Power distribution network real-time scheduling method and device based on source network load storage situation awareness

Abstract

The invention discloses a power distribution network real-time scheduling method and device based on source network load storage situation awareness, and the method comprises the steps: collecting the power generation data of each distributed power supply, the load data of each node and the energy storage data of each energy storage device in a power distribution network, carrying out the load high-low peak clustering analysis of all nodes, the method comprises the following steps: obtaining load high-low peak shunting nodes and load uniform distribution nodes, performing power generation staged clustering analysis on all distributed power supplies to obtain stable power generation power supplies and power generation power supplies in all stages, obtaining a matched power supply of each node, calculating an energy storage support matching factor of each node and each energy storage device, and calculating the energy storage support matching factor of each node and each energy storage device. Obtaining matched energy storage equipment of each node; and scheduling the power distribution network based on the matched power supplies corresponding to all the nodes and the matched energy storage devices corresponding to all the nodes. According to the power distribution network real-time scheduling method and device based on source network load storage situation awareness provided by the embodiment of the invention, the real-time performance of power distribution network scheduling is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network control, and in particular to a real-time dispatching method and device for a distribution network based on source-grid-load-storage situation awareness. Background Art

[0002] Source-grid-load-storage organically integrates energy sources, power grids, power loads and energy storage systems to form a comprehensive energy system to achieve efficient energy utilization and optimize the balance between energy supply and demand. Distribution network dispatching can reasonably adjust the operation mode of the power grid according to the operation status of source-grid-load-storage to achieve optimal allocation of power resources.

[0003] However, since the data sources of source, grid, load and storage are wide and complex, including the power generation data of distributed power sources, the load data of nodes, and the energy storage data of energy storage equipment, the existing technology is difficult to efficiently integrate and integrate the source, grid, load and storage data, resulting in low real-time performance of distribution network scheduling.

[0004] It can be seen that how to improve the real-time performance of distribution network dispatching has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0005] The present invention provides a real-time dispatching method and device for a distribution network based on source-grid-load-storage situation awareness, so as to solve the technical problem that it is difficult to efficiently integrate and integrate source-grid-load-storage data in the prior art, resulting in low real-time dispatching of the distribution network.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a real-time dispatching method for a distribution network based on source-grid-load-storage situation awareness.

[0007] Collect the power generation data of each distributed power source in the distribution network, the load data of each node and the energy storage data of each energy storage device;

[0008] Performing load peak and low peak clustering analysis on all the nodes to obtain each load peak and low peak shunting node and each load evenly distributed node; performing power generation stage clustering analysis on all the distributed power sources to obtain each stable power generation source and each stage power generation source;

[0009] Based on the matching characteristics of the load peak and low peak shunting nodes and the stage power generation power supply, and the matching characteristics of the load uniform distribution nodes and the stable power generation power supply, the matching power supply of each node is obtained;

[0010] Calculate the energy storage support matching factor of each node and each energy storage device based on the complementary characteristics of the load data of each node, the power generation data of the corresponding matching power source, and the energy storage data of each energy storage device;

[0011] All the energy storage support matching factors are sorted and analyzed, and the matching energy storage device of each node is obtained based on the results of the sorting and analysis; the distribution network is dispatched based on the matching power sources corresponding to all the nodes and the matching energy storage devices corresponding to all the nodes.

[0012] As one of the preferred solutions, the load peak and low peak clustering analysis is performed on all the nodes to obtain each load peak and low peak shunt node and each load evenly distributed node, including:

[0013] Calculate the load high and low peak shunt index of each node according to the peak point distribution characteristics of the load data of each node, cluster all the nodes based on all the load high and low peak shunt indexes to obtain each load high and low peak shunt node and each load uniform distribution node;

[0014] The step of performing a generation stage cluster analysis on all the distributed power sources to obtain each stable generation power source and each stage generation power source includes:

[0015] The power generation data of all the distributed power sources are subjected to long-term comparative processing to obtain the stable power generation contribution factor of each distributed power source; all the distributed power sources are clustered based on all the stable power generation contribution factors to obtain each stable power generation source and each stage power generation source.

