Self-adaptive cooperative control method and system for power grid stability

Abstract

The invention provides a self-adaptive cooperative control method and system for the stability of a power grid, belongs to the technical field of power grids, and aims to solve the problem that the dynamic response of an existing regulation and control strategy based on a consistency protocol is insufficient. The method comprises the following steps: constructing a new energy power supply matrix R and a load power matrix L, and obtaining frequency deviation; dynamically determining an active power output instruction and a load power demand according to the frequency deviation, and updating the state space model; judging whether the new energy active power output is out of limit or grid-connected / off-grid conditions exist, if so, reconstructing the matrix and repeating the previous steps; and updating the frequency deviation in real time, if the frequency deviation exceeds an allowable range, starting additional frequency modulation control, calculating an adaptive frequency correction coefficient, and returning to iteration. Through dynamic adjustment and iterative control, the stability of the power grid is improved, and the dynamic response capability is improved.

Description

Technical Field

[0001] This invention belongs to the field of power grid technology, specifically relating to an adaptive cooperative control method and system for power grid stability. Background Technology

[0002] With the advancement of my country's strategic goal of "peaking carbon and achieving carbon neutrality," the construction of a new power system dominated by a high proportion of renewable energy has become an important development direction. Against this backdrop, the stable operation of the power system under large-scale renewable energy grid integration has become a critical technical challenge that urgently needs to be addressed. Due to the significant intermittent, random, and fluctuating characteristics of renewable energy generation, coupled with the continuous integration of new electricity loads, the power grid exhibits complex characteristics of uncertainty on both the source and load sides, posing a severe challenge to system power balance and voltage quality control.

[0003] Distributed control algorithms have attracted much attention due to their strong robustness in communication-constrained scenarios. Their ability to achieve global coordination through local information interaction can effectively protect data privacy and reduce communication costs. Among existing distributed methods, while consensus-based control strategies can partially solve the control problem in weak power grids, they still rely on centralized information stations to aggregate and calculate global state variables (such as power and frequency), leading to two drawbacks: Incomplete distribution: the need for a central node to integrate state variables violates the "decentralization" principle of distributed architecture; Insufficient dynamic response: simplified models based on scalar consensus variables cannot accurately characterize the dynamic coupling relationship of time-varying uncertainties on both the source and load sides, making it difficult to guarantee power stability.

[0004] To address the aforementioned issues, there is an urgent need to construct a collaborative control framework based on state-space modeling. By modeling the dynamic behavior of the system as multidimensional state-space equations, a full-state description of source-load dual-side uncertainties, network topology changes, and dynamic constraints can be achieved. Furthermore, by combining a distributed state observer with an adaptive feedback control law, global stability regulation can be achieved through local state information interaction without centralized computation, effectively suppressing frequency / power oscillations caused by renewable energy fluctuations and improving the power supply reliability of high-proportion renewable energy power grids. Summary of the Invention

[0005] In view of this, the present invention provides an adaptive cooperative control method and system for power grid stability, in order to solve the problem that, although the control strategy based on consensus protocol can partially solve the control problem of weak power grids, it leads to insufficient dynamic response.

[0006] The technical solution adopted in this invention is as follows:

[0007] An adaptive cooperative control method for power grid stability includes:

[0008] Step 1: Based on the power generation cost and load power of each new energy source, construct the new energy power matrix R and the load power matrix L, and obtain the frequency deviation through the frequency detection system.

[0009] Step 1, which involves constructing the new energy power matrix R and the load power matrix L based on the power generation costs and load power of each new energy source, specifically includes the following steps:

[0010] Step A1: Based on the power generation cost function of new energy sources, calculate the power generation cost of new energy sources, as shown in the following formula:

[0011]

[0012] The power consumption is calculated based on the power consumption function of the electrical load, as shown in the following formula:

[0013]

[0014] In the formula, α i β i γ i These are the constant term, linear coefficient, and quadratic coefficient of the electricity cost function, respectively. j b j c j These are the constant term, the coefficient of the first term, and the coefficient of the second term of the power function, respectively. Gi For the output power of renewable energy i, P Dj For the power demand of load j, C i For incremental costs, B j For electricity efficiency;

[0015] Step A2: Obtain the new energy power source matrix R based on the cost function of each new energy power generation, as shown in the following formula:

[0016] R = [R1 … R] i ]

[0017] The load power matrix L is obtained based on the electrical load power function, as shown in the following equation:

[0018] L = [L1 … L] i ].

