient to obtain the information entropy flow components between each pair of physics fields; finally, perform vector synthesis on all inter-field information entropy flow components to obtain the total value of the cross-field information entropy flow of the multi-physics field, so as to quantify the information transfer intensity and dynamic interaction direction between different physics fields and accurately reflect the dynamic characteristics of information flow in the multi-field coupling process.

[0014] Furthermore, the mathematical expression of the multi-physics synergistic entropy integration formula is as follows: ;in, Let t be the overall cooperative entropy of the multiphysics system at time t; The entropy flow integration coefficient; These are the weighting coefficients for the cross-field information entropy flows; This is the system dispersion correction coefficient; Let be the standard deviation of the three types of cross-field information entropy flows at time t; The standard deviation of the three types of cross-field information entropy flows is used as the benchmark. The calculation steps are as follows: First, obtain the marginal entropy value of each physical field and the joint entropy value of any number of physical fields. Calculate the inter-field synergistic correlation coefficient by the difference between the joint entropy and the marginal entropy. Second, according to the weight ratio of each physical field in the system, perform a weighted summation of all inter-field synergistic correlation coefficients to obtain the total level of synergistic correlation of the multi-physics field. Finally, substitute the total level of synergistic correlation into the entropy function model to calculate the overall synergistic entropy value of the multi-physics field. This is used to quantify the overall orderliness and synergistic evolution degree of the multi-physics field system and reflect the synergistic stability state of the system during dynamic operation.

[0015] Furthermore, the physical operation safety thresholds of the equipment are specifically set as follows: the thermal runaway threshold for the energy storage battery is 60℃, and the warning value is 48℃; the thermal safety threshold for the power grid line is 75℃, and the warning value is 60℃; the mechanical vibration safety threshold for the transformer is 180Hz, and the warning value is 144Hz; the multi-field information coupling order threshold is specifically set as follows: the cross-field information entropy threshold is 0.05bit / s, the cooperative entropy threshold is 1.8bit, and the warning values ​​are all 80% of the corresponding thresholds.

[0016] Furthermore, the mathematical expression of the multiphysics two-dimensional health assessment formula is as follows: ;in, The overall health of the multiphysics system at time t; , These are the dimension weight coefficients; The total number of core physical parameters of the equipment participating in the evaluation; This represents the real-time value of the nth core physical parameter at time t. This is the normal operating baseline value for the nth core physical parameter; The safety threshold for the nth core physical parameter; Let t be the overall cooperative entropy of the multiphysics system at time t; The critical threshold for the overall collaborative entropy of the multi-physics system is defined as follows: First, the real-time physical parameters of the device are normalized with a preset safety threshold to obtain a health assessment value for the physical parameter dimension. Second, the overall collaborative entropy value of the multi-physics system is normalized with a preset orderliness threshold to obtain a health assessment value for the information orderliness dimension. Finally, the normalized assessment values ​​of the two dimensions are weighted and fused to calculate the comprehensive health value of the device's multi-physics system, providing a core quantitative basis for subsequent dynamic weight adjustment and control command generation.

[0017] Furthermore, the comprehensive optimization target weight adjustment rules are as follows: when the physical parameters do not exceed the warning value and the collaborative entropy does not exceed the threshold, the weight ratio of power grid electrical operation safety, energy storage battery thermal safety, power grid equipment thermal / mechanical safety, and multi-field information network orderliness is 0.25; when the physical parameters exceed the warning value, the combined weight of energy storage battery thermal safety and power grid equipment thermal / mechanical safety increases to 0.6; when the collaborative entropy exceeds the threshold, the weight of multi-field information network orderliness increases to 0.7; when the physical parameters exceed the warning value and the collaborative entropy exceeds the threshold, the combined weight of energy storage battery thermal safety and power grid equipment thermal / mechanical safety is 0.5, and the weight of multi-field information network orderliness is 0.5.

[0018] Furthermore, the distributed energy storage charge / discharge rate adjustment range is 0.1C to 1.5C, and the power adjustment step size is 50kW / step; the grid-side cooling system start / stop delay does not exceed 1s, and the load adjustment amplitude does not exceed 10% / time; the system core processing terminal recalculates the comprehensive health status every 10 seconds, and when the comprehensive health status is ≥0.8 for 10 consecutive cycles, the current control process is terminated; if the standard is not met, the control command is regenerated and executed; the parameter range is set based on typical energy storage system operation tests and grid safety specifications.

