A stable control method for a networked power system based on multimodal collaboration

Abstract

The present invention discloses a stable control method for a network-forming power system based on multimodal collaboration. The power grid system is connected with multiple energy units for inputting electric energy into the power grid system; key state data of the power grid is obtained through a synchronous phasor measurement unit and sensors; based on the key state data, the virtual inertia value is adjusted in real time through a dynamic virtual inertia nonlinear adaptive algorithm, and an improved Nash bargaining game model is adopted for optimizing the multi-energy power distribution; when a power grid fault is detected, a time-varying delay virtual impedance model is constructed to dynamically adjust the impedance delay time; through the above several models and parameters, the operating parameters of the energy units and the operating parameters of the interfaces between the energy units and the power grid system are adjusted to achieve the stability control of the power grid system. The control method of the present invention has the advantages of being able to improve the system inertia support ability and frequency stability, enhance the fault ride-through ability, ensure the frequency stability, power balance and voltage stability of the power grid system, etc.

Description

Technical Field

[0001] The present invention relates to a control method for an electric power system, in particular to a stable control method for a grid-type electric power system based on multi-modal collaboration. Background Art

[0002] A microgrid refers to a small power generation and distribution system consisting of distributed generation (DG), energy storage devices, energy conversion devices, loads, monitoring and protection devices, etc. Distributed generation, energy storage devices, loads, etc. are all connected to the DC bus, and the DC network is then connected to the external AC grid through a power electronic inverter (inverter). With the large-scale access of renewable energy (such as photovoltaics and wind power), the stability of the power system faces severe challenges. Due to the randomness and intermittent nature of renewable energy output, traditional power systems based on rotating mechanical inertia cannot provide sufficient inertial support, resulting in frequent system frequency fluctuations and difficulty in maintaining power balance. In addition, in the event of a grid failure or large-scale disturbance, the fault ride-through capability of the new energy unit is low, which can easily lead to system instability or even grid collapse.

[0003] In order to deal with the above problems, researchers at home and abroad have proposed methods such as virtual synchronous generator (VSG), improved inertia control and power allocation optimization strategy. Although these technologies have improved the dynamic response capability and stability of the system to a certain extent, there are still some shortcomings.

[0004] Virtual synchronous generator is a new grid-connected power generation technology that improves the stability and reliability of the power system by simulating the behavior of traditional synchronous generators. In order to ensure the performance of virtual synchronous generators: VSG must simulate the rotor inertia of synchronous generators to suppress rapid frequency fluctuations. VSG must simulate the speed regulation and excitation controller of synchronous generator sets to achieve steady-state support of frequency and voltage.

[0005] Virtual inertia control is a technology that controls the rate of change of current and voltage by introducing specific control strategies into power electronic equipment, thereby achieving an inertial effect similar to that of a rotating mass. The main application of virtual inertia control is in power systems. By adjusting various control devices in the power system, the power system can respond quickly to disturbances and achieve an inertial response effect similar to that of traditional mechanical generators. Virtual inertia control technology can simulate the inertial response effect of traditional mechanical generators. Virtual inertia technology can effectively reduce the frequency deviation of the power system, thereby improving the stability and reliability of the power system. There is a problem of insufficient inertia control in the virtual inertia control process of the prior art. The existing virtual inertia control algorithm is prone to drastic fluctuations in inertia values ​​under large disturbance conditions, and cannot smoothly transition, affecting system stability.

[0006] Power distribution optimization is to distribute energy to different loads as needed, ensuring that each load can obtain just the right amount of energy supply. The optimal distribution of active power in a power system means that, on the premise of ensuring the power supply quality and stability of the power grid, through reasonable power dispatching and optimal control, the active power distribution of each node in the power system reaches the optimal state. There is a problem of unbalanced power distribution in power distribution optimization. Traditional power distribution strategies do not fully consider the operating states of multiple energy units, resulting in low energy utilization efficiency and poor system stability; the operating states include SOC (State of Charge), output efficiency, etc.

