A high-penetration photovoltaic grid-connected coordinated control method and related devices

Abstract

This application belongs to the field of power system technology and discloses a high-penetration photovoltaic grid-connected coordinated control method and related devices. The method includes: calculating voltage deviation and frequency deviation based on grid connection point voltage and frequency, respectively; correcting the inverter output power based on the dynamic virtual impedance calculated from the voltage and frequency deviations and the grid equivalent impedance; dynamically allocating energy storage charging and discharging power based on the corrected inverter output power and the state of charge of the energy storage system; constructing an optimization model with the objective of minimizing the sum of voltage deviation, frequency deviation, energy storage cost, and curtailed photovoltaic power; solving the optimization model based on energy storage charging and discharging power and meteorological data to obtain the optimal curtailed photovoltaic power and optimal energy storage cost; and then updating the grid equivalent impedance and the maximum photovoltaic output power. The updated grid equivalent impedance and the maximum photovoltaic output power are then used to correct the inverter output power. This application improves the technical problems of photovoltaic volatility and insufficient grid inertia.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a high-penetration photovoltaic grid-connected collaborative control method and related devices. Background Technology

[0002] Voltage and frequency are crucial parameters for power grid quality; stable grid operation requires controlling voltage and frequency deviations within certain levels. With the grid integration of highly distributed photovoltaic (PV) systems, the stability of distribution network voltage and frequency faces challenges. As new energy power systems transition to a higher proportion of renewable energy, distribution networks with PV penetration exceeding 80% face two core contradictions: the strong volatility of PV power generation and the weakening of the grid's inertial support capacity. The interaction of these two factors poses a severe challenge to system security.

[0003] The volatility of photovoltaic (PV) power stems from its physical characteristics and weather dependence. PV power generation is affected by factors such as irradiance, temperature, and cloud cover, exhibiting random fluctuations ranging from minutes to hours. For example, cloud movement can cause PV output to drop by more than 30% within seconds, while the alternation of day and night can cause a daily peak-to-valley power difference exceeding 80%. This volatility, combined with the regular changes in traditional loads, significantly increases the probability of power supply and demand imbalance in the distribution network. Furthermore, after grid connection, PV inverters exhibit "current source" characteristics, lacking the voltage source support capability of synchronous generator sets, leading to a decrease in system short-circuit capacity and further weakening the grid's resilience to disturbances.

[0004] Insufficient grid inertia is directly related to the substitution effect of synchronous generator units. Traditional thermal / hydropower generator units buffer power disturbances through rotor kinetic energy, but after photovoltaic grid connection replaces synchronous generator units, the system's equivalent inertial time constant drops from 6-10 seconds in traditional grids to less than 2 seconds. The sensitivity of low-inertia systems to power deficits increases exponentially; even a small power imbalance can trigger a rapid frequency drop. For example, when system inertia decreases by 50%, the rate of frequency change under the same power disturbance doubles, increasing the risk of frequency exceeding the limit within 0.5 seconds by more than three times.

[0005] The coupling effect between the two triggers multiple cascading risks, including:

[0006] 1. Voltage instability: Sudden changes in photovoltaic output cause frequent reversals in the power flow direction of the distribution network, and the voltage fluctuation at the end of the feeder can reach ±15% of the rated value, inducing malfunction of protection devices;

[0007] 2. Frequency collapse: Insufficient inertia causes the system to lose its ability to buffer against short-term photovoltaic power shortages. In extreme cases, the frequency deviation can exceed 0.5Hz within 200ms, triggering the low-frequency load shedding device.

[0008] 3. Resonant amplification: Many grid-connected inverters exhibit negative impedance characteristics in weak grid environments, which interact with the line impedance to cause wide-frequency oscillations. Actual test cases show that the resonant frequency covers the range of 200Hz-2kHz, leading to insulation breakdown of the equipment.

[0009] 4. Economic degradation: In order to smooth out fluctuations, traditional solutions need to reserve 20%-30% of spinning reserve capacity and force a curtailment rate of more than 15%, which significantly increases the cost of power supply. Summary of the Invention

[0010] This application provides a high-penetration photovoltaic grid-connected collaborative control method and related device to improve the technical problems of strong photovoltaic volatility and insufficient grid inertia in the distribution network.

[0011] In view of this, the first aspect of this application provides a high-penetration photovoltaic grid-connected coordinated control method, comprising:

[0012] The voltage deviation and frequency deviation are calculated based on the grid connection point voltage and grid connection point frequency, respectively. The inverter output power is then corrected based on the dynamic virtual impedance calculated from the voltage deviation and the frequency deviation, as well as the grid equivalent impedance.

[0013] The energy storage charging and discharging power is dynamically allocated based on the corrected inverter output power and the energy storage system's state of charge.

