Control method and system for optimizing coordinated frequency modulation of optical storage system based on fuzzy control

Abstract

The application discloses a control method and system for optimizing frequency modulation of a light storage system based on fuzzy control, and belongs to the technical field of light storage system optimization. The control method for optimizing frequency modulation of the light storage system based on fuzzy control constructs an inertial droop control model, a follow-up factor change model, an output power calculation model, a fuzzy control optimization model and a light storage system coordinated frequency modulation model, and calculates a virtual inertia control participation factor and a droop control participation factor based on the influence of virtual inertia response on frequency disturbance and frequency drop time, so that the values of the virtual inertia coefficient and the droop coefficient are changed by using the virtual inertia control participation factor and the droop control participation factor, thereby the frequency modulation advantage of the energy storage can be fully exerted, the frequency stability of the power system after the wind and light electricity penetration rate is ensured, and the frequency safety and stability problem of the new energy grid-connected system is effectively solved.

Description

Technical Field

[0001] This invention relates to a control method and system for optimizing the coordinated frequency modulation of a photovoltaic and energy storage system based on fuzzy control, belonging to the field of photovoltaic and energy storage system optimization technology. Background Technology

[0002] The large-scale grid connection of new energy sources, represented by wind and solar power, while accelerating the green and low-carbon transformation of energy and promoting the achievement of "dual-carbon" goals, also poses serious risks to the safe and stable operation of the power system due to their random fluctuations, uncertainties, and the decoupling from the system frequency caused by maximum power point tracking (MPPT) control via converters. New energy power plants possessing a certain frequency regulation capability are gradually becoming a technical requirement and development trend for high-proportion new energy grid-connected systems. However, new energy power plants, especially photovoltaic (PV) power plants, which rely on additional frequency regulation control, still face limitations in frequency regulation due to their own reserve capacity. Utilizing idle, adjustable energy storage devices on the grid for rapid power regulation, and participating in frequency regulation together with PV power plants, has become one of the effective means to solve the above problems.

[0003] Currently, research on photovoltaic (PV) frequency regulation focuses on variable load shedding, virtual synchronous machine (VSM) technology, and additional energy storage inertial response to enhance the frequency regulation capabilities of PV power plants and improve system frequency response characteristics. Some studies propose enabling PV to achieve a certain level of frequency regulation capability by reducing the maximum PV power output, which is more economical than additional energy storage. Others propose PV-assisted frequency regulation control methods incorporating droop control, virtual inertial control, or a combination of both, to enable PV to participate in grid frequency regulation. Still others propose VSM technology for PV inverters, simulating the rotor motion equations of traditional synchronous generators to give PV systems performance similar to synchronous generators. Some studies have designed and proposed parallel energy storage devices connected to the high-voltage side of PV inverters, using VSM control technology to allow energy storage to handle power response regulation in response to system frequency changes. However, these studies do not consider the adaptive nature of PV active power reserve frequency regulation under different system frequency response adjustments, nor do they consider utilizing idle and controllable grid energy storage to enhance the frequency regulation contribution of PV power plants through additional frequency regulation control. Therefore, the frequency regulation effect and economic efficiency still need improvement.

[0004] Furthermore, Chinese patent (publication number: CN115833229 A) provides a primary frequency regulation method for a wind-storage integrated system based on multivariate fuzzy logic control. The method includes: establishing a wind-storage integrated system model; dividing wind speed zones; establishing a multivariate fuzzy logic control method; in the medium wind speed zone, the DFIG simulates the grid inertia process using virtual inertial control and employs overspeed load shedding control to provide frequency regulation reserves; the supercapacitor energy storage simulates the grid's primary frequency regulation process using virtual droop control; and based on the droop characteristics, controlling its active power output according to the frequency deviation. The aforementioned invention provides a primary frequency regulation method for a wind-storage integrated system based on multivariate fuzzy logic control, which enables power allocation for wind power and energy storage participating in primary frequency regulation.

[0005] However, the virtual inertia factor coefficient and droop control coefficient of the above schemes and existing technologies are often selected based on the frequency regulation margin and frequency regulation power reserve design. Therefore, the virtual inertia factor coefficient and droop control coefficient generally remain unchanged in specific scenarios, which cannot give full play to the frequency regulation advantages of energy storage, cannot ensure the frequency stability of the power system after the penetration of wind and solar power, and cannot effectively solve the frequency security and stability problem of new energy grid-connected systems.

[0006] The information disclosed in this background section is only for understanding the background of the inventive concept, and therefore may include information that does not constitute prior art. Summary of the Invention

[0007] To address the aforementioned problems, or one of them, the present invention aims to provide a scientific, reasonable, and feasible control method for photovoltaic-storage system coordinated frequency regulation based on fuzzy control. This method involves constructing an inertial droop control model, a follower factor variation model, an output power calculation model, a fuzzy control optimization model, and a photovoltaic-storage system coordinated frequency regulation model to obtain virtual inertial coefficients and droop coefficients. Based on the influence of the virtual inertial response on frequency disturbances and frequency drop time, virtual inertial control participation factors and droop control participation factors are calculated. Furthermore, based on these factors, the photovoltaic additional frequency regulation control output power command is calculated. According to the photovoltaic additional frequency regulation control output power command, the real-time change in photovoltaic grid-connected active power load after photovoltaic active power reserve control and converter control response is obtained. Based on the real-time change in photovoltaic grid-connected active power load and deviation data, a photovoltaic-storage coordinated fuzzy control rule table is formulated. Finally, based on this rule table, photovoltaic-storage coordinated frequency regulation control is achieved. This method is scientific, reasonable, and feasible.

[0008] To address the aforementioned problems, or one of them, the second objective of this invention is to provide a control system based on fuzzy control optimization for photovoltaic-storage system collaborative frequency regulation. This system obtains virtual inertia coefficients and droop coefficients by setting up an inertia droop control module, a follow-up factor variation module, an output power calculation module, a fuzzy control optimization module, and a photovoltaic-storage system collaborative frequency regulation module. Then, based on the influence of the virtual inertia response on frequency disturbances and frequency drop time, virtual inertia control participation factors and droop control participation factors are calculated. Based on these factors, the photovoltaic additional frequency regulation control output power command is calculated. According to the photovoltaic additional frequency regulation control output power command, the real-time change in photovoltaic grid-connected active power load after photovoltaic active power reserve control and converter control response is obtained. Based on the real-time change in photovoltaic grid-connected active power load and deviation data, a photovoltaic-storage collaborative fuzzy control rule table is formulated. Finally, based on this rule table, photovoltaic-storage collaborative frequency regulation control is achieved. This scientific, reasonable, and feasible control system is based on fuzzy control optimization for photovoltaic-storage system collaborative frequency regulation.

