Dynamic partitioning method and system for distribution system control based on dynamic source-load matching

Abstract

The present invention discloses a method and system for dynamic zoning of distribution system control based on dynamic source-load matching; the method comprises: obtaining parameter data and power supply history data of each node in the distribution system control area; determining a number of nodes as reference nodes based on the power supply history data and preset screening conditions; determining the modularity of the node and the reference node based on the parameter data of each node; dividing the initial partition corresponding to the reference node based on the modularity and the reference node; judging whether the source-load matching composed of the modularity, the reactive power margin and the source-load active power balance satisfies the preset control constraint conditions; when the control constraint conditions are not satisfied, adjusting each initial partition using a genetic algorithm to obtain a target partition that satisfies the control constraint conditions, thereby reducing the control complexity and, at the same time, reducing the problems of electrical parameter fluctuations caused by frequent issuance of control instructions and reduced equipment life caused by frequent operation of power equipment.

Description

Technical Field

[0001] Embodiments of the present invention relate to power distribution system control technology, and more particularly to a method and system for dynamic partitioning of power distribution system control based on dynamic source-load matching. Background Art

[0002] Clean energy, energy storage, and electrical loads in the distribution network are primarily manifested in the large-scale integration of distributed power sources, distributed energy storage, and distributed sources, loads, and storage, such as electric vehicles, electric refrigeration, and electric heating. These resources are characterized by significant non-determinism, randomness, and volatility. Furthermore, these distributed resources are connected in large numbers on the low-voltage side, and the low-voltage distribution network is characterized by poor observability, measurability, controllability, and adjustability. This further exacerbates the difficulty of regulating these diverse sources and loads, and can easily lead to node voltage over-limits, power reverse, equipment and line overloads, increased line losses, and other issues that impact the safety, reliability, and economy of the distribution system.

[0003] For medium and low voltage active distribution networks, especially low voltage distribution networks, establishing a good interactive relationship between sources, loads and storage, selecting reasonable control objects and units, and configuring appropriate communication conditions are key links in achieving safe, reliable and economical operation of distribution networks under large-scale distributed resource access. It is also an important basic guarantee for realizing hierarchical and regional control of active distribution networks. Summary of the Invention

[0004] The object of the present invention is to provide a method and system for dynamic zoning of distribution system control based on dynamic source-load matching, so as to realize zoning control of the distribution system.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a method for dynamic partitioning of a power distribution system based on dynamic source-load matching, comprising:

[0007] Obtain parameter data and power supply history data of each node in the distribution system control area;

[0008] Determining a plurality of the nodes as reference nodes based on the power supply history data and preset screening conditions;

[0009] determining the modularity of the node and the reference node based on parameter data of each of the nodes;

[0010] Dividing an initial partition corresponding to the reference node based on the modularity and the reference node;

[0011] Calculating the reactive power margin and source-load active power balance of each of the initial partitions based on the parameter data of each of the nodes;

[0012] Determining whether the source-load matching degree of each of the initial partitions, which is composed of the modularity, the reactive power margin, and the source-load active power balance, meets the preset control constraint conditions;

[0013] When the control constraint conditions are not met, a genetic algorithm is used to adjust each of the initial partitions to obtain a target partition that meets the control constraint conditions.

[0014] Optionally, the determining of the plurality of nodes as reference nodes based on the power supply history data and preset screening conditions includes:

[0015] Based on the power supply history data, several nodes having large power demands and / or large power returned to the upper power grid are determined as reference nodes.

[0016] Optionally, determining the modularity of the node and the reference node based on the parameter data of each node includes:

[0017] determining a voltage sensitivity of each of the nodes based on parameter data of each of the nodes;

[0018] determining an edge weight between the node and the reference node based on the voltage sensitivity;

[0019] The modularity of the node and the reference node is calculated based on the edge weight and a preset modularity calculation formula.

[0020] Optionally, determining the voltage sensitivity of each of the nodes based on the parameter data of each of the nodes includes:

[0021] The voltage sensitivity of each of the nodes is based on the following formula:

[0022] ΔU=S PU ΔP+S QU ΔQ

[0023] Wherein, ΔU is the voltage change of the node, S PU is the voltage sensitivity under active power, S QU is the voltage sensitivity under reactive power, ΔP is the injected active power deviation matrix of the node, and ΔQ is the reactive power deviation matrix of the node.

