Power distribution network frame optimization method and system

Abstract

The invention discloses a power distribution network frame optimization method and system. The method comprises the following steps: acquiring voltage and current of each node in a power distribution network, and calculating load data of each node according to the voltage and the current; based on the load data, constructing a load characteristic feature vector through data analysis; obtaining the output power of each distributed power supply in the power distribution network, identifying the fluctuation characteristics of the output power, and constructing the output characteristic feature vectors of the distributed power supplies; calculating the power variation of each node based on the load characteristic eigenvector and the distributed power supply output characteristic eigenvector; and updating the topological structure of the power grid according to the power variation of each node. According to the method, the load characteristic eigenvector of each node and the output characteristic eigenvector of the distributed power supply are constructed, and the fine-grained power balance analysis is performed node by node, so that the stability and flexibility of power supply are improved. And through a topology reconstruction mechanism, the utilization efficiency of electric energy is remarkably improved, and the power transmission loss is reduced.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and in particular to a method and system for optimizing power distribution network structure. Background Technology

[0002] With the continuous growth of electricity demand and the large-scale integration of distributed energy sources (such as photovoltaics and wind power) into distribution networks, the operation of traditional distribution networks faces multiple challenges. Traditional distribution network architecture designs are typically based on fixed loads and centralized power supply, making it difficult to adapt to complex load fluctuations and the stochastic characteristics of distributed energy sources. This static structural design often leads to problems such as overload, high power transmission losses, and unstable power supply in practical applications, failing to meet the stability and efficiency requirements of modern distribution networks.

[0003] First, the integration of distributed energy sources increases the volatility of power supply in the distribution network. Because distributed energy sources are greatly affected by environmental factors (for example, photovoltaic power generation is affected by sunshine conditions and wind power generation is affected by wind speed), their output power has significant randomness and volatility. Traditional load dispatching methods are difficult to coordinate the dynamically changing supply and demand balance, resulting in instability in the operation of the power grid.

[0004] Secondly, the load demand of each node in the distribution network varies significantly. Different nodes will experience load peaks, average loads, and periodic load fluctuations at different times. Traditional load dispatching strategies usually only consider the overall power balance and ignore the fine-grained load differences between nodes, resulting in low power supply efficiency. At the same time, overload or underload phenomena are prone to occur when the load fluctuates greatly.

[0005] In addition, the traditional distribution network topology lacks flexibility and it is difficult to quickly adjust the connection relationship between nodes under different load conditions. Since long-distance transmission increases power loss, how to quickly and efficiently transmit power from surplus nodes to high-load nodes has become an urgent problem to be solved. Summary of the Invention

[0006] This invention provides a method and system for optimizing the power distribution network structure, which can effectively improve the stability and flexibility of power grid operation.

[0007] According to one aspect of the present invention, a method for optimizing a power distribution network is provided, comprising:

[0008] Collect the voltage and current of each node in the distribution network, and calculate the load data of each node based on the voltage and current.

[0009] Based on the load data, a load characteristic feature vector is constructed through data analysis;

[0010] The output power of each distributed power source in the distribution network is obtained, and the fluctuation characteristics of the output power are identified to construct a feature vector of the output characteristics of the distributed power source.

[0011] Based on the load characteristic feature vector and the distributed power source output characteristic feature vector, the power change of each node is calculated;

[0012] The power grid topology is updated based on the power changes at each node.

[0013] Optionally, collecting the voltage and current of each node in the distribution network, and calculating the load data of each node based on the voltage and current, includes:

[0014] Voltage and current at each node in the distribution network are collected using voltage and current sensors, respectively.

[0015] The load data for each node is calculated based on the product of the voltage, the current, and the power factor.

[0016] Optionally, based on the load data, constructing a load characteristic feature vector through data analysis includes:

[0017] Based on the load data, the load characteristics of each node are identified through data analysis, and a load characteristic feature vector including load peak, average load, and load variation cycle is constructed.