[0007] The existing technology also has the problem of insufficient fault ride-through ability. Under the conditions of power grid faults, the impedance regulation strategy lacks dynamic adaptability and is difficult to effectively suppress transient overvoltage and short-term power oscillation.

[0008] Therefore, there is an urgent need for an innovative control method to optimize inertia support, power distribution, and impedance adjustment through a multi-modal collaborative strategy, so as to improve the frequency stability, power balance ability, and fault ride-through ability of the system, and thus meet the requirements of a power system with a high proportion of new energy access. Summary of the Invention

[0009] The present invention is to avoid the deficiencies existing in the above-mentioned prior art, and provides a grid-forming power system stability control method based on multi-modal collaboration to avoid frequency instability phenomena caused by excessive or insufficient inertia, and improve the anti-disturbance ability and operation reliability of the power grid system.

[0010] The present invention adopts the following technical solutions to solve the technical problems.

[0011] A grid-forming power system stability control method based on multi-modal collaboration of the present invention is characterized in that a plurality of energy units for inputting electric energy into the power grid system are connected to the power grid system; the method includes the following steps:

[0012] Step 1: Data acquisition step; obtain key state data of the power grid through a phasor measurement unit (PMU) and sensors;

[0013] Step 2: Based on the key state data, adjust the virtual inertia value J(t) in real time through a dynamic virtual inertia nonlinear adaptive algorithm;

[0014] Step 3: Based on the key state data, adopt an improved Nash bargaining game model for multi-energy power distribution optimization;

[0015] Step 4: When a power grid fault is detected, construct a time-varying delay virtual impedance model and dynamically adjust the impedance delay time ΔT;

[0016] Step 5: According to the virtual inertia value J(t) calculated in Step 2, the result of the multi-energy power distribution optimization in Step 3, and the impedance delay time ΔT in Step 4, adjust the operating parameters of each energy unit in the multiple energy units and the operating parameters of the interface between each energy unit and the power grid system to achieve the stability control of the power grid system.

[0017] The structural feature of a grid-forming power system stability control method based on multi-modal collaboration of the present invention also lies in:

[0018] During specific implementation, in Step 1, the grid frequency deviation Δf, the frequency change rate dΔf / dt, and the state of charge SOC of the energy storage system are collected through a synchronized phasor measurement unit; the voltage U, current I, angular frequency ω of each distributed power source in the microgrid system, and the power change data of the inverter are collected in real time through sensors.

[0019] During specific implementation, in Step 2, the following formula (1) is used to calculate the virtual inertia value J(t);

[0020] (1);

[0021] In the formula (1), J(t) is the virtual inertia value that dynamically changes with time t, J0 is the basic inertia value, which is a constant; k is the adjustment gain coefficient; η is the change attenuation coefficient; Δf is the grid frequency deviation, and dΔf / dt is the change rate of the grid frequency deviation Δf with time t.

[0022] During specific implementation, in Step 3, the calculation process of the multi-energy power distribution optimization is shown in the following formula (2);

[0023] (2);

[0024] In the formula (2), is the maximum output command power of the i-th energy unit; ω i is the state weight value related to the priority and operating efficiency of the i-th energy unit; C i (P i ) is the power output cost function of the i-th energy unit, P i is the output power of the i-th energy unit; a i , b i and c i are parameters related to the characteristics of the i-th energy unit itself; ε i is the balance offset of the i-th energy unit; λ i is the secondary weighting coefficient related to the SOC of the i-th energy unit; θ is the state sensitivity adjustment coefficient; SOC iis the current charging state of the i-th energy unit; SOC ref is the SOC reference value; P d is the total load demand of the power grid system; P i,max is the maximum output power of the i-th energy unit.