[0024] Optionally, determining the edge weight between the node and the reference node based on the voltage sensitivity includes:

[0025] The edge weight between the node and the reference node is calculated based on the following formula:

[0026]

[0027] Among them, Aij is the edge weight between the node and the reference node, S PU is the voltage sensitivity under active power, S QU is the voltage sensitivity under reactive power.

[0028] Optionally, the calculating the modularity of the node and the reference node based on the edge weight and a preset modularity calculation formula includes:

[0029] The modularity of the node and the benchmark node is calculated based on the following formula:

[0030]

[0031] Wherein, ρ is the modularity, m is the sum of all the edge weights in the network, ki represents the sum of the edge weights connected to k nodes, S PU is the voltage sensitivity under active power, S QU is the voltage sensitivity under reactive power, δ(i, j) indicates whether nodes i and j are in the same area. If they are in the same area, δ(i, j)=1, otherwise δ(i, j)=0.

[0032] Optionally, dividing the initial partition corresponding to the reference node based on the modularity and the reference node includes:

[0033] The nodes whose modularity is within a preset module range are divided into the same partitions as the corresponding reference nodes to obtain initial partitions corresponding to the number of the reference nodes.

[0034] In a second aspect, an embodiment of the present invention further provides a device for controlling dynamic partitioning of a power distribution system, comprising:

[0035] An acquisition module is used to obtain parameter data and power supply history data of each node in the distribution system control area;

[0036] A reference module, configured to determine a plurality of the nodes as reference nodes based on the power supply history data and preset screening conditions;

[0037] a determination module, configured to determine the modularity of the node and the reference node based on parameter data of each node;

[0038] a partitioning module, configured to partition an initial partition corresponding to the reference node based on the modularity and the reference node;

[0039] A constraint module, configured to determine whether each of the initial partitions satisfies a preset control constraint condition based on parameter data of each of the nodes;

[0040] The adjustment module is used to adjust each of the initial partitions using a genetic algorithm when the control constraint condition is not met, so as to obtain a target partition that meets the control constraint condition.

[0041] In a third aspect, an embodiment of the present invention further provides a power distribution system control dynamic partitioning device, the device comprising:

[0042] one or more processors;

[0043] a storage device for storing one or more programs;

[0044] When the one or more programs are executed by the one or more processors, the one or more processors implement the dynamic partitioning method for power distribution system regulation as described in the first aspect.

[0045] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, are used to execute the dynamic partitioning method for controlling the power distribution system as described in the first aspect.

[0046] The present invention obtains parameter data and power supply history data of each node in the distribution system control area, determines several nodes as reference nodes, then calculates the modularity between each node and the reference node to partition the nodes, and then verifies the control constraints of the initial partitions obtained by division. Finally, a genetic algorithm is used to adjust the initial partitions that do not meet the control constraints, so as to dynamically divide the nodes of the distribution system into multiple areas, thereby minimizing the number of regulated objects and reducing the control complexity to the greatest extent. At the same time, it reduces problems such as electrical parameter fluctuations caused by frequent issuance of control instructions and reduced equipment life caused by frequent operation of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flow chart of a method for dynamic partitioning of power distribution system control provided by an embodiment of the present invention;

[0048] Figure 2 A schematic diagram of the structure of a dynamic partitioning device for controlling power distribution system according to an embodiment of the present invention;

[0049] Figure 3 A schematic diagram of the structure of a dynamic partitioning device for controlling power distribution system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0051] Figure 1 This is a flow chart of a method for dynamically zoning a power distribution system according to an embodiment of the present invention. This embodiment is applicable to situations where dynamic zoning control is performed on a power distribution network. The method can be performed by a device for dynamically zoning a power distribution system, and specifically includes the following steps:

[0052] Step 110: Obtain parameter data and power supply history data of each node in the power distribution system control area.

