A distributed energy control method, system, device and medium

Abstract

The present invention discloses a distributed energy control method, system, device and medium, which relate to the technical field of power system. By respectively using distributed new energy installed capacity data, user electricity consumption information data and distribution network side network structure data to calculate the impact factor, a capacity impact factor matrix, a time impact factor matrix and a topology impact factor matrix are constructed; based on the capacity impact factor matrix, the time impact factor matrix and the topology impact factor matrix, the new energy response sensitivity is calculated to generate multiple new energy response sensitivities; based on the new energy response sensitivity, energy control is performed on each user in the distribution network to generate energy control data. By performing energy control on each user in the distribution network based on the new energy response sensitivity, the regulating effect of the user response capability in the distributed new energy control process can be fully utilized, and while ensuring the stability of the power quality of the distributed resources, the economic benefits of local users can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a distributed energy control method, system, equipment and medium. Background Art

[0002] In recent years, distributed energy (DRE) has rapidly developed due to its advantages of easy installation, flexible switching, and low cost, and its penetration rate in traditional power grids has steadily increased. While a high proportion of DRE connected to the grid optimizes my country's energy supply, it can also lead to numerous problems, such as power flow reversal, uncoordinated energy consumption, and degraded power quality on the distribution network. Relying solely on the grid's own control capabilities and implementation methods is insufficient to precisely regulate the vast amount of small-scale DRE. Since local load users benefit from the green, low-cost electricity provided by DRE, and their loads have a more direct impact on the distribution network's regulatory capabilities, they should bear greater responsibility for responding to issues related to DRE. This responsibility can be implemented through measures such as load reduction and compliance with regional electricity price changes. However, this responsibility must be shared by all local load users. Currently, research on the allocation of response responsibility is relatively limited, primarily focusing on approaches that allocate responsibility based on power quality insurance mechanisms and dynamic on-grid pricing mechanisms.

[0003] The power quality insurance mechanism assesses the response responsibility of each distributed energy user based on the quality of the electricity they generate. The lower the quality of the electricity generated by the user's installed capacity, the greater the response responsibility they must bear. During peak power generation periods, this portion of electricity is priced higher, placing it at a disadvantage in the market, thereby alleviating the pressure on renewable energy consumption. However, the power quality of distributed energy is affected by multiple subjective and objective factors and cannot be fully adjusted by the user, making this solution difficult to ensure fairness in practice. The dynamic on-grid electricity price mechanism considers factors such as power generation, distribution network load capacity, ambient temperature, and real-time electricity prices to establish a daily dynamic electricity price for energy on-grid settlement. However, this method primarily considers the impact of grid structure and the external environment on responsibility allocation, and does not yet consider the role of user response capabilities in responsibility allocation. Summary of the Invention

[0004] The present invention provides a distributed energy control method, system, device and medium, which solves the technical problem that the existing distributed energy control method is easily affected by multiple subjective and objective factors, and mainly considers the impact of the power grid structure and external environment on responsibility allocation, but does not consider the role of user response capability on responsibility allocation, resulting in poor energy control effect.

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

[0006] Obtain the distributed new energy installed capacity data, user electricity consumption information data and distribution network side network structure data corresponding to the distribution network;

[0007] The distributed new energy installed capacity data, the user electricity consumption information data and the distribution network side network structure data are respectively used to calculate the impact factors, and a capacity impact factor matrix, a time impact factor matrix and a topology impact factor matrix are constructed;

[0008] Performing new energy response sensitivity calculation based on the capacity impact factor matrix, the time impact factor matrix, and the topology impact factor matrix to generate multiple new energy response sensitivities;

[0009] Based on the new energy response sensitivity, energy regulation is performed on each user in the distribution network to generate energy regulation data.

[0010] Optionally, the step of respectively using the distributed new energy installed capacity data, the user electricity consumption information data, and the distribution network side network structure data to calculate the impact factors and construct a capacity impact factor matrix, a time impact factor matrix, and a topology impact factor matrix includes:

[0011] Using the distributed new energy installed capacity data to construct a matrix, generating a distributed new energy installed capacity information matrix;

[0012] The distributed new energy installed capacity information matrix is:

[0013] ;

[0014] in, It is the information matrix of distributed new energy installed capacity; The number of local distributed new energy installed capacity; The initial capacity of the first distributed new energy unit; is the initial capacity of the second distributed new energy unit; For the Initial capacity of distributed new energy units;

[0015] Calculate the impact factor based on the distributed new energy installed capacity information matrix to construct a capacity impact factor matrix;

[0016] Matrix construction is performed using the daily load curve of each user within a preset time period in the user electricity consumption information data to generate a user initial load curve information matrix;

[0017] Calculate the impact factor based on the user's initial load curve information matrix and construct a time impact factor matrix;

[0018] The impact factors are calculated based on the distribution network side network structure data to construct a topology impact factor matrix.

[0019] Optionally, the step of calculating the impact factor based on the distributed new energy installed capacity information matrix and constructing the capacity impact factor matrix includes:

[0020] Divide the new energy installed capacity corresponding to the distributed new energy installed capacity information matrix into bins according to preset bin division data to generate capacity bin data;

[0021] Using the capacity division data to update the distributed new energy installed capacity information matrix, generating a local distributed new energy installed capacity matrix after initial binning;

[0022] The local distributed new energy installed capacity matrix after the initial binning is:

[0023] ;

[0024] ;

[0025] in, is the local distributed renewable energy installed capacity matrix after initial binning; for Middle Distributed renewable energy installed capacity of units; for The distributed renewable energy installed capacity of the first unit; for The distributed renewable energy installed capacity of the second unit; for Middle The distributed renewable energy installed capacity of each unit; p is the unit number, p = 1, 2, ..., 9; is the gear capacity interval; for Maximum value; It is the information matrix of distributed new energy installed capacity;

[0026] Normalizing the initial binned local distributed renewable energy installed capacity matrix to generate a target binned local distributed renewable energy installed capacity matrix;

[0027] The local distributed new energy installed capacity matrix after the target binning is:

[0028] ;

[0029] ;

[0030] in, The local distributed renewable energy installed capacity matrix after target binning; for Middle Distributed renewable energy installed capacity of units; for The distributed renewable energy installed capacity of the first unit; for The distributed renewable energy installed capacity of the second unit; for Middle Distributed renewable energy installed capacity of units; for Middle Distributed renewable energy installed capacity of units; for Maximum value; for Minimum value; is the local distributed renewable energy installed capacity matrix after initial binning;

[0031] Calculate the new energy low installed capacity threshold by using the maximum capacity and the minimum capacity in the local distributed new energy installed capacity matrix after the initial binning to generate the distributed new energy low installed capacity threshold;

[0032] The threshold value of low installed capacity of distributed new energy is:

[0033] ;

[0034] in, It is the low installed capacity threshold of distributed new energy; for Maximum value; for Minimum value; is the local distributed renewable energy installed capacity matrix after initial binning;

[0035] The capacity impact factor matrix is ​​constructed by using the distributed new energy low installed capacity threshold and the local distributed new energy installed capacity matrix after target binning to generate a capacity impact factor matrix;

[0036] The capacity impact factor matrix is:

[0037] ;

[0038] ;

[0039] in, is the capacity impact factor matrix; for Middle Capacity impact factors of user responsiveness; for The capacity impact factor of the user response capability of the first new energy installed capacity; for The capacity impact factor of the second new energy installed capacity user response capability; for Middle The capacity impact factor of the user response capability of the new energy installed capacity; for Middle Distributed renewable energy installed capacity of units; The local distributed renewable energy installed capacity matrix after target binning; It is the low installed capacity threshold for distributed new energy.

[0040] Optionally, the step of calculating the impact factor based on the user initial load curve information matrix and constructing the time impact factor matrix includes:

[0041] Calculating the average load rate corresponding to each user in the user initial load curve information matrix using a preset average load rate formula to generate multiple average load rates;

[0042] The preset average load rate formula is:

[0043] ;

[0044] in, For users Average load rate at hour h in the past 90 days; For users Average load at h on day d; for Load curve data at h on day d;

[0045] The average load rate is used to calculate the impact factor and construct a time impact factor matrix;

[0046] The time impact factor matrix is:

[0047] ;

[0048] ;

[0049] in, is the time impact factor matrix; is the time impact factor of user 1's responsiveness; is the time impact factor of user 2's responsiveness; is the time impact factor of user sn responsiveness; For users Time impact factor of responsiveness; For users The average load rate at hour h in the past 90 days.

