A power system distributed energy storage collaborative scheduling system

Abstract

This invention discloses a distributed energy storage collaborative scheduling system for power systems, relating to the field of distributed energy storage optimization technology. It achieves quantitative assessment of the transient stability of the distribution network through transient energy functions, quantifies the system stability margin, and converts it into energy storage output commands. By constructing a two-dimensional hierarchical scheduling mechanism, it divides energy storage levels based on electrical distance and power sensitivity coefficients, and differentiates the allocation of near-end damping output and far-end balancing output to achieve optimal allocation of spatial resources. Simultaneously, it establishes a multi-timescale hierarchical control system, performing hierarchical control based on the response speed of energy storage devices, fully leveraging the advantages of fast and slow energy storage characteristics, and covering the entire transient process of a fault. Furthermore, it adopts a fully closed-loop adaptive scheduling architecture, with the energy storage cluster providing real-time feedback on its operating status, driving the system to dynamically adjust scheduling strategies until the distribution network returns to steady-state operation.

Description

Technical Field

[0001] This invention relates to the field of distributed energy storage optimization technology, specifically a distributed energy storage collaborative scheduling system for power systems. Background Technology

[0002] With the continuous advancement of energy transition, the large-scale integration of high-proportion distributed power sources and distributed energy storage into modern distribution networks has become an inevitable trend. While this development trend enhances the flexibility of distribution networks and the capacity for renewable energy absorption, it also makes the grid topology increasingly complex, significantly increases the number of disturbance sources, and causes frequent transient disturbances such as short-circuit faults and load surges. These disturbances can easily lead to stability problems such as system power angle oscillations and voltage drops, seriously threatening the power supply security and stable operation of distribution networks. Against this backdrop, how to effectively improve the stability support capability of distribution networks under fault transients has become a key issue that urgently needs to be addressed in the current power system field.

[0003] Existing distributed energy storage dispatching technologies have the following significant drawbacks when dealing with transient faults in distribution networks: First, they lack quantitative means of assessing transient stability. Traditional methods rely heavily on empirical judgment or qualitative analysis, which cannot accurately quantify the degree of transient energy imbalance in the system. This results in low accuracy and adaptability of energy storage output commands, making it difficult to achieve dynamic matching with the intensity of disturbances. Second, they fail to consider the spatial distribution characteristics of energy storage for differentiated dispatching. Existing technologies typically treat energy storage clusters as a whole, without dividing them into levels based on parameters such as the electrical distance between energy storage units and the fault point, and the power sensitivity coefficient. This leads to the inability of near-end energy storage to quickly provide local damping support, and the failure of far-end energy storage to effectively maintain global power balance, making it easy for the fault range to expand. In addition, they do not fully utilize the response time characteristics of energy storage to achieve multi-timescale collaborative control. The time-scale advantages of fast energy storage (such as flywheels and supercapacitors) and slow energy storage (such as lithium batteries) are not fully utilized. The timing linkage between millisecond-level damping in the early stage of the fault and subsequent second-level power maintenance is missing, resulting in slow transient recovery and poor oscillation suppression. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a distributed energy storage collaborative dispatch system for power systems. This system can quantitatively assess the transient stability of the distribution network through transient energy functions, quantify the system's stability margin, and convert it into energy storage output commands. By constructing a two-dimensional hierarchical dispatch mechanism, it divides energy storage levels based on electrical distance and power sensitivity coefficients, and differentiates the allocation of near-end damping output and far-end balancing output to achieve optimal allocation of spatial resources. Simultaneously, it establishes a multi-timescale hierarchical control system, performing hierarchical control based on the response speed of energy storage devices, fully leveraging the advantages of both fast and slow energy storage characteristics, and covering the entire transient process of a fault. Furthermore, it adopts a fully closed-loop adaptive dispatch architecture, with the energy storage cluster providing real-time feedback on its operating status, driving the system to dynamically adjust dispatch strategies until the distribution network returns to steady-state operation.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a distributed energy storage collaborative dispatch system for power systems, the system comprising: a power grid status monitoring module, a transient energy analysis module, a stability margin decision module, a hierarchical energy storage dispatch module, a multi-timescale collaborative control module, and an energy storage cluster execution module; The power grid status monitoring module collects electrical operating parameters of the distribution network in real time, identifies fault disturbance signals and locates fault points, and triggers transient scheduling processes. The transient energy analysis module calculates the generator transient energy accumulation value and the load transient energy consumption value through the transient energy function to obtain the transient energy difference; The stability margin decision module quantifies the stability margin of the distribution network system based on the transient energy difference and converts the stability margin into a total coordinated output command for the distributed energy storage cluster. The energy storage hierarchical scheduling module divides near-end energy storage units and far-end energy storage units based on the electrical distance between the energy storage unit and the fault point and the power sensitivity coefficient, and generates hierarchical power distribution instructions; The multi-timescale collaborative control module performs hierarchical control based on the transient response speed of the energy storage device, matches the power support requirements of different timescales, and generates the final execution command. The energy storage cluster execution module receives execution commands, outputs corresponding power, and simultaneously provides feedback on real-time operating status, forming a closed-loop adaptive scheduling and control.

