Power grid frequency intelligent adjustment method and device, adjustment equipment and storage medium

Abstract

Embodiments of the present application provide a power grid frequency intelligent adjustment method and device, an adjustment apparatus and a storage medium. The method is applied to a battery energy storage system, which comprises an energy management system and a converter. The method comprises obtaining current state information; distributing output proportions of droop control and output proportions of virtual inertia control according to the current state information by using fuzzy logic rules; generating a charging and discharging depth by using model predictive control according to the current state information; determining a total output power according to the output proportions of the droop control, the output proportions of the virtual inertia control and the charging and discharging depth, and sending a control signal to the converter through the energy management system. The method provided by the embodiments can quickly respond to power grid frequency changes, ensure power grid frequency stability, avoid overcharging and overdischarging problems caused by the battery energy storage system in the frequency adjustment process, thereby significantly prolonging the service life of the battery energy storage device and reducing the replacement and maintenance costs of the system.

Description

Technical Field

[0001] This application relates to the field of power system control technology, specifically to a method, device, regulating equipment, and storage medium for intelligent regulation of power grid frequency. Background Technology

[0002] Currently, with the high proportion of new energy sources and power electronic equipment connected to the grid, the proportion of traditional thermal power units in the power system has relatively decreased, leading to a significant reduction in system inertia. This change increases the fluctuation amplitude of grid frequency during active power disturbances, thus posing a greater challenge to frequency security. Battery energy storage systems, due to their rapid response and flexible adjustment characteristics, have become an important means of grid regulation. However, existing frequency regulation control strategies have not fully explored the potential of battery energy storage in frequency regulation, and also struggle to effectively avoid overcharging and over-discharging problems during frequency regulation. Therefore, it is urgent to propose new control strategies to improve the frequency regulation capability of battery energy storage and ensure the frequency stability of the power system. Summary of the Invention

[0003] In view of the above problems, embodiments of this application provide a method, apparatus, regulating device, and storage medium for intelligent regulation of power grid frequency to solve the above technical problems.

[0004] The embodiments of this application are implemented using the following technical solutions: In a first aspect, some embodiments of this application provide a method for intelligent regulation of grid frequency, applied to a battery energy storage system. The battery energy storage system includes an energy management system and a converter. The method includes acquiring current state information; allocating the output weight of droop control and the output weight of virtual inertial control according to the current state information using fuzzy logic rules; generating charge / discharge depth using model predictive control according to the current state information; determining the total output power according to the output weight of droop control, the output weight of virtual inertial control, and the charge / discharge depth; and sending a control signal to the converter through the energy management system.

[0005] Secondly, some embodiments of this application also provide a smart grid frequency regulation device applied to a battery energy storage system. The battery energy storage system includes an energy management system and a converter. The device includes a data acquisition module, a control module, a constraint module, and a communication module. The data acquisition module is used to acquire current state information; the control module is used to allocate the output ratio of droop control and the output ratio of virtual inertial control according to the current state information using fuzzy logic rules; the constraint module is used to generate charge and discharge depths using model predictive control according to the current state information; and the communication module is used to determine the total output power according to the output ratio of droop control, the output ratio of virtual inertial control, and the charge and discharge depths, and send control signals to the converter through the energy management system.

[0006] Thirdly, embodiments of this application also provide a regulating device, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are called by the processor, the above-described intelligent power grid frequency regulating method is executed.

[0007] Fourthly, embodiments of this application also provide a computer-readable storage medium storing program code, wherein the above-described intelligent power grid frequency regulation method is executed when the program code is run by a processor.

[0008] This application provides a method, apparatus, regulating device, and storage medium for intelligent grid frequency regulation. The method is applied to a battery energy storage system, which includes an energy management system and a converter. The method includes acquiring current state information; allocating the output weight of droop control and the output weight of virtual inertial control based on the current state information using fuzzy logic rules; generating charge / discharge depth using model predictive control based on the current state information; determining the total output power based on the output weight of droop control, the output weight of virtual inertial control, and the charge / discharge depth; and sending a control signal to the converter through the energy management system. This allows for rapid response to grid frequency changes, ensuring grid frequency stability, avoiding overcharging and over-discharging problems caused by frequency regulation in the battery energy storage system, significantly extending the service life of the battery energy storage device, and reducing system replacement and maintenance costs.

[0009] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A schematic diagram of a battery energy storage system provided in an embodiment of this application is shown.

[0012] Figure 2 A flowchart illustrating an intelligent power grid frequency regulation method provided in an embodiment of this application is shown.

[0013] Figure 3 A flowchart illustrating another intelligent power grid frequency regulation method provided in an embodiment of this application is shown.

[0014] Figure 4 A block diagram of a smart grid frequency regulation device provided in an embodiment of this application is shown.

