A power distribution network energy storage control method and system based on multi-feeder hierarchical identification

Abstract

The application discloses a power distribution network energy storage control method and system based on multi-feeder level identification. The method comprises the following steps: identifying all feeders and nodes of the power distribution network, determining the feeder to which the energy storage node belongs through a membership function; sorting the energy storage nodes according to the topological distance of the nodes from the root node, dividing the action priority and level, and the greater the distance, the higher the priority; obtaining the active power of the photovoltaic grid-connected point, and removing noise by using median filtering; applying moving average filtering to the filtered power, obtaining smooth power and calculating the suppression power required by each energy storage node; based on the instantaneous fluctuation rate of the smooth power, the low-level energy storage node is preferentially triggered, and the time delay is set for the high-level node. The system comprises corresponding modules. The application does not need communication, effectively improves the suppression effect on the large power fluctuation of the power distribution network, optimizes the energy storage resource configuration, and prolongs the service life of the equipment.

Description

Technical Field

[0001] This invention relates to the field of distribution network energy storage access configuration technology, specifically to a distribution network energy storage control method and system based on multi-feeder level identification. Background Technology

[0002] With the advancement of the "dual carbon" goals, the penetration rate of distributed photovoltaic (PV) power in distribution networks is continuously increasing. However, the randomness, intermittency, and volatility of PV power output pose serious challenges to the safe and stable operation of distribution networks. Especially in scenarios with a high proportion of PV integration, drastic power fluctuations caused by changes in sunlight can lead to problems such as voltage exceeding limits, frequency deviations, and line overloads in the distribution network, severely affecting power quality and even threatening system stability.

[0003] To mitigate photovoltaic (PV) power fluctuations, energy storage systems are considered an effective technological means due to their rapid response and flexible adjustment capabilities. Currently, methods for power mitigation using energy storage can be mainly divided into two categories: centralized control and distributed local control. Centralized control relies on a dispatch center to collect global information for optimization calculations. While theoretically it can achieve global optimization, it suffers from high communication dependence, computational complexity, and poor real-time performance, making it difficult to cope with frequent rapid fluctuations in the distribution network. Distributed local control, on the other hand, is based on local measurement data from the PV grid connection point, controlling the charging and discharging of nearby installed energy storage devices. Although it offers rapid response, it often lacks system-level coordination. When multiple energy storage nodes operate independently, control conflicts can easily arise, leading to "overcompensation" or "undercompensation," and it cannot optimize the utilization of the entire network's energy storage resources. This may result in some energy storage being overcharged / overdischarged while others remain idle, reducing overall control efficiency and economy.

[0004] Furthermore, existing power smoothing algorithms, such as simple low-pass filtering or moving average filtering, are ineffective in handling sudden, pulse-like power drops caused by cloud cover or other factors. These algorithms may misinterpret sharp noise signals as fluctuations requiring smoothing, or they may fail to adapt to different fluctuation characteristics due to fixed filtering parameters. This can result in the smoothed power still not meeting grid connection requirements, or cause unnecessary frequent operation of energy storage equipment, shortening its lifespan.

[0005] Therefore, there is an urgent need for a new control method that can automatically identify the distribution network topology, coordinate the control of multiple energy storage nodes, and intelligently handle power fluctuations and noise, so as to achieve efficient, economical, and reliable suppression of high power fluctuations in the distribution network. Summary of the Invention

[0006] The purpose of this invention is to provide a distribution network energy storage control method and system based on multi-feeder level identification, so as to solve the problems of low intelligence of the smoothing algorithm and the inability of energy storage nodes to coordinate control in the prior art.

[0007] To achieve the above objectives, the present invention provides a distribution network energy storage control method based on multi-feeder level identification, comprising the following steps: S1: Based on the node and branch connection relationship of the distribution network system, identify all feeders and their contained nodes, and determine the feeder to which each energy storage node belongs through the membership function; S2: Sort the energy storage nodes according to the topological distance of each node from the root node, divide the action priority of each energy storage node and define the action level; the larger the topological distance of a node from the root node, the higher the action priority of the energy storage node and the smaller the action level. S3: Obtain the local active power data of the photovoltaic grid connection point in the distribution network as the photovoltaic-storage combined output power, and use the median filtering method to remove noise from the photovoltaic-storage combined output power to obtain the noise-removed photovoltaic-storage combined output power sequence. S4: Apply a moving average filtering algorithm to the noise-removed photovoltaic-storage joint output power sequence to obtain the smoothed photovoltaic-storage joint output power, and calculate the smoothing power required by each energy storage node. S5: Based on the instantaneous fluctuation rate of the smoothed combined output power of photovoltaic and energy storage, and according to the action priority and action level of each energy storage node, the energy storage node with the lower action level is triggered first to perform control actions, and a time delay is set for the control actions of the energy storage node with the higher action level.

