Rail transit multi-station coordinated energy storage virtual inertia response method and system

Abstract

The application discloses a kind of track traffic multi-station collaborative energy storage virtual inertia response method and system, applied to the energy storage site of scattered deployment along track traffic line.Method includes: in each site, based on local monitoring energy storage state and grid-connected point electrical quantity information, through integrated virtual synchronous machine control model, local active, reactive and harmonic compensation instruction is generated;The local state information of each site is collected through inter-station communication network, and global frequency, voltage deviation and harmonic current amplitude are calculated using weighted average consistency algorithm;According to the local ability weight of each site, the power demand determined based on global parameters is apportioned, and the final instruction of each site is generated to control the output compensation current of energy storage converter.The application realizes the collaborative control of virtual inertia support and voltage, harmonic control of multi-station energy storage aggregation, solves the problem of equipment redundancy and poor collaboration of traditional scheme.

Description

Technical Field

[0001] This invention relates to the technical fields of power supply or distribution circuit devices or systems and energy storage systems, and in particular to a virtual inertia response method and system for multi-station collaborative energy storage in rail transit. Background Technology

[0002] With the continuous expansion of the scale and the increasing electrification of rail transit systems, their power supply networks exhibit significant dynamic fluctuation characteristics. On the one hand, traction loads exhibit strong time-varying impact characteristics during different operating phases such as train start-up, acceleration, and braking, causing rapid voltage fluctuations and an increase in harmonic components in the distribution network along the line, affecting the stability and power quality of the power supply system. On the other hand, with the continuous increase in the proportion of renewable energy integration, the overall inertia of the power system is showing a downward trend, making the power grid more prone to significant deviations under frequency disturbances, thus increasing the demand for external dynamic support capabilities.

[0003] In rail transit systems, distributed energy storage devices are primarily used for traction load support and regenerative braking energy absorption. Their operation is relatively independent, lacking the ability to proactively coordinate with the dynamic characteristics of the power grid. Under complex operating conditions, single-site energy storage regulation often only achieves localized responses, failing to address multi-dimensional regulation needs such as frequency support, voltage stability, and harmonic suppression. When multiple energy storage sites operate simultaneously, the lack of effective information exchange and coordination mechanisms can lead to mutual interference or inconsistent responses among the sites, limiting the overall control effectiveness.

[0004] Furthermore, there is often competition between different operational objectives in rail transit scenarios. For example, while the power grid has inertia support requirements, train traction or braking loads may cause voltage or harmonic levels to rise. In this case, the energy storage system needs to cope with multiple dynamic requirements simultaneously. Without a refined objective allocation and coordination mechanism, problems such as response lag, insufficient compensation, or overcompensation can easily occur, making it difficult for the system to achieve ideal overall performance.

[0005] Against this backdrop, there is an urgent need for a technical solution that can be applied to multi-station energy storage clusters, simultaneously address multiple objectives of frequency, voltage, and harmonic regulation, and possess a unified model-driven collaborative response capability, in order to improve the overall robustness and rapid response capability of rail transit energy systems to external disturbances. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to provide a virtual inertial response method and system for multi-station collaborative energy storage in rail transit, which can improve the overall robustness and rapid response capability of rail transit energy systems to external disturbances.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a virtual inertial response method for multi-station collaborative energy storage in rail transit, applied to multiple energy storage sites distributed along rail transit lines, each of which is connected to the common connection point of the power distribution network via an energy storage converter; the method includes:

[0008] Step S1: For each energy storage site, based on the energy storage status information and point of common coupling electrical quantity information obtained from local monitoring, the integrated virtual synchronous machine control model is used to process the information to generate the local active power reference command, local reactive power compensation command, and local harmonic compensation current command for that site; wherein, the energy storage status information includes at least the state of charge and the health status, and the point of common coupling electrical quantity information includes at least voltage, current, and grid frequency;

[0009] Step S2: Collect local status information of each energy storage site through a preset inter-site communication network. The local status information includes at least the local frequency deviation, local voltage deviation, and local harmonic current amplitude calculated from the electrical quantity information of the common connection point. Process all the collected local status information based on the weighted average consensus algorithm to calculate the global frequency deviation, global voltage deviation, and global harmonic current amplitude that characterize the overall system status.

[0010] Step S3: Based on the local capability weights of each energy storage site, the global power demand determined based on the global frequency deviation, global voltage deviation, and global harmonic current amplitude is allocated to generate the final active power command, final reactive power command, and final harmonic compensation current command for each site; wherein, the local capability weights are dynamically determined based on the energy storage status information and rated power of the energy storage converter of the corresponding site; each site controls its energy storage converter to output the corresponding compensation current according to the final active power command, final reactive power command, and final harmonic compensation current command.

[0011] Furthermore, the integrated virtual synchronous machine control model in step S1 adopts a three-layer nested control architecture, specifically including:

[0012] The underlying control architecture dynamically adjusts the virtual inertia parameters and damping parameters based on the energy storage status information, and generates the local active power reference command based on the virtual rotor motion equation and the grid frequency.

[0013] Mid-level control architecture: Based on the deviation between the voltage and the rated voltage in the electrical quantity information of the common connection point, dynamically adjust the virtual excitation voltage and generate the local reactive power compensation command for voltage compensation;

[0014] Top-level control architecture: The fundamental frequency and harmonic frequency of the current in the electrical quantity information of the common connection point are separated, and the reverse local harmonic compensation current command is generated based on the separated harmonic current components.

[0015] Further, step S2 includes:

[0016] Step S21: Divide the plurality of energy storage sites into at least one cluster; each site in a cluster uploads its local status information to the cluster head node through the intra-cluster communication subnet.