[0018] Optionally, based on the load data, the load characteristics of each node are identified through data analysis, and a load characteristic feature vector including load peak, average load, and load variation cycle is constructed, including:

[0019] The load characteristic feature vector should satisfy the following formula:

[0020] In the formula, This is the load characteristic feature vector. For peak load, For average load, For load variation cycle;

[0021] The load peak: This involves statistically analyzing load data within a historical time window, identifying and recording the load peak for each node. ,in, Indicates a time window. For load data;

[0022] The average load is calculated based on load data within historical time windows, determining the average load for each node. ,in, Indicates time window The total number of time points within;

[0023] The load variation cycle is determined by analyzing the variation cycle of load data using spectral analysis methods, and identifying and recording the load variation cycle of each node.

[0024] Optionally, the output power of each distributed power source within the distribution network is obtained, and the fluctuation characteristics of the output power are identified. Constructing a feature vector of the distributed power source output characteristics includes:

[0025] Real-time acquisition of output voltage, output current, and power factor of each distributed power source;

[0026] The output power of each distributed power source in the distribution network is obtained based on the output voltage, the output current and the power factor.

[0027] Time series analysis of output power is performed to identify the fluctuation characteristics of distributed power source output power;

[0028] Based on the output characteristics of distributed power sources, an output model is constructed, and feature vectors of distributed power source output characteristics are generated.

[0029] Optionally, the fluctuation characteristics of the distributed power source output power include peak output power, average output power, and fluctuation period;

[0030] The peak output power: In the formula, This indicates the sampling time point, and d represents the number of the distributed power source. For distributed power sources In time Output power at that time This represents the peak output power.

[0031] The average output power: ; Average output power;

[0032] The characteristic vector of the distributed power source output should satisfy:

[0033] ; The distributed power source output characteristic feature vector, which represents the power output characteristics of the distributed power source under different load conditions, is used to indicate the fluctuation period.

[0034] Optionally, calculating the power change of each node based on the load characteristic feature vector and the distributed power source output characteristic feature vector includes:

[0035] Calculate the required power of each node based on its load characteristic feature vector;

[0036] Calculate the distributed power supply power based on the characteristic vector of the distributed power source output characteristics;

[0037] Calculate the power change of each node based on the node's power demand and the distributed power supply allocated by the distributed power source.

[0038] Optionally, the node power demand is calculated based on the load characteristic feature vector of each node, including:

[0039] The node power requirement is calculated using the following formula:

[0040] In the formula, Represents a node The average load, Represents a node The load variation cycle, Indicates the load fluctuation amplitude coefficient. Indicates the power required by the node;

[0041] Based on the characteristic vector of distributed power source output, the calculation of the distributed power supply allocation includes:

[0042] The following formula is used to calculate the distributed power supply:

[0043] In the formula, Distribute power to distributed power sources. Indicates power supply to the node The total number of distributed power sources, Distributed power source Average output power Distributed power source The fluctuation cycle, Distributed power source The output fluctuation amplitude coefficient reflects the magnitude of power supply output fluctuations. Distributed power source Assigned to node The power ratio coefficient;

[0044] Based on the node's power demand and the distributed power supply, the power change of each node is calculated, including:

[0045] The power change at each node is calculated using the following formula:

[0046] In the formula, This represents the change in power.

[0047] Optionally, updating the power grid topology based on the power changes at each node includes:

[0048] Based on the power change of each node, the nodes are prioritized to determine the geographical location and network topology of high-load nodes and surplus nodes.

[0049] Based on the geographical location and network topology of high-load nodes and surplus nodes, the optimal connection relationship is determined, and the high-load nodes and surplus nodes are connected through switch control and circuit breaker control.

[0050] Dynamic adjustments are implemented at distributed power access points near high-load nodes;

[0051] After completing node connection optimization and distributed power source access point adjustment, the power grid topology is updated, and the actual topology reconfiguration is achieved through remote control of switches and circuit breakers.