[0015] Figure 5 A block diagram of an adjustment device provided in an embodiment of this application is shown. Figure 6 A block diagram of a computer storage medium provided in an embodiment of this application is shown. Detailed Implementation

[0016] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0017] With the increasing global demand for renewable energy, battery energy storage systems (BESS) are being used more and more widely in modern power systems. BESS effectively addresses the challenges of the intermittent and unstable nature of renewable energy generation, providing solutions for balancing supply and demand, stabilizing grid frequency, and improving power quality. Battery energy storage technologies include various types, such as lithium-ion batteries, lead-acid batteries, sodium-sulfur batteries, and flow batteries. Among these, lithium-ion batteries have become the mainstream choice in the current market due to their high energy density, long lifespan, and fast charge / discharge characteristics. Meanwhile, with continuous advancements in battery technology and declining manufacturing costs, the economics and feasibility of BESS have significantly improved.

[0018] Currently, with the high proportion of new energy sources and power electronic equipment connected to the grid, the proportion of traditional thermal power units in the power system has relatively decreased, leading to a significant reduction in system inertia. This change increases the fluctuation amplitude of grid frequency during active power disturbances, thus posing a greater challenge to frequency security. Battery Energy Storage (BESS), due to its rapid response and flexible adjustment characteristics, has become an important means of grid regulation. However, existing frequency regulation control strategies have not fully explored the potential of battery energy storage in frequency regulation, and also struggle to effectively avoid overcharging and over-discharging problems during frequency regulation. Therefore, it is urgent to propose new control strategies to improve the frequency regulation capability of battery energy storage and ensure the frequency stability of the power system.

[0019] After long-term research, the inventors have proposed a method, device, regulating equipment, and storage medium for intelligent grid frequency regulation. This method is applied to a Battery Energy Storage System (BESS), which includes an energy management system and a converter. The method includes: acquiring current state information; allocating the output weight of droop control and virtual inertial control based on the current state information using fuzzy logic rules; generating charge / discharge depth using model predictive control based on the current state information; determining the total output power based on the output weight of droop control, the output weight of virtual inertial control, and the charge / discharge depth; and sending a control signal to the converter through the energy management system. This enables rapid response to grid frequency changes, ensures grid frequency stability, avoids overcharging and over-discharging problems caused by frequency regulation in the battery energy storage system, significantly extends the service life of the battery energy storage device, and reduces system replacement and maintenance costs.

[0020] like Figure 1 As shown, Figure 1 A battery energy storage system 10 is provided in this application embodiment. The battery energy storage system 10 includes: an energy management system 11, a converter 12, a battery energy storage unit 13, and a battery management system 14. The energy management system 11 can be any device with communication and storage functions, such as a smartphone, desktop computer, laptop computer, tablet computer, smart control panel, smart gateway, or other smart communication device with network connectivity. The energy management system 11 is responsible for the coordination and optimization control of the entire system, ensuring the efficient operation of the BESS in different operating modes. The energy management system 11 optimizes the battery charging and discharging strategy by receiving grid status information, battery status information from the battery management system 14, and other external environmental data, maximizing system efficiency. The energy management system 11 can adjust the operating mode of the converter 12 according to load demand and battery energy storage status, thereby realizing intelligent scheduling and optimization control of the BESS. The converter 12 is used for bidirectional conversion between direct current (DC) and alternating current (AC). The inverter 12 can convert the DC power in the battery energy storage unit 13 into AC power for output to the grid, or convert the AC power from the grid into DC power for storage in the battery energy storage unit 13, according to system requirements. The battery energy storage unit 13 mainly consists of multiple battery cells (e.g., lithium-ion batteries), each providing a certain energy storage capacity, which can be adjusted according to actual needs. The battery management system 14 is responsible for monitoring and managing each battery cell in the energy storage unit, including real-time monitoring of parameters such as battery charging, discharging, temperature, capacity, and health status, and transmits the above information to the energy management system 11 in real time.

[0021] like Figure 2 As shown, Figure 2The diagram shows a flowchart of a smart grid frequency regulation method 100 provided in an embodiment of this application. The smart grid frequency regulation method 100 can be applied to the battery energy storage system 10 described above. Specifically, the smart grid frequency regulation method 100 can be applied to the energy management system 11 in the battery energy storage system 10, so that it can adjust the working mode of the converter 12 according to the load demand and the battery energy storage status, thereby realizing intelligent scheduling and optimization control of the BESS.

[0022] In this embodiment, the intelligent power grid frequency regulation method 100 may include the following steps S110 to S140.

[0023] Step S110: Obtain the current status information.

[0024] In this embodiment, the current grid frequency deviation and frequency change rate can be obtained, and the energy storage state of charge can also be obtained.

[0025] In some implementations, grid frequency deviation and frequency change rate can also be collected from grid dispatch centers, grid operator interfaces, frequency measurement equipment, etc.

