A high-penetration photovoltaic grid-connected voltage stability control method and related device

Abstract

The present application belongs to the field of power grid technology, and discloses a high-penetration photovoltaic grid-connected voltage stability control method and related devices. The method includes: calculating a dynamic virtual impedance based on the current change, power fluctuation, and voltage reference deviation of the photovoltaic cluster, updating the admittance matrix of the distribution network through the dynamic virtual impedance, and then calculating the state matrix of the distribution network; optimizing the voltage distribution function through the state matrix to obtain optimized voltage distribution parameters; adjusting the voltage reference value and adjustment parameters through the optimized voltage distribution parameters, inputting the adjusted voltage reference value and adjustment parameters into the global optimization model for calculation, and updating the reactive output adjustment amount of each photovoltaic node according to the calculation results; and feeding back the updated reactive output adjustment amount of each photovoltaic node to the photovoltaic inverter controller for reactive output adjustment. The present application corrects the admittance matrix through dynamic virtual impedance to compensate for the voltage deviation caused by photovoltaic output fluctuations, thereby improving the operational stability of the power grid.

Description

Technical Field

[0001] The present application relates to the field of power grid technology, and in particular to a high-penetration photovoltaic grid-connected voltage stability control method and related devices. Background Art

[0002] As renewable energy penetration continues to climb, distribution network voltage stability control faces unprecedented technical challenges. Current mainstream control methods, primarily based on static impedance compensation, centralized PI regulation, and manual threshold setting, are increasingly revealing serious limitations when dealing with high-proportion photovoltaic integration. First, fixed-parameter virtual impedance models struggle to adapt to the minute-by-second fluctuations in photovoltaic output, leading to voltage deviations that consistently exceed safety limits. Second, centralized control architectures require processing high-dimensional state data from the entire network, resulting in significant computational latency and an inability to meet real-time requirements. Furthermore, when photovoltaic penetration exceeds 60%, traditional control strategies are prone to local optimization traps, triggering cascading voltage instabilities. Summary of the Invention

[0003] The present application provides a high-penetration photovoltaic grid-connected voltage stability control method and related devices, which are used to improve the technical problem that the static virtual impedance model used in the existing technology cannot adapt to the second-level fluctuations of photovoltaic output, resulting in low grid stability.

[0004] In view of this, the first aspect of the present application provides a high-penetration photovoltaic grid-connected voltage stability control method, comprising:

[0005] Calculating a dynamic virtual impedance based on the current variation, power fluctuation, and voltage reference deviation of the photovoltaic cluster, updating the admittance matrix of the distribution network using the dynamic virtual impedance, and calculating the voltage, current, and power of the photovoltaic node using the updated admittance matrix to generate a state matrix of the distribution network;

[0006] Constructing a voltage distribution function, optimizing the voltage distribution function using the state matrix, and obtaining optimized voltage distribution parameters;

[0007] The voltage reference value and the adjustment parameter are adjusted using the optimized voltage distribution parameter, the adjusted voltage reference value and the adjusted adjustment parameter are input into a global optimization model for calculation, and the reactive output adjustment amount of each photovoltaic node is updated according to the calculation result; the global optimization model aims to minimize the sum of the voltage deviation, reactive output deviation and reactive output adjustment amount of all photovoltaic nodes;

[0008] The updated reactive power output regulation value of each photovoltaic node is fed back to the photovoltaic inverter controller for reactive power output regulation.

[0009] Optionally, the process of obtaining the voltage reference deviation includes:

[0010] The voltage of each photovoltaic node in the photovoltaic cluster is collected, and the voltage difference between the voltage of each photovoltaic node and the current voltage reference value is calculated. The average value of the voltage difference of all photovoltaic nodes in the photovoltaic cluster is calculated to obtain the voltage reference deviation of the photovoltaic cluster.

[0011] Optionally, the method further includes:

[0012] Performing dimensionality reduction processing on the state matrix to obtain a state matrix after dimensionality reduction;

[0013] Accordingly, constructing the voltage distribution function, optimizing the voltage distribution function using the state matrix, and obtaining optimized voltage distribution parameters include:

[0014] A voltage distribution function is constructed, and the voltage distribution function is optimized using the reduced-dimensional state matrix to obtain optimized voltage distribution parameters.

[0015] Optionally, the voltage distribution parameters include a voltage mean of the photovoltaic cluster and a contribution weight of the photovoltaic cluster to global voltage stability;

[0016] Adjusting the voltage reference value using the optimized voltage distribution parameter includes:

[0017] According to the contribution weight of the optimized photovoltaic cluster to the global voltage stability, the voltage mean of the optimized photovoltaic cluster is weighted and summed to obtain an adjusted voltage reference value; the baseline reference voltage is set according to the distribution network operation standard;

[0018] Alternatively, the deviation between the optimized photovoltaic cluster voltage mean and the baseline reference voltage is calculated to obtain the photovoltaic cluster voltage deviation;

[0019] The voltage deviation of the photovoltaic cluster is weighted and summed using the optimized contribution weight of the photovoltaic cluster to the global voltage stability to obtain the voltage adjustment amount;

[0020] The baseline reference voltage is adjusted according to the voltage adjustment amount to obtain an adjusted voltage reference value.

