Distributed new energy active power regulation and control method, device and equipment and storage medium

Abstract

The invention discloses a distributed new energy active power regulation and control method, device and equipment and a storage medium, and is used for solving the technical problems that an existing distributed new energy regulation and control mode is low in accuracy and poor in self-adaptability and real-time performance. Comprising the following steps: calculating a distribution network voltage compensation vector by adopting voltage deviation values of all distribution network nodes and an inverse matrix of a voltage sensitivity matrix; equally dividing the whole network power regulation quantity according to the number of the distributed energy equipment accessed to the distribution network node to generate a main network demand regulation vector; performing weighted fusion on the main network demand adjustment vector and the distribution network voltage compensation vector to obtain a global active adjustment target vector; calculating an equipment adaptation index of the distributed energy equipment by adopting an equipment adjustment capability parameter of the distributed energy equipment, a dominant frequency component in a global active adjustment target vector, a main network real-time power margin and a voltage deviation value; and allocating the adjustment task in the global active adjustment target vector to the distributed energy equipment for execution by adopting the dominant frequency component and the equipment adaptation index.

Description

Technical Field

[0001] The present invention relates to the field of distributed new energy technology, and in particular to a distributed new energy active power control method, device, equipment and storage medium. Background Art

[0002] As installed capacity of renewable energy power generation continues to grow rapidly, wind and photovoltaic power generation are increasingly contributing to a larger share of total installed capacity. This high penetration of renewable energy presents unprecedented challenges to power system operation. Distributed renewable energy sources are characterized by significant intermittency, volatility, and uncertainty. Their large-scale grid integration is transforming distribution networks from traditional "passive" to "active" networks, leading to increasingly complex power interactions between the main and distribution networks.

[0003] At the operational level, fluctuations in renewable energy output can cause grid frequency deviations, voltage overshoots, and other issues. Furthermore, the integration of massive distributed energy devices has led to an exponential increase in grid operational data. To address these challenges, the power industry is actively exploring new control technologies and proposing the construction of a multi-source coordinated control system encompassing "source, grid, load, and storage." Current mainstream technologies include static optimization control based on historical data, dynamic allocation using edge computing, and coordinated control of the main and distribution networks. These technologies have, to a certain extent, enhanced the grid's ability to accommodate distributed renewable energy. However, as renewable energy penetration continues to increase, higher requirements are placed on the real-time, precision, and adaptability of control technologies. Summary of the Invention

[0004] The present invention provides a distributed renewable energy active power control method, device, equipment and storage medium, which are used to solve the technical problems of low accuracy, poor adaptability and real-time performance of existing distributed renewable energy control methods.

[0005] The present invention provides a distributed new energy active power control method, comprising:

[0006] Calculate the real-time power margin of the main network and receive the power adjustment value of the entire network;

[0007] Obtaining the inverse matrix of the voltage sensitivity matrix of each distribution network node, the number of distributed energy devices connected to the distribution network node, and the device regulation capability parameters of the distributed energy devices connected to the distribution network node;

[0008] Obtaining the voltage deviation values ​​of all distribution network nodes in real time, and calculating the distribution network voltage compensation vector in combination with the inverse matrix;

[0009] Evenly divide the power regulation amount of the entire network according to the number of the distributed energy devices to generate a main network demand regulation vector;

[0010] Weighted fusion of the main grid demand regulation vector and the distribution network voltage compensation vector to obtain a global active power regulation target vector;

[0011] extracting a dominant frequency component from the global active power regulation target vector;

[0012] Calculating a device adaptation index of a distributed energy device connected to a distribution network node by using the device regulation capability parameter, the dominant frequency component, the main network real-time power margin, and the voltage deviation value;

[0013] The dominant frequency component and the device adaptation index are used to allocate the regulation tasks in the global active power regulation target vector to the distributed energy devices of the distribution network nodes for execution.

[0014] Optionally, the step of calculating the real-time power margin of the primary network and receiving the power adjustment amount of the entire network includes:

[0015] Real-time collection of real-time transmission power data of each transmission line;

[0016] Obtain thermal stability limit power parameters from the preset dispatch center;

[0017] The real-time transmission power data and the thermal stability limit power parameter are used to calculate the real-time power margin of the main network, and the power adjustment amount of the entire network is received.

[0018] Optionally, the step of acquiring the voltage deviation values ​​of all distribution network nodes in real time and calculating the distribution network voltage compensation vector in combination with the inverse matrix includes:

[0019] Real-time monitoring of the actual voltage values ​​of all the distribution network nodes;

[0020] Calculating a voltage deviation between the actual voltage value and the rated voltage value;

[0021] The voltage deviation values ​​of all distribution network nodes are used to generate a voltage deviation vector;

[0022] The voltage deviation vector is inversely calculated using the inverse matrix to obtain a distribution network voltage compensation vector.

[0023] Optionally, the step of weightedly fusing the main network demand regulation vector and the distribution network voltage compensation vector to obtain a global active power regulation target vector includes:

[0024] Calculating the main network demand weight according to the main network real-time power margin, and calculating the distribution network demand weight according to the voltage deviation value;

[0025] The main network demand regulation vector and the distribution network voltage compensation vector are weightedly fused based on the main network demand weight and the distribution network demand weight to obtain a global active power regulation target vector.