[0019] In step 1, frequency detection is applicable to any system capable of detecting frequencies and is not limited to a specific frequency detection method.

[0020] Step 2: Based on the frequency deviation output by the frequency detection system, determine the active power output command and load power demand of each new energy power source in real time and dynamically update the state space model.

[0021] In step 2, the state-space model S is calculated as follows:

[0022]

[0023] In the formula, S is the state-space model, is the new energy power source matrix, and L is the load power matrix; P Gref For the new energy output power command matrix, P Dn Load power demand matrix:

[0024] P Gref =[P G1_ref …P Gi_ref ]

[0025] P Dn =[P D1_n …P Dj_n ]

[0026] Step 2 specifically includes the following steps:

[0027] Step 2.1: Based on the state-space variables of the distributed renewable energy source at node i in the k-th iteration, obtain the active power output command of the renewable energy source, as shown in the following formula:

[0028]

[0029] In the formula, Let β be the state-space variable of the distributed renewable energy source at node i in the k-th iteration. i γ i These are the coefficients of the first and second terms of the power generation cost function, respectively, P. Gi_ref The output power command for new energy i;

[0030] Step 2.2: Based on the state-space variables of the flexible load at node j in the k-th iteration, obtain the power demand of the load, as shown in the following formula:

[0031]

[0032] In the formula, Let b be the state-space variable of the flexible load at node j in the kth iteration. j c j These are the coefficients of the first and second terms of the power function, respectively, P. Dj_n Let j be the power demand of load j;

[0033] The state-space variables for each new energy source and each load are:

[0034]

[0035] In the formula, Let i be the state-space variable of the new energy source in the k-th iteration. Let β be the state-space variable of the load of node j in the k-th iteration. i γ i These are the coefficients of the first and second terms of the power generation cost function, respectively, b. j c j These are the coefficients of the first and second terms of the power function, respectively, P. Gi_ref For the output power command of new energy i, P Dj_n Let j be the power demand of load j.

[0036] Step 2.3: Update the state-space variables of the new energy source, as shown in the following equation:

[0037]

[0038] Update the state-space variables of the load as shown in the following equation:

[0039]

[0040] Wherein: H * (k)=ΩΔf(k), Δf(k)=f * -f(k), Let i be the state-space variable of the new energy source in the k-th iteration. Let ξH be the state-space variable of the load at node j in the kth iteration. * (k) represents the adaptive adjustment term for frequency control, ξ is the adjustment coefficient with a value of [-0.005, 0.005], Ω is the adaptive frequency correction coefficient, Δf(k) is the frequency deviation value in the k-th iteration, and f(k) is the frequency measured by the frequency detection system in the k-th iteration. * This is the system's rated frequency.

[0041] Step 3: Determine whether the active power output of each new energy source exceeds the limit and whether there is grid connection / grid disconnection. If either situation exists, repeat steps 1 and 2. If neither situation exists, proceed to step 4.

[0042] Step 3 specifically includes: if the active power output of the new energy source in the current step exceeds the maximum power limit, the new energy source operates at the maximum constrained power, and the state space variables and state space model of the energy source are updated; if the active power output of the new energy source in the current step is less than the minimum power limit, the new energy source operates at the minimum constrained power, and the state space variables and state space model of the new energy source are updated; if the new energy source is connected to the grid or disconnected from the grid, the new energy source power matrix R, as well as the corresponding state space variables and state space model, are recalculated.

[0043] Step 4: Update the system frequency deviation measurement value in real time. If the frequency deviation still exceeds the allowable error range, trigger the frequency modulation control in step 5.

[0044] Step 5: Start the additional frequency modulation control, calculate the adaptive frequency correction coefficient, and return to Step 2 for the next round of iteration calculation.