[0009] To address the aforementioned problems, or one of them, the third objective of this invention is to provide a control method and system based on fuzzy control optimization of the coordinated frequency regulation of photovoltaic-storage systems. This method is based on the influence of virtual inertial response on frequency disturbances and frequency drop time, calculating virtual inertial control participation factors and droop control participation factors. By using these virtual inertial control participation factors and droop control participation factors to change the values ​​of virtual inertial coefficients and droop coefficients, the frequency regulation advantages of energy storage can be fully utilized, ensuring frequency stability after the penetration of wind and solar power systems. This effectively solves the frequency security and stability problem of new energy grid-connected systems.

[0010] To achieve one of the above objectives, the first technical solution of the present invention is as follows:

[0011] The control method for optimizing the coordinated frequency modulation of a photovoltaic-storage system based on fuzzy control includes the following steps:

[0012] The first step is to obtain the system frequency deviation data;

[0013] The second step is to obtain the virtual inertia coefficient and sag coefficient based on the deviation data and using the pre-built inertia sag control model.

[0014] The third step involves calculating the virtual inertia control participation factor and the droop control participation factor based on the virtual inertia coefficient and the droop coefficient, using a pre-built follower factor variation model and considering the influence of the virtual inertia response on frequency disturbances and the frequency drop time.

[0015] The fourth step involves using a pre-built output power calculation model and, based on the virtual inertial control participation factor, droop control participation factor, virtual inertial coefficient, and droop coefficient, calculating the photovoltaic additional frequency modulation control output power command.

[0016] The fifth step is to obtain the real-time load shedding of the photovoltaic grid-connected active power after the photovoltaic active power reserve control and converter control response, based on the output power command of the photovoltaic additional frequency regulation control.

[0017] The sixth step is to formulate a fuzzy control rule table for photovoltaic-storage collaborative control based on the real-time changes in active power load reduction and deviation data of photovoltaic grid connection, using a pre-built fuzzy control optimization model.

[0018] The seventh step is to utilize a pre-built frequency modulation model of the photovoltaic-storage system and, based on the photovoltaic-storage collaborative fuzzy control rule table, to achieve photovoltaic-storage collaborative frequency modulation control.

[0019] This invention, through continuous exploration and experimentation, constructs an inertial droop control model, a follower factor variation model, an output power calculation model, a fuzzy control optimization model, and a photovoltaic-storage system coordinated frequency modulation model to obtain virtual inertial coefficients and droop coefficients. Based on the influence of the virtual inertial response on frequency disturbances and frequency drop time, virtual inertial control participation factors and droop control participation factors are calculated. Furthermore, based on these factors, the photovoltaic additional frequency modulation control output power command is calculated. According to the photovoltaic additional frequency modulation control output power command, the real-time load shedding of photovoltaic grid-connected active power after photovoltaic active power reserve control and converter control response is obtained. Based on the real-time load shedding of photovoltaic grid-connected active power and deviation data, a photovoltaic-storage coordinated fuzzy control rule table is formulated. Finally, based on this rule table, photovoltaic-storage coordinated frequency modulation control is achieved. The scheme is scientific, reasonable, and feasible.

[0020] Furthermore, based on the influence of virtual inertial response on frequency disturbance and frequency drop time, this invention constructs a follower factor variation model to calculate the virtual inertial control participation factor and droop control participation factor. By utilizing these factors, the values ​​of the virtual inertial coefficient and droop coefficient can be altered, thereby fully leveraging the frequency regulation advantages of energy storage, ensuring frequency stability after wind and solar power penetration, and effectively solving the frequency security and stability problem of new energy grid-connected systems.

[0021] As a preferred technical measure:

[0022] In the second step, the formulas for calculating the virtual inertia coefficient and the droop coefficient are as follows:

[0023]

[0024]

[0025] Where: K D K is the virtual inertia coefficient. P P is the droop coefficient.mpp The maximum photovoltaic power generation is given by Δf, where max(df / dt) is the maximum allowable rate of change of the grid frequency; Δf max This represents the maximum depth of photovoltaic (PV) participation in grid frequency regulation.

[0026] As a preferred technical measure:

[0027] In the third step, the calculation formula for the virtual inertial control participation factor is as follows:

[0028]

[0029] In the formula, L D (t) represents the virtual inertial control participation factor, L0, L max r and r respectively determine L D The three parameters of the function form (t) are L0, L... max The initial and final values ​​of the function are determined by r, which measures the rate of change of the function. All three parameters are greater than zero, and t is time.

[0030] As a preferred technical measure:

[0031] In the third step, the formula for calculating the droop control participation factor is as follows:

[0032]

[0033] In the formula, L P (t) represents the droop control participation factor, L0, L... max r and r respectively determine L D The three parameters of the function form (t) are L0, L... max The initial and final values ​​of the function are determined by r, which measures the rate of change of the function. All three parameters are greater than zero, and t is time.

[0034] As a preferred technical measure:

[0035] In the fourth step, the calculation formula for the photovoltaic additional frequency modulation control output power command is as follows:

[0036]

[0037] In the formula, △P PV For photovoltaic additional frequency modulation control output power command; K D K P These are the virtual inertia coefficient and droop coefficient, respectively; Δf is the system frequency deviation. L is the system frequency change rate; D (t), L P (t) represents the virtual inertial control participation factor and the droop control participation factor, respectively, which exhibit an S-shaped change during the frequency response process.

[0038] As a preferred technical measure:

[0039] In the fifth step, the calculation formula for the real-time change in active power load reduction of the photovoltaic grid connection is as follows:

[0040]

[0041] In the formula, Δd represents the change in photovoltaic load reduction, and P d P represents the photovoltaic active power reserve when the system frequency is normal. pv For real-time output power of photovoltaics, P mmp This represents the maximum tracking power of the photovoltaic system.

[0042] As a preferred technical measure:

[0043] In the sixth step, the method for formulating the fuzzy control rule table for optical-storage collaborative operation is as follows:

[0044] When the absolute value of the system frequency deviation Δf is large or the photovoltaic load reduction Δd changes significantly, the additional frequency regulation output of energy storage should be increased.

[0045] When the absolute value of the system frequency deviation Δf is small or the change in photovoltaic load reduction Δd is close to zero, the additional frequency regulation output of energy storage should be reduced to restore frequency stability.

[0046] The fuzzy control rule table for optical-storage collaborative control is shown below:

[0047]

[0048] in, The frequency regulation power command is added to the energy storage, △f is the system frequency deviation, △d is the photovoltaic load reduction, NB is the negative large fuzzy subset, NM is the negative medium fuzzy subset, NS is the negative small fuzzy subset, ZO is the zero fuzzy subset, PS is the positive small fuzzy subset, PM is the positive medium fuzzy subset, and PB is the positive large fuzzy subset.