[0019] On the other hand, a power distribution system based on the aforementioned power big data automatic reasoning platform includes:

[0020] Data acquisition module: used to deploy high-precision sensors at key monitoring locations of distributed energy storage batteries, power grid lines, and transformers, build a multi-physics field data acquisition network, collect real-time operating data of electrical, thermal, and mechanical fields, and output multi-physics field raw datasets;

[0021] Cross-field information modeling module: It is used to abstract the interaction between electrical field, thermal field and mechanical field into cross-field information flow. Based on the original multi-physics dataset, it calculates the cross-field information entropy flow through the cross-field information entropy flow quantification formula, and then substitutes it into the multi-physics collaborative entropy integration formula to calculate the overall collaborative entropy of the multi-physics.

[0022] Health assessment module: It is used to combine the equipment's factory technical standards, smart grid operation specifications, and the calculation results of cross-field information entropy flow and multi-physics overall collaborative entropy to construct a multi-physics health assessment system and define the equipment's physical operation safety threshold and the multi-field information coupling order threshold.

[0023] Dynamic weight decision module: It is used to calculate the comprehensive health score using the comprehensive optimization objectives of power grid electrical operation safety, energy storage battery thermal safety, power grid equipment thermal / mechanical safety, and multi-field information network orderliness. It dynamically adjusts the weight of the comprehensive optimization objectives based on the comprehensive health score and generates distributed energy storage charging and discharging control instructions and power grid-side auxiliary equipment linkage instructions.

[0024] Control execution and closed-loop verification module: It is used to receive and execute control commands, adjust the charging and discharging rate and power of distributed energy storage, start and stop the grid-side cooling system, optimize the operating load of grid equipment, and collect operating data in real time and feed it back to the data acquisition module to complete the whole process of data processing and operating status verification, forming a control closed loop.

[0025] Compared with existing technologies, this method and system for coordinated regulation of distributed energy storage based on smart grids has the following advantages:

[0026] I. This invention constructs a multi-physics field data acquisition network to comprehensively capture operational data across all dimensions of electrical, thermal, and mechanical fields. It abstracts the interactions between different physical fields into cross-field information flows, and uses scientific quantification methods to accurately characterize the intensity and dynamic features of inter-field information interaction. Furthermore, it combines equipment technical standards and power grid operation specifications to construct a two-dimensional health assessment system, enabling a comprehensive judgment of the physical operating status of equipment and the orderliness of multi-field information coupling. This multi-dimensional, full-scenario analysis method breaks the limitations of single-physical-field assessment, making health status assessment more systematic and comprehensive. It provides solid data support for subsequent regulation, effectively avoids regulation deviations caused by incomplete information, ensures the safety and stability of smart grid and distributed energy storage system operation, promotes the transformation of regulation decision-making from experience-driven to data-driven, and enhances adaptability to complex operating scenarios.

[0027] Second, this invention establishes multi-dimensional comprehensive optimization goals, dynamically adjusts the weights of each goal with overall health as the core, generates targeted control instructions, and relies on a closed-loop verification mechanism to provide real-time feedback on the operating status and continuously optimize the control strategy. This dynamically adaptable decision-making mode and the full-process closed-loop control design can accurately respond to changes in the system's operating status, realize the coordinated linkage between energy storage charging and discharging and grid auxiliary equipment, avoid the control rigidity problem caused by fixed-weight decision-making, and improve the execution efficiency and adaptability of control instructions by adjusting operating parameters in real time and optimizing equipment load distribution. It can also balance the safety requirements of various dimensions with the orderly operation of the system, reduce unnecessary energy consumption and equipment wear, extend the service life of related equipment, improve the overall operating efficiency and reliability of the smart grid distributed energy storage system, and provide strong support for the consumption of new energy and the stable operation of the grid.

[0028] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0030] Figure 1 It is a framework diagram of the steps of the distributed energy storage collaborative regulation method based on the smart grid;

[0031] Figure 2 It is a module flow chart of the distributed energy storage collaborative regulation system based on the smart grid;

[0032] Figure 3 It is a diagram of the input-output relationship of each step of the distributed energy storage collaborative regulation based on the smart grid. Specific Embodiments

[0033] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will describe in detail the specific embodiments, structures, features and their effects of the present invention in combination with the drawings and preferred embodiments.

