Energy-storage synchronous and coordinated management method and system based on virtual synchronization technique

Abstract

Provided in the present invention are an energy-storage synchronous and coordinated management method and system based on a virtual synchronization technique. The method comprises: selecting a suitable energy storage device on the basis of grid requirements, and collecting parameters of the energy storage device; on the basis of the parameters of the energy storage device, establishing a dynamic model of a virtual rotator, and setting a rotator rotational inertia and a torque parameter; establishing an adaption layer, so as to realize the interaction between the virtual rotator and a physical energy storage device; providing a line monitoring apparatus in a grid so as to collect real-time data of the energy storage device and detect, in real time, whether a fault occurs, and the adaption layer executing a safety operation when a fault occurs, and performing dynamic control and prediction planning when no fault occurs, so as to solve the problem of the uneven distribution of resources; and the adaption layer determining whether grid nodes and the real-time data of the energy storage device are normal, and if the grid nodes and the real-time data of the energy storage device are abnormal, performing detection and setting again, and if the grid nodes and the real-time data of the energy storage device are normal, saving a log and ending the process. In the present invention, an intelligent means is used to replace a conventional experiential control method, such that the dependence on experience is reduced, and dynamic optimization can be performed to increase the level of automation of energy storage control.

Description

A method and system for energy storage synchronization coordination management based on virtual synchronization technology Technical Field

[0001] The present invention relates to the technical field of energy storage management, and in particular to a method and system for energy storage synchronization coordination management based on virtual synchronization technology. Background Art

[0002] As the proportion of renewable energy continues to increase, the power grid faces challenges of increasing volatility and uncertainty. Energy storage technology is needed to help the grid shift peaks and valleys and achieve load balancing. At the same time, grid operations are also threatened by failures and accidents, requiring energy storage systems to provide emergency response capabilities. Therefore, achieving coordinated joint control of the power grid and energy storage resources to improve grid security and stability is a key technical challenge facing the development of the current power system. Current power grid systems are unable to simultaneously coordinate the use of energy storage resources and provide emergency response support for failures.

[0003] The research on energy storage technology and grid coordinated control technology is of great significance to promoting the high proportion of clean energy consumption, ensuring the safe and efficient operation of the power grid, and realizing the optimization of energy structure.

[0004] Summary of the Invention

[0005] In view of the above-mentioned prior art, the purpose of the present invention is to provide a method and system for energy storage synchronization coordination management based on virtual synchronization technology, so as to achieve predictive control of system status and perform dynamic optimization scheduling of resources.

[0006] To achieve the above-mentioned object, the technical solution provided in the first aspect of the present invention is:

[0007] A method for energy storage synchronization coordination management based on virtual synchronization technology includes the following steps:

[0008] S1. Collect energy storage equipment parameters;

[0009] S2. Establish a dynamic model of the virtual rotor based on the energy storage device parameters and set the rotor moment of inertia and torque parameters;

[0010] S3. Establish an adaptation layer to realize the interaction between the virtual rotor and the physical energy storage device;

[0011] S4. Install line monitoring devices in the power grid to collect real-time data from energy storage devices and detect faults in real time. The adaptation layer performs safe operations when a fault occurs and performs dynamic control and predictive planning when there is no fault, to address the problem of uneven resource allocation.

[0012] S5. The adaptation layer determines whether the real-time data of the grid nodes and the energy storage device are normal. If not, re-check and set. If normal, save the log and end.

[0013] Preferably, the S1 specifically includes:

[0014] Obtain the technical manual of the selected equipment and collect the precise parameters of the equipment, including efficiency, response time, number of cycles, temperature, energy storage capacity, charge and discharge power curve, and conversion efficiency curve;

[0015] Perform charge and discharge tests on actual equipment to obtain working characteristic data, calibrate the parameters in the manual, and summarize all technical parameters obtained from the manual and tests as basic data for subsequent modeling.

[0016] Preferably, the S2 specifically includes:

[0017] Obtain the rotor moment of inertia J of the rotor from the equipment parameter table in kg·m 2 As a unit, it is calculated by the following formula: J = ∫r 2 dm,

[0018] Where r is the distance from the mass element to the axis of rotation, and dm is the mass element;

[0019] The step of establishing a dynamic model of a virtual rotor includes:

[0020] Define the state variables, select the rotor angular velocity ω as the first state sub-variable, denoted as x1; select the rotor angular acceleration α as the second state sub-variable, denoted as x2; then the state variables are expressed as: x = [x1; x2] = [ω; α],

[0021] According to Newton's second law, the dynamic equation of the rotor is: J*a=T-Bw,

[0022] Where J is the rotor moment of inertia, B is the damping coefficient, and T is the torque;

[0023] Perform Laplace transform on the rotor's dynamic equation and obtain the state equation:

[0024] The output equation is derived by selecting the rotor angular velocity ω, that is, the first state sub-variable x1 as the output variable y, and the output equation is expressed as: y=C*x=[1 0]*[x1;x2]=x1,

[0025] The above equations can be organized into state space equations:

[0026] In the formula, the state matrix Input Matrix Output matrix C = [1 0];

[0027] To simulate the state-space equation, create two integration modules in Matlab / Simulink to integrate the state equation to obtain the first state sub-variable x1 and the second state sub-variable x2. Then create a gain module to calculate the state matrix A and the input matrix F. Finally, create an output module to calculate the output matrix C to obtain the output variable y. Connect the above modules to form a Simulink model of the state-space model.

[0028] Create a standard test signal module and select step, DC, and sine as the torque input signals. Connect them to the input of the state-space model and observe the response of the first state subvariable x1 (i.e., the rotor angular velocity ω) in the Scope module. Observe the shape, rise time, and resting error of the response curve. Finally, adjust the damping coefficient B and rotor moment of inertia J in the state matrix A to gradually bring the shape and dynamic characteristics of the response curve closer to those of the actual system.

[0029] The damping coefficient B and rotor moment of inertia J in the state matrix A are adjusted to make the shape and dynamic characteristics of the response curve gradually approach the actual system.