[0008] To optimize the above technical solution, the specific measures also include: In step S1, the process of identifying all feeders and their contained nodes based on the node and branch connection relationships of the distribution network system is as follows: Based on the node and branch connection relationships of the distribution network system, a node set is defined. and adjacency matrix The adjacency matrix The rows and columns are node numbers, and the adjacency matrix is... Middle elements Reflects the electrical connection relationships between nodes; From the root node of the distribution network system Departure, Definition The set of visited nodes; For an unvisited node, the set of its unvisited neighboring nodes is: :

[0009] in, Represents nodes The set of all directly connected nodes; set up From the root node Departure to Node The exploration path, for each unvisited node ,exist Update the set of visited nodes :

[0010] Recursively execute until the final node is reached, which satisfies... At this point, the current path forms a complete feeder; feeder This represents a connected path from the root node to the terminal node:

[0011]

[0012] in, This represents the total number of feeders identified in the distribution network. This represents the total number of nodes contained in the currently identified feeder; It is the root node of the power distribution network system; This refers to the end node of the currently identified feeder; express The Middle line, number The elements of the column.

[0013] Further, in step S1, the specific process of determining the feeder to which each energy storage node belongs through the membership function is as follows: Define the set of energy storage nodes ,in , Let be the set of nodes in the distribution network system; assuming energy storage nodes. The feeder is located at , , and The subordinate relationship between them is determined by the membership function. Make a judgment: if ,but Conversely, ; definition The feeder subordinate set is :

[0014] Feeder attribution mapping for:

[0015] in, The input set for this mapping represents the set of all energy storage nodes; This is the output set of this mapping, representing the power set of the feeder identifiers.

[0016] In step S2, the energy storage nodes are sorted according to their topological distance from the root node, and the action priorities and action levels of each energy storage node are defined. The specific process is as follows: Based on adjacency matrix compute nodes to the root node Shortest topological distance:

[0017] in, Indicates the connection node and Any connected path; Representing a path Topological distance; Furthermore, the shortest topological distance vector of the nodes is obtained. The corresponding main diagonal matrix is ​​the node distance matrix. , Representation matrix diagonal elements, ;matrix The mathematical expression is:

[0018] Define energy storage access indication vector Characterizes the location of energy storage nodes in the distribution network system. Energy storage access indication value:

[0019] Based on matrix and energy storage access indication vector The effective distance vector of the energy storage node is obtained through matrix multiplication. The formula is:

[0020] definition For energy storage nodes, the effective distance value is... For other nodes, ; For the identified feeder Extract the subset of energy storage nodes it contains, and represent it as a set. :

[0021] Furthermore, based on the set of effective distance value pairs Perform descending sorting to obtain an ordered sequence. :

[0022] in, Indicates feeder The number of energy storage nodes included. Indicated in the feeder The middle is sorted in descending order of effective distance value. indivual ; Based on the ordered sequence Define the action hierarchy of the energy storage node.

[0023] In step S3, the local active power data of the photovoltaic grid-connected point in the distribution network is obtained as the combined output power of photovoltaic and energy storage. Median filtering is used to remove noise from the combined output power of photovoltaic and energy storage, resulting in a noise-removed sequence of the combined output power of photovoltaic and energy storage. The specific process is as follows: Set the median filter window length The local active power data of the photovoltaic grid-connected points in the distribution network is obtained and used as the combined output power of photovoltaic and energy storage to obtain the combined output power of photovoltaic and energy storage. At each point in time , , build a Centered sliding data window ,from A segment of data extracted in chronological order, containing data from time [time]. arrive The power data subset is:

[0024] in, It is half the length of a window; This represents the total number of sampling points; This is a boundary handling function; This is a boundary handling function; Furthermore, on The combined output power of the optical and energy storage systems is sorted in ascending order according to its numerical value to obtain an ordered sequence. :

[0025] Pick The median as Filtered output value :

[0026] Traversal Filter the output values ​​at all times The combined output power sequence of the photovoltaic and energy storage systems, arranged in chronological order and processed by median filtering, is obtained. .