[0016] As one of the preferred solutions, the load high-peak and low-peak load diversion index of each node is calculated according to the peak point distribution characteristics of the load data of each node, including the specific method of:

[0017] The average value of all the load data of each node plus the standard deviation is used as the upper limit of the average load of each node, and the average value of all the load data of each node minus the standard deviation is used as the lower limit of the average load of each node;

[0018] When the load data is greater than the corresponding upper limit of the average load, the collection time corresponding to the load data is used as the peak load point of each node; when the load data is less than the corresponding lower limit of the average load, the collection time corresponding to the load data is used as the low-peak load point of each node; when the load data is greater than or equal to the corresponding lower limit of the average load and less than or equal to the corresponding upper limit of the average load, the collection time corresponding to the load data is used as the stable load point of each node;

[0019] When each collection moment and the next adjacent collection moment are both the peak load points, each collection moment is recorded as a peak holding point; when each collection moment and the next adjacent collection moment are both the low-peak load points, each collection moment is recorded as a low-peak holding point;

[0020] The sum of the number of the high peak holding points of each node and the number of the low peak holding points of each node is divided by the total number of corresponding collection moments as the high-peak and low-peak load diversion index of each node.

[0021] As one of the preferred solutions, the long-term comparison processing of the power generation data of all the distributed power sources to obtain the stable power generation contribution factor of each distributed power source includes the following specific methods:

[0022] Arrange all the power generation data of each distributed power source in ascending order according to the time sequence of collection to obtain a power generation time series of each distributed power source; use an adaptive piecewise constant approximation algorithm to divide each power generation time series into a plurality of power generation sub-sequences;

[0023] The average value of the Hurst exponents of all the power generation subsequences of each power generation time series is used as the autocorrelation coefficient of the power generation stage of the corresponding distributed power source; the average value of all the power generation data of each power generation subsequence is used as the average power generation contribution index of each power generation subsequence, and the range of the average power generation contribution index of all the power generation subsequences of each power generation time series is used as the corresponding staged power generation coefficient of the distributed power source;

[0024] Taking the sum of all the power generation data of each of the distributed power sources as the power generation contribution index of each of the distributed power sources;

[0025] Based on the linear relationship between the power generation stage autocorrelation coefficient, the staged power generation coefficient and the power generation contribution index, the stable power generation contribution factor of each distributed power source is calculated.

[0026] As one of the preferred solutions, the matching power supply of each node is obtained based on the matching characteristics of the load peak and low peak shunting nodes and the stage power generation power supply, and the matching characteristics of the load uniform distribution nodes and the stable power generation power supply, including:

[0027] Perform time-division supply-demand matching processing on the stage power generation source and the load high-peak and low-peak shunting nodes to obtain a first matching factor between each stage power generation source and each load high-peak and low-peak shunting node;

[0028] Performing a first sorting process on all the first matching factors, and obtaining a matching power supply for each of the load high-peak and low-peak shunting nodes based on the result of the first sorting process;

[0029] Performing supply-demand matching processing on the stable power generation source and the load evenly distributed node in different time periods to obtain a second matching factor for each stable power generation source and each load evenly distributed node;

[0030] A second sorting process is performed on all the second matching factors, and a matching power source for each of the load evenly distributed nodes is obtained based on a result of the second sorting process.

[0031] As one of the preferred solutions, the stage power generation source and the load high and low peak shunt nodes are subjected to time-divided supply and demand matching processing to obtain a first matching factor for each stage power generation source and each load high and low peak shunt node, including:

[0032] Arrange all the load data of each of the load high and low peak shunting nodes in ascending order according to the time sequence of collection to obtain a first load time series of each of the load high and low peak shunting nodes;

[0033] The Pearson correlation coefficient between the power generation time series of each of the power generation sources at the stage and the first load time series of each of the load high and low peak shunting nodes is used as the first correlation unidirectional factor between each of the power generation sources at the stage and each of the load high and low peak shunting nodes;

[0034] Based on the difference between the power generation time series of each of the power generation sources in the stage and the first load time series of each of the load high and low peak shunting nodes, the first matching factor of each of the power generation sources in the stage and each of the load high and low peak shunting nodes is calculated in combination with the first associated same direction factor:

[0035]

[0036] in, For the i 1 The power source of the jth stage and 1 The first matching factor of the load peak and low peak distribution nodes, For the i 1 The power source of the jth stage and 1 The first associated unidirectional factor of the load peak and low peak diversion nodes, exp() is an exponential function with a natural constant as the base, For the i 1 The mth element value of the generation time series of the power generation source in the stage, For the jth 1The mth element value of the first load time series of a load peak-to-peak diversion node, where n is the number of collection moments.