[0030] Preferably, the S3 specifically includes:

[0031] The adaptation layer receives the output control signal of the virtual rotor, establishes a mapping relationship between the speed w and the reference power Pref through neural network training, and converts the output control signal into a control instruction that can be executed by physical energy storage identification, as follows:

[0032] Define the neural network structure: the input layer is set with 1 node, representing the reference power Pref; the output layer is set with 1 node, representing the speed w; the hidden layer is set with m nodes;

[0033] Assume that the parameters from the input layer to the hidden layer are weight matrix W1, the parameters from the hidden layer to the output layer are weight matrix W2, and set the neural network prediction speed w to: w=f(Pref)=W2g(W1Pref+b1)+b2,

[0034] Where g is the hidden layer activation function, b1 is the hidden layer bias vector, b2 is the output layer bias, f is the mapping function, Pref is the input power, W1 is the weight matrix connecting Pref and the hidden layer, and W2 is the weight matrix connecting the hidden layer and the output;

[0035] Collect training data {Pref, w}, calculate the mean square error between the network's predicted speed w and the actual speed w as the loss function L, optimize W1, W2, b1, and b2 through the error backpropagation algorithm to minimize the loss L, repeatedly train the network, gradually reduce the loss function, and obtain the final mapping model. Based on the new Pref, use the model f(Pref) to predict the corresponding speed w;

[0036] In real-time control, the adaptation layer receives the real-time rotor speed w, calculates the corresponding Pref through the mapping function f, converts the Pref value into a control command based on the standard communication protocol, and sends it to the control system of the energy storage device. The adaptation layer and the device control system are connected through a reliable industrial communication link to ensure that the command arrives on time. After receiving the command, the local controller of the device activates the power control closed loop and drives the inverter and other components to ensure that the real-time power P of the device tracks the reference value Pref.

[0037] Preferably, the S3 further includes establishing constraints between the adaptation layer and the energy storage device, and the constraints between the adaptation layer and the energy storage device specifically include:

[0038] If the speed fluctuation output by the virtual rotor is abnormal, the adaptation layer needs to set a low-pass filter to smooth the speed signal to avoid issuing violently fluctuating control instructions to the energy storage device;

[0039] If there is hysteresis or inertia effect inside the energy storage device, the adaptation layer needs to add historical states to the neural network model to improve the adaptability to dynamic hysteresis characteristics;

[0040] If the actual device's power tracking performance is poor, the adaptation layer needs to appropriately increase the margin between the control command and the actual power value to avoid the impact of frequent switching on the device.

[0041] If packet loss or delay occurs in the industrial communication network, the adaptation layer will activate the local predictive model compensation control when it detects the fault to ensure system stability.

[0042] Preferably, the security operation in S4 specifically includes:

[0043] Line detection devices are set up at key nodes of the power grid. Current transformers are installed on the power grid lines to detect line current in real time. When the current exceeds the threshold, an alarm is triggered. Voltage transformers are installed at the nodes to monitor the voltage amplitude and phase in real time to determine voltage faults.

[0044] Collect the voltage data of each node when the power grid is operating normally and calculate the maximum voltage U max , minimum value U min and the average value U avg ;

[0045] Determine the ultra-high voltage threshold: U high =U max +a%*(U max -U avg );

[0046] Determine the ultra-low voltage threshold: U low =U min -β%*(Uavg -U min );

[0047] Among them, a and β are empirical values, ranging from 0 to 100;

[0048] Statistical analysis of the average current I of each line during normal power supply avg and the maximum current I max ,

[0049] Determine the overload alarm point: I over =k1*I max ,

[0050] Determine the overcurrent fault point: I fault =k2*I max ,

[0051] Where k1 represents the normal load rate of the reference line,

[0052] k2 represents 120% to 130% of the rated current of the reference line;

[0053] During the test, if the detected voltage is higher than U high， If the detected voltage is lower than U low， If the current is higher than I over If the current exceeds I fault， If the time lasts longer than t4, it is judged as an overcurrent fault;

[0054] Among them, the setting range of t1 is 2-3 seconds, the setting range of t2 is 1-2 seconds, the setting range of t3 is 10-20 seconds, and the setting range of t4 is 0.5-1 second;

[0055] If the adaptation layer detects an ultra-high voltage fault, it sends a boost charging command to the energy storage device to increase the DC bus voltage. It also sends a reactive power compensation command to the distribution network and a reduction in excitation current command to the LCU units of the hydro-turbine units within the domain, helping to reduce the grid-side voltage.

[0056] If the adaptation layer detects an ultra-low voltage fault, it sends a voltage reduction and discharge command to the energy storage device to help maintain the bus voltage. It also sends a reactive power compensation command to the distribution network and an excitation current increase command to the LCU units of the hydro-turbine units in the domain to help increase the grid-side voltage.

[0057] If the adaptation layer detects that the fault is an overload fault, it will immediately send a maximum discharge power command to the energy storage device to divert and reduce the load, and at the same time send a load cutting instruction to the load side to reduce the overload;

[0058] If the adaptation layer detects an overcurrent fault, it immediately sends a trip command to the circuit breaker to cut off the faulty section and simultaneously sends an emergency stop command to the energy storage device to prevent the device from being affected.

[0059] An overload fault indicates that the device is subjected to a current or load exceeding its rated current or power for a short period of time, which causes the device to overheat, be damaged, or cause other safety issues. This is a partial device failure. An overcurrent fault indicates that the instantaneous current in the circuit exceeds the rated current of the device or circuit design.

[0060] If no fault is found in real-time monitoring, the real-time data of all energy storage devices are collected and sent to the adaptation layer.

[0061] Preferably, the dynamic control and prediction planning in S4 includes determining whether the balance is achieved, and the adaptation layer issues a balance instruction for dynamic coordination.

[0062] The determination of whether the balance is achieved includes the following specific steps:

[0063] Calculate the average SOC value of all devices avg , count the number of devices with current SOC that is too high or too low;

[0064] Determine whether there is SOC imbalance in the system;

[0065] Check whether the power and power balance is established;

[0066] Set tolerance control deviation range;

[0067] The adaptation layer issues balancing instructions for dynamic coordination, including the adaptation layer sending discharge control instructions to devices with too high SOC and sending charging instructions to devices with too low SOC; using closed-loop control to adjust the charge and discharge power Pi of each device, repeatedly calculating and issuing control instructions at a certain time interval, and performing dynamic coordination until the SOC of each device is restored to balance.