[0050] Optionally, the step of calculating the impact factor based on the distribution network side network structure data and constructing a topology impact factor matrix includes:

[0051] The voltage data and power data corresponding to each node in the distribution network structure data are used to construct a Jacobi equation;

[0052] The Jacobi equation is:

[0053] ;

[0054] in, For users in the power network The power at For nodes Power to Node With node Partial derivative of the phase angle difference between voltages; For nodes power; For nodes With node Voltage phase angle; For nodes Power to Node Partial derivative of voltage amplitude; For nodes Voltage amplitude; For nodes Voltage and user The difference in voltage phase angle; For nodes The voltage amplitude disturbance applied on

[0055] respectively calculating the inverse operation of the partial derivatives of the node power with respect to the voltage amplitude in the Jacobi equation to generate a plurality of original voltage sensitivities;

[0056] The original voltage sensitivity is:

[0057] ;

[0058] in, For nodes About User Load Node The original voltage sensitivity of For nodes Power to Node Partial derivative of voltage amplitude; For nodes power; For nodes The voltage amplitude; For nodes and The electrical conductivity between For nodes With node The inter-susceptance For nodes With node Voltage phase angle;

[0059] The original voltage sensitivity is used to construct a matrix to generate an original voltage sensitivity matrix;

[0060] The original voltage sensitivity matrix is:

[0061] ;

[0062] in, is the original voltage sensitivity matrix; For nodes About User Load Node The original voltage sensitivity of For nodes About User Load Node The original voltage sensitivity of For nodes About User Load Node The original voltage sensitivity of For nodes About User Load Node The original voltage sensitivity of For nodes About User Load Node The original voltage sensitivity of

[0063] Normalizing each element in the original voltage sensitivity matrix to generate a topology impact factor matrix;

[0064] The topology impact factor matrix is:

[0065] ;

[0066] ;

[0067] in, is the topological impact factor matrix; For users Load on the node Voltage sensitivity; The number of local users; The number of distributed new energy grid-connected points; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For nodes About User Load Node The original voltage sensitivity.

[0068] Optionally, the step of calculating the new energy response sensitivity based on the capacity impact factor matrix, the time impact factor matrix, and the topology impact factor matrix to generate a plurality of new energy response sensitivities includes:

[0069] Calculating the mutual information value between each influencing factor in the capacity influencing factor matrix and the user load data corresponding to the distribution network using a preset mutual information value calculation formula to generate a plurality of capacity mutual information values;

[0070] The preset mutual information value calculation formula is:

[0071] ;

[0072] in, for and The mutual information value between is the k-th impact factor index; y is the user load data; is the influencing factor of the kth category user responsiveness Item value; The first Item value; Impact Factor exist Probability of occurrence; For user load Probability of appearing in y; for and The joint probability density of

[0073] Calculating the mutual information value between each impact factor in the time impact factor matrix and the user load data using the preset mutual information value calculation formula to generate a plurality of time mutual information values;

[0074] Calculating the mutual information value between each influencing factor in the topology influencing factor matrix and the user load data using the preset mutual information value calculation formula to generate a plurality of topology mutual information values;

[0075] Selecting the maximum values ​​of the capacity mutual information value, the time mutual information value, and the topology mutual information value respectively to generate an initial maximum capacity mutual information value, an initial maximum time mutual information value, and an initial maximum topology mutual information value;

[0076] Normalizing the initial maximum capacity mutual information value, the initial maximum time mutual information value, and the initial maximum topology mutual information value to generate a target maximum capacity mutual information value, a target maximum time mutual information value, and a target maximum topology mutual information value;

[0077] constructing a correlation matrix using the target maximum capacity mutual information value, the target maximum time mutual information value, and the target maximum topology mutual information value to generate a correlation parameter matrix;

[0078] Calculating new energy response sensitivities using the correlation parameter matrix, the capacity impact factor matrix, the time impact factor matrix, and the topology impact factor matrix to generate a plurality of new energy response sensitivities;

[0079] The new energy response sensitivity is:

[0080] ;

[0081] in, For users For Node New energy response sensitivity; is the target maximum capacity mutual information value; For users Capacity impact factors of responsiveness; is the target maximum time mutual information value; For users Time impact factor of responsiveness; is the target maximum topological mutual information value; For users For Node Voltage sensitivity.

[0082] Optionally, the step of performing energy regulation on each user in the distribution network based on the new energy response sensitivity and generating energy regulation data includes:

[0083] Calculating the deviation between the real-time voltage value of each node in the distribution network and its corresponding rated voltage respectively, and generating a plurality of node deviations;

[0084] The node deviation and the new energy response sensitivity corresponding to the node deviation are used to distribute the voltage deviation that each user in the distribution network needs to bear, thereby generating multiple user voltage deviations;

[0085] The user voltage deviation is:

[0086] ;

[0087] in, For users Nodes required User voltage deviation; For nodes The node voltage deviation; For users For Node New energy response sensitivity; For local users on the node New energy response sensitivity;

[0088] Calculating user load reduction amounts by respectively using the user voltage deviation amounts to generate multiple user load reduction amounts;

[0089] The user load reduction amount is:

[0090] ;

[0091] in, For users Responsible Node The amount of user load reduction corresponding to the voltage deviation; for; For nodes Power to Node Partial derivative of voltage amplitude; For nodes power; For nodes Voltage amplitude;

[0092] respectively calculating the product of the user load reduction amount and the load reduction electricity price corresponding to the user load reduction amount to generate the economic benefit corresponding to the user load reduction amount;

[0093] The economic benefits are:

[0094] ;

[0095] in, For users Participating regulation nodes Economic benefits of voltage; For users Responsible Node The amount of user load reduction corresponding to the voltage deviation; To reduce load electricity prices;

[0096] All of the economic benefits are used to construct energy regulation data.

[0097] The present invention also provides a distributed energy control system, comprising:

[0098] The data acquisition module is used to obtain the distributed new energy installed capacity data, user electricity consumption information data and distribution network side network structure data corresponding to the distribution network;

[0099] A matrix construction module is used to calculate the impact factors using the distributed new energy installed capacity data, the user electricity consumption information data and the distribution network side network structure data, and construct a capacity impact factor matrix, a time impact factor matrix and a topology impact factor matrix;

[0100] a new energy response sensitivity generation module, configured to calculate the new energy response sensitivity based on the capacity impact factor matrix, the time impact factor matrix, and the topology impact factor matrix, and generate a plurality of new energy response sensitivities;

[0101] The energy regulation data generation module is used to perform energy regulation on each user in the distribution network based on the new energy response sensitivity and generate energy regulation data.

[0102] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of implementing any of the above-mentioned distributed energy control methods.

[0103] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, it implements any of the above-mentioned distributed energy control methods.

[0104] It can be seen from the above technical solutions that the present invention has the following advantages:

[0105] By regulating energy consumption for each user in the distribution network based on the sensitivity of renewable energy response, this invention fully leverages the regulatory role of user response capabilities in the distributed renewable energy regulation process. This approach ensures stable power quality from distributed resources while improving the economic benefits of local users. This addresses the technical issue of existing distributed energy regulation methods, which are susceptible to multiple subjective and objective factors and primarily consider the impact of grid structure and the external environment on responsibility allocation, without considering the impact of user response capabilities on responsibility allocation, leading to poor energy regulation effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0106] 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.

[0107] Figure 1 A flowchart of the steps of a distributed energy control method provided in Example 1 of the present invention;

[0108] Figure 2 A flowchart of the steps of a distributed energy control method provided in the second embodiment of the present invention;

[0109] Figure 3 This is a schematic diagram of the binning processing results of the installed capacity data of 100 households in the region provided by the third embodiment of the present invention;

[0110] Figure 4 A schematic diagram showing calculation results of the impact factors of the response capability capacity of each user provided in the third embodiment of the present invention;

[0111] Figure 5 A schematic diagram of the response capability time impact factors of 100 households from 12:00 to 13:00 provided in the third embodiment of the present invention;

[0112] Figure 6 A schematic diagram of topological impact factors of each user's response capability provided in the third embodiment of the present invention;

[0113] Figure 7 A schematic diagram comparing the voltage conditions at a new energy grid connection point before and after the implementation of the third embodiment of the present invention;

[0114] Figure 8 This is a table diagram showing the economic benefit distribution results of 10 users from 12:00 to 13:00 provided in Example 3 of the present invention;

[0115] Figure 9A structural block diagram of a distributed energy control system provided in the fourth embodiment of the present invention;

[0116] Figure 10 This is a structural block diagram of an electronic device provided in Example 5 of the present invention. DETAILED DESCRIPTION

[0117] The embodiments of the present invention provide a distributed energy control method, system, device and medium, which are used to solve the technical problem that the existing distributed energy control methods are easily affected by multiple subjective and objective factors, and mainly consider the impact of the power grid structure and external environment on responsibility allocation, but have not considered the role of user response capability on responsibility allocation, resulting in poor energy control effect.