[0026] Optionally, the step of extracting a dominant frequency component from the global active power regulation target vector includes:

[0027] A fast Fourier transform is performed on the global active power regulation target vector, and a frequency band whose spectrum energy ratio exceeds a preset spectrum energy threshold is extracted as a dominant frequency component.

[0028] Optionally, the device regulation capability parameter includes an actual regulation rate and an actual response time; and the step of calculating the device adaptation index of the distributed energy device connected to the distribution network node using the device regulation capability parameter, the dominant frequency component, the main network real-time power margin, and the voltage deviation value includes:

[0029] Calculating the ratio of the actual regulation rate to a preset regulation rate reference value to obtain a regulation rate score;

[0030] Calculate the ratio of the preset response time benchmark value to the actual response time to obtain a response time score;

[0031] Obtaining a historical optimal operating frequency of the device, and calculating an absolute difference between the historical optimal operating frequency of the device and the dominant frequency component;

[0032] Converting the absolute difference into a frequency matching score by a reciprocal function;

[0033] Calculating a main network margin impact factor using the main network real-time power margin and a preset safety margin threshold;

[0034] Calculating a voltage deviation impact factor based on the voltage deviation value and a preset voltage over-limit threshold;

[0035] Calculating the difference between the dominant frequency component and the historical optimal operating frequency of the device, and converting the difference into a frequency matching factor using a reciprocal function;

[0036] Calculating a first product of the main grid margin impact factor and the regulation rate score, a second product of the voltage deviation impact factor and the response time score, and a third product of the frequency matching factor and the frequency matching score;

[0037] The first product, the second product and the third product are added together to obtain a device adaptation index of the distributed energy device connected to the distribution network node.

[0038] Optionally, the step of using the dominant frequency component and the device adaptation index to allocate the regulation tasks in the global active power regulation target vector to the distributed energy devices of the distribution network nodes includes:

[0039] Determining the task type of the adjustment task according to the dominant frequency component and a preset high-frequency determination threshold;

[0040] Prioritizing all distributed energy devices on the same distribution network node based on the device adaptation index;

[0041] When the task type is a high-frequency regulation task, a preset proportion of distributed energy devices are selected as the main execution objects according to the priority order from high to low;

[0042] Calculating a first total index of device adaptation indexes of all the main execution objects;

[0043] Calculating a first ratio of the device adaptation index of each of the main execution objects to the first total index;

[0044] allocating an adjustment amount to each of the main execution objects according to the first ratio;

[0045] When the task type is a low-frequency regulation task, the distributed energy equipment other than the primary execution object is used as a secondary execution object;

[0046] Calculate a second overall index of the equipment adaptation index of all distributed energy equipment;

[0047] Calculate a second ratio of the device adaptation index of each of the secondary objects to the second total index;

[0048] The adjustment amount is allocated to each of the secondary execution objects according to the second ratio.

[0049] The present invention also provides a distributed new energy active power control device, comprising:

[0050] The main network real-time power margin and the whole network power adjustment amount acquisition module is used to calculate the main network real-time power margin and receive the whole network power adjustment amount;

[0051] An acquisition module is used to obtain the inverse matrix of the voltage sensitivity matrix of each distribution network node, the number of distributed energy devices connected to the distribution network node, and the device regulation capability parameters of the distributed energy devices connected to the distribution network node;

[0052] A distribution network voltage compensation vector calculation module is used to obtain the voltage deviation values ​​of all distribution network nodes in real time and calculate the distribution network voltage compensation vector in combination with the inverse matrix;

[0053] A distributed energy device quantity acquisition module is used to obtain the number of distributed energy devices connected to the distribution network node;

[0054] A main demand regulation vector generation module is used to evenly divide the power regulation amount of the entire network according to the number of the distributed energy devices to generate a main network demand regulation vector;

[0055] A global active power regulation target vector generation module is configured to perform weighted fusion of the main network demand regulation vector and the distribution network voltage compensation vector to obtain a global active power regulation target vector;

[0056] A dominant frequency component extraction module, configured to extract a dominant frequency component from the global active power regulation target vector;

[0057] The device regulation capability parameter acquisition module is used to obtain the device regulation capability parameters of the distributed energy equipment connected to the distribution network node;

[0058] a device adaptation index calculation module, configured to calculate the device adaptation index of the distributed energy device connected to the distribution network node by using the device adjustment capability parameter, the dominant frequency component, the main network real-time power margin, and the voltage deviation value;

[0059] The allocation module is used to use the dominant frequency component and the device adaptation index to allocate the regulation tasks in the global active power regulation target vector to the distributed energy devices of the distribution network nodes for execution.

[0060] The present invention further provides an electronic device, comprising a processor and a memory:

[0061] The memory is used to store program code and transmit the program code to the processor;

[0062] The processor is used to execute the distributed renewable energy active power control method as described in any one of the above items according to the instructions in the program code.

[0063] The present invention also provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the distributed new energy active power control method as described in any one of the above items.