[0045] Step 5, specifically calculating the adaptive frequency correction coefficient, includes:

[0046] Step B1: When the frequency deviation exceeds the critical value, both the power supply side and the load side participate in frequency regulation simultaneously. The formula for calculating the adaptive frequency correction coefficient is as follows:

[0047] Ω=(K s +K d ) / K d

[0048] In the formula, K s K represents the unit regulation power of new energy sources. d The unit regulating power of the load.

[0049] Step B2: When the frequency deviation is less than the frequency deviation threshold, the power supply side participates in frequency modulation independently. The formula for calculating the adaptive frequency correction coefficient is as follows:

[0050] Ω=K s

[0051] In the formula, K s This refers to the unit regulating power of new energy sources.

[0052] The permissible frequency deviation range is determined according to the "Power Quality Power System Frequency Permissible Deviation". For systems with a capacity of 3 million kilowatts and above, the permissible frequency deviation range is [-0.2Hz, 0.2Hz]; for systems with a capacity of 3 million kilowatts and below, the permissible frequency deviation range is [-0.5Hz, 0.5Hz].

[0053] An adaptive cooperative control system for power grid stability includes:

[0054] The first module: Based on the power generation cost and load power of each new energy source, a new energy power matrix R and a load power matrix L are constructed, and the frequency deviation is obtained through a frequency detection system.

[0055] The second module: Based on the frequency deviation output by the frequency detection system, the active power output command and load power demand of each new energy power source are determined in real time and dynamically updated, and the state space model is updated dynamically.

[0056] The third module: Determine whether the active power output of each new energy source exceeds the limit and whether there is grid connection / grid disconnection. If either situation exists, repeat steps 1 and 2. If neither situation exists, proceed to step 4.

[0057] Module 4: Update the system frequency deviation measurement value in real time. If the frequency deviation still exceeds the allowable error range, trigger the frequency modulation control in step 5.

[0058] Module 5: Start additional frequency modulation control, calculate the adaptive frequency correction coefficient, and return to step 2 for the next round of iterative calculation.

[0059] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0060] This invention can achieve a full-state description of source-load dual-side uncertainties, network topology changes, and dynamic constraints; it can achieve global stability control through local state information interaction without centralized computing, effectively suppressing frequency / power oscillations caused by renewable energy fluctuations and improving the power supply reliability of high-proportion renewable energy power grids. Attached Figure Description

[0061] The present invention will be described by way of example and with reference to the accompanying drawings, wherein:

[0062] Figure 1 This is a flowchart of the control method of the present invention.

[0063] Figure 2 This is the topology diagram of the IEEE 39-node system.

[0064] Figure 3 This is an iterative graph of state variables.

[0065] Figure 4 Simulation diagram of active power output for various renewable energy sources.

[0066] Figure 5 This is a diagram showing the total power variation on both sides of the "source-load" axis.

[0067] Figure 6 This is an iterative diagram of state variables for load fluctuation testing.

[0068] Figure 7 The graph shows the changes in active power output of various renewable energy sources for load fluctuation testing.

[0069] Figure 8 This is a graph showing the total power change on both sides of the "source-load" axis for load fluctuation testing.

[0070] Figure 9 This is an iterative diagram of the state variables for the power generation unit fluctuation test.

[0071] Figure 10A graph showing the changes in active power output of various renewable energy sources for power generation unit fluctuation testing.

[0072] Figure 11 The graph shows the total power variation on both sides of the "source-load" axis for the power generation unit fluctuation test. Detailed Implementation

[0073] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0074] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0075] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined with each other.

[0076] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0077] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0078] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined with each other.

[0079] Example 1

[0080] like Figures 1-5 As shown in the figure, an adaptive cooperative control method for power grid stability is disclosed in this embodiment of the invention, including:

[0081] Step 1: Based on the power generation cost and load power of each new energy source, construct the new energy power matrix R and the load power matrix L, and obtain the frequency deviation through the frequency detection system.