[0017] Step S22: Each cluster head node averages the local status information of all stations within its cluster to obtain the cluster average status information; the cluster head nodes exchange the cluster average status information through the inter-cluster communication network.

[0018] Step S23: Each cluster head node calculates the global frequency deviation, global voltage deviation, and global harmonic current amplitude based on the average state information of all clusters obtained through interaction and using the weighted average consensus algorithm. When calculating the global frequency deviation, the weight assigned to the average state information of each cluster is positively correlated with the rated power of the corresponding site in the cluster and negatively correlated with the electrical distance.

[0019] Furthermore, the method also includes step S4, which involves identifying the current operating condition in real time and dynamically determining the priority weights of virtual inertia response, power quality management, and traction energy dispatch according to a preset operating condition-priority mapping rule; and dynamically correcting the final active power command, final reactive power command, and final harmonic compensation current command generated in step S3 based on the priority weights.

[0020] Furthermore, step S4 also includes a local optimization sub-step:

[0021] A portion of the energy storage capacity at each energy storage site is preset as an emergency power quality buffer capacity, which is used first to respond to voltage compensation and harmonic suppression requirements.

[0022] When the state of charge of this site is detected to be lower than the first preset threshold, or the voltage deviation of the point of common coupling exceeds the second preset threshold, the active power response depth of this site is automatically reduced or the emergency buffer capacity is invoked first.

[0023] A multi-station collaborative energy storage virtual inertial response system for rail transit, used to implement the method described above, the system comprising:

[0024] Multiple distributed energy storage power stations are deployed along the rail transit line to collect local energy storage status information and common connection point electrical quantity information at the station. The energy storage power station includes energy storage battery packs, energy storage converters, and power quality monitoring devices.

[0025] Multiple integrated control modules are installed in the energy storage converters of each of the energy storage power stations and connected to the power quality monitoring devices of the energy storage power stations. They are used to receive local energy storage status information and common connection point electrical quantity information, and process them through the built-in integrated virtual synchronous machine control model to generate local control instruction sets.

[0026] An inter-station collaborative communication network connects all the integrated control modules and is used to collect local status information of each of the energy storage power stations. The local status information includes at least the local frequency deviation, local voltage deviation and local harmonic current amplitude calculated from the electrical quantity information of the common connection point of each station.

[0027] The collaborative computing module, connected to the inter-station collaborative communication network, is used to process all the collected local state information according to the weighted average consensus algorithm to calculate the global frequency deviation, global voltage deviation, and global harmonic current amplitude; and to allocate the global power demand determined based on the global frequency deviation, global voltage deviation, and global harmonic current amplitude according to the energy storage state information of each energy storage station and the local capacity weight dynamically determined by the rated power of the energy storage converter, to generate the final power instruction set for each station, and to send it to the corresponding integrated control module through the inter-station collaborative communication network.

[0028] Each integrated control module controls the energy storage converter to output a corresponding compensation current according to the final power command set.

[0029] Furthermore, the integrated control module includes:

[0030] The virtual inertia control unit is used to dynamically adjust the virtual inertia parameters and damping parameters according to the received local energy storage status information, and generate active power reference commands based on the virtual rotor motion equation and the grid frequency.

[0031] The voltage support control unit is used to dynamically adjust the virtual excitation voltage based on the received deviation between the common coupling voltage and the rated voltage, and generate a reactive power compensation command for voltage compensation.

[0032] The harmonic suppression control unit is used to separate the fundamental frequency and harmonics of the received common coupling point current, and generate a harmonic compensation current command based on the separated harmonic current components.

[0033] The instruction synthesis unit, connected to the virtual inertia control unit, the voltage support control unit, and the harmonic suppression control unit, is used to fuse the locally generated active power reference instruction, reactive power compensation instruction, and harmonic compensation current instruction with the final power instruction set issued by the collaborative computing module to generate the final current control instruction of the energy storage converter.

[0034] Furthermore, the inter-station collaborative communication network adopts a clustered architecture, including:

[0035] Multiple intra-cluster communication subnets, each intra-cluster communication subnet connects the integrated control module of all energy storage power stations within a cluster and the cluster head node of that cluster, and is used to transmit the local status information and the final power instruction set;

[0036] An inter-cluster communication network, connecting the cluster head nodes of each cluster, is used to exchange average intra-cluster state information between the cluster head nodes;

[0037] The collaborative computing module is distributed across each cluster head node.

[0038] Furthermore, the system also includes a dynamic optimization management module, which is connected to the collaborative computing module and each of the energy storage power stations. The module is used to receive real-time system operating condition information collected and uploaded by the power quality monitoring devices of each of the energy storage power stations, and dynamically determine the priority weights of virtual inertial response, power quality management and traction energy scheduling according to the preset operating condition-priority mapping rules, and generate a weight adjustment signal to send to the collaborative computing module.

[0039] The collaborative computing module corrects the calculation and allocation process of the global power demand based on the received weight adjustment signal.

[0040] Furthermore, the power quality monitoring device is deployed at the common connection point of each energy storage power station to collect voltage and current signals at high frequency and calculate the local frequency deviation, local voltage deviation and local harmonic current amplitude.

[0041] The collaborative computing module is integrated into the regional collaborative control host. The regional collaborative control host communicates with all energy storage power stations through the inter-station collaborative communication network and connects to the upper-level power grid dispatching system through the standard power communication protocol interface.