[0025] In specific implementation, in step 4, the time-varying delay virtual impedance model is as shown in the following formula (3);

[0026] (3);

[0027] In the formula (3), Z virtual (S) is the virtual impedance, S is the Laplace operator; τ is the time-varying delay parameter, ω cut is the cut-off frequency; K p , T d and T f are respectively the proportional gain coefficient, the differential time constant and the filtering time constant (parameters of the dynamic adjustment controller); V fault , V th and f grid are respectively the voltage sag amplitude, the voltage sag threshold and the grid rated frequency.

[0028] In specific implementation, in step 5, the operating parameters to be adjusted include the virtual synchronous machine control parameters of each energy unit in multiple energy units, the energy storage charge and discharge power of the energy storage inverter power supply, the power factor adjustment of each energy unit in multiple energy units, and the voltage support ability of the power grid system.

[0029] In specific implementation, in step 3, a multi-modal cooperative control strategy is used to calculate the reference power, and the reference power is input to the improved Nash bargaining game model, and the improved Nash bargaining game model combines the reference power to optimize the multi-energy power distribution.

[0030] The present invention also discloses an electronic device, including at least one processor and a memory communicatively connected to the at least one processor; characterized in that the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the stable control method of the network-forming power system based on multi-modal cooperation.

[0031] The present invention also discloses a non-transitory computer-readable storage medium storing computer instructions, characterized in that the computer instructions are used to make the computer execute the stable control method of the network-forming power system based on multi-modal cooperation.

[0032] The present invention also discloses a computer program product, including a computer program; its characteristic is that when the computer program is executed by a processor, it implements the grid-type power system stability control method based on multi-modal collaboration.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0034] The invention discloses a grid-type power system stability control method based on multi-modal collaboration, wherein a power grid system is connected with a plurality of energy units for inputting electric energy into the power grid system; key state data of the power grid is obtained through a synchronous phasor measurement unit and a sensor; based on the key state data, a virtual inertia value is adjusted in real time through a dynamic virtual inertia nonlinear adaptive algorithm, and a multi-energy power distribution optimization is performed using an improved Nash bargaining game model; when a power grid fault is detected, a time-varying delayed virtual impedance model is constructed, and the impedance delay time is dynamically adjusted; through the above models and parameters, the operating parameters of the energy unit and the operating parameters of the interface between the energy unit and the power grid system are adjusted to achieve stability control of the power grid system.

[0035] The present invention discloses a grid-type power system stability control method based on multi-modal collaboration, which is suitable for power systems with a high proportion of renewable energy access, such as microgrids, isolated island power grids, industrial park smart grids and other scenarios. It aims to collaboratively optimize the system's frequency stability, power balance and fault ride-through capability through multiple control technologies.

[0036] The grid-type power system stability control method based on multi-modal collaboration of the present invention has the advantages of improving the system inertial support capacity and frequency stability, ensuring system reliability and economy, enhancing fault ride-through capability, and ensuring frequency stability, power balance and voltage stability of the power grid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a principle block diagram of a grid-type power system stability control method based on multi-modal collaboration of the present invention.

[0038] Figure 2 This is a flow chart of a grid-type power system stability control method based on multi-modal collaboration of the present invention.

[0039] The present invention will be further described below through specific implementation modes in conjunction with the accompanying drawings. DETAILED DESCRIPTION

[0040] See also Figures 1 to 2 The invention provides a grid-type power system stability control method based on multi-modal collaboration, which is characterized in that a power grid system is connected to a plurality of energy units for inputting electric energy to the power grid system; and comprises the following steps:

[0041] Step 1: Data acquisition step; obtaining key state data of the power grid through a synchronized phasor measurement unit (PMU) and sensors;

[0042] Step 2: Based on the key state data, adjusting the virtual inertia value J(t) in real time through a dynamic virtual inertia non-linear adaptive algorithm;

[0043] Step 3: Based on the key state data, adopting an improved Nash bargaining game model to optimize the multi-energy power distribution;

[0044] Step 4: When a power grid fault is detected, constructing a time-varying delay virtual impedance model and dynamically adjusting the impedance delay time ΔT;

[0045] Step 5: According to the virtual inertia value J(t) calculated in Step 2, the result of the multi-energy power distribution optimization in Step 3, and the impedance delay time ΔT in Step 4, adjusting the operating parameters of each energy unit in multiple energy units and the operating parameters of the interface between each energy unit and the power grid system to achieve the stability control of the power grid system.