[0064] It can be seen from the above technical solutions that the present invention has the following advantages: the present invention first calculates the real-time power margin based on the difference between the real-time transmission power of the main network and the thermal stability limit, quantifies the power regulation capability of the main network, and receives the total power regulation demand of the entire network at the same time; on the distribution network side, the voltage sensitivity matrix of each node is obtained through offline flow calculation, the node voltage deviation is monitored in real time and a voltage deviation vector is generated, and the distribution network voltage compensation vector is solved inversely using the inverse matrix of the sensitivity matrix to accurately map the voltage deviation to the active power compensation demand; then, the main network demand weight is dynamically adjusted through the main network margin influencing factor to ensure that the regulation priority is improved when the main network margin is insufficient, and at the same time, the distribution network demand weight is generated based on the ratio of the sum of the voltage deviations of the distribution network nodes to the reference value through a piecewise linear function to balance the local voltage repair demand; the main network demand adjustment vector is compared with the distribution network After the voltage compensation vectors are fused according to the weights, the dominant frequency components whose spectrum energy exceeds the threshold are extracted through fast Fourier transform to identify the core frequency band of the power grid fluctuation. Furthermore, the device adaptation index is calculated based on the actual adjustment rate, response time, historical best frequency matching degree, and real-time main grid margin and voltage deviation status of the equipment, and the comprehensive adaptability of the equipment to the current power grid status is quantified. Finally, the adjustment tasks are divided into high-frequency and low-frequency types according to the dominant frequency components. High-frequency tasks are preferentially assigned to the top 30% of high-adaptability index devices, and low-frequency tasks are assigned to the remaining devices according to the index ratio. At the same time, the adjustment error is corrected in real time through closed-loop monitoring and dynamic reallocation mechanism, so as to achieve multi-objective coordinated regulation of main grid power stability, distribution network voltage optimization and equipment utilization improvement, and effectively respond to the random fluctuations of new energy and the multi-time scale regulation needs of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0066] Figure 1 A flowchart of a distributed renewable energy active power control method provided by an embodiment of the present invention;

[0067] Figure 2 This is a structural block diagram of a distributed new energy active power control device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The embodiments of the present invention provide a distributed renewable energy active power control method, apparatus, device and storage medium, which are used to solve the technical problems of low accuracy, poor adaptability and real-time performance of existing distributed renewable energy control methods.

[0069] In order to make the purpose, features, and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below 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 making creative work are within the scope of protection of the present invention.

[0070] See also Figure 1 , Figure 1 A flowchart of the steps of a distributed renewable energy active power control method provided by an embodiment of the present invention.

[0071] The present invention provides a distributed renewable energy active power control method, which may specifically include the following steps:

[0072] Step 101: Calculate the real-time power margin of the primary network and receive the power adjustment value of the entire network;

[0073] In this embodiment of the present invention, step 101 may include the following sub-steps:

[0074] S11, real-time collection of transmission power data of each transmission line;

[0075] S12, obtaining thermal stability limit power parameters from a preset dispatching center;

[0076] S13, using the real-time transmission power data and the thermal stability limit power parameter to calculate the real-time power margin of the main network, and receiving the power adjustment amount of the entire network.

[0077] In the specific implementation, the real-time transmission power data of each transmission line can be collected in real time through the wide-area measurement system deployed at the key nodes of the main network. Combined with the thermal stability limit power parameters pre-stored in the preset dispatching center, the sum of the differences between the real-time transmission power of each line and its corresponding thermal stability limit power parameters is calculated as the real-time power margin of the main network. At the same time, the power regulation demand instructions of the entire network issued by the superior energy management system are received, including the regulation direction and the total regulation amount, and the real-time power margin and the power regulation amount of the entire network are transmitted to the subsequent processing module. The real-time power margin is used to characterize the current power regulation capability that the main network can bear, and the power regulation amount of the entire network is used to indicate the total value of power that needs to be increased or reduced in the entire network. The two together provide dynamic input data for the subsequent main network demand weight calculation and regulation vector construction.

[0078] Step 102: Obtain the inverse matrix of the voltage sensitivity matrix of each distribution network node, the number of distributed energy devices connected to the distribution network node, and the device regulation capability parameters of the distributed energy devices connected to the distribution network node;

[0079] Distributed energy equipment refers to small-scale energy production and storage systems that are distributed and close to the user side. They can improve energy utilization efficiency, reduce transmission losses, and enhance the resilience of the power grid.

[0080] In the embodiment of the present invention, the voltage sensitivity matrix of each distribution network node can be obtained through offline power flow calculation, and then the inverse matrix of the voltage sensitivity matrix can be calculated.

[0081] Offline power flow calculation is one of the most basic and important calculations in power system analysis. It refers to the calculation of the voltage amplitude, phase angle and power distribution in the power system under a certain steady-state operation mode, given the power system network topology, component parameters and boundary conditions such as load and generator.

[0082] The voltage sensitivity matrix is ​​an important tool in power system analysis. It is used to quantify the interactions between system variables, and is particularly important in voltage stability analysis and reactive power optimization. The voltage sensitivity matrix describes the sensitivity of system node voltages to changes in controlled variables.

[0083] Obtaining the voltage sensitivity matrix through offline power flow calculation is a conventional technical means in this field and will not be described in detail here.

[0084] Step 103: Obtain the voltage deviation values ​​of all distribution network nodes in real time, and calculate the distribution network voltage compensation vector in combination with the inverse matrix;

[0085] In the embodiment of the present invention, the voltage deviation values ​​of all distribution network nodes can be acquired in real time, and the distribution network voltage compensation vector can be calculated in combination with the inverse matrix of the voltage sensitivity matrix.

[0086] In one example, step 103 may include the following sub-steps:

[0087] S31, real-time monitoring of the actual voltage values ​​of all distribution network nodes;

[0088] S32, calculating a voltage deviation between the actual voltage value and the rated voltage value;

[0089] S33, generating a voltage deviation vector using the voltage deviation values ​​of all distribution network nodes;

[0090] S34, using an inverse matrix to perform inverse calculation on the voltage deviation vector to obtain a distribution network voltage compensation vector.