[0118] 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.

[0119] Example 1

[0120] See also Figure 1 , Figure 1 This is a flowchart of the steps of a distributed energy control method provided in Example 1 of the present invention.

[0121] A distributed energy control method provided in Example 1 of the present invention includes:

[0122] Step 101: Obtain the distributed new energy installed capacity data, user electricity consumption information data and distribution network side network structure data corresponding to the distribution network.

[0123] In this embodiment of the present invention, local distributed renewable energy installed capacity data is collected in the distribution network to obtain distributed renewable energy installed capacity data. Daily load curve information for each user over the past 90 days is collected to obtain user electricity consumption information data. Local distribution network topology information is collected in the distribution network to obtain distribution network side network structure data.

[0124] Step 102: Distributed new energy installed capacity data, user electricity consumption information data, and distribution network structure data are used to calculate impact factors, and a capacity impact factor matrix, a time impact factor matrix, and a topology impact factor matrix are constructed.

[0125] In an embodiment of the present invention, a matrix is ​​constructed using distributed renewable energy installed capacity data to generate a distributed renewable energy installed capacity information matrix. Impact factors are calculated based on the distributed renewable energy installed capacity information matrix to construct a capacity impact factor matrix. A matrix is ​​constructed using the daily load curves of each user within a preset time period from user electricity consumption data to generate a user initial load curve information matrix. Impact factors are calculated based on the user initial load curve information matrix to construct a time impact factor matrix. Impact factors are calculated based on distribution network structure data to construct a topology impact factor matrix.

[0126] Step 103 : Calculate new energy response sensitivities based on the capacity impact factor matrix, the time impact factor matrix, and the topology impact factor matrix to generate multiple new energy response sensitivities.

[0127] In an embodiment of the present invention, a preset mutual information value calculation formula is used to calculate the mutual information value between each impact factor in the capacity impact factor matrix and the user load data corresponding to the distribution network, thereby generating multiple capacity mutual information values. A preset mutual information value calculation formula is used to calculate the mutual information value between each impact factor in the time impact factor matrix and the user load data, thereby generating multiple time mutual information values. A preset mutual information value calculation formula is used to calculate the mutual information value between each impact factor in the topology impact factor matrix and the user load data, thereby generating multiple topology mutual information values. The maximum values ​​among the capacity mutual information value, the time mutual information value, and the topology mutual information value are respectively selected to generate an initial maximum capacity mutual information value, an initial maximum time mutual information value, and an initial maximum topology mutual information value. The initial maximum capacity mutual information value, the initial maximum time mutual information value, and the initial maximum topology mutual information value are normalized to generate a target maximum capacity mutual information value, a target maximum time mutual information value, and a target maximum topology mutual information value. The target maximum capacity mutual information value, the target maximum time mutual information value, and the target maximum topology mutual information value are used to construct a correlation matrix, thereby generating a correlation parameter matrix. The new energy response sensitivity is calculated using the correlation parameter matrix, capacity influence factor matrix, time influence factor matrix and topology influence factor matrix to generate multiple new energy response sensitivities.

[0128] Step 104: Perform energy regulation on each user in the distribution network based on the new energy response sensitivity to generate energy regulation data.

[0129] In an embodiment of the present invention, the deviation between the real-time voltage value of each node in the distribution network and its corresponding rated voltage is calculated to generate multiple node deviations. The node deviations and the corresponding new energy response sensitivities are used to allocate the voltage deviations required of each user in the distribution network to generate multiple user voltage deviations. The user voltage deviations are used to calculate user load reductions to generate multiple user load reductions. The product of the user load reduction and the load reduction electricity price corresponding to the user load reduction is calculated to generate the economic benefits corresponding to the user load reduction. The total economic benefits are used to construct energy regulation data.

[0130] In an embodiment of the present invention, distributed renewable energy installed capacity data, user electricity consumption data, and distribution network structure data corresponding to the distribution network are obtained. Impact factors are calculated using these data, constructing a capacity impact factor matrix, a time impact factor matrix, and a topology impact factor matrix. New energy response sensitivities are calculated based on the capacity impact factor matrix, the time impact factor matrix, and the topology impact factor matrix to generate multiple new energy response sensitivities. Energy regulation is then performed on each user in the distribution network based on the new energy response sensitivities to generate energy regulation data. By performing energy regulation on each user in the distribution network based on the new energy response sensitivities, the regulatory role of user response capabilities in the distributed renewable energy regulation process can be fully utilized. This ensures stable power quality for distributed resources while increasing the economic benefits of local users. This addresses the technical issue of existing distributed energy regulation methods being susceptible to multiple subjective and objective factors and primarily considering the impact of grid structure and the external environment on responsibility allocation, while failing to consider the role of user response capabilities in responsibility allocation, resulting in poor energy regulation effectiveness.

[0131] Example 2

[0132] See also Figure 2 , Figure 2 This is a flow chart of the steps of a distributed energy control method provided in Example 2 of the present invention.

[0133] Another distributed energy control method provided in Example 2 of the present invention includes:

[0134] Step 201: Obtain the distributed new energy installed capacity data, user electricity consumption information data and distribution network side network structure data corresponding to the distribution network.

[0135] In the embodiment of the present invention, the specific implementation process of step 201 is similar to that of step 101 and will not be repeated here.

[0136] Step 202: Use the distributed new energy installed capacity data to construct a matrix to generate a distributed new energy installed capacity information matrix.

[0137] In the embodiment of the present invention, the local distributed new energy installed capacity data, i.e., the distributed new energy installed capacity data, is collected and a matrix is ​​constructed to obtain the distributed new energy installed capacity information matrix. , the information matrix of distributed new energy installed capacity is:

[0138] ;

[0139] in, It is the information matrix of distributed new energy installed capacity; The number of local distributed new energy installed capacity; The initial capacity of the first distributed new energy unit; is the initial capacity of the second distributed new energy unit; For the The initial capacity of distributed new energy units.

[0140] Step 203: Calculate the impact factor based on the distributed new energy installed capacity information matrix to construct a capacity impact factor matrix.

[0141] Furthermore, step 203 may include the following sub-steps S11-S15:

[0142] S11. Divide the new energy installed capacity corresponding to the distributed new energy installed capacity information matrix into bins according to the preset bin division data to generate capacity bin data.

[0143] S12. Use the capacity division data to update the distributed new energy installed capacity information matrix to generate a local distributed new energy installed capacity matrix after initial binning.

[0144] S13. Normalize the local distributed renewable energy installed capacity matrix after initial binning to generate a local distributed renewable energy installed capacity matrix after target binning.

[0145] S14. Calculate the new energy low installed capacity threshold by using the maximum capacity and the minimum capacity in the local distributed new energy installed capacity matrix after initial binning to generate the distributed new energy low installed capacity threshold.

[0146] S15. Use the distributed new energy low installed capacity threshold and the local distributed new energy installed capacity matrix after target binning to construct a capacity impact factor matrix to generate a capacity impact factor matrix.

[0147] In an embodiment of the present invention, the new energy installed capacity corresponding to the distributed new energy installed capacity information matrix is ​​divided into bins according to the preset bin division data to generate capacity bin data. Specifically, the initial distributed new energy installed capacity is divided into 9 bins according to the size, and the capacity interval of each bin is:

[0148] ;

[0149] in, is the gear capacity interval; It is the maximum value in the distributed new energy installed capacity information matrix; It is the minimum value in the distributed new energy installed capacity information matrix.