[0034] Embodiment 1:

[0035] An embodiment of the distributed energy storage collaborative regulation method based on the smart grid, in the scenario of an energy storage power station in an urban commercial park.

[0036] A 10MW distributed energy storage power station supporting an urban core commercial park is connected to the 110kV smart grid,承担园区高峰用电削峰、低谷填谷及应急供电任务,采用本发明方法开展日常运行调控,具体实施过程如 Figure 1 shown:

[0037] S1, Multi-physics data acquisition: Temperature, current, internal resistance, and wind speed sensors are installed on the surface of individual battery cells, positive and negative busbars, battery boxes, and heat dissipation channels of distributed energy storage batteries; current, temperature, leakage current, and temperature and humidity sensors are installed on the conductors, line joints, insulator strings, and surrounding environment of power grid lines; temperature, vibration, oil temperature, oil level, and contact resistance sensors are installed on the core, windings, oil tank, tap changer, and foundation of transformers to build a multi-physics data acquisition network covering key equipment. The system collects real-time operational data across electrical, thermal, and mechanical fields using a multi-physics data acquisition network. The electrical field data includes the voltage, current, and power of the energy storage battery; the voltage, current, power, and losses of the power grid line; and the primary and secondary voltages, currents, and losses of the transformer. The thermal field data includes the body temperature of the energy storage battery, power grid line, and transformer, as well as the ambient temperature and the temperature of related media. The mechanical field data includes the vibration frequency, vibration amplitude, and related vibration angles of the energy storage battery, power grid line, and transformer. This results in a comprehensive and accurate multi-physics raw dataset, providing complete data support for subsequent cross-field information modeling and health assessment.

[0038] S2, Cross-field Information Quantization Modeling: The interaction between electrical, thermal, and mechanical fields is abstracted as a cross-field information flow. Based on the original multi-physics dataset, the cross-field information entropy flow between physical fields is calculated using the cross-field information entropy flow quantification formula. The mathematical expression of the cross-field information entropy flow quantification formula is as follows: ;in, Let be the cross-field information entropy flow between field i and field j at time t; Let be the mutual information value between field i and field j at time t; , This is the operating condition correction factor, used to calibrate the calculation results according to the actual operating conditions and adapt to different operating scenarios; Let be the parameter coupling coefficient between field i and field j at time t; The energy transfer difference between field i and field j at time t; Let be the difference in parameter deviation between field i and field j at time t; The parameter reference deviation between field i and field j is used; during the calculation process, a condition correction coefficient is set in combination with the actual operating conditions of the power plant to accurately present the information transmission intensity and dynamic interaction direction between different physical fields; then, the obtained cross-field information entropy flow is substituted into the multi-physics field collaborative entropy integration formula, and the mathematical expression of the multi-physics field collaborative entropy integration formula is: ;in, Let t be the overall cooperative entropy of the multiphysics system at time t; The entropy flow integration coefficient; These are the weighting coefficients for the cross-field information entropy flows; This is the system dispersion correction coefficient; Let be the standard deviation of the three types of cross-field information entropy flows at time t; The baseline standard deviation of the three types of cross-field information entropy flows is used. By combining parameters such as the entropy flow integration coefficient, the weight coefficient of each cross-field information entropy flow, and the system dispersion correction coefficient, the overall collaborative entropy of the multi-physics field is calculated, which clearly reflects the collaborative stability state of the system during dynamic operation and provides key quantitative indicators for subsequent health assessment.

[0039] S3, Dual-Dimensional Health Assessment Construction: Combining the factory technical standards of equipment such as energy storage batteries, power grid lines, and transformers with local smart grid operation specifications, and referencing the calculation results of cross-field information entropy flow and multi-physics overall collaborative entropy, a scientific and comprehensive multi-physics health assessment system is constructed. Clear physical operation safety thresholds are defined: the thermal runaway threshold for energy storage batteries is set at 60℃, with a warning value of 48℃, effectively preventing the risk of battery thermal runaway; the thermal safety threshold for power grid lines is set at 75℃, with a warning value of 60℃, ensuring stable line operation; the mechanical vibration safety threshold for transformers is set at 180Hz, with a warning value of 144Hz, avoiding equipment damage due to excessive vibration. Multi-field information coupling order thresholds are defined: the cross-field information entropy threshold is 0.05 bit / s, and the collaborative entropy threshold is 1.8 bits, with warning values ​​for both at 80% of their corresponding thresholds, providing a clear standard for judging the orderliness of system information interaction.