[0014] An optimization model is constructed with the goal of minimizing the sum of voltage deviation, frequency deviation, energy storage cost, and curtailment of solar power. The optimization model is solved based on the energy storage charging and discharging power and meteorological data to obtain the optimal curtailment of solar power and the optimal energy storage cost.

[0015] The grid equivalent impedance and photovoltaic maximum output are updated based on the optimal curtailment amount, the optimal energy storage cost, the grid connection point voltage, the grid connection point current, and meteorological data. The inverter output power is then corrected using the updated grid equivalent impedance and the updated photovoltaic maximum output.

[0016] Optionally, the inverter output power includes the inverter's active power output and reactive power output, and the correction formula for the inverter output power is:

[0017]

[0018] In the formula, Let t be the active power output of the inverter. The inverter's reactive power output at time t. Let t be the photovoltaic maximum power point tracking output. Let be the dynamic virtual impedance at time t. Let be the equivalent impedance of the power grid at time t. Let be the grid connection point voltage at time t. For reference voltage, This is the reactive power regulation coefficient.

[0019] Optionally, the step of dynamically allocating energy storage charging and discharging power based on the corrected inverter output power and the energy storage system's state of charge includes:

[0020] Calculate the difference between the load active power and the corrected inverter active power output to obtain the power deviation;

[0021] Calculate the difference between the baseline reference value of the energy storage system's state of charge and the energy storage system's state of charge to obtain the load deviation;

[0022] The power deviation and the load deviation are weighted and summed to obtain the allocated energy storage charging and discharging power.

[0023] Optionally, the step of correcting the inverter output power using the updated grid equivalent impedance and the updated photovoltaic maximum output includes:

[0024] The photovoltaic maximum power point output is updated by updating the photovoltaic maximum power point output, and the inverter active power output is updated by updating the photovoltaic maximum power point output and the grid equivalent impedance.

[0025] The inverter reactive power output is updated by updating the grid equivalent impedance.

[0026] Optionally, the objective function of the optimization model is:

[0027]

[0028] The constraints of the optimization model include:

[0029]

[0030] In the formula, Let be the voltage deviation at time t. The frequency deviation at time t, Let be the energy storage cost at time t. Let t be the amount of light discarded at time t, and T be the adjustment period; , , , These are the weighting coefficients for voltage deviation, frequency deviation, energy storage cost, and curtailment of solar power, respectively. For the photovoltaic output at time t, Let be the energy storage charging and discharging power at time t. The inverter's reactive power output at time t. Let be the active power of the load at time t. Let be the static reactive power compensation power at time t; Let be the reactive power of the load at time t; Let t be the state of charge of the energy storage system. This is the state-of-charge limit of the energy storage system. This represents the upper limit of the state of charge of the energy storage system.

[0031] Optionally, the calculation process for wasted light includes:

[0032] Predict photovoltaic output based on predicted irradiance;

[0033] Determine whether the photovoltaic output is greater than the sum of the maximum power of energy storage charging and discharging and the active power of the load;

[0034] If so, the amount of curtailed solar power is determined based on the difference between the photovoltaic output, the maximum power of the energy storage charging and discharging, and the active power of the load.

[0035] If not, set the discarded light amount to 0.

[0036] A second aspect of this application provides a high-penetration photovoltaic grid-connected collaborative control system, comprising:

[0037] The real-time control layer is used to calculate the voltage deviation and frequency deviation based on the grid connection point voltage and grid connection point frequency, and to correct the inverter output power based on the dynamic virtual impedance calculated by the voltage deviation and the frequency deviation and the grid equivalent impedance.

[0038] The short-term coordination layer is used to dynamically allocate the energy storage charging and discharging power based on the corrected inverter output power and the energy storage system's state of charge.

[0039] The long-term optimization layer is used to construct an optimization model with the goal of minimizing the sum of voltage deviation, frequency deviation, energy storage cost, and curtailment. The optimization model is solved based on the energy storage charging and discharging power and meteorological data to obtain the optimal curtailment and optimal energy storage cost.

[0040] The grid equivalent impedance and photovoltaic maximum output are updated based on the optimal curtailment amount, the optimal energy storage cost, the grid connection point voltage, grid connection point current, and meteorological data uploaded by the real-time control layer. The inverter output power is then corrected using the updated grid equivalent impedance and updated photovoltaic maximum output.

[0041] Optionally, the inverter output power includes the inverter's active power output and reactive power output, and the correction formula for the inverter output power is:

[0042]

[0043] In the formula, Let t be the active power output of the inverter. The inverter's reactive power output at time t. Let t be the photovoltaic maximum power point tracking output. Let be the dynamic virtual impedance at time t. Let be the equivalent impedance of the power grid at time t. Let be the grid connection point voltage at time t. For reference voltage, This is the reactive power regulation coefficient.