[0091] In specific implementation, the voltage sensitivity matrix of each distribution network node can be pre-calculated and stored through offline power flow simulation, the actual voltage values ​​of all distribution network nodes can be monitored in real time and compared with the rated voltage values, the absolute value of the voltage deviation of each node can be calculated to form a voltage deviation vector, and then the voltage sensitivity matrix can be inverted to obtain its inverse matrix. The inverse matrix is ​​used to reversely solve the voltage deviation vector to generate the distribution network voltage compensation vector.

[0092] The offline calculated voltage sensitivity matrix reflects the sensitivity of node active power changes to voltage. The real-time voltage deviation vector quantifies the voltage deviation state of each node. The inverse calculation process of the inverse matrix converts the voltage deviation into the corresponding active power compensation amount, thereby accurately guiding the active power regulation direction and amplitude of the distribution network nodes, ensuring that the voltage compensation strategy dynamically matches the real-time grid status.

[0093] Step 104 , evenly divide the power regulation amount of the entire network according to the number of distributed energy devices to generate a main network demand regulation vector;

[0094] In an embodiment of the present invention, the power regulation amount of the entire network can be evenly divided according to the number of distributed energy devices connected to the distribution network node to construct a main network demand regulation vector to ensure that the total power demand of the entire network can be quickly sent to all devices, taking into account real-time, fairness and robustness.

[0095] Step 105: weighted fusion of the main grid demand regulation vector and the distribution network voltage compensation vector to obtain a global active power regulation target vector;

[0096] In the embodiment of the present invention, the main grid demand regulation vector and the distribution network voltage compensation vector are weighted and fused to obtain a global active power regulation target vector.

[0097] In one example, step 105 may include the following sub-steps:

[0098] S51, calculating the main network demand weight according to the real-time power margin of the main network, and calculating the distribution network demand weight according to the voltage deviation value;

[0099] S52 , based on the main network demand weight and the distribution network demand weight, the main network demand regulation vector and the distribution network voltage compensation vector are weightedly integrated to obtain a global active power regulation target vector.

[0100] In specific implementation, the calculation formula for the main network demand weight is as follows:

[0101]

[0102] in, is the margin adjustment coefficient, The real-time power margin of the main network, is the reference margin, is the main network demand weight, and k is the main network weight coefficient.

[0103] In one example, the sum of the absolute values ​​of the voltage deviations of all distribution network nodes can be proportionally calculated to a preset reference voltage deviation value, and the proportional value can be mapped to the distribution network demand weight according to a preset piecewise linear function. When the total deviation ratio is lower than a preset first threshold, the distribution network weight increases linearly, and the weight saturates after exceeding a preset second threshold, ensuring that the distribution network demand weight is stable and controllable when the voltage exceeds the limit.

[0104] After determining the main network demand weight and distribution network demand weight, the main network demand weight can be multiplied by the main network demand regulation vector, and the distribution network demand weight can be multiplied by the distribution network voltage compensation vector. The products of the two can then be added together to obtain the global active power regulation target vector.

[0105] Step 106: extracting the dominant frequency component from the global active power regulation target vector;

[0106] In a specific implementation, the global active power regulation target vector includes multiple frequency components. Extracting the dominant frequency vector from the global active power regulation target vector can be achieved by performing a fast Fourier transform on the global active power regulation target vector and extracting the frequency band whose spectral energy ratio exceeds a preset spectral energy threshold as the dominant frequency component.

[0107] In a specific implementation, the global active power regulation target vector can be subjected to fast Fourier transform to calculate the energy proportion of each frequency band in its spectrum, and the frequency band whose spectrum energy proportion exceeds a preset spectrum energy threshold is selected as the dominant frequency component.

[0108] It should be noted that the main grid demand weight is generated by the ratio of the real-time power margin to the reference margin through logical function mapping, and the distribution network demand weight is calculated by the ratio of the sum of the absolute values ​​of the voltage deviations of all distribution network nodes to the reference voltage deviation value through a piecewise linear function. The global vector fusion process takes into account the dynamic attenuation of the main grid power margin and the overall over-limit level of the distribution network voltage, and the extraction of the dominant frequency component is judged by the spectrum energy threshold to ensure that the control target focuses on the main frequency characteristics of the power grid fluctuations, providing accurate frequency input for the subsequent equipment adaptation index calculation.

[0109] Step 107, using the device regulation capability parameter, the dominant frequency component, the main network real-time power margin, and the voltage deviation value, calculate the device adaptation index of the distributed energy device connected to the distribution network node;

[0110] In the embodiment of the present invention, the device adjustment capability parameters include real-time adjustment rate and actual response time; step 107 may specifically include the following sub-steps:

[0111] S71, calculating the ratio of the actual adjustment rate to the preset adjustment rate reference value to obtain an adjustment rate score;

[0112] S72, calculating the ratio of the preset response time reference value to the actual response time to obtain a response time score;

[0113] S73, obtaining the historical best operating frequency of the device, and calculating the absolute difference between the historical best operating frequency of the device and the dominant frequency component;

[0114] S74, converting the absolute difference into a frequency matching score using a reciprocal function;

[0115] S75, calculating a main network margin impact factor using the main network real-time power margin and a preset safety margin threshold;

[0116] S76, calculating a voltage deviation impact factor based on the voltage deviation value and a preset voltage over-limit threshold;

[0117] S77, calculating the difference between the dominant frequency component and the historical optimal operating frequency of the device, and converting the difference into a frequency matching factor using a reciprocal function;

[0118] S78, calculating a first product of the main grid margin impact factor and the regulation rate score, a second product of the voltage deviation impact factor and the response time score, and a third product of the frequency matching factor and the frequency matching score;

[0119] S79: Add the first product, the second product, and the third product to obtain a device adaptation index of the distributed energy device connected to the distribution network node.