[0021] Optionally, the voltage distribution parameter also includes the voltage standard deviation of the photovoltaic cluster; the regulation parameter includes the reactive power regulation coefficient and the voltage regulation coefficient;

[0022] Adjusting the regulation parameters using the optimized voltage distribution parameters includes:

[0023] According to the contribution weight of the optimized photovoltaic cluster to the global voltage stability, the voltage standard deviation of the optimized photovoltaic cluster is weighted and summed to obtain the adjusted reactive power regulation coefficient;

[0024] The reciprocal of the maximum value of the voltage standard deviation of the optimized photovoltaic cluster is used as the voltage regulation coefficient to obtain the adjusted voltage regulation coefficient.

[0025] Optionally, the global optimization model is:

[0026]

[0027]

[0028] Where U i is the potential function of photovoltaic node i, is the voltage of photovoltaic node i, n is the total number of photovoltaic nodes in the photovoltaic cluster, is the reference voltage, is the voltage regulation coefficient, is the reactive power output of photovoltaic node i, is the reactive power reference value, is the reactive power regulation coefficient; through the voltage regulation coefficient and reactive power regulation coefficient , potential function U i Ability to perform dimensionless operations; is the set of reactive output regulation quantities of all photovoltaic nodes, is the reactive power output regulation of photovoltaic node n, is the L2 norm, is the regularization coefficient.

[0029] Optionally, the voltage distribution function is:

[0030]

[0031] Where, is the mean voltage of PV cluster j, is the voltage of PV node i in PV cluster j; is the voltage standard deviation of PV cluster j; is the contribution weight of PV cluster j to global voltage stability, and N is the number of PV clusters.

[0032] A second aspect of the present application provides a high-penetration photovoltaic grid-connected voltage stability control device, comprising:

[0033] A state calculation unit is configured to calculate a dynamic virtual impedance based on the current variation, power fluctuation, and voltage reference deviation of the photovoltaic cluster, update the admittance matrix of the distribution network using the dynamic virtual impedance, calculate the voltage, current, and power of the photovoltaic node using the updated admittance matrix, and generate a state matrix of the distribution network;

[0034] a parameter optimization unit, configured to construct a voltage distribution function, optimize the voltage distribution function using the state matrix, and obtain optimized voltage distribution parameters;

[0035] a model calculation unit, configured to adjust a voltage reference value and a regulation parameter using the optimized voltage distribution parameter, input the adjusted voltage reference value and the adjusted regulation parameter into a global optimization model for calculation, and update a reactive output regulation value of each photovoltaic node based on the calculation result; the global optimization model aims to minimize the sum of the voltage deviation of all photovoltaic nodes and the reactive output regulation value of all photovoltaic nodes;

[0036] The feedback regulation unit is used to feed back the updated reactive output regulation value of each photovoltaic node to the photovoltaic inverter controller for reactive output regulation.

[0037] A third aspect of the present application provides an electronic device, the device comprising a processor and a memory;

[0038] The memory is used to store program code and transmit the program code to the processor;

[0039] The processor is configured to execute any one of the high-penetration photovoltaic grid-connected voltage stability control methods described in the first aspect according to instructions in the program code.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium for storing program code. When the program code is executed by a processor, the high-penetration photovoltaic grid-connected voltage stability control method described in any one of the first aspects is implemented.

[0041] It can be seen from the above technical solutions that this application has the following advantages:

[0042] The high-penetration photovoltaic grid-connected voltage stability control method provided in this application introduces an adaptive adjustment mechanism of dynamic virtual impedance. The virtual impedance is dynamically updated based on the photovoltaic current change and power fluctuation. The dynamic virtual impedance is used to dynamically correct the admittance matrix, thereby compensating for voltage deviations caused by photovoltaic output fluctuations, thereby improving grid operation stability.

[0043] Furthermore, the voltage reference value is adjusted by optimizing the voltage mean and the optimized PV cluster contribution weight to global voltage stability, avoiding the control rigidity caused by using a fixed threshold, breaking through the limitations of traditional fixed thresholds, and adapting to extreme operating conditions with a penetration rate greater than 85%.