[0052] According to another aspect of the present invention, a power distribution network optimization system is provided, comprising:

[0053] The acquisition module is used to acquire the voltage and current of each node in the power distribution network and calculate the load data of each node.

[0054] A load characteristic analysis module is used to construct a load characteristic feature vector based on the load data through data analysis.

[0055] A distributed power source output characteristic analysis module is used to obtain the output power of each distributed power source in the distribution network, identify the fluctuation characteristics of the output power, and construct a distributed power source output characteristic feature vector.

[0056] The calculation module is used to calculate the power change of each node based on the load characteristic feature vector and the distributed power output characteristic feature vector;

[0057] An update module is used to update the power grid topology based on the power change of each node.

[0058] This invention provides a method and system for optimizing a power distribution network. The method includes: collecting voltage and current data from each node in the power distribution network and calculating load data for each node based on the voltage and current; constructing a load characteristic feature vector based on the load data through data analysis; obtaining the output power of each distributed power source in the power distribution network and identifying the fluctuation characteristics of the output power, constructing a distributed power source output characteristic feature vector; calculating the power change of each node based on the load characteristic feature vector and the distributed power source output characteristic feature vector; and updating the power grid topology based on the power change of each node. The technical solution provided by this invention, by constructing load characteristic feature vectors and distributed power source output characteristic feature vectors for each node, performs power balance analysis node by node, thereby calculating the power deficit or surplus of each node in real time. This fine-grained power balance analysis allows for more accurate identification of load demand and dynamic adjustment of power supply based on high-frequency real-time data, ensuring dynamic power balance between nodes. This avoids the overload and uneven power supply problems caused by traditional coarse load analysis, improving the stability and flexibility of power supply. The topology reconfiguration mechanism significantly improves power utilization efficiency, enabling the system to not only improve power supply quality but also effectively control overall power transmission loss.

[0059] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 A flowchart of a power distribution network optimization method provided in an embodiment of the present invention;

[0062] Figure 2 A flowchart of another power distribution network optimization method provided in an embodiment of the present invention;

[0063] Figure 3 A flowchart of another power distribution network optimization method provided in this embodiment of the invention;

[0064] Figure 4 A flowchart illustrating another method for optimizing a power distribution network structure provided in an embodiment of the present invention;

[0065] Figure 5A flowchart illustrating another method for optimizing a power distribution network structure provided in an embodiment of the present invention;

[0066] Figure 6 This is a schematic diagram of the structure of a power distribution network optimization system provided in an embodiment of the present invention;

[0067] Figure 7 This is a schematic diagram of the structure of an electronic device for a power distribution network optimization method provided in an embodiment of the present invention. Detailed Implementation

[0068] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0069] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0070] Figure 1 This is a flowchart illustrating a distribution network optimization method provided in an embodiment of the present invention. This embodiment is applicable to situations involving complex load fluctuations and the stochastic characteristics of distributed energy resources, enabling the rapid and efficient transfer of electrical energy from surplus nodes to high-load nodes. This method can be executed by a distribution network optimization system, which can be implemented in hardware and / or software and can be configured in any electronic device with communication capabilities. See also... Figure 1 The method includes:

[0071] S110. Collect the voltage and current of each node in the distribution network, and calculate the load data of each node based on the voltage and current.

[0072] Specifically, voltage and current sensors are installed at each node in the distribution network. The voltage and current of each node in the distribution network are collected by the voltage and current sensors, and the voltage and current are multiplied (in practice, power factor and other correction terms will be taken into account) to calculate the load data of each node.

[0073] S120. Based on load data, construct load characteristic feature vectors through data analysis.

[0074] Specifically, based on load data, a load characteristic feature vector is constructed through data analysis, and the results of various calculations are integrated to build a node. The load characteristic feature vector is defined as follows: peak load, average load, and load variation cycle.

[0075] S130. Obtain the output power of each distributed power source in the distribution network, identify the fluctuation characteristics of the output power, and construct the characteristic vector of the output characteristics of the distributed power source.