[0150] Use the capacity division data to update the distributed new energy installed capacity information matrix, and obtain the local distributed new energy installed capacity matrix after the initial binning. Reassign the values ​​in :

[0151] ;

[0152] in, is the local distributed renewable energy installed capacity matrix after binning, i.e. the local distributed renewable energy installed capacity matrix after initial binning; for Middle Distributed renewable energy installed capacity of units; for The distributed renewable energy installed capacity of the first unit; for The distributed renewable energy installed capacity of the second unit; for Middle The distributed renewable energy installed capacity of each unit; p is the unit number, p = 1, 2, ..., 9; is the gear capacity interval; for Maximum value; It is the information matrix of distributed new energy installed capacity.

[0153] The local distributed new energy installed capacity matrix after the initial binning is normalized to obtain the local distributed new energy installed capacity matrix after data preprocessing, that is, the local distributed new energy installed capacity matrix after the target binning. The local distributed new energy installed capacity matrix after the target binning is:

[0154] ;

[0155] ;

[0156] in, The local distributed renewable energy installed capacity matrix after target binning; for Middle Distributed renewable energy installed capacity of units; for The installed capacity of distributed renewable energy for the first unit; for The distributed renewable energy installed capacity of the second unit; for Middle Distributed renewable energy installed capacity of units; for Middle Distributed renewable energy installed capacity of units; for Maximum value; for Minimum value; It is the local distributed renewable energy installed capacity matrix after initial binning.

[0157] The maximum and minimum capacity values ​​in the local distributed new energy installed capacity matrix after initial binning are used to calculate the new energy low installed capacity threshold value, and generate the distributed new energy low installed capacity threshold value. That is, the first three boxes in the capacity bin data are the low capacity range, and the distributed new energy low installed capacity threshold value is for:

[0158] ;

[0159] in, is the threshold value of low installed capacity of distributed new energy, which is 0.25; for The maximum value is 9; for The minimum value is 1; It is the local distributed renewable energy installed capacity matrix after initial binning.

[0160] Calculate the capacity impact factor matrix of user responsiveness , it is believed that when the normalized distributed renewable energy installed capacity is less than the low installed capacity threshold, the capacity impact factor of the installed user response capability is 0, that is:

[0161] ;

[0162] in, is the capacity impact factor matrix; for Middle Capacity impact factors of user responsiveness; for The capacity impact factor of the user response capability of the first new energy installed capacity; for The capacity impact factor of the second new energy installed capacity user response capability; for Middle The capacity impact factor of the user response capability of the new energy installed capacity; for Middle Distributed renewable energy installed capacity of units; The local distributed renewable energy installed capacity matrix after target binning; It is the low installed capacity threshold for distributed new energy.

[0163] Step 204: construct a matrix using the daily load curve of each user within a preset time period in the user electricity consumption information data to generate a user initial load curve information matrix.

[0164] In the embodiment of the present invention, the daily load curve of each user within the preset time in the user electricity information data is collected, that is, the daily load curve of each user in the past 90 days is collected to construct the user initial load curve information matrix ,in, is the user's initial load curve information matrix, in which to It is the daily load curve of each user within 1 to 90 days.

[0165] Step 205: Calculate the impact factor based on the user's initial load curve information matrix and construct a time impact factor matrix.

[0166] Furthermore, step 205 may include the following sub-steps S21-S22:

[0167] S21. Calculate the average load rate corresponding to each user in the user initial load curve information matrix using a preset average load rate formula to generate multiple average load rates.

[0168] S22. Use the average load rate to calculate the impact factor and construct a time impact factor matrix.

[0169] In the embodiment of the present invention, the user's initial load curve information matrix The load curve data in the table is used as the user load time domain sample to calculate the average load rate of each user in each hour of the day:

[0170] ;

[0171] in, For users Average load rate at hour h in the past 90 days; For users Average load at h on day d; for Load curve data at time h on day d.

[0172] According to the hourly average load rate of users, the time impact factor of user response capability is determined, and the time impact factor matrix is ​​constructed. The time impact factor matrix is:

[0173] ;

[0174] ;

[0175] in, is the time impact factor matrix; is the time impact factor of user 1's responsiveness; is the time impact factor of user 2's responsiveness; is the time impact factor of user sn responsiveness; For users Time impact factor of responsiveness; For users The average load rate at hour h in the past 90 days, when When it is not less than 0.8, the user For high-load users, we will take on more responsibilities for responding to new energy. When it is less than 0.3, the user For low-load users, the time impact factor of their user response capability is 0.

[0176] Step 206: Calculate the impact factor based on the distribution network side network structure data and construct a topology impact factor matrix.

[0177] Furthermore, step 206 may include the following sub-steps S31-S34:

[0178] S31. Use the voltage data and power data corresponding to each node in the distribution network structure data to construct a Jacobi equation.

[0179] S32. Calculate the inverse operation of the partial derivatives of the node power with respect to the voltage amplitude in the Jacobi equation respectively to generate a plurality of original voltage sensitivities.

[0180] S33. Matrix construction is performed using the original voltage sensitivity to generate an original voltage sensitivity matrix.

[0181] S34. Normalize each element in the original voltage sensitivity matrix to generate a topology influence factor matrix.

[0182] In this embodiment of the present invention, the line impedance, reactive compensation device reactance, and transformer impedance parameters in the local distribution network topology are calculated based on the distribution network side network structure data. These parameters are used to calculate the topology impact factor of the user response capability and solve the power flow. The line parameters are calculated as follows:

[0183] ;

[0184] in, For the line Impedance value; is the line impedance per unit length; For the line length.

[0185] ;

[0186] in, For the The reactance value of each reactive power compensation device; The reactance of the reactive compensation device per unit capacity; For the The capacity of the reactive power compensation device.

[0187] ;

[0188] in, For the The equivalent reactance of the transformer; For the The short-circuit voltage percentage of each transformer; is the rated voltage value; is the rated capacity.

[0189] Based on the network topology, the Jacobian equation for the power change of distributed renewable energy grid-connected nodes is written as:

[0190] ;

[0191] in, For users in the power network The power at For nodes Power to Node With node Partial derivative of the phase angle difference between voltages; For nodes power; For nodes With node Voltage phase angle; For nodes Power to Node Partial derivative of voltage amplitude; For nodes Voltage amplitude; For nodes Voltage and user The difference in voltage phase angle; For nodes The voltage amplitude disturbance applied on .

[0192] Calculate the inverse operation of the partial derivative of node power with respect to voltage amplitude and calculate the original voltage sensitivity matrix of the grid-connected node with respect to each user load :

[0193] ;

[0194] ;

[0195] in, For nodes About User Load Node The original voltage sensitivity of For nodes Power to Node Partial derivative of voltage amplitude; For nodes power; For nodes The voltage amplitude; For nodes and The electrical conductivity between For nodes With node The inter-susceptance For nodes With node Phase angle between voltages.

[0196] right The elements in are normalized and the topological impact factors of user response capability are calculated to construct the topological impact factor matrix:

[0197] ;

[0198] ;

[0199] in, is the topological impact factor matrix; For users Load on the node Voltage sensitivity; The number of local users; The number of distributed new energy grid-connected points; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For nodes About User Load Node The original voltage sensitivity.

[0200] Step 207 : Calculate new energy response sensitivities based on the capacity impact factor matrix, the time impact factor matrix, and the topology impact factor matrix to generate multiple new energy response sensitivities.

[0201] Furthermore, step 207 may include the following sub-steps S41-S47:

[0202] S41. Using a preset mutual information value calculation formula, respectively calculate the mutual information value between each impact factor in the capacity impact factor matrix and the user load data corresponding to the distribution network to generate multiple capacity mutual information values.

[0203] S42: Calculate the mutual information value between each impact factor in the time impact factor matrix and the user load data using a preset mutual information value calculation formula to generate a plurality of time mutual information values.

[0204] S43: Using a preset mutual information value calculation formula, respectively calculate the mutual information value between each impact factor in the topology impact factor matrix and the user load data to generate a plurality of topology mutual information values.

[0205] S44. Select the maximum values ​​of the capacity mutual information value, the time mutual information value, and the topology mutual information value respectively to generate an initial maximum capacity mutual information value, an initial maximum time mutual information value, and an initial maximum topology mutual information value.

[0206] S45 , normalizing the initial maximum capacity mutual information value, the initial maximum time mutual information value, and the initial maximum topology mutual information value to generate a target maximum capacity mutual information value, a target maximum time mutual information value, and a target maximum topology mutual information value.