[0044] Furthermore, the regulation parameters are adjusted by the voltage standard deviation of the photovoltaic cluster and the optimized contribution weight of the photovoltaic cluster to the global voltage stability to achieve dynamic coordination between photovoltaic nodes;

[0045] Furthermore, by reducing the dimensionality of the multi-dimensional state matrix, it helps to improve the computational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A schematic flow chart of a high-penetration photovoltaic grid-connected voltage stability control method provided in an embodiment of the present application;

[0048] Figure 2 Another flow chart of a high-penetration photovoltaic grid-connected voltage stability control method provided in an embodiment of the present application;

[0049] Figure 3 A structural schematic diagram of a high-penetration photovoltaic grid-connected voltage stability control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] 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 ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] For easier understanding, please refer to Figure 1 , an embodiment of the present application provides a high-penetration photovoltaic grid-connected voltage stability control method, comprising:

[0052] Step 110: Calculate a dynamic virtual impedance based on the current variation, power fluctuation, and voltage reference deviation of the PV cluster, update the admittance matrix of the distribution network using the dynamic virtual impedance, calculate the voltage, current, and power of the PV node using the updated admittance matrix, and generate a state matrix of the distribution network;

[0053] Real-time detection of the current and power of each photovoltaic node in the photovoltaic cluster, and calculation of the change relative to the previous moment, to obtain the current change and power change of each photovoltaic node in each time period. The fluctuation rate of the total current and total power of the photovoltaic cluster can be obtained by weighted or arithmetic averaging the current change and power change of each photovoltaic node in the photovoltaic cluster, such as:

[0054]

[0055]

[0056] Where, is the current change of the photovoltaic cluster in the kth period, is the current change of the i-th photovoltaic node in the photovoltaic cluster in the k-th period, is the power fluctuation of the photovoltaic cluster in the kth period, is the power fluctuation of the i-th photovoltaic node in the photovoltaic cluster in the k-th time period, and n is the total number of photovoltaic nodes in the photovoltaic cluster.

[0057] Collect the voltage of each photovoltaic node in the photovoltaic cluster and calculate the voltage of each photovoltaic node and the current voltage reference value The voltage difference of the photovoltaic cluster is calculated, and the average voltage difference of all photovoltaic nodes in the photovoltaic cluster is calculated to obtain the voltage reference deviation of the photovoltaic cluster. .

[0058] Calculate the dynamic virtual impedance based on the current change, power fluctuation and voltage reference deviation of the PV cluster:

[0059]

[0060] Where, is the dynamic virtual impedance of the PV cluster in the kth period, is the voltage reference deviation of the photovoltaic cluster in the kth period, 、 Both are damping coefficients, which are used to adjust the dynamic response speed of dynamic virtual impedance.

[0061] Update the admittance matrix of the distribution network through dynamic virtual impedance:

[0062]

[0063] Where, is the admittance matrix of the distribution network after updating in the kth period, is the initial admittance matrix, represents a diagonal matrix;

[0064] The updated admittance matrix is ​​used to calculate the photovoltaic node voltage, current and power, and generate the state matrix ,in, is the voltage amplitude of photovoltaic node n, is the voltage phase angle of photovoltaic node n, is the injection current amplitude of photovoltaic node n, is the active power of PV node n, is the reactive power of photovoltaic node n. Reflects the correction of virtual impedance to the equivalent structure of the power grid to ensure the state matrix Contains global state information under dynamic conditions. Calculating node voltage, current, and power through the admittance matrix is ​​a prior art technique, and its specific calculation process will not be described in detail here.

[0065] Step 120: construct a voltage distribution function, optimize the voltage distribution function using a state matrix, and obtain optimized voltage distribution parameters;

[0066] The voltage distribution function in the embodiment of the present application is:

[0067]

[0068] Where, is the mean voltage of PV cluster j, is the voltage of PV node i in PV cluster j; is the voltage standard deviation of PV cluster j, representing the intensity of voltage fluctuation; is the contribution weight of PV cluster j to global voltage stability, and N is the number of PV clusters. , initial voltage standard deviation The initial weight coefficient is obtained from historical data statistics. Obtained from the eigenvector of the state matrix X:

[0069]

[0070] Where, is the j-th column eigenvector of the state matrix X, is the kth column eigenvector of the state matrix X, is the number of eigenvector columns of the state matrix X.

[0071] After constructing the voltage distribution function, the voltage distribution optimization objective function is constructed based on the voltage distribution function and the voltage reference distribution:

[0072]

[0073] Where, is the current voltage distribution of photovoltaic node i, is the voltage reference distribution (such as normal distribution), is the regularization coefficient; L is the graph Laplace matrix, which reflects the topological connection relationship of the power grid; by adjusting make Approach , reduce the voltage limit probability; tr is the trace of the matrix; through the regularization constraint ,make sure Smooth changes within the network space and avoid local mutations.

[0074] The voltage distribution parameters can be iteratively updated using the gradient descent method , until the convergence condition is reached (such as the difference between the voltage of each photovoltaic node in the photovoltaic cluster j and the reference voltage is less than the preset voltage deviation threshold and the weight coefficient The change rate of the voltage distribution parameter is lower than the preset change rate threshold), and the latest updated voltage distribution parameters are output to obtain the optimized voltage distribution parameters. .