[0040] S4, Multi-objective Dynamic Weight Decision: Taking the electrical operation safety of the power grid, the thermal safety of energy storage batteries, the thermal / mechanical safety of power grid equipment, and the orderliness of the multi-field information network as comprehensive optimization objectives, the comprehensive health of the system is calculated using a multi-physics field two-dimensional health assessment formula. The mathematical expression of the multi-physics field two-dimensional health assessment formula is as follows: ;in, The overall health of the multiphysics system at time t; , These are the dimension weight coefficients; The total number of core physical parameters of the equipment participating in the evaluation; This represents the real-time value of the nth core physical parameter at time t. This is the normal operating baseline value for the nth core physical parameter; The safety threshold for the nth core physical parameter; Let t be the overall cooperative entropy of the multiphysics system at time t; This represents the critical threshold for the overall collaborative entropy of the multi-physics system. The weights of each optimization objective are dynamically adjusted based on the comprehensive health results. During the initial operation phase, when physical parameters do not exceed the warning value and collaborative entropy does not exceed the threshold, the weight of each of the four optimization objectives is set to 0.25, achieving balanced optimization across multiple objectives. During operation, if the energy storage battery temperature reaches 52℃, exceeding the warning value of 48℃, due to increased electricity load in the park, the combined weight of energy storage battery thermal safety and grid equipment thermal / mechanical safety is increased to 0.6, prioritizing the physical safety of the equipment. If collaborative entropy exceeds the threshold, the weight of the multi-physics information network's orderliness is increased to 0.7, prioritizing the restoration of the system's information interaction orderliness. If both physical parameters and collaborative entropy exceed the threshold simultaneously, the combined weight of energy storage battery thermal safety and grid equipment thermal / mechanical safety is set to 0.5, and the weight of the multi-physics information network's orderliness is also set to 0.5, balancing equipment safety and system orderliness. Based on the adjusted weights, targeted distributed energy storage charging and discharging control commands and grid-side auxiliary equipment linkage commands are generated to ensure that the control direction aligns with the actual needs of the system.

[0041] S5, Control Execution and Closed-Loop Verification: After receiving the control command, the energy storage power station control system immediately executes the relevant operations, adjusting the distributed energy storage charge / discharge rate to 0.8C, which is within the reasonable adjustment range of 0.1C to 1.5C. The output power is precisely adjusted in steps of 50kW / step. Simultaneously, the grid-side cooling system is activated with a start / stop delay controlled within 1 second, optimizing the grid equipment operating load by no more than 10% per cycle, rapidly responding to control demands. During control execution, the data acquisition network continuously collects various operating data in real time and feeds it back to the core processing terminal for full-process processing, comprehensively verifying the system's operating status. The system's core processing terminal recalculates the comprehensive health every 10 seconds. When the comprehensive health is ≥0.8 for 10 consecutive cycles, the system's operating status is stable, and the current control process is terminated. If the standard is not met, a new control command is generated and executed, forming a complete and efficient control closed loop, ensuring the stable coordinated operation of the energy storage power station and the grid. Figure 3 As shown.

[0042] In summary, this embodiment fully implements a distributed energy storage collaborative control method based on a smart grid in an urban commercial park energy storage power station scenario. A comprehensive dataset is constructed through multi-physics field data acquisition. Inter-field interaction and system collaboration quantification are achieved using cross-field information entropy flow formulas and multi-physics field collaborative entropy integration formulas. A two-dimensional health assessment system is built based on defined thresholds. Target weights are dynamically adjusted according to the calculation results of the multi-physics field two-dimensional health assessment formula. Finally, a complete process is formed through standardized control execution and closed-loop verification. The entire process strictly adheres to established parameter standards, achieving dual protection of equipment safety and system order, ensuring that the energy storage power station and the smart grid respond collaboratively to load changes and efficiently perform peak shaving, valley filling, and emergency power supply functions.

[0043] Example 2:

[0044] An example of a distributed energy storage collaborative control system based on a smart grid. A smart grid scenario in an industrial park.

