Multi-modal cooperation-based stability control method for network construction type power system

Abstract

The invention discloses a multi-modal cooperation-based stability control method for a network-building type power system. A power grid system is connected with a plurality of energy units for inputting electric energy to the power grid system; acquiring key state data of the power grid through the synchronous phasor measurement unit and the 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 an improved Nash bargaining game model is adopted to carry out multi-energy power distribution optimization; when a power grid fault is detected, constructing a time-varying delay virtual impedance model, and dynamically adjusting impedance delay time; through the above models and parameters, the operation parameters of the energy unit and the operation parameters of the interface of the energy unit and the power grid system are adjusted, and the stability control of the power grid system is realized. The control method disclosed by the invention has the advantages of improving the inertial support capability and the frequency stability of the system, enhancing the fault ride-through capability, ensuring the frequency stability, the power balance and the voltage stability of the power grid system and the like.

Description

Technical Field

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

[0002] A microgrid is a small power generation and distribution system consisting of distributed generation (DG), energy storage devices, energy conversion devices, loads, monitoring and protection devices, etc. The DG, energy storage devices, and loads are all connected to the DC bus, which is then connected to the external AC grid via power electronic inverters (inverters). With the large-scale integration of renewable energy sources (such as photovoltaic 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 maintaining power balance. In addition, in the event of a grid fault or large-scale disturbance, the fault ride-through capability of the new energy units is low, which can easily lead to system instability or even grid collapse.

[0003] To address these issues, researchers at home and abroad have proposed methods such as virtual synchronous generators (VSGs), improved inertia control, and power allocation optimization strategies. While these technologies have improved the system's dynamic response and stability to a certain extent, they still have some shortcomings.

[0004] The virtual synchronous generator (VSG) is a new grid-connected power generation technology that improves power system stability and reliability by simulating the behavior of traditional synchronous generators. To ensure the performance of the virtual synchronous generator, the VSG must simulate the rotor inertia of the synchronous generator to suppress rapid frequency fluctuations. The VSG must also simulate the speed regulation and excitation controllers of the synchronous generator set to achieve steady-state frequency and voltage support.

[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 a traditional mechanical generator. 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. The virtual inertia control process of the existing technology has the problem of insufficient inertia control. The existing virtual inertia control algorithm is prone to drastic fluctuations in inertia values ​​under large disturbance conditions, and cannot make smooth transitions, affecting system stability.

[0006] Power distribution optimization involves allocating energy to different loads on demand, ensuring that each load receives the appropriate energy supply. Optimal active power allocation in a power system involves optimizing the active power distribution at each node in the system through reasonable power dispatch and optimized control, while ensuring the quality and stability of the grid's power supply. Power distribution optimization can pose the problem of uneven power distribution. Traditional power distribution strategies fail to fully consider the operating status of multiple energy units, resulting in low energy utilization efficiency and poor system stability. Operating status includes SOC (State of Charge) and output efficiency.

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

[0008] Therefore, an innovative control method is urgently needed to optimize inertia support, power distribution and impedance adjustment through a multi-modal collaborative strategy to improve the system's frequency stability, power balancing capability and fault ride-through capability, thereby adapting to the needs of power systems with a high proportion of new energy access. Summary of the Invention

[0009] The present invention aims to overcome the shortcomings of the above-mentioned existing technologies and provide a grid-type power system stability control method based on multi-modal collaboration to avoid frequency instability caused by excessive or insufficient inertia and improve the anti-disturbance capability and operational reliability of the power grid system.

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

[0011] The present invention provides a grid-type power system stability control method based on multi-modal collaboration, characterized in that a power grid system is connected to a plurality of energy units for inputting electric energy into the power grid system; and comprises the following steps:

[0012] Step 1: Data acquisition: Acquire key grid status data through synchronized phasor measurement units (PMUs) and sensors.

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

[0014] Step 3: Based on the key state data, the improved Nash bargaining game model is used to optimize the multi-energy power allocation;

[0015] 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;

[0016] Step 5: Based on 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 stability control of the power grid system.

[0017] The structural characteristics of the grid-type power system stability control method based on multi-modal collaboration of the present invention are also as follows:

[0018] In the 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 the 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 through sensors.

[0019] In specific implementation, in step 2, the virtual inertia value J(t) is calculated using the following formula (1):

[0020] (1);

[0021] In the formula (1), J(t) is the virtual inertia value that changes dynamically with time t, J0 is the basic inertia value and 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.