[0049] As a preferred technical measure:

[0050] In the seventh step, the method for achieving coordinated frequency modulation control of photovoltaic and energy storage is as follows:

[0051] S61, based on the optical-storage collaborative fuzzy control rule table, obtain several input fuzzy subsets;

[0052] S62, based on several input fuzzy subsets and using the Midani-type fuzzy language controller, perform inference operations to obtain several output fuzzy subsets, forming an output fuzzy set;

[0053] S63 employs a weighted average method to defuzzify the output fuzzy set, obtaining continuous numerical values ​​for the energy storage additional frequency modulation command.

[0054] S64, according to the energy storage additional frequency regulation command Coordinated frequency modulation control of the photovoltaic-storage system.

[0055] As a preferred technical measure:

[0056] The weighted average method is an algebraic approximation method for the area centroid method integral calculation. It calculates the weighted average of the centers of the output fuzzy subset, and the weight is the height of the corresponding fuzzy subset.

[0057] To achieve one of the above objectives, the second technical solution of the present invention is as follows:

[0058] The control system based on fuzzy control to optimize the coordinated frequency modulation of the photovoltaic and energy storage system includes an inertial droop control module, a follower factor variation module, an output power calculation module, a fuzzy control optimization module, and a coordinated frequency modulation module for the photovoltaic and energy storage system.

[0059] The inertia droop control module is used to obtain the virtual inertia coefficient and droop coefficient based on the deviation data;

[0060] The follow-up factor variation module is used to process the virtual inertia coefficient and droop coefficient to calculate the virtual inertia control participation factor and droop control participation factor.

[0061] The output power calculation module is used to calculate the photovoltaic additional frequency modulation control output power command based on the virtual inertial control participation factor, droop control participation factor, virtual inertial coefficient, and droop coefficient.

[0062] The fuzzy control optimization module is used to formulate a fuzzy control rule table for photovoltaic-storage collaborative operation based on real-time changes and deviations in active power load of photovoltaic grid connection.

[0063] The photovoltaic-storage system collaborative frequency modulation module is used to realize photovoltaic-storage collaborative frequency modulation control according to the photovoltaic-storage collaborative fuzzy control rule table.

[0064] This invention, through continuous exploration and experimentation, obtains virtual inertia coefficients and droop coefficients by setting up an inertia droop control module, a follow-up factor variation module, an output power calculation module, a fuzzy control optimization module, and a photovoltaic-storage system coordinated frequency modulation module. Then, based on the influence of the virtual inertial response on frequency disturbances and the frequency drop time, virtual inertial control participation factors and droop control participation factors are calculated. Based on these factors, the photovoltaic additional frequency modulation control output power command is calculated. According to the photovoltaic additional frequency modulation control output power command, the real-time change in photovoltaic grid-connected active power load after photovoltaic active power reserve control and converter control response is obtained. Based on the real-time change in photovoltaic grid-connected active power load and deviation data, a photovoltaic-storage coordinated fuzzy control rule table is formulated. Finally, based on this rule table, photovoltaic-storage coordinated frequency modulation control is achieved. The scheme is scientific, reasonable, and feasible.

[0065] Furthermore, based on the influence of virtual inertial response on frequency disturbance and frequency drop time, this invention calculates the virtual inertial control participation factor and droop control participation factor by setting a follow-up factor variation module. The virtual inertial control participation factor and droop control participation factor are then used to change the values ​​of the virtual inertial coefficient and droop coefficient, thereby fully leveraging the frequency regulation advantages of energy storage, ensuring frequency stability after wind and solar power penetration, and effectively solving the frequency security and stability problem of new energy grid-connected systems.

[0066] Compared with existing technical solutions, the present invention has the following beneficial effects:

[0067] This invention, through continuous exploration and experimentation, constructs an inertial droop control model, a follower factor variation model, an output power calculation model, a fuzzy control optimization model, and a photovoltaic-storage system coordinated frequency modulation model to obtain virtual inertial coefficients and droop coefficients. Based on the influence of the virtual inertial response on frequency disturbances and frequency drop time, virtual inertial control participation factors and droop control participation factors are calculated. Furthermore, based on these factors, the photovoltaic additional frequency modulation control output power command is calculated. According to the photovoltaic additional frequency modulation control output power command, the real-time load shedding of photovoltaic grid-connected active power after photovoltaic active power reserve control and converter control response is obtained. Based on the real-time load shedding of photovoltaic grid-connected active power and deviation data, a photovoltaic-storage coordinated fuzzy control rule table is formulated. Finally, based on this rule table, photovoltaic-storage coordinated frequency modulation control is achieved. The scheme is scientific, reasonable, and feasible.

[0068] This invention, through continuous exploration and experimentation, obtains virtual inertia coefficients and droop coefficients by setting up an inertia droop control module, a follow-up factor variation module, an output power calculation module, a fuzzy control optimization module, and a photovoltaic-storage system coordinated frequency modulation module. Then, based on the influence of the virtual inertial response on frequency disturbances and the frequency drop time, virtual inertial control participation factors and droop control participation factors are calculated. Based on these factors, the photovoltaic additional frequency modulation control output power command is calculated. According to the photovoltaic additional frequency modulation control output power command, the real-time change in photovoltaic grid-connected active power load after photovoltaic active power reserve control and converter control response is obtained. Based on the real-time change in photovoltaic grid-connected active power load and deviation data, a photovoltaic-storage coordinated fuzzy control rule table is formulated. Finally, based on this rule table, photovoltaic-storage coordinated frequency modulation control is achieved. The scheme is scientific, reasonable, and feasible.

[0069] Furthermore, based on the influence of virtual inertial response on frequency disturbance and frequency drop time, this invention calculates the virtual inertial control participation factor and droop control participation factor. By using the virtual inertial control participation factor and droop control participation factor to change the values ​​of the virtual inertial coefficient and droop coefficient, the frequency regulation advantages of energy storage can be fully utilized, ensuring frequency stability after the penetration of wind and solar power systems, and effectively solving the frequency security and stability problem of new energy grid-connected systems. Attached Figure Description

[0070] Figure 1 L is the virtual inertial control participation factor of this invention. D (t) Schematic diagram of the tuning design process;

[0071] Figure 2 L is the droop control participation factor in this invention. P (t) Schematic diagram of the tuning design process;

[0072] Figure 3 This is a block diagram of the photovoltaic-storage fuzzy collaborative control strategy considering the real-time photovoltaic load shedding rate in this invention;

[0073] Figure 4 This is a schematic diagram of the input and output fuzzification of the optical-storage collaborative fuzzy controller of the present invention;

[0074] Figure 5 This is a schematic diagram of the input-output relationship surface of the optical-storage collaborative fuzzy controller of the present invention;

[0075] Figure 6 This is a schematic diagram of the simulation model of the three-machine nine-node system including photovoltaic storage of the present invention;

[0076] Figures 7-12 This is a schematic diagram of the simulation verification results of the photovoltaic adaptive frequency modulation control of the present invention;

[0077] Figures 13-18 This is a schematic diagram illustrating the simulation verification results of the photovoltaic-storage fuzzy collaborative control considering the real-time photovoltaic load shedding rate in this invention. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0079] Conversely, this invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of the invention as defined in the claims. Furthermore, to provide a better understanding of the invention, certain specific details are described in detail below. However, those skilled in the art will fully understand the invention even without these detailed descriptions.