[0037] As one of the preferred solutions, the stable power generation source and the load evenly distributed node are matched with each other in different time periods to obtain a second matching factor for each stable power generation source and each load evenly distributed node, including:

[0038] Arrange all the load data of each load evenly distributed node in ascending order according to the time sequence of collection to obtain a second load time series of each load evenly distributed node;

[0039] The Pearson correlation coefficient between the power generation time series of each of the stable power generation sources and the second load time series of each of the evenly distributed load nodes is used as a second correlation unidirectional factor between each of the stable power generation sources and each of the evenly distributed load nodes;

[0040] Based on the difference between the power generation time series of each of the stable power generation sources and the second load time series of each of the load evenly distributed nodes, the second matching factor of each of the stable power generation sources and each of the load evenly distributed nodes is calculated in combination with the second associated unidirectional factor:

[0041]

[0042] in, For the i 2 The jth stable power source and 2 The second matching factor for evenly distributed load nodes is For the i 2 The jth stable power source and 2 The second associated isotropic factor of the load evenly distributed node, exp() is an exponential function with a natural constant as the base, For the i 2 The mth element value of the generation time series of a stable power source, For the jth 2 The mth element value of the second load time series of the node with uniform load distribution, n is the number of collection moments.

[0043] As one of the preferred solutions, the energy storage support matching factor of each node and each energy storage device is calculated based on the complementary characteristics of the load data of each node, the power generation data of the corresponding matching power source, and the energy storage data of each energy storage device, including:

[0044] The difference between the load data of each node and the power generation data of the corresponding matching power source at each acquisition moment is used as the power generation load supplement index of each node at each acquisition moment; all the power generation load supplement indexes of each node are arranged in ascending order according to the time sequence of acquisition as the power generation load supplement sequence of each node;

[0045] Arrange all the energy storage data of each energy storage device in ascending order according to the time sequence of collection as the energy storage time series of each energy storage device;

[0046] Calculate the energy storage support deviation factor of each node and each energy storage device based on the difference characteristics between the power generation load supplement sequence of each node and the energy storage time series of each energy storage device;

[0047] The calculation result of an exponential function with a natural constant as the base and the opposite number of the energy storage support deviation factor as the exponent is used as the energy storage support matching factor for each of the nodes and each of the energy storage devices.

[0048] As one of the preferred solutions, the energy storage support deviation factor of each node and each energy storage device is calculated based on the difference characteristics between the power generation load supplement sequence of each node and the energy storage time sequence of each energy storage device, including:

[0049] Subtract the energy storage time series of each energy storage device from the power generation load supplement sequence of each node to obtain an energy storage support difference sequence of each node and each energy storage device;

[0050] The absolute value of each element in each of the energy storage support difference sequences is taken as the energy storage support deviation sequence of each of the nodes and each of the energy storage devices, and the sum of all element values ​​in the energy storage support deviation sequence of each of the nodes and each of the energy storage devices is taken as the energy storage support deviation factor of each of the nodes and each of the energy storage devices.

[0051] Another embodiment of the present invention provides a real-time dispatching device for a distribution network based on source, grid, load and storage situation awareness, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the real-time dispatching method for a distribution network based on source, grid, load and storage situation awareness as described above is implemented.

[0052] Compared with the prior art, the embodiments of the present invention have the following advantages:

[0053] (1) Collect the power generation data of each distributed power source in the distribution network, the load data of each node, and the energy storage data of each energy storage device. Considering that the data sources of source, grid, load and storage are extensive and complex, first analyze the distribution characteristics of the peak points of the node load to obtain each load peak and low peak diversion node and each load uniform distribution node; at the same time, analyze the stage characteristics of distributed power generation to obtain each stable power source and each stage power source;

[0054] (2) Based on the matching characteristics of load peak and low peak shunting nodes and stage power generation sources, and the matching characteristics of load evenly distributed nodes and stable power generation sources, the matching power source of each node is obtained, and only the load peak and low peak shunting nodes are matched with the stage power generation sources, and the load evenly distributed nodes are matched with the stable power generation sources, which narrows the matching range between nodes and distributed power sources and improves the real-time scheduling of the distribution network;