[0053] In the embodiments of the present invention, the section of the power system from the step-down distribution substation (high-voltage distribution substation) to the user end is referred to as the distribution system. A distribution system is a power network system composed of various distribution equipment (or components) and distribution facilities that converts voltage and directly distributes electricity to end users. Clean energy, energy storage, and electrical loads in the distribution network are primarily manifested in the large-scale integration of distributed power sources, distributed energy storage, and distributed sources, loads, and storage, such as electric vehicles, electric refrigeration, and electric heating. These resources exhibit significant uncertainty, randomness, and volatility. Furthermore, the large number of these distributed resources connected to the low-voltage side, coupled with the poor observability, measurability, controllability, and adjustability of low-voltage distribution networks, further exacerbates the difficulty of regulating multiple sources and loads. This can easily lead to node voltage violations, power reverse flow, equipment / line overloads, and increased line losses, impacting the safety, reliability, and economic efficiency of the distribution system. However, a significant portion of existing research has focused on single-voltage distribution networks, and the resulting conclusions cannot meet the regulatory requirements of actual medium- and low-voltage, multi-voltage distribution networks.

[0054] In the embodiments of the present invention, the main focus is on distributed power sources in the power distribution system. The general definition of distributed power sources refers to power sources distributed at the user end, connected to a voltage level of 35kV and below, and mainly consumed locally. They generally include distributed power generation systems such as photovoltaic power generation, wind power generation, micro gas turbine power generation, and fuel cell power generation. In a broad sense, they also include various energy storage devices. Further exploring the above definition in combination with the characteristics of distributed power sources (high non-determinism and strong randomness) can yield the following basic principles for the development of distributed power sources, namely:

[0055] (1) Distributed power sources are preferentially consumed at the voltage level to which they are connected, so as to reduce various line losses and equipment losses caused by the transmission of distributed power across multiple voltage levels;

[0056] (2) Distributed power sources should be consumed at lower voltage levels first to prevent them from being sent to higher voltage levels, thereby reducing the impact of high non-determinism and strong randomness of distributed power sources on higher voltage levels (such as 110 kV and above, which are usually more important);

[0057] (3) Distributed power sources should be consumed locally through autonomous or adaptive means as much as possible, which can significantly reduce the centralized computing and communication pressures of the grid dispatching department, while improving the timeliness and economy of responding to rapid changes in output, and further reducing the dimension and complexity of regulation.

[0058] In this embodiment of the present invention, parameter data and power supply history data are collected for each node within the distribution system's control area. The parameter data may include the node's real-time physical connectivity with surrounding nodes, location information, active voltage, active current, reactive voltage, reactive current, real-time load, active power, and reactive power. The power supply history data may include the node's historical parameter data and power generation and consumption data.

[0059] Step 120: Determine several nodes as reference nodes based on power supply history data and preset screening conditions.

[0060] During implementation, this embodiment of the present invention primarily implements regional control on nodes that require frequent regulation, such as those with high power demands or those that feed high power back to the upstream grid. This prevents the distribution system from having to adjust all nodes simultaneously, reducing the adjustment workload per unit time. Therefore, in this embodiment of the present invention, nodes with high power demands or those that feed high power back to the upstream grid serve as reference nodes.

[0061] In an embodiment of the present invention, several nodes having large power demands and / or large power returned to a superior power grid may be determined as reference nodes based on power supply history data.

[0062] Step 130: Determine the modularity of each node and a reference node based on the parameter data of each node.

[0063] In this embodiment of the present invention, the modularity of a node relative to a reference node is defined to characterize the network community structure between the node and the reference node. Community structure is a key property of complex networks. Generally speaking, nodes within a community often have similar characteristics and are significantly different from nodes within other communities.

[0064] Optionally, higher modularity indicates closer internal community relationships, looser external relationships (less mutual influence between communities), and more reasonable community structure division within the complex network. Considering the inherent characteristics of power systems, parameters reflecting electrical connectivity should be embedded in the modularity metric. Voltage sensitivity can be selected to reflect the relationship between nodes and partitions.

[0065] Step 140: Divide the initial partitions corresponding to the reference nodes based on the modularity and the reference nodes.

[0066] In the aforementioned steps, the modularity between the reference node and other nodes is calculated, and nodes with a higher modularity (geographically in the same region and whether they are directly physically connected) than the reference node are obtained. In this step, the nodes are divided into partitions within each reference node based on the modularity, thereby obtaining the initial partitions in the embodiment of the present invention. For example, nodes with a modularity within a preset module range are divided into the same partition as the corresponding reference node, obtaining initial partitions corresponding to the number of reference nodes.