[0042] The beneficial effects of this invention are:

[0043] By constructing an integrated virtual inertia, voltage compensation, and harmonic suppression collaborative control mechanism for multi-site energy storage clusters, the embodiments of the present invention can achieve the following beneficial effects:

[0044] 1. Achieving localized and precise response at energy storage sites, enhancing dynamic regulation capabilities: Employing an integrated virtual synchronous machine control model, this approach combines energy storage status information with electrical quantity information from the point of common coupling (PCC). This enables each site to rapidly generate local reference commands for active power, reactive power, and harmonic compensation under changing operating conditions. This method gives the energy storage system dynamic response characteristics similar to a synchronous machine, significantly improving its real-time regulation capabilities against frequency disturbances, voltage fluctuations, and harmonic variations.

[0045] 2. Obtaining a unified system-level regulation target through inter-station status information fusion: Utilizing inter-station communication networks and a weighted average consensus algorithm, global frequency deviation, global voltage deviation, and global harmonic amplitude are obtained, transforming multi-station energy storage from decentralized, independent control to a coordinated response based on the overall system status. This mechanism avoids problems such as local overcompensation and inconsistent responses caused by single-station regulation, improving the stability and consistency of multi-station coordination.

[0046] 3. The capacity-weighted power allocation mechanism significantly improves the rationality and fairness of regulation: This invention dynamically determines the capacity weight based on the energy storage status of each site and the rated power of the converter, and allocates the global power demand accordingly, making the output command more aligned with the remaining capacity and health status of each site. This strategy effectively avoids the problem of excessive burden on a single site or inefficient participation of some sites, improving the overall reliability and utilization efficiency of the energy storage cluster.

[0047] 4. Achieving unified coordination of inertia support, voltage stability, and harmonic suppression: This invention simultaneously generates active, reactive, and harmonic compensation commands within a single control framework, avoiding the equipment redundancy and asynchronous response problems caused by the need for multiple independent devices to perform functions in traditional solutions. It can achieve dynamic balance among multiple objectives under different operating conditions, ensuring the overall optimization of inertia support depth, voltage control effect, and harmonic suppression efficiency.

[0048] 5. Enhancing the adaptability and robustness of multi-station energy storage clusters to complex rail transit conditions: This invention achieves dual-level decision-making based on local information and global status, enabling the energy storage system to quickly respond to local disturbances near stations while maintaining overall electrical consistency along the line. Even under complex conditions such as frequent train starts, braking, and regenerative energy feedback, it can maintain stable dynamic regulation quality, improving the power quality and operational reliability of rail transit.

[0049] 6. Reduce system construction costs and operational complexity: By implementing three types of functions (virtual inertia, voltage compensation, and harmonic suppression) through a unified virtual synchronous machine model, the need for independent equipment deployment and maintenance of multiple control systems is reduced. This lowers system investment while ensuring performance and improves the long-term maintainability of the system. Attached Figure Description

[0050] Figure 1This is a flowchart of the steps of the virtual inertial response method for multi-station collaborative energy storage in rail transit in this invention;

[0051] Figure 2 This is a flowchart of step S2 in this invention;

[0052] Figure 3 This is a schematic diagram of the structure of the multi-station collaborative energy storage virtual inertial response system for rail transit in this invention.

[0053] Reference numerals: 1. Energy storage power station; 11. Energy storage battery pack; 12. Energy storage converter; 13. Power quality monitoring device; 2. Integrated control module; 21. Virtual inertia control unit; 22. Voltage support control unit; 23. Harmonic suppression control unit; 24. Command synthesis unit; 3. Inter-station collaborative communication network; 31. Intra-cluster communication subnet; 32. Inter-cluster communication network; 4. Collaborative computing module; 5. Dynamic optimization management module. Detailed Implementation

[0054] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Identical components are denoted by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "upper," and "lower" used in the following description refer to directions in the accompanying drawings, and the terms "bottom surface," "top surface," "inner," and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0055] Example 1, refer to Figures 1 to 2 This is the first embodiment of the present invention, which provides a virtual inertial response method for multi-station collaborative energy storage in rail transit, applied to the power supply system of Metro Line 3 in a certain city. The line is 25 kilometers long, with 10 energy storage power stations 1 (labeled C1-C10) deployed along the line. Each power station is connected to the common connection point of the rail transit traction distribution network through a 500kW energy storage converter 12. The power supply system also includes a regional collaborative control host deployed in the metro control center, and an inter-station collaborative communication network 3 connecting each power station and the control host.

[0056] Working principle of Example 1:

[0057] The core of this embodiment's method is to achieve comprehensive and coordinated control of the power grid's virtual inertia, point-of-combination voltage, and harmonics through a closed-loop process of "generating commands through local integrated control, forming a global strategy through multi-station collaboration, and allocating and executing commands according to capacity." The specific workflow is as follows:

[0058] Step S1 (Integrated Local Control Command Generation Step): Each energy storage power station 1 (taking C5 as an example) first operates its local control independently. Its power quality monitoring device 13 deployed at the point of common coupling (e.g., a HIOKI PW6001 power quality analyzer) collects local energy storage status information (including the battery pack's state of charge (SOC) and state of health (SOH)) and point of common coupling electrical quantity information (including voltage, current, and grid frequency) in real time at a sampling frequency of no less than 2kHz. This information is then sent to the integrated control module 2 embedded in the station's energy storage converter 12 (implemented using an embedded controller based on a Xilinx Zynq-7000 series FPGA and an ARM Cortex-A9 core).

[0059] The integrated control module 2 runs an integrated virtual synchronous machine control model, which adopts a three-layer nested control architecture, including:

[0060] The underlying control architecture (virtual inertia control layer): This layer receives the State of Charge (SOC) and the State of Health (SOH). First, the virtual inertia constant J and the damping coefficient D are dynamically adjusted. The adjustment rules are as follows:

[0061] When SOC ≥ 80% or SOH ≤ 85%, the virtual inertia constant J and damping coefficient D are set to 1.2 times and 1.1 times the rated values, respectively, to improve the system response stability.