[0055] (3) Based on the complementary characteristics of the load data of each node, the power generation data of the corresponding matching power source and the energy storage data of each energy storage device, the energy storage support matching factor of each node and each energy storage device is calculated; all energy storage support matching factors are sorted and analyzed, and the matching energy storage device of each node is obtained based on the results of the sorting analysis. The distribution network is dispatched based on the matching power sources corresponding to all nodes and the matching energy storage devices corresponding to all nodes. The load data of the node and the power generation data of the distributed power source are first analyzed, and the node and the distributed power source are matched. Then, according to the matching results of the node and the distributed power source, the corresponding energy storage device is selected so that the load demand of all nodes can be met to the greatest extent, avoiding the problem of complex calculation caused by analyzing all data of the source, grid, load and storage at the same time, and further improving the real-time dispatch of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of a real-time dispatching method of a distribution network based on source-grid-load-storage situation awareness in one embodiment of the present invention;

[0057] Figure 2 It is a schematic diagram of obtaining a matching power supply in one embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0060] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0061] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0062] An embodiment of the present invention provides a real-time dispatching method for a distribution network based on source-grid-load-storage situation awareness. For details, see Figure 1-2 , Figure 1 The flowchart of the real-time dispatching method of the distribution network based on source-grid-load-storage situation awareness in one embodiment of the present invention is shown. Figure 2 It is a schematic diagram showing the acquisition of a matching power supply in one embodiment of the present invention.

[0063] The flowchart of the real-time dispatching method of the distribution network based on source-grid-load-storage situation awareness in one embodiment of the present invention includes the following steps S1 to S5, which are as follows:

[0064] Step S1: Collect the power generation data of each distributed power source in the distribution network, the load data of each node and the energy storage data of each energy storage device.

[0065] It should be noted that power generation data, load data and energy storage data reflect the supply and demand relationship and operating status of the power system. By comprehensively analyzing these data, we can fully understand the operating conditions of the power system and provide a basis for dispatching decisions.

[0066] Specifically, the power generation data of each distributed power source in the distribution network and the load data and energy storage data of each node are collected, and the collected data are cleaned, calibrated and normalized to ensure the accuracy and availability of the data.

[0067] Step S2: Perform load peak and low-peak clustering analysis on all nodes to obtain each load peak and low-peak shunt node and each load uniform distribution node; perform power generation stage clustering analysis on all distributed power sources to obtain each stable power generation source and each stage power generation source.

[0068] It should be noted that since the data sources of source, grid, load and storage are extensive and complex, including the power generation data of distributed power sources, the load data of nodes and the energy storage data of energy storage equipment, in order to improve the real-time scheduling of the distribution network, the distribution characteristics of the peak points of the node load and the stage characteristics of the distributed power generation are first analyzed, so as to classify the nodes and distributed power sources respectively.

[0069] Specifically, the average value of all load data of each node plus the standard deviation is taken as the upper limit of the average load of each node, and the average value of all load data of each node minus the standard deviation is taken as the lower limit of the average load of each node.

[0070] When the load data is greater than the corresponding average load upper limit, the collection time corresponding to the load data is used as the peak load point of each node; when the load data is less than the corresponding average load lower limit, the collection time corresponding to the load data is used as the low-peak load point of each node; when the load data is greater than or equal to the corresponding average load lower limit and less than or equal to the corresponding average load upper limit, the collection time corresponding to the load data is used as the stable load point of each node.

[0071] When each collection moment and the next adjacent collection moment are both peak load points, each collection moment is recorded as a peak holding point; when each collection moment and the next adjacent collection moment are both low-peak load points, each collection moment is recorded as a low-peak holding point.

[0072] The sum of the number of peak holding points of each node and the number of low peak holding points of each node divided by the total number of corresponding collection moments is taken as the high-peak and low-peak load diversion index of each node.

[0073] It should be noted that the peak holding point and the low peak holding point represent the peak period and low peak period of the load data respectively. When the proportion of the peak period and the low peak period in the load data is higher, it means that the load demand of the corresponding node is more likely to show a high and low peak trend, and the load high and low peak diversion index value is larger.

[0074] All nodes are clustered based on all load peak and low peak diversion indexes to obtain various load peak and low peak diversion nodes and various load uniform distribution nodes.

[0075] All power generation data of each distributed power source are arranged in ascending order according to the time sequence of collection to obtain the power generation time series of each distributed power source. Each power generation time series is divided into several power generation sub-sequences using an adaptive piecewise constant approximation algorithm.

[0076] The average value of the Hurst exponents of all the power generation sub-sequences of each power generation time series is taken as the autocorrelation coefficient of the corresponding distributed power generation stage; the average value of all the power generation data of each power generation sub-sequence is taken as the average power generation contribution index of each power generation sub-sequence, the range of the average power generation contribution index of all the power generation sub-sequences of each power generation time series is taken as the corresponding distributed power generation coefficient by stage, and the sum of all the power generation data of each distributed power source is taken as the power generation contribution index of each distributed power source.