[0045] Large industrial parks are planned and constructed with smart grid systems, equipped with 20MW distributed energy storage devices to balance fluctuations in renewable energy generation and ensure stable power supply for production. The system described in this invention is used to achieve coordinated control. The system's composition and implementation are as follows: Figure 2 As shown:

[0046] Data Acquisition Module: Responsible for the core task of multi-physics data acquisition. Based on the distribution of energy storage devices and power grid equipment in the industrial park, high-precision temperature, current, internal resistance, wind speed, leakage current, temperature and humidity, vibration, oil temperature, oil level and contact resistance sensors are comprehensively deployed at key monitoring locations such as the surface of battery cells, positive and negative busbars, battery boxes and heat dissipation channels of distributed energy storage batteries, conductors, line joints, insulator strings and surrounding environment of power grid lines, and iron cores, windings, oil tanks, tap changers and foundations of transformers. This constructs a multi-physics data acquisition network covering all key equipment in the park. The system collects real-time operational data across electrical, thermal, and mechanical fields using a multi-physics data acquisition network. Electrical field data includes energy storage battery voltage, current, and power; power grid line voltage, current, power, and losses; and transformer primary and secondary voltage, current, and losses. Thermal field data includes the body temperature of various equipment, ambient temperature, and the temperature of related media. Mechanical field data includes the vibration frequency, vibration amplitude, and related vibration angles of various equipment. The system ultimately outputs a complete multi-physics raw dataset, providing a high-quality data foundation for subsequent cross-field information modeling and health assessment modules, ensuring the accuracy and reliability of subsequent calculation and analysis results.

[0047] Cross-field information modeling module: Receives the multi-physics raw dataset output by the data acquisition module. First, it abstracts the interactions between electrical, thermal, and mechanical fields into cross-field information flow. Then, based on the multi-physics raw dataset, it calculates the cross-field information entropy flow between physical fields using the cross-field information entropy flow quantification formula. The mathematical expression of the cross-field information entropy flow quantification formula is: ;in, Let be the cross-field information entropy flow between field i and field j at time t; Let be the mutual information value between field i and field j at time t; , This is the operating condition correction factor, used to calibrate the calculation results according to the actual operating conditions and adapt to different operating scenarios; Let be the parameter coupling coefficient between field i and field j at time t; The energy transfer difference between field i and field j at time t; Let be the difference in parameter deviation between field i and field j at time t; The parameter baseline deviation between field i and field j is used. During the calculation, the operating condition correction coefficient is calibrated according to the actual operating conditions such as the fluctuation of power load in the industrial park and changes in ambient temperature to accurately quantify the information transmission intensity and dynamic interaction direction between fields. Then, the obtained cross-field information entropy flow is substituted into the multi-physics field collaborative entropy integration formula. Combined with the preset entropy flow integration coefficient, the weight coefficient of each cross-field information entropy flow, the system dispersion correction coefficient, and other parameters, the overall collaborative entropy of the multi-physics field is calculated. The mathematical expression of the multi-physics field collaborative entropy integration formula is: ;in, Let t be the overall cooperative entropy of the multiphysics system at time t; The entropy flow integration coefficient; These are the weighting coefficients for the cross-field information entropy flows; This is the system dispersion correction coefficient; Let be the standard deviation of the three types of cross-field information entropy flows at time t; The benchmark standard deviation of the three types of cross-field information entropy flow effectively quantifies the overall orderliness and degree of collaborative evolution of multi-physics field systems, providing key data support for the health assessment module and helping to accurately construct the health assessment system.

[0048] Health Assessment Module: Combining the factory technical standards of equipment such as energy storage batteries, power grid lines, and transformers, the smart grid operation specifications of industrial parks, and the cross-field information entropy flow and multi-physics overall collaborative entropy calculation results output by the cross-field information modeling module, a highly targeted multi-physics health assessment system is constructed. Clearly defined physical operation safety thresholds are established: thermal runaway threshold for energy storage batteries is 60℃ with a warning value of 48℃; thermal safety threshold for power grid lines is 75℃ with a warning value of 60℃; and mechanical vibration safety threshold for transformers is 180Hz with a warning value of 144Hz, providing a clear basis for judging the physical safety status of equipment. Simultaneously, multi-field information coupling order thresholds are defined: cross-field information entropy threshold is 0.05 bit / s, collaborative entropy threshold is 1.8 bits, and warning values ​​are all 80% of their corresponding thresholds, accurately judging the orderliness of system information interaction and providing a clear evaluation standard for the dynamic weight decision-making module, ensuring that the decision-making direction is scientific and reasonable.