[0026] Step S120: Based on the current state information, use fuzzy logic rules to allocate the output weight of droop control and the output weight of virtual inertial control.

[0027] During frequency regulation, when the power grid is affected by disturbances such as load changes and renewable energy fluctuations, the rate of frequency change rapidly reaches its maximum value, while the frequency deviation remains almost unchanged. At this instant, the Baseline Variable Escalator (BESS) needs to employ virtual inertial control for frequency regulation. As the BESS intervenes in frequency regulation, the change in system frequency gradually slows down, but the frequency deviation gradually increases until the system frequency deviation reaches its maximum value, at which point the rate of frequency change approaches zero. At this stage, the BESS needs to switch to droop control mode to continue frequency regulation. Therefore, the frequency regulation process of the BESS requires a transition from virtual inertial control to droop control.

[0028] Furthermore, this embodiment allocates the output weights of virtual inertial control and droop control during frequency regulation by formulating fuzzy logic rules. Specifically, based on the frequency change rate, frequency deviation, and the output weight of virtual inertial control, corresponding fuzzy subsets and membership functions are set. Using frequency deviation and frequency change rate as inputs and the output weight of virtual inertial control as outputs, corresponding fuzzy logic rules are formulated to achieve a smooth transition from virtual inertial control to droop control during the BESS frequency regulation process.

[0029] It is worth noting that the sum of the output ratio of droop control and the output ratio of virtual inertia control is always 1. The output ratio of droop control can be obtained through the output ratio of virtual inertia control.

[0030] Step S130: Generate the depth of charge / discharge using Model Predictive Control (MPC) based on the current state information.

[0031] In this embodiment, a state-space model is constructed using frequency deviation and energy storage state of charge (SBC) as state variables. Specifically, the frequency deviation is related to variables such as the previous time step frequency deviation, grid system inertia, generator active power output, load changes, renewable energy fluctuations, energy storage output, and charge / discharge depth. The energy storage SBC is related to variables such as the previous time step energy storage SBC, the rated capacity of the BESS, the energy storage output, and charge / discharge depth. Among these, the charge / discharge depth serves as a predictor of MPC, varying between 0 and 1. When the charge / discharge depth is 0, the BESS does not output any active power; when the charge / discharge depth is 1, it indicates that no charge / discharge constraints are applied to the BESS.

[0032] It is worth noting that the energy storage principle output is the sum of the active power output of virtual inertial control and the active power output of droop control. Among them, the active power output of virtual inertial control is the product of the frequency change rate, the virtual inertia coefficient, and the output ratio of virtual inertial control, while the active power output of droop control is the product of the frequency deviation, the droop coefficient, and the output ratio of droop control.

[0033] Furthermore, frequency deviation and energy storage state of charge are used as output variables, and depth of charge / discharge is used as the controlled input. Based on the state-space model, corresponding system control matrices, input control matrices, output control matrices, and system disturbance matrices are set to construct an MPC model. By predicting the depth of charge / discharge, the frequency deviation and energy storage state of charge are made to approximate the ideal state. The controlled input corresponding to the predicted optimal state of the output variables is taken as the optimal solution, thereby generating the depth of charge / discharge.

[0034] Step S140: Determine the total output power based on the output ratio of droop control, the output ratio of virtual inertia control, and the depth of charge and discharge, and send a control signal to the converter through the energy management system.

[0035] In this embodiment, the energy storage unit's output power is multiplied by the depth of charge / discharge to obtain the total output power. A control signal is generated based on the total output power and sent by the energy management system to the converter. The converter then adjusts the active power output of the battery energy storage unit. The energy storage unit's output power can be referred to in step S130 above, and will not be repeated here.

[0036] The intelligent grid frequency regulation method provided in this application is applied to a Battery Energy Storage System (BESS), which includes an energy management system and a converter. The method includes: acquiring current state information; allocating the output weight of droop control and virtual inertial control using fuzzy logic rules based on the current state information; generating charge / discharge depth using model predictive control based on the current state information; determining the total output power based on the output weight of droop control, the output weight of virtual inertial control, and the charge / discharge depth; and sending a control signal to the converter through the energy management system. This allows for rapid response to grid frequency changes, ensuring grid frequency stability, avoiding overcharging and over-discharging problems caused by frequency regulation in the battery energy storage system, significantly extending the service life of the battery energy storage device, and reducing system replacement and maintenance costs.

[0037] like Figure 3 As shown, Figure 3 This application illustrates another intelligent grid frequency regulation method 200 provided in an embodiment of the present application. This intelligent grid frequency regulation method 200 can also be applied to BESS (Browser Emergency Response System). Furthermore, the intelligent grid frequency regulation method 200 may include the following steps S201 to S214.

[0038] Step S201: Obtain the current status information.