[0044] A third aspect of this application provides an electronic device, the device including a processor and a memory;

[0045] The memory is used to store program code and transmit the program code to the processor;

[0046] The processor is used to execute any one of the high-penetration photovoltaic grid-connected collaborative control methods described in the first aspect according to the instructions in the program code.

[0047] A fourth aspect of this application provides a computer-readable storage medium for storing program code that, when executed by a processor, implements the high-penetration photovoltaic grid-connected collaborative control method described in any of the first aspects.

[0048] As can be seen from the above technical solutions, this application has the following advantages:

[0049] The high-penetration photovoltaic grid-connected collaborative control method provided in this application suppresses instantaneous fluctuations through a real-time control layer, compensates for second-level power surges in photovoltaics and adjusts dynamic virtual impedance through the rapid response of the inverter, and dynamically adjusts the inverter output impedance by real-time detection of grid connection point voltage and frequency deviations to offset voltage flicker caused by sudden drops in photovoltaic output within 10ms; smooths minute-level fluctuations through a short-time coordination layer, fills minute-level power gaps caused by photovoltaic cloud shading by utilizing the rapid charging and discharging characteristics of energy storage, and dynamically allocates charging and discharging tasks according to the state of charge of each energy storage system; and reconstructs grid operation resilience through a long-term optimization layer and solves the problem of systemic inertia loss caused by high proportion of photovoltaics through clustered coordinated optimization.

[0050] Furthermore, this application adopts a synergistic efficiency mechanism. The millisecond-level disturbance information processed by the real-time control layer is uploaded to the short-term coordination layer to correct the energy storage action strategy. Through the architecture of layered blocking and collaborative defense, this application decomposes the impact of photovoltaic fluctuations on the power grid into different time scales for targeted processing. At the same time, the equivalent power grid inertia is reconstructed through spatiotemporal collaboration, which effectively reduces the voltage over-limit rate and reduces the amount of curtailed solar power.

[0051] Furthermore, this application decomposes the network-wide control into three layers: a real-time control layer (node ​​level), a short-term coordination layer (cluster level), and a long-term optimization layer (region level). Each layer only needs to process the corresponding scale variables, which improves computational efficiency compared to traditional centralized control methods. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 A schematic flowchart of a high-penetration photovoltaic grid-connected collaborative control method provided in this application embodiment;

[0054] Figure 2 This is a schematic diagram of a high-penetration photovoltaic grid-connected collaborative control device provided in an embodiment of this application. Detailed Implementation

[0055] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0056] For easier understanding, please refer to Figure 1 This application provides a high-penetration photovoltaic grid-connected collaborative control method, including:

[0057] Step 110: Calculate the voltage deviation and frequency deviation based on the grid connection point voltage and grid connection point frequency, and correct the inverter output power based on the dynamic virtual impedance calculated from the voltage deviation and frequency deviation and the grid equivalent impedance.

[0058] This application decomposes the overall network control into three layers: a real-time control layer, a short-term coordination layer, and a long-term optimization layer. The real-time control layer can obtain the voltage at the high-penetration photovoltaic grid connection point in real time through a phasor measurement unit (PMU). Grid connection point current, and real-time acquisition of grid connection point frequency. Meteorological data such as irradiance and temperature from weather stations. The real-time control layer uses grid connection point voltage... Grid connection frequency Calculate the voltage deviation separately Frequency deviation According to voltage deviation and frequency deviation The dynamic virtual impedance is calculated and used to correct the inverter's power output. Based on small-signal stability analysis, the real-time control layer constructs a dynamic virtual impedance model to calculate the dynamic virtual impedance, thereby correcting the inverter's output impedance in real time and suppressing resonance and voltage fluctuations caused by high-penetration photovoltaics.

[0059] The dynamic virtual impedance model is as follows:

[0060]

[0061] In the formula, Let be the dynamic virtual impedance at time t; This is the equivalent reference impedance; This is the voltage sensitivity coefficient; Let be the grid connection point voltage at time t. For reference voltage, Let be the voltage deviation at time t; Let be the grid connection frequency at time t. Nominal frequency, The frequency deviation at time t; These are the frequency integral weighting coefficients;

[0062] The formula for correcting the inverter output power is:

[0063]

[0064] In the formula, Let t be the active power output of the inverter. The inverter's reactive power output at time t. Power output for photovoltaic maximum power point tracking. Let be the dynamic virtual impedance at time t. Let be the equivalent impedance of the power grid at time t. Let be the grid connection point voltage at time t. For reference voltage, This is the reactive power regulation coefficient.

[0065] The real-time control layer (capable of millisecond-level control) is used to suppress transient fluctuations. It compensates for second-level power surges in photovoltaic power and adjusts dynamic virtual impedance through the inverter's rapid response: it detects grid connection point voltage and frequency deviations in real time and dynamically adjusts the inverter's output impedance, which can offset voltage flicker caused by sudden drops in photovoltaic output within 10ms. For example, when a voltage drop is detected, the inverter's virtual impedance is instantly increased, limiting current output to prevent overcurrent.