[0067] Step 150: Calculate the reactive power margin and source-load active power balance of each initial partition based on the parameter data of each node;

[0068] Step 160: Determine whether the source-load matching degree of each initial partition, which consists of modularity, reactive power margin, and source-load active power balance, meets the preset control constraint conditions;

[0069] In an embodiment of the present invention, in the aforementioned steps, only the initial partitions are obtained by dividing the nodes into partitions where different reference nodes are located based on modularity. There may be situations where the reactive capacity reserve does not meet the adjustment requirements of voltage fluctuations, the power balance conditions between the distributed power output and the load demand, and the constraints of the conventional power system. Therefore, in this step, it is necessary to verify the constraints of the initial partitions divided in the aforementioned steps to ensure that the division of each partition can meet the adjustment requirements of voltage fluctuations, the power balance conditions between the distributed power output and the load demand, and the constraints of the conventional power system, so as to ensure the safe and reliable operation of the distribution system.

[0070] In practice, reactive power is a factor closely tied to the power system. Sufficient reactive capacity reserves help a region adjust to voltage fluctuations under various operating conditions. Therefore, this factor should be considered when optimizing source-load matching within a source region. Compared to traditional distribution networks, new distribution systems with large-scale distributed power generation have more complex reactive power distribution. For example, distributed photovoltaic inverters possess certain reactive power absorption and emission capabilities.

[0071] Taking into account distributed power sources, energy storage devices and other source-load-storage inverter devices, as well as feeder capacitors, SVCs, SVGs and other reactive power regulation devices in the new power distribution system, the main problem in the case of large-scale distributed power generation is that the voltage of most nodes exceeds the upper limit. In this embodiment of the present invention, the reactive power margin is defined as follows:

[0072]

[0073] Among them, U maxis the highest voltage in this area based on load forecast, U up，lim The upper voltage limit of this voltage level; S i With P DG，i The capacity and grid-connected active power of the inverters equipped for the distributed power sources or energy storage devices in this area; Q k，SVG / SVC / SCB The capacity of reactive power regulation devices such as SVG, SVC, and SCB configured in this area; S QU，imax is the reactive-voltage sensitivity coefficient of the node with the highest voltage. Based on this, the denominator in the formula represents the maximum voltage regulation value within the region that can be achieved through reactive regulation. When ξ > 1, it indicates that the reactive capacity within the region can meet the regulation requirements.

[0074] When the distributed power supply is under-generated or not generated, the main problem is that the voltage of most nodes exceeds the lower limit. In the embodiment of the present invention, the reactive power margin is defined as follows:

[0075]

[0076] Among them, U min is the lowest voltage in this area based on load forecast, U down，lim The lower voltage limit of this voltage level; S i With P DG，i The capacity and grid-connected active power of the inverters equipped for the distributed power sources or energy storage devices in this area; Q k，SVG / SVC / SCB The capacity of reactive power regulation devices such as SVG, SVC, and SCB configured in this area; S QD，imin is the reactive-voltage sensitivity coefficient of the node where the lowest voltage occurs. Based on this, the denominator in the formula represents the maximum voltage regulation value within the region that can be achieved through reactive regulation. When ξ > 1, it indicates that the reactive capacity within the region can meet the regulation requirements.

[0077] In an embodiment of the present invention, source-load active power balance is also defined. Source-load active power balance refers to the balance of power output of distributed power sources and load demand over a period of time. By reducing the active power within the region that is fed back to the superior power grid or excessive active power dispatched from other partitions through a zoning strategy, the frequency of distribution network regulation and control can be reduced, and fewer objects need to be regulated.

[0078] The source-load active balance is as follows:

[0079]

[0080]

[0081] Among them, Pa is the adjustable value of active power in this area. ΔP DG，I , ΔP ES，j , ΔP flec-load, k are the distributed power output, energy storage device output / energy storage and load regulation value that can participate in load demand response in this area. ΔP demand are the active increase / decrease values ​​of the rigid load in the area, and η is the active balance degree of source and load. When zoning, more zones should be considered to maximize η.

[0082] Combining the indicators of the above three dimensions, the indicators of source-load matching are defined as follows:

[0083] σ=ω1ρ+ω2ξ+ω3η

[0084] ω1+ω2+ω3=1

[0085] The network modularity, reactive power margin (voltage safety) and source-load active power balance are combined in the form of weighted average to form the source-load matching degree σ, where ω1, ω2 and ω3 are weights, indicating the importance of the three dimensions under different working conditions. In actual application, they can be selected as needed.