[0049] Dynamic Weighted Decision Module: Taking the electrical operation safety of the power grid, the thermal safety of energy storage batteries, the thermal / mechanical safety of power grid equipment, and the orderliness of the multi-field information network as comprehensive optimization objectives, it calls the multi-physics field dual-dimensional health assessment formula and, combined with the threshold set by the health assessment module, calculates the real-time comprehensive health of the system. The mathematical expression of the multi-physics field dual-dimensional health assessment formula is: ;in, The overall health of the multiphysics system at time t; , These are the dimension weight coefficients; The total number of core physical parameters of the equipment participating in the evaluation; This represents the real-time value of the nth core physical parameter at time t. This is the normal operating baseline value for the nth core physical parameter; The safety threshold for the nth core physical parameter; Let t be the overall cooperative entropy of the multiphysics system at time t; This represents the critical threshold for the overall collaborative entropy of the multi-physics system. The weights of each optimization objective are dynamically adjusted based on the overall health status. When the park's electricity consumption is in off-peak hours, and the equipment's physical parameters do not exceed the warning value and the collaborative entropy does not exceed the threshold, the weights of all four objectives are 0.25, achieving balanced multi-objective control. When a production line in the park operates at full load, causing the grid line temperature to reach 65℃, exceeding the warning value of 60℃, the combined weights of the energy storage battery's thermal safety and the grid equipment's thermal / mechanical safety are increased to 0.6, prioritizing equipment operational safety. If the cross-field information entropy reaches 0.045 bit / s, exceeding the warning value of 0.04 bit / s (i.e., the collaborative entropy exceeds the relevant warning standard), the weight of the multi-field information network's orderliness is increased to 0.7, prioritizing the restoration of the system's information collaborative orderliness. If both physical parameters and collaborative entropy exceed the warning values ​​simultaneously, the combined weights of the energy storage battery's thermal safety and the grid equipment's thermal / mechanical safety are set to 0.5, and the weight of the multi-field information network's orderliness is 0.5, balancing equipment safety and system orderliness. Based on the adjusted weights, precise distributed energy storage charging and discharging control commands and grid-side auxiliary equipment linkage commands are generated, providing a clear basis for control execution.

[0050] The regulation execution and closed-loop verification module immediately executes regulation operations upon receiving the regulation instructions generated by the dynamic weight decision module. It adjusts the distributed energy storage charge / discharge rate to 1.2C, within a reasonable range of 0.1C-1.5C, adjusting the charge / discharge power in steps of 50kW / step. It starts and stops the grid-side cooling system with a delay of no more than 1 second, optimizing the grid equipment load by no more than 10% per cycle, and quickly implementing regulation measures. During regulation, it collects equipment operation data in real time and feeds it back to the data acquisition module, completing the entire process of data processing and operational status verification. The system's core processing end recalculates the comprehensive health score every 10 seconds. If the comprehensive health score is ≥0.8 for 10 consecutive cycles, the system is considered stable, and the current regulation process is terminated. If the standard is not met, the dynamic weight decision module is triggered to regenerate the regulation instructions, which are then executed by this module, forming a continuous closed-loop regulation mechanism. This effectively balances fluctuations in new energy power generation in the industrial park, ensures stable power supply for production, and guarantees the efficient operation of the smart grid and distributed energy storage system in the industrial park.

[0051] In summary, this embodiment, targeting a smart grid scenario in an industrial park, deploys a distributed energy storage collaborative control system comprised of five modules. The data acquisition module provides precise data support, the cross-field information modeling module completes the quantification of inter-field and system collaboration, the health assessment module clarifies scientific assessment standards, the dynamic weight decision-making module generates targeted control commands, and the control execution and closed-loop verification module implements operations and continuously optimizes. The system applies core formulas throughout, strictly adheres to parameter specifications, and through efficient inter-module collaboration and dynamic closed-loop control, effectively balances fluctuations in renewable energy generation, mitigates equipment operation risks, ensures continuous and stable power supply for industrial production in the park, and fully realizes the efficient collaborative operation of the smart grid and the distributed energy storage system.