[0207] S46. Use the target maximum capacity mutual information value, the target maximum time mutual information value, and the target maximum topology mutual information value to construct a correlation matrix to generate a correlation parameter matrix.

[0208] S47. Calculate the new energy response sensitivity using the correlation parameter matrix, the capacity impact factor matrix, the time impact factor matrix, and the topology impact factor matrix to generate multiple new energy response sensitivities.

[0209] In the embodiment of the present invention, the maximum mutual information value between the three types of impact factors and user load is calculated. That is, a preset mutual information value calculation formula is used to calculate the mutual information value between each influencing factor in the capacity impact factor matrix and the user load data corresponding to the distribution network, generating multiple capacity mutual information values. A preset mutual information value calculation formula is used to calculate the mutual information value between each influencing factor in the time impact factor matrix and the user load data, generating multiple time mutual information values. A preset mutual information value calculation formula is used to calculate the mutual information value between each influencing factor in the topology impact factor matrix and the user load data, generating multiple topology mutual information values. The maximum value among the capacity mutual information value, the time mutual information value, and the topology mutual information value is then selected to generate the initial maximum capacity mutual information value, the initial maximum time mutual information value, and the initial maximum topology mutual information value.

[0210] Among them, the preset mutual information value calculation formula is:

[0211] ;

[0212] in, for and The mutual information value between is the k-th impact factor index; y is the user load data; is the influencing factor of the kth category user responsiveness Item value; The first Item value; Impact Factor exist Probability of occurrence; For user load Probability of appearing in y; for and The joint probability density of .

[0213] The initial maximum capacity mutual information value, the initial maximum time mutual information value, and the initial maximum topology mutual information value are normalized to obtain the target maximum capacity mutual information value, the target maximum time mutual information value, and the target maximum topology mutual information value. The normalization formula is:

[0214] ;

[0215] in, for The maximum information coefficient between y and y; For The number of data partitions in the direction; The number of data divisions in the y direction; for and The mutual information value between them; B is the total number of mutual information values.

[0216] The correlation between the three types of influencing factors and user load is judged by the maximum information coefficient, that is, the correlation matrix is ​​constructed using the target maximum capacity mutual information value, the target maximum time mutual information value and the target maximum topology mutual information value to obtain the correlation parameter matrix ,in, is the target maximum capacity mutual information value, i.e., the correlation between the capacity impact factor and the user load; is the target maximum time mutual information value, i.e., the correlation between the time impact factor and the user load; is the target maximum topological mutual information value, that is, the correlation between the topology impact factor and the user load.

[0217] The correlation analysis results are used as the weights of each influencing factor. That is, the higher the correlation of the influencing factor to the user load, the greater the weight of the influencing factor in the user response capability. The capacity influencing factor, time influencing factor and topology influencing factor are superimposed to calculate the user's new energy response sensitivity. The new energy response sensitivity is:

[0218] ;

[0219] in, For users For Node New energy response sensitivity; is the target maximum capacity mutual information value; For users Capacity impact factors of responsiveness; is the target maximum time mutual information value; For users Time impact factor of responsiveness; is the target maximum topological mutual information value; For users For Node Voltage sensitivity.

[0220] Step 208: Perform energy regulation on each user in the distribution network based on the new energy response sensitivity to generate energy regulation data.

[0221] Furthermore, step 208 may include the following sub-steps S51-S55:

[0222] S51. Calculate the deviation between the real-time voltage value of each node in the distribution network and its corresponding rated voltage respectively, and generate multiple node deviations.

[0223] S52. Distribute the voltage deviation that each user in the distribution network needs to bear using the node deviation and the new energy response sensitivity corresponding to the node deviation, and generate multiple user voltage deviations.

[0224] S53 , respectively calculating user load reduction amounts using the user voltage deviation amounts to generate multiple user load reduction amounts.

[0225] S54 , respectively calculating the product of the user load reduction amount and the load reduction electricity price corresponding to the user load reduction amount, to generate the economic benefit corresponding to the user load reduction amount.

[0226] S55. Use all economic benefits to construct energy regulation data.

[0227] In the embodiment of the present invention, the real-time power flow of the local distribution network is solved to obtain the real-time voltage value of each distributed new energy grid-connected node. ,in, For the Voltage value of distributed new energy grid-connected nodes; is the number of local distributed renewable energy grid-connected nodes.

[0228] Calculate the deviation of the real-time voltage value of the distributed renewable energy grid-connected node from the rated voltage of the point, and obtain the deviation of multiple nodes:

[0229] ;

[0230] ;

[0231] in, is the deviation matrix constructed from all node deviations; For the Voltage deviation of distributed new energy grid-connected nodes; For the Voltage value of distributed new energy grid-connected nodes; For nodes Rated voltage value.

[0232] According to the node The new energy response sensitivity is allocated to each user to bear the user voltage deviation amount:

[0233] The user voltage deviation is:

[0234] ;

[0235] in, For users Nodes required User voltage deviation; For nodes The node voltage deviation; For users For Node New energy response sensitivity; For local users on the node New energy response sensitivity.

[0236] Calculate the nodes required for each user The voltage deviation corresponds to the user load reduction amount as follows: ;

[0237] ;

[0238] in, For users Responsible Node The amount of user load reduction corresponding to the voltage deviation; for; For nodes Power to Node Partial derivative of voltage amplitude; For nodes power; For nodes Voltage amplitude.

[0239] According to the amount of each user's new energy response, the compensation for users participating in the regulation node is calculated by reducing the load electricity price. Economic benefits of voltage ;

[0240] ;

[0241] in, For users Participating regulation nodes Economic benefits of voltage; For users Responsible Node The amount of user load reduction corresponding to the voltage deviation; In order to reduce the load electricity price, all economic benefits corresponding to all nodes are finally used to construct energy control data.

[0242] In an embodiment of the present invention, local renewable energy generation and user electricity consumption data are collected, namely, distributed renewable energy installed capacity data corresponding to the distribution network, user electricity consumption data, and distribution network structure data. Data preprocessing is performed by binning the installed capacity. Specifically, the renewable energy installed capacity corresponding to the distributed renewable energy installed capacity information matrix is ​​binned according to preset bin positions to generate capacity binned data. Impact factors are calculated based on the distributed renewable energy installed capacity information matrix to construct a capacity impact factor matrix; impact factors are calculated based on the user initial load curve information matrix to construct a time impact factor matrix; and impact factors are calculated based on the distribution network structure data to construct a topology impact factor matrix. Correlation analysis is performed between the three impact factor matrices and user loads. Based on the correlation analysis results, the weights of each impact factor are determined and the user's renewable energy response sensitivity is calculated. The power flow is then solved, and the voltage deviation value at the grid connection point is adjusted based on user response. The economic benefits of the adjustment are distributed according to each user's renewable energy response. This invention fully utilizes the regulatory role of user response capabilities in the distributed renewable energy regulation process, ensuring stable power quality when consuming distributed resources while increasing the economic benefits of local users.

[0243] Example 3

[0244] All local users are equipped with distributed new energy units and have loads. A park distribution network with 100 distributed new energy users is selected as a sample to simulate the voltage regulation and calculate the economic benefits of users.

[0245] In this embodiment, the control time interval is selected as 1h and the period T=24h (1 day). The safe operating range of the network node voltage is [0.95, 1.05], and the unit is pu. Among the installed capacity of 100 households in the park, the minimum power is 8kW and the maximum power is 380kW. According to the method of the present invention, the installed capacity data of each user is divided into boxes and processed, and the effect is as shown in the attached figure. Figure 3 As shown; Based on the binning results, the capacity impact factors of 100 households are further calculated, and the calculation results are shown in the attached Figure 4 Then, based on the time domain characteristics of user load, the time impact factor of user response ability in 24 periods of a day is solved. Taking the period of 12:00-13:00 as an example, the solution result is as follows: Figure 5 As shown in Table 1; Based on the network structure of the distribution network side, the topology impact factor of the user response capability is calculated, and the calculation results are shown in the attached Figure 6 shown.