[0062] When the state of charge (SOC) is between 20% and 80%, the virtual inertia constant J and damping coefficient D decrease linearly with the SOC (e.g., J decreases by 5% and D by 3% for every 10% decrease in SOC) to prevent deep charge / discharge cycles from affecting battery life. Subsequently, based on the adjusted virtual inertia constant J and damping coefficient D, and the monitored grid frequency... The local active power reference command is generated by solving the virtual rotor motion equations. The virtual rotor motion equations are configured as follows: This equation simulates the rotor dynamics of a synchronous generator, where... This is the virtual prime mover torque (related to frequency deviation). This is the virtual electromagnetic torque (related to the output current). Here, D is the virtual power angle, D is the damping coefficient, and J is the virtual inertia constant. Local active power reference command. The generation process is as follows:

[0063] Virtual electromagnetic torque This is calculated using the equation of motion combined with frequency deviation closed-loop control. According to the principle of electromechanical energy conversion, active power P equals torque multiplied by angular velocity. Therefore, the local active power reference command... Determined by the following formula: This process enables the energy storage converter to spontaneously provide or absorb active power according to changes in grid frequency, just like a synchronous generator, thereby providing dynamic virtual inertia support for the grid.

[0064] Mid-level control architecture (voltage support control layer): This layer receives the voltage at the common connection point. Calculate its relationship with the rated voltage. Deviation (e.g., 35kV) Based on voltage deviation According to the formula Dynamically adjust the virtual excitation voltage ,in This is the rated excitation voltage. This is the voltage regulation coefficient (a larger value, such as 1.0, is used when the voltage drops, and a smaller value, such as 0.6, is used when the voltage rises). Local reactive power compensation command. The generation process is as follows: The virtual excitation voltage Uf directly determines the strength of the electromotive force within the simulated synchronous generator, thereby controlling its ability to output reactive power. To establish a direct control relationship, Generated by the mapping function:

[0065] .in, The set reactive power gain coefficient, This is a sign function used to determine the direction of reactive power (inductive or capacitive). For example, when... When the voltage drops to >0 (voltage dip), the control converter outputs inductive reactive power to boost the voltage. This adjustment process simulates the automatic voltage regulation function of a synchronous generator, with the goal of making the voltage rise. Stable at 0.95 Up to 1.05 Within the range.

[0066] Top-level control architecture (harmonic suppression control layer): This layer receives the current signal at the point of common coupling. An improved synchronous reference coordinate system algorithm, supplemented by a notch filter, is used to separate the current signal into the fundamental component i1 and harmonic components. (Focus on separating the 3rd, 5th, and 7th harmonics). Subsequently, based on the harmonic current... Generate a reverse local harmonic compensation current command. ,in The compensation factor is set to 0.95. This instruction aims to output a compensation current from the converter that is equal in magnitude and opposite in direction to the harmonic current, thereby canceling the harmonics in the line and reducing the total harmonic distortion rate. Less than 5%.

[0067] At this point, C5 station has generated data containing... , and The local control instruction set.

[0068] Step S2 (Global Coordination Parameter Generation Step), refer to Figure 2 ,include:

[0069] Step S21: Divide multiple energy storage sites into at least one cluster; each site in a cluster uploads its local status information to the cluster head node through the intra-cluster communication subnet 31.

[0070] In step S22, each cluster head node calculates the average local status information of all stations within its cluster to obtain the average status information within the cluster; the cluster head nodes exchange the average status information within the cluster through the inter-cluster communication network 32.

[0071] In step S23, each cluster head node calculates the global frequency deviation, global voltage deviation, and global harmonic current amplitude based on the average state information of all clusters obtained through interaction and using a weighted average consensus algorithm. Among them, when calculating the global frequency deviation, the weight assigned to the average state information of each cluster is positively correlated with the rated power of the corresponding site in the cluster and negatively correlated with the electrical distance.

[0072] Specifically, in this embodiment, to achieve multi-site collaboration, the local status information of each site needs to be aggregated and merged. This embodiment adopts a clustered collaborative architecture. First, the 10 sites are divided into two clusters according to geographical distance (approximately every 5-8 kilometers): Cluster A (C1-C5) and Cluster B (C6-C10). Within each cluster, a communication subnet is built through an industrial optical network (latency ≤10ms), and between clusters, they are connected through a 5G slicing network (latency ≤20ms). Sites C3 and C8 are selected as cluster head nodes because of their good SOC (approximately 70% and 75% respectively) and SOH (both >90%) status.

[0073] Subsequently, each station (such as C5) reports its local status information (including the local frequency deviation calculated from the electrical quantity information used in step S1). Local voltage deviation Local harmonic current amplitude The local status information (including SOC, SOH, and rated power) is uploaded to the cluster head node (C3) via the cluster's industrial optical network. Cluster head node S3 averages the local status information of all sites within cluster A to obtain the average status information of cluster A (such as average frequency deviation). Average voltage deviation Average harmonic current amplitude Next, cluster head nodes C3 and C8 exchange their intra-cluster average state information via the 5G network.

[0074] Finally, based on the received average state information within all clusters, each cluster head node calculates the global collaborative parameters characterizing the overall system state using a weighted average consensus algorithm. The specific calculation formula and technical principles are as follows:

[0075] Global frequency deviation calculate: ;

[0076] Weight The calculation formula is:

[0077] . For clusters Average rated power of internal stations, For clusters Average electrical distance to the grid connection point The total number of clusters, For cluster index (k,j=1,2,…,N). For clusters The average frequency deviation.

[0078] The aforementioned weighting design enables clusters with larger rated power (stronger active power support capability) and closer electrical distance (more direct frequency response) to play a leading role in global frequency regulation, optimizing the efficiency and speed of inertia support.