[0076] Specifically, by installing output voltage and current sensors on each distributed power source device within the distribution network, the output voltage, output current, and power factor of each distributed power source are collected in real time to obtain the actual output power at each point in time. Since the power generation of distributed power sources is greatly affected by natural conditions, time series analysis is required on the collected output power data to identify the fluctuation characteristics of the distributed power source's output power, including peak output power, average output power, and fluctuation period. The identified fluctuation characteristics are then fixed in the form of a numerical list, forming a "feature set"—the distributed power source output characteristic feature vector—that intuitively reflects the power generation pattern. This feature vector is used to represent the output characteristics of the distributed power source under different load conditions.

[0077] S140. Based on the load characteristic feature vector and the distributed power source output characteristic feature vector, calculate the power change of each node.

[0078] Specifically, based on load characteristic feature vectors and distributed generation output characteristic feature vectors, fine-grained power balance analysis is performed node by node to calculate the power deficit or surplus of each node. The load characteristic feature vectors provide the power demand at that node—electricity demand. The distributed generation output characteristic feature vectors provide the allocated power supply at that node—local generation. Subtracting the electricity demand from the local generation yields the power change at that node at a given moment. The purpose of calculating power change is twofold: firstly, to identify nodes with drastic power fluctuations—these nodes are weak links in grid stability and require focused energy storage and line adjustments; secondly, to anticipate when a node will experience "overgeneration" or "power deficit," allowing for proactive allocation of surrounding power sources to avoid waste or power shortages.

[0079] S150. Update the power grid topology based on the power changes at each node.

[0080] Specifically, the topology of a distribution network is not fixed. It adjusts the connections of lines, switches, and equipment in real time or periodically based on changes in the "power shortage" or "power surplus" of each node. The "topology" can be understood as a wiring diagram of the distribution network, clearly defining the connection relationships of all key elements in the network, mainly including: line connections between nodes, the on / off status of switches, and the access methods of key equipment. Based on the power gap or surplus of each node through fine-grained power balance analysis, the nodes that need adjustment and their connections are determined. Through switch control, circuit breaker control, and adjustments to the access points of distributed energy sources, a new node connection topology is constructed, ensuring that high-load nodes receive power from nearby sources and reducing power loss over long distances. When the power change is positive, it indicates that the node has a power surplus at a certain time; when the power change is negative, it indicates that the node has a power gap at a certain time. Actual topology reconfiguration can be achieved through remote control of switches and circuit breakers, ensuring that high-load nodes receive a stable and nearby power supply in the new topology.

[0081] This invention provides a method and system for optimizing a power distribution network. The method includes: collecting voltage and current data from each node in the power distribution network and calculating load data for each node based on the voltage and current; constructing a load characteristic feature vector based on the load data through data analysis; obtaining the output power of each distributed power source in the power distribution network and identifying the fluctuation characteristics of the output power, constructing a distributed power source output characteristic feature vector; calculating the power change of each node based on the load characteristic feature vector and the distributed power source output characteristic feature vector; and updating the power grid topology based on the power change of each node. The technical solution provided by this invention, by constructing load characteristic feature vectors and distributed power source output characteristic feature vectors for each node, performs power balance analysis node by node, thereby calculating the power deficit or surplus of each node in real time. This fine-grained power balance analysis allows for more accurate identification of load demand and dynamic adjustment of power supply based on high-frequency real-time data, ensuring dynamic power balance between nodes. This avoids the overload and uneven power supply problems caused by traditional coarse load analysis, improving the stability and flexibility of power supply. The topology reconfiguration mechanism significantly improves power utilization efficiency, enabling the system to not only improve power supply quality but also effectively control overall power transmission loss.

[0082] Figure 2 This is a flowchart of another power distribution network optimization method provided by an embodiment of the present invention. The embodiment of the present invention further refines the aforementioned embodiments based on the previous embodiments. Optionally, step S110 specifically includes:

[0083] S210. The voltage and current of each node in the distribution network are collected by voltage and current sensors respectively; the load data of each node is calculated based on the product of voltage and current and power factor.