[0075] Step 130: adjusting the voltage reference value and the regulation parameter using the optimized voltage distribution parameter, inputting the adjusted voltage reference value and the adjusted regulation parameter into the global optimization model for calculation, and updating the reactive power regulation amount of each photovoltaic node according to the calculation result;

[0076] The global optimization model constructed in the embodiment of the present application is:

[0077]

[0078]

[0079] in, is the potential function of photovoltaic node i, is the voltage of photovoltaic node i, is the reference voltage, is the voltage regulation coefficient, is the reactive power output of photovoltaic node i, is the reactive power reference value, is the reactive power regulation coefficient, is the set of reactive output regulation quantities of all photovoltaic nodes, , is the reactive power output regulation of photovoltaic node n, It is the L2 norm of the reactive power regulation set of all photovoltaic nodes, which is used to punish the sharp fluctuation of reactive power regulation of the whole network to avoid over-regulation or oscillation. is a global constraint, through Limit the total amount of regulation across the entire network to avoid over-regulation. is the regularization coefficient.

[0080] It should be noted that the voltage regulation coefficient and reactive power regulation coefficient , potential function U i Able to perform dimensionless operations, you can use coefficients and Normalization is performed: by coefficient Convert the voltage difference to per-unit value (e.g. reference voltage 10kV→1.0 pu), the coefficient According to the PV inverter capacity calibration (e.g., 1MVA→1.0 pu), the square of the final voltage deviation and the square of the reactive output deviation are both converted into dimensionless per-unit squares to achieve physical additivity.

[0081] In one embodiment, the contribution weight of the optimized photovoltaic cluster to the global voltage stability can be calculated based on the weight of the optimized photovoltaic cluster to the global voltage stability. , the voltage mean of the optimized photovoltaic cluster Perform weighted summation to obtain the current adjusted voltage reference value The average voltage of the photovoltaic cluster By weight Weighted fusion is performed to form a global voltage reference value, which is the voltage target for collaborative control, ensuring that the voltage of each photovoltaic node converges to the optimized statistical mean.

[0082] In another embodiment, the optimized average voltage of the photovoltaic cluster can be calculated With baseline reference voltage Deviation, get the voltage deviation of the photovoltaic cluster , where the baseline reference voltage can be set according to the distribution network operation standard (such as IEEE 1547) For example, the baseline reference voltage is set to ±5% of the nominal voltage; according to the contribution weight of the optimized photovoltaic cluster to the global voltage stability , the voltage deviation of the photovoltaic cluster Perform weighted summation to obtain a voltage adjustment amount; adjust the baseline reference voltage according to the voltage adjustment amount to obtain an adjusted voltage reference value, namely:

[0083]

[0084] Where, is the adjusted voltage reference value for the kth period, and N is the number of PV clusters.

[0085] In the embodiment of the present application, the adjustment parameters are adjusted by the optimized voltage distribution parameters, including:

[0086] The contribution weight of the optimized photovoltaic cluster to the global voltage stability The voltage standard deviation of the optimized photovoltaic cluster Perform weighted summation to obtain the adjusted reactive power regulation coefficient , Adjust the reactive power regulation priority of different clusters; take the inverse of the maximum value of the voltage standard deviation of the optimized photovoltaic cluster as the voltage regulation coefficient to obtain the adjusted voltage regulation coefficient . Characterization The voltage fluctuation intensity of a photovoltaic cluster, The larger the value, the worse the stability of the area. and Achieve dynamic response. The bigger, The smaller it is, the lower the voltage deviation penalty weight is to avoid overshoot; The bigger, The larger the value is, the higher the reactive power regulation intensity is, and the high fluctuation area is suppressed preferentially.

[0087] In the embodiment of the present application, the adjusted voltage reference value and the adjusted tuning parameters ( 、 ) is input into the global optimization model for calculation, and the process of updating the reactive power regulation of each photovoltaic node according to the calculation results is as follows:

[0088] First, the adjusted voltage reference value and the adjusted regulation parameter obtained above are used as the potential function The baseline value;

[0089] Secondly, the gradient descent method is used to solve (the calculation process of the gradient descent method is to solve the minimum value along the direction of gradient descent): Ask about Q i The partial derivative of , we can get the adjustment direction:

[0090]

[0091] Then, a distributed update is used: each photovoltaic node iteratively calculates according to the learning rate and updates the reactive power regulation of each photovoltaic node:

[0092]

[0093] Where, is the reactive power regulation of photovoltaic node i in the k+1th period, is the reactive power of photovoltaic node i in the kth period, is the reactive power of photovoltaic node s in the kth period, is the set of neighbor nodes of photovoltaic node i, and photovoltaic node s is the neighbor node of photovoltaic node i; is the coordination amount between neighbor nodes; 、 are learning rates, Used to control the gradient descent step size, Used to control the coordination strength between neighboring nodes, , is the preset coefficient, It affects the coordination strength between neighboring nodes. PV nodes with high weights have a stronger pulling effect on the control quantity of neighboring nodes, forming an optimization effect of point-to-surface optimization.