[0052] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for coordinated control of distributed energy storage based on smart grids, characterized in that, The specific steps of this method are as follows: S1, Multi-physics data acquisition: High-precision sensors are deployed at key monitoring locations of distributed energy storage batteries, power grid lines, and transformers to build a multi-physics data acquisition network, which collects real-time operational data of electrical, thermal, and mechanical fields to form a multi-physics raw dataset. S2, Cross-field information quantification modeling: The interaction between electrical field, thermal field and mechanical field is abstracted into cross-field information flow. Based on the original dataset of multi-physics field, the cross-field information entropy flow between physical fields is calculated by the cross-field information entropy flow quantification formula. Then, the cross-field information entropy flow is substituted into the multi-physics field collaborative entropy integration formula to calculate the overall collaborative entropy of multi-physics field. S3, Construction of a dual-dimensional health assessment: Combining the equipment's factory technical standards, smart grid operation specifications, and the calculation results of cross-field information entropy flow and multi-physics overall collaborative entropy, a multi-physics health assessment system is constructed to define the equipment's physical operation safety threshold and the multi-field information coupling order threshold. S4, Multi-objective dynamic weight decision-making: Taking the electrical operation safety of the power grid, the thermal safety of energy storage batteries, the thermal / mechanical safety of power grid equipment, and the orderliness of multi-field information networks as comprehensive optimization objectives, the comprehensive health is calculated using the multi-physics field dual-dimensional health assessment formula, the weight of the comprehensive optimization objectives is dynamically adjusted according to the comprehensive health, and distributed energy storage charging and discharging control commands and grid-side auxiliary equipment linkage commands are generated. S5, Regulation Execution and Closed-Loop Verification: Receives and executes regulation commands, adjusts the charging and discharging rate and power of distributed energy storage, starts and stops the grid-side cooling system, optimizes the operating load of grid equipment, and collects data in real time for full-process processing to verify the operating status and form a regulation closed loop.

2. The method for coordinated control of distributed energy storage based on smart grids according to claim 1, characterized in that, In step S1, the deployment of high-precision sensors at key monitoring locations of distributed energy storage batteries, power grid lines, and transformers specifically includes: deploying temperature, current, internal resistance, and wind speed sensors on the surface of individual battery cells, positive and negative busbars, battery boxes, and heat dissipation channels of distributed energy storage batteries; deploying current, temperature, leakage current, and temperature and humidity sensors on the conductors, line joints, insulator strings, and surrounding environment of power grid lines; and deploying temperature, vibration, oil temperature, oil level, and contact resistance sensors on the core, windings, oil tank, tap changer, and foundation of transformers.

3. The method for coordinated control of distributed energy storage based on smart grids according to claim 1, characterized in that, In step S1, the real-time collected full-dimensional operational data of electrical field, thermal field, and mechanical field specifically includes: electrical field data including the voltage, current, and power of distributed energy storage batteries, the voltage, current, power, and losses of power grid lines, and the primary and secondary voltages, currents, and losses of transformers; thermal field data including the body temperature, ambient temperature, and related medium temperature of distributed energy storage batteries, power grid lines, and transformers; and mechanical field data including the vibration frequency, vibration amplitude, and related vibration angles of distributed energy storage batteries, power grid lines, and transformers.

4. The method for coordinated control of distributed energy storage based on smart grids according to claim 1, characterized in that, In step S2, the mathematical expression of the cross-field information entropy flow formula is: ;in, Let be the cross-field information entropy flow between field i and field j at time t; Let be the mutual information value between field i and field j at time t; , This is the operating condition correction factor, used to calibrate the calculation results according to the actual operating conditions and adapt to different operating scenarios; Let be the parameter coupling coefficient between field i and field j at time t; The energy transfer difference between field i and field j at time t; Let be the difference in parameter deviation between field i and field j at time t; The parameter reference deviation between field i and field j.

5. The method for coordinated control of distributed energy storage based on smart grids according to claim 1, characterized in that, In step S2, the mathematical expression of the multiphysics synergistic entropy integration formula is: ;in, Let t be the overall cooperative entropy of the multiphysics system at time t; The entropy flow integration coefficient; These are the weighting coefficients for the cross-field information entropy flows; This is the system dispersion correction coefficient; Let be the standard deviation of the three types of cross-field information entropy flows at time t; This represents the benchmark standard deviation of the three types of cross-field information entropy flows.