[0120] In the specific implementation, first, the ratio of the actual regulation rate to the preset regulation rate reference value can be calculated to obtain the regulation rate score, and the ratio of the preset response time reference value to the actual response time can be calculated to obtain the response time score. The absolute difference between the historical best operating frequency of the device and the dominant frequency component is converted into a frequency matching score through the inverse function.

[0121] Then, the main network margin impact factor is calculated based on the real-time power margin of the main network and the preset safety margin threshold.

[0122] The calculation formula for the main network margin impact factor is as follows:

[0123]

[0124] Among them, A is the main network margin influencing factor, is the main network benchmark margin, is the real-time power margin, is the preset safety margin threshold, is the adjustment factor.

[0125] Next, the voltage deviation impact factor is calculated based on the voltage deviation value and the preset voltage over-limit threshold. The frequency matching factor is calculated using a reciprocal function based on the difference between the dominant frequency component and the device's historical optimal operating frequency.

[0126] Among them, when the absolute value of the voltage deviation of the distribution network node does not exceed the preset voltage over-limit threshold, the reference voltage deviation impact factor is directly used. If the voltage deviation exceeds the limit, the ratio of the difference between the deviation value and the threshold to the threshold is calculated, multiplied by the preset adjustment coefficient, and then added to the reference value to obtain the voltage deviation impact factor.

[0127] Finally, the main grid margin impact factor is multiplied by the regulation rate score, the voltage deviation impact factor is multiplied by the response time score, and the frequency matching factor is multiplied by the frequency matching score. The three are added together to obtain the equipment adaptation index. This index quantifies the comprehensive adaptability of the equipment to the current grid frequency characteristics, main grid power urgency and node voltage over-limit status. It provides a decision-making basis for prioritizing the allocation of subsequent high-frequency tasks to the top 30% high-index equipment and allocating low-frequency tasks according to the index ratio, ensuring that high-regulation rate equipment responds to main grid margin crises, fast-response equipment prioritizes repairing voltage-over-limit nodes, and the best frequency adaptation equipment accurately matches the dominant fluctuation component.

[0128] Step 108 : Using the dominant frequency component and the device adaptation index, the regulation tasks in the global active power regulation target vector are allocated to the distributed energy devices at the distribution network nodes for execution.

[0129] In this embodiment of the present invention, step 108 may include the following sub-steps:

[0130] S81, determining the task type of the adjustment task according to the dominant frequency component and a preset high-frequency determination threshold;

[0131] S82, prioritizing all distributed energy devices on the same distribution network node based on the device adaptation index;

[0132] S83, when the task type is a high-frequency regulation task, selecting a preset proportion of distributed energy devices as main execution objects according to the priority order from high to low;

[0133] S84, calculating a first total index of device adaptation indexes of all main execution objects;

[0134] S85, calculating a first ratio of the device adaptation index of each main execution object to the first total index;

[0135] S86, allocating adjustment amounts to each main execution object according to the first ratio;

[0136] S87, when the task type is a low-frequency regulation task, the distributed energy equipment other than the primary execution object is regarded as a secondary execution object;

[0137] S88, calculating a second total index of the equipment adaptation index of all distributed energy devices;

[0138] S89, calculating a second ratio of the device adaptation index of each secondary object to the second total index;

[0139] S810: Allocate an adjustment amount to each secondary execution object according to a second ratio.

[0140] In the specific implementation, the type of regulation task is first determined based on the dominant frequency component of the global active regulation target vector. If the dominant frequency component is greater than or equal to the preset high-frequency judgment threshold, for example, 0.1 Hz, it is classified as a high-frequency regulation task, otherwise it is classified as a low-frequency regulation task; then, all distributed energy devices at the same distribution network node are prioritized based on the device adaptation index. The higher the index, the better the comprehensive regulation capability of the device; for high-frequency regulation tasks, the distributed energy devices with high adaptation indexes in the top 30% of the priority ranking are selected as the main execution objects, and the regulation amount is allocated according to the proportion of the adaptation index of each distributed energy device to the total index of the top 30% of the devices, to ensure that high-frequency fluctuations are borne first by devices with fast regulation rate, short response time and high frequency matching; for For low-frequency regulation tasks, the remaining 70% of distributed energy devices are treated as secondary execution objects, and the regulation amount is allocated according to the ratio of the adaptation index of the secondary execution object to the total index of all distributed energy devices. At the same time, the actual output upper limit of each distributed energy device is adjusted according to the real-time dynamic weight of the main network margin influencing factor and the voltage deviation influencing factor. If the main network margin is lower than the safety threshold or the node voltage deviation exceeds the limit, the regulation weight of the corresponding distributed energy device is automatically increased to prioritize the suppression of system risks; finally, the allocation result is encapsulated as a power regulation instruction and sent to each device controller, and the execution effect is monitored in real time. If the actual output of the distributed energy device deviates from the target by more than the tolerance value, the dynamic reallocation mechanism is triggered, the adaptation index is recalculated, and the task allocation strategy is updated to achieve closed-loop control of the entire process.