[0082] Step 1, which involves constructing the new energy power matrix R and the load power matrix L based on the power generation costs and load power of each new energy source, specifically includes the following steps:

[0083] Step A1: Based on the power generation cost function of new energy sources, calculate the power generation cost of new energy sources, as shown in the following formula:

[0084]

[0085] The power consumption is calculated based on the power consumption function of the electrical load, as shown in the following formula:

[0086]

[0087] In the formula, α i β i γ i These are the constant term, linear coefficient, and quadratic coefficient of the electricity cost function, respectively. j b j c j These are the constant term, the coefficient of the first term, and the coefficient of the second term of the power function, respectively. Gi For the output power of renewable energy i, P Dj For the power demand of load j, C i For incremental costs, B j For electricity efficiency;

[0088] Step A2: Obtain the new energy power source matrix R based on the cost function of each new energy power generation, as shown in the following formula:

[0089] R = [R1 … R] i ]

[0090] The load power matrix L is obtained based on the electrical load power function, as shown in the following equation:

[0091] L = [L1…L i ].

[0092] In step 1, frequency detection is applicable to any system capable of detecting frequencies and is not limited to a specific frequency detection method.

[0093] Step 2: Based on the frequency deviation output by the frequency detection system, determine the active power output command and load power demand of each new energy power source in real time and dynamically update the state space model.

[0094] In step 2, the state-space model S is calculated as follows:

[0095]

[0096] In the formula, S is the state-space model, is the new energy power source matrix, and L is the load power matrix; P Gref For the new energy output power command matrix, P Dn Load power demand matrix:

[0097] P Gref =[P G1_ref …P Gi_ref ]

[0098] P Dn =[P D1_n …P Dj_n ]

[0099] Step 2 specifically includes the following steps:

[0100] Step 2.1: Based on the state-space variables of the distributed renewable energy source at node i in the k-th iteration, obtain the active power output command of the renewable energy source, as shown in the following formula:

[0101]

[0102] In the formula, Let β be the state-space variable of the distributed renewable energy source at node i in the k-th iteration. i γ i These are the coefficients of the first and second terms of the power generation cost function, respectively, P. Gi_ref The output power command for new energy i;

[0103] Step 2.2: Based on the state-space variables of the flexible load at node j in the k-th iteration, obtain the power demand of the load, as shown in the following formula:

[0104]

[0105] In the formula, Let b be the state-space variable of the flexible load at node j in the kth iteration. j c j These are the coefficients of the first and second terms of the power function, respectively, P. Dj_n Let j be the power demand of load j;

[0106] The state-space variables for each new energy source and each load are:

[0107]

[0108] In the formula, Let i be the state-space variable of the new energy source in the k-th iteration. Let β be the state-space variable of the load of node j in the k-th iteration. i γ i These are the coefficients of the first and second terms of the power generation cost function, respectively, b. j c j These are the coefficients of the first and second terms of the power function, respectively, P. Gi_ref For the output power command of new energy i, P Dj_n Let j be the power demand of load j.

[0109] Step 2.3: Update the state-space variables of the new energy source, as shown in the following equation:

[0110]

[0111] Update the state-space variables of the load as shown in the following equation:

[0112]

[0113] Wherein: H * (k)=ΩΔf(k), Δf(k)=f * -f(k), Let i be the state-space variable of the new energy source in the k-th iteration. Let ξH be the state-space variable of the load at node j in the kth iteration. * (k) represents the adaptive adjustment term for frequency control, ξ is the adjustment coefficient with a value of [-0.005, 0.005], Ω is the adaptive frequency correction coefficient, Δf(k) is the frequency deviation value in the k-th iteration, and f(k) is the frequency measured by the frequency detection system in the k-th iteration. * This is the system's rated frequency.

[0114] Step 3: Determine whether the active power output of each new energy source exceeds the limit and whether there is grid connection / grid disconnection. If either situation exists, repeat steps 1 and 2. If neither situation exists, proceed to step 4.

[0115] Step 3 specifically includes: if the active power output of the new energy source in the current step exceeds the maximum power limit, the new energy source operates at the maximum constrained power, and the state space variables and state space model of the energy source are updated; if the active power output of the new energy source in the current step is less than the minimum power limit, the new energy source operates at the minimum constrained power, and the state space variables and state space model of the new energy source are updated; if the new energy source is connected to the grid or disconnected from the grid, the new energy source power matrix R, as well as the corresponding state space variables and state space model, are recalculated.