[0084] Step S120 specifically includes:

[0085] S220. Based on load data, identify the load characteristics of each node through data analysis and construct a load characteristic feature vector including load peak, average load, and load variation cycle.

[0086] Specifically, the load characteristic eigenvector should satisfy the following equation:

[0087] In the formula, This is the load characteristic feature vector. For peak load, For average load, This refers to the load variation cycle.

[0088] Peak load: Statistically analyze load data within historical time windows, identify and record the peak load for each node. ,in, Indicates a time window. This is load data.

[0089] Average load: Calculate the average load for each node based on load data within a historical time window. ,in, Indicates time window The total number of time points within a specified time window; average load represents the number of nodes within a specified time window. The average load level within the area.

[0090] Load variation cycle: Analyze the variation cycle of load data using spectrum analysis methods to identify and record the load variation cycle of each node.

[0091] The frequency spectrum analysis method is used to analyze the variation cycle of load data, identify and record the load fluctuation cycle of each node, and capture the periodic load characteristics. , Represents a node The load variation cycle.

[0092] Load variation period is used to identify the periodic characteristics of load changes over time. Discrete Fourier transform is used to analyze the periodicity of load data, and the load frequency component is defined. for:

[0093] ,in, Indicates frequency, Represents the imaginary unit. Represents a node The load at frequency Find the frequency corresponding to the maximum frequency component in the spectral composition. And calculate its period. To obtain the main cycle: .

[0094] Figure 3 This is a flowchart illustrating another distribution network optimization method provided by an embodiment of the present invention. The embodiments of the present invention further refine the aforementioned embodiments. Optionally, step S130 specifically includes:

[0095] S310: Real-time acquisition of the output voltage, output current, and power factor of each distributed power source; based on the output voltage, output current, and power factor, obtain the output power of each distributed power source in the distribution network.

[0096] Specifically, by installing output voltage and output current sensors on each distributed power source device in the distribution network, the output voltage, output current, and power factor of each distributed power source are collected in real time using the output voltage and output current sensors to obtain the actual output power at each point in time.

[0097] S320 performs time series analysis on the output power to identify the fluctuation characteristics of the distributed power source's output power.

[0098] Optionally, the fluctuation characteristics of the distributed power source output power include peak output power, average output power, and fluctuation period;

[0099] Peak output power: In the formula, This indicates the sampling time point, and d represents the number of the distributed power source. For distributed power sources In time Output power at that time This represents the peak output power.

[0100] Average output power: ; This represents the average output power.

[0101] S330. Based on the output characteristics of distributed power sources, construct an output model and generate feature vectors of distributed power source output characteristics.

[0102] Specifically, the characteristic vector of distributed power source output should satisfy:

[0103] ; The distributed power source output characteristic feature vector, which represents the power output characteristics of the distributed power source under different load conditions, is used to indicate the fluctuation period.

[0104] The technical solution provided by this invention constructs load characteristic feature vectors and distributed power supply output characteristic feature vectors for each node, and performs fine-grained power balance analysis on a node-by-node basis. This allows for real-time calculation of the power deficit or surplus of each node. This fine-grained power balance analysis enables more accurate identification of load demand and dynamic adjustment of power supply based on high-frequency real-time data, ensuring dynamic power balance between nodes. It avoids the problems of overload and uneven power supply caused by traditional coarse load analysis, and improves the stability and flexibility of power supply.

[0105] Figure 4 This is a flowchart illustrating another distribution network optimization method provided by an embodiment of the present invention. The embodiments of the present invention further refine the aforementioned embodiments. Optionally, step S140 specifically includes:

[0106] S410. Calculate the required power of each node based on the load characteristic feature vector of each node.