[0006] Furthermore, the power grid status monitoring module collects electrical operating parameters of the distribution network in real time, including: distribution network node voltage, line transmission power, generator power angle, load active power, load reactive power, and fault type and location parameters.

[0007] Furthermore, after the distribution network fault disturbance is identified, the transient energy analysis module constructs a transient energy function model adapted to the distributed power source access characteristics of the distribution network based on real-time operating data such as generator power angle, rotor speed, active and reactive power of nodes, and equivalent load impedance uploaded by the power grid status monitoring module. By integrating the change in generator rotor kinetic energy and the accumulation of electromagnetic potential energy in the time domain during the fault transient process, the transient energy accumulation value of the generator is solved. At the same time, combined with the load dynamic response characteristics and equivalent damping parameters, the transient energy consumption value on the load side is calculated by integration. Then, the two types of transient energy parameters are subtracted in real time to finally obtain the transient energy difference between the generator and the load.

[0008] Furthermore, after receiving the transient energy difference value output by the transient energy analysis module, the stability margin decision module establishes a quantitative correspondence between the energy difference value and the system's transient stability margin based on a preset critical stability energy threshold and the standard energy reference value under the rated operating conditions of the distribution network. By comparing the deviation between the transient energy difference value and the critical threshold value in real time, the current grid stability margin value is calculated. The larger the energy difference value, the more severe the source-load energy imbalance, the lower the corresponding stability margin, and the higher the risk of power angle oscillation and voltage instability. After completing the stability margin determination, the stability margin index is converted into a total collaborative output command that can be executed by the energy storage cluster according to the built-in stability margin-total output mapping model. Taking into account the transient voltage support requirements of the distribution network, the power angle damping suppression requirements, and the maximum adjustable output limit of the distributed energy storage cluster, the total active and reactive power output target values ​​required to meet the system's stable recovery are determined through linear interpolation and dynamic correction calculations.

[0009] Furthermore, the stability margin decision module calculates the current grid stability margin value by comparing the deviation between the transient energy difference and the critical threshold in real time. The calculation formula is as follows: Where K is the transient stability margin of the distribution network. It is the absolute value of the transient energy difference. The calculation method is as follows: , It is the cumulative transient energy value of the generator, representing the total cumulative amount of rotor kinetic energy and electromagnetic potential energy during the fault transient process. This is the load transient energy consumption value, which characterizes the total amount of transient energy absorbed and consumed by the load side during a fault. It is the preset critical stability energy threshold of the distribution network.

[0010] Furthermore, the stability margin decision module, based on a built-in stability margin-total output mapping model, transforms the stability margin index into a total coordinated output command executable by the energy storage cluster. The mapping model expression is as follows: ,in, This represents the total coordinated output command value for the energy storage cluster. The maximum adjustable active power output that can be put into operation by all distributed energy storage units in the distribution network is defined as k, which is a dynamic correction coefficient that is adjusted in real time according to the fault type, voltage drop depth, and power angle oscillation rate to adapt to the output correction under different disturbance intensities.

[0011] Furthermore, the stability margin decision module dynamically adjusts the response speed and amplitude coefficient of the output command based on the decay rate of the stability margin. When the system stability margin drops rapidly, it automatically increases the strength of the output command and shortens the response delay. When the margin recovers to the safe range, it synchronously and gradually reduces the output scale. In addition, the stability margin decision module combines the real-time load fluctuations of the power grid and the output changes of distributed power sources to make slight corrections to the total output command, forming a complete scheduling command that includes the total output size, response timing, and adjustment rate, which is directly transmitted to the energy storage hierarchical scheduling module.