[0079] Global voltage deviation calculate: Among them, weight . and Clusters ,cluster Average electrical impedance to the point of common coupling (PCC), For clusters The average voltage deviation. This weighting design is based on the reciprocal of the electrical distance, allowing clusters that are highly sensitive to PCC voltage and have significant regulation effects to undertake more voltage compensation tasks, avoiding ineffective compensation at distant sites, and improving the accuracy of voltage control.

[0080] Global harmonic current amplitude calculate: Among them, weight . and For clusters ,cluster Average apparent power capacity of in-site stations, For clusters The average harmonic current amplitude of the internal sites. This weighting design is proportional to the current output capacity of the cluster, ensuring that the harmonic suppression task is mainly undertaken by sites with larger current margins, guaranteeing the overall harmonic suppression effect, and preventing overload of small-capacity sites.

[0081] Through the weighted fusion based on multidimensional physical characteristics described above, the global frequency deviation that accurately guides global collaborative control is obtained. Global voltage deviation and global harmonic current amplitude .

[0082] Step S3 (Power Command Allocation and Execution Step): The collaborative computing module 4 in the regional collaborative control host (an industrial server configured with an Intel Xeon Gold 6348 CPU and 64GB of memory) first calculates the global collaborative parameters based on step S2. , and Determine the global power requirements of the system (such as total active power requirements, total reactive power requirements, and total harmonic compensation requirements).

[0083] Then, based on the local capability weights of each site. These global demands should be distributed fairly and efficiently. Dynamic calculation, for example, for station C5, the calculation process is as follows:

[0084] ;

[0085] in, The current state of charge of station C5. For the health status of site C5, The rated power capacity of site C5, This is the sum of the rated power capacities of all stations in the system.

[0086] The above formula takes into account the real-time energy level (SOC), health status (SOH), and rated capacity (P_N) of the site, ensuring that sites in good condition and with strong capabilities can undertake more regulation tasks, thus achieving optimal resource allocation.

[0087] After the cost was allocated, station C5 received its final instructions:

[0088] Active power command: .in, This is the active power-frequency regulation coefficient, which represents the total amount of active power (kW) required to compensate for a 1Hz frequency deviation. This coefficient is determined by the system characteristics. This represents the total active power adjustment required for the entire system to restore frequency stability.

[0089] Reactive power command: .in, The reactive power-voltage regulation coefficient represents the total amount of reactive power (in kVar) required to compensate for a 1kV voltage deviation. This represents the total reactive power adjustment required for the entire system to restore voltage stability.

[0090] Harmonic compensation current command: .in, This is a sign function. It is used to indicate the direction of harmonic compensation, ensuring that the compensation current is out of phase with the detected harmonic current, thereby achieving the purpose of cancellation (compensation).

[0091] Ultimately, the integrated control module 2 of station C5 synthesizes these final instructions into a current control signal for the energy storage converter 12, driving the converter to output a composite current containing active, reactive, and harmonic compensation components, and accurately executes the coordinated control objective.

[0092] Step S4 (Dynamic Priority Management and Instruction Correction Step – Optimized Implementation): To address target conflicts under complex operating conditions, the system adds a dynamic optimization management module 5. This module analyzes information from various stations and the power grid dispatch in real time to identify the current operating condition. For example, when a power grid frequency drop exceeding 0.5Hz is detected, it is determined to be an "extreme inertia demand" condition, and the priority weights are automatically set as follows: inertia response 0.6, power quality management 0.2, and traction energy dispatch 0.2. Based on this, the collaborative calculation module 4 adjusts the global power demand calculation in step S3, prioritizing active power support and temporarily relaxing the voltage and harmonic management accuracy requirements (e.g., allowing THD to temporarily not exceed 8%), thereby ensuring power grid stability during crisis moments.

[0093] Local optimization: At each site, such as C5, the battery management system reserves 18% of its energy storage capacity as an "emergency power quality buffer." This capacity is specifically used to respond to sudden voltage compensation or harmonic suppression needs and does not participate in regular inertial response or traction energy recovery, ensuring rapid response capabilities for power quality management. Simultaneously, preset protection logic: When the site's SOC is detected to be below a first preset threshold of 20%, the system automatically sends received or locally generated active power commands... Reduce by 40% to avoid over-discharge of the battery; when the voltage deviation at the common connection point of this station exceeds the second preset threshold by 10% (i.e. In the event of a power outage, the aforementioned emergency buffer capacity is prioritized for reactive power compensation, and participation in other adjustments occurs only after voltage recovery. This local optimization mechanism adds a final layer of safety and optimization barriers on top of global coordination.

[0094] Technical Results: Through the implementation of this embodiment, the subway line energy storage system achieves integrated and coordinated control of grid frequency, voltage, and harmonics. Tests show that when the grid experiences a 0.5Hz frequency drop, the system can provide effective inertial support within 200ms to help the frequency recover; when train start-up causes an 8% voltage drop, the voltage can recover to the normal range within 150ms; and the system suppresses harmonics generated by train braking by more than 70%, keeping THD below 3%. Compared to traditional solutions that require the deployment of independent VSG, SVG, and APF devices, this embodiment significantly reduces equipment costs and floor space, and through multi-station collaboration and dynamic optimization, greatly improves the system's robustness and overall performance in dealing with multiple and conflicting demands.

[0095] Example 2 is the second embodiment of the present invention, referred to Figure 3 This embodiment provides a virtual inertial response system for multi-station collaborative energy storage in rail transit, used to implement the method described in Embodiment 1. The system is constructed on a subway line in a certain city and aims to physically realize virtual inertial support for multi-station energy storage aggregation and collaborative power quality management through a combination of hardware and software.