[0080] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the invention.

[0081] The first specific embodiment of the control method for optimizing the coordinated frequency modulation of a photovoltaic-storage system based on fuzzy control in this application is as follows:

[0082] A control method for optimizing the coordinated frequency modulation of a photovoltaic-storage system based on fuzzy control includes the following:

[0083] First, based on the analysis of the system's frequency response process and its demands on inertial power and primary frequency modulation power, a virtual inertial factor L based on the Logistic function is defined and designed in detail. D (t) and droop control participation factor L P (t), and proposes a photovoltaic adaptive integrated inertial active power reserve frequency regulation control strategy based on the Logistic function.

[0084] Secondly, considering the limitations of photovoltaic active power reserve frequency regulation and the rapid power regulation characteristics of energy storage, the system frequency deviation Δf and the real-time change of photovoltaic load shedding rate Δd are used as fuzzy control inputs, and the energy storage is used as an additional frequency regulation power command. To produce the output, a fuzzy control rule table for photovoltaic-storage collaborative control is formulated, and a fuzzy collaborative control strategy for photovoltaic-storage collaborative control that takes into account real-time load shedding of photovoltaic power is proposed.

[0085] Finally, simulation studies were used to verify the effectiveness of the proposed strategy.

[0086] The second specific embodiment of the control method for optimizing the coordinated frequency modulation of a photovoltaic-storage system based on fuzzy control in this application is as follows:

[0087] A control method based on fuzzy control for optimizing the coordinated frequency modulation of a photovoltaic-storage system is described, which is a control strategy based on the Logistic function and fuzzy control for optimizing the coordinated frequency modulation of a photovoltaic-storage system. The method includes:

[0088] Photovoltaic adaptive integrated inertial control based on Logistic function:

[0089] Based on the analysis of the inertial response requirements and primary frequency modulation power requirements at different stages of the system's frequency drop disturbance response, the system's frequency change rate is at its maximum at the instant of the frequency disturbance. At this moment, the system's maximum frequency change rate is related to the virtual inertial coefficient K. D They exhibit an inverse correlation, and as the frequency decreases, the rate of change of the system frequency becomes smaller, resulting in a smaller demand for inertial response power. Typically, the virtual inertial coefficient K... D The system is designed and tuned based on the safety requirements of the maximum rate of change of the system frequency at the moment of disturbance. Therefore, the degree of participation of virtual inertial control should be greater in the early stage of disturbance. Furthermore, at the moment of disturbance and in the early stage, the system frequency deviation is small and the system power deficit is small. Therefore, the degree of participation of droop control should be smaller.

[0090] As the frequency drops, the system rate of change gradually decreases, and the participation of virtual inertial control should gradually diminish to reserve photovoltaic power for droop control. However, as the absolute value of the system deviation increases, droop control should intervene quickly. When the system frequency begins to recover, the rate of change becomes negative, and the output of the virtual inertial response is opposite to the frequency regulation demand of the system's power deficit, causing secondary interference. Therefore, virtual inertial control should exit during the frequency recovery phase. During this phase, the system frequency deviation remains negative and its absolute value gradually decreases, and the system's total power generation is still insufficient. Therefore, to ensure the system's active power quickly reaches a new equilibrium, and considering the absolute value of the steady-state frequency deviation at the end of the first frequency regulation and the droop coefficient K... P Inversely proportional to the frequency recovery phase, droop control should continue to participate, but the rate of increase in its participation can be slowed down. Based on the above analysis, this application selects the Logistic function with S-shaped curve characteristics and proposes the following photovoltaic adaptive integrated inertial frequency modulation control strategy:

[0091]

[0092] In the formula, △P PV This is the output power command for photovoltaic additional frequency modulation control; Δf is the system frequency deviation. L is the system frequency change rate; D (t), L P (t) represents the virtual inertial control participation factor and the droop control participation factor, respectively, which exhibit an S-shaped change during the frequency response process.

[0093] K D KP The following design is typically based on frequency regulation margin and photovoltaic frequency modulation power reserve:

[0094]

[0095]

[0096] In the formula: P mpp The maximum photovoltaic power generation is given by Δf, where max(df / dt) is the maximum allowable rate of change of the grid frequency; Δf max The maximum frequency regulation depth of photovoltaic power grid participation is given. All variables in the above calculations are per-unit values ​​(pu).

[0097] The virtual inertial control participation factor L of this invention D A specific embodiment of the tuning design of (t):

[0098] Virtual inertial control participation factor L D (t) is defined and specifically designed as follows:

[0099]

[0100] In the formula, L0, L max r and r respectively determine L D The three parameters of the function form (t) are L0, L... max The initial and final values ​​of the function are determined by r, which measures how fast the function changes. All three parameters are greater than zero.

[0101] From Equation 4, we can see that L D (t) represents an inverse S-curve shape, meaning it decays in an inverse S-shape from the initial value 1-L0 to 1-L. max Since it is desired that the virtual inertial control will eventually exit during the frequency recovery phase, i.e., the virtual inertial control participation factor L... D (t) The final value should be zero, so set L. max =1. For example... Figure 1 L is given D (t) The function form of t under different values ​​of L0 and r. Figure 1 As shown in (a), when r = 10 remains constant, L0 determines the starting position of the curve. The larger L0 is, the earlier the S-curve begins to decay. This application aims to maintain a large value for the virtual inertial response in the early stages of frequency disturbance, therefore L0 = 0.001 is selected. When L0 = 0.001 remains constant, r determines the rate of change of the curve, thus the decay rate to zero differs. Based on the synchronous generator inertial response time being approximately 1–2 seconds, if the time for the virtual inertial control participation factor to decay to zero is taken as 1 second, then... Figure 1 As shown in (b), r = 12 can be selected.

[0102] Final virtual inertial control participation factor L D The expression for (t) is:

[0103]

[0104] Drooping control participation factor L P (t) Define and design in detail:

[0105]

[0106] In the formula, since droop control is mainly related to the system frequency deviation, the target of the droop control participation factor is L, which is based on the moment when the system frequency drops the most. P (t) = 100%, and for ease of analysis and comparison, we take L. P The final value of (t) is 200%, that is, setting L max =2.