[0094] By optimizing and solving the global optimization model, the updated reactive power output regulation of each photovoltaic node in the photovoltaic cluster is obtained. .

[0095] Step 140: Feedback the updated reactive output regulation value of each photovoltaic node to the photovoltaic inverter controller for reactive output regulation;

[0096] The updated reactive power output regulation of each photovoltaic node Feedback to the photovoltaic inverter controller for reactive power output adjustment, after the reactive power is adjusted, it affects the power fluctuation of the photovoltaic cluster in the next period , thus forming a closed-loop feedback.

[0097] The high-penetration photovoltaic grid-connected voltage stability control method provided in this application introduces an adaptive adjustment mechanism of dynamic virtual impedance. The virtual impedance is dynamically updated based on the photovoltaic current change and power fluctuation. The dynamic virtual impedance is used to dynamically correct the admittance matrix, thereby compensating for voltage deviations caused by photovoltaic output fluctuations, thereby improving grid operation stability.

[0098] Furthermore, the voltage reference value is adjusted by optimizing the voltage mean and the optimized PV cluster contribution weight to global voltage stability, avoiding the control rigidity caused by using a fixed threshold, breaking through the limitations of traditional fixed thresholds, and adapting to extreme operating conditions with a penetration rate greater than 85%.

[0099] Furthermore, the regulation parameters are adjusted by the voltage standard deviation of the photovoltaic cluster and the optimized contribution weight of the photovoltaic cluster to the global voltage stability to achieve dynamic coordination between photovoltaic nodes;

[0100] The above is an embodiment of a high-penetration photovoltaic grid-connected voltage stability control method provided by the present application. The following is another embodiment of a high-penetration photovoltaic grid-connected voltage stability control method provided by the present application.

[0101] Please refer to Figure 2 , an embodiment of the present application provides a high-penetration photovoltaic grid-connected voltage stability control method, comprising:

[0102] Step 210: Calculate the dynamic virtual impedance based on the current variation, power fluctuation, and voltage reference deviation of the PV cluster, update the admittance matrix of the distribution network using the dynamic virtual impedance, calculate the voltage, current, and power of the PV node using the updated admittance matrix, and generate a state matrix of the distribution network;

[0103] The specific content of step 210 is consistent with the specific content of the aforementioned step 110 and will not be repeated here.

[0104] Step 220: Perform dimensionality reduction processing on the state matrix to obtain a state matrix after dimensionality reduction;

[0105] Construct the weight matrix B according to the state matrix X:

[0106]

[0107] Where W is the weight matrix, which is determined by node sensitivity or electrical distance. High weights are assigned to key nodes (such as photovoltaic access points or load centers) to enhance their influence on feature extraction. is a diagonal matrix with diagonal elements Calculated by node voltage sensitivity or power contribution, for example:

[0108]

[0109] Where, is the voltage of photovoltaic node r, P r is the active power of photovoltaic node r, is the active power of PV node i, and n is the number of PV nodes.

[0110] Weighting Matrix The symmetry of ensures that the real solutions of the eigendecomposition have off-diagonal elements Represents a photovoltaic node and The state correlation strength of . Perform eigendecomposition on the weight matrix B , extract the dominant mode, and then obtain the state matrix after dimensionality reduction ,in, is an eigenvector matrix, where each column represents a grid operation mode (e.g., voltage-dominant mode, power oscillation mode); is a diagonal matrix of eigenvalues, with elements arranged in descending order ( ), the eigenvalue reflects the contribution of the mode to the grid stability; To retain The eigenvectors corresponding to the largest eigenvalues ​​(i.e., the dominant mode set) are usually selected. The eigenvector corresponding to the largest eigenvalue is used to filter out noise and minor modes; For the front The diagonal matrix composed of eigenvalues ​​is the energy distribution of the dominant mode.

[0111] Step 230: construct a voltage distribution function, optimize the voltage distribution function using the reduced-dimensional state matrix, and obtain optimized voltage distribution parameters;

[0112] After constructing the voltage distribution function, the voltage distribution optimization objective function is constructed based on the voltage distribution function and the voltage reference distribution:

[0113]

[0114] The voltage distribution function is:

[0115]

[0116] Initial weight coefficient The state matrix after dimensionality reduction The characteristic vector of is obtained:

[0117]

[0118] Where, is the state matrix after dimensionality reduction The j-th eigenvector of is the state matrix after dimensionality reduction The g-th column eigenvector of is the state matrix after dimensionality reduction The voltage distribution function is optimized by the state matrix after dimensionality reduction to obtain the optimized voltage distribution parameters.

[0119] Step 240: Adjust the voltage reference value and the regulation parameter using the optimized voltage distribution parameter, input the adjusted voltage reference value and the adjusted regulation parameter into the global optimization model for calculation, and update the reactive power regulation value of each photovoltaic node based on the calculation result;

[0120] Step 250: Feedback the updated reactive power output regulation value of each photovoltaic node to the photovoltaic inverter controller for reactive power regulation.