[0096] System composition and connection relationships:

[0097] The system described above specifically includes the following core modules, which together form an organic whole through defined connections:

[0098] Multiple distributed energy storage power stations 1: Deployed along rail transit lines (e.g., 10 stations). Each power station serves as a physical node, including:

[0099] Energy storage battery pack 11: It uses lithium iron phosphate batteries with a rated capacity of 1MWh. Its battery management system provides local energy storage status information in real time, including state of charge (SOC) and state of health (SOH).

[0100] Energy storage converter 12: Rated power of 500kW, supports four-quadrant operation, and serves as an actuator for energy exchange with the grid.

[0101] Power quality monitoring device 13: Deployed at the common connection point of the power station, using a high-precision device (such as HIOKIPW6001), it collects voltage and current signals at a frequency of not less than 2kHz and calculates the local frequency deviation in real time. ), local voltage deviation ( ) and local harmonic current amplitude ( These pieces of information together constitute the electrical quantity information of the common connection point.

[0102] Multiple integrated control modules 2: This is the core for realizing local intelligent control. Each module is set in the control cabinet of the energy storage converter 12 of the corresponding energy storage power station 1 in the form of an embedded controller (e.g., using a Xilinx Zynq-7000 series FPGA combined with an ARM Cortex-A9 core). This module is connected to the power quality monitoring device 13 and the battery management system of the station through hardware lines to receive the local energy storage status information and the electrical quantity information of the point of common coupling. Internally, it operates an integrated virtual synchronous machine control model, specifically including the following logic units:

[0103] Virtual inertia control unit 21: Receives SOC, SOH, and grid frequency. It dynamically adjusts the virtual inertia constant J and damping coefficient D based on the SOC, and according to the virtual rotor motion equation... Generate active power reference commands. The function of this equation is to enable the converter to autonomously generate active power commands with inertial response and damping characteristics based on the differential (acceleration) and deviation of the grid frequency by simulating the rotor mechanical motion of the synchronous generator. This is the core algorithm for providing virtual inertia.

[0104] Voltage support control unit 22: Receives the point of common coupling voltage. Calculates its deviation from the rated voltage. According to the formula Dynamically adjust the virtual excitation voltage This leads to the generation of reactive power compensation commands. The function of this model is to simulate the excitation regulator of a synchronous generator, and to control the magnitude and direction of the reactive power output by adjusting the internal electromotive force, thereby achieving rapid closed-loop compensation of the point of common coupling voltage.

[0105] Harmonic suppression control unit 23: Receives the point of common coupling current. It uses an improved synchronous reference coordinate system algorithm to separate the harmonic current components. And generate a reverse harmonic compensation current command. The algorithm's function is to accurately extract harmonic components and instruct the converter to output offset current, thereby suppressing harmonic pollution at its source.

[0106] Instruction synthesis unit 24: It is connected to the three control units mentioned above and is responsible for merging the locally generated instructions with the global instructions from the collaborative computing module 4 to generate the final current control instructions that act on the power devices of the energy storage converter 12.

[0107] Inter-station collaborative communication network 3: This is the nervous system that enables information exchange between multiple stations. It adopts a clustered architecture.

[0108] Intra-cluster communication subnet 31: For example, 10 power stations are divided into 2 clusters according to their geographical proximity. A wired communication subnet (delay ≤10ms) is built within the cluster using industrial Ethernet or optical network to connect the integrated control module 2 of all power stations in the cluster and the cluster head node of the cluster.

[0109] Inter-cluster communication network 32: Clusters are interconnected wirelessly via 5G network slicing and other methods to connect cluster head nodes and achieve wide-area communication (latency ≤20ms).

[0110] Cluster head node: It is a power station with good communication conditions and excellent energy storage status (such as SOC≥60%) within the cluster. Its integrated control module 2 additionally carries the function of distributed collaborative computing.

[0111] Collaborative Computing Module 4: This is the system's "decision-making brain." Its functions are deployed in a distributed manner across the cluster head nodes, ultimately converging at the regional collaborative control host (an industrial server deployed in the metro control center, such as one using an Intel Xeon processor). This module connects to the integrated control module 2 of all power stations via the inter-station collaborative communication network 3. Its core functions include:

[0112] Information aggregation and global calculation: Receive local status information from each station collected via the network, and calculate the global frequency deviation using a weighted average consensus algorithm. ), global voltage deviation ( ) and global harmonic current amplitude ( The weights in the algorithm (such as those based on rated power and electrical distance) ensure that the calculated global parameters reflect the physical characteristics of the power grid and the capabilities of the sites, guiding the formation of optimal collaborative strategies.

[0113] Command allocation and issuance: The local capacity weight is dynamically calculated based on the energy storage status information and converter rated power of each power station. The formula can be: ,in, This represents the current state of charge of the site. For the health status of the site, The rated power capacity of the site, This is the sum of the rated power capacities of all stations in the system. Based on this weight, the global power demand, determined by global parameters, is allocated to generate the final power instruction set for each power station. , , And, it is transmitted to the integrated control module of the corresponding power station via the network.

[0114] Dynamic Optimization Management Module 5: As the system's "dispatch commander," this module can be integrated into the regional collaborative control host. It receives real-time operating condition information from each power station through the inter-station collaborative communication network 3 and connects to the collaborative computing module 4. It embeds "operating condition identification-priority matching" logic, for example, a preset rule: when a grid frequency drop of ≥0.5Hz is detected, it is determined as an "extreme inertia demand" condition, and the priority weights of virtual inertia response, power quality management, and traction energy dispatch are dynamically set to 0.6:0.2:0.2. This module sends the weight adjustment signal to the collaborative computing module 4, which then corrects the calculation and allocation of global power demand accordingly, achieving dynamic and refined resolution of multi-objective conflicts.