[0012] Furthermore, the stability margin decision module calculates the electrical distance between the grid connection point and the fault point of each energy storage unit based on the impedance matrix and impedance magnitude calculation. Simultaneously, it uses power flow sensitivity analysis to solve for the ratio of the output change of each energy storage unit to the voltage at the fault point and the change in the system power angle, obtaining the power sensitivity coefficient of each energy storage unit. Through built-in preset electrical distance thresholds and power sensitivity coefficient thresholds (pre-set based on the distribution network topology, rated operating parameters, and fault type), energy storage units that simultaneously meet the requirements of an electrical distance less than the preset threshold and a power sensitivity coefficient greater than the preset threshold are classified as near-end energy storage units, while the remaining energy storage units are classified as far-end energy storage units. Based on the total coordinated output command of the energy storage cluster output by the stability margin decision module, and following the principle of near-end priority and far-end supplementation, the module allocates the damping power output ratio of near-end energy storage units and the power balance output ratio of far-end energy storage units, clarifying the specific output amplitude, start-up sequence, and adjustment rate of each energy storage unit, generating a hierarchical output allocation command, and transmitting it in real time to the multi-timescale coordinated control module.

[0013] Furthermore, the multi-timescale collaborative control module receives the hierarchical power allocation command transmitted by the energy storage hierarchical scheduling module, synchronously collects the inherent transient response parameters and real-time operating status parameters of each distributed energy storage unit, quantifies and classifies the transient response speed of each energy storage unit, divides different response levels according to the differences in response speed, clarifies the response delay and adjustment rate parameter range of each level of energy storage unit, combines the power support timing requirements of different time stages during the fault transient process, and aligns with the total output requirements of each energy storage unit in the hierarchical power allocation command, calculates and determines the start-up timing, output duration, and output amplitude allocation ratio of each response level energy storage unit, and dynamically corrects the output amplitude and adjustment rate based on the real-time operating status parameters of each energy storage unit. Subsequently, it integrates the specific execution parameters of the start-up time, output magnitude, and adjustment rate of each energy storage unit, transforms the hierarchical power allocation command into specific operation commands that each energy storage unit can directly execute, forming the final energy storage execution command set.

[0014] Compared with existing technologies, this distributed energy storage collaborative dispatch system for power systems has the following advantages: This invention introduces a transient energy function to achieve accurate quantitative assessment of the transient stability of the distribution network, significantly improving the accuracy and adaptability of energy storage output commands. A dual-dimensional hierarchical scheduling mechanism rationally divides energy storage levels based on electrical distance and power sensitivity coefficients, enabling near-end energy storage to quickly exert a damping effect and far-end energy storage to effectively maintain power balance, rapidly suppressing local disturbances and preventing the expansion of fault range. A multi-timescale hierarchical control system fully utilizes the advantages of fast energy storage such as flywheels and supercapacitors, as well as the slow energy storage of lithium batteries, balancing response speed and power continuity. A fully closed-loop adaptive scheduling architecture provides real-time feedback on the operating status of the energy storage cluster and dynamically adjusts the scheduling strategy, adapting to different fault types and disturbance intensities. It is highly versatile, effectively suppressing power angle oscillations and voltage drops, significantly improving the transient stability of the distribution network and the reliability of power supply.

[0015] 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

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0017] Figure 1 This is a structural block diagram of a distributed energy storage collaborative dispatch system for a power system; Figure 2 A flowchart of a stability margin decision module for a distributed energy storage collaborative dispatch system in a power system; Figure 3 This is a flowchart of a distributed energy storage collaborative dispatch system for a power system. Detailed Implementation