[0141] The present invention first calculates the real-time power margin based on the difference between the real-time transmission power of the main network and the thermal stability limit, quantifies the power regulation capability of the main network, and receives the total power regulation demand of the entire network at the same time; on the distribution network side, the voltage sensitivity matrix of each node is obtained through offline power flow calculation, the node voltage deviation is monitored in real time and a voltage deviation vector is generated, and the distribution network voltage compensation vector is solved inversely using the inverse matrix of the sensitivity matrix to accurately map the voltage deviation to the active power compensation demand; then, the main network demand weight is dynamically adjusted through the main network margin influencing factor to ensure that the regulation priority is improved when the main network margin is insufficient, and at the same time, the distribution network demand weight is generated based on the ratio of the total voltage deviation of the distribution network nodes to the reference value through a piecewise linear function to balance the local voltage repair demand; the main network demand adjustment vector and the distribution network voltage compensation vector are fused according to the weight Then, the dominant frequency components whose spectrum energy exceeds the threshold are extracted through fast Fourier transform to identify the core frequency band of power grid fluctuations; further, the actual adjustment rate, response time, historical best frequency matching degree of the equipment, as well as the real-time main grid margin and voltage deviation status are combined to calculate the equipment adaptation index and quantify the comprehensive adaptability of the equipment to the current grid status; finally, the adjustment tasks are divided into high-frequency and low-frequency types according to the dominant frequency components, and high-frequency tasks are preferentially assigned to the top 30% of high-adaptability index devices, and low-frequency tasks are assigned to the remaining devices according to the index ratio. At the same time, the adjustment error is corrected in real time through closed-loop monitoring and dynamic reallocation mechanism, so as to realize multi-objective coordinated regulation of main grid power stability, distribution network voltage optimization and equipment utilization improvement, and effectively respond to the random fluctuations of new energy and the multi-time scale regulation needs of the power grid.

[0142] See also Figure 2 , Figure 2 This is a structural block diagram of a distributed new energy active power control device provided by an embodiment of the present invention.

[0143] An embodiment of the present invention provides a distributed new energy active power control device, comprising:

[0144] The main network real-time power margin and the whole network power adjustment amount acquisition module 201 is used to calculate the main network real-time power margin and receive the whole network power adjustment amount;

[0145] An acquisition module 202 is configured to acquire an inverse matrix of a voltage sensitivity matrix of each distribution network node, the number of distributed energy devices connected to the distribution network node, and device regulation capability parameters of the distributed energy devices connected to the distribution network node;

[0146] The distribution network voltage compensation vector calculation module 203 is used to obtain the voltage deviation values ​​of all distribution network nodes in real time and calculate the distribution network voltage compensation vector in combination with the inverse matrix;

[0147] The main demand regulation vector generation module 204 is used to evenly divide the power regulation amount of the entire network according to the number of distributed energy devices to generate a main network demand regulation vector;

[0148] The global active power regulation target vector generation module 205 is used to perform weighted fusion of the main network demand regulation vector and the distribution network voltage compensation vector to obtain the global active power regulation target vector;

[0149] A dominant frequency component extraction module 206 is configured to extract a dominant frequency component from the global active power regulation target vector;

[0150] The device adaptation index calculation module 207 is used to calculate the device adaptation index of the distributed energy device connected to the distribution network node using the device adjustment capability parameter, the dominant frequency component, the main network real-time power margin, and the voltage deviation value;

[0151] The allocation module 208 is configured to allocate the regulation tasks in the global active power regulation target vector to the distributed energy devices at the distribution network nodes for execution by using the dominant frequency component and the device adaptation index.

[0152] In an embodiment of the present invention, the main network real-time power margin and the whole network power adjustment amount acquisition module 201 includes:

[0153] Real-time transmission power data acquisition submodule, used to collect real-time transmission power data of each transmission line;

[0154] The thermal stability limit power parameter acquisition submodule is used to obtain the thermal stability limit power parameter from the preset dispatching center;

[0155] The whole network power regulation amount receiving submodule is used to calculate the real-time power margin of the main network using real-time transmission power data and thermal stability limit power parameters, and receive the whole network power regulation amount.

[0156] In the embodiment of the present invention, the distribution network voltage compensation vector calculation module 203 includes:

[0157] The actual voltage value monitoring submodule is used to monitor the actual voltage values ​​of all distribution network nodes in real time;

[0158] The voltage deviation value calculation submodule is used to calculate the voltage deviation value between the actual voltage value and the rated voltage value;

[0159] A voltage deviation vector generation submodule is used to generate a voltage deviation vector using the voltage deviation values ​​of all distribution network nodes;

[0160] The distribution network voltage compensation vector generation submodule is used to perform inverse calculation on the voltage deviation vector using an inverse matrix to obtain the distribution network voltage compensation vector.

[0161] In the embodiment of the present invention, the global active power regulation target vector generation module 205 includes:

[0162] The main network demand weight and distribution network demand weight calculation submodule is used to calculate the main network demand weight according to the real-time power margin of the main network and the distribution network demand weight according to the voltage deviation value;

[0163] The global active power regulation target vector calculation submodule is used to weightedly fuse the main network demand regulation vector and the distribution network voltage compensation vector based on the main network demand weight and the distribution network demand weight to obtain the global active power regulation target vector.