[0107] Specifically, the node's power demand is calculated using the node's average load and load variation cycle; that is, the node's power demand is calculated using the following formula:

[0108] In the formula, Represents a node The average load, Represents a node The load variation cycle, Indicates the load fluctuation amplitude coefficient. This represents the node's required power. Here, represents the magnitude of load fluctuations, obtained by fitting historical load data. This formula shows that the load fluctuates periodically over time, and the calculated node required power... It dynamically reflects the time demand of node load.

[0109] S420. Calculate the distributed power supply power based on the characteristic vector of the distributed power source output.

[0110] Specifically, the distributed power supply is calculated using the following formula:

[0111] In the formula, Distribute power to distributed power sources. Indicates power supply to the node The total number of distributed power sources, Distributed power source Average output power Distributed power source The fluctuation cycle, Distributed power source The output fluctuation amplitude coefficient reflects the magnitude of power supply output fluctuations. Distributed power source Assigned to node The power proportionality coefficient; where, This coefficient can be dynamically adjusted based on load demand and power distribution strategies.

[0112] S430. Calculate the power change of each node based on the node's required power and the distributed power supply.

[0113] Specifically, the power change at each node is calculated using the following formula:

[0114] In the formula, This represents the change in power. Where:

[0115] Represents a node In time There is excess power; Represents a node In time There is a power shortage.

[0116] Based on the power balance calculation results, each node is marked with a state:

[0117] like , mark node As a surplus node, it can provide power support to other nodes;

[0118] like , mark node As a gap node, it needs to obtain power supplementation from nearby nodes or distributed power sources.

[0119] Figure 5 This is a flowchart illustrating another distribution network optimization method provided by an embodiment of the present invention. The embodiments of the present invention further refine the aforementioned embodiments based on the previous embodiments. See also... Figure 5 Optionally, step S150 specifically includes:

[0120] S510. Based on the power change of each node, prioritize each node and determine the geographical location and network topology of high-load nodes and surplus nodes.

[0121] Specifically, based on the power gap or surplus of each node, high-load nodes are prioritized according to the size of their power gap, so that nodes with larger power gaps are adjusted first; similarly, surplus nodes are prioritized according to the size of their power surplus, so that nodes with larger power surplus are given priority for power supply adjustment.

[0122] S520: Based on the geographical location and network topology of high-load nodes and surplus nodes, determine the optimal connection relationship, and connect high-load nodes and surplus nodes through switch control and circuit breaker control.

[0123] Specifically, based on the geographical location and network topology of high-load nodes and surplus nodes, the optimal connection relationship is determined, including calculating the distance between high-load nodes and nearby surplus nodes, prioritizing the selection of the nearest surplus node, and connecting high-load nodes and surplus nodes through switch control and circuit breaker control to meet the power supply needs of high-load nodes and reduce power transmission losses over long distances.

[0124] S530 implements dynamic adjustments at distributed power access points near high-load nodes.

[0125] Specifically, dynamic adjustments are implemented at distributed power supply access points near high-load nodes to adjust the output distribution of distributed power sources, prioritizing power supply to high-load nodes with large power deficits. The allocated power supply is This avoids power loss during long-distance power transmission and improves the response speed of the power grid.

[0126] S540 After completing node connection optimization and distributed power source access point adjustment, update the power grid topology and realize actual topology reconfiguration through remote control of switches and circuit breakers.

[0127] Specifically, after completing node connection optimization and distributed energy access adjustments, the grid topology is updated. This involves remotely controlling switches and circuit breakers to achieve actual topology reconfiguration, ensuring that high-load nodes receive stable and readily available power in the new topology. Following topology reconfiguration, the load status of nodes is monitored in real time, and a priority ranking mechanism is used to dynamically adjust load allocation and power output ratios to ensure that high-load nodes receive nearby power support.

[0128] To determine the optimal connectivity between high-load nodes and redundant nodes, the distance between high-load nodes and neighboring redundant nodes is calculated using Euclidean distance. Let the nodes... The coordinates are ,node The coordinates are Then the node and The distance between them is The calculation is as follows:

[0129] This formula calculates the nodes. and nodes The spatial distance is used to select the nearest redundant node to reduce power loss in long-distance transmission.