[0086] For example, the two indicators of Dimensions 2 and 3 mentioned above should also be set with corresponding constraints during subsequent zoning optimization, such as 1 < ξ < 1.05 and 0.95 < η < 1.05. The selection of these constraints is based on different regions, different grid structures, and different load importance.

[0087] Step 170: When the control constraint conditions are not met, a genetic algorithm is used to adjust each initial partition to obtain a target partition that meets the control constraint conditions.

[0088] In an embodiment of the present invention, when the initial partition cannot satisfy the constraint conditions, the allocation of the initial partition is adjusted through a genetic algorithm to further satisfy the target partition that controls the constraint conditions.

[0089] The technical solution of this embodiment obtains parameter data and power supply history data of each node in the distribution system control area, determines several nodes as reference nodes, and then calculates the modularity between each node and the reference node to partition the nodes. The control constraints of the initial partitions obtained are then verified. Finally, a genetic algorithm is used to adjust the initial partitions that do not meet the control constraints, so as to dynamically divide the nodes of the distribution system into multiple areas, thereby minimizing the number of regulated objects and reducing the complexity of control. At the same time, it reduces problems such as electrical parameter fluctuations caused by frequent issuance of control instructions and reduced equipment life caused by frequent operation of power equipment.

[0090] In one example of the present invention, the modularity of a node and a reference node may be calculated based on the following formula:

[0091]

[0092] Where ρ is the modularity, m is the sum of all edge weights in the network, ki is the sum of the edge weights connected to k nodes, S PU is the voltage sensitivity under active power, S QU is the voltage sensitivity under reactive power, δ(i, j) indicates whether nodes i and j are in the same area. If they are in the same area, δ(i, j) = 1, otherwise δ(i, j) = 0.

[0093] In the embodiment of the present invention, based on the concept of community, the modularity function is used to measure the structural characteristics of the complex network community, as shown in the following formula:

[0094]

[0095] Wherein, ρ is the modularity described in the embodiment of the present invention, A ij is the weight of the edge connecting node i and node j, which is used to judge the connectivity between nodes. That is, when node i is connected to node j, A ij =1, otherwise A ij = 0. ki represents the sum of the weights of the edges connected to node k, and m represents the sum of the weights of all edges in the network. δ(i, j) is used to determine whether nodes i and j are in the same region. If they are in the same region, δ(i, j) = 1; otherwise, δ(i, j) = 0.

[0096] According to the above definition, the higher the modularity, the closer the internal relationship of the community, the looser the external relationship (the less mutual influence between communities), and the more reasonable the community structure division of the complex network.

[0097] Taking into account the inherent characteristics of the power system, parameters reflecting the electrical connection relationship should be embedded in the modularity index. The present invention selects voltage sensitivity to reflect the correlation relationship between nodes and partitions.

[0098] Existing studies usually only consider the reactive-voltage sensitivity relationship. However, in this invention, the active-reactive-voltage sensitivity concept is proposed, taking into account the access characteristics of the distribution network and distributed power sources, to reflect the electrical connection relationship in the edge weight.

[0099] The voltage sensitivity of the system nodes is usually obtained from the Jacobian matrix in the power flow calculation. The Jacobian matrix can be expressed as follows:

[0100]

[0101] Among them, ΔP and ΔQ are the node-injected active and reactive power deviation matrices, Δθ and ΔU are the node voltage and phase angle variation matrices, and J is the Jacobian matrix. The inverse matrix can be obtained as follows:

[0102]

[0103] Replace S in the formula Pθ 、S Qθ 、S PU 、S QU That is, the sensitivity of the phase angle and the sensitivity of the voltage. In the present invention, the voltage sensitivity is the main focus, and the change in node voltage ΔU can be obtained as:

[0104] ΔU=S PU ΔP+S QU ΔQ

[0105] Get S PU 、S QU After calculating the voltage sensitivity, we combine it with the edge weight to get:

[0106]

[0107] In summary, the calculation formula for modularity in the embodiment of the present invention is as follows:

[0108]

[0109] Wherein, ρ is the modularity described in the embodiment of the present invention, A ij is the weight of the edge connecting node i and node j, which is used to judge the connectivity between nodes. That is, when node i is connected to node j, A ij =1, otherwise A ij = 0. ki represents the sum of the weights of the edges connected to node k, and m represents the sum of the weights of all edges in the network. δ(i, j) is used to determine whether nodes i and j are in the same region. If they are in the same region, δ(i, j) = 1; otherwise, δ(i, j) = 0.