[0066] Step 120: Dynamically allocate energy storage charging and discharging power based on the corrected inverter output power and the energy storage system state of charge;

[0067] The short-time coordination layer calculates the difference between the load's active power and the corrected inverter's active power output to obtain the power deviation; it calculates the difference between the reference value of the energy storage system's state of charge and the energy storage system's state of charge to obtain the load deviation; and it performs a weighted summation of the power deviation and load deviation to obtain the allocated energy storage charging and discharging power. The short-time coordination layer dynamically allocates energy storage charging and discharging power to smooth photovoltaic fluctuations and achieve power balance.

[0068] The energy storage power allocation formula is as follows:

[0069]

[0070] In the formula, Let be the energy storage charging and discharging power at time t. The power allocation factor, This is the state of charge adjustment coefficient. Let be the active power of the load at time t. This serves as a reference value for the state of charge of the energy storage system. Let t be the state of charge of the energy storage system at time t.

[0071] The short-term coordination layer (capable of second-level control) smooths out minute-level fluctuations, utilizing the rapid charging and discharging characteristics of energy storage to fill minute-level power gaps caused by photovoltaic cloud obstruction. It dynamically allocates charging and discharging tasks based on the state of charge (SOC) of each energy storage system. For short- to medium-term changes such as gradual changes in photovoltaic output and periodic load fluctuations, the control cycle is synchronized with the frequency of weather forecast updates (e.g., satellite cloud image updates). When dynamically allocating energy storage power, it dynamically adjusts the charging and discharging priorities of energy storage based on ultra-short-term photovoltaic output forecasts (e.g., the next 30 seconds) and load trends. For example, if it is predicted that clouds will cover the photovoltaic power station in 10 seconds, it can instruct energy storage to discharge at maximum power to fill the power gap in advance. It can coordinate equipment such as capacitor banks to achieve refined reactive power compensation on a second-level timescale.

[0072] Step 130: Construct an optimization model with the goal of minimizing the sum of voltage deviation, frequency deviation, energy storage cost, and curtailment of solar power. Solve the optimization model based on energy storage charging and discharging power and meteorological data to obtain the optimal curtailment of solar power and the optimal energy storage cost.

[0073] The long-term optimization layer constructs an optimization model with the goal of minimizing the sum of voltage deviation, frequency deviation, energy storage cost, and curtailment. This achieves multi-objective coordinated optimization of voltage, frequency, and economics. The optimization model is solved based on energy storage charging and discharging power and meteorological data to obtain the optimal model values ​​after compromises in multi-objective coordinated optimization, including optimal curtailment, optimal energy storage cost, optimal grid connection voltage, and optimal grid connection frequency. Voltage and frequency control of photovoltaic grid connection can then be performed based on the optimal grid connection voltage and frequency.

[0074] The objective function of the optimization model is:

[0075]

[0076] The constraints of the optimization model include:

[0077]

[0078] In the formula, Let be the voltage deviation at time t. The frequency deviation at time t, Let be the energy storage cost at time t. Let be the amount of light discarded at time t; T be the adjustment period. , , , These are the weighting coefficients for voltage deviation, frequency deviation, energy storage cost, and curtailment of solar power, respectively. For the photovoltaic output at time t, Let be the energy storage charging and discharging power at time t. The inverter's reactive power output at time t. Let be the active power of the load at time t. Let be the static reactive power compensation power at time t; Let be the reactive power of the load at time t; Let t be the state of charge of the energy storage system. This is the state-of-charge limit of the energy storage system. This represents the upper limit of the state of charge of the energy storage system.

[0079] Energy storage SOC is updated through the short-term layer, with SOC serving as a constraint condition. Limit the feasible range of energy storage charging and discharging power to prevent overcharging / over-discharging of energy storage, while optimizing its economics.

[0080] Through the energy storage cost term in the objective function This reflects the impact of SOC changes on battery life, namely:

[0081]

[0082] In the formula, The energy storage attenuation coefficient is denoted by SOC(t). The lower the SOC(t), the higher the charging and discharging cost.

[0083] Meteorological data can be used to adjust energy storage and curtailment strategies in advance to cope with photovoltaic fluctuations. The predicted maximum photovoltaic output at time t. = As input to the power balance constraint:

[0084]

[0085] In the formula, For the photovoltaic output at time t, The predicted irradiance at time t can be obtained from meteorological data. For photovoltaic conversion efficiency, This refers to the effective light-receiving area of ​​the photovoltaic array; Let t be the maximum power of energy storage charging and discharging at time t.

[0086] The calculation of curtailment is influenced by photovoltaic (PV) output. Specifically, PV output is predicted based on the predicted irradiance; it is then determined whether the PV output exceeds the sum of the maximum power of energy storage charging / discharging and the active power of the load. If so, the curtailment is determined based on the difference between the PV output and the maximum power of energy storage charging / discharging and the active power of the load; otherwise, the curtailment is set to 0. .