6. The method for coordinated regulation of distributed energy storage based on smart grids according to claim 1, characterized in that, In step S3, the physical operation safety thresholds of the equipment are specifically set as follows: the thermal runaway threshold of the energy storage battery is 60℃, and the warning value is 48℃; the thermal safety threshold of the power grid line is 75℃, and the warning value is 60℃; the mechanical vibration safety threshold of the transformer is 180Hz, and the warning value is 144Hz; the multi-field information coupling order threshold is specifically set as follows: the cross-field information entropy threshold is 0.05bit / s, the cooperative entropy threshold is 1.8bit, and the warning values ​​are all 80% of the corresponding thresholds.

7. The method for coordinated control of distributed energy storage based on smart grids according to claim 1, characterized in that, In step S4, the mathematical expression of the multiphysics two-dimensional health assessment formula is: ;in, The overall health of the multiphysics system at time t; , These are the dimension weight coefficients; The total number of core physical parameters of the equipment participating in the evaluation; This represents the real-time value of the nth core physical parameter at time t. This is the normal operating baseline value for the nth core physical parameter; The safety threshold for the nth core physical parameter; Let t be the overall cooperative entropy of the multiphysics system at time t; It represents the critical threshold of the overall cooperative entropy of a multiphysics system.

8. The method for coordinated control of distributed energy storage based on smart grids according to claim 1, characterized in that, In step S4, the comprehensive optimization target weight adjustment rule is as follows: when the physical parameters do not exceed the warning value and the collaborative entropy does not exceed the threshold, the weight ratio of power grid electrical operation safety, energy storage battery thermal safety, power grid equipment thermal / mechanical safety, and multi-field information network orderliness is 0.25; when the physical parameters exceed the warning value, the combined weight of energy storage battery thermal safety and power grid equipment thermal / mechanical safety increases to 0.6; when the collaborative entropy exceeds the threshold, the weight of multi-field information network orderliness increases to 0.7; when the physical parameters exceed the warning value and the collaborative entropy exceeds the threshold, the combined weight of energy storage battery thermal safety and power grid equipment thermal / mechanical safety is 0.5, and the weight of multi-field information network orderliness is 0.

5.

9. The method for coordinated control of distributed energy storage based on smart grids according to claim 1, characterized in that, In step S5, the distributed energy storage charge / discharge rate adjustment range is 0.1C to 1.5C, and the power adjustment step size is 50kW / step; the grid-side cooling system start / stop delay does not exceed 1s, and the load adjustment amplitude does not exceed 10% / time; the system core processing terminal recalculates the comprehensive health every 10 seconds, and when the comprehensive health is ≥0.8 for 10 consecutive cycles, the current control process is terminated; if the standard is not met, the control command is regenerated and executed; the parameter range is set based on typical energy storage system operation tests and grid safety specifications.

10. A distributed energy storage collaborative control system based on a smart grid, the system being applicable to the distributed energy storage collaborative control method based on a smart grid as described in any one of claims 1-9, characterized in that, The system includes: Data acquisition module: used to deploy high-precision sensors at key monitoring locations of distributed energy storage batteries, power grid lines, and transformers, build a multi-physics field data acquisition network, collect real-time operating data of electrical, thermal, and mechanical fields, and output multi-physics field raw datasets; Cross-field information modeling module: It is used to abstract the interaction between electrical field, thermal field and mechanical field into cross-field information flow. Based on the original multi-physics dataset, it calculates the cross-field information entropy flow through the cross-field information entropy flow quantification formula, and then substitutes it into the multi-physics collaborative entropy integration formula to calculate the overall collaborative entropy of the multi-physics. Health assessment module: It is used to combine the equipment's factory technical standards, smart grid operation specifications, and the calculation results of cross-field information entropy flow and multi-physics overall collaborative entropy to construct a multi-physics health assessment system and define the equipment's physical operation safety threshold and the multi-field information coupling order threshold. Dynamic weight decision module: It is used to calculate the comprehensive health score using the comprehensive optimization objectives of power grid electrical operation safety, energy storage battery thermal safety, power grid equipment thermal / mechanical safety, and multi-field information network orderliness. It dynamically adjusts the weight of the comprehensive optimization objectives based on the comprehensive health score and generates distributed energy storage charging and discharging control instructions and power grid-side auxiliary equipment linkage instructions. Control execution and closed-loop verification module: It is used to receive and execute control commands, adjust the charging and discharging rate and power of distributed energy storage, start and stop the grid-side cooling system, optimize the operating load of grid equipment, and collect operating data in real time and feed it back to the data acquisition module to complete the whole process of data processing and operating status verification, forming a control closed loop.

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