[0046] Such as Figure 1 In the power grid system of the present invention, three energy units such as a battery energy storage inverter power supply, a photovoltaic inverter power supply, and a wind power generation inverter power supply are connected. The power grid system accesses three inverter power supply units of energy storage, photovoltaic, and wind power. Each unit acquires key state data such as the power grid frequency, voltage, current, and power in real time through sensors and a synchronized phasor measurement unit. First, the virtual inertia regulation module dynamically adjusts the virtual inertia value according to the frequency deviation and its change rate through a dynamic virtual inertia non-linear adaptive algorithm to improve the inertial support ability of the system. Then, the power distribution optimization module realizes the power coordination among multiple energies through an improved Nash game model based on the states of each energy unit (such as the state of charge (SOC) of the energy storage and the maximum output capacity). If a power grid fault is detected, the time-varying delay virtual impedance module will dynamically adjust the impedance parameters to suppress overvoltage and power oscillation. Finally, the electrical control unit uniformly adjusts the operating parameters of each inverter power supply according to the reference values output by each control module to achieve the stable control of the frequency, voltage, and power of the power grid system.

[0047] Such as Figure 2It is a flowchart of a grid-forming power system stability control method based on multi-modal collaboration. By collecting data such as grid frequency deviation, rate of change, and active power output in real time, a non-linear adaptive virtual inertia adjustment algorithm is used to dynamically optimize the virtual inertia value, enhancing the system's inertia support ability and frequency stability; based on an improved Nash bargaining game model, combined with the SOC information of the energy storage system, the power of photovoltaic, wind power, and energy storage is optimized and allocated to coordinate the power flow of multiple energy sources, ensuring the reliability and economy of the system; when a grid fault occurs, a time-varying delay virtual impedance model is constructed to dynamically adjust the delay parameters to suppress transient overvoltage and power oscillation, enhancing the fault ride-through ability; finally, through a multi-modal collaborative control strategy, the operating parameters of new energy units and the grid interface are adjusted in real time to maintain the frequency stability, power balance, and voltage stability of the grid, improving the system's anti-disturbance ability and operating reliability.

[0048] Among them, in the process of the collaborative effect of virtual inertia adjustment and power distribution optimization, the stability of the system frequency is ensured through the adjustment of virtual inertia, while power distribution optimization further ensures power balance during system operation by balancing the outputs of photovoltaic, wind power, and energy storage units. Frequency stability and power balance affect each other. Power fluctuations affect frequency, and frequency fluctuations affect power regulation.

[0049] In the process of the interaction between virtual inertia and fault ride-through ability, dynamic virtual inertia adjustment helps the system to make a smooth transition when a fault occurs, thereby reducing the impact of power fluctuations and voltage dips on the system. And time-varying delay virtual impedance adjustment further suppresses the power oscillation caused by the fault, ensuring that the power grid can quickly return to a stable state.

[0050] The relationship between power distribution and fault ride-through ability is that power distribution optimization can not only improve the economy of system operation but also enhance the emergency ability of the power grid during a fault; reasonable power distribution ensures that when a fault occurs in the system, the operation balance of the power grid can be maintained through timely adjustment of the energy storage unit and renewable energy unit, reducing the impact of the fault on the system.

[0051] The connection among virtual inertia adjustment, power distribution optimization, and fault ride-through ability is very close. Virtual inertia and power distribution optimization directly affect the frequency and power stability of the system, while fault ride-through ability ensures that the system can quickly recover and maintain stability.