[0018] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0019] This invention provides a distributed energy storage collaborative dispatch system for power systems. It achieves quantitative assessment of the transient stability of the distribution network through transient energy functions, quantifies the system's stability margin, and converts it into energy storage output commands. By constructing a two-dimensional hierarchical dispatch mechanism, it divides energy storage levels based on electrical distance and power sensitivity coefficients, and differentiates the allocation of near-end damping output and far-end balancing output to achieve optimal spatial resource allocation. Simultaneously, it establishes a multi-timescale hierarchical control system, performing hierarchical control based on the response speed of energy storage devices to fully leverage the advantages of both fast and slow energy storage, covering the entire transient process of a fault. Furthermore, it adopts a fully closed-loop adaptive dispatch architecture, with the energy storage cluster providing real-time feedback on its operating status, driving the system to dynamically adjust dispatch strategies until the distribution network returns to steady-state operation. This invention provides a distributed energy storage collaborative dispatch system for power systems, such as... Figure 1 As shown, the system consists of a power grid status monitoring module, a transient energy analysis module, a stability margin decision-making module, an energy storage hierarchical scheduling module, a multi-timescale collaborative control module, and an energy storage cluster execution module. These modules interact with each other in real time via a dedicated power communication network to transmit control commands. Specifically, the power grid status monitoring module serves as the system's data acquisition front-end; the transient energy analysis module and the stability margin decision-making module constitute the core decision-making layer; the energy storage hierarchical scheduling module and the multi-timescale collaborative control module form the scheduling execution adaptation layer; and the energy storage cluster execution module is the terminal execution layer. While completing power output, the energy storage cluster execution module fully feeds back the real-time operating data of each energy storage unit to the front-end modules, forming a closed-loop adaptive scheduling control mechanism encompassing data acquisition, intelligent analysis, dynamic decision-making, precise scheduling, and execution feedback, ensuring the real-time performance and accuracy of the scheduling strategy.

[0020] The power grid status monitoring module is deployed at all monitoring and control nodes in the distribution network, establishing regular real-time communication connections with various monitoring and control terminals within the distribution network. This enables synchronous acquisition of electrical parameters under all operating conditions and comprehensive fault detection. The module continuously collects key operating parameters of the distribution network, including basic electrical data such as voltage amplitude and phase angle at each node, active and reactive power transmission of transmission lines, synchronous generator power angle, real-time rotor speed, active and reactive power on the load side, and equivalent load impedance. The module incorporates fault feature identification and location algorithms. By constructing a baseline library of rated operating parameters, it compares the deviation characteristics between the collected data and the baseline data in real time, quickly identifying various disturbance fault types such as three-phase short circuits, single-phase grounding, load abrupt changes, and distributed power fluctuations. Combining node impedance matrices and topology correlation algorithms, it calculates and locates the physical nodes and lines where faults occur. When the module identifies a valid fault disturbance signal, it immediately triggers the system's transient emergency dispatch process and packages and uploads all collected real-time operating data, fault type information, and fault location data to the transient energy analysis module, providing complete data support for subsequent energy analysis.

[0021] The transient energy analysis module receives real-time operating data and fault information uploaded by the power grid condition monitoring module. Addressing the transient characteristic changes caused by the high penetration rate of distributed generation in the distribution network, it constructs a customized transient energy function model adapted to the grid-connected characteristics of distributed generation, eliminating the interference of distributed generation output fluctuations on energy calculation accuracy. Using the fault trigger moment as the integration starting point, the transient energy analysis module performs continuous time-domain integration on the entire fault transient process, quantifying and calculating the transient energy parameters on both the generator and load sides. On the generator side, it integrates the dynamic change in rotor kinetic energy and the cumulative change in electromagnetic potential energy, summing them to obtain the cumulative transient energy value of the generator. This parameter fully characterizes the energy accumulation on the generator side during the fault transient process; on the load side, the load transient energy consumption value is obtained by integral calculation by combining the load dynamic response characteristics, equivalent damping coefficient and power consumption characteristics. This represents the total transient energy absorbed and consumed on the load side during a fault. After calculating the energy on both sides, the module performs a real-time difference operation to obtain the transient energy difference between the source and load sides. The calculation formula is: The energy difference is then transmitted in real time to the stability margin decision module, serving as the core basis for the quantitative assessment of the system's transient stability state. The stability margin decision module uses transient energy difference as the core input parameter to accurately quantify the transient stability margin of the distribution network and generate a total coordinated output command for the distributed energy storage cluster that adapts to the system's stability requirements, such as... Figure 2 As shown, the specific implementation steps are as follows: Transient stability margin quantification calculation: Pre-configure the critical stability energy threshold to adapt to the target distribution network A quantitative mapping relationship between energy difference and system stability margin is established, and the real-time transient stability margin K of the distribution network is calculated using a standardized formula: In the formula, K represents the absolute value of the transient energy difference. The value of K is positively correlated with the stability of the system. The smaller the value, the more severe the energy imbalance between the source and load, and the higher the risk level of grid power angle oscillation and voltage instability.