[0164] In the embodiment of the present invention, the dominant frequency component extraction module 206 extracts the dominant frequency component from the global active power regulation target vector, including:

[0165] The dominant frequency component extraction submodule is used to perform fast Fourier transform on the global active power regulation target vector and extract the frequency band whose spectrum energy ratio exceeds the preset spectrum energy threshold as the dominant frequency component.

[0166] In the embodiment of the present invention, the device adjustment capability parameters include the actual adjustment rate and the actual response time; the device adaptation index calculation module 207 includes:

[0167] The regulation rate score calculation submodule is used to calculate the ratio of the actual regulation rate to the preset regulation rate reference value to obtain the regulation rate score;

[0168] The response time score calculation submodule is used to calculate the ratio of the preset response time benchmark value to the actual response time to obtain the response time score;

[0169] The absolute difference calculation submodule is used to obtain the historical best operating frequency of the device and calculate the absolute difference between the historical best operating frequency of the device and the dominant frequency component;

[0170] The frequency matching score conversion submodule is used to convert the absolute difference into a frequency matching score through a reciprocal function;

[0171] The main network margin impact factor calculation submodule is used to calculate the main network margin impact factor using the main network real-time power margin and the preset safety margin threshold;

[0172] A voltage deviation impact factor calculation submodule is used to calculate the voltage deviation impact factor based on the voltage deviation value and a preset voltage over-limit threshold;

[0173] The frequency matching factor conversion submodule is used to calculate the difference between the dominant frequency component and the historical optimal operating frequency of the device, and convert the difference into a frequency matching factor through an inverse function;

[0174] A product calculation submodule is used to calculate the first product of the main grid margin impact factor and the regulation rate score, the second product of the voltage deviation impact factor and the response time score, and the third product of the frequency matching factor and the frequency matching score;

[0175] The device adaptation index submodule is used to add the first product, the second product and the third product to obtain the device adaptation index of the distributed energy device connected to the distribution network node.

[0176] In this embodiment of the present invention, the allocation module 208 includes:

[0177] A task type judgment submodule is used to judge the task type of the adjustment task according to the dominant frequency component and the preset high-frequency judgment threshold;

[0178] The priority sorting submodule is used to prioritize all distributed energy devices on the same distribution network node based on the device adaptation index;

[0179] The main execution object determination submodule is used to select a preset proportion of distributed energy devices as the main execution objects according to the priority order from high to low when the task type is a high-frequency regulation task;

[0180] A first total index calculation submodule, configured to calculate a first total index of device adaptation indices of all main execution objects;

[0181] A first ratio calculation submodule, configured to calculate a first ratio of the device adaptation index of each main execution object to the first total index;

[0182] A first allocation submodule, configured to allocate an adjustment amount to each main execution object according to a first ratio;

[0183] A secondary execution object determination submodule is used to determine the distributed energy equipment other than the primary execution object as a secondary execution object when the task type is a low-frequency regulation task;

[0184] A second total index calculation submodule, used to calculate a second total index of the device adaptation index of all distributed energy devices;

[0185] A second ratio calculation submodule, configured to calculate a second ratio of the device adaptation index of each secondary object to the second total index;

[0186] The second allocation submodule is configured to allocate the adjustment amount to each secondary execution object according to a second ratio.

[0187] An embodiment of the present invention further provides an electronic device, the device including a processor and a memory:

[0188] The memory is used to store program codes and transmit the program codes to the processor;

[0189] The processor is used to execute the distributed renewable energy active power control method according to the instructions in the program code.

[0190] An embodiment of the present invention further provides a computer-readable storage medium, which is used to store program code, and the program code is used to execute the distributed new energy active power control method of the embodiment of the present invention.

[0191] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0192] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0193] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, embodiments of the present invention may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0194] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0195] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable terminal device. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0197] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0198] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0199] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0200] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. 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 invention.

Claims

1. A distributed new energy active power control method, characterized in that: include: Calculate the real-time power margin of the main network and receive the power adjustment value of the entire network; Obtaining the inverse matrix of the voltage sensitivity matrix of each distribution network node, the number of distributed energy devices connected to the distribution network node, and the device regulation capability parameters of the distributed energy devices connected to the distribution network node; Obtaining the voltage deviation values ​​of all distribution network nodes in real time, and calculating the distribution network voltage compensation vector in combination with the inverse matrix; Evenly divide the power regulation amount of the entire network according to the number of the distributed energy devices to generate a main network demand regulation vector; Weighted fusion of the main grid demand regulation vector and the distribution network voltage compensation vector to obtain a global active power regulation target vector; extracting a dominant frequency component from the global active power regulation target vector; Calculating a device adaptation index of a distributed energy device connected to a distribution network node by using the device regulation capability parameter, the dominant frequency component, the main network real-time power margin, and the voltage deviation value; The dominant frequency component and the device adaptation index are used to allocate the regulation tasks in the global active power regulation target vector to the distributed energy devices of the distribution network nodes for execution.

2. The method according to claim 1, characterized in that The step of calculating the real-time power margin of the primary network and receiving the power adjustment amount of the entire network includes: Real-time collection of real-time transmission power data of each transmission line; Obtain thermal stability limit power parameters from the preset dispatch center; The real-time transmission power data and the thermal stability limit power parameter are used to calculate the real-time power margin of the main network, and the power adjustment amount of the entire network is received.