[0039] In this embodiment, the current status information is obtained. Further, step S201 can be referred to the aforementioned step S110, and will not be repeated here.

[0040] As one implementation method, the energy storage health status in the battery management system can also be obtained.

[0041] As another implementation method, the energy storage temperature status in the battery management system can also be obtained.

[0042] As another implementation method, energy storage capacity information in the battery management system can also be obtained.

[0043] Step S202: Determine whether the signs of the frequency deviation and the rate of change of frequency are consistent.

[0044] In this embodiment, the power grid frequency regulation process can be divided into a droop phase and a recovery phase. During the droop phase, the frequency deviation and the rate of change of frequency have the same sign, and the virtual inertial control suppresses the droop in the power grid frequency. When the frequency deviation reaches its maximum value, the rate of change of frequency decays to zero, and the power grid frequency enters the recovery phase. During the recovery phase, the rate of change of frequency and the frequency deviation have different signs, and the virtual inertial control suppresses the recovery of the power grid frequency. Therefore, before allocating the output weights of virtual inertial control and droop control, it is necessary to determine whether the signs of the frequency deviation and the rate of change of frequency are consistent. If the result is inconsistent, step S203 can be continued. If the result is consistent, step S204 can be continued.

[0045] Step S203: Change the sign of the virtual inertia coefficient.

[0046] In this embodiment, when the frequency regulation process enters the recovery phase, the signs of the frequency change rate and the frequency deviation are inconsistent—that is, the trend of frequency change is opposite to the direction of frequency deviation. Virtual inertial control, as a control method to suppress frequency changes, will inhibit the recovery of the grid frequency at this stage. To avoid this situation, the virtual inertial coefficient needs to undergo sign transformation processing.

[0047] Step S204: The sign of the virtual inertia coefficient remains unchanged.

[0048] In this embodiment, when the frequency adjustment process is in the drop phase, the virtual inertial control aims to suppress the frequency drop, so the preset sign of the virtual inertial coefficient must remain unchanged.

[0049] Step S205: Based on the current state information, use fuzzy logic rules to allocate the output weight of droop control and the output weight of virtual inertial control.

[0050] In this embodiment, the method of allocating the output weight of droop control and the output weight of virtual inertial control according to the current state information using fuzzy logic rules can refer to the aforementioned step S120, and will not be repeated here.

[0051] Furthermore, as an implementation method, since the allowable frequency deviation range of my country's large power grid is typically between -0.2 Hz and 0.2 Hz, the control threshold range for frequency deviation can be set within this allowable range. This ensures that the proportional coefficient of the droop control reaches its maximum value before the absolute value of the frequency deviation reaches 0.2 Hz, thereby better leveraging the frequency modulation potential of the BESS. Optionally, to increase the sensitivity of the input signal, seven fuzzy subsets can be set on average within the corresponding control threshold range for the input signal. Similarly, five fuzzy subsets can be set on average within the control threshold range for the output signal.

[0052] For example, the control threshold range for frequency deviation is set between -0.1 Hz and 0.1 Hz, and within this range, seven fuzzy subsets—"negative large," "negative medium," "negative small," "zero," "positive small," "positive medium," and "positive large"—are uniformly set. Specifically, the membership function of "negative large" takes a maximum value of 1 at -0.1 Hz and linearly decays to a minimum value of 0 at -0.067 Hz. The membership function of "negative medium" linearly increases from a minimum value of 0 to a maximum value of 1 from -0.1 Hz to -0.067 Hz, and then linearly decays from a maximum value of 1 to a minimum value of 0 from -0.067 Hz to -0.033 Hz. The membership function of "negative small" linearly increases from a minimum value of 0 to a maximum value of 1 from -0.067 Hz to -0.033 Hz, and then linearly decays from a maximum value of 1 to a minimum value of 0 from -0.033 Hz to 0 Hz. The membership function for "zero" rises linearly from -0.033 Hz to 0 Hz, increasing from a minimum of 0 to a maximum of 1, and then decays back to a minimum of 0 from 0 Hz to 0.033 Hz. The membership function for "small" rises linearly from 0 Hz to 0.033 Hz, increasing from a minimum of 0 to a maximum of 1, and then decays back to a minimum of 0 from 0.033 Hz to 0.067 Hz. The membership function for "medium" rises linearly from 0.033 Hz to 0.067 Hz, increasing from a minimum of 0 to a maximum of 1, and then decays back to a minimum of 0 from 0.067 Hz to 0.1 Hz. The membership function for "large" has a minimum of 0 at 0.067 Hz and rises linearly to a maximum of 1 at 0.1 Hz.

[0053] Similarly, the control threshold range for the frequency change rate can be set between -0.1 Hz / s and 0.1 Hz / s. The specific logic for setting the fuzzy subset and its membership function can be found in the above-mentioned fuzzy logic for frequency deviation and the setting method for its membership function, which will not be elaborated here.