[0068] Preferably,

[0069] The statistics of the number of devices with too high or too low SOC include:

[0070] Calculate the average SOC value of all devices avg , set the allowed SOC floating range; if the SOC of a device is higher than SOC avg If the SOC of a device is lower than the SOC avg If the SOC of the device exceeds the allowable lower floating range, it is determined that the SOC of the device is too low; set the number of devices with too high SOC to n high , set the number of SOC too low devices to n low ;

[0071] The determining whether the system has SOC imbalance includes:

[0072] Set the tolerance percentage P for SOC imbalance allow， The upper limit of the number of devices with SOC imbalance is calculated as follows: threshold =n total *P allow ;

[0073] If n high >n threshold or n low >n threshold , it is determined that the system has SOC imbalance;

[0074] Checking whether power and electricity balance is established includes:

[0075] Determine the capacity Ci and current SOCi. For devices with excessively high SOC, calculate the amount of energy that exceeds the average: ΔQi = Ci × (SOCi - SOCavg);

[0076] For devices with too low SOC, calculate the amount of electricity required to replenish: ΔQi=Ci×(SOCavg-SOCi);

[0077] Summarize the total discharge capacity ΔQ_discharge of all devices with too high SOC and the total charge capacity ΔQ_charge of devices with too low SOC, and map ΔQi to the corresponding charge and discharge power Pi:

[0078] Where Δt is the step size, and the power Pi is limited so that it cannot exceed the rated power of a single device;

[0079] Check whether the power and energy balance is established: ∑Pi*Δt=∑ΔQi;

[0080] The setting tolerance control deviation range includes:

[0081] Test the response time T of different types of energy storage devices to execute standard power control instructions response , measuring the power control accuracy error ε of the device under different SOC and temperature conditions precision , set the control period T control ;

[0082] Calculate the response time tolerance: δt response =T response -T control ;

[0083] Calculation accuracy tolerance range: δP precision =2*ε precision ;

[0084] Comprehensively determine the total tolerance deviation range of power control: ΔP tol =δP response +δP precision ;

[0085] If a control command is sent, ΔP is considered tol The margin is: P cmd =P ideal ±ΔP tol , where P ideal is the target power;

[0086] If the detection feedback is correct, the actual power of the device is kept within the upper and lower limits: P ideal -ΔP tol ≤P real ≤P ideal +ΔP tol , where P real is the actual power.

[0087] A second aspect of the present invention provides an energy storage synchronization coordination management system based on virtual synchronization technology, the system comprising:

[0088] Equipment parameter acquisition module, used to collect energy storage equipment parameters;

[0089] The model building module is used to build a dynamic model of the virtual rotor according to the parameters of the energy storage device and set the rotor moment of inertia and torque parameters;

[0090] An adaptation layer establishment module is used to establish an adaptation layer to realize the interaction between the virtual rotor and the physical energy storage device;

[0091] Line detection devices are installed in the power grid to collect real-time data from energy storage devices and detect whether faults have occurred in real time. The adaptation layer performs safe operations when a fault occurs and performs dynamic control and predictive planning when there are no faults, thus solving the problem of uneven resource allocation.

[0092] The adaptation layer is used to determine whether the real-time data of the grid nodes and energy storage devices are normal. If not, re-detect and set them. If normal, save the log and end.

[0093] The beneficial effects of the present invention are:

[0094] The present invention provides a method and system for energy storage synchronization coordination management based on virtual synchronization technology. This system incorporates a line monitoring device that can monitor power grid faults in real time, resolving the issue of slow grid fault response. By quickly identifying the fault point, emergency measures can be implemented promptly, significantly improving the safety and reliability of the power grid. An adaptation layer is established to implement safe operations during grid faults and provide dynamic control during normal operations. The design of the adaptation layer significantly enhances the coordination between the power grid and the energy storage system. On the one hand, the adaptation layer can actively participate in regulation during grid faults, improving the grid's self-recovery capabilities. On the other hand, the adaptation layer can optimize the scheduling of the energy storage system, improving resource allocation efficiency. The virtual rotor model and the adaptation layer collaborate to achieve state monitoring and predictive control of the energy storage equipment. Therefore, the present invention better balances multiple requirements, including system safety and economic efficiency, and provides an effective technical means for the coordinated and symbiotic control of the power grid and energy storage. This helps enhance the flexibility and coordination of the system and achieve intelligent upgrades to the power system. The present invention also uses intelligent methods to replace traditional empirical control methods, reducing reliance on empirical knowledge while enabling dynamic optimization and significantly improving the automation level of energy storage control. BRIEF DESCRIPTION OF THE DRAWINGS

[0095] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0096] FIG1 is a flow chart of a method for energy storage synchronization coordination management based on virtual synchronization technology provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0097] The principles and features of the present invention are described below with reference to the accompanying drawings. The enumerated embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0098] 1 , this embodiment provides a method and system for energy storage synchronization coordination management based on virtual synchronization technology, including the following steps:

[0099] S1. Collect energy storage device parameters.

[0100] S2. According to the parameters of the energy storage device, a dynamic model of the virtual rotor is established, and the rotor moment of inertia and torque parameters are set.

[0101] S3. Establish an adaptation layer to realize the interaction between the virtual rotor and the physical energy storage device.

[0102] S4. Line monitoring devices are installed in the power grid to collect real-time data from energy storage devices and detect whether a fault has occurred in real time. The adaptation layer performs safety operations when a fault occurs and performs dynamic control and predictive planning when there is no fault, which is used to solve the problem of uneven resource allocation.

[0103] S5. The adaptation layer determines whether the real-time data of the grid nodes and the energy storage device are normal. If not, re-check and set. If normal, save the log and end.

[0104] Said S1 specifically includes:

[0105] Obtain the technical manual of the selected equipment and collect the precise parameters of the equipment, including efficiency, response time, number of cycles, temperature, energy storage capacity, charge and discharge power curve, and conversion efficiency curve;

[0106] Perform charge and discharge tests on actual equipment to obtain working characteristic data, calibrate the parameters in the manual, and summarize all technical parameters obtained from the manual and tests as basic data for subsequent modeling.