[0130] The technical solution provided by this invention optimizes the connection relationship between high-load nodes and surplus nodes, enabling power transmission over shorter distances and reducing losses associated with long-distance power transmission. Simultaneously, adjustments are made to the distributed power supply access points near high-load nodes, prioritizing power supply to nodes with larger power deficits, further reducing power loss during long-distance transmission. This innovative topology reconfiguration mechanism significantly improves power utilization efficiency, enhancing both power quality and overall power transmission loss control. Furthermore, the technical solution employs a priority ranking mechanism based on node power deficits and surpluses, ensuring high-load nodes receive priority power support from nearby surplus nodes and distributed power sources, and dynamically adjusting the power allocation ratio. This innovative design enables rapid response to load fluctuations in the power grid, prioritizing the power supply needs of critical nodes and ensuring the stability and continuity of important loads.

[0131] Figure 6 This is a schematic diagram of a power distribution network optimization system provided in an embodiment of the present invention. (See attached diagram.) Figure 6 The system includes: a data acquisition module 610, a load characteristic analysis module 620, a distributed power source output characteristic analysis module 630, a calculation module 640, and an update module 650.

[0132] The acquisition module 610 is used to acquire the voltage and current of each node in the distribution network and calculate the load data of each node.

[0133] The load characteristic analysis module 620 is used to construct load characteristic feature vectors based on load data through data analysis.

[0134] The distributed power generation output characteristic analysis module 630 is used to obtain the output power of each distributed power source in the distribution network, identify the fluctuation characteristics of the output power, and construct the distributed power generation output characteristic feature vector.

[0135] The calculation module 640 is used to calculate the power change of each node based on the load characteristic feature vector and the distributed power source output characteristic feature vector.

[0136] The update module 650 is used to update the power grid topology based on the power changes at each node.

[0137] The power distribution network optimization system provided in this embodiment of the invention can execute the power distribution network optimization method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0138] Figure 7 This is a schematic diagram of an electronic device for a power distribution network optimization method provided in an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0139] like Figure 7 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0140] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0141] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a power distribution network optimization method.

[0142] In some embodiments, a power distribution network optimization method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the power distribution network optimization method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a power distribution network optimization method by any other suitable means (e.g., by means of firmware).

[0143] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0144] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0145] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable 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 thereof.

[0146] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0147] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0148] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0149] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0150] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A power distribution network framework optimization method, characterized in that, The method comprises the following steps: Collecting the voltage and current of each node in the power distribution network, and calculating the load data of each node according to the voltage and current; Based on the load data, a load characteristic feature vector is constructed through data analysis; Obtaining the output power of each distributed power source in the power distribution network, and identifying the fluctuation characteristics of the output power to construct a distributed power source output characteristic feature vector; Based on the load characteristic feature vector and the distributed power source output characteristic feature vector, the power variation of each node is calculated; According to the power variation of each node, the power grid topology is updated.

2. The method of claim 1, wherein, Collecting the voltage and current of each node in the power distribution network, and calculating the load data of each node according to the voltage and current comprises: Collecting the voltage and current of each node in the power distribution network through voltage sensors and current sensors respectively; Based on the product of the voltage, the current and the power factor, the load data of each node is calculated.

3. The method of claim 1, wherein, Based on the load data, a load characteristic feature vector is constructed through data analysis, which comprises: Based on the load data, the load characteristics of each node are identified through data analysis, and a load characteristic feature vector including load peak value, average load and load variation period is constructed.

4. The method of claim 3, wherein, Based on the load data, the load characteristics of each node are identified through data analysis, and a load characteristic feature vector including load peak value, average load and load variation period is constructed, which comprises: The load characteristic feature vector should satisfy the following formula: wherein is a load characteristic feature vector, is a load peak, is an average load, is a load variation period; The load peak value: the load data in the statistical history time window, identifying and recording the load peak value of each node, wherein, denotes the time window, is the load data; The average load: based on the load data in the historical time window, the average load of each node is calculated, wherein, represents the total number of time points within the time window ; The load variation period: the change period of the load data is analyzed by using the frequency spectrum analysis method, and the load variation period of each node is identified and recorded.