[0087] By solving the above optimization model, the optimal amount of curtailed solar power, the optimal energy storage cost, the optimal grid connection voltage, and the optimal grid connection frequency are finally obtained.

[0088] The long-term optimization layer (achieving minute-level control) is used to optimize system resilience. Through clustered coordinated optimization, it solves the problem of systemic inertia loss caused by a high proportion of photovoltaic power.

[0089] Step 140: Update the grid equivalent impedance and photovoltaic maximum output based on the optimal curtailment amount, optimal energy storage cost, grid connection point voltage, grid connection point current and meteorological data uploaded by the real-time control layer, and correct the inverter output power using the updated grid equivalent impedance and updated photovoltaic maximum output.

[0090] The long-term optimization layer updates the grid equivalent impedance and maximum photovoltaic output using a recursive least squares method, based on the optimal curtailment rate, optimal energy storage cost, grid connection point voltage, grid connection point current, and meteorological data uploaded by the real-time control layer. Specifically, the long-term optimization layer uses the grid equivalent impedance... and the maximum output of photovoltaic power For the parameters to be identified, the optimal light discard rate is used. Optimal energy storage cost Grid connection point voltage Grid connection point current The input feature vector is generated from the irradiance, and a model for updating the parameters to be identified is constructed:

[0091]

[0092] in, Let be the parameters to be identified at time t. Let be the input feature vector at time t. , , Let be the grid connection point voltage and grid connection point current at time t, respectively. Let be the normalized irradiance at time t. Let be the light rejection rate at time t. Let be the percentage of energy storage cost at time t. To maximize energy storage cost, Here is the Kalman gain matrix. This represents the actual measured output value of the system at time t, which is used to compare with the model's predicted value and drive parameter updates. Typically, the data includes the following two categories:

[0093] (1) To : Re() represents taking the real part of a complex number;

[0094] (2) To : .

[0095] In the model for updating parameters to be identified Compared with the predicted value The error function is:

[0096]

[0097] error It directly participates in parameter correction. The parameters to be identified at time t-1 are given.

[0098] When updating parameters, the Kalman gain matrix is ​​used. Implement parameter updates. The weight of the new measurement data in the parameter estimation is determined:

[0099]

[0100] in, Let be the covariance matrix at time t-1. , This is the forgetting factor (used to reduce the influence of old data). The parameter update process is as follows:

[0101] First, calculate the error: through Compared with model predictions The deviation generates an error signal ;

[0102] Then, adjust the gain: based on the current input features. Covariance Matrix Dynamically adjust the Kalman gain matrix ;

[0103] Finally, adjust the parameters: using the current Kalman gain matrix. Sum of error signals Iterative updates to the parameter vector, i.e. .

[0104] Furthermore, when the optimal energy storage cost Exceeding the threshold ,limit Update range :

[0105]

[0106] In the formula, This is the cost sensitivity coefficient, typically ranging from 0.1 to 0.3. The embodiments in this application update the economic boundary using energy storage cost constraint parameters.

[0107] After obtaining the updated grid equivalent impedance and the updated maximum photovoltaic output, the inverter output power is corrected using these updated parameters. Specifically, firstly, the maximum power point tracking (MPPT) output is updated using the updated maximum photovoltaic output. Photovoltaic maximum power point tracking output The benchmark value is based on the maximum output of photovoltaic power. Dynamically updated:

[0108]

[0109] In the formula, This refers to the rated capacity of the photovoltaic system. Based on current meteorological data such as irradiance and temperature, the baseline value for maximum power point tracking is dynamically adjusted to avoid overload or curtailment caused by sudden environmental changes.

[0110] The inverter's active power output is updated by updating the grid equivalent impedance and the photovoltaic maximum power point tracking output, and the inverter's reactive power output is updated by updating the grid equivalent impedance.

[0111] Secondly, the energy storage power allocation is adjusted by updating the maximum photovoltaic output and the updated grid equivalent impedance. Because the inverter has active power output The real-time value is affected by the maximum output of photovoltaic power. Equivalent impedance of the power grid The impact on the active power output of the upgraded inverter Then, the energy storage power allocation is adjusted. When photovoltaic power output changes abruptly (e.g., due to cloud cover), the maximum photovoltaic output will be utilized. Rapidly update energy storage charging and discharging requirements to maintain power balance.

[0112] Finally, the amount of curtailed solar power is updated by adjusting the maximum output of the photovoltaic system. : In the long-term optimization layer, the maximum photovoltaic output is based on the prediction. Adjust the curtailment strategy to balance system security and economy.