[0027] In step S4, the moving average filtering algorithm is applied to the noise-removed photovoltaic-storage joint output power sequence to obtain the smoothed photovoltaic-storage joint output power, and the smoothing power required by each energy storage node is calculated. The specific process is as follows: Initialize the moving average filter algorithm parameters, including the baseline power volatility threshold. With window length Calculation coefficients ,in control Sensitivity to volatility response; and initialization according to Descending priority queue ; For each moment , Calculate the smoothed combined output power of light and energy storage instantaneous volatility : If the instantaneous volatility is below the threshold, the original power is output directly; otherwise, it is calculated according to the following formula:

[0028] Furthermore, based on Dynamically calculate temporary window length The specific calculation formula is as follows:

[0029] in, and They are respectively The minimum and maximum values, Indicates rounding up; renew And determine the effective window; the newly calculated tuples join in ,in Its remaining effective duration; take The first element of the middle The current effective window length ; Furthermore, a moving average filter based on a dynamic window is performed, using a defined... right After applying a moving average filter, the smoothed combined output power of the optical storage system is obtained. :

[0030] Calculate the damping power required from the energy storage nodes. :

[0031] when When, the energy storage node discharges to compensate for the power deficit; when At that time, the energy storage nodes are charged to absorb the power surplus.

[0032] In step S5, based on the instantaneous fluctuation rate of the smoothed combined photovoltaic and energy storage output power, and according to the action priority and action level of each energy storage node, the control actions of energy storage nodes with lower action levels are triggered first, and a time delay is set for the control actions of energy storage nodes with higher action levels. The specific process is as follows: After identifying the nodes requiring mitigation and their effective action directions, instantaneous volatility is calculated in real time. When instantaneous volatility is detected threshold If the power fluctuation has been effectively suppressed, the system will no longer trigger subsequent energy storage nodes with higher action levels. Furthermore, when instantaneous volatility is detected > Threshold At that time, this moment Defined as the initial moment that triggers the energy storage action, and the feeder Energy storage nodes in according to The order is sorted, and the lower-level energy storage nodes are activated first to suppress power, while a time delay is set for the higher-level energy storage nodes to trigger their actions. Define hierarchy of exist Action triggering time function in:

[0033] in, Time delay function for hierarchical dependencies:

[0034] in, The basic unit of time delay; For belonging to multiple categories of Its final moment of action Take all belonging Earliest moment in China: .

[0035] As another important technical solution, the present invention also provides a distribution network energy storage control system based on multi-feeder level identification, comprising: The feeder and node identification module is used to identify all feeders and their contained nodes based on the node and branch connection relationship of the distribution network system, and to determine the feeder to which each energy storage node belongs through the membership function. The energy storage action hierarchy configuration module is used to sort energy storage nodes according to the topological distance of each node from the root node, divide the action priority of each energy storage node and define the action hierarchy; the greater the topological distance of a node from the root node, the higher the action priority of the energy storage node and the smaller the action hierarchy. The power data acquisition and preprocessing module is used to acquire local active power data of photovoltaic grid connection points in the distribution network as the combined output power of photovoltaic and energy storage. The median filtering method is used to remove noise from the combined output power of photovoltaic and energy storage to obtain the combined output power sequence of photovoltaic and energy storage after noise removal. The power fluctuation smoothing calculation module is used to apply a moving average filtering algorithm to the noise-removed photovoltaic-storage joint output power sequence to obtain the smoothed photovoltaic-storage joint output power and calculate the smoothing power that each energy storage node needs to provide. The energy storage coordination control execution module is used to trigger control actions of energy storage nodes with lower action levels based on the instantaneous fluctuation rate of the smoothed photovoltaic-energy storage combined output power and according to the action priority and action level of each energy storage node, while setting a time delay for the control actions of energy storage nodes with higher action levels.

[0036] The present invention also proposes an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a distribution network energy storage control method based on multi-feeder level identification as described above.

[0037] The present invention also proposes a computer-readable storage medium storing a computer program that enables a computer to execute a distribution network energy storage control method based on multi-feeder level identification as described above.

[0038] Compared with the prior art, the beneficial effects of the present invention are: This invention achieves coordinated control from far to near by introducing automatic identification of feeder topology and energy storage level, thus avoiding action conflicts.

[0039] This invention, based on a hierarchical action strategy using topological distance, enables the time-ordered allocation of energy storage resources, avoiding simultaneous actions and over-adjustment.

[0040] This invention combines a two-stage filtering strategy of median filtering and adaptive moving average filtering, which effectively eliminates sudden noise and smooths out periodic fluctuations.

[0041] This invention automatically identifies the feeder structure through localized measurements and formulates a coordinated control strategy based on topological distance, proceeding from far to near and in a time-ordered manner. This effectively smooths out high-power fluctuations and improves system stability without relying on communication.