[0110] In the embodiment of the present invention, a genetic algorithm is used as the preferred algorithm. The genetic algorithm is suitable for solving "population problems" with large network scale and high accuracy requirements, and can achieve a balance between computational complexity / computation time and accuracy. The optimization objective function is shown as follows:

[0111] maxσ=ω1ρ+ω2ξ+ω3η

[0112] That is, the maximum source-load matching is taken as the optimization goal, and the partitioning scheme is obtained.

[0113] For example, in the embodiments of the present invention, the control constraint conditions are shown in the following formulas:

[0114]

[0115] I ij.min ≤I ij ≤jij.max

[0116] V min ≤V i ≤V max

[0117] P DG.imin ≤P DG.i ≤P DG.imax

[0118] P pcc-i.min ≤P i ≤P pcc-i.max

[0119] Q i.min ≤Q i ≤Q i.max

[0120] T i.min ≤T i ≤T i.max

[0121] SOC i，min ≤SOC i ≤SOC i，max

[0122] The above formulas are respectively the power flow constraint, branch load constraint, distributed generation active power output constraint, substation active power output constraint, reactive power compensation device capacity constraint, transformer tap position adjustment constraint, and energy storage device SOC state constraint.

[0123] Figure 2 A schematic diagram of a structure of a power distribution system control dynamic partitioning device provided by an embodiment of the present invention is shown as follows: Figure 2 As shown, the dynamic partitioning device for power distribution system control includes an acquisition module 21, a reference module 22, a determination module 23, a division module 24, a calculation module 25, a constraint module 26 and an adjustment module 27, wherein:

[0124] An acquisition module 21 is used to acquire parameter data and power supply history data of each node in the distribution system control area;

[0125] A reference module 22 is configured to determine a number of nodes as reference nodes based on power supply history data and preset screening conditions;

[0126] a determination module 23 for determining the modularity of a node and a reference node based on parameter data of each node;

[0127] A partitioning module 24 is configured to partition an initial partition corresponding to a reference node based on the modularity and the reference node;

[0128] A calculation module 25 is configured to calculate the reactive power margin and source-load active power balance of each of the initial partitions based on the parameter data of each of the nodes;

[0129] The constraint module 26 determines whether the source-load matching degree of each initial partition, which is composed of the modularity, the reactive power margin and the source-load active power balance, meets the preset control constraint conditions.

[0130] The adjustment module 27 is used to adjust each initial partition using a genetic algorithm when the control constraint conditions are not met, so as to obtain a target partition that meets the control constraint conditions.

[0131] The reference module 22 includes:

[0132] The reference unit is used to determine, based on the power supply history data, several nodes having large power demands and / or large power returned to the upper power grid as reference nodes.

[0133] The determination module 23 includes:

[0134] a voltage sensitivity unit, configured to determine a voltage sensitivity of each node based on parameter data of each node;

[0135] an edge weight unit, for determining an edge weight between a node and a reference node based on voltage sensitivity;

[0136] The modularity unit is used to calculate the modularity of a node and a benchmark node based on edge weights and a preset modularity calculation formula.

[0137] The voltage sensitivity of each node is determined based on the parameter data of each node, including:

[0138] The voltage sensitivity of each node is based on the following formula:

[0139] ΔU=S PU ΔP+S QU ΔQ

[0140] Where ΔU is the voltage change of the node, S PU is the voltage sensitivity under active power, S QU is the voltage sensitivity under reactive power, ΔP is the injected active power deviation matrix of the node, and ΔQ is the reactive power deviation matrix of the node.

[0141] The edge weight between the node and the reference node is determined based on the voltage sensitivity, including:

[0142] The edge weight between a node and a reference node is calculated based on the following formula:

[0143]

[0144] Among them, Aij is the edge weight between the node and the benchmark node, S PU is the voltage sensitivity under active power, S QU is the voltage sensitivity under reactive power.

[0145] The modularity of a node and a benchmark node is calculated based on edge weights and a preset modularity calculation formula, including:

[0146] The modularity of a node relative to a benchmark node is calculated based on the following formula:

[0147]

[0148] Where ρ is the modularity, m is the sum of all edge weights in the network, ki is the sum of the edge weights connected to k nodes, S PU is the voltage sensitivity under active power, S QU is the voltage sensitivity under reactive power, δ(i, j) indicates whether nodes i and j are in the same area. If they are in the same area, δ(i, j) = 1, otherwise δ(i, j) = 0.