[0113] The online identification results are fed back into the parameter update model in step 140, forming a closed-loop update to ensure that the model parameters are continuously optimized as the environment changes, thereby improving system adaptability. Through multi-level parameter transmission and closed-loop feedback, multi-objective coordinated optimization of voltage, frequency, and economic efficiency in a high-penetration photovoltaic distribution network is achieved. In this embodiment, the long-term layer can generate a scheduling baseline every 5 minutes (the specific interval can be set according to actual conditions), the short-term layer fine-tunes the action strategy every second, and the real-time layer executes closed-loop control every 10 milliseconds, forming a nested structure of "coarse adjustment-fine adjustment-micro adjustment". Through multi-level coordination, the maximum photovoltaic output becomes the core parameter connecting the physical model and data-driven operation, achieving precise control of photovoltaic output and improved system stability.

[0114] The high-penetration photovoltaic grid-connected collaborative control method provided in this application suppresses instantaneous fluctuations through a real-time control layer, compensates for second-level power surges in photovoltaics and adjusts dynamic virtual impedance through the rapid response of the inverter, and dynamically adjusts the inverter output impedance by real-time detection of grid connection point voltage and frequency deviations to offset voltage flicker caused by sudden drops in photovoltaic output within 10ms; smooths minute-level fluctuations through a short-time coordination layer, fills minute-level power gaps caused by photovoltaic cloud shading by utilizing the rapid charging and discharging characteristics of energy storage, and dynamically allocates charging and discharging tasks according to the state of charge of each energy storage system; and reconstructs grid operation resilience through a long-term optimization layer and solves the problem of systemic inertia loss caused by high proportion of photovoltaics through clustered coordinated optimization.

[0115] Furthermore, this application adopts a synergistic efficiency mechanism. The millisecond-level disturbance information processed by the real-time control layer is uploaded to the short-term coordination layer to correct the energy storage action strategy. Through the architecture of layered blocking and collaborative defense, this application decomposes the impact of photovoltaic fluctuations on the power grid into different time scales for targeted processing. At the same time, the equivalent power grid inertia is reconstructed through spatiotemporal collaboration, which effectively reduces the voltage over-limit rate and reduces the amount of curtailed solar power.

[0116] Most existing technologies employ centralized control of voltage and frequency. Centralized control methods require unified modeling of all node parameters (voltage, power, etc.) across the entire network, leading to an exponential increase in the number of optimization variables with the number of nodes. For example, the power flow equations for a power grid with N nodes have a dimension of 2N×2N; when N>100, the computational complexity exceeds the processing capacity of a conventional scheduling server. Millisecond-level fast control requires frequent calls to the entire network state estimate, but a single state estimate in a centralized architecture can take hundreds of milliseconds, failing to meet the demands of high-frequency regulation. All measurement data must be uploaded to the central controller for processing; communication latency in complex topologies can exceed 100ms, especially in rural distribution networks where the deployment of PMUs leads to significant transmission congestion caused by the surge in data volume.

[0117] This application decomposes the network-wide control into three layers: a real-time control layer (node ​​level), a short-term coordination layer (cluster level), and a long-term optimization layer (region level). Each layer only needs to process the corresponding scale variables, which improves computational efficiency compared to traditional centralized control methods.

[0118] Existing technologies typically employ centralized control methods, but centralized frequency modulation response delays exceed the second level, making it impossible to track millisecond-level photovoltaic fluctuations. Fixed virtual impedance models are prone to over-adjustment under operating conditions, and offline parameter tuning results in poor controller adaptability. This application addresses these issues by employing multi-timescale collaborative control to suppress fluctuations in real time, dynamic virtual impedance to reshape system damping characteristics, data-driven parameter identification to improve model robustness, and multi-objective optimization to achieve a safe and economical equilibrium. This forms a complete solution spanning "physical response - data perception - optimization decision-making," with its technological advantages precisely mapping to the root causes of the problems, providing a new breakthrough path for high-penetration photovoltaic grid connection.

[0119] The above is an embodiment of a high-penetration photovoltaic grid-connected collaborative control method provided in this application. The following is an embodiment of a high-penetration photovoltaic grid-connected collaborative control system provided in this application.

[0120] Please refer to Figure 2 This application provides a high-penetration photovoltaic grid-connected collaborative control system, comprising:

[0121] The real-time control layer 210 is used to calculate the voltage deviation and frequency deviation based on the grid connection point voltage and grid connection point frequency, and to correct the inverter output power based on the dynamic virtual impedance calculated by the voltage deviation and frequency deviation and the grid equivalent impedance.

[0122] Short-time coordination layer 220 is used to dynamically allocate energy storage charging and discharging power according to the corrected inverter output power and the energy storage system state of charge.

[0123] Long-term optimization layer 230 is used to construct an optimization model with the goal of minimizing the sum of voltage deviation, frequency deviation, energy storage cost and curtailment. The optimization model is solved based on energy storage charging and discharging power and meteorological data to obtain the optimal curtailment and optimal energy storage cost.