[0042] This invention significantly improves the smoothing effect of high power fluctuations in the distribution network, enhances system stability, optimizes energy storage resource allocation, and extends equipment life. Attached Figure Description

[0043] Figure 1 This is a flowchart illustrating the present invention.

[0044] Figure 2 This is a schematic diagram of the topology of the IEEE 33 system in an embodiment of the present invention.

[0045] Figure 3 This is a comparison curve of the combined output power fluctuation of the photovoltaic and energy storage nodes 1 in this embodiment of the invention.

[0046] Figure 4 This is a comparison diagram of the combined output power of photovoltaic and energy storage at energy storage node 1 in this embodiment of the invention.

[0047] Figure 5 This is a comparison curve of the combined output power of photovoltaic and energy storage at energy storage node 2 in this embodiment of the invention.

[0048] Figure 6 This is a comparison diagram of the combined output power of photovoltaic and energy storage at energy storage node 2 in this embodiment of the invention.

[0049] Figure 7 This is a comparison curve of the combined output power of photovoltaic and energy storage at energy storage node 3 in this embodiment of the invention.

[0050] Figure 8 This is a comparison diagram of the combined output power of photovoltaic and energy storage at energy storage node 3 in this embodiment of the invention. Detailed Implementation

[0051] The present invention will be further described in detail below through specific embodiments, but it should not be construed as limiting the scope of the subject matter of the present invention to the following embodiments. All technologies implemented based on the above content of the present invention fall within the scope of the present invention.

[0052] like Figure 1 As shown, this invention provides a distribution network energy storage control method based on multi-feeder level identification, comprising the following steps: S1: Based on the node and branch connection relationship of the distribution network system, identify all feeders and their contained nodes, and determine the feeder to which each energy storage node belongs through the membership function; Based on the node and branch connection relationships of the distribution network as the basic data, according to Connectivity characteristics of a node distribution network topology, defining the node set. and adjacency matrix Adjacency matrix Both rows and columns are node numbers. It is an adjacency matrix The elements in express The Middle line, number The elements of the column reflect the nodes. With nodes The electrical connection between them.

[0053] In some implementations, if , representing a node and nodes Directly connected, meaning there is a branch between these two nodes; if , representing a node and nodes Not connected.

[0054] From the root node of the distribution network system Departure, Definition The set of visited nodes, initially . For an unvisited node, calculate its set of unexplored neighboring nodes. :

[0055] in, Represents nodes The set of all directly connected nodes.

[0056] set up From the root node Departure to Node The exploration path, for each unvisited node ,exist Update the set of visited nodes :

[0057] Recursively execute until the final node is reached, which satisfies... The node ( If a node is an empty set, then the current path constitutes a complete feeder.

[0058] feeder This represents a connected path from the root node to the terminal node:

[0059]

[0060] in, This represents the total number of feeders identified in the distribution network. This represents the total number of nodes contained in the currently identified feeder; It is the root node of the power distribution network system; This refers to the end node of the currently identified feeder; express The Middle line, number Column elements; Represents nodes Adjacent and compared The node with a node number greater than 1.

[0061] By systematically traversing all possible paths, all paths in the distribution network can be fully identified. and accurately record The included nodes.

[0062] Define the set of feeders to be identified ,in, It corresponds to an ordered sequence of nodes.

[0063] In some implementations, such as Figure 2 As shown, it was obtained through exploration. , Feeder 1 is: ; Feeder 2 is ; Feeder 3 is Feeder 4 is .

[0064] As energy storage nodes, there are a total of [number] nodes in the distribution network system. indivual , forming a set ,in .

[0065] set up The feeder is located at , , and The subordinate relationship between them is determined by the membership function. Make a judgment: if Then let ;like Then let .

[0066] Based on this membership function, define The feeder subordinate set is :

[0067] Representing a set Size, for It may belong to a single item ,Right now It may also belong to multiple categories simultaneously. ,Right now .

[0068] Feeder attribution mapping for:

[0069] in, The input set for this mapping, i.e. the domain, represents the set of all energy storage nodes; This is the output set of this mapping, i.e., the range, representing the power set of the feeder identifier; numbers They represent feeders respectively .

[0070] This mapping fully describes With or to The correspondence between them:

[0071] In some implementations, such as Figure 2 As shown, the IEEE 33-node distribution system has a total of 7 photovoltaic (PV) systems. In the PV aggregation path, energy storage is selected at nodes 6, 14, and 31 as energy storage node 1, energy storage node 2, and energy storage node 3, respectively. Here, PV represents connected PV, and ESS represents connected energy storage. S2: Sort the energy storage nodes according to the topological distance of each node from the root node, divide the action priority of each energy storage node and define the action level; the larger the topological distance of a node from the root node, the higher the action priority of the energy storage node and the smaller the action level. Based on adjacency matrix compute nodes to the root node Shortest topological distance:

[0072] in, Indicates the connection node and Any connected path; Representing a path The topological distance, i.e., the number of branches traversed; Self-distance is defined as The distance between directly connected nodes is 1, and so on.