[0116] Step 4: Update the system frequency deviation measurement value in real time. If the frequency deviation still exceeds the allowable error range, trigger the frequency modulation control in step 5.

[0117] Step 5: Start the additional frequency modulation control, calculate the adaptive frequency correction coefficient, and return to Step 2 for the next round of iteration calculation.

[0118] Step 5, specifically calculating the adaptive frequency correction coefficient, includes:

[0119] Step B1: When the frequency deviation exceeds the critical value, both the power supply side and the load side participate in frequency regulation simultaneously. The formula for calculating the adaptive frequency correction coefficient is as follows:

[0120] Ω=(K s +K d ) / K d

[0121] In the formula, K s K represents the unit regulation power of new energy sources. d The unit regulating power of the load.

[0122] Step B2: When the frequency deviation is less than the frequency deviation threshold, the power supply side participates in frequency modulation independently. The formula for calculating the adaptive frequency correction coefficient is as follows:

[0123] Ω=K s

[0124] In the formula, K s This refers to the unit regulating power of new energy sources.

[0125] The permissible frequency deviation range is determined according to the "Power Quality Power System Frequency Permissible Deviation". For systems with a capacity of 3 million kilowatts and above, the permissible frequency deviation range is [-0.2Hz, 0.2Hz]; for systems with a capacity of 3 million kilowatts and below, the permissible frequency deviation range is [-0.5Hz, 0.5Hz].

[0126] Taking the IEEE-39 node 10-machine 19-load system as an example, Figure 2 This is a communication topology diagram of the IEEE 39-node system. Nodes 1-10 represent new energy power generation units G1-G1, respectively. 10 Nodes 11-29 represent loads 11-29 respectively. The system includes 10 distributed renewable energy generation units and 19 flexible loads. The parameters of all generation units and flexible loads are shown in Table 1.

[0127] Table 110 Load System Parameters

[0128]

[0129] The following simulation calculations for three scenarios are provided for verification:

[0130] (1) Scenario 1: Verify that the control method can effectively maintain system stability through collaborative control.

[0131] A simulation model was constructed using renewable energy unit G1 as a typical power generation unit and flexible load 11 as the controlled object. The sampling period for state observation and control was set to 0.02s. The simulation results are as follows: Figures 3 to 5 As shown. Figure 3 This is an iterative diagram of state-space variables, representing the values ​​of each state-space variable during the iteration process; Figure 4 The simulation diagram shows the active power output of each new energy source, representing the magnitude of the active power output of each new energy power generation unit. Figure 5 This is a graph showing the total power variation on both sides of the "source-load" axis, indicating the magnitude of the source-side output power and the power required by the load. Figure 3 It can be seen that all state-space variables in the system converge to the same optimal value and remain stable; from Figure 4 It can be seen that the active power output of each renewable energy source ultimately remains stable; Figure 5 The power balance characteristics of the source-load sides shown further demonstrate that the total output of the source side matches the load demand in real time, and the system achieves dynamic supply and demand balance under state-space regulation. This confirms that the method effectively suppresses random fluctuations through multi-dimensional state observation and collaborative feedback mechanisms, ensuring the global stability of the high-proportion renewable energy power grid.

[0132] (2)Scenario 2: Verify that the control method can cope with load fluctuations.

[0133] Based on Scenario 1, it is assumed that load 29 disconnects from the grid at 24 seconds and reconnects to the grid at 48 seconds. Simulation results are shown below. Figures 6 to 8 ,in, Figure 6 This is an iterative diagram of state-space variables, representing the values ​​of each state-space variable during the iteration process; Figure 7 The graph shows the changes in active power output of each renewable energy source, indicating the magnitude of active power output from each renewable energy generation unit. Figure 8 The diagram shows the total power variation on both the source and load sides, representing the output power of the source and the power required by the load. Simulation results show that when t = 24s, load 29 disconnects from the grid, the state-space variables converge to a new steady-state value and remain stable; the active power output of each renewable energy source is redistributed, and the total supply and demand power of the system reaches a new stable equilibrium state. When t = 48s, flexible load 29 reconnects to the grid, the state-space variables return to their original steady-state values, the active power output of each renewable energy source returns to its original output power value, and the supply and demand power returns to the original stable equilibrium state. Therefore, the described control method can cope with load fluctuations.