[0052] The multi-modal collaborative strategy ensures that the system can still operate efficiently and stably in the face of large-scale disturbances, frequent faults, and high-proportion new energy access by simultaneously optimizing three technologies: virtual inertia adjustment, power distribution optimization, and fault ride-through ability.

[0053] In specific implementation, in step 1, the power grid frequency deviation Δf, the frequency change rate dΔf / dt, and the state of charge SOC of the energy storage system are collected through a synchronized phasor measurement unit; the voltage U, current I, angular frequency ω of each distributed power source in the microgrid system, and the power change data of the inverter are collected in real time through sensors.

[0054] The power change data includes the active power fluctuation ΔP(t), reactive power fluctuation ΔQ(t) of the inverter, and the dynamic virtual impedance Z vi parameters such as (s, t), etc. The key state data collected in step 1 will be used as input parameters for subsequent steps, especially in several subsequent steps such as the virtual inertia adjustment step, power distribution optimization step, and fault detection and impedance adjustment step.

[0055] In specific implementation, in step 2, the virtual inertia value J(t) is calculated using the following formula (1);

[0056] (1);

[0057] In formula (1), J(t) is the virtual inertia value that changes dynamically with time t, J0 is the basic inertia value, which is a constant; k is the adjustment gain coefficient; η is the change attenuation coefficient; Δf is the power grid frequency deviation, and dΔf / dt is the change rate of the power grid frequency deviation Δf with time t.

[0058] The dynamic adjustment algorithm of the virtual inertia value J(t) in formula (1) of the present invention adopts an inertia coefficient adaptive mechanism based on variable-speed non-linear adjustment. First, the sensitivity of the virtual inertia value J(t) is determined by the adjustment gain coefficient k. Secondly, the change attenuation coefficient η is used for the smooth transition of the virtual inertia value J(t), and when the power grid frequency deviation Δf changes violently in a short time, it avoids large fluctuations in the calculated value of the virtual inertia value J(t) under large disturbances.

[0059] Based on the power grid frequency deviation Δf and the frequency change rate dΔf / dt, the virtual inertia value J(t) is adjusted in real time through a dynamic virtual inertia non-linear adaptive algorithm. This process can enhance the response ability of the power grid system to load fluctuations and frequency fluctuations, thereby ensuring that the power grid system has the ability of smooth transition and fast response when being disturbed. The virtual inertia value J(t) changes the performance of the grid-forming control technology to ensure the coordinated operation of the inverter frequency regulation and power distribution of the energy storage inverter power supply.

[0060] In specific implementation, in step 3, the calculation process of multi-energy power distribution optimization is shown in the following formula (2);

[0061] (2);

[0062] In the formula (2), is the maximum output command power of the i-th energy unit; ω i is the state weight value related to the priority and operating efficiency of the i-th energy unit; C i (P i ) is the power output cost function of the i-th energy unit, P i is the output power of the i-th energy unit; a i , b i and c i are parameters related to the characteristics of the i-th energy unit; ε i is the balance offset of the i-th energy unit; λ i is the secondary weighting coefficient related to the SOC of the i-th energy unit; θ is the state sensitivity adjustment coefficient; SOC i is the current charge state of the i-th energy unit; SOC ref is the SOC reference value; P d is the total load demand of the power grid system; P i,max is the maximum output power of the i-th energy unit.

[0063] a i , b i and c i are parameters related to the characteristics of the i-th energy unit (energy storage inverter power supply, photovoltaic inverter power supply, wind power generation inverter power supply); for example, for the energy storage inverter power supply, a1 represents the increased loss at high power, b1 represents the energy consumption of general power output, and c1 is the fixed cost for maintaining operation; for the photovoltaic inverter power supply, a2 represents the non-linear loss, b2 represents the energy consumption brought by the output power, and c2 is the basic operation cost of the photovoltaic system; for the wind power generation inverter power supply, a3 represents the non-linear output caused by the wind speed change, b3 represents the energy consumption of the control system, and c3 is the basic operation cost.