[0073] Obtain the node distance vector The corresponding main diagonal matrix is ​​the node distance matrix. , Representation matrix The Middle Okay, number Column elements, i.e., matrix diagonal elements, ;matrix The mathematical expression is:

[0074] The matrix was obtained through calculation. As shown in the following formula:

[0075] Define energy storage access indication vector Characterizes the location of energy storage access nodes in the distribution network system. Energy storage access indication value:

[0076] Based on matrix and energy storage access indication vector The effective distance vector of the energy storage node is obtained through matrix multiplication. The formula is:

[0077] The vector was obtained through calculation. With the final vector As shown in the following formula:

[0078] definition For energy storage nodes, the effective distance value is... For other nodes, ; For the identified feeder It is necessary to extract its contents. A subset is represented as a set. :

[0079] Based on the set of effective distance value pairs Perform descending sorting to obtain an ordered sequence. :

[0080] in, express Included quantity; Indicates in Chinese Press The first in descending order indivual Based on this sorting, define exist Ranking function in: ;in,

[0081] As a preferred option, ,express exist China has the largest Value, i.e., distance The furthest away has the highest action priority.

[0082] definition exist Action hierarchy in the middle:

[0083] in, This indicates the highest action priority; the higher the level number, the lower the action priority.

[0084] because It may belong to multiple categories at the same time. , For different Each has a corresponding level value. .

[0085] exist Complete hierarchical mapping in:

[0086] In some embodiments, feeder 1 has energy storage nodes 6 and 14, and feeder 2 has energy storage nodes 6 and 31. According to... Energy storage node 14 and energy storage node 31 were assigned to action level 1, and energy storage node 6 was assigned to action level 2.

[0087] S3: Obtain the local active power data of the photovoltaic grid connection point in the distribution network as the photovoltaic-storage combined output power, and use the median filtering method to remove noise from the photovoltaic-storage combined output power to obtain the noise-removed photovoltaic-storage combined output power sequence. In some implementations, the median filter window length is set. It is an odd number. For minute-level sampling data, The value range is 5 to 15 minutes. This invention takes... .

[0088] Obtain the local active power data of the photovoltaic grid-connected points in the distribution network, use it as the combined output power of photovoltaic and energy storage, and obtain the combined output power of photovoltaic and energy storage. At each point in time , Construct a sliding data window centered on that point. ,from A segment of data extracted in chronological order, containing data from time [time]. arrive The power data subset is:

[0089] in, This represents the total number of sampling points; For boundary handling functions, ensure that the starting index is not less than 1. This is a boundary handling function that ensures the ending index does not exceed the total data length. .when When near the start or end boundary of the sequence, only the actual data points are taken.

[0090] right The combined output power of the optical and energy storage systems is sorted in ascending order according to its numerical value to obtain an ordered sequence. :

[0091] Among them, in the middle of the sequence At the boundary .

[0092] After sorting, take The median as Filtered output value :

[0093] Traversal Filter the output values ​​at all times The combined output power sequence of the photovoltaic and energy storage systems, arranged in chronological order and processed by median filtering, is obtained. .

[0094] S4: Apply a moving average filtering algorithm to the noise-removed photovoltaic-storage joint output power sequence to obtain the smoothed photovoltaic-storage joint output power, and calculate the smoothing power required by each energy storage node. Initialize the moving average filter algorithm parameters, including the baseline power volatility threshold. With window length Calculation coefficients ,in control Sensitivity to volatility response; and initialization according to Descending priority queue .

[0095] Preferably, for each time step , Calculate the smoothed combined output power of light and energy storage instantaneous volatility If the instantaneous volatility is below the threshold, the original power is output directly; otherwise, it is calculated according to the following formula:

[0096] based on Dynamically calculate temporary window length :when At that time, set a larger To enhance the smoothing effect; when When, set a smaller To preserve the true trend of change, the specific calculation formula is as follows:

[0097] in, and They are respectively The minimum and maximum values, This indicates rounding up to the nearest integer.

[0098] As a preferred option, update And determine the effective window; the newly calculated tuples join in ,in Its remaining effective duration; take The first element of the middle The current effective window length is used as the timeframe; the remaining effective duration of all elements in the queue at each time step is decremented by 1, and all elements are removed. Element.