[0107] During a frequency modulation process, because the time from the start of the speed controller's operation to the deepest frequency drop is shorter than the time for the frequency to recover and stabilize, and the parameter r determines the rate of change of the participation factor, the initial value is set to L0 = 1. Based on L... P The time it takes for the curve to rise from its initial value of 1 to its final value (corresponding to the time it takes for the frequency to recover and rise after one modulation) is used to set the value of r. Figure 2 L is given P (t) The function shape under different values ​​of L0 and r. According to Figure 2 L under different r in (a) P Based on the (t) curve and considering the regulations governing photovoltaic primary frequency regulation in my country's power grid, the adjustment time should not exceed 15 seconds. If the frequency drop time is approximately 1 second, then the recovery time for the photovoltaic primary frequency regulation is approximately 14 seconds. Therefore, r = 0.5 is selected. L0 determines L... P The initial position of the (t) curve affects the time it takes for it to rise from its initial value to 1. Therefore, according to Figure 2 (b) L under different L0 conditions P (t) curve, select L0 = 0.75.

[0108] The droop control participation factor L in this invention P A specific embodiment of the tuning design of (t):

[0109] Ultimate droop control participation factor L P The expression for (t) is:

[0110]

[0111] A specific embodiment of the photovoltaic-storage fuzzy collaborative control strategy considering the real-time photovoltaic load shedding rate is as follows:

[0112] Numerous uncertainties exist in photovoltaic-storage systems, such as changes in weather conditions and model establishment errors, all of which can affect the output. Energy storage needs to constantly adjust its additional frequency regulation output based on the real-time operating status of the photovoltaic power plant, a complex, time-varying, and multi-coupled process involving the large power grid, making it difficult to establish accurate mathematical models. Therefore, a photovoltaic-storage coordinated control strategy is designed using the highly adaptable and robust fuzzy control theory. In the photovoltaic-storage coordination process, in addition to leveraging its own power characteristics and simulating the droop characteristics of a synchronous generator to participate in power system frequency regulation based on the system frequency deviation, energy storage should also provide additional output when the frequency regulation capacity of the photovoltaic power plant is insufficient. This better complements photovoltaic and energy storage, leveraging the rapid power regulation advantages of energy storage resources to compensate for the insufficient frequency regulation capacity of the photovoltaic power plant and improve the system's dynamic frequency response. Therefore, this application uses the system frequency deviation Δf and the real-time load shedding rate change Δd of the photovoltaic power plant as inputs, and the additional frequency regulation power command from the energy storage as input. To determine the output, the following fuzzy collaborative control strategy for photovoltaic and energy storage, taking into account the real-time photovoltaic load shedding rate, is proposed:

[0113] Therefore, the present invention is characterized by defining and designing, in detail, a virtual inertia factor and droop control participation factor based on the Logistic function, according to the analysis of the inertial power and primary frequency regulation power demand of the system frequency response process. A photovoltaic adaptive integrated inertial active power reserve frequency regulation control strategy is proposed. This strategy can better utilize photovoltaic reserve power, enhance the active frequency support capability of photovoltaic power plants, and improve the system frequency response characteristics. Furthermore, considering the limitations of photovoltaic active power reserve frequency regulation and the rapid power adjustment characteristics of energy storage, a photovoltaic-energy storage fuzzy collaborative control strategy considering real-time photovoltaic load shedding is proposed, using system frequency deviation and real-time photovoltaic load reduction rate as fuzzy control inputs and energy storage additional frequency regulation power commands as outputs. This strategy can leverage the complementary advantages of photovoltaic and energy storage, utilizing existing idle and controllable energy storage devices in the grid to add frequency regulation output in coordination with photovoltaic power plant frequency regulation, resulting in better economic efficiency. Simultaneously, it compensates for the insufficient frequency regulation capability of photovoltaic power plants alone, improving the system frequency response characteristics.

[0114] This application presents a third specific embodiment of a control method for optimizing coordinated frequency modulation in a photovoltaic-storage system based on fuzzy control:

[0115] A control method for optimizing the coordinated frequency modulation of a photovoltaic-storage system based on fuzzy control is a photovoltaic adaptive integrated inertial control based on the Logistic function, which includes the following:

[0116] Based on the analysis of the inertial response requirements and primary frequency regulation power requirements at different stages of the system frequency drop disturbance response, a Logistic function with S-shaped curve characteristics is selected, and the following photovoltaic adaptive integrated inertial frequency regulation control strategy is proposed:

[0117]

[0118] In the formula, △P PV For photovoltaic additional frequency modulation control output power command; K D K P These are the virtual inertia coefficient and droop coefficient, respectively; Δf is the system frequency deviation. L is the system frequency change rate; D (t), L P (t) represents the virtual inertial control participation factor and the droop control participation factor, respectively, which exhibit an S-shaped change during the frequency response process.

[0119] K D K P The following design is typically based on frequency regulation margin and photovoltaic frequency modulation power reserve:

[0120]

[0121]

[0122] In the formula: P mpp The maximum photovoltaic power generation is given by Δf, where max(df / dt) is the maximum allowable rate of change of the grid frequency; Δf max The maximum frequency regulation depth of photovoltaic power grid participation is given. All variables in the above calculations are per-unit values ​​(pu).

[0123] Virtual inertial control participation factor L D (t) Define and design in detail:

[0124]

[0125] In the formula, L0, L max r and r respectively determine L D The three parameters of the function form (t) are L0, L... max The initial and final values ​​of the function are determined by r, which measures how fast the function changes. All three parameters are greater than zero.

[0126] From Equation 4, we can see that L D (t) represents an inverse S-curve shape, meaning it decays in an inverse S-shape from the initial value 1-L0 to 1-L. max Since it is desired that the virtual inertial control will eventually exit during the frequency recovery phase, i.e., the virtual inertial control participation factor L... D (t) The final value should be zero, so set L. max =1. For example... Figure 1 L is given D (t) The function form of t under different values ​​of L0 and r. Figure 1As shown in (a), when r = 10 remains constant, L0 determines the starting position of the curve. The larger L0 is, the earlier the S-curve begins to decay. This application aims to maintain a large value for the virtual inertial response in the early stage of frequency disturbance, therefore L0 = 0.001 is selected. When L0 = 0.001 remains constant, r determines the rate of change of the curve, thus the decay speed to zero is different. Based on the synchronous generator inertial response time being approximately 1–2 seconds, if the time for the virtual inertial control participation factor to decay to zero is taken as 1 second, from Figure... Figure 1 As shown in (b), r = 12 can be selected.