[0107] The S2 specifically includes:

[0108] Obtain the rotor moment of inertia J of the rotor from the equipment parameter table in kg·m 2 The unit is ∫r. The moment of inertia is related to the shape, size, material and speed of the rotor and is calculated by the following formula: J = ∫r 2 dm,

[0109] Where r is the distance from the mass element to the axis of rotation, and dm is the mass element;

[0110] Obtain the shaft torque parameter KT, which represents the torque output at a given current (in N·m / A). This parameter reflects the relationship between the motor's electromagnetic torque and current. Obtain the motor torque parameter KM, which represents the motor's torque output at a given voltage / current (in N·m / V or N·m / A). This parameter can be determined from the motor's speed-torque curve.

[0111] The second-order inertia link is used to describe the rotor motion, and the state space equation is established with the rotor angular velocity as the state variable.

[0112] The step of establishing a dynamic model of a virtual rotor includes:

[0113] Define the state variables, select the rotor angular velocity ω as the first state sub-variable, denoted as x1; select the rotor angular acceleration α as the second state sub-variable, denoted as x2; then the state variables are expressed as: x = [x1; x2] = [ω; α],

[0114] According to Newton's second law, the dynamic equation of the rotor is: J*a=T-Bw,

[0115] Where J is the rotor moment of inertia, B is the damping coefficient, and T is the torque;

[0116] Perform Laplace transform on the rotor's dynamic equation and obtain the state equation:

[0117] The output equation is derived by selecting the rotor angular velocity ω, that is, the first state sub-variable x1 as the output variable y, and the output equation is expressed as: y=C*x=[1 0]*[x1;x2]=x1,

[0118] The above equations can be organized into state space equations:

[0119] In the formula, the state matrix Input Matrix Output matrix C = [1 0];

[0120] To simulate the state-space equation, create two integration modules in Matlab / Simulink to integrate the state equation to obtain the first state sub-variable x1 and the second state sub-variable x2. Then create a gain module to calculate the state matrix A and the input matrix F. Finally, create an output module to calculate the output matrix C to obtain the output variable y. Connect the above modules to form a Simulink model of the state-space model.

[0121] Create a standard test signal module and select step, DC, and sine as the torque input signals. Connect them to the input of the state-space model and observe the response of the first state subvariable x1 (i.e., the rotor angular velocity ω) in the Scope module. Observe the shape, rise time, and resting error of the response curve. Finally, adjust the damping coefficient B and rotor moment of inertia J in the state matrix A to gradually bring the shape and dynamic characteristics of the response curve closer to those of the actual system.

[0122] The damping coefficient B and rotor moment of inertia J in the state matrix A are adjusted to make the shape and dynamic characteristics of the response curve gradually approach the actual system.

[0123] The S3 specifically includes:

[0124] The virtual rotor is a mathematical model based on the dynamic model and cannot directly interact with the physical energy storage device, so an adaptation layer needs to be established to realize the information exchange and control interface between the two.

[0125] The adaptation layer is used to receive the output control signal of the virtual rotor. The adaptation layer establishes a mapping relationship between the speed w and the reference power Pref through neural network training, and converts the output control signal into a control instruction that can be executed by physical energy storage identification, as follows:

[0126] Define the neural network structure: the input layer is set with 1 node, representing the reference power Pref; the output layer is set with 1 node, representing the speed w; the hidden layer is set with m nodes;

[0127] Assume that the parameters from the input layer to the hidden layer are weight matrix W1, the parameters from the hidden layer to the output layer are weight matrix W2, and set the neural network prediction speed w to: w=f(Pref)=W2g(W1 Pref+b1)+b2,

[0128] Where g is the hidden layer activation function, b1 is the hidden layer bias vector, b2 is the output layer bias, f is the mapping function, Pref is the input power, W1 is the weight matrix connecting Pref and the hidden layer, and W2 is the weight matrix connecting the hidden layer and the output;

[0129] Collect training data {Pref, w}, calculate the mean square error between the network's predicted speed w and the actual speed w as the loss function L, optimize W1, W2, b1, and b2 through the error backpropagation algorithm to minimize the loss L, repeatedly train the network, gradually reduce the loss function, and obtain the final mapping model. Based on the new Pref, use the model f(Pref) to predict the corresponding speed w;

[0130] In real-time control, the adaptation layer receives the real-time rotor speed w, calculates the corresponding Pref through the mapping function f, converts the Pref value into a control command based on the standard communication protocol, and sends it to the control system of the energy storage device. The adaptation layer and the device control system are connected through a reliable industrial communication link to ensure that the command arrives on time. After receiving the command, the local controller of the device activates the power control closed loop and drives the inverter and other components to ensure that the real-time power P of the device tracks the reference value Pref.

[0131] S3 further includes establishing constraints between the adaptation layer and the energy storage device. The constraints between the adaptation layer and the energy storage device specifically include:

[0132] If the speed fluctuation output by the virtual rotor is abnormal, the adaptation layer needs to set a low-pass filter to smooth the speed signal to avoid issuing violently fluctuating control instructions to the energy storage device;

[0133] If there is hysteresis or inertia effect inside the energy storage device, the adaptation layer needs to add historical states to the neural network model to improve the adaptability to dynamic hysteresis characteristics;

[0134] If the actual device's power tracking performance is poor, the adaptation layer needs to appropriately increase the margin between the control command and the actual power value to avoid the impact of frequent switching on the device.

[0135] If packet loss or delay occurs in the industrial communication network, the adaptation layer will activate the local predictive model compensation control when it detects the fault to ensure system stability.

[0136] The fluctuation amplitude is compared with the absolute value of the rotational speed. If the fluctuation amplitude exceeds 10% of the rotational speed, it is determined to be abnormal fluctuation. If the device frequently switches from high power mode to low power mode (or vice versa) within a short period of time, this indicates that the device control system cannot stably maintain a specific power level, resulting in poor power tracking performance of the actual device.

[0137] Furthermore, the adaptation layer also needs to set up a physical interface, specifically using existing standard industrial engineering interfaces to connect to the local control system, and adopt DENET technology, configure QoS priorities, and isolate and control data traffic to ensure real-time communication.