[0099] Perform a moving average filter based on a dynamic window, using a deterministic... right After applying a moving average filter, the smoothed combined output power of the optical storage system is obtained. :

[0100] Calculate the damping power required from the energy storage system. :

[0101] when When, the stored energy is discharged to compensate for the power deficit; when At that time, the energy storage is charged to absorb the power surplus.

[0102] S5: Based on the instantaneous fluctuation rate of the smoothed combined output power of photovoltaic and energy storage, and according to the action priority and action level of each energy storage node, the energy storage node with the lower action level is triggered first to perform control actions, and a time delay is set for the control actions of the energy storage node with the higher action level.

[0103] In some implementations, after determining the nodes that need to be smoothed and their effective action directions, the instantaneous volatility is calculated in real time. When instantaneous volatility is detected threshold If the power fluctuation has been effectively suppressed, the system will no longer trigger subsequent energy storage nodes with higher action levels. When instantaneous volatility is detected > Threshold At that time, this moment Defined as the initial moment that triggers the energy storage action, and the feeder Energy storage nodes in according to The order is sorted, and the lower-level energy storage nodes are activated first to suppress power, while a time delay is set for the higher-level energy storage nodes to trigger their actions. If at the level After the energy storage node operates, instantaneous volatility is detected. Drop to the threshold If the power fluctuation is within a certain range, it is determined that the power fluctuation has been effectively suppressed, and the action level will no longer be triggered. The subsequent energy storage nodes; conversely, the action level. The energy storage node is triggered to act after a time delay that depends on a specific level; define the level. of exist Action triggering time function in:

[0104] in, Time delay function for hierarchical dependencies:

[0105] in, The basic unit of time delay; For belonging to multiple categories of Its final moment of action Take all belonging Earliest moment in China:

[0106] like Figures 3 to 8 As shown, after filtering, the combined output power fluctuation of the photovoltaic and energy storage nodes is significantly reduced. At the same time, based on the energy storage action level coordination mode designed by S5, only the energy storage unit of level 1 needs to be activated for smoothing, which can effectively control the power fluctuation without calling the energy storage of level 2. This strategy helps to reduce the overall number of actions of the energy storage device while ensuring the smoothing effect, thereby extending its system life.

[0107] In another embodiment, the present invention also provides a distribution network energy storage control system based on multi-feeder level identification, comprising: The feeder and node identification module is used to identify all feeders and their contained nodes based on the node and branch connection relationship of the distribution network system, and to determine the feeder to which each energy storage node belongs through the membership function. The energy storage action hierarchy configuration module is used to sort energy storage nodes according to the topological distance of each node from the root node, divide the action priority of each energy storage node and define the action hierarchy; the greater the topological distance of a node from the root node, the higher the action priority of the energy storage node and the smaller the action hierarchy. The power data acquisition and preprocessing module is used to acquire local active power data of photovoltaic grid connection points in the distribution network as the combined output power of photovoltaic and energy storage. The median filtering method is used to remove noise from the combined output power of photovoltaic and energy storage to obtain the combined output power sequence of photovoltaic and energy storage after noise removal. The power fluctuation smoothing calculation module is used to apply a moving average filtering algorithm to the noise-removed photovoltaic-storage joint output power sequence to obtain the smoothed photovoltaic-storage joint output power and calculate the smoothing power that each energy storage node needs to provide. The energy storage coordination control execution module is used to trigger control actions of energy storage nodes with lower action levels based on the instantaneous fluctuation rate of the smoothed photovoltaic-energy storage combined output power and according to the action priority and action level of each energy storage node, while setting a time delay for the control actions of energy storage nodes with higher action levels.

[0108] In another embodiment of the present invention, an electronic device is proposed, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a distribution network energy storage control method based on multi-feeder level identification as described above.

[0109] In another embodiment of the present invention, a computer-readable storage medium is provided storing a computer program that causes a computer to execute a distribution network energy storage control method based on multi-feeder level identification as described above.

[0110] In the embodiments disclosed in this application, a computer storage medium may be a tangible medium that may contain or store programs for use by or in conjunction with an instruction execution system, apparatus, or device. The computer storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of computer storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0111] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications, equivalent substitutions, and improvements made by those skilled in the art to the above embodiments without departing from the scope of the technical solution of the present invention, based on the technical essence of the present invention, shall still fall within the protection scope of the technical solution of the present invention.