[0022] Total Coordinated Output Command Generation: The stability margin decision module has a built-in stability margin-total output mapping model, which converts the quantified stability margin index into a total active power output command that the energy storage cluster can directly execute. The core mapping expression is: ,in, This represents the total coordinated output command value for the energy storage cluster. The maximum adjustable active power output of all distributed energy storage units in the distribution network is defined as k, which is a dynamic correction coefficient that can be adaptively set in real time according to the fault type, voltage drop depth, and power angle oscillation rate to meet the power regulation requirements under different disturbance intensities.

[0023] Dynamic adaptive optimization of commands: The stability margin decision module monitors the dynamic decay rate of the stability margin in real time and adjusts the response speed and amplitude coefficient of the output command accordingly. When the stability margin drops rapidly, it automatically increases the strength of the output command and reduces the command response delay to achieve rapid power support. When the stability margin recovers to the preset safe range, it gradually reduces the output scale to avoid power surges. At the same time, the stability margin decision module combines the real-time load fluctuations of the power grid and the random fluctuations of distributed power output to make small dynamic corrections to the total output command, generating a complete total scheduling command that includes output amplitude, response timing, and power regulation rate, and then sends it to the energy storage hierarchical scheduling module.

[0024] The energy storage hierarchical scheduling module, based on a dual-dimensional evaluation index of electrical distance and power sensitivity, realizes the hierarchical division and differentiated output allocation of distributed energy storage units, maximizing the efficiency of energy storage power regulation. The specific implementation method is as follows: Energy storage unit hierarchical classification: The energy storage hierarchical scheduling module is based on the impedance matrix of the distribution network nodes. Through impedance modulus calculation, it accurately calculates the electrical distance between the grid connection point and the fault point of each energy storage unit. Using the power flow sensitivity analysis method, it solves the ratio of the output change of each energy storage unit to the voltage fluctuation of the fault point and the change of the system power angle to obtain the power sensitivity coefficient of each unit. The energy storage hierarchical scheduling module pre-configures the electrical distance threshold and the power sensitivity coefficient threshold. These thresholds can be pre-set and optimized according to the distribution network topology, rated operating parameters, and fault type. Energy storage units that simultaneously meet the requirements of electrical distance less than the threshold and power sensitivity coefficient greater than the threshold are classified as near-end energy storage units, and the remaining energy storage units are uniformly classified as far-end energy storage units.

[0025] Differentiated output allocation: Following the core scheduling principle of prioritizing near-end control and supplementing far-end support, differentiated output tasks are allocated. Near-end energy storage units undertake high-frequency damping power output to quickly suppress power angle oscillations and voltage dips caused by faults; far-end energy storage units undertake steady-state power balancing output to maintain system power balance and voltage stability in the long term. The energy storage tiered scheduling module accurately allocates the output ratio of the two types of energy storage units based on the overall coordinated output command, clarifies the target output amplitude, start-up sequence, and power regulation rate of each energy storage unit, generates tiered output allocation commands, and transmits them to the multi-timescale coordinated control module in real time.

[0026] The multi-timescale collaborative control module focuses on the differences in transient response characteristics of energy storage units, achieving precise matching between power demand and energy storage units across multiple timescales. It converts hierarchical output commands into directly executable terminal control commands. The specific implementation process is as follows: The multi-timescale collaborative control module receives hierarchical power allocation commands and synchronously collects characteristic parameters such as the inherent transient response delay and maximum power regulation rate of each distributed energy storage unit, as well as real-time status parameters such as state of charge and operating conditions. Based on the response speed quantification index, all energy storage units are classified into three levels: millisecond-level fast response, second-level medium-speed response, and minute-level slow response. The corresponding response delay range and regulation rate parameter range for each level are clearly defined. Combined with the time-series power demand throughout the entire fault transient process, hierarchical matching control is completed: millisecond-level fast response energy storage... The energy storage unit provides instantaneous power support during the initial stage of a fault, suppressing initial disturbances; the second-level medium-speed response unit dynamically adjusts power during transient processes, smoothing out power angle and voltage fluctuations; the minute-level slow-speed response unit supplements power during the steady-state recovery phase, maintaining long-term system stability. The multi-timescale collaborative control module calculates and determines the start-up sequence, output duration, and amplitude distribution ratio of each level of energy storage unit, dynamically corrects the output parameters based on real-time operating status, and integrates and generates a standardized execution instruction set containing start-up time, output magnitude, and adjustment rate, which is then sent to the energy storage cluster execution module.