[0127] Final virtual inertial control participation factor L D The expression for (t) is:

[0128]

[0129] Drooping control participation factor L P (t) Define and design in detail:

[0130]

[0131] In the formula, since droop control is mainly related to the system frequency deviation, the target of the droop control participation factor is L, which is based on the moment when the system frequency drops the most. P (t) = 100%, and for ease of analysis and comparison, we take L. P The final value of (t) is 200%, that is, setting L max =2.

[0132] During a frequency modulation process, because the time from the start of the speed controller's operation to the deepest frequency drop is shorter than the time for the frequency to recover and stabilize, and the parameter r determines the rate of change of the participation factor, the initial value is set to L0 = 1. Based on L... P The time it takes for the curve to rise from its initial value of 1 to its final value (corresponding to the time it takes for the frequency to recover and rise after one modulation) is used to set the value of r. Figure 2 (a) in the middle gives L P (t) The function shape under different values ​​of L0 and r. According to Figure 2 L under different r in (a) P Based on the (t) curve and considering the regulations governing photovoltaic primary frequency regulation in my country's power grid, the adjustment time should not exceed 15 seconds. If the frequency drop time is approximately 1 second, then the recovery time for the photovoltaic primary frequency regulation is approximately 14 seconds. Therefore, r = 0.5 is selected. L0 determines L... P The initial position of the (t) curve affects the time it takes for it to rise from its initial value to 1. Therefore, according to Figure 2 (b) L under different L0 conditions P (t) curve, select L0 = 0.75.

[0133] Ultimate droop control participation factor L P The expression for (t) is:

[0134]

[0135] To better complement solar power and energy storage, leverage the rapid power regulation advantages of energy storage resources, compensate for the insufficient frequency regulation capabilities of solar power plants, and improve the dynamic frequency response characteristics of the system, this study uses the system frequency deviation Δf and the real-time load shedding rate change Δd of the solar power plant as inputs, and adds an additional frequency regulation power command to the energy storage system. For output quantity, propose Figure 3 The diagram illustrates a fuzzy collaborative control strategy for photovoltaic and energy storage that considers the real-time load shedding rate of photovoltaic power.

[0136] Figure 3 In this context, MPPT stands for Maximum Power Point Tracking; VSC stands for Grid-connected Inverter; BESS stands for Battery Energy Storage System; PV stands for Photovoltaic Power Grid; S1 and S2 are switches, connecting the Photovoltaic Maximum Power Point Tracking Control mode and the Active Power Reserve Frequency Regulation Control mode, respectively; PLL stands for Phase-Locked Loop; and s stands for Differential Operation.

[0137] One specific embodiment of the fuzzy controller in this application:

[0138] The specific design process of the fuzzy controller for realizing the fuzzy collaborative control strategy of optical storage is as follows:

[0139] The first step is fuzzification of the input and output. According to my country's power quality regulations, the allowable normal frequency deviation for a power system is ±0.2Hz. When the system capacity is small, the frequency deviation can be relaxed to ±0.5Hz. This application appropriately uses ±0.3Hz. It is set that when |Δf| reaches above 0.3Hz, the energy storage releases / absorbs active power at its maximum available frequency regulation power output. Therefore, the domain of discourse for the fuzzy control input Δf is [-0.3 0.3]Hz. The photovoltaic system typically experiences a load shearing at its maximum generating power P during normal operation. mmp 10% is used for active power reserve frequency regulation, so the maximum increment of active power regulation upward and downward to cope with frequency disturbances is 10%P. mmp Therefore, taking the load reduction rate during normal photovoltaic operation as the reference point, the universe of discourse for the fuzzy control input photovoltaic load reduction change Δd is [-10% 10%], where -10% to 0% is the universe of discourse when the frequency is lowered and 0% to 10% is the universe of discourse when the frequency is highered.

[0140] Furthermore, using the energy storage frequency regulation capacity of the collaborative photovoltaic power station as the benchmark value, with discharging as positive and charging as negative, a fuzzy control output is set. The domain of discourse is [-1 1]pu.

[0141] The above variables are all defined with 7 fuzzy subsets: NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), and PB (positive large). The membership functions of each variable are as follows: Figure 4 As shown.

[0142] The second step is the formulation and reasoning of fuzzy rules. To simulate the frequency regulation characteristics of a conventional synchronous generator, this application formulates the fuzzy control rule table for photovoltaic-storage coordination, as shown in Table 1, based on the system frequency deviation Δf, the real-time load reduction change of photovoltaic power Δd, and the additional frequency regulation power increment command of energy storage Δd. The basic reasoning principle is as follows:

[0143] When the absolute value of the system frequency deviation Δf is large (near the lowest and highest points of the system frequency) or the photovoltaic load reduction Δd changes significantly (at which point the photovoltaic output active power increment is close to the upper and lower limits, corresponding to the initial moment of frequency disturbance), the additional frequency regulation output of energy storage should be increased as much as possible.

[0144] When the absolute value of the system frequency deviation Δf is small or the change in photovoltaic load reduction Δd is close to zero, the additional frequency regulation output of energy storage should be reduced to restore the frequency to stability as soon as possible.

[0145] After formulating the fuzzy control rules, this application selects the most common Mamdani-type fuzzy language controller and performs approximate inference of the output fuzzy subset based on the input fuzzy subset.

[0146] Table 1. Fuzzy Control Rules for Photovoltaic-Storage Collaborative Control

[0147]

[0148] The third step is to defuzzify the output. The weighted average method is an algebraic approximation method based on the area centroid method of integral calculation. It calculates the weighted average of the centers of the fuzzy subsets of the fuzzy control output, with the weights being the height of the corresponding fuzzy set. This method is simple and reasonable in calculation.

[0149] Therefore, this application employs a weighted average method to defuzzify the output fuzzy set obtained from fuzzy inference, thereby acquiring continuous numerical values ​​for energy storage additional frequency modulation commands. Output. Based on the above design process of the optical-storage fuzzy collaborative controller, the final controller input-output relationship surface is as follows: Figure 5 As shown.

[0150] A simulation embodiment of the present invention:

[0151] This application is based on Matlab / Simulink. Figure 6The simulation model shown is a three-machine nine-node system containing photovoltaic and energy storage. The system base capacity is 100MVA, and the loads L1, L2, and L3 are set to 0.5+j0.15 pu, 0.3+j0.1p.u., and 0.4+j0.2 pu, respectively. Figure 6 In the diagram, 1-3 represent the grid-connected power supply buses of the system, and 4-6 represent the high-voltage buses of the system ring network; G1-G3 represent synchronous generators; PV represents photovoltaic power generation; and BESS represents battery energy storage system. The photovoltaic power station uses a single-unit equivalent model with a capacity of 0.01 pu × 100; the energy storage system uses a power-current dual closed-loop control inverter connected to the grid (model 9) with a capacity of 0.1 pu. To study the effectiveness of the additional power control strategy that provides frequency support for energy storage in conjunction with the photovoltaic power station, therefore... Figure 3 Energy storage P bref0 Set to 0. In the simulation, the photovoltaic power station operates with limited power (10% reserve power). The load L3 is set to increase active power by 0.1 pu every 5.0s to simulate the system frequency drop disturbance for simulation study.