[0077] Based on the linear relationship between the autocorrelation coefficient of the power generation stage, the phased power generation coefficient and the power generation contribution index, the stable power generation contribution factor of each distributed power source is calculated.

[0078] It should be noted that the larger the autocorrelation coefficient of the power generation stage of each distributed power source, the larger the power generation contribution index, and the smaller the staged power generation coefficient, the larger and more stable the power generation of the distributed power source, and the larger the corresponding stable power generation contribution factor value.

[0079] As an embodiment of the present application, the sum of the autocorrelation coefficient of each power generation stage and the corresponding power generation contribution index minus the corresponding staged power generation coefficient is used as the stable power generation contribution factor of the corresponding distributed power source.

[0080] All distributed power sources are clustered based on all stable power generation contribution factors to obtain each stable power generation source and each stage power generation source.

[0081] Step S3: Based on the matching characteristics of the load peak and low peak shunting nodes and the staged power generation sources, and the matching characteristics of the load evenly distributed nodes and the stable power generation sources, the matching power source of each node is obtained.

[0082] It should be noted that the source, grid, load and storage involve a large amount of complex data. If all the data of the source, grid, load and storage are analyzed at the same time, the calculation will become complicated, which will increase the time resource consumption of distribution network scheduling. In order to solve this problem, the load data of the node and the power generation data of the distributed power source are first analyzed, and the node and distributed power source are matched.

[0083] Specifically, all load data of each load high and low peak shunt node are arranged in ascending order according to the time sequence of collection to obtain the first load time series of each load high and low peak shunt node; the Pearson correlation coefficient between the power generation time series of each stage of the power generation source and the first load time series of each load high and low peak shunt node is used as the first associated unidirectional factor between the power generation source at each stage and each load high and low peak shunt node.

[0084] Based on the difference between the power generation time series of each stage power generation source and the first load time series of each load peak and low peak shunting node, the first matching factor of each stage power generation source and each load peak and low peak shunting node is calculated in combination with the first associated unidirectional factor:

[0085]

[0086] in, For the i 1 The power source of the jth stage and 1 The first matching factor of the load peak and low peak distribution nodes, For the i 1 The power source of the jth stage and 1 The first associated unidirectional factor of the load peak and low peak diversion nodes, exp() is an exponential function with a natural constant as the base, For the i 1 The mth element value of the power generation time series of the power generation source in the stage, For the jth 1 The mth element value of the first load time series of a load peak-to-peak diversion node, where n is the number of collection moments.

[0087] It should be noted that, when the first associated unidirectional factor of the stage power generation source and the load high and low peak diversion node is larger, and the difference in the element values ​​of the simultaneous power generation time series and the first load time series at the same position is smaller, it means that the power generation of the stage power generation source is more likely to just meet the load demand of the load high and low peak diversion node at all times, and the higher the degree of matching between the two, the more the stage power generation source should be used as the power support equipment of the load high and low peak diversion node.

[0088] A first sorting process is performed on all first matching factors, and a matching power supply of each load high-peak and low-peak shunting node is obtained based on the result of the first sorting process.

[0089] All load data of each load evenly distributed node are arranged in ascending order according to the time sequence of collection to obtain the second load time series of each load evenly distributed node; the Pearson correlation coefficient between the power generation time series of each stable power generation source and the second load time series of each load evenly distributed node is used as the second associated unidirectional factor between each stable power generation source and each load evenly distributed node.

[0090] Based on the difference between the power generation time series of each stable power generation source and the second load time series of each load evenly distributed node, the second matching factor of each stable power generation source and each load evenly distributed node is calculated in combination with the second associated unidirectional factor:

[0091]

[0092] in, For the i 2 The jth stable power source and 2 The second matching factor for evenly distributed load nodes is For the i 2 The jth stable power source and 2 The second associated isotropic factor of the load evenly distributed node, exp() is an exponential function with a natural constant as the base, For the i 2 The mth element value of the generation time series of a stable power source, For the jth 2 The mth element value of the second load time series of the node with uniform load distribution, n is the number of collection moments.

[0093] It should be noted that, when the second associated unidirectional factor of the smooth power generation source and the evenly distributed load node is larger and the difference in the element values ​​of the power generation time series and the second load time series at the same position is smaller, it means that the power generation of the smooth power generation source is more likely to just meet the load demand of the evenly distributed load node at all times, and the higher the degree of matching between the two, the more the smooth power generation source should be used as the power support equipment of the evenly distributed load node.