[0054] Similarly, the output weight threshold range of virtual inertial control can be set between 0 and 1, and five fuzzy subsets of "zero", "small", "medium", "large" and "maximum" can be evenly set within this range. The specific logic for setting the membership function corresponding to the fuzzy subsets can refer to the setting method of the fuzzy logic corresponding to the membership function of the frequency deviation mentioned above, and will not be repeated here.

[0055] Furthermore, corresponding fuzzy logic rules are formulated based on the fuzzy subsets of the input and output signals that have been set. The specific rules can be shown in Table 1 below. Table 1 For example, if the frequency change rate is "negatively large" and the frequency deviation is "negatively small", then according to the above rule correspondence, the output weight of the virtual inertial control can be determined to be "relatively large"; if the frequency change rate is "negatively small" and the frequency deviation is "negatively large", then according to the above rule correspondence, the output weight of the virtual inertial control can be determined to be "relatively small". Finally, the output weight of the virtual inertial control is output according to the rule results.

[0056] Step S206: Construct a prediction model for MPC based on the current state information.

[0057] In this embodiment, the grid frequency and energy storage state of charge are used as state variables to construct a state-space model. Specifically, the grid frequency and energy storage state can be represented as: (1) In the formula, f ( k )express k The power grid frequency at any given time; P G ( k () represents the active power output of the unit at time k; M This represents the power grid inertia, which is constant. P b ( k )express k The principle of energy storage at all times; u d ( k )express k Depth of charge / discharge at any given time; P L ( k )express k Load changes over time; S ( k ) represents k The state of charge of the stored energy at any given moment; Q B This indicates the rated capacity of BESS.

[0058] As one implementation method, grid frequency can also be correlated with renewable energy fluctuations.

[0059] Furthermore, based on the above state variables, an MPC model is constructed, which can be expressed as: (2) In the formula, x ( k )express k The state variable at any given time; w ( k )express k The perturbation variable at any given time;u ( k )express k The controlled input variable at any given time; y ( k )express k Output variables at any given time; A , B , C , L These represent the system control matrix, input control matrix, output control matrix, and system disturbance matrix, respectively. k The state variable at time t, k The perturbation variables at time and k The output variable at time t can be represented as: (3) in, A , B , C , L It can be represented as: (4) As one implementation method, renewable energy fluctuations can also be considered as one of the disturbance variables.

[0060] Step S207: Set the input weights to constant values.

[0061] In this embodiment, the MPC cost function comprises two parts: an error-weighted sum and an input-weighted sum. The error weights are used to calculate the error-weighted sum, and the input weights are used to calculate the input-weighted sum. The input-weighted sum is used to calculate the sum of the controlled inputs within the prediction period. Since the optimization of the charge / discharge depth aims to avoid overcharging and over-discharging of the BESS, it is not necessary to consider the magnitude of the sum of the controlled inputs within the prediction period; therefore, the input weights can be set to a constant smaller than the error weights.

[0062] Step S208: Automatically adjust the frequency weights during charging and discharging based on the energy storage state of charge.

[0063] In this embodiment, the error weights can be divided into frequency weights and energy storage state of charge weights. Before calculating the error weighted sum, the grid frequency reference value can be set to 50 Hz, and the energy storage state of charge reference value can be set to 0.5. Therefore, the error matrix variables can be: (5) In the formula, e ( k )express k Error variables at time points.

[0064] Under ideal state of charge of energy storage, the frequency weight can be kept at its maximum value, so that MPC focuses on reducing frequency deviation and thus calculates the optimal solution for depth of charge and discharge. Under poor state of charge of energy storage, the frequency weight can be reduced, so that MPC reduces its optimization for minimizing frequency deviation and instead focuses on tracking the minimization of state of charge deviation of energy storage.

[0065] Specifically, taking BESS discharge as an example, when the frequency deviation is less than 0, the energy storage state of charge can be divided into ideal and suboptimal states. As one implementation method, the energy storage state of charge interval [0.2, 0.35) is set as the suboptimal state, and [0.35, 0.8] is set as the ideal state. In the suboptimal state, the frequency weight can be smoothly increased from the minimum value to the maximum value in the form of an S-shaped curve from 0.2 to 0.35. In the ideal state, the frequency weight is always kept at the maximum value.

[0066] Similarly, taking BESS charging as an example, when the frequency deviation is greater than 0, as one implementation method, the energy storage state of charge range [0.2, 0.65] can be set as the ideal state, and (0.65, 0.8] as the suboptimal state. In the ideal state, the frequency weight is always kept at the maximum value, and in the suboptimal state, the frequency weight can be smoothly reduced from the maximum value to the minimum value in the form of an S-shaped curve from 0.65 to 0.8.