[0134] (3) Scenario 3: Verify that the control method can cope with the fluctuations of the power generation unit.

[0135] Based on Scenario 1, it is assumed that at t = 24s, power generation unit G2 is disconnected from the grid; and at t = 48s, power generation unit G2 is reconnected to the grid. Simulation results are shown below. Figures 9 to 11 ,in, Figure 9 This is an iterative diagram of state-space variables, representing the values ​​of each state-space variable during the iteration process; Figure 10 The graph shows the changes in active power output of each new energy source, indicating the magnitude of active power output from each renewable energy power generation unit. Figure 11 The diagram shows the total power variation on both the source and load sides, representing the output power of the source and the power required by the load. Simulation results show that when t = 24s, power generation unit G2 is disconnected from the grid, the state-space variables converge to a new steady-state value, the active power output of each distributed renewable energy source is redistributed, and the system's supply and demand power reaches a new stable equilibrium state. When t = 48s, power generation unit G2 is reconnected to the grid, the state-space variables return to their original steady-state values, the active power output of each distributed renewable energy source returns to its original output power value, and the supply and demand power returns to the original stable equilibrium state. Therefore, the described control method can cope with fluctuations in power generation units.

[0136] Example 2

[0137] This embodiment proposes an adaptive cooperative control system for power grid stability, including:

[0138] The first module: Based on the power generation cost and load power of each new energy source, a new energy power matrix R and a load power matrix L are constructed, and the frequency deviation is obtained through a frequency detection system.

[0139] The second module: Based on the frequency deviation output by the frequency detection system, the active power output command and load power demand of each new energy power source are determined in real time and dynamically updated, and the state space model is updated dynamically.

[0140] The third module: Determine whether the active power output of each new energy source exceeds the limit and whether there is grid connection / grid disconnection. If either situation exists, repeat steps 1 and 2. If neither situation exists, proceed to step 4.

[0141] Module 4: Update the system frequency deviation measurement value in real time. If the frequency deviation still exceeds the allowable error range, trigger the frequency modulation control in step 5.

[0142] Module 5: Start additional frequency modulation control, calculate the adaptive frequency correction coefficient, and return to step 2 for the next round of iterative calculation.

[0143] The circuits, electronic components, and modules involved are all existing technologies, which can be fully implemented by those skilled in the art, and need not be elaborated upon. The scope of protection of this invention does not involve any improvement to the software and methods.

[0144] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0145] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An adaptive cooperative control method for power grid stability, characterized in that, include: Step 1: Based on the power generation cost and load power of each new energy source, construct the new energy power matrix R and the load power matrix L, and obtain the frequency deviation through the frequency detection system. Step 2: Based on the frequency deviation output by the frequency detection system, determine the active power output command and load power demand of each new energy power source in real time and dynamically update the state space model. Step 3: Determine whether the active power output of each new energy source exceeds the limit and whether there is grid connection / grid disconnection. If either situation exists, repeat steps 1 and 2. If neither situation exists, proceed to step 4. Step 4: Update the system frequency deviation measurement value in real time. If the frequency deviation still exceeds the allowable error range, trigger the frequency modulation control in step 5. Step 5: Start the additional frequency modulation control, calculate the adaptive frequency correction coefficient, and return to Step 2 for the next round of iteration calculation.

2. The adaptive cooperative control method for power grid stability according to claim 1, characterized in that, Step 1, which involves constructing the new energy power matrix R and the load power matrix L based on the power generation costs and load power of each new energy source, specifically includes the following steps: Step A1: Based on the power generation cost function of new energy sources, calculate the power generation cost of new energy sources, as shown in the following formula: The power consumption is calculated based on the power consumption function of the electrical load, as shown in the following formula: In the formula, α i β i γ i These are the constant term, linear coefficient, and quadratic coefficient of the electricity cost function, respectively. j b j c j These are the constant term, the coefficient of the first term, and the coefficient of the second term of the power function, respectively. Gi For the output power of renewable energy i, P Dj For the power demand of load j, C i For incremental costs, B j For electricity efficiency; Step A2: Obtain the new energy power source matrix R based on the cost function of each new energy power generation, as shown in the following formula: R=[R1 … R i ] The load power matrix L is obtained based on the electrical load power function, as shown in the following equation: L=[L1…L i ]。 3. The adaptive cooperative control method for power grid stability according to claim 1, characterized in that, In step 1, frequency detection is applicable to any system capable of detecting frequencies and is not limited to a specific frequency detection method.