[0121] The specific contents of step 240 to step 250 are consistent with the specific contents of the aforementioned step 130 to step 140, and will not be repeated here.

[0122] The high-penetration photovoltaic grid-connected voltage stability control method provided in this application introduces an adaptive adjustment mechanism of dynamic virtual impedance. The virtual impedance is dynamically updated based on the photovoltaic current change and power fluctuation. The dynamic virtual impedance is used to dynamically correct the admittance matrix, thereby compensating for voltage deviations caused by photovoltaic output fluctuations, thereby improving grid operation stability.

[0123] Furthermore, the voltage reference value is adjusted by optimizing the voltage mean and the optimized PV cluster contribution weight to global voltage stability, avoiding the control rigidity caused by using a fixed threshold, breaking through the limitations of traditional fixed thresholds, and adapting to extreme operating conditions with a penetration rate greater than 85%.

[0124] Furthermore, the regulation parameters are adjusted by the voltage standard deviation of the photovoltaic cluster and the optimized contribution weight of the photovoltaic cluster to the global voltage stability to achieve dynamic coordination between photovoltaic nodes;

[0125] Furthermore, by reducing the dimensionality of the multi-dimensional state matrix, it helps to improve the computational efficiency.

[0126] Please refer to Figure 3 , an embodiment of the present application provides a high-penetration photovoltaic grid-connected voltage stability control device, comprising:

[0127] A state calculation unit 310 is configured to calculate a dynamic virtual impedance based on the current variation, power fluctuation, and voltage reference deviation of the PV cluster, update the admittance matrix of the distribution network using the dynamic virtual impedance, calculate the voltage, current, and power of the PV node using the updated admittance matrix, and generate a state matrix of the distribution network;

[0128] A parameter optimization unit 320 is used to construct a voltage distribution function, optimize the voltage distribution function through a state matrix, and obtain optimized voltage distribution parameters;

[0129] The model calculation unit 330 is configured to adjust the voltage reference value and the adjustment parameter using the optimized voltage distribution parameter, input the adjusted voltage reference value and the adjusted adjustment parameter into the global optimization model for calculation, and update the reactive output adjustment value of each photovoltaic node based on the calculation result. The global optimization model aims to minimize the voltage deviation, reactive output deviation, and reactive output adjustment value of all photovoltaic nodes.

[0130] The feedback regulation unit 340 is used to feed back the updated reactive output regulation value of each photovoltaic node to the photovoltaic inverter controller for reactive output regulation.

[0131] As a further improvement, the device further comprises:

[0132] The voltage reference deviation acquisition unit is used to collect the voltage of each photovoltaic node in the photovoltaic cluster, calculate the voltage difference between the voltage of each photovoltaic node and the current voltage reference value, and calculate the average value of the voltage difference of all photovoltaic nodes in the photovoltaic cluster to obtain the voltage reference deviation of the photovoltaic cluster.

[0133] As a further improvement, the device further comprises:

[0134] A dimensionality reduction processing unit is used to perform dimensionality reduction processing on the state matrix to obtain a state matrix after dimensionality reduction;

[0135] Accordingly, the parameter optimization unit 320 is specifically configured to construct a voltage distribution function, optimize the voltage distribution function using the state matrix after dimensionality reduction, and obtain optimized voltage distribution parameters.

[0136] As a further improvement, the voltage distribution parameters include the average voltage of the photovoltaic cluster and the contribution weight of the photovoltaic cluster to the global voltage stability; when the model calculation unit 330 is used to adjust the voltage reference value based on the optimized voltage distribution parameters, it is specifically used to:

[0137] The voltage reference value is adjusted through the optimized voltage distribution parameters, including:

[0138] According to the contribution weight of the optimized photovoltaic cluster to the global voltage stability, the voltage mean value of the optimized photovoltaic cluster is weighted summed to obtain the adjusted voltage reference value;

[0139] Alternatively, a deviation between the optimized photovoltaic cluster voltage mean and a baseline reference voltage is calculated to obtain the photovoltaic cluster voltage deviation; the baseline reference voltage is set according to the distribution network operation standard;

[0140] The voltage deviation of the photovoltaic cluster is weighted and summed using the optimized contribution weight of the photovoltaic cluster to the global voltage stability to obtain the voltage adjustment amount;

[0141] The baseline reference voltage is adjusted according to the voltage adjustment amount to obtain an adjusted voltage reference value.

[0142] As a further improvement, the voltage distribution parameter also includes the voltage standard deviation of the photovoltaic cluster; the adjustment parameter includes a reactive power adjustment coefficient and a voltage adjustment coefficient; when the model calculation unit 330 is used to adjust the adjustment parameter using the optimized voltage distribution parameter, it is specifically used to:

[0143] According to the contribution weight of the optimized photovoltaic cluster to the global voltage stability, the voltage standard deviation of the optimized photovoltaic cluster is weighted and summed to obtain the adjusted reactive power regulation coefficient;

[0144] The reciprocal of the maximum value of the voltage standard deviation of the optimized photovoltaic cluster is used as the voltage regulation coefficient to obtain the adjusted voltage regulation coefficient.