[0022] In specific implementation, in step 3, the calculation process of multi-energy power allocation 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 is a parameter 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 iis 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.

[0025] In specific implementation, in step 4, the time-varying delay virtual impedance model is 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 cutoff frequency; K p 、T d and T f are proportional gain coefficient, differential time constant and filter time constant (dynamic adjustment controller parameters); V fault 、V th and f grid They are voltage sag amplitude, voltage sag threshold and grid rated frequency respectively.

[0028] During specific implementation, 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.

[0029] In specific implementation, in step 3, a multi-modal collaborative control strategy is used to calculate the reference power, and the reference power is input into the improved Nash bargaining game model, which is combined with the reference power to optimize the multi-energy power allocation.

[0030] The present invention also discloses an electronic device, comprising at least one processor and a memory communicatively connected to the at least one processor; wherein 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 grid-type power system stability control method based on multimodal collaboration.

[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 enable the computer to execute the multi-modal collaboration-based grid-type power system stability control method.

[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 existing technology, the beneficial effects of the present invention are embodied in:

[0034] The present invention discloses a grid-type power system stability control method based on multimodal collaboration, wherein a plurality of energy units for inputting electric energy into the grid system are connected to the grid system; key state data of the grid is obtained through synchronized phasor measurement units 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 used to optimize multi-energy power distribution; when a grid fault is detected, a time-varying delay virtual impedance model is constructed to dynamically adjust the impedance delay time; and through the above models and parameters, the operating parameters of the energy units and the operating parameters of the interface between the energy units and the grid system are adjusted to achieve stability control of the grid system.

[0035] The present invention discloses a grid-type power system stability control method based on multimodal collaboration, which is suitable for power systems with a high proportion of renewable energy access, such as microgrids, 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 capability 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 This 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 methods in conjunction with the accompanying drawings. DETAILED DESCRIPTION

[0040] See also Figures 1 and 2 The present 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 into the power grid system; and comprises the following steps:

[0041] Step 1: Data acquisition step: Acquire key status data of the power grid through synchronized phasor measurement units (PMUs) and sensors;

[0042] Step 2: Based on key state data, the virtual inertia value J(t) is adjusted in real time through the dynamic virtual inertia nonlinear adaptive algorithm;

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

[0044] 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;

[0045] Step 5: Based on 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 stability control of the power grid system.

[0046] like Figure 1 The power grid system of the present invention is connected to three energy units: an energy storage inverter, a photovoltaic inverter, and a wind power inverter. The power grid system is connected to the energy storage, photovoltaic, and wind power inverter units. Each unit uses sensors and synchronized phasor measurement units to collect key status data such as grid frequency, voltage, current, and power in real time. First, the virtual inertia adjustment module dynamically adjusts the virtual inertia value based on the frequency deviation and its rate of change using a dynamic virtual inertia nonlinear adaptive algorithm to improve the system's inertial support capacity. Next, the power distribution optimization module uses a modified Nash game model to achieve power coordination among multiple energy sources based on the status of each energy unit (such as the energy storage SOC and maximum output capacity). If a grid fault is detected, the time-varying delay virtual impedance module dynamically adjusts the impedance parameters to suppress overvoltage and power oscillations. Finally, the electrical control unit uniformly adjusts the operating parameters of each inverter based on the reference values ​​output by each control module to achieve stable control of the frequency, voltage, and power of the power grid system.

[0047] like Figure 2This is a flow chart of a grid-type power system stability control method based on multi-modal collaboration of the present invention. By real-time collection of data such as grid frequency deviation, change rate and active output, a nonlinear adaptive virtual inertia adjustment algorithm is used to dynamically optimize the virtual inertia value, thereby improving the system's inertial support capability and frequency stability; based on an improved Nash bargaining game model and combined with the SOC information of the energy storage system, the power of photovoltaic, wind power and energy storage is optimized and distributed, and the flow of multi-energy power is coordinated to ensure system reliability and economy; when a grid fault occurs, a time-varying delay virtual impedance model is constructed, and the delay parameters are dynamically adjusted to suppress transient overvoltage and power oscillation, thereby enhancing the fault ride-through capability; finally, through a multi-modal collaborative control strategy, the operating parameters of the new energy unit and the grid interface are adjusted in real time to maintain the frequency stability, power balance and voltage stability of the grid, thereby improving the system's anti-disturbance capability and operational reliability.