[0152] To verify the effectiveness of the proposed adaptive integrated inertial photovoltaic active power reserve frequency regulation strategy, we now conduct... Figure 6 In the simulation model shown, photovoltaics employs five frequency regulation control strategies: no additional frequency regulation, droop control, virtual inertial control, conventional integrated inertial control, and adaptive integrated inertial control. Energy storage does not participate in frequency regulation control. The droop coefficient K... P and virtual inertia coefficient K D Set to K respectively P =0.1, K D =0.12.

[0153] Figure 7 The curves show a comparison of system frequencies under five control modes. Figure 8 for Figure 7 Enlarged view at point A. The figure shows that compared to control without additional frequency modulation, droop control is beneficial for reducing the frequency drop depth, and virtual inertial control is beneficial for reducing the initial frequency change rate. The combined inertial control of droop and virtual inertia can both reduce the system frequency drop depth and improve the system frequency recovery characteristics. Furthermore, the adaptive combined inertial strategy adopted in this application, compared to other control strategies, not only reduces the maximum system frequency drop depth but also accelerates the time for the frequency to recover and stabilize, which is beneficial for the safe operation of the system.

[0154] Figure 9The comparison results of photovoltaic output power under different frequency regulation control strategies are further presented. It can be seen that virtual inertial control can respond immediately to provide active power support during frequency disturbances, but it cannot provide support for a long time. Droop control can provide active power support for frequency recovery when the frequency drops significantly. Conventional integrated inertial control can combine the advantages of both, giving full play to their respective advantages over a long time scale. Compared with conventional integrated inertial control, the adaptive integrated inertial control proposed in this application can generate a large amount of active power for inertial response during frequency disturbances. At the same time, as the frequency drops and recovers, it reduces the participation of virtual inertial response and increases the participation of droop control. Therefore, it can use droop control to generate more active power during the deepest frequency drop and frequency recovery stages to reduce system frequency deviation, accelerate frequency recovery, and thus better improve the system frequency response characteristics.

[0155] Figure 10 , Figure 11 The dynamic response processes of the active power output of synchronous generators G1 and G2 in the simulation model were compared under three frequency regulation control strategies: no control, conventional integrated inertial control, and adaptive integrated inertial control. Compared to the control strategy without frequency support, the active power output of the synchronous generator is reduced because the active power reserve of the photovoltaic power generation system can be supplemented by frequency regulation to increase the active power output of the photovoltaic system during the frequency support phase. Compared to conventional integrated inertial control, combined with... Figure 9 It can be seen that, because the adaptive integrated inertial control enables the photovoltaic system to output more active power during the frequency recovery phase, the active power output of the synchronous generators G1 and G2 will change less during this phase, the time for the rotor to recover to its rated speed will be shorter, and the system's frequency dynamic response characteristics will be better. Meanwhile, Figure 12 The power angle difference response process of synchronous generators G1 and G2 shown also demonstrates that the designed adaptive integrated inertial control can improve the dynamic characteristics of the system to recover synchronous stability after frequency disturbance.

[0156] To verify the effectiveness of the fuzzy collaborative control strategy for photovoltaic power generation and energy storage that takes into account the real-time load shedding rate of photovoltaic power, the photovoltaic power station adopted the aforementioned verified effective adaptive integrated inertial frequency modulation control strategy, while the energy storage adopted three control methods: no additional frequency modulation control, droop control, and photovoltaic model collaboration, and a simulation comparison study was conducted.

[0157] Figure 13 A comparison of system frequency response under three energy storage control methods is presented. It can be seen that, compared to the scenario where only the photovoltaic power station participates in system frequency regulation without energy storage, the participation of energy storage in frequency regulation can significantly improve frequency characteristics. Furthermore, comparing the results with the result of energy storage independently regulating frequency through droop control, it can be seen that the proposed fuzzy collaborative control strategy of photovoltaic and energy storage can reduce the system frequency drop depth and improve the system frequency recovery characteristics.

[0158] Figure 14 , Figure 15The comparison of energy storage and photovoltaic output power during the frequency support process shows that, compared to energy storage participating in frequency regulation based on its own frequency deviation droop control, the proposed photovoltaic-energy storage coordinated control strategy allows energy storage to increase its output according to the established photovoltaic-energy storage coordination fuzzy rules when the photovoltaic output approaches its upper limit during frequency disturbances. Simultaneously, near the deepest frequency drop, it maintains a high output state based on the larger frequency deviation to support system frequency recovery. Therefore, the designed photovoltaic-energy storage fuzzy coordinated control not only enables energy storage to respond with active power output according to frequency deviation but also adjusts its output according to the real-time photovoltaic load shedding rate. This leverages the technical advantage of rapid power response from energy storage, compensates for the shortcomings of photovoltaic frequency regulation capabilities, and helps improve the system's dynamic frequency response process.

[0159] Figure 16 , Figure 17 Simulation results of the active power output of synchronous generators G1 and G2 during frequency regulation were compared under different energy storage control methods. Compared with no energy storage support, utilizing energy storage in frequency regulation can significantly reduce the active power output of synchronous generators G1 and G2. Simultaneously, combined with... Figure 14 It can be seen that the proposed fuzzy collaborative control strategy of photovoltaic and energy storage enables the energy storage to output more frequency-modulated power during the deepest frequency drop and the frequency recovery phase. Therefore, the change in active power output of generators G1 and G2 supporting frequency recovery is reduced compared to the situation without collaborative control. Figure 18 The simulation results of the power angle difference between synchronous generators G1 and G2 shown further demonstrate that the proposed optical-storage fuzzy collaborative control can improve the dynamic characteristics of the system to recover synchronous stability after frequency disturbances.

[0160] An embodiment of a device applying the method of the present invention:

[0161] A computer device comprising:

[0162] One or more processors;

[0163] Storage device for storing one or more programs;

[0164] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-described control method for optimizing the coordinated frequency modulation of the optical storage system based on fuzzy control.

[0165] An embodiment of a computer medium applying the method of the present invention:

[0166] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned control method for optimizing the coordinated frequency modulation of a photoelectric storage system based on fuzzy control.