[0138] It should be noted that the adaptation layer is also responsible for collecting real-time status data of the physical energy storage device, such as current power, voltage, and current parameters, and feeding it back to the virtual rotor model to complete the status update.

[0139] The security operations described in S4 specifically include:

[0140] Line detection devices are installed at key nodes of the power grid, such as substations and ring network intersections. Current transformers are installed on the power grid lines to detect line currents in real time. When the current exceeds the threshold, an alarm is triggered. Voltage transformers are installed at nodes to monitor voltage amplitude and phase in real time to identify voltage faults.

[0141] Collect the voltage data of each node when the power grid is operating normally and calculate the maximum voltage U max , minimum value U min and the average value U avg；

[0142] Determine the ultra-high voltage threshold: U high =U max +a%*(U max -U avg );

[0143] Determine the ultra-low voltage threshold: U low =U min -β%*(U avg -U min );

[0144] Among them, a and β are empirical values, ranging from 0 to 100. According to the settings required in the current project, the general empirical value is 10%;

[0145] Statistical analysis of the average current I of each line during normal power supply avg and the maximum current I max ,

[0146] Determine the overload alarm point: I over =k1*I max ,

[0147] Determine the overcurrent fault point: I fault =k2*I max ,

[0148] Where k1 represents the normal load rate of the reference line, with a certain margin reserved. It is empirically set to 80% to 85% of the rated current. In addition, considering the load changes in different seasons, sufficient margin is given to adapt to the load increase.

[0149] k2 represents 120% to 130% of the reference line rated current; k1 and k2 values ​​need to be flexibly adjusted according to the transformer neutral point grounding method and device configuration;

[0150] During the test, if the detected voltage is higher than U high， If the detected voltage is lower than U low， If the current is higher than I over If the current exceeds I fault , and it lasts longer than t4, it is judged as an overcurrent fault;

[0151] The setting range for t1 is 2-3 seconds, the setting range for t2 is 1-2 seconds, the setting range for t3 is 10-20 seconds, and the setting range for t4 is 0.5-1 second. Ultra-high and ultra-low voltages have a greater impact on the system, so the judgment time limit is set slightly shorter. The system can withstand overload conditions for a certain period of time, so the alarm time limit is set longer. Overcurrent faults have a huge impact on the system and require a quick judgment and response, so the time limit is set shorter.

[0152] If the adaptation layer detects that the fault is an ultra-high voltage fault, it sends a boost charging command to the energy storage device to increase the DC bus voltage. At the same time, it sends a reactive power compensation command to the distribution network and a command to reduce the excitation current to the LCU units of the hydro-turbine units in the domain. This adds command control to the LCU excitation control units of the hydro-turbine units to help reduce the grid-side voltage.

[0153] If the adaptation layer detects an ultra-low voltage fault, it sends a voltage reduction and discharge command to the energy storage device to help maintain the bus voltage. At the same time, it sends a reactive power compensation command to the distribution network and an excitation current increase command to the LCU unit of the hydro-turbine unit in the domain. This increases the command control of the hydro-turbine excitation control unit (LCU) to help increase the grid-side voltage.

[0154] If the adaptation layer detects that the fault is an overload fault, it will immediately send a maximum discharge power command to the energy storage device to divert and reduce the load, and at the same time send a load cutting instruction to the load side to reduce the overload;

[0155] If the adaptation layer detects an overcurrent fault, it immediately sends a trip command to the circuit breaker to cut off the faulty section and simultaneously sends an emergency stop command to the energy storage device to prevent the device from being affected.

[0156] An overload fault indicates that the device is subjected to a current or load exceeding its rated current or power for a short period of time, which causes the device to overheat, be damaged, or cause other safety issues. This is a partial device failure. An overcurrent fault indicates that the instantaneous current in the circuit exceeds the rated current of the device or circuit design.

[0157] If no fault is found during real-time monitoring, real-time data of all energy storage devices, including SOC margin, voltage, current, temperature parameters, etc., are collected and sent to the adaptation layer.

[0158] The dynamic control and prediction planning described in S4 include judging whether the balance is achieved, and the adaptation layer issues a balance instruction for dynamic coordination.

[0159] The determination of whether the balance is achieved includes the following specific steps:

[0160] Calculate the average SOC value of all devices avg , count the number of devices with current SOC that is too high or too low;

[0161] Determine whether there is SOC imbalance in the system;

[0162] Check whether the power and power balance is established;

[0163] Set tolerance control deviation range;

[0164] The adaptation layer issues balancing instructions for dynamic coordination, including the adaptation layer sending discharge control instructions to devices with too high SOC and sending charging instructions to devices with too low SOC; using closed-loop control to adjust the charge and discharge power Pi of each device, repeatedly calculating and issuing control instructions at a certain time interval, and performing dynamic coordination until the SOC of each device is restored to balance.

[0165] The statistics of the number of devices with too high or too low SOC include:

[0166] Calculate the average SOC value of all devices avg , set the allowed SOC upper and lower floating range; generally it is ±5% SOC avg ; If a device's SOC is higher than SOC avg If the SOC of a device is lower than the SOCavg If the SOC of the device exceeds the allowable lower floating range, it is determined that the SOC of the device is too low; set the number of devices with too high SOC to n high , set the number of SOC too low devices to n low ;

[0167] The determining whether the system has SOC imbalance includes:

[0168] Set the tolerance percentage P for SOC imbalance allow， The upper limit of the number of devices with SOC imbalance is calculated as follows: threshold =n total *P allow ;

[0169] If n high >n threshold or n low >n threshold , it is determined that the system has SOC imbalance;

[0170] Among them, P allow According to the battery capacity, in the current embodiment, P is set. allow 3%.