[0115] Working principle of Example 2:

[0116] After the system is powered on, it enters a continuous closed-loop operating state:

[0117] Data Acquisition and Local Calculation: Each power quality monitoring device 13 and battery management system continuously collects local information. The virtual inertia, voltage support, and harmonic suppression units of each integrated control module 2 perform parallel calculations to generate local active power, reactive power, and harmonic compensation commands.

[0118] Information Upload and Collaborative Decision-Making: Local status information of each station (including calculated data) The data (including SOC and SOH) is uploaded to the cluster head node via the intra-cluster communication subnet 31. The cluster head node first calculates the intra-cluster average information, and then exchanges information with other cluster head nodes via the inter-cluster communication network 32. The collaborative computing module 4 (distributed among the cluster head nodes and the control host) uses a weighted average consensus algorithm to fuse all information and calculate the global collaborative parameters. , and .

[0119] Global command generation and optimization: The collaborative computing module 4 generates the total power demand based on global parameters and distributes it fairly according to the dynamically updated local capacity weights of each station, forming a preliminary final power command set. At the same time, the dynamic optimization management module 5 analyzes real-time operating conditions (such as detecting a frequency drop of ≥0.5Hz), triggers priority adjustment, and notifies the collaborative computing module 4 to correct the command set (such as increasing the weight of active power commands).

[0120] Command Issuance and Fusion Execution: The revised final power command set is issued to the integrated control module 2 of each power station through the inter-station collaborative communication network 3. The command synthesis unit 24 of each station fuses and optimizes the global command with the locally generated command (for example, in case of conflict, the global command takes precedence, or a weighted average is used) to generate the final current control command, which drives the energy storage converter 12 of the station to accurately output the required compensation current.

[0121] Technical effects of Example 2:

[0122] This embodiment of the system physically implements the invention through the aforementioned modular and networked hardware architecture and hierarchical control logic, achieving significant results:

[0123] High hardware integration: Through an integrated control module 2 and its internal units, the functions of a virtual synchronous machine (VSG), a static var generator (SVG), and an active power filter (APF) are integrated on a single energy storage converter 12, replacing three independent power electronic devices in the traditional solution, reducing equipment costs by about 40% and floor space by 60%.

[0124] Precise collaborative control: The design of the clustered communication network and the distributed collaborative computing module 4 ensures low-latency and high-reliability interaction of massive status information. The weighted consensus algorithm based on physical characteristics (capacity, distance) makes global decision-making more in line with the actual situation of the power grid, and the command error of multi-station collaboration can be controlled within 5%, which is significantly better than the traditional average algorithm (more than 15%).

[0125] Dynamic response intelligence: The dynamic optimization management module 5, along with preset priority rules, enables the system to automatically identify operating conditions such as "extreme inertia requirements" (frequency drop ≥ 0.5Hz) and dynamically adjust control objectives. This resolves multi-objective conflicts, ensuring that frequency stability is prioritized during the most critical moments (such as large grid disturbances), while prioritizing power quality (THD ≤ 5%) during normal times.

[0126] High engineering reliability: The modular design facilitates deployment and maintenance. The digital twin platform can be used for preliminary simulation verification based on the system architecture of this embodiment, reducing the risk of on-site debugging. The system supports hot redundancy and automatic switching of cluster head nodes, and local failures do not affect global functionality, demonstrating good robustness.

[0127] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principle of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A virtual inertial response method for multi-station collaborative energy storage in rail transit, applied to multiple energy storage sites distributed along rail transit lines, wherein each energy storage site is connected to the point of common connection of the power distribution network through an energy storage converter, characterized in that, The method includes: Step S1: For each energy storage site, based on the energy storage status information and point of common coupling electrical quantity information obtained from local monitoring, the integrated virtual synchronous machine control model is used to process the information to generate the local active power reference command, local reactive power compensation command, and local harmonic compensation current command for that site; wherein, the energy storage status information includes at least the state of charge and the health status, and the point of common coupling electrical quantity information includes at least voltage, current, and grid frequency; Step S2: Collect local status information of each energy storage site through a preset inter-site communication network. The local status information includes at least the local frequency deviation, local voltage deviation, and local harmonic current amplitude calculated from the electrical quantity information of the common connection point. Process all the collected local status information based on the weighted average consensus algorithm to calculate the global frequency deviation, global voltage deviation, and global harmonic current amplitude that characterize the overall system status. Step S3: Based on the local capability weights of each energy storage site, the global power demand determined based on the global frequency deviation, global voltage deviation, and global harmonic current amplitude is allocated to generate a final active power command, a final reactive power command, and a final harmonic compensation current command for each site to form a final power command set. The locally generated local active power reference command, the local reactive power compensation command, and the local harmonic compensation current command are fused with the final power command set to generate the final current control command for the energy storage converter. The local capability weights are dynamically determined based on the energy storage status information and the rated power of the energy storage converter of the corresponding site. Each site controls its energy storage converter to output the corresponding compensation current according to the final current control command.

2. The method according to claim 1, characterized in that, The integrated virtual synchronous machine control model in step S1 adopts a three-layer nested control architecture, specifically including: The underlying control architecture dynamically adjusts the virtual inertia parameters and damping parameters based on the energy storage status information, and generates the local active power reference command based on the virtual rotor motion equation and the grid frequency. Mid-level control architecture: Based on the deviation between the voltage and the rated voltage in the electrical quantity information of the common connection point, dynamically adjust the virtual excitation voltage and generate the local reactive power compensation command for voltage compensation; Top-level control architecture: The fundamental frequency and harmonic frequency of the current in the electrical quantity information of the common connection point are separated, and the reverse local harmonic compensation current command is generated based on the separated harmonic current components.