[0048] The synergistic effect of virtual inertia adjustment and power allocation optimization ensures system frequency stability through virtual inertia adjustment, while power allocation optimization further ensures power balance during system operation by balancing the output of photovoltaic, wind power, and energy storage units. Frequency stability and power balance influence each other: power fluctuations affect frequency, and frequency fluctuations affect power regulation.

[0049] The interaction between virtual inertia and fault ride-through capability allows for dynamic virtual inertia adjustment to help the system transition smoothly during a fault, mitigating the impact of power fluctuations and voltage sags. Time-varying virtual impedance adjustment further suppresses power oscillations caused by faults, ensuring the grid can quickly recover to a stable state.

[0050] The relationship between power distribution and fault ride-through capability is as follows: power distribution optimization not only improves the economic efficiency of system operation, but also enhances the grid's emergency response capabilities in the event of a fault. Reasonable power distribution ensures that when a fault occurs, the system can maintain the grid's operational balance and reduce the impact of the fault on the system through timely adjustment of energy storage units and renewable energy units.

[0051] Virtual inertia adjustment, power distribution optimization, and fault ride-through capability are closely linked. Virtual inertia and power distribution optimization directly impact the system's frequency and power stability, while fault ride-through capability ensures rapid system recovery and stability.

[0052] The multimodal collaborative strategy ensures that the system can maintain efficient and stable operation when facing large-scale disturbances, frequent failures and a high proportion of new energy access by simultaneously optimizing three technologies: virtual inertia adjustment, power distribution optimization and fault ride-through capability.

[0053] In the 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 the 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 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 calculated by ΔP(t) and ΔQ(t). vi The key status data collected in step 1 will be used as input parameters in subsequent steps, especially in the virtual inertia adjustment step, power distribution optimization step, fault detection and impedance adjustment step, etc.

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

[0056] (1);

[0057] In the formula (1), J(t) is the virtual inertia value that changes dynamically with time t, J0 is the basic inertia value and 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.

[0058] The dynamic adjustment algorithm for the virtual inertia value J(t) in formula (1) of the present invention employs an inertia coefficient adaptive mechanism based on variable speed nonlinear regulation. First, the sensitivity of the virtual inertia value J(t) is determined by the adjustment gain coefficient k. Second, the variable attenuation coefficient η is used to smooth the transition of the virtual inertia value J(t). This prevents large fluctuations in the calculated value of the virtual inertia value J(t) under large disturbances when the grid frequency deviation Δf changes dramatically within a short period of time.

[0059] Based on the grid frequency deviation Δf and the frequency change rate dΔf / dt, a dynamic virtual inertia nonlinear adaptive algorithm adjusts the virtual inertia value J(t) in real time. This process enhances the grid system's responsiveness to load and frequency fluctuations, ensuring smooth transitions and rapid response to disturbances. The virtual inertia value J(t) alters the performance of the grid control technology to ensure coordinated operation of the energy storage inverter's inverter frequency regulation and power distribution.

[0060] In specific implementation, in step 3, the calculation process of multi-energy power allocation 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 is a parameter 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 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.

[0063] a i 、b i and c i It is a parameter related to the inherent characteristics of the i-th energy unit (energy storage inverter, photovoltaic inverter, wind power inverter); for example, for the energy storage inverter, a1 represents the increased loss under high power, b1 represents the energy consumption of general power output, and c1 is the fixed cost of maintaining operation; for the photovoltaic inverter, a2 represents the nonlinear loss, b2 represents the energy consumption caused by the output power, and c2 is the basic operating cost of the photovoltaic system; for the wind power inverter, a3 represents the nonlinear output caused by wind speed changes, b3 represents the energy consumption of the control system, and c3 is the basic operating cost.

[0064] like Figure 1 , the energy units include energy storage inverter power supply, photovoltaic inverter power supply, wind power inverter power supply, etc. These energy units input electrical energy to the power grid system. ε i It is used to prevent the over-allocation of a single energy source in these energy units. The state sensitivity adjustment coefficient θ is used to balance the dynamic impact of SOC. ref Used to ensure that energy storage is dispatched 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, the improved Nash bargaining game model is used to optimize the multi-energy power allocation. The improved Nash bargaining game model of the present invention considers the output capacity of each energy unit (such as the power output cost function C of the energy unit) i (P i )), device status (such as energy storage SOC status SOC i , photovoltaic or wind power maximum output power P i,max situation), load demand (such as 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 output command power of a certain energy unit By changing the inverter reference power of each energy unit, the output power of each energy unit to the grid system is limited, and the power flow between different energy units is coordinated to improve the efficiency and stability of the overall system.