[0167] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0168] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0169] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0170] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A control method for optimizing the coordinated frequency modulation of a photovoltaic-storage system based on fuzzy control. Its features are, Includes the following steps: The first step is to obtain the system frequency deviation data; The second step is to obtain the virtual inertia coefficient and sag coefficient based on the deviation data and using the pre-built inertia sag control model. The third step involves calculating the virtual inertia control participation factor and the droop control participation factor based on the virtual inertia coefficient and the droop coefficient, using a pre-built follower factor variation model and considering the influence of the virtual inertia response on frequency disturbances and the frequency drop time. The formula for calculating the virtual inertial control participation factor is as follows: In the formula, L D ( t ) represents the virtual inertial control participation factor. L 0、 L max , r They decided respectively L D ( t The three parameters of the function form, L 0、 L max This determines the positions of the function's initial and final values. r To measure how fast a function changes, all three parameters must be greater than zero. t For time; The formula for calculating the droop control participation factor is as follows: In the formula, L P ( t ) is a factor involved in droop control. L 0、 L max , r They decided respectively L D ( t The three parameters of the function form, L 0、 L max This determines the positions of the function's initial and final values. r To measure how fast a function changes, all three parameters must be greater than zero. t For time; The fourth step involves using a pre-built output power calculation model and, based on the virtual inertial control participation factor, droop control participation factor, virtual inertial coefficient, and droop coefficient, calculating the photovoltaic additional frequency modulation control output power command. The fifth step is to obtain the real-time load shedding of the photovoltaic grid-connected active power after the photovoltaic active power reserve control and converter control response, based on the output power command of the photovoltaic additional frequency regulation control. The formula for calculating the real-time change in active power load reduction of grid-connected photovoltaic systems is as follows: In the formula, △ d For photovoltaic load reduction changes, P d This represents the photovoltaic active power reserve when the system frequency is normal. P pv For real-time output power of photovoltaics, P mmp This represents the maximum tracking power of the photovoltaic system. The sixth step is to formulate a fuzzy control rule table for photovoltaic-storage collaborative control based on the real-time changes in active power load reduction and deviation data of photovoltaic grid connection, using a pre-built fuzzy control optimization model. The seventh step is to utilize a pre-built frequency modulation model of the photovoltaic-storage system and, based on the photovoltaic-storage collaborative fuzzy control rule table, to achieve photovoltaic-storage collaborative frequency modulation control.

2. The control method for optimizing the coordinated frequency modulation of a photovoltaic-storage system based on fuzzy control as described in claim 1, characterized in that, In the second step, the formulas for calculating the virtual inertia coefficient and the droop coefficient are as follows: In the formula: K D For virtual inertia coefficients, K P Sag coefficient ，P mpp The maximum power generation of photovoltaic power, max(d) f / d t ) represents the maximum allowable rate of change of the power grid frequency; △ f max This represents the maximum depth of photovoltaic (PV) participation in grid frequency regulation.

3. The control method for optimizing the coordinated frequency modulation of a photovoltaic-storage system based on fuzzy control as described in claim 1, characterized in that, In the fourth step, the calculation formula for the photovoltaic additional frequency modulation control output power command is as follows: In the formula, △ P PV Add frequency modulation control output power command to photovoltaic system; K D , K P These are the virtual inertia coefficient and the sagging coefficient, respectively; △ f For system frequency deviation, The system frequency change rate; L D ( t ), L P ( t These are the virtual inertial control participation factor and the droop control participation factor, respectively, which exhibit an S-shaped change during the frequency response process.

4. The control method for optimizing the coordinated frequency modulation of a photovoltaic-storage system based on fuzzy control as described in claim 3, characterized in that, The sixth step, the method for formulating the fuzzy control rule table for photovoltaic-storage collaborative operation, is as follows: When the system frequency deviation Δ f Large absolute value or photovoltaic load reduction △ d When there are significant changes, the additional frequency regulation output of energy storage should be increased; When the system frequency deviation Δ f Small absolute value or photovoltaic load reduction △ d When the change approaches zero, the additional frequency regulation output of the energy storage should be reduced to restore the frequency to stability; The fuzzy control rule table for optical-storage collaborative control is shown below: Among them, △ P* b represents the additional frequency regulation power command for energy storage, △ f For the system frequency deviation, Δ d For photovoltaic load reduction, NB is the negative large fuzzy subset, NM is the negative medium fuzzy subset, NS is the negative small fuzzy subset, ZO is the zero fuzzy subset, PS is the positive small fuzzy subset, PM is the positive medium fuzzy subset, and PB is the positive large fuzzy subset.

5. The control method for optimizing the coordinated frequency modulation of a photovoltaic-storage system based on fuzzy control as described in claim 1, characterized in that, In the seventh step, the method for achieving coordinated frequency modulation control of photovoltaic and energy storage is as follows: S61, based on the optical-storage collaborative fuzzy control rule table, obtain several input fuzzy subsets; S62, based on several input fuzzy subsets and using the Midani-type fuzzy language controller, perform inference operations to obtain several output fuzzy subsets, forming an output fuzzy set; S63 uses a weighted average method to defuzzify the output fuzzy set and obtains the continuous numerical values ​​of the energy storage additional frequency modulation command △. P* b; S64, according to the energy storage additional frequency regulation command △ P* b. Perform coordinated frequency modulation control on the photovoltaic-storage system.

6. The control method for optimizing the coordinated frequency modulation of a photovoltaic-storage system based on fuzzy control as described in claim 5, characterized in that, The weighted average method is an algebraic approximation method for the area centroid method integral calculation. It calculates the weighted average of the centers of the output fuzzy subset, and the weight is the height of the corresponding fuzzy subset.

7. A control system for optimizing the coordinated frequency modulation of a photovoltaic-storage system based on fuzzy control, characterized in that, The control method based on fuzzy control optimization for coordinated frequency modulation of a photovoltaic-storage system, as described in any one of claims 1-6, is adopted; it includes an inertial droop control module, a follower factor variation module, an output power calculation module, a fuzzy control optimization module, and a coordinated frequency modulation module for the photovoltaic-storage system; The inertia droop control module is used to obtain the virtual inertia coefficient and droop coefficient based on the deviation data; The follow-up factor variation module calculates the virtual inertial control participation factor and droop control participation factor based on the influence of virtual inertial response on frequency disturbance and frequency drop time. The output power calculation module is used to calculate the photovoltaic additional frequency modulation control output power command based on the virtual inertial control participation factor, droop control participation factor, virtual inertial coefficient, and droop coefficient. The fuzzy control optimization module is used to formulate a fuzzy control rule table for photovoltaic-storage collaborative operation based on real-time changes and deviations in active power load of photovoltaic grid connection. The photovoltaic-storage system collaborative frequency modulation module is used to realize photovoltaic-storage collaborative frequency modulation control according to the photovoltaic-storage collaborative fuzzy control rule table.

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