Claims

1. A distribution network energy storage control method based on multi-feeder level identification, characterized in that, Includes the following steps: S1: Based on the node and branch connection relationship of the distribution network system, identify all feeders and their contained nodes, and determine the feeder to which each energy storage node belongs through the membership function; S2: Sort the energy storage nodes according to the topological distance of each node from the root node, divide the action priority of each energy storage node and define the action level; the larger the topological distance of a node from the root node, the higher the action priority of the energy storage node and the smaller the action level. S3: Obtain the local active power data of the photovoltaic grid connection point in the distribution network as the photovoltaic-storage combined output power, and use the median filtering method to remove noise from the photovoltaic-storage combined output power to obtain the noise-removed photovoltaic-storage combined output power sequence. In step S3, the local active power data of the photovoltaic grid-connected point in the distribution network is obtained as the combined output power of photovoltaic and energy storage. The median filtering method is used to remove noise from the combined output power of photovoltaic and energy storage to obtain the noise-removed combined output power sequence. The specific process is as follows: Set the median filter window length The local active power data of the photovoltaic grid-connected points in the distribution network is obtained and used as the combined output power of photovoltaic and energy storage to obtain the combined output power of photovoltaic and energy storage. At each point in time , , build a Centered sliding data window ,from A segment of data extracted in chronological order, containing data from time [time]. arrive The power data subset is: in, It is half the length of a window; This represents the total number of sampling points; This is a boundary handling function; This is a boundary handling function; right The combined output power of the optical and energy storage systems is sorted in ascending order of numerical value to obtain an ordered sequence. : Pick The median as Filtered output value : Traversal Filter the output values ​​at all times The combined output power sequence of the photovoltaic and energy storage systems, arranged in chronological order and processed by median filtering, is obtained. ; S4: Apply a moving average filtering algorithm to the noise-removed photovoltaic-storage joint output power sequence to obtain the smoothed photovoltaic-storage joint output power, and calculate the smoothing power required by each energy storage node. S5: Based on the instantaneous fluctuation rate of the smoothed combined output power of photovoltaic and energy storage, and according to the action priority and action level of each energy storage node, the energy storage node with the lower action level is triggered first to perform control actions, and a time delay is set for the control actions of the energy storage node with the higher action level.

2. The distribution network energy storage control method based on multi-feeder level identification according to claim 1, characterized in that: In step S1, the process of identifying all feeders and their contained nodes based on the node and branch connection relationships of the distribution network system is as follows: Based on the node and branch connection relationships of the distribution network system, a node set is defined. and adjacency matrix The adjacency matrix The rows and columns are node numbers, and the adjacency matrix is... Middle elements Reflects the electrical connection relationships between nodes; From the root node of the distribution network system Departure, Definition The set of visited nodes; For an unvisited node, the set of its unvisited neighboring nodes is: : in, Represents nodes The set of all directly connected nodes; set up From the root node Departure to Node The exploration path, for each unvisited node ,exist Update the set of visited nodes : Recursively execute until the final node is reached, which satisfies... At this point, the current path forms a complete feeder; feeder This represents a connected path from the root node to the terminal node: in, This represents the total number of feeders identified in the distribution network. This represents the total number of nodes contained in the currently identified feeder; It is the root node of the power distribution network system; This refers to the end node of the currently identified feeder; express The Middle line, number The elements of the column.

3. The distribution network energy storage control method based on multi-feeder level identification according to claim 2, characterized in that: In step S1, the specific process of determining the feeder to which each energy storage node belongs through membership functions is as follows: Define the set of energy storage nodes ,in , Let be the set of nodes in the distribution network system; assuming energy storage nodes. The feeder is located at , , and The subordinate relationship between them is determined by the membership function. Make a judgment: if ,but Conversely, ; definition The feeder subordinate set is : Feeder attribution mapping for: in, The input set for this mapping represents the set of all energy storage nodes; This is the output set of this mapping, representing the power set of the feeder identifiers.

4. The distribution network energy storage control method based on multi-feeder level identification according to claim 3, characterized in that: In step S2, the energy storage nodes are sorted according to their topological distance from the root node, and the action priorities and action levels of each energy storage node are defined. The specific process is as follows: Based on adjacency matrix compute nodes to the root node Shortest topological distance: in, Indicates the connection node and Any connected path; Representing a path Topological distance; Obtain the shortest topological distance vector of the nodes The corresponding main diagonal matrix is ​​the node distance matrix. , Representation matrix diagonal elements, ;matrix The mathematical expression is: Define energy storage access indication vector Characterizes the location of energy storage nodes in the distribution network system. Energy storage access indication value: Based on matrix and energy storage access indication vector The effective distance vector of the energy storage node is obtained through matrix multiplication. The formula is: definition For energy storage nodes, the effective distance value is... For other nodes, ; For the identified feeder Extract the subset of energy storage nodes it contains, and represent it as a set. : Based on the set of effective distance value pairs Perform descending sorting to obtain an ordered sequence. : in, Indicates feeder The number of energy storage nodes included. Indicated in the feeder The middle is sorted in descending order of effective distance value. indivual ; Based on the ordered sequence Define the action hierarchy of the energy storage node.