[0066] In specific implementation, in step 4, the time-varying delay virtual impedance model is 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 cutoff frequency; K p 、T d and T f are proportional gain coefficient, differential time constant and filter time constant (dynamic adjustment controller parameters); V fault 、V th and f grid They are voltage sag amplitude, voltage sag threshold and grid rated frequency respectively.

[0069] When a grid fault is detected, a time-varying delay virtual impedance model is constructed, and the voltage drop amplitude is calculated online to dynamically adjust the delay parameter τ. cut Dynamically suppress high-frequency resonance and improve fault ride-through capability. The time-varying delay virtual impedance model of the present invention can suppress transient overvoltage and short-term power oscillations, achieve fault depth perception and response buffering through time-varying delay parameters, and avoid overvoltage shocks; suppress high-frequency power fluctuations and short-term oscillations through low-pass filtering and dynamic adjustment controllers, thereby improving the voltage stability and power stability of the system during the fault process. Furthermore, the fault ride-through capability can be improved to ensure the safe operation of the new energy unit. The adjusted virtual impedance Z virtual(S) is used to regulate the recovery process of the power grid system after a power grid fault. The time-varying delayed virtual impedance model enhances the stability of the system during the fault period by dynamically reshaping the impedance.

[0070] During specific implementation, 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.

[0071] During implementation, the operating parameters of the new energy units (such as photovoltaic and wind power), energy storage units, and grid interfaces are adjusted in real time based on the calculation results from steps 2 to 4. 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 grid voltage support capability.

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

[0073] The present invention also discloses an electronic device, comprising at least one processor and a memory communicatively connected to the at least one processor; wherein 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 grid-type power system stability control method based on multimodal collaboration.

[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 enable the computer to execute the multi-modal collaboration-based grid-type power system stability control method.

[0075] 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.

[0076] In this invention, the multimodal collaborative control strategy combines centralized and distributed control, introduces a dynamic adaptive control strategy based on predictive compensation and a multi-level feedback mechanism, and employs a multiscale coordinated control optimization method in both the time and frequency domains. The formula for calculating the reference power using the multimodal collaborative 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 (time t) reference power of the local controller; P i,ref (t) is the system reference power setting value (at time t); f meas is the current 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), △t is the time interval used for advance scheduling; γ is the first-order prediction compensation coefficient; ϕ is the second-order prediction compensation coefficient.

[0079] The present invention provides a grid-type power system stability control method based on multi-modal collaboration, which has the following technical features.

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

[0081] 2. Improved Nash bargaining power allocation model: By introducing a multi-level state weighted function, it optimizes the power allocation of multiple energy units such as photovoltaics, wind power, and energy storage, taking into account output costs, equipment status, and system requirements, thereby improving system operation efficiency and energy utilization.

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

[0083] The present invention proposes a grid-type power system stability control method based on multimodal collaboration, which achieves innovative designs in voltage inertia, angular frequency inertia, and dynamic virtual impedance, significantly improving the dynamic performance and stability of microgrids. It has broad application potential in both island and grid-connected operations.

[0084] The present invention proposes a method for stabilizing a grid-type power system based on multimodal collaboration. This method addresses the existing problems of poor frequency stability, power oscillation, and low fault ride-through success rate through a dynamic virtual inertia nonlinear adaptive algorithm, an improved Nash bargaining power allocation model, and time-varying delay impedance reshaping technology. At the same time, through a multimodal collaborative control strategy, the frequency stability, power balance, and voltage stability of the power grid are maintained, enhancing the system's anti-disturbance capability and operational reliability. Significant improvements have been achieved in terms of system dynamic response, power allocation optimization, and safe operation under fault conditions.

[0085] In summary, this invention, through a multimodal collaborative control strategy, comprehensively improves the stability, reliability, and economic efficiency of the power system by addressing three key dimensions: inertia support, power distribution, and fault ride-through. This provides important technical support for the future development of new energy grids. Consequently, this invention demonstrates excellent adaptability in power grids with a high proportion of renewable energy access, and is widely applicable in scenarios such as isolated microgrids, industrial park smart grids, and weak grids.

[0086] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

[0087] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods 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 through the dynamic virtual inertia nonlinear adaptive algorithm; 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 2, 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.

4. 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.

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 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.

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 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.

7. The method for controlling the stability of a grid-type 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.

8. 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 1-7.

9. 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 1-7.

10. 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 7.

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