[0246] In order to verify the optimization effect of the proposed implementation method on voltage safety, a comparative analysis of the voltage conditions of a new energy grid connection point before and after the implementation in 24 hours a day is conducted, as shown in the attached figure. Figure 7As shown in the figure, before the implementation of the method of the present invention, the voltage at this point was over-limit, such as during the peak period of new energy output from 11:00 to 16:00 and the low period of new energy output from 3:00 to 8:00. After the proposed method was adopted, the voltage over-limit problem was effectively regulated and the occurrence of voltage over-limit was curbed. In terms of economic benefits, the economic benefits obtained from the regulation were distributed according to the new energy response amount of each user. The economic benefits distribution results of 10 users from 12:00 to 13:00 are shown as follows: Figure 8 As shown in Table 2, when the user response is large, that is, the load reduction is large or the price range of distributed renewable energy electricity is large, the economic benefits obtained from the allocation are higher.

[0247] Example 4

[0248] See also Figure 9 , Figure 9 This is a structural block diagram of a distributed energy control system provided in Example 4 of the present invention.

[0249] A distributed energy control system provided in Example 3 of the present invention includes:

[0250] The data acquisition module 901 is used to obtain the distributed new energy installed capacity data, user electricity consumption information data and distribution network side network structure data corresponding to the distribution network.

[0251] The matrix construction module 902 is used to calculate the impact factors using the distributed new energy installed capacity data, user power consumption information data and distribution network side network structure data, and construct the capacity impact factor matrix, time impact factor matrix and topology impact factor matrix.

[0252] The new energy response sensitivity generation module 903 is configured to calculate the new energy response sensitivity based on the capacity impact factor matrix, the time impact factor matrix, and the topology impact factor matrix, and generate multiple new energy response sensitivities.

[0253] The energy regulation data generation module 904 is configured to perform energy regulation on each user in the distribution network based on the new energy response sensitivity and generate energy regulation data.

[0254] Optionally, the matrix construction module 902 includes:

[0255] The distributed new energy installed capacity information matrix generation module is used to construct a matrix using distributed new energy installed capacity data to generate a distributed new energy installed capacity information matrix.

[0256] The information matrix of distributed new energy installed capacity is:

[0257] ;

[0258] in, It is the information matrix of distributed new energy installed capacity; The number of local distributed new energy installed capacity; The initial capacity of the first distributed new energy unit; is the initial capacity of the second distributed new energy unit; For the The initial capacity of distributed new energy units.

[0259] The capacity impact factor matrix construction module is used to calculate the impact factor based on the distributed new energy installed capacity information matrix and construct the capacity impact factor matrix.

[0260] The user initial load curve information matrix generation module is used to construct a matrix using the daily load curve of each user within a preset time in the user electricity consumption information data to generate the user initial load curve information matrix.

[0261] The time impact factor matrix construction module is used to calculate the impact factor based on the user's initial load curve information matrix and construct the time impact factor matrix.

[0262] The topology impact factor matrix construction module is used to calculate the impact factor based on the distribution network side network structure data and construct the topology impact factor matrix.

[0263] Optionally, the capacity impact factor matrix building module may perform the following steps:

[0264] Divide the new energy installed capacity corresponding to the distributed new energy installed capacity information matrix into bins according to the preset bin division data to generate capacity bin data;

[0265] Use capacity division data to update the distributed new energy installed capacity information matrix and generate the local distributed new energy installed capacity matrix after initial binning;

[0266] After initial binning, the local distributed new energy installed capacity matrix is:

[0267] ;

[0268] ;

[0269] in, is the local distributed renewable energy installed capacity matrix after initial binning; for Middle Distributed renewable energy installed capacity of units; for The distributed renewable energy installed capacity of the first unit; for The distributed renewable energy installed capacity of the second unit; for Middle The distributed renewable energy installed capacity of each unit; p is the unit number, p = 1, 2, ..., 9; is the gear capacity interval; for Maximum value; It is the information matrix of distributed new energy installed capacity;

[0270] Normalize the local distributed renewable energy installed capacity matrix after initial binning to generate the local distributed renewable energy installed capacity matrix after target binning;

[0271] The local distributed new energy installed capacity matrix after target binning is:

[0272] ;

[0273] ;

[0274] in, The local distributed renewable energy installed capacity matrix after target binning; for Middle Distributed renewable energy installed capacity of units; for The installed capacity of distributed renewable energy for the first unit; for The distributed renewable energy installed capacity of the second unit; for Middle Distributed renewable energy installed capacity of units; for Middle Distributed renewable energy installed capacity of units; for Maximum value; for Minimum value; is the local distributed renewable energy installed capacity matrix after initial binning;

[0275] The maximum and minimum capacity values ​​in the local distributed new energy installed capacity matrix after initial binning are used to calculate the new energy low installed capacity threshold to generate the distributed new energy low installed capacity threshold;

[0276] The threshold for the low installed capacity of distributed new energy is:

[0277] ;

[0278] in, It is the low installed capacity threshold of distributed new energy; for Maximum value; for Minimum value; is the local distributed renewable energy installed capacity matrix after initial binning;

[0279] The capacity impact factor matrix is ​​constructed by using the distributed new energy low installed capacity threshold and the local distributed new energy installed capacity matrix after target binning to generate the capacity impact factor matrix;

[0280] The capacity impact factor matrix is:

[0281] ;

[0282] ;

[0283] in, is the capacity impact factor matrix; for Middle Capacity impact factors of user responsiveness; for The capacity impact factor of the user response capability of the first new energy installed capacity; for The capacity impact factor of the second new energy installed capacity user response capability; for Middle The capacity impact factor of the user response capability of the new energy installed capacity; for Middle Distributed renewable energy installed capacity of units; The local distributed renewable energy installed capacity matrix after target binning; It is the low installed capacity threshold for distributed new energy.

[0284] Optionally, the time impact factor matrix construction module may perform the following steps:

[0285] The preset average load rate formula is used to calculate the average load rate corresponding to each user in the user initial load curve information matrix to generate multiple average load rates;

[0286] The default average load factor formula is:

[0287] ;

[0288] in, For users Average load rate at hour h in the past 90 days; For users Average load at h on day d; for Load curve data at h on day d;

[0289] The influence factors are calculated using the average load rate and the time influence factor matrix is ​​constructed;

[0290] The time impact factor matrix is:

[0291] ;

[0292] ;

[0293] in, is the time impact factor matrix; is the time impact factor of user 1's responsiveness; is the time impact factor of user 2's responsiveness; is the time impact factor of user sn responsiveness; For users Time impact factor of responsiveness; For users The average load rate at hour h in the past 90 days.

[0294] Optionally, the topology impact factor matrix building module may perform the following steps:

[0295] The Jacobi equation is constructed using the voltage and power data corresponding to each node in the distribution network structure data.

[0296] The Jacobi equation is:

[0297] ;

[0298] in, For users in the power network The power at For nodes Power to Node With node Partial derivative of the phase angle difference between voltages; For nodes power; For nodes With node Voltage phase angle; For nodes Power to Node Partial derivative of voltage amplitude; For nodes Voltage amplitude; For nodes Voltage and user The difference in voltage phase angle; For nodes The voltage amplitude disturbance applied on

[0299] The inverse operation of the partial derivative of the node power with respect to the voltage amplitude in the Jacobi equation is calculated respectively to generate multiple raw voltage sensitivities;

[0300] The raw voltage sensitivity is:

[0301] ;

[0302] in, For nodes About User Load Node The original voltage sensitivity of For nodes Power to Node Partial derivative of voltage amplitude; For nodes power; For nodes The voltage amplitude; For nodes and The electrical conductivity between For nodes With node The inter-susceptance For nodes With node Voltage phase angle;

[0303] The original voltage sensitivity is used to construct a matrix to generate an original voltage sensitivity matrix;

[0304] The original voltage sensitivity matrix is:

[0305] ;

[0306] in, is the original voltage sensitivity matrix; For nodes About User Load Node The original voltage sensitivity of For nodes About User Load Node The original voltage sensitivity of For nodes About User Load Node The original voltage sensitivity of For nodes About User Load Node The original voltage sensitivity of For nodes About User Load Node The original voltage sensitivity of

[0307] Normalize each element in the original voltage sensitivity matrix to generate a topology impact factor matrix;

[0308] The topological impact factor matrix is:

[0309] ;

[0310] ;

[0311] in, is the topological impact factor matrix; For users Load on the node Voltage sensitivity; The number of local users; The number of distributed new energy grid-connected points; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity.