[0027] The energy storage cluster execution module establishes a point-to-point control communication link with all distributed energy storage units in the distribution network, and has the functions of multi-unit synchronous control and status feedback. The multi-timescale collaborative control module receives the execution command set issued by the multi-timescale collaborative control module, and drives each energy storage unit to accurately output the target active power and reactive power according to the command requirements, so as to achieve rapid power response and stable output. At the same time, the multi-timescale collaborative control module collects real-time operating data of each energy storage unit, such as actual output amplitude, state of charge, operating temperature, and working mode, and uploads it back to the grid status monitoring module and each decision and scheduling module through the communication link to realize real-time verification and dynamic iterative optimization of scheduling commands. When the transient stability margin of the distribution network recovers to the preset safety threshold and remains stable, the system gradually exits the transient scheduling mode, and the energy storage cluster execution module smoothly reduces the output to the normal operating state, completing the entire fault disturbance control process and realizing closed-loop adaptive scheduling control of the entire system.

[0028] like Figure 3 As shown, the specific workflow of the distributed energy storage collaborative dispatch system for power systems provided by this invention is as follows: The power grid status monitoring module collects electrical operating parameters of the distribution network across the entire domain, inspects the power grid operating status in real time, and maintains standby monitoring mode when there is no fault. Identify fault disturbance signals and locate the fault point, trigger the transient scheduling process, and package and upload all operational data and fault information; A transient energy function model is constructed, and the cumulative transient energy value of the generator and the transient energy consumption value of the load are calculated by time-domain integration. The source-load transient energy difference is then solved. Based on the critical stable energy threshold, the transient stability margin of the system is calculated, and the total collaborative output command of the energy storage cluster is generated and dynamically optimized through the mapping model. Based on electrical distance and power sensitivity, near-end and far-end energy storage units are divided, and differentiated output allocation is completed according to the near-end priority principle to generate hierarchical instructions; Based on the energy storage response speed classification, the power demand is matched to different time scales of faults, the output parameters are corrected and a standardized terminal execution instruction set is generated. Drive each energy storage unit to output the target power, provide real-time feedback on the operating status, and form a closed-loop control; The system's stability margin has been restored to a safe range, and the energy storage output has been gradually reduced, exiting the transient dispatch and resuming normal operation.

[0029] In one embodiment, the present invention is applied to a distribution network of an urban industrial park containing photovoltaic distributed power sources. The distribution network is connected to multiple sets of distributed energy storage units. During normal operation, the park load is stable, the grid parameters are stable, and the system is in a standby monitoring state.

[0030] When a single-phase grounding fault occurs on a feeder in a power distribution network park, the power grid status monitoring module collects abnormal signals such as node voltage drops and line power fluctuations in real time, quickly identifies the fault type and locates the fault node, and immediately triggers the transient dispatch process, uploading real-time electrical data to the transient energy analysis module.

[0031] The transient energy analysis module constructs a transient energy function adapted to photovoltaic grid connection. The integral calculation shows that the cumulative value of generator transient energy is significantly higher than the value of load transient energy consumption. The source-load transient energy difference is too large, and this difference is simultaneously transmitted to the stability margin decision module.

[0032] The stability margin decision module calls the preset critical stability energy threshold, calculates that the system transient stability margin is lower than the safety threshold, and determines that the power grid has the risk of voltage instability and power angle oscillation. It generates the total coordinated output command of the energy storage cluster through the mapping model, and optimizes the output amplitude and response speed by combining the voltage drop depth setting dynamic correction coefficient, and issues the total dispatch command.

[0033] The energy storage hierarchical scheduling module calculates the electrical distance and power sensitivity between each energy storage unit and the fault node, divides the three groups of energy storage units around the fault into near-end energy storage units, and divides the four groups of energy storage units at the far end of the park into far-end energy storage units; allocates 70% of the damping power output to the near-end energy storage units and 30% of the power balancing output to the far-end energy storage units, clarifies the start-up sequence and adjustment rate of each unit, and generates hierarchical instructions.