[0064] Such as Figure 1 , the energy unit includes an energy storage inverter power supply, a photovoltaic inverter power supply, a wind power generation inverter power supply, etc., and these energy units input electric energy into the power grid system. ε i is used to prevent the over-allocation of a single energy source among these energy units. The state sensitivity adjustment coefficient θ is used to balance the dynamic influence of SOC. SOC ref is used to ensure the scheduling of the energy storage within the optimal working range.

[0065] Based on the SOC information of the energy storage system collected in step 1 and key status data such as the output power signals of photovoltaic, wind power, and energy storage units, an improved Nash bargaining game model is used to optimize the multi-energy power distribution. The improved Nash bargaining game model of the present invention, by considering the output capacity of each energy unit (such as the power output cost function C i (P i ))), equipment status (such as the SOC status SOC i , the maximum output power P i,max of photovoltaic or wind power), load demand (such as the total load demand P d ), and grid parameters (such as grid voltage and frequency also determine the output capacity of each energy unit), according to the maximum available output command power of a certain energy unit, change the inverter reference power of each such energy unit, thereby limiting the output power of each energy unit to the grid system, coordinating the power flow between different energy units, so as to improve the efficiency and stability of the overall system.

[0066] Specifically in implementation, in step 4, the time-varying delay virtual impedance model is as shown in the following formula (3);

[0067] (3);

[0068] In the formula (3), Z virtual (S) is the virtual impedance, S is the Laplace operator; τ is the time-varying delay parameter, ω cut is the cut-off frequency; K p , T d and T f are respectively the proportional gain coefficient, differential time constant, and filter time constant (for dynamically adjusting the controller parameters); V fault , V th and f grid are respectively the voltage drop amplitude, voltage drop threshold, and grid rated frequency.

[0069] When a grid fault is detected, a time-varying delay virtual impedance model is constructed, and the time-varying delay parameter τ is dynamically adjusted by online calculating the voltage drop amplitude. The cut-off frequency ω cut is introduced to dynamically suppress high-frequency resonance and improve the fault ride-through ability. The time-varying delay virtual impedance model of the present invention can suppress transient overvoltage and short-time power oscillation, realize fault depth perception and response buffering through the time-varying delay parameter, and avoid overvoltage impact; suppress high-frequency power fluctuation and short-time oscillation through low-pass filtering and dynamic adjustment of the controller, thereby improving the voltage stability and power smoothness of the system during the fault process. Further, the fault ride-through ability can be improved to ensure the safe operation of new energy units. The adjusted virtual impedance Z virtual(S), which is used to regulate the recovery process of the power grid system after a power grid fault. The time-varying delay virtual impedance model enhances the system stability during the fault through impedance dynamic reshaping.

[0070] In specific implementation, in step 5, the operating parameters to be adjusted include the inverter virtual synchronous machine control parameters of each energy unit in multiple energy units, the energy storage charge and discharge power of the energy storage inverter power supply, the power factor adjustment of each energy unit in multiple energy units, and the voltage support ability of the power grid system.

[0071] In specific implementation, according to the calculation results of steps 2 to 4, the operating parameters of new energy units (such as photovoltaic and wind power), energy storage units, and power grid interfaces are adjusted in real time. These operating parameters include but are not limited to: inverter virtual synchronous machine control parameters, energy storage charge and discharge power, power factor adjustment, and power grid voltage support ability, etc.

[0072] In specific implementation, in step 3, a multi-modal cooperative control strategy is used to calculate the reference power, and the reference power is input into the improved Nash bargaining game model, and the improved Nash bargaining game model combines the reference power to optimize the multi-energy power distribution.

[0073] The present invention also discloses an electronic device, including at least one processor and a memory communicatively connected to the at least one processor; characterized in that the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the power grid system stability control method based on multi-modal cooperation.

[0074] The present invention also discloses a non-transitory computer-readable storage medium storing computer instructions, characterized in that the computer instructions are used to cause the computer to execute the power grid system stability control method based on multi-modal cooperation.