[0094] A second sorting process is performed on all the second matching factors, and a matching power source for each load uniformly distributed node is obtained based on the result of the second sorting process.

[0095] It should be noted that only the load peak and low peak diversion nodes are matched with the stage power generation sources, and the load uniform distribution nodes are matched with the smooth power generation sources, which narrows the matching range between the nodes and the distributed power sources and improves the real-time scheduling of the distribution network.

[0096] Step S4: Calculate the energy storage support matching factor of each node and each energy storage device based on the complementary characteristics of the load data of each node, the power generation data of the corresponding matching power source, and the energy storage data of each energy storage device.

[0097] Specifically, the difference between the load data of each node at each collection moment and the power generation data of the corresponding matching power source is used as the power generation load supplement index of each node at each collection moment; all the power generation load supplement indexes of each node are arranged in ascending order according to the time sequence of acquisition as the power generation load supplement sequence of each node.

[0098] All energy storage data of each energy storage device are arranged in ascending order according to the time sequence of collection as the energy storage time series of each energy storage device.

[0099] Furthermore, the energy storage time series of each energy storage device and the power generation load supplement sequence of each node are subtracted to obtain the energy storage support difference sequence of each node and each energy storage device.

[0100] The absolute value of each element in each energy storage support difference sequence is taken as the energy storage support deviation sequence of each node and each energy storage device, and the sum of all element values ​​in the energy storage support deviation sequence of each node and each energy storage device is taken as the energy storage support deviation factor of each node and each energy storage device.

[0101] The calculation result of the exponential function with the natural constant as the base and the opposite number of the energy storage support deviation factor as the exponent is used as the energy storage support matching factor for each node and each energy storage device.

[0102] It should be noted that when the difference between the power generation load replenishment sequence and the energy storage time series at the same location is smaller, it means that the combination of the corresponding energy storage equipment and the corresponding distributed power source is more likely to meet the load demand of the node at all times, the smaller the energy storage support deviation factor value, and the larger the energy storage support matching factor value.

[0103] Step S5: sort and analyze all energy storage support matching factors, and obtain the matching energy storage device for each node based on the results of the sorting analysis; and dispatch the distribution network based on the matching power sources corresponding to all nodes and the matching energy storage devices corresponding to all nodes.

[0104] Specifically, the energy storage support matching factors of each node and all energy storage devices are arranged in descending order, and the energy storage device corresponding to the largest energy storage support matching factor is used as the matching energy storage device for each node.

[0105] The matching power supply of each node is used as the power support device of each node, and the matching energy storage device of each node is used as the energy storage support device of each node. Through the joint action of the power support device and the energy storage support device, the load demand of each node is met and the dispatch of the distribution network is realized.

[0106] It should be noted that the load data of the node and the power generation data of the distributed power source are first analyzed, the node and the distributed power source are matched, and then the corresponding energy storage device is selected according to the matching result of the node and the distributed power source, so as to meet the load demand of all nodes to the greatest extent, avoiding the problem of complex calculation caused by analyzing all data of source network load storage at the same time, and further improving the real-time scheduling of the distribution network.

[0107] Correspondingly, another embodiment of the present invention provides a real-time dispatching device for a distribution network based on source, grid, load and storage situation awareness, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the real-time dispatching method for a distribution network based on source, grid, load and storage situation awareness as described above.

[0108] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A real-time dispatching method for distribution network based on source-grid-load-storage situation awareness, characterized in that: The method comprises: Collect the power generation data of each distributed power source in the distribution network, the load data of each node and the energy storage data of each energy storage device; Performing load peak and low peak clustering analysis on all the nodes to obtain each load peak and low peak shunting node and each load evenly distributed node; performing power generation stage clustering analysis on all the distributed power sources to obtain each stable power generation source and each stage power generation source; Based on the matching characteristics of the load peak and low peak shunting nodes and the stage power generation power supply, and the matching characteristics of the load uniform distribution nodes and the stable power generation power supply, the matching power supply of each node is obtained; Calculate the energy storage support matching factor of each node and each energy storage device based on the complementary characteristics of the load data of each node, the power generation data of the corresponding matching power source, and the energy storage data of each energy storage device; All the energy storage support matching factors are sorted and analyzed, and the matching energy storage device of each node is obtained based on the results of the sorting and analysis; the distribution network is dispatched based on the matching power sources corresponding to all the nodes and the matching energy storage devices corresponding to all the nodes.