[0312] Optionally, the new energy response sensitivity generation module 903 may perform the following steps:

[0313] The preset mutual information value calculation formula is used to calculate the mutual information value between each influencing factor in the capacity influencing factor matrix and the user load data corresponding to the distribution network, and multiple capacity mutual information values ​​are generated;

[0314] The preset mutual information value calculation formula is:

[0315] ;

[0316] in, for and The mutual information value between is the k-th impact factor index; y is the user load data; is the influencing factor of the kth category user responsiveness Item value; The first Item value; Impact Factor exist Probability of occurrence; For user load Probability of appearing in y; for and The joint probability density of

[0317] The mutual information value between each impact factor in the time impact factor matrix and the user load data is calculated using a preset mutual information value calculation formula to generate multiple time mutual information values;

[0318] The mutual information value between each influencing factor in the topology influencing factor matrix and the user load data is calculated using a preset mutual information value calculation formula to generate multiple topology mutual information values;

[0319] Select the maximum values ​​of the capacity mutual information value, the time mutual information value, and the topology mutual information value respectively to generate the initial maximum capacity mutual information value, the initial maximum time mutual information value, and the initial maximum topology mutual information value;

[0320] Normalize the initial maximum capacity mutual information value, the initial maximum time mutual information value, and the initial maximum topology mutual information value to generate the target maximum capacity mutual information value, the target maximum time mutual information value, and the target maximum topology mutual information value;

[0321] The target maximum capacity mutual information value, the target maximum time mutual information value and the target maximum topology mutual information value are used to construct the correlation matrix and generate the correlation parameter matrix;

[0322] The new energy response sensitivity is calculated using the correlation parameter matrix, capacity impact factor matrix, time impact factor matrix and topology impact factor matrix to generate multiple new energy response sensitivities;

[0323] The new energy response sensitivity is:

[0324] ;

[0325] in, For users For Node New energy response sensitivity; is the target maximum capacity mutual information value; For users Capacity impact factors of responsiveness; is the target maximum time mutual information value; For users Time impact factor of responsiveness; is the target maximum topological mutual information value; For users For Node Voltage sensitivity.

[0326] Optionally, the energy control data generation module 904 may perform the following steps:

[0327] Calculate the deviation between the real-time voltage value of each node in the distribution network and its corresponding rated voltage respectively, and generate multiple node deviations;

[0328] The node deviation and the new energy response sensitivity corresponding to the node deviation are used to allocate the voltage deviation that each user in the distribution network needs to bear, and multiple user voltage deviations are generated;

[0329] The user voltage deviation is:

[0330] ;

[0331] in, For users Nodes required User voltage deviation; For nodes The node voltage deviation; For users For Node New energy response sensitivity; For local users on the node New energy response sensitivity;

[0332] The user voltage deviation is respectively used to calculate the user load reduction amount to generate multiple user load reduction amounts;

[0333] The user load reduction is:

[0334] ;

[0335] in, For users Responsible Node The amount of user load reduction corresponding to the voltage deviation; for; For nodes Power to Node Partial derivative of voltage amplitude; For nodes power; For nodes Voltage amplitude;

[0336] Calculate the product of the user load reduction amount and the load reduction electricity price corresponding to the user load reduction amount respectively to generate the economic benefits corresponding to the user load reduction amount;

[0337] The economic benefits are:

[0338] ;

[0339] in, For users Participating regulation nodes Economic benefits of voltage; For users Responsible Node The amount of user load reduction corresponding to the voltage deviation; To reduce load electricity prices; use all economic benefits to build energy regulation data.

[0340] Example 5

[0341] See also Figure 10 , Figure 10 This is a structural block diagram of an electronic device provided in Example 5 of the present invention.

[0342] An electronic device according to an embodiment of the present invention includes: a memory 1001 and a processor 1002, wherein the memory 1001 stores a computer program; when the computer program is executed by the processor 1002, the processor 1002 executes a distributed energy control method as described in any of the above embodiments.

[0343] Memory 1001 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Memory 1001 has storage space 1003 for program code 1013 for executing any of the steps in the above-described method. For example, storage space 1003 for program code may include program code 1013 for implementing various steps in the above-described method. This program code may be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards, or floppy disks. The program code may be compressed, for example, in a suitable format. When executed by a processing device, this code causes the processing device to execute the various steps in the distributed energy resource control method described above.

[0344] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the distributed energy control method according to any of the above embodiments is implemented.

[0345] 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.

[0346] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only 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 an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0347] Units described as separate components may or may not be physically separate, and 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.

[0348] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, 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.

[0349] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0350] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned 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 various embodiments of the present invention.

Claims

1. A distributed energy control method, characterized in that: include: Obtain the distributed new energy installed capacity data, user electricity consumption information data and distribution network side network structure data corresponding to the distribution network; The distributed new energy installed capacity data, the user electricity consumption information data and the distribution network side network structure data are respectively used to calculate the impact factors, and a capacity impact factor matrix, a time impact factor matrix and a topology impact factor matrix are constructed; Performing new energy response sensitivity calculation based on the capacity impact factor matrix, the time impact factor matrix, and the topology impact factor matrix to generate multiple new energy response sensitivities; Based on the new energy response sensitivity, energy regulation is performed on each user in the distribution network to generate energy regulation data; The capacity impact factor matrix is: ； ； in, is the capacity impact factor matrix; for Middle Capacity impact factors of user responsiveness; for The capacity impact factor of the user response capability of the first new energy installed capacity; for The capacity impact factor of the second new energy installed capacity user response capability; for Middle The capacity impact factor of the user response capability of the new energy installed capacity; for Middle Distributed renewable energy installed capacity of units; It is the low installed capacity threshold of distributed new energy; The time impact factor matrix is: ； ； in, is the time impact factor matrix; is the time impact factor of user 1's responsiveness; is the time impact factor of user 2's responsiveness; is the time impact factor of user sn responsiveness; For users Time impact factor of responsiveness; For users Average load rate at hour h in the past 90 days; The topology impact factor matrix is: ； ； in, is the topological impact factor matrix; For users Load on the node Voltage sensitivity; The number of local users; The number of distributed new energy grid-connected points; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For nodes About User Load Node The original voltage sensitivity.

2. The distributed energy control method according to claim 1, characterized in that: The steps of respectively using the distributed new energy installed capacity data, the user electricity consumption information data, and the distribution network side network structure data to calculate the impact factors and construct a capacity impact factor matrix, a time impact factor matrix, and a topology impact factor matrix include: Using the distributed new energy installed capacity data to construct a matrix, generating a distributed new energy installed capacity information matrix; The distributed new energy installed capacity information matrix is: ； in, It is the information matrix of distributed new energy installed capacity; The number of local distributed new energy installed capacity; The initial capacity of the first distributed new energy unit; is the initial capacity of the second distributed new energy unit; For the Initial capacity of distributed new energy units; Calculate the impact factor based on the distributed new energy installed capacity information matrix to construct a capacity impact factor matrix; Matrix construction is performed using the daily load curve of each user within a preset time period in the user electricity consumption information data to generate a user initial load curve information matrix; Calculate the impact factor based on the user's initial load curve information matrix and construct a time impact factor matrix; The impact factors are calculated based on the distribution network side network structure data to construct a topology impact factor matrix.

3. The distributed energy control method according to claim 2, characterized in that: The step of calculating the impact factor based on the distributed new energy installed capacity information matrix and constructing the capacity impact factor matrix includes: Divide the new energy installed capacity corresponding to the distributed new energy installed capacity information matrix into bins according to preset bin division data to generate capacity bin data; Using the capacity division data to update the distributed new energy installed capacity information matrix, generating a local distributed new energy installed capacity matrix after initial binning; The local distributed new energy installed capacity matrix after the initial binning is: ； ； in, is the local distributed renewable energy installed capacity matrix after initial binning; for Middle Distributed renewable energy installed capacity of units; for The distributed renewable energy installed capacity of the first unit; for The distributed renewable energy installed capacity of the second unit; for Middle The distributed renewable energy installed capacity of each unit; p is the unit number, p = 1, 2, ..., 9; is the gear capacity interval; for Maximum value; It is the information matrix of distributed new energy installed capacity; Normalizing the initial binned local distributed renewable energy installed capacity matrix to generate a target binned local distributed renewable energy installed capacity matrix; The local distributed new energy installed capacity matrix after the target binning is: ； ； in, The local distributed renewable energy installed capacity matrix after target binning; for Middle Distributed renewable energy installed capacity of units; for The distributed renewable energy installed capacity of the first unit; for The distributed renewable energy installed capacity of the second unit; for Middle Distributed renewable energy installed capacity of units; for Middle Distributed renewable energy installed capacity of units; for Maximum value; for Minimum value; is the local distributed renewable energy installed capacity matrix after initial binning; Calculate the new energy low installed capacity threshold by using the maximum capacity and the minimum capacity in the local distributed new energy installed capacity matrix after the initial binning to generate the distributed new energy low installed capacity threshold; The threshold value of low installed capacity of distributed new energy is: ； in, It is the low installed capacity threshold of distributed new energy; for Maximum value; for Minimum value; is the local distributed renewable energy installed capacity matrix after initial binning; The distributed new energy low installed capacity threshold and the local distributed new energy installed capacity matrix after target binning are used to construct a capacity impact factor matrix to generate a capacity impact factor matrix.