[0075] The present invention also discloses a computer program product, including a computer program; characterized in that the computer program realizes the power grid system stability control method based on multi-modal cooperation when executed by a processor.

[0076] In the present invention, based on the combination of central control and distributed control, the multi-modal cooperative control strategy introduces a dynamic adaptive control strategy based on prediction compensation and multi-level feedback mechanism, and adopts a multi-scale coordinated control optimization method in the time domain and frequency domain. The calculation formula for calculating the reference power using the multi-modal cooperative control strategy is shown in the following formula (4).

[0077] (4);

[0078] In the formula (4), P i,local (t) is the current real-time reference power of the local controller (at time t); P i,ref (t) is the system reference power setting value (at time t); f meas is the currently measured frequency; f ref is the target frequency; k local is the local frequency control gain coefficient; ψ is the integral coefficient of the frequency change rate, which is used to smooth the system response; P i,pred (t + Δt) is the predicted power value (at time t + △t), and △t is the time interval, which is used for advance scheduling; γ is the first-order prediction compensation coefficient; ϕ is the second-order prediction compensation coefficient.

[0079] A grid-forming power system stability control method based on multimodal collaboration of the present invention has the following technical characteristics.

[0080] 1. Dynamic virtual inertia nonlinear adaptive algorithm: According to the system disturbance intensity and the frequency change rate, the virtual inertia value is adjusted in real time to avoid the frequency instability phenomenon caused by too large or too small inertia, and realize the smooth transition and fast response of the frequency.

[0081] 2. Improved Nash bargaining power distribution model: By introducing a multi-level state weighting function, the power distribution of multiple energy units such as photovoltaic, wind power, and energy storage is optimized, taking into account the output cost, equipment status, and system requirements, and improving the system operation efficiency and energy utilization rate.

[0082] 3. Time-varying delay virtual impedance reshaping technology: According to the fault depth (such as voltage drop amplitude, duration), the virtual impedance and delay time are adjusted in real time to suppress transient overvoltage and power oscillation, and enhance the fault ride-through ability of the system.

[0083] A grid-forming power system stability control method based on multimodal collaboration of the present invention has achieved innovative designs in voltage inertia, angular frequency inertia, and dynamic virtual impedance, significantly improving the dynamic performance and stability of the microgrid, and having broad application potential in both islanding and grid-connected operations.

[0084] A grid-forming power system stability control method based on multimodal collaboration of the present invention solves the problems of poor frequency stability, power oscillation, and low fault ride-through success rate in the prior art through the dynamic virtual inertia nonlinear adaptive algorithm, the improved Nash bargaining power distribution model, and the time-varying delay impedance reshaping technology. At the same time, through the multimodal collaborative control strategy, the frequency stability, power balance, and voltage stability of the power grid are maintained, the anti-disturbance ability and operation reliability of the system are improved, and significant improvements have been achieved in aspects such as system dynamic response, power distribution optimization, and safe operation under fault conditions.

[0085] In summary, through the multi-modal collaborative control strategy, the present invention starts from three key dimensions: inertia support, power distribution, and fault ride-through, comprehensively improving the stability, reliability, and economy of the power system, and providing important technical support for the development of future new energy power grids. Therefore, the present invention exhibits excellent adaptability in power grid systems with a high proportion of renewable energy access and can be widely applied to scenarios such as island microgrids, industrial park smart grids, and weak power grids.