2. The real-time dispatching method for distribution network based on source-grid-load-storage situation awareness according to claim 1 is characterized in that: The load peak and low peak clustering analysis is performed on all the nodes to obtain each load peak and low peak shunting node and each load evenly distributed node, including: Calculate the load high and low peak shunt index of each node according to the peak point distribution characteristics of the load data of each node, cluster all the nodes based on all the load high and low peak shunt indexes to obtain each load high and low peak shunt node and each load uniform distribution node; The step of performing a generation stage cluster analysis on all the distributed power sources to obtain each stable generation power source and each stage generation power source includes: The power generation data of all the distributed power sources are subjected to long-term comparative processing to obtain the stable power generation contribution factor of each distributed power source; all the distributed power sources are clustered based on all the stable power generation contribution factors to obtain each stable power generation source and each stage power generation source.

3. The real-time dispatching method for distribution network based on source-grid-load-storage situation awareness according to claim 2 is characterized in that: The specific method of calculating the load high-peak and low-peak diversion index of each node according to the peak point distribution characteristics of the load data of each node includes: The average value of all the load data of each node plus the standard deviation is used as the upper limit of the average load of each node, and the average value of all the load data of each node minus the standard deviation is used as the lower limit of the average load of each node; When the load data is greater than the corresponding upper limit of the average load, the collection time corresponding to the load data is used as the peak load point of each node; When the load data is less than the corresponding lower limit of the average load, the collection time corresponding to the load data is used as the low peak load point of each node; When the load data is greater than or equal to the corresponding lower limit of the average load and less than or equal to the corresponding upper limit of the average load, the collection time corresponding to the load data is used as the stable load point of each node; When each collection moment and the next adjacent collection moment are both the peak load points, each collection moment is recorded as a peak holding point; when each collection moment and the next adjacent collection moment are both the low-peak load points, each collection moment is recorded as a low-peak holding point; The sum of the number of the high peak holding points of each node and the number of the low peak holding points of each node is divided by the total number of corresponding collection moments as the high-peak and low-peak load diversion index of each node.

4. The real-time dispatching method for distribution network based on source-grid-load-storage situation awareness according to claim 2 is characterized in that: The specific method of performing long-term comparison processing on the power generation data of all the distributed power sources to obtain the stable power generation contribution factor of each distributed power source includes: Arrange all the power generation data of each distributed power source in ascending order according to the time sequence of collection to obtain the power generation time series of each distributed power source; use an adaptive piecewise constant approximation algorithm to divide each power generation time series into a plurality of power generation sub-sequences; The average value of the Hurst exponents of all the power generation subsequences of each power generation time series is used as the autocorrelation coefficient of the power generation stage of the corresponding distributed power source; the average value of all the power generation data of each power generation subsequence is used as the average power generation contribution index of each power generation subsequence, and the range of the average power generation contribution index of all the power generation subsequences of each power generation time series is used as the corresponding staged power generation coefficient of the distributed power source; Taking the sum of all the power generation data of each of the distributed power sources as the power generation contribution index of each of the distributed power sources; Based on the linear relationship between the power generation stage autocorrelation coefficient, the staged power generation coefficient and the power generation contribution index, the stable power generation contribution factor of each distributed power source is calculated.

5. The real-time dispatching method for distribution network based on source-grid-load-storage situation awareness according to claim 4 is characterized in that: The obtaining of the matching power source of each node based on the matching characteristics of the load peak and low peak shunting nodes and the stage power generation power source, and the matching characteristics of the load uniform distribution nodes and the stable power generation power source, comprises: Perform time-division supply-demand matching processing on the stage power generation source and the load high-peak and low-peak shunting nodes to obtain a first matching factor between each stage power generation source and each load high-peak and low-peak shunting node; Performing a first sorting process on all the first matching factors, and obtaining a matching power supply for each of the load high-peak and low-peak shunting nodes based on the result of the first sorting process; Performing supply-demand matching processing on the stable power generation source and the load evenly distributed node in different time periods to obtain a second matching factor between each stable power generation source and each load evenly distributed node; A second sorting process is performed on all the second matching factors, and a matching power source for each of the load evenly distributed nodes is obtained based on a result of the second sorting process.