[0124] The grid equivalent impedance and maximum photovoltaic output are updated based on the optimal curtailment amount, optimal energy storage cost, grid connection point voltage, grid connection point current, and meteorological data uploaded by the real-time control layer. The inverter output power is then corrected using the updated grid equivalent impedance and updated maximum photovoltaic output.

[0125] As a further improvement, the inverter output power includes the inverter's active power output and reactive power output. The corrected formula for the inverter output power is as follows:

[0126]

[0127] In the formula, Let t be the active power output of the inverter. The inverter's reactive power output at time t. Let t be the photovoltaic maximum power point tracking output. Let be the dynamic virtual impedance at time t. Let be the equivalent impedance of the power grid at time t. Let be the grid connection point voltage at time t. For reference voltage, This is the reactive power regulation coefficient.

[0128] The high-penetration photovoltaic grid-connected collaborative control method provided in this application suppresses instantaneous fluctuations through a real-time control layer, compensates for second-level power surges in photovoltaics and adjusts dynamic virtual impedance through the rapid response of the inverter, and dynamically adjusts the inverter output impedance by real-time detection of grid connection point voltage and frequency deviations to offset voltage flicker caused by sudden drops in photovoltaic output within 10ms; smooths minute-level fluctuations through a short-time coordination layer, fills minute-level power gaps caused by photovoltaic cloud shading by utilizing the rapid charging and discharging characteristics of energy storage, and dynamically allocates charging and discharging tasks according to the state of charge of each energy storage system; and reconstructs grid operation resilience through a long-term optimization layer and solves the problem of systemic inertia loss caused by high proportion of photovoltaics through clustered coordinated optimization.

[0129] Furthermore, this application adopts a synergistic efficiency mechanism. The millisecond-level disturbance information processed by the real-time control layer is uploaded to the short-term coordination layer to correct the energy storage action strategy. Through the architecture of layered blocking and collaborative defense, this application decomposes the impact of photovoltaic fluctuations on the power grid into different time scales for targeted processing. At the same time, the equivalent power grid inertia is reconstructed through spatiotemporal collaboration, which effectively reduces the voltage over-limit rate and reduces the amount of curtailed solar power.

[0130] This application decomposes the network-wide control into three layers: a real-time control layer (node ​​level), a short-term coordination layer (cluster level), and a long-term optimization layer (region level). Each layer only needs to process the corresponding scale variables, which improves computational efficiency compared to traditional centralized control methods.

[0131] This application also provides an electronic device, which includes a processor and a memory;

[0132] The memory is used to store program code and transfer the program code to the processor;

[0133] The processor is used to execute the high-penetration photovoltaic grid-connected coordinated control method in the foregoing method embodiments according to the instructions in the program code.

[0134] This application also provides a computer-readable storage medium for storing program code, which, when executed by a processor, implements the high-penetration photovoltaic grid-connected coordinated control method described in the foregoing method embodiments.

[0135] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0136] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product, or apparatus.

[0137] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0139] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0140] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0141] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of this application through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0142] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A high-penetration photovoltaic grid-connected collaborative control method, characterized in that, include: The voltage deviation and frequency deviation are calculated based on the grid connection point voltage and grid connection point frequency, respectively. The inverter output power is then corrected based on the dynamic virtual impedance calculated from the voltage deviation and the frequency deviation, as well as the grid equivalent impedance. The formula for calculating the dynamic virtual impedance is as follows: ； In the formula, Let be the dynamic virtual impedance at time t; This is the equivalent reference impedance; This is the voltage sensitivity coefficient; Let be the grid connection point voltage at time t. For reference voltage, Let be the voltage deviation at time t; Let be the grid connection frequency at time t. Nominal frequency, The frequency deviation at time t; These are the frequency integral weighting coefficients; The inverter output power includes the inverter's active power output and reactive power output. The corrected formula for the inverter output power is: ； In the formula, Let t be the active power output of the inverter. The inverter's reactive power output at time t. Let t be the photovoltaic maximum power point tracking output. Let be the dynamic virtual impedance at time t. Let be the equivalent impedance of the power grid at time t. Let be the grid connection point voltage at time t. For reference voltage, This is the reactive power regulation coefficient; The energy storage charging and discharging power is dynamically allocated based on the corrected inverter output power and the energy storage system's state of charge. An optimization model is constructed with the goal of minimizing the sum of voltage deviation, frequency deviation, energy storage cost, and curtailment of solar power. The optimization model is solved based on the energy storage charging and discharging power and meteorological data to obtain the optimal curtailment of solar power and the optimal energy storage cost. The grid equivalent impedance and photovoltaic maximum output are updated based on the optimal curtailment amount, the optimal energy storage cost, the grid connection point voltage, the grid connection point current, and meteorological data. The inverter output power is then corrected using the updated grid equivalent impedance and the updated photovoltaic maximum output. The method of correcting the inverter output power using the updated grid equivalent impedance and the updated photovoltaic maximum output includes: The photovoltaic maximum power point output is updated by updating the photovoltaic maximum power point output, and the inverter active power output is updated by updating the photovoltaic maximum power point output and the grid equivalent impedance. The inverter reactive power output is updated by updating the grid equivalent impedance.