5. The distribution network energy storage control method based on multi-feeder level identification according to claim 1, characterized in that: In step S4, the moving average filtering algorithm is applied to the noise-removed photovoltaic-storage joint output power sequence to obtain the smoothed photovoltaic-storage joint output power, and the smoothing power required by each energy storage node is calculated. The specific process is as follows: Initialize the moving average filter algorithm parameters, including the baseline power volatility threshold. With window length Calculation coefficients ,in control Sensitivity to volatility response; and initialization according to Descending priority queue ; For each moment , Calculate the smoothed combined output power of light and energy storage instantaneous volatility : If the instantaneous volatility is below the threshold, the original power is output directly; otherwise, it is calculated according to the following formula: based on Dynamically calculate temporary window length The specific calculation formula is as follows: in, and They are respectively The minimum and maximum values, Indicates rounding up; renew And determine the effective window; the newly calculated tuples join in ,in Its remaining effective duration; take The first element of the middle The current effective window length ; Perform a moving average filter based on a dynamic window, using a deterministic... right After applying a moving average filter, the smoothed combined output power of the optical storage system is obtained. : Calculate the damping power required from the energy storage nodes. : when When, the energy storage node discharges to compensate for the power deficit; when At that time, the energy storage nodes are charged to absorb the power surplus.

6. The distribution network energy storage control method based on multi-feeder level identification according to claim 4, characterized in that: In step S5, based on the instantaneous fluctuation rate of the smoothed combined photovoltaic and energy storage output power, and according to the action priority and action level of each energy storage node, the control actions of energy storage nodes with lower action levels are triggered first, and a time delay is set for the control actions of energy storage nodes with higher action levels. The specific process is as follows: After determining the nodes requiring smoothing and their effective action directions, instantaneous volatility is calculated in real time. When instantaneous volatility is detected threshold If the power fluctuation has been effectively suppressed, the system will no longer trigger subsequent energy storage nodes with higher action levels. When instantaneous volatility is detected > Threshold At that time, this moment Defined as the initial moment that triggers the energy storage action, and the feeder Energy storage nodes in according to The order is sorted, and the lower-level energy storage nodes are activated first to suppress power, while a time delay is set for the higher-level energy storage nodes to trigger their actions. Define hierarchy of exist Action triggering time function in: in, Time delay function for hierarchical dependencies: in, The basic unit of time delay; For belonging to multiple categories of Its final moment of action Retrieve all belonging Earliest moment in China: 。 7. A system for executing the distribution network energy storage control method based on multi-feeder level identification as described in claim 1, characterized in that, include: The feeder and node identification module is used to identify all feeders and their contained nodes based on the node and branch connection relationship of the distribution network system, and to determine the feeder to which each energy storage node belongs through the membership function. The energy storage action hierarchy configuration module is used to sort energy storage nodes according to the topological distance of each node from the root node, divide the action priority of each energy storage node and define the action hierarchy; the greater the topological distance of a node from the root node, the higher the action priority of the energy storage node and the smaller the action hierarchy. The power data acquisition and preprocessing module is used to acquire local active power data of photovoltaic grid connection points in the distribution network as the combined output power of photovoltaic and energy storage. The median filtering method is used to remove noise from the combined output power of photovoltaic and energy storage to obtain the combined output power sequence of photovoltaic and energy storage after noise removal. The power fluctuation smoothing calculation module is used to apply a moving average filtering algorithm to the noise-removed photovoltaic-storage joint output power sequence to obtain the smoothed photovoltaic-storage joint output power and calculate the smoothing power that each energy storage node needs to provide. The energy storage coordination control execution module is used to trigger control actions of energy storage nodes with lower action levels based on the instantaneous fluctuation rate of the smoothed photovoltaic-energy storage combined output power and according to the action priority and action level of each energy storage node, while setting a time delay for the control actions of energy storage nodes with higher action levels.

8. An electronic device, characterized in that, include: The device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a distribution network energy storage control method based on multi-feeder level identification as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program causes the computer to execute a distribution network energy storage control method based on multi-feeder level identification as described in any one of claims 1 to 6.

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