[0171] Checking whether power and electricity balance is established includes:

[0172] Determine the capacity Ci and current SOCi. For devices with excessively high SOC, calculate the amount of energy that exceeds the average: ΔQi = Ci × (SOCi - SOCavg);

[0173] For devices with too low SOC, calculate the amount of electricity required to replenish: ΔQi=Ci×(SOCavg-SOCi);

[0174] Summarize the total discharge capacity ΔQ_discharge of all devices with too high SOC and the total charge capacity ΔQ_charge of devices with too low SOC, and map ΔQi to the corresponding charge and discharge power Pi:

[0175] Where Δt is the step size, and the power Pi is limited so that it cannot exceed the rated power of a single device;

[0176] Check whether the power and energy balance is established: ∑Pi*Δt=∑ΔQi;

[0177] Fine-tune power distribution to meet balance, convert the final Pi into a speed control signal based on the speed-power mapping relationship, and consider the impact of communication delay on control, set the timing relationship to compensate for the impact of network delay; consider the response time and accuracy error of different devices, and set the tolerance control deviation range.

[0178] The setting tolerance control deviation range includes:

[0179] Test the response time T of different types of energy storage devices to execute standard power control instructions response , measuring the power control accuracy error ε of the device under different SOC and temperature conditions precision , set the control period T control ;

[0180] Calculate the response time tolerance: δt response =T response -T control ;

[0181] Calculation accuracy tolerance range: δP precision =2*ε precision ;

[0182] Comprehensively determine the total tolerance deviation range of power control: ΔP tol =δP response +δP precision ;

[0183] If a control command is sent, ΔP is considered tol The margin is: P cmd =P ideal ±ΔP tol , where P ideal is the target power;

[0184] If the detection feedback is correct, the actual power of the device is kept within the upper and lower limits: P ideal -ΔP tol ≤P real ≤P ideal +ΔP tol , where P real is the actual power.

[0185] When sending control instructions, considering the margin of ΔP_tol is P cmd =P ideal ±ΔP tol , when testing feedback, as long as the actual power of the device is within the upper and lower limits: P ideal -ΔP tol ≤P real ≤P ideal +ΔP tol That is, the control is considered effective and unnecessary fluctuation adjustments are avoided. The measurement tolerance range is calibrated regularly and online adaptive optimization is performed.

[0186] Based on the same inventive concept as the aforementioned method embodiment, another embodiment of the present invention provides an energy storage synchronization coordination management system based on virtual synchronization technology, the system comprising:

[0187] Equipment parameter acquisition module, used to collect energy storage equipment parameters;

[0188] The model building module is used to build a dynamic model of the virtual rotor according to the parameters of the energy storage device and set the rotor moment of inertia and torque parameters;

[0189] An adaptation layer establishment module is used to establish an adaptation layer to realize the interaction between the virtual rotor and the physical energy storage device;

[0190] Line detection devices are installed in the power grid to collect real-time data from energy storage devices and detect whether faults have occurred in real time. The adaptation layer performs safe operations when a fault occurs and performs dynamic control and predictive planning when there are no faults, thus solving the problem of uneven resource allocation.

[0191] The adaptation layer is used to determine whether the real-time data of the grid nodes and energy storage devices are normal. If not, re-detect and set them. If normal, save the log and end.

[0192] The working principle and beneficial effects of the system embodiment are the same as those of the aforementioned method embodiment and will not be repeated here.

[0193] Another embodiment of the present invention further provides a computer device, which is applicable to the energy storage synchronization coordination management method based on virtual synchronization technology, and includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the energy storage synchronization coordination management method based on virtual synchronization technology proposed in the above embodiment.

[0194] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0195] Another embodiment of the present invention further provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the energy storage synchronization coordination management method based on virtual synchronization technology as proposed in the above embodiment.

[0196] In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0197] To verify the functionality of this method, a three-dimensional simulation environment of a power station and energy storage station was established, which includes a distributed photovoltaic system with a total installed capacity of 300MW and a 100MW / 400MWh energy storage station.

[0198] Install line monitoring devices at the access point of the photovoltaic power station and the entrance substation of the distribution network. The equipment parameters are as follows:

[0199] Table 1 Related equipment parameter information

[0200] Set the voltage upper and lower alarm limits, current overload and overcurrent alarm limits.

[0201] During operation, the voltage at a key distribution network node measured 38.5kV, below 85% of the lower limit of 32kV, for more than two seconds. The line monitoring module identified this as an undervoltage fault. The module detected the fault within 2.1 seconds and sent a signal to the adaptation layer within 50 milliseconds. Upon receiving the fault signal, the adaptation layer issued a discharge command to the energy storage management system within 1.2 seconds, activating the energy storage system's maximum power (80MW). Power was activated within 2.1 seconds, helping to restore the distribution network voltage.

[0202] Using a SELECTION-500 server as the operating platform, a neural network was used to establish a speed-to-power mapping relationship, accurately predict the energy storage system output, and build an adaptation layer. PSCAD simulations determined the parameters of the virtual rotor model: moment of inertia J = 8000 kg·m², torque constant KT = 10 N·m / A. The adaptation layer platform issues coordination commands every five minutes to optimize the operating points of the four 100MW / 400MWh lithium-ion battery energy storage systems and maintain a balanced state of charge (SOC). The experiments significantly increased the photovoltaic grid-connected capacity, shortened grid fault response time by 58%, and increased peak load regulation by 35%.

[0203] The comparison between the specific technical solutions of the present invention and the prior art is shown in Table 2:

[0204] Table 2 Comparison between the present invention and prior art

[0205] It can be clearly seen from the table that the present invention has significant improvements and advantages over the existing technology in many aspects such as fault detection response time, precise positioning, coordinated control, and resource optimization configuration. The comprehensive performance is better than the existing technology and it is easy to promote and apply.

[0206] These are specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. The scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for energy storage synchronization coordination management based on virtual synchronization technology, characterized in that: The steps include: S1. Collect energy storage equipment parameters; S2. According to the parameters of the energy storage device, a dynamic model of the virtual rotor is established, and the rotor moment of inertia and torque parameters are set; S3, establish an adaptation layer to realize the interaction between the virtual rotor and the physical energy storage device; S4. Line monitoring devices are installed in the power grid to collect real-time data of energy storage equipment and detect whether a fault occurs in real time. The adaptation layer performs safe operations when a fault occurs and performs dynamic control and predictive planning when there is no fault, so as to solve the problem of uneven resource allocation. S5. The adaptation layer determines whether the real-time data of the grid nodes and the energy storage device are normal. If not, re-check and set. If normal, save the log and end.