6. The real-time dispatching method for distribution network based on source-grid-load-storage situation awareness according to claim 5 is characterized in that: The step of performing time-division supply-demand matching processing on the stage power generation source and the load high-peak and low-peak shunt nodes to obtain a first matching factor between each stage power generation source and each load high-peak and low-peak shunt node includes: Arrange all the load data of each of the load high and low peak shunting nodes in ascending order according to the time sequence of collection to obtain a first load time series of each of the load high and low peak shunting nodes; The Pearson correlation coefficient between the power generation time series of each of the power generation sources at the stage and the first load time series of each of the load high and low peak shunting nodes is used as the first correlation unidirectional factor between each of the power generation sources at the stage and each of the load high and low peak shunting nodes; Based on the difference between the power generation time series of each of the power generation sources in the stage and the first load time series of each of the load high and low peak shunting nodes, the first matching factor of each of the power generation sources in the stage and each of the load high and low peak shunting nodes is calculated in combination with the first associated same direction factor: in, is the first matching factor between the i1th stage power source and the j1th load peak and low peak diversion node, is the first associated unidirectional factor between the i1th stage power source and the j1th load peak and low peak diversion node, exp() is an exponential function with a natural constant as the base, is the mth element value of the power generation time series of the i1th stage power generation source, is the mth element value of the first load time series of the j1th load peak and low peak diversion node, and n is the number of collection times.

7. The real-time dispatching method for distribution network based on source-grid-load-storage situation awareness according to claim 5 is characterized in that: The step of performing time-division supply-demand matching processing on the stable power generation source and the load evenly distributed node to obtain a second matching factor for each stable power generation source and each load evenly distributed node includes: Arrange all the load data of each load evenly distributed node in ascending order according to the time sequence of collection to obtain a second load time series of each load evenly distributed node; The Pearson correlation coefficient between the power generation time series of each of the stable power generation sources and the second load time series of each of the evenly distributed load nodes is used as a second correlation unidirectional factor between each of the stable power generation sources and each of the evenly distributed load nodes; Based on the difference between the power generation time series of each of the stable power generation sources and the second load time series of each of the load evenly distributed nodes, the second matching factor of each of the stable power generation sources and each of the load evenly distributed nodes is calculated in combination with the second associated unidirectional factor: in, is the second matching factor of the i2th stable power source and the j2th load uniform distribution node, is the second associated unidirectional factor between the i2th stable power source and the j2th load uniform distribution node, exp() is an exponential function with a natural constant as the base, is the mth element value of the generation time series of the i2th stable power source, is the mth element value of the second load time series of the j2th load uniformly distributed node, and n is the number of collection times.

8. The method for real-time dispatching of distribution network based on source-grid-load-storage situation awareness according to claim 1 is characterized in that: The calculating of the energy storage support matching factor of each node and each energy storage device based on the complementary characteristics of the load data of each node, the power generation data of the corresponding matching power source and the energy storage data of each energy storage device comprises: The difference between the load data of each node and the power generation data of the corresponding matching power source at each acquisition moment is used as the power generation load supplement index of each node at each acquisition moment; all the power generation load supplement indexes of each node are arranged in ascending order according to the time sequence of acquisition as the power generation load supplement sequence of each node; Arrange all the energy storage data of each energy storage device in ascending order according to the time sequence of collection as the energy storage time series of each energy storage device; Calculate the energy storage support deviation factor of each node and each energy storage device based on the difference characteristics between the power generation load supplement sequence of each node and the energy storage time series of each energy storage device; The calculation result of an exponential function with a natural constant as the base and the opposite number of the energy storage support deviation factor as the exponent is used as the energy storage support matching factor for each of the nodes and each of the energy storage devices.

9. The real-time dispatching method for distribution network based on source-grid-load-storage situation awareness according to claim 8 is characterized in that: The calculating of the energy storage support deviation factor of each node and each energy storage device based on the difference characteristics between the power generation load supplement sequence of each node and the energy storage time sequence of each energy storage device comprises: Subtract the energy storage time series of each energy storage device from the power generation load supplement sequence of each node to obtain an energy storage support difference sequence of each node and each energy storage device; The absolute value of each element in each of the energy storage support difference sequences is taken as the energy storage support deviation sequence of each of the nodes and each of the energy storage devices, and the sum of all element values ​​in the energy storage support deviation sequence of each of the nodes and each of the energy storage devices is taken as the energy storage support deviation factor of each of the nodes and each of the energy storage devices.

10. A real-time dispatching device for distribution network based on source-grid-load-storage situation awareness, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the real-time scheduling method of distribution network based on source-grid-load-storage situation awareness as described in any one of claims 1 to 9.

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