3. The method according to claim 1, characterized in that The step of acquiring the voltage deviation values ​​of all distribution network nodes in real time and calculating the distribution network voltage compensation vector in combination with the inverse matrix includes: Real-time monitoring of the actual voltage values ​​of all the distribution network nodes; Calculating a voltage deviation between the actual voltage value and the rated voltage value; The voltage deviation values ​​of all distribution network nodes are used to generate a voltage deviation vector; The voltage deviation vector is inversely calculated using the inverse matrix to obtain a distribution network voltage compensation vector.

4. The method according to claim 1, wherein The step of weightedly fusing the main network demand regulation vector and the distribution network voltage compensation vector to obtain a global active power regulation target vector includes: Calculating the main network demand weight according to the main network real-time power margin, and calculating the distribution network demand weight according to the voltage deviation value; The main network demand regulation vector and the distribution network voltage compensation vector are weightedly fused based on the main network demand weight and the distribution network demand weight to obtain a global active power regulation target vector.

5. The method according to claim 1, wherein The step of extracting the dominant frequency component from the global active power regulation target vector comprises: A fast Fourier transform is performed on the global active power regulation target vector, and a frequency band whose spectrum energy ratio exceeds a preset spectrum energy threshold is extracted as a dominant frequency component.

6. The method according to claim 1, wherein The device regulation capability parameters include an actual regulation rate and an actual response time; and the step of calculating the device adaptation index of the distributed energy device connected to the distribution network node using the device regulation capability parameters, the dominant frequency component, the main network real-time power margin, and the voltage deviation value includes: Calculating the ratio of the actual regulation rate to a preset regulation rate reference value to obtain a regulation rate score; Calculate the ratio of the preset response time benchmark value to the actual response time to obtain a response time score; Obtaining a historical optimal operating frequency of the device, and calculating an absolute difference between the historical optimal operating frequency of the device and the dominant frequency component; Converting the absolute difference into a frequency matching score by a reciprocal function; Calculating a main network margin impact factor using the main network real-time power margin and a preset safety margin threshold; Calculating a voltage deviation impact factor based on the voltage deviation value and a preset voltage over-limit threshold; Calculating the difference between the dominant frequency component and the historical optimal operating frequency of the device, and converting the difference into a frequency matching factor using a reciprocal function; Calculating a first product of the main grid margin impact factor and the regulation rate score, a second product of the voltage deviation impact factor and the response time score, and a third product of the frequency matching factor and the frequency matching score; The first product, the second product and the third product are added together to obtain a device adaptation index of the distributed energy device connected to the distribution network node.

7. The method according to claim 1, characterized in that The step of using the dominant frequency component and the device adaptation index to allocate the regulation tasks in the global active power regulation target vector to the distributed energy devices of the distribution network nodes for execution includes: Determining the task type of the adjustment task according to the dominant frequency component and a preset high-frequency determination threshold; Prioritizing all distributed energy devices on the same distribution network node based on the device adaptation index; When the task type is a high-frequency regulation task, a preset proportion of distributed energy devices are selected as the main execution objects according to the priority order from high to low; Calculating a first total index of device adaptation indexes of all the main execution objects; Calculating a first ratio of the device adaptation index of each of the main execution objects to the first total index; allocating an adjustment amount to each of the main execution objects according to the first ratio; When the task type is a low-frequency regulation task, the distributed energy equipment other than the primary execution object is used as a secondary execution object; Calculate a second overall index of the equipment adaptation index of all distributed energy equipment; Calculate a second ratio of the device adaptation index of each of the secondary objects to the second total index; The adjustment amount is allocated to each of the secondary execution objects according to the second ratio.

8. A distributed new energy active power control device, characterized in that: include: The main network real-time power margin and the whole network power adjustment amount acquisition module is used to calculate the main network real-time power margin and receive the whole network power adjustment amount; An acquisition module is used to obtain the inverse matrix of the voltage sensitivity matrix of each distribution network node, the number of distributed energy devices connected to the distribution network node, and the device regulation capability parameters of the distributed energy devices connected to the distribution network node; A distribution network voltage compensation vector calculation module is used to obtain the voltage deviation values ​​of all distribution network nodes in real time and calculate the distribution network voltage compensation vector in combination with the inverse matrix; A main demand regulation vector generation module is used to evenly divide the power regulation amount of the entire network according to the number of the distributed energy devices to generate a main network demand regulation vector; A global active power regulation target vector generation module is configured to perform weighted fusion of the main network demand regulation vector and the distribution network voltage compensation vector to obtain a global active power regulation target vector; A dominant frequency component extraction module, configured to extract a dominant frequency component from the global active power regulation target vector; a device adaptation index calculation module, configured to calculate the device adaptation index of the distributed energy device connected to the distribution network node by using the device adjustment capability parameter, the dominant frequency component, the main network real-time power margin, and the voltage deviation value; The allocation module is used to use the dominant frequency component and the device adaptation index to allocate the regulation tasks in the global active power regulation target vector to the distributed energy devices of the distribution network nodes for execution.

9. 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 distributed renewable energy active power control method according to any one of claims 1 to 7 according to the instructions in the program code.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium is used to store program code, and the program code is used to execute the distributed renewable energy active power control method according to any one of claims 1 to 7.

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