[0145] This application introduces an adaptive adjustment mechanism of dynamic virtual impedance, which dynamically updates the virtual impedance based on the change in photovoltaic current and power fluctuation. The dynamic virtual impedance dynamically corrects the admittance matrix to compensate for the voltage deviation caused by photovoltaic output fluctuations, thereby improving the stability of grid operation.

[0146] Furthermore, the voltage reference value is adjusted by optimizing the voltage mean and the optimized PV cluster contribution weight to global voltage stability, avoiding the control rigidity caused by using a fixed threshold, breaking through the limitations of traditional fixed thresholds, and adapting to extreme operating conditions with a penetration rate greater than 85%.

[0147] Furthermore, the regulation parameters are adjusted by the voltage standard deviation of the photovoltaic cluster and the optimized contribution weight of the photovoltaic cluster to the global voltage stability to achieve dynamic coordination between photovoltaic nodes;

[0148] Furthermore, by reducing the dimensionality of the multi-dimensional state matrix, it helps to improve the computational efficiency.

[0149] An embodiment of the present application further provides an electronic device, the device including a processor and a memory;

[0150] The memory is used to store program codes and transmit the program codes to the processor;

[0151] The processor is configured to execute the high-penetration photovoltaic grid-connected voltage stability control method in the aforementioned method embodiment according to instructions in the program code.

[0152] An embodiment of the present application further provides a computer-readable storage medium for storing program code, which, when executed by a processor, implements the high-penetration photovoltaic grid-connected voltage stability control method in the aforementioned method embodiment.

[0153] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0154] In the specification of this application and the above-mentioned drawings, the terms "first," "second," "third," "fourth," etc. (if any) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, product, or apparatus.

[0155] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or plural.

[0156] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0157] 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.

[0158] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0159] If the integrated unit is implemented in the form of 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 the present application, 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. The computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name: Read-Only Memory, English abbreviation: ROM), random access memory (full name: Random Access Memory, English abbreviation: RAM), disk or optical disk, and other media that can store program code.

[0160] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A high-penetration photovoltaic grid-connected voltage stability control method, characterized in that: include: Calculating a dynamic virtual impedance based on the current variation, power fluctuation, and voltage reference deviation of the photovoltaic cluster, updating the admittance matrix of the distribution network using the dynamic virtual impedance, and calculating the voltage, current, and power of the photovoltaic node using the updated admittance matrix to generate a state matrix of the distribution network; A voltage distribution function is constructed, and the voltage distribution function is optimized by the state matrix to obtain optimized voltage distribution parameters; the voltage distribution function is: ； Where, is the mean voltage of PV cluster j, is the voltage of PV node i in PV cluster j; is the voltage standard deviation of PV cluster j; is the contribution weight of PV cluster j to global voltage stability, N is the number of PV clusters; The voltage reference value and the adjustment parameter are adjusted using the optimized voltage distribution parameters, the adjusted voltage reference value and the adjusted adjustment parameter are input into the global optimization model for calculation, and the reactive output adjustment amount of each photovoltaic node is updated according to the calculation results; the global optimization model aims to minimize the voltage deviation, reactive output deviation, and reactive output adjustment amount of all photovoltaic nodes; the global optimization model is: ； ； Where U i is the potential function of photovoltaic node i, is the voltage of photovoltaic node i, n is the total number of photovoltaic nodes in the photovoltaic cluster, is the reference voltage, is the voltage regulation coefficient, is the reactive power output of photovoltaic node i, is the reactive power reference value, is the reactive power regulation coefficient; through the voltage regulation coefficient and reactive power regulation coefficient , potential function U i Ability to perform dimensionless operations; is the set of reactive output regulation quantities of all photovoltaic nodes, is the reactive power regulation of photovoltaic node n, is the L2 norm, is the regularization coefficient; The voltage distribution parameters include the voltage mean of the photovoltaic cluster and the contribution weight of the photovoltaic cluster to the global voltage stability. The voltage reference value is adjusted according to the optimized voltage distribution parameters, including: According to the contribution weight of the optimized photovoltaic cluster to the global voltage stability, the voltage mean value of the optimized photovoltaic cluster is weighted summed to obtain the adjusted voltage reference value; Alternatively, a deviation between the optimized photovoltaic cluster voltage mean and a baseline reference voltage is calculated to obtain the photovoltaic cluster voltage deviation; the baseline reference voltage is set according to the distribution network operation standard; The voltage deviation of the photovoltaic cluster is weighted and summed using the optimized contribution weight of the photovoltaic cluster to the global voltage stability to obtain the voltage adjustment amount; adjusting the baseline reference voltage according to the voltage adjustment amount to obtain an adjusted voltage reference value; The updated reactive power output regulation value of each photovoltaic node is fed back to the photovoltaic inverter controller for reactive power output regulation.