[0067] Step S209: Automatically adjust the weights of the energy storage state of charge during charging and discharging based on the energy storage state of charge.

[0068] In this embodiment, in order for the MPC to focus on reducing frequency deviation under ideal energy storage state of charge conditions, the energy storage state of charge weight must be kept at its minimum value when the frequency weight is at its maximum value. Similarly, in order for the MPC to focus on reducing energy storage state of charge deviation under suboptimal energy storage state of charge conditions, the energy storage state of charge weight needs to be increased accordingly when the frequency weight decreases.

[0069] As one implementation method, taking BESS discharge as an example, when the frequency deviation is less than 0, the energy storage state of charge interval [0.2, 0.35) is set as the undesirable state, and [0.35, 0.8] is set as the ideal state. In the undesirable state, the energy storage state of charge weight can be smoothly reduced from the maximum value to the minimum value in the form of an S-shaped curve from 0.2 to 0.35. In the ideal state, the energy storage state of charge weight is always kept at the minimum value.

[0070] Similarly, taking BESS charging as an example, when the frequency deviation is greater than 0, as one implementation method, the energy storage state of charge range [0.2, 0.65] can be set as the ideal state, and (0.65, 0.8] as the suboptimal state. In the ideal state, the energy storage state of charge weight is always kept at the minimum value. In the suboptimal state, the energy storage state of charge weight can be smoothly increased from the minimum value to the maximum value in the form of an S-shaped curve from 0.65 to 0.8.

[0071] It is worth noting that the maximum value of the frequency weight must be much greater than the minimum value of the energy storage state of charge weight, and the maximum value of the energy storage state of charge weight must be much greater than the minimum value of the frequency weight, so that MPC can have a clear focus target in different energy storage states of charge in order to calculate the optimal solution of charge and discharge depth.

[0072] Step S210: Generate the cost function.

[0073] In this embodiment, the MPC cost function can be expressed as: (6) In the formula, J Represents the MPC cost function. Q , P , F These represent the error weight matrix, input weight matrix, and final error weight matrix, respectively. N This represents the prediction step size. The input weight matrix is ​​a constant, and the error weight matrix contains... i The frequency weights and energy storage state of charge weights at each time point, and the final error weight matrix includes... k + N The frequency weights and energy storage state of charge weights change constantly based on the predicted values ​​of the energy storage state of charge. Q and F It can be represented as: (7) In the formula, q f ( i )express i Frequency weighting at time points q s ( i )express i Weights of the energy storage state of charge at any given time.

[0074] It is worth noting that the response speed of MPC is related to the prediction step size, sampling time, and system configuration. As one implementation method, the sampling time can be set to 0.1 s and the prediction step size to 5.

[0075] Step S211: Calculate the minimum value of the cost function based on the set constraints, and thereby determine the charge / discharge depth.

[0076] In this embodiment, the cost function is transformed into a quadratic programming form to find its minimum value, thereby obtaining the optimal solution for the corresponding charge / discharge depth. Specifically, since the cost function of MPC includes both error variables and controlled input variables, and the error variable at each time step can be represented by the initial error variable and the controlled input variable, the general quadratic programming form of the cost function of MPC can be obtained by eliminating the error variable, thus finding its minimum value. Specifically, with k For example, MPC in time k Time's up k + N The error matrix at time step can be represented as: (8) In the formula, with e ( k +1| k For example, the left side of the vertical line inside the parentheses represents the predicted time, and the right side represents the actual time. Therefore, this formula expresses the meaning that MPC in k Time prediction k Error matrix at time +1. r k represent k The reference matrix for each time point includes grid frequency reference values ​​and energy storage state of charge reference values. Since both are constant values, k The reference matrix at time step can be represented as: (9) Furthermore, the minimum value of the cost function of MPC can be obtained based on the established constraints. As one implementation method, the constraints can be set as follows: (10) Step S212: Multiply the frequency change rate by the virtual inertia coefficient, the output ratio of the virtual inertia control, and the charge / discharge depth to calculate the active power of the virtual inertia control.

[0077] In this embodiment, the active power of virtual inertial control is the product of the current frequency change rate, the virtual inertia coefficient, the output ratio of virtual inertial control, and the depth of charge and discharge.

[0078] In this embodiment, the active power of virtual inertial control is the product of the current frequency change rate, the virtual inertia coefficient, the output ratio of virtual inertial control, and the depth of charge and discharge.

[0079] Step S213: Multiply the frequency deviation by the output ratio of droop control, the droop coefficient, and the charge / discharge depth to calculate the active power of droop control.

[0080] In this embodiment, the active power of droop control is the product of the current frequency deviation, the droop coefficient, the output weight of droop control, and the depth of charge and discharge.

[0081] Step S214: Add the active power of the virtual inertial control to the active power of the droop control to determine the total output power, and send the control signal to the converter through the energy management system.