4. The adaptive cooperative control method for power grid stability according to claim 1, characterized in that, Step 2 specifically includes the following steps: Step 2.1: Based on the state-space variables of the distributed renewable energy source at node i in the k-th iteration, obtain the active power output command of the renewable energy source, as shown in the following formula: In the formula, Let β be the state-space variable of the distributed renewable energy source at node i in the k-th iteration. i γ i These are the coefficients of the first and second terms of the power generation cost function, respectively, P. Gi_ref The output power command for new energy i; Step 2.2: Based on the state-space variables of the flexible load at node j in the k-th iteration, obtain the power demand of the load, as shown in the following formula: In the formula, Let b be the state-space variable of the flexible load at node j in the kth iteration. j c j These are the coefficients of the first and second terms of the power function, respectively, P. Dj_n Let j be the power demand of load j; Step 2.3: Update the state-space variables of the new energy source, as shown in the following equation: Update the state-space variables of the load as shown in the following equation: Wherein: H * (k)=ΩΔf(k), Δf(k)=f * -f(k), Let i be the state-space variable of the new energy source in the k-th iteration. Let ξH be the state-space variable of the load at node j in the kth iteration. * (k) represents the adaptive adjustment term for frequency control, ξ is the adjustment coefficient with a value of [-0.005, 0.005], Ω is the adaptive frequency correction coefficient, Δf(k) is the frequency deviation value in the k-th iteration, and f(k) is the frequency measured by the frequency detection system in the k-th iteration. * This is the system's rated frequency.

5. The adaptive cooperative control method for power grid stability according to claim 1, characterized in that, Step 3 specifically includes: if the active power output of the new energy source in the current step exceeds the maximum power limit, the new energy source operates at the maximum constrained power, and the state space variables and state space model of the energy source are updated; if the active power output of the new energy source in the current step is less than the minimum power limit, the new energy source operates at the minimum constrained power, and the state space variables and state space model of the new energy source are updated; if the new energy source is connected to the grid or disconnected from the grid, the new energy source power matrix R, as well as the corresponding state space variables and state space model, are recalculated.

6. The adaptive cooperative control method for power grid stability according to claim 1, characterized in that, Step 5, specifically calculating the adaptive frequency correction coefficient, includes: Step B1: When the frequency deviation exceeds the critical value, both the power supply side and the load side participate in frequency regulation simultaneously. The formula for calculating the adaptive frequency correction coefficient is as follows: Ω=(K s +K d ) / K d In the formula, K s K represents the unit regulation power of new energy sources. d The unit regulating power of the load. Step B2: When the frequency deviation is less than the frequency deviation threshold, the power supply side participates in frequency modulation independently. The formula for calculating the adaptive frequency correction coefficient is as follows: Ω=K s In the formula, K s This refers to the unit regulating power of new energy sources.

7. An adaptive cooperative control system for power grid stability, used to implement the adaptive cooperative control method for power grid stability as described in claims 1-6, characterized in that, include: The first module: Based on the power generation cost and load power of each new energy source, a new energy power matrix R and a load power matrix L are constructed, and the frequency deviation is obtained through a frequency detection system. The second module: Based on the frequency deviation output by the frequency detection system, the active power output command and load power demand of each new energy power source are determined in real time and dynamically updated, and the state space model is updated dynamically. The third module: Determine whether the active power output of each new energy source exceeds the limit and whether there is grid connection / grid disconnection. If either situation exists, repeat steps 1 and 2. If neither situation exists, proceed to step 4. Module 4: Update the system frequency deviation measurement value in real time. If the frequency deviation still exceeds the allowable error range, trigger the frequency modulation control in step 5. Module 5: Start additional frequency modulation control, calculate the adaptive frequency correction coefficient, and return to step 2 for the next round of iterative calculation.

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