2. The energy storage synchronization coordination management method based on virtual synchronization technology according to claim 1, characterized in that: The S1 specifically includes: Obtain the technical manual of the selected equipment and collect the precise parameters of the equipment, including efficiency, response time, number of cycles, temperature, energy storage capacity, charge and discharge power curve, and conversion efficiency curve; Perform charge and discharge tests on actual equipment to obtain working characteristic data, calibrate the parameters in the manual, and summarize all technical parameters obtained from the manual and tests as basic data for subsequent modeling.

3. The energy storage synchronization coordination management method based on virtual synchronization technology according to claim 1, characterized in that: The S2 specifically includes: Obtain the rotor moment of inertia J of the rotor from the equipment parameter table in kg·m 2 As a unit, it is calculated by the following formula: J=∫r 2 dm, Where r is the distance from the mass element to the axis of rotation, and dm is the mass element; The step of establishing a dynamic model of a virtual rotor comprises: Define the state variables, select the rotor angular velocity ω as the first state sub-variable, denoted as x1; select the rotor angular acceleration α as the second state sub-variable, denoted as x2; then the state variables are expressed as: x=[x1;x2]=[ω;α], According to Newton's second law, the dynamic equation of the rotor is: J*a=T-Bw, Where J is the rotor moment of inertia, B is the damping coefficient, and T is the torque; According to the Laplace transform of the rotor's dynamic equation, the state equation is obtained: The output equation is derived by selecting the rotor angular velocity ω, that is, the first state sub-variable x1 as the output variable y, and the output equation is expressed as: y=C*x=[1 0]*[x1; x2]=x1, The above equations can be reorganized into state space equations: In the formula, the state matrix Input Matrix Output matrix C = [1 0]; Model simulation of state-space equations. Create two integration modules in Matlab / Simulink to integrate the state equations to obtain the first state sub-variable x1 and the second state sub-variable x2. Then create a gain module to calculate the state matrix A and the input matrix F. Finally, create an output module to calculate the output matrix C to obtain the output variable y. Connect the above modules to form a Simulink model of the state-space model. Create a standard test signal module, select step, DC, and sine as the torque input signal, connect it to the input of the state space model, and observe the response of the first state subvariable x1, that is, the rotor angular velocity ω, in the Scope module, observe the shape of the response curve, rise time, and resting error, and finally adjust the state The damping coefficient B and the rotor moment of inertia J in the matrix A make the shape and dynamic characteristics of the response curve gradually approach the actual system; The damping coefficient B and the rotor moment of inertia J in the state matrix A are adjusted to make the shape and dynamic characteristics of the response curve gradually approach the actual system.

4. The energy storage synchronization coordination management method based on virtual synchronization technology according to claim 1, characterized in that: The S3 specifically includes: The adaptation layer receives the output control signal of the virtual rotor, establishes a mapping relationship between the speed w and the reference power Pref through neural network training, and converts the output control signal into a control instruction that can be executed by physical energy storage identification, as follows: Define the neural network structure: the input layer is set with 1 node, indicating the reference power Pref; the output layer is set with 1 node, indicating the speed w; the hidden layer is set with m nodes; Assume that the parameters from the input layer to the hidden layer are weight matrix W1, the parameters from the hidden layer to the output layer are weight matrix W2, and the neural network prediction speed w is set to: w=f(Pref)=W2g(W1Pref+b1)+b2, Where g is the hidden layer activation function, b1 is the hidden layer bias vector, b2 is the output layer bias, f is the mapping function, Pref is the input power, W1 is the weight matrix connecting Pref and the hidden layer, and W2 is the weight matrix connecting the hidden layer and the output; Collect training data {Pref, w}, calculate the mean square error between the network predicted speed w and the actual speed w as the loss function L, optimize W1, W2, b1, b2 through the error back propagation algorithm to minimize the loss L, repeatedly train the network, gradually reduce the loss function, and obtain the final mapping model. On the new Pref, use the model f(Pref) to predict the corresponding speed w; In real-time control, the adaptation layer receives the real-time speed w of the rotor, calculates the corresponding Pref through the mapping function f, converts the Pref value into a control command of the standard communication protocol, and sends it to the control system of the energy storage device. The adaptation layer and the device control system are connected through a reliable industrial communication link to ensure that the instructions can arrive on time. After receiving the instructions, the local controller of the device will activate the power control closed loop and drive the inverter and other components to make the real-time power P of the device track the reference value Pref.

5. The method for energy storage synchronization coordination management based on virtual synchronization technology according to claim 4, characterized in that: S3 also includes establishing constraints between the adaptation layer and the energy storage device, and the constraints between the adaptation layer and the energy storage device specifically include: If the speed fluctuation of the virtual rotor output is abnormal, the adaptation layer needs to set a low-pass filter to smooth the speed signal to avoid issuing violently fluctuating control instructions to the energy storage device; If there is hysteresis or inertia effect inside the energy storage device, the adaptation layer needs to add historical states to the neural network model to improve the adaptability to dynamic sluggish characteristics; If the power tracking performance of the actual device is poor, the adaptation layer needs to appropriately expand the tolerance between the control command and the actual power value to avoid the impact of frequent hesitant switching on the device; If packet loss or delay occurs in the industrial communication network, the adaptation layer will activate the local predictive model compensation control when the fault is detected to ensure system stability.