[0082] In this embodiment, the active power of droop control is added to the active power of virtual inertial control to obtain the total output power. A control signal is generated based on the total output power and sent by the energy management system to the converter, which adjusts the active power output of the battery energy storage unit.

[0083] The intelligent grid frequency regulation method provided in this application is applied to a Battery Energy Storage System (BESS), which includes an energy management system and a converter. The method includes: acquiring current state information; allocating the output weight of droop control and virtual inertial control using fuzzy logic rules based on the current state information; generating charge / discharge depth using model predictive control based on the current state information; determining the total output power based on the output weight of droop control, the output weight of virtual inertial control, and the charge / discharge depth; and sending a control signal to the converter through the energy management system. This allows for rapid response to grid frequency changes, ensuring grid frequency stability, avoiding overcharging and over-discharging problems caused by frequency regulation in the battery energy storage system, significantly extending the service life of the battery energy storage device, and reducing system replacement and maintenance costs.

[0084] like Figure 4 As shown, Figure 4 This application illustrates an intelligent power grid frequency regulation device 300, which may include a data acquisition module 310, a control module 350, a constraint module 360, and a communication module 370. The data acquisition module 310 acquires current state information; the control module 350 allocates the output weight of droop control and the output weight of virtual inertial control based on the current state information using fuzzy logic rules; the constraint module 360 ​​generates charge / discharge depth using model predictive control based on the current state information; and the communication module 370 determines the total output power based on the output weight of droop control, the output weight of virtual inertial control, and the charge / discharge depth, and sends control signals to the converter through the energy management system.

[0085] Furthermore, the constraint module 360 ​​includes an MPC model construction unit 361, an input weight unit 362, a frequency weight unit 363, a state of charge weight unit 364, a cost function unit 365, and a calculation unit 366. Specifically, the MPC model construction unit 361 is used to construct a prediction model for MPC based on the current state information; the input weight unit 362 is used to set the input weights to constant values; the frequency weight unit 363 is used to automatically adjust the frequency weights under charging and discharging states based on the energy storage state of charge; the state of charge weight unit 364 is used to automatically adjust the energy storage state of charge weights under charging and discharging states based on the energy storage state of charge; the cost function unit 365 is used to generate a cost function; and the calculation unit 366 is used to calculate the minimum value of the cost function based on the set constraints, and thereby determine the depth of charge / discharge.

[0086] Furthermore, the communication module 370 includes a virtual inertial unit 371, a droop unit 372, and a communication unit 373. The virtual inertial unit 371 calculates the active power of virtual inertial control by multiplying the frequency change rate by the virtual inertial coefficient, the output ratio of virtual inertial control, and the depth of charge / discharge. The droop unit 372 calculates the active power of droop control by multiplying the frequency deviation by the output ratio of droop control, the droop coefficient, and the depth of charge / discharge. The communication unit 373 adds the active power of virtual inertial control to the active power of droop control to determine the total output power and sends a control signal to the converter through the energy management system.

[0087] In some embodiments, the intelligent power grid frequency regulation device 300 may further include a judgment module 320, a negative inertia module 330, and an inertia module 340. The judgment module 320 is used to determine whether the signs of the frequency deviation and the rate of frequency change are consistent; the negative inertia module 330 is used to change the sign of the virtual inertia coefficient; and the inertia module 340 is used to keep the sign of the virtual inertia coefficient unchanged.

[0088] The intelligent grid frequency regulation device provided in this application embodiment is applied to a Battery Energy Storage System (BESS). The BESS includes an energy management system and a converter. The intelligent grid frequency regulation device first acquires the current state information; then, based on the current state information, it uses fuzzy logic rules to allocate the output weight of droop control and the output weight of virtual inertial control; next, it uses model predictive control to generate the charge / discharge depth based on the current state information; finally, it determines the total output power based on the output weight of droop control, the output weight of virtual inertial control, and the charge / discharge depth, and sends a control signal to the converter through the energy management system. This allows for rapid response to grid frequency changes, ensuring grid frequency stability, avoiding overcharging and over-discharging problems caused by the battery energy storage system during frequency regulation, significantly extending the service life of the battery energy storage device, and reducing system replacement and maintenance costs.

[0089] like Figure 5 As shown, Figure 5 The diagram shows a module block diagram of a regulating device 400 provided in an embodiment of this application. The intelligent control panel 400 includes a processor 410 and a memory 420. The memory 420 stores program instructions, which, when executed by the processor 410, implement the above-described intelligent power grid frequency regulating method.

[0090] Processor 410 may include one or more processing cores. Processor 410 connects to various parts of the battery management system using various interfaces and lines, and performs various functions and processes data of the battery management system by running or executing instructions, programs, code sets, or instruction sets stored in memory 420, and by calling data stored in memory 420. Optionally, processor 410 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 410 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into processor 410 and may be implemented separately using a communication chip.