5. The method of claim 1, wherein, Obtaining the output power of each distributed power source in the power distribution network, and identifying the fluctuation characteristics of the output power to construct a distributed power source output characteristic feature vector comprises: Real-time collection of output voltage, output current and power factor of each distributed power source; According to the output voltage, the output current and the power factor, the output power of each distributed power source in the power distribution network is obtained; Time series analysis is performed on the output power to identify the fluctuation characteristics of the distributed power source output power; Based on the output characteristics of the distributed power source, an output model is constructed to generate a distributed power source output characteristic feature vector.

6. The method of claim 5, wherein, The fluctuation characteristics of the distributed power source output power include peak output power, average output power and fluctuation period; the peak output power: ; wherein, denotes a sampling time point, d denotes the number of the distributed power supply, is the distributed power supply at time , and is the peak output power; the average output power: ; is the average output power; The distributed power source output characteristic feature vector should satisfy: ; is a fluctuation period, and the distributed power output characteristic feature vector is used to represent the output characteristics of the distributed power under different load conditions.

7. The method of claim 1, wherein, Based on the load characteristic feature vector and the distributed power source output characteristic feature vector, the power variation of each node is calculated, which comprises: According to the load characteristic feature vector of each node, the node demand power is calculated; According to the distributed power source output characteristic feature vector, the distributed power source distributed power supply power is calculated; According to the node demand power and the distributed power source distributed power supply power, the power variation of each node is calculated.

8. The method of claim 7, wherein, According to the load characteristic feature vector of each node, the node demand power is calculated, which comprises: The node demand power is calculated by using the following formula: wherein, denotes the average load of a node denotes the load variation period of a node denotes the load fluctuation amplitude coefficient of a node denotes the load fluctuation amplitude coefficient of a node denotes the load fluctuation amplitude coefficient of a node denotes the load fluctuation amplitude coefficient of a node According to the distributed power source output characteristic feature vector, the distributed power source distributed power supply power is calculated, which comprises: The distributed power source distributed power supply power is calculated by using the following formula: In the formula, Distribute power to distributed power sources. Indicates power supply to the node The total number of distributed power sources, Distributed power source Average output power Distributed power source The fluctuation cycle, Distributed power source The output fluctuation amplitude coefficient reflects the magnitude of power supply output fluctuations. Distributed power source Assigned to node The power ratio coefficient; According to the node demand power and the distributed power source distributed power supply power, the power variation of each node is calculated, which comprises: The power change amount of each node is calculated by using the following formula: wherein is the power variation.

9. The method of claim 1, wherein, According to the power change amount of each node, the power grid topology is updated, comprising: Based on the power change amount of each node, the priority of each node is sorted, and the geographical location and network topology of the high-load node and the surplus node are determined; Based on the geographical location and network topology of the high-load node and the surplus node, the optimal connection relationship is determined, and the high-load node and the surplus node are connected through switch control and circuit breaker control; The distributed power supply access point near the high-load node is adjusted dynamically; After the node connection optimization and the distributed power supply access point adjustment are completed, the power grid topology is updated, and the actual topology reconstruction is realized through remote control of switches and circuit breakers.

10. A power distribution network framework optimization system, characterized in that, Comprising: The acquisition module is used to acquire the voltage and current of each node in the power distribution network, and calculate the load data of each node; The load characteristic analysis module is used to construct a load characteristic feature vector based on the load data through data analysis; The distributed power supply output characteristic analysis module is used to obtain the output power of each distributed power supply in the power distribution network, and identify the fluctuation characteristics of the output power, and construct a distributed power supply output characteristic feature vector; The calculation module is used to calculate the power change amount of each node based on the load characteristic feature vector and the distributed power supply output characteristic feature vector; The update module is used to update the power grid topology according to the power change amount of each node.

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