[0034] The multi-timescale collaborative control module classifies the response of the energy storage units. The two near-end energy storage units are millisecond-level fast response units, and the remaining units are second-level medium-speed response units. It matches the power demand for instantaneous support in the early stage of a fault and suppression of transient process fluctuations, corrects the output parameters, and generates a precise execution instruction set for each unit.

[0035] After receiving the command, the energy storage cluster execution module immediately outputs power to suppress voltage drop at the millisecond level, and the second-level unit synchronously follows up to smooth out power angle oscillation. At the same time, it transmits the state of charge and output data of each energy storage unit in real time. The system continuously optimizes the scheduling strategy through closed-loop feedback. Within 10 seconds, the stability margin of the distribution network recovers to the safety threshold, and the voltage and power angle return to the rated operating range.

[0036] Ultimately, the system smoothly reduced the output of the energy storage cluster, exited the transient dispatch mode, and the distribution network returned to steady-state operation. This completed the coordinated dispatch and control of distributed energy storage under the fault disturbance, effectively avoiding power outage accidents and ensuring the power supply stability of the industrial park.

[0037] 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 distributed energy storage collaborative dispatch system for power systems, characterized in that, The system includes: a power grid status monitoring module, a transient energy analysis module, a stability margin decision module, a hierarchical energy storage scheduling module, a multi-timescale collaborative control module, and an energy storage cluster execution module; The power grid status monitoring module collects electrical operating parameters of the distribution network in real time, identifies fault disturbance signals and locates fault points, and triggers transient scheduling processes. The transient energy analysis module calculates the generator transient energy accumulation value and the load transient energy consumption value through the transient energy function to obtain the transient energy difference; The stability margin decision module quantifies the stability margin of the distribution network system based on the transient energy difference and converts the stability margin into a total coordinated output command for the distributed energy storage cluster. The energy storage hierarchical scheduling module divides near-end energy storage units and far-end energy storage units based on the electrical distance between the energy storage unit and the fault point and the power sensitivity coefficient, and generates hierarchical power distribution instructions; The multi-timescale collaborative control module performs hierarchical control based on the transient response speed of the energy storage device, matches the power support requirements of different timescales, and generates the final execution command. The energy storage cluster execution module receives execution commands, outputs corresponding power, and simultaneously provides feedback on real-time operating status, forming a closed-loop adaptive scheduling and control.

2. The power system distributed energy storage collaborative dispatch system according to claim 1, characterized in that, The power grid status monitoring module collects electrical operating parameters of the distribution network in real time, including: distribution network node voltage, line transmission power, generator power angle, load active power, load reactive power, and fault type and location parameters.

3. The power system distributed energy storage collaborative dispatch system according to claim 1, characterized in that, After a fault disturbance in the distribution network is identified, the transient energy analysis module constructs a transient energy function model adapted to the characteristics of distributed power source access in the distribution network based on real-time operating data such as generator power angle, rotor speed, active and reactive power of nodes, and equivalent load impedance uploaded by the power grid status monitoring module. By integrating the change in generator rotor kinetic energy and the accumulation of electromagnetic potential energy in the time domain during the fault transient process, the module solves for the generator transient energy accumulation value. At the same time, combined with the load dynamic response characteristics and equivalent damping parameters, the module integrates to calculate the transient energy consumption value on the load side. Then, the two types of transient energy parameters are subtracted in real time to finally obtain the transient energy difference between the generator and the load.

4. The power system distributed energy storage collaborative dispatch system according to claim 1, characterized in that, After receiving the transient energy difference value output by the transient energy analysis module, the stability margin decision module establishes a quantitative correspondence between the energy difference value and the system's transient stability margin based on a preset critical stability energy threshold and the standard energy reference value under the rated operating conditions of the distribution network. By comparing the deviation between the transient energy difference value and the critical threshold value in real time, the module calculates the current grid stability margin value. The larger the energy difference value, the more severe the source-load energy imbalance, the lower the corresponding stability margin, and the higher the risk of power angle oscillation and voltage instability. After completing the stability margin determination, the module converts the stability margin index into a total collaborative output command that can be executed by the energy storage cluster according to the built-in stability margin-total output mapping model. Taking into account the transient voltage support requirements of the distribution network, the power angle damping suppression requirements, and the maximum adjustable output limit of the distributed energy storage cluster, the module determines the total active and reactive power output target values ​​required to meet the system's stable recovery through linear interpolation and dynamic correction calculations.