[0091] The memory 420 may include random access memory (RAM) or read-only memory (ROM). The memory 420 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 420 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the various method embodiments described below. The data storage area may also store data created during the use of the electronic device (such as phonebook data, audio / video data, chat log data, etc.).

[0092] like Figure 6As shown, this application embodiment also provides a computer-readable storage medium 500, which stores computer program instructions 510, which can be called by a processor to execute the methods described in the above embodiments.

[0093] Computer-readable storage media can be electronic storage devices such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, computer-readable storage media include non-transitory computer-readable storage media. Computer-readable storage medium 600 has storage space for program code that performs any of the method steps described above. This program code can be read from or written to one or more computer program products. The program code can be compressed, for example, in a suitable form.

[0094] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Although this application has disclosed preferred embodiments as above, it is not intended to limit this application. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the technical solution of this application. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of this application without departing from the scope of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A method for intelligent regulation of power grid frequency, characterized in that, Applied to a battery energy storage system, the battery energy storage system including an energy management system and a converter, the method includes: Get the current status information; Based on the current state information, the output weight of droop control and the output weight of virtual inertial control are allocated using fuzzy logic rules. Based on the current state information, the depth of charge / discharge is generated using Model Predictive Control (MPC); and The total output power is determined based on the output ratio of the droop control, the output ratio of the virtual inertia control, and the charge / discharge depth, and a control signal is sent to the converter through the energy management system.

2. The intelligent power grid frequency regulation method as described in claim 1, characterized in that, The current status information includes frequency deviation, frequency change rate, load change, unit active power output, energy storage charge status, and energy storage health status.

3. The intelligent power grid frequency regulation method as described in claim 2, characterized in that, Before allocating the output weight of droop control and the output weight of virtual inertial control based on the current state information using fuzzy logic rules, the method further includes: Determine whether the signs of the frequency deviation and the rate of change of frequency are consistent; and If the sign of the frequency deviation is inconsistent with that of the frequency change rate, the sign of the virtual inertia coefficient is changed, wherein the virtual inertia coefficient is used to calculate the active power of the virtual inertial control.

4. The intelligent power grid frequency regulation method as described in claim 3, characterized in that, After determining whether the signs of the frequency deviation and the frequency change rate are consistent, the method further includes: If the sign of the frequency deviation is consistent with that of the frequency change rate, then the sign of the virtual inertia coefficient remains unchanged.

5. The intelligent power grid frequency regulation method according to any one of claims 2 to 4, characterized in that, The step of generating the charge / discharge depth using MPC based on the current state information includes: The prediction model for the MPC is constructed based on the current state information; Set error weights and input weights, which are used to jointly generate a cost function. The error weights include frequency weights and energy storage state of charge weights. Generate the cost function; and The minimum value of the cost function is calculated based on the set constraints, and the charge / discharge depth is determined therefrom.

6. The intelligent power grid frequency regulation method as described in claim 5, characterized in that, The setting of error weights and input weights includes: Set the input weights to constant values; Based on the energy storage state of charge, the frequency weights are automatically adjusted during charging and discharging; and The weights of the energy storage state of charge are automatically adjusted during charging and discharging based on the energy storage state of charge.

7. The intelligent power grid frequency regulation method according to any one of claims 5 to 6, characterized in that, The step of determining the total output power based on the output power ratio of the droop control, the output power ratio of the virtual inertia control, and the charge / discharge depth, and sending a control signal to the converter through the energy management system, includes: The active power of the virtual inertial control is calculated by multiplying the frequency change rate by the virtual inertial coefficient, the output ratio of the virtual inertial control, and the charge / discharge depth. The active power of the droop control is calculated by multiplying the frequency deviation by the output ratio of the droop control, the droop coefficient, and the charge / discharge depth. The active power of the virtual inertial control is added to the active power of the droop control to determine the total output power, and a control signal is sent to the converter through the energy management system.

8. A smart power grid frequency regulation device, characterized in that, Applied to a battery energy storage system, the battery energy storage system including an energy management system and a converter, the device includes: The data acquisition module is used to obtain current status information; The control module is used to allocate the output weight of the droop control and the output weight of the virtual inertial control according to the current state information using fuzzy logic rules. The constraint module is used to generate the charge / discharge depth using model prediction control based on the current state information; and The communication module is used to determine the total output power based on the output ratio of the droop control, the output ratio of the virtual inertial control, and the charge / discharge depth, and to send control signals to the converter through the energy management system.

9. An adjustment device, characterized in that, It includes a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are invoked by the processor, they execute the intelligent power grid frequency regulation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code, wherein the intelligent power grid frequency regulation method according to any one of claims 1 to 7 is executed when the program code is run by a processor.

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