6. The energy storage synchronization coordination management method based on virtual synchronization technology according to claim 1, characterized in that: The security operations described in S4 specifically include: Line detection devices are set up at key nodes of the power grid. Current transformers are installed on the power grid lines to detect line currents in real time. When the current exceeds the threshold, an alarm is triggered. Voltage transformers are installed at nodes to monitor voltage amplitude and phase in real time to determine voltage faults. Collect the voltage data of each node when the power grid is operating normally and calculate the maximum voltage U max , minimum value U min and the average value U avg； Determine the ultra-high voltage threshold: U high =U max +a%*(U max -U avg ); Determine the ultra-low voltage threshold: U low =U min -β%*(U avg -U min ); Among them, a and β are empirical values, ranging from 0 to 100. Count the average current I of each line during normal power supply avg and the maximum current I max , Determine the overload alarm point: I over =k1*I max , Determine the overcurrent fault point: I fault =k2*I max , Where k1 represents the normal load rate of the reference line, k2 represents 120% to 130% of the rated current of the reference line; During the test, if the detected voltage is higher than U high , and the duration exceeds t1 seconds, it is judged as an ultra-high voltage fault; if the detected voltage is lower than U low， If the current is higher than I over If the current exceeds I fault， If the time lasts longer than t4, it is considered an overcurrent fault. Among them, the setting range of t1 is 2-3 seconds, the setting range of t2 is 1-2 seconds, the setting range of t3 is 10-20 seconds, and the setting range of t4 is 0.5-1 second; If the adaptation layer detects that the fault is an ultra-high voltage fault, the adaptation layer sends a boost charging command to the energy storage device to increase the DC bus voltage, and at the same time sends a reactive power compensation command to the distribution network and sends a reduction excitation current command to the LCU unit of the hydro-turbine unit in the domain to help reduce the grid-side voltage; If the adaptation layer detects that the fault is an ultra-low voltage fault, the adaptation layer sends a voltage reduction discharge command to the energy storage device to help maintain the bus voltage. At the same time, it sends a reactive power compensation command to the distribution network and sends an excitation current increase command to the LCU unit of the hydro-turbine unit in the domain to help increase the grid-side voltage. If the adaptation layer detects that the fault is an overload fault, the adaptation layer immediately sends a maximum discharge power command to the energy storage device to shunt and reduce the load, and at the same time sends a load cutting instruction to the load side to reduce the overload; If the adaptation layer detects that the fault is an overcurrent fault, it immediately sends a trip command to the circuit breaker to cut off the fault section, and at the same time sends an emergency stop command to the energy storage device to avoid affecting the equipment; Among them, overload fault means that the equipment is subjected to current or load exceeding its rated current or power in a short period of time, which causes the equipment to overheat, be damaged or cause other safety problems, and is a partial equipment failure; overcurrent fault means that the instantaneous current in the circuit exceeds the rated current of the equipment or circuit design; If no fault is found in real-time monitoring, the real-time data of all energy storage devices are collected and sent to the adaptation layer.

7. The energy storage synchronization coordination management method based on virtual synchronization technology according to claim 1, characterized in that: The dynamic control and prediction planning in S4 includes determining whether the balance is achieved, and the adaptation layer issues a balance instruction for dynamic coordination. The determination of whether the balance is achieved includes the following specific steps: Calculate the average SOC value of all devices avg , count the number of devices with current SOC that is too high or too low; Determine whether there is SOC imbalance in the system; Check whether the power and power balance is established; Set tolerance control deviation range; The adaptation layer issues a balancing instruction for dynamic coordination, including the adaptation layer sending a discharge control instruction to the device with too high SOC and sending a charging instruction to the device with too low SOC; using closed-loop control to adjust the charge and discharge power Pi of each device, repeating the calculation and issuing of control instructions at a certain time interval, and performing dynamic coordination until the SOC of each device is restored to balance.

8. The energy storage synchronization coordination management method based on virtual synchronization technology according to claim 7, characterized in that: The statistics of the number of devices with too high or too low SOC include: Calculate the average SOC value of all devices avg , set the allowed SOC upper and lower floating range; if the SOC of a device is higher than SOC avg If the SOC of a device is lower than the allowable upper floating range, the SOC of the device is judged to be too high. avg If it exceeds the allowable lower floating range, the device SOC is judged to be too low; set the number of devices with too high SOC to n high , set the number of devices with low SOC to n low ; The determining whether the system has SOC imbalance phenomenon includes: Set the tolerance percentage P of SOC imbalance allow， The upper limit of the number of devices with SOC imbalance allowed is calculated as follows: n threshold =n total *P allow ; If high >n threshold or low >n threshold , it is determined that the system has SOC imbalance; The checking whether the power and electricity balance is established includes: Determine the capacity Ci and current SOCi. For devices with too high SOC, calculate the amount of electricity that exceeds the average: ΔQi=Ci×(SOCi-SOCavg); For devices with low SOC, calculate the amount of electricity that needs to be replenished: ΔQi=Ci×(SOCavg-SOCi); Summarize the total discharge capacity ΔQ_discharge of all devices with too high SOC and the total charge capacity ΔQ_charge of all devices with too low SOC, and map ΔQi to the corresponding charge and discharge power Pi: Where Δt is the step length, and the power Pi is limited so that it cannot exceed the rated power of a single device; Check whether the power and charge balance is established: ∑Pi*Δt=∑ΔQi; The setting tolerance control deviation range includes: Test the response time T of different types of energy storage devices to execute standard power control instructions response , measuring the power control accuracy error ε of the device under different SOC and temperature conditions precision , set the control period T control ; Calculate the response time tolerance: δt response =T response -T control ; Calculation accuracy tolerance range: δP precision =2*e precision ; Comprehensively determine the total tolerance deviation range of power control: ΔP tol =δP response +δP precision ; If a control command is sent, ΔP is considered tol The margin is: P cmd =P ideal ±ΔP tol , where P ideal is the target power; If the detection feedback is received, the actual power of the equipment is kept within the upper and lower limits: P ideal -ΔP tol ≤P real ≤P ideal +ΔP tol , where P real is the actual power.

9. A storage energy synchronization coordination management system based on virtual synchronization technology, characterized in that: The system comprises: Equipment parameter acquisition module, used to collect energy storage equipment parameters; A model building module is used to build a dynamic model of the virtual rotor and set the rotor moment of inertia and torque parameters according to the parameters of the energy storage device; An adaptation layer establishment module is used to establish an adaptation layer to realize the interaction between the virtual rotor and the physical energy storage device; Line detection device, installed in the power grid, is used to collect real-time data of energy storage equipment and detect Whether a failure occurs, and the adaptation layer performs safe operations when a failure occurs, and performs dynamic control and predictive planning when there is no failure to solve the problem of uneven resource allocation; The adaptation layer is used to determine whether the real-time data of the grid nodes and energy storage devices are normal. If not, re-detect and set; if normal, save the log and end.

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