3. The method according to claim 1, characterized in that, Step S2 includes: Step S21: Divide the plurality of energy storage sites into at least one cluster; each site in a cluster uploads its local status information to the cluster head node through the intra-cluster communication subnet. Step S22: Each cluster head node averages the local status information of all stations within its cluster to obtain the cluster average status information; the cluster head nodes exchange the cluster average status information through the inter-cluster communication network. Step S23: Each cluster head node calculates the global frequency deviation, global voltage deviation, and global harmonic current amplitude based on the average state information of all clusters obtained through interaction and using the weighted average consensus algorithm. When calculating the global frequency deviation, the weights assigned to the average state information within each cluster are positively correlated with the rated power of the corresponding site within the cluster and negatively correlated with the electrical distance.

4. The method according to claim 1, characterized in that, The method further includes step S4, which involves identifying the current operating condition in real time and dynamically determining the priority weights of virtual inertia response, power quality management, and traction energy scheduling according to a preset operating condition-priority mapping rule. Based on the priority weights, the final active power command, final reactive power command, and final harmonic compensation current command generated in step S3 are dynamically corrected.

5. The method according to claim 4, characterized in that, Step S4 also includes a local optimization sub-step: A portion of the energy storage capacity at each energy storage site is preset as an emergency power quality buffer capacity, which is used first to respond to voltage compensation and harmonic suppression requirements. When the state of charge of this site is detected to be lower than the first preset threshold, or the voltage deviation of the point of common coupling exceeds the second preset threshold, the active power response depth of this site is automatically reduced or the emergency buffer capacity is invoked first.

6. A multi-station collaborative energy storage virtual inertial response system for rail transit, used to implement the method according to any one of claims 1 to 5, characterized in that, The system includes: Multiple distributed energy storage power stations are deployed along the rail transit line to collect local energy storage status information and common connection point electrical quantity information at the station. The energy storage power station includes energy storage battery packs, energy storage converters, and power quality monitoring devices. Multiple integrated control modules are installed in the energy storage converters of each of the energy storage power stations and connected to the power quality monitoring devices of the energy storage power stations. They are used to receive local energy storage status information and common connection point electrical quantity information, and process them through the built-in integrated virtual synchronous machine control model to generate local control instruction sets. An inter-station collaborative communication network connects all the integrated control modules and is used to collect local status information of each of the energy storage power stations. The local status information includes at least the local frequency deviation, local voltage deviation and local harmonic current amplitude calculated from the electrical quantity information of the common connection point of each station. The collaborative computing module, connected to the inter-station collaborative communication network, is used to process all the collected local state information according to the weighted average consensus algorithm to calculate the global frequency deviation, global voltage deviation, and global harmonic current amplitude; and to allocate the global power demand determined based on the global frequency deviation, global voltage deviation, and global harmonic current amplitude according to the energy storage state information of each energy storage station and the local capacity weight dynamically determined by the rated power of the energy storage converter, to generate the final power instruction set for each station, and to send it to the corresponding integrated control module through the inter-station collaborative communication network. The integrated control module includes: The virtual inertia control unit is used to dynamically adjust the virtual inertia parameters and damping parameters according to the received local energy storage status information, and generate local active power reference commands based on the virtual rotor motion equation and the grid frequency. The voltage support control unit is used to dynamically adjust the virtual excitation voltage based on the received deviation between the common coupling voltage and the rated voltage, and generate a local reactive power compensation command for voltage compensation. The harmonic suppression control unit is used to separate the fundamental frequency and harmonics of the received common coupling point current, and generate a local harmonic compensation current command based on the separated harmonic current components. The instruction synthesis unit is connected to the virtual inertia control unit, the voltage support control unit, and the harmonic suppression control unit, respectively. It is used to fuse the locally generated local active power reference instruction, local reactive power compensation instruction, and local harmonic compensation current instruction with the final power instruction set issued by the collaborative computing module to generate the final current control instruction of the energy storage converter. Each integrated control module controls the energy storage converter to output a corresponding compensation current according to the final current control command.

7. The system according to claim 6, characterized in that, The inter-station collaborative communication network adopts a clustered architecture, including: Multiple intra-cluster communication subnets, each intra-cluster communication subnet connects the integrated control module of all energy storage power stations within a cluster and the cluster head node of that cluster, and is used to transmit the local status information and the final power instruction set; An inter-cluster communication network, connecting the cluster head nodes of each cluster, is used to exchange average intra-cluster state information between the cluster head nodes; The collaborative computing module is distributed across each cluster head node.

8. The system according to claim 6, characterized in that, The system also includes a dynamic optimization management module, which is connected to the collaborative computing module and each of the energy storage power stations. The module is used to receive real-time system operating condition information collected and uploaded by the power quality monitoring devices of each of the energy storage power stations, and dynamically determine the priority weights of virtual inertial response, power quality management and traction energy scheduling according to the preset operating condition-priority mapping rules, and generate a weight adjustment signal to send to the collaborative computing module. The collaborative computing module corrects the calculation and allocation process of the global power demand based on the received weight adjustment signal.

9. The system according to claim 6, characterized in that, The power quality monitoring device is deployed at the common connection point of each energy storage power station to collect voltage and current signals at high frequency and calculate the local frequency deviation, local voltage deviation and local harmonic current amplitude. The collaborative computing module is integrated into the regional collaborative control host. The regional collaborative control host communicates with all energy storage power stations through the inter-station collaborative communication network and connects to the upper-level power grid dispatching system through the standard power communication protocol interface.

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