2. The high-penetration photovoltaic grid-connected coordinated control method according to claim 1, characterized in that, The dynamic allocation of energy storage charging and discharging power based on the corrected inverter output power and the energy storage system's state of charge includes: Calculate the difference between the load active power and the corrected inverter active power output to obtain the power deviation; Calculate the difference between the baseline reference value of the energy storage system's state of charge and the energy storage system's state of charge to obtain the load deviation; The power deviation and the load deviation are weighted and summed to obtain the allocated energy storage charging and discharging power.

3. The high-penetration photovoltaic grid-connected coordinated control method according to claim 1, characterized in that, The objective function of the optimization model is: ； The constraints of the optimization model include: ； In the formula, Let be the voltage deviation at time t. The frequency deviation at time t, Let be the energy storage cost at time t. Let t be the amount of light discarded at time t, and T be the adjustment period; , , , These are the weighting coefficients for voltage deviation, frequency deviation, energy storage cost, and curtailment of solar power, respectively. For the photovoltaic output at time t, Let be the energy storage charging and discharging power at time t. The inverter's reactive power output at time t. Let be the active power of the load at time t. Let be the static reactive power compensation power at time t; Let be the reactive power of the load at time t; Let t be the state of charge of the energy storage system. This is the state-of-charge limit of the energy storage system. This represents the upper limit of the state of charge of the energy storage system.

4. The high-penetration photovoltaic grid-connected coordinated control method according to claim 1, characterized in that, The calculation process for light waste includes: Predict photovoltaic output based on predicted irradiance; Determine whether the photovoltaic output is greater than the sum of the maximum power of energy storage charging and discharging and the active power of the load; If so, the amount of curtailed solar power is determined based on the difference between the photovoltaic output, the maximum power of the energy storage charging and discharging, and the active power of the load. If not, set the discarded light amount to 0.

5. A high-penetration photovoltaic grid-connected collaborative control system, characterized in that, include: The real-time control layer is used to calculate the voltage deviation and frequency deviation based on the grid connection point voltage and grid connection point frequency, and to correct the inverter output power based on the dynamic virtual impedance calculated by the voltage deviation and the frequency deviation and the grid equivalent impedance. The formula for calculating the dynamic virtual impedance is as follows: ； In the formula, Let be the dynamic virtual impedance at time t; This is the equivalent reference impedance; This is the voltage sensitivity coefficient; Let be the grid connection point voltage at time t. For reference voltage, Let be the voltage deviation at time t; Let be the grid connection frequency at time t. Nominal frequency, The frequency deviation at time t; These are the frequency integral weighting coefficients; The inverter output power includes the inverter's active power output and reactive power output. The corrected formula for the inverter output power is: ； In the formula, Let t be the active power output of the inverter. The inverter's reactive power output at time t. Let t be the photovoltaic maximum power point tracking output. Let be the dynamic virtual impedance at time t. Let be the equivalent impedance of the power grid at time t. Let be the grid connection point voltage at time t. For reference voltage, This is the reactive power regulation coefficient; The short-term coordination layer is used to dynamically allocate the energy storage charging and discharging power based on the corrected inverter output power and the energy storage system's state of charge. The long-term optimization layer is used to construct an optimization model with the goal of minimizing the sum of voltage deviation, frequency deviation, energy storage cost, and curtailment. The optimization model is solved based on the energy storage charging and discharging power and meteorological data to obtain the optimal curtailment and optimal energy storage cost. The grid equivalent impedance and photovoltaic maximum output are updated based on the optimal curtailment amount, the optimal energy storage cost, the grid connection point voltage, grid connection point current and meteorological data uploaded by the real-time control layer, and the inverter output power is corrected by the updated grid equivalent impedance and updated photovoltaic maximum output. The method of correcting the inverter output power using the updated grid equivalent impedance and the updated photovoltaic maximum output includes: The photovoltaic maximum power point output is updated by updating the photovoltaic maximum power point output, and the inverter active power output is updated by updating the photovoltaic maximum power point output and the grid equivalent impedance. The inverter reactive power output is updated by updating the grid equivalent impedance.

6. An electronic device, characterized in that, The device includes a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the high-penetration photovoltaic grid-connected collaborative control method according to any one of claims 1-4, based on the instructions in the program code.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code, which, when executed by a processor, implements the high-penetration photovoltaic grid-connected collaborative control method according to any one of claims 1-4.

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