5. A distributed energy storage collaborative dispatch system for power systems according to claim 4, characterized in that, The stability margin decision module calculates the current grid stability margin by comparing the deviation between the transient energy difference and the critical threshold in real time. The calculation formula is as follows: ,in, It is the transient stability margin of the distribution network. It is the absolute value of the transient energy difference. The calculation method is as follows: , It is the cumulative transient energy value of the generator, representing the total cumulative amount of rotor kinetic energy and electromagnetic potential energy during the fault transient process. It is the transient energy consumption value of the load, which, combined with the load dynamic response characteristics and equivalent damping parameters, characterizes the total amount of transient energy absorbed and consumed by the load side during a fault. It is the preset critical stability energy threshold of the distribution network.

6. A distributed energy storage collaborative dispatch system for power systems according to claim 5, characterized in that, The stability margin decision module, based on its built-in stability margin-total output mapping model, transforms the stability margin index into a total coordinated output command that the energy storage cluster can execute. The mapping model expression is as follows: ,in, This represents the total coordinated output command value for the energy storage cluster. The maximum adjustable active power output that can be put into operation by all distributed energy storage units in the distribution network is defined as k, which is a dynamic correction coefficient that is adjusted in real time according to the fault type, voltage drop depth, and power angle oscillation rate to adapt to the output correction under different disturbance intensities.

7. A distributed energy storage collaborative dispatch system for power systems according to claim 1, characterized in that, The stability margin decision module dynamically adjusts the response speed and amplitude coefficient of the output command based on the decay rate of the stability margin. When the system stability margin drops rapidly, it automatically increases the strength of the output command and shortens the response delay. When the margin recovers to the safe range, it synchronously and gradually reduces the output scale. In addition, the stability margin decision module combines the real-time load fluctuations of the power grid and the output changes of distributed power sources to make slight corrections to the total output command, forming a complete scheduling command that includes the total output size, response timing, and adjustment rate, which is directly transmitted to the energy storage hierarchical scheduling module.

8. A distributed energy storage collaborative dispatch system for power systems according to claim 1, characterized in that, The energy storage hierarchical scheduling module calculates the electrical distance between the grid connection point and the fault point of each energy storage unit based on the node impedance matrix and through impedance modulus calculation. At the same time, it solves the ratio of the output change of each energy storage unit to the voltage of the fault point and the change of the system power angle through the power flow sensitivity analysis method, and obtains the power sensitivity coefficient of each energy storage unit. Through the built-in preset electrical distance threshold and power sensitivity coefficient threshold, which are pre-set according to the distribution network topology, rated operating parameters and fault type, the energy storage units that simultaneously meet the requirements of electrical distance less than the preset threshold and power sensitivity coefficient greater than the preset threshold are classified as near-end energy storage units, and the remaining energy storage units are classified as far-end energy storage units. Based on the total coordinated output command of the energy storage cluster output by the stability margin decision module, the damping power output ratio of the near-end energy storage units and the power balance output ratio of the far-end energy storage units are allocated according to the principle of near-end priority and far-end supplementation. The specific output amplitude, start-up sequence and adjustment rate of each energy storage unit are clarified, and a hierarchical output allocation command is generated and transmitted to the multi-timescale coordinated control module in real time.

9. A distributed energy storage collaborative dispatch system for power systems according to claim 1, characterized in that, The multi-timescale collaborative control module receives the hierarchical power allocation command transmitted by the energy storage hierarchical scheduling module, synchronously collects the inherent transient response parameters and real-time operating status parameters of each distributed energy storage unit, quantifies and classifies the transient response speed of each energy storage unit, divides different response levels according to the differences in response speed, clarifies the response delay and regulation rate parameter range of each level of energy storage unit, combines the power support timing requirements of different time stages during the fault transient process, and aligns with the total output requirements of each energy storage unit in the hierarchical power allocation command to calculate and determine the start-up timing, output duration, and output amplitude allocation ratio of each response level energy storage unit. At the same time, it dynamically corrects the output amplitude and regulation rate based on the real-time operating status parameters of each energy storage unit, and then integrates the specific execution parameters of the start-up time, output magnitude, and regulation rate of each energy storage unit to transform the hierarchical power allocation command into specific operation commands that each energy storage unit can directly execute, forming the final energy storage execution command set.

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