4. The distributed energy control method according to claim 2, characterized in that: The step of calculating the impact factor based on the user's initial load curve information matrix and constructing a time impact factor matrix includes: Calculating the average load rate corresponding to each user in the user initial load curve information matrix using a preset average load rate formula to generate multiple average load rates; The preset average load rate formula is: ； in, For users Average load rate at hour h in the past 90 days; For users Average load at h on day d; for Load curve data at h on day d; The average load rate is used to calculate the impact factor and construct a time impact factor matrix.

5. The distributed energy control method according to claim 2, characterized in that: The step of calculating the impact factor based on the distribution network side network structure data and constructing a topology impact factor matrix includes: The voltage data and power data corresponding to each node in the distribution network structure data are used to construct a Jacobi equation; The Jacobi equation is: ； in, For users in the power network The power at For nodes Power to Node With node Partial derivative of the phase angle difference between voltages; For nodes power; For nodes With node Voltage phase angle; For nodes Power to Node Partial derivative of voltage amplitude; For nodes Voltage amplitude; For nodes Voltage and user The difference in voltage phase angle; For nodes The voltage amplitude disturbance applied on respectively calculating the inverse operation of the partial derivatives of the node power with respect to the voltage amplitude in the Jacobi equation to generate a plurality of original voltage sensitivities; The original voltage sensitivity is: ； in, For nodes About User Load Node The original voltage sensitivity of For nodes Power to Node Partial derivative of voltage amplitude; For nodes power; For nodes The voltage amplitude; For nodes and The electrical conductivity between For nodes With node The inter-susceptance For nodes With node Voltage phase angle; The original voltage sensitivity is used to construct a matrix to generate an original voltage sensitivity matrix; The original voltage sensitivity matrix is: ; in, is the original voltage sensitivity matrix; For nodes About User Load Node The original voltage sensitivity of For nodes About User Load Node The original voltage sensitivity of For nodes About User Load Node The original voltage sensitivity of For nodes About User Load Node The original voltage sensitivity of For nodes About User Load Node The original voltage sensitivity of Each element in the original voltage sensitivity matrix is ​​normalized to generate a topology influence factor matrix.

6. The distributed energy control method according to claim 1, characterized in that: The step of calculating the new energy response sensitivity based on the capacity impact factor matrix, the time impact factor matrix, and the topology impact factor matrix to generate a plurality of new energy response sensitivities includes: Calculating the mutual information value between each influencing factor in the capacity influencing factor matrix and the user load data corresponding to the distribution network using a preset mutual information value calculation formula to generate a plurality of capacity mutual information values; The preset mutual information value calculation formula is: ； in, for and The mutual information value between is the k-th impact factor index; y is the user load data; is the influencing factor of the kth category user responsiveness Item value; The first Item value; Impact Factor exist Probability of occurrence; For user load Probability of appearing in y; for and The joint probability density of Calculating the mutual information value between each impact factor in the time impact factor matrix and the user load data using the preset mutual information value calculation formula to generate a plurality of time mutual information values; Calculating the mutual information value between each influencing factor in the topology influencing factor matrix and the user load data using the preset mutual information value calculation formula to generate a plurality of topology mutual information values; Selecting the maximum values ​​of the capacity mutual information value, the time mutual information value, and the topology mutual information value respectively to generate an initial maximum capacity mutual information value, an initial maximum time mutual information value, and an initial maximum topology mutual information value; Normalizing the initial maximum capacity mutual information value, the initial maximum time mutual information value, and the initial maximum topology mutual information value to generate a target maximum capacity mutual information value, a target maximum time mutual information value, and a target maximum topology mutual information value; constructing a correlation matrix using the target maximum capacity mutual information value, the target maximum time mutual information value, and the target maximum topology mutual information value to generate a correlation parameter matrix; Calculating new energy response sensitivities using the correlation parameter matrix, the capacity impact factor matrix, the time impact factor matrix, and the topology impact factor matrix to generate a plurality of new energy response sensitivities; The new energy response sensitivity is: ； in, For users For Node New energy response sensitivity; is the target maximum capacity mutual information value; For users Capacity impact factors of responsiveness; is the target maximum time mutual information value; For users Time impact factor of responsiveness; is the target maximum topological mutual information value; For users For Node Voltage sensitivity.

7. The distributed energy control method according to claim 1, characterized in that: The step of performing energy regulation on each user in the distribution network based on the new energy response sensitivity and generating energy regulation data includes: Calculating the deviation between the real-time voltage value of each node in the distribution network and its corresponding rated voltage respectively, and generating a plurality of node deviations; The node deviation and the new energy response sensitivity corresponding to the node deviation are used to distribute the voltage deviation that each user in the distribution network needs to bear, thereby generating multiple user voltage deviations; The user voltage deviation is: ； in, For users Nodes required User voltage deviation; For nodes The node voltage deviation; For users For Node New energy response sensitivity; For local users on the node New energy response sensitivity; Calculating user load reduction amounts by respectively using the user voltage deviation amounts to generate multiple user load reduction amounts; The user load reduction amount is: ； in, For users Responsible Node The amount of user load reduction corresponding to the voltage deviation; for; For nodes Power to Node Partial derivative of voltage amplitude; For nodes power; For nodes Voltage amplitude; respectively calculating the product of the user load reduction amount and the load reduction electricity price corresponding to the user load reduction amount to generate the economic benefit corresponding to the user load reduction amount; The economic benefits are: ； in, For users Participating regulation nodes Economic benefits of voltage; For users Responsible Node The amount of user load reduction corresponding to the voltage deviation; To reduce load electricity prices; All of the economic benefits are used to construct energy regulation data.

8. A distributed energy control system, characterized in that: include: The data acquisition module is used to obtain the distributed new energy installed capacity data, user electricity consumption information data and distribution network side network structure data corresponding to the distribution network; A matrix construction module is used to calculate the impact factors using the distributed new energy installed capacity data, the user electricity consumption information data and the distribution network side network structure data, and construct a capacity impact factor matrix, a time impact factor matrix and a topology impact factor matrix; a new energy response sensitivity generation module, configured to calculate the new energy response sensitivity based on the capacity impact factor matrix, the time impact factor matrix, and the topology impact factor matrix, and generate a plurality of new energy response sensitivities; An energy regulation data generation module, configured to perform energy regulation on each user in the distribution network based on the new energy response sensitivity, and generate energy regulation data; The capacity impact factor matrix is: ； ； in, is the capacity impact factor matrix; for Middle Capacity impact factors of user responsiveness; for The capacity impact factor of the user response capability of the first new energy installed capacity; for The capacity impact factor of the second new energy installed capacity user response capability; for Middle The capacity impact factor of the user response capability of the new energy installed capacity; for Middle Distributed renewable energy installed capacity of units; It is the low installed capacity threshold of distributed new energy; The time impact factor matrix is: ； ； in, is the time impact factor matrix; is the time impact factor of user 1's responsiveness; is the time impact factor of user 2's responsiveness; is the time impact factor of user sn responsiveness; For users Time impact factor of responsiveness; For users Average load rate at hour h in the past 90 days; The topology impact factor matrix is: ； ； in, is the topological impact factor matrix; For users Load on the node Voltage sensitivity; The number of local users; The number of distributed new energy grid-connected points; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For users Load on the node Voltage sensitivity; For nodes About User Load Node The original voltage sensitivity.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the distributed energy control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the distributed energy control method according to any one of claims 1 to 7 is implemented.

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