[0086] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

[0087] In addition, it should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative manner of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A grid-type power system stability control method based on multi-modal collaboration, characterized in that: The power grid system is connected to a plurality of energy units for inputting electric energy into the power grid system; the steps include: Step 1: Data acquisition step: obtaining key status data of the power grid through synchronized phasor measurement units and sensors; Step 2: Based on the key state data, the virtual inertia value J(t) is adjusted in real time by a dynamic virtual inertia nonlinear adaptive algorithm; the virtual inertia value J(t) is calculated using the following formula (1); （1）； In the formula (1), J(t) is the virtual inertia value that changes dynamically with time t, J0 is the basic inertia value, which is a constant; k is the adjustment gain coefficient; η is the change attenuation coefficient; Δf is the grid frequency deviation, and dΔf / dt is the rate of change of the grid frequency deviation Δf with time t; Step 3: Based on the key status data, the improved Nash bargaining game model is used to optimize the multi-energy power allocation; Step 4: When a grid fault is detected, a time-varying delay virtual impedance model is constructed to dynamically adjust the impedance delay time ΔT; Step 5: According to the virtual inertia value J(t) calculated in step 2, the result of multi-energy power allocation optimization in step 3 and the impedance delay time ΔT in step 4, adjust the operating parameters of each energy unit in the multiple energy units and the operating parameters of the interface between each energy unit and the power grid system to achieve stability control of the power grid system.

2. According to claim 1, a grid-type power system stability control method based on multi-modal collaboration is characterized in that: In step 1, the grid frequency deviation Δf, the frequency change rate dΔf / dt and the state of charge SOC of the energy storage system are collected by a synchronous phasor measurement unit; the voltage U, current I, angular frequency ω of each distributed power source in the microgrid system and the power change data of the inverter are collected in real time by sensors.

3. According to claim 1, a grid-type power system stability control method based on multi-modal collaboration is characterized in that: In step 3, the calculation process of multi-energy power allocation optimization is shown in the following formula (2); （2）； In the formula (2), is the maximum output command power of the i-th energy unit; ω i is the state weight value related to the priority and operating efficiency of the i-th energy unit; C i (P i ) is the power output cost function of the i-th energy unit, P i is the output power of the i-th energy unit; a i 、b i and c i is a parameter related to the intrinsic characteristics of the i-th energy unit; ε i is the balance offset of the i-th energy unit; i is the secondary weighting coefficient related to the SOC of the i-th energy unit; θ is the state sensitivity adjustment coefficient; SOC i is the current state of charge of the i-th energy unit; SOC ref is the SOC reference value; P d is the total load demand of the power grid system; P i,max is the maximum output power of the i-th energy unit.

4. According to claim 1, a grid-type power system stability control method based on multi-modal collaboration is characterized in that: In step 4, the time-varying delay virtual impedance model is shown in the following formula (3); （3）； In the formula (3), Z virtual (S) is the virtual impedance, S is the Laplace operator; τ is the time-varying delay parameter, ω cut is the cut-off frequency; K p 、T d and T f are proportional gain coefficient, differential time constant and filter time constant respectively; V fault 、V th and f grid They are voltage sag amplitude, voltage sag threshold and grid rated frequency respectively.

5. The method for controlling the stability of a grid-connected power system based on multi-modal collaboration according to claim 1 is characterized in that: In step 5, the operating parameters that need to be adjusted include the inverter virtual synchronous machine control parameters of each energy unit in the multiple energy units, the energy storage charging and discharging power of the energy storage inverter power supply, the power factor adjustment of each energy unit in the multiple energy units and the voltage support capability of the power grid system.

6. The method for controlling the stability of a grid-connected power system based on multi-modal collaboration according to claim 1 is characterized in that: In step 3, a multi-modal collaborative control strategy is used to calculate the reference power, and the reference power is input into an improved Nash bargaining game model, and the improved Nash bargaining game model is combined with the reference power to optimize the multi-energy power allocation.

7. An electronic device comprising at least one processor and a memory connected to the at least one processor in communication; wherein: The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the grid-type power system stability control method based on multi-modal collaboration as described in any one of claims 1-6.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable the computer to execute the grid-type power system stability control method based on multi-modal collaboration according to any one of claims 1-6.

9. A computer program product comprising a computer program; wherein: When the computer program is executed by a processor, the computer program implements the grid-type power system stability control method based on multi-modal collaboration according to any one of claims 1 to 6.

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