2. The high-penetration photovoltaic grid-connected voltage stability control method according to claim 1, characterized in that: The process of obtaining the voltage reference deviation includes: The voltage of each photovoltaic node in the photovoltaic cluster is collected, and the voltage difference between the voltage of each photovoltaic node and the current voltage reference value is calculated. The average value of the voltage difference of all photovoltaic nodes in the photovoltaic cluster is calculated to obtain the voltage reference deviation of the photovoltaic cluster.

3. The high penetration photovoltaic grid-connected voltage stability control method according to claim 1, characterized in that: The method further comprises: Performing dimensionality reduction processing on the state matrix to obtain a state matrix after dimensionality reduction; Accordingly, constructing the voltage distribution function, optimizing the voltage distribution function using the state matrix, and obtaining optimized voltage distribution parameters include: A voltage distribution function is constructed, and the voltage distribution function is optimized using the reduced-dimensional state matrix to obtain optimized voltage distribution parameters.

4. The high-penetration photovoltaic grid-connected voltage stability control method according to claim 1, characterized in that: Voltage distribution parameters also include the voltage standard deviation of the photovoltaic cluster; regulation parameters include reactive power regulation coefficient and voltage regulation coefficient; Adjusting the regulation parameters using the optimized voltage distribution parameters includes: According to the contribution weight of the optimized photovoltaic cluster to the global voltage stability, the voltage standard deviation of the optimized photovoltaic cluster is weighted and summed to obtain the adjusted reactive power regulation coefficient; The reciprocal of the maximum value of the voltage standard deviation of the optimized photovoltaic cluster is used as the voltage regulation coefficient to obtain the adjusted voltage regulation coefficient.

5. A high-penetration photovoltaic grid-connected voltage stability control device, characterized in that: include: A state calculation unit is configured to calculate a dynamic virtual impedance based on the current variation, power fluctuation, and voltage reference deviation of the photovoltaic cluster, update the admittance matrix of the distribution network using the dynamic virtual impedance, calculate the voltage, current, and power of the photovoltaic node using the updated admittance matrix, and generate a state matrix of the distribution network; The parameter optimization unit is used to construct a voltage distribution function, optimize the voltage distribution function through the state matrix, and obtain optimized voltage distribution parameters; the voltage distribution function is: ； Where, is the mean voltage of PV cluster j, is the voltage of PV node i in PV cluster j; is the voltage standard deviation of PV cluster j; is the contribution weight of PV cluster j to global voltage stability, N is the number of PV clusters; A model calculation unit is configured to adjust a voltage reference value and a regulation parameter using the optimized voltage distribution parameter, input the adjusted voltage reference value and the adjusted regulation parameter into a global optimization model for calculation, and update the reactive output regulation value of each photovoltaic node based on the calculation result; the global optimization model aims to minimize the voltage deviation, reactive output deviation, and reactive output regulation value of all photovoltaic nodes; the global optimization model is: ； ； Where U i is the potential function of photovoltaic node i, is the voltage of photovoltaic node i, n is the total number of photovoltaic nodes in the photovoltaic cluster, is the reference voltage, is the voltage regulation coefficient, is the reactive power output of photovoltaic node i, is the reactive power reference value, is the reactive power regulation coefficient; through the voltage regulation coefficient and reactive power regulation coefficient , potential function U i Ability to perform dimensionless operations; is the set of reactive output regulation quantities of all photovoltaic nodes, is the reactive power regulation of photovoltaic node n, is the L2 norm, is the regularization coefficient; The voltage distribution parameters include the voltage mean of the photovoltaic cluster and the contribution weight of the photovoltaic cluster to the global voltage stability. The voltage reference value is adjusted according to the optimized voltage distribution parameters, including: According to the contribution weight of the optimized photovoltaic cluster to the global voltage stability, the voltage mean value of the optimized photovoltaic cluster is weighted summed to obtain the adjusted voltage reference value; Alternatively, a deviation between the optimized photovoltaic cluster voltage mean and a baseline reference voltage is calculated to obtain the photovoltaic cluster voltage deviation; the baseline reference voltage is set according to the distribution network operation standard; The voltage deviation of the photovoltaic cluster is weighted and summed using the optimized contribution weight of the photovoltaic cluster to the global voltage stability to obtain the voltage adjustment amount; adjusting the baseline reference voltage according to the voltage adjustment amount to obtain an adjusted voltage reference value; The feedback regulation unit is used to feed back the updated reactive output regulation value of each photovoltaic node to the photovoltaic inverter controller for reactive output regulation.

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 voltage stability control method according to any one of claims 1 to 4 according to 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, and when the program code is executed by a processor, the high-penetration photovoltaic grid-connected voltage stability control method according to any one of claims 1 to 4 is implemented.

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