[0149] The division module 24 includes:

[0150] The partitioning unit is used to partition the nodes whose modularity is within a preset module range into the same partitions as the corresponding reference nodes, and obtain initial partitions corresponding to the number of reference nodes.

[0151] The power distribution system control dynamic zoning device provided in the embodiment of the present invention can execute the power distribution system control dynamic zoning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0152] Figure 3 A schematic diagram of a structure of a power distribution system control dynamic partitioning device provided by an embodiment of the present invention, such as Figure 3 As shown, the device includes a processor 30, a memory 31, a communication module 32, an input device 33 and an output device 34; the number of processors 30 in the device can be one or more. Figure 3 In the embodiment, a processor 30 is used as an example; the processor 30, the memory 31, the communication module 32, the input device 33 and the output device 34 in the device can be connected by a bus or other means. Figure 3 The bus connection is taken as an example.

[0153] Memory 31, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the subject update method in the embodiments of the present invention (for example, the acquisition module 21, reference module 22, determination module 23, partitioning module 24, constraint module 25, and adjustment module 26 in the power distribution system control dynamic partitioning device). Processor 30 executes the software programs, instructions, and modules stored in memory 31 to execute various functional applications and data processing of the device, thereby implementing the above-mentioned power distribution system control dynamic partitioning method.

[0154] The memory 31 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function; the data storage area may store data created based on the use of the terminal, etc. Furthermore, the memory 31 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state memory device. In some instances, the memory 31 may further include memory remotely located relative to the processor 30, and these remote memories may be connected to the device via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0155] The communication module 32 is used to establish a connection with the display screen and implement data exchange with the display screen. The input device 33 can be used to receive input digital or character information and generate key signal input related to user settings and function control of the electronic device. The output device 34 can include a display device such as a display screen.

[0156] The present embodiment provides a power distribution system control dynamic zoning device that can execute the power distribution system control dynamic zoning method provided in any embodiment of the present invention, with specific corresponding functions and beneficial effects.

[0157] An embodiment of the present invention further provides a storage medium containing computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, are used to perform a method for dynamically partitioning a power distribution system, the method comprising:

[0158] Obtain parameter data and power supply history data of each node in the distribution system control area;

[0159] Determine several nodes as benchmark nodes based on historical power supply data and preset screening conditions;

[0160] Determine the modularity of the node and the benchmark node based on the parameter data of each node;

[0161] Divide the initial partitions corresponding to the benchmark nodes based on the modularity and the benchmark nodes;

[0162] Based on the parameter data of each node, determine whether each initial partition meets the preset control constraints;

[0163] When the control constraints are not met, the genetic algorithm is used to adjust the initial partitions to obtain the target partitions that meet the control constraints.

[0164] Of course, the storage medium containing computer-executable instructions provided in an embodiment of the present invention is not limited to the method operations described above, and can also execute related operations in the distribution system control dynamic partitioning method provided in any embodiment of the present invention.

[0165] Through the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented with the help of software and necessary general-purpose hardware, and of course it can also be implemented through hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the distribution system control dynamic partitioning method described in each embodiment of the present invention.

[0166] It is worth noting that in the embodiment of the above-mentioned power distribution system control dynamic zoning device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0167] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the concept of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for dynamic partitioning of distribution system control based on dynamic source-load matching, characterized in that: include: Obtain parameter data and power supply history data of each node in the distribution system control area; Determining a plurality of the nodes as reference nodes based on the power supply history data and preset screening conditions; determining the modularity of the node and the reference node based on parameter data of each of the nodes; Dividing an initial partition corresponding to the reference node based on the modularity and the reference node; Calculating the reactive power margin and source-load active power balance of each of the initial partitions based on the parameter data of each of the nodes; Determining whether the source-load matching degree of each of the initial partitions, which is composed of the modularity, the reactive power margin, and the source-load active power balance, meets the preset control constraint conditions; When the control constraint condition is not satisfied, adjusting each of the initial partitions using a genetic algorithm to obtain a target partition that satisfies the control constraint condition; The determining the modularity of the node and the reference node based on the parameter data of each node includes: determining a voltage sensitivity of each of the nodes based on parameter data of each of the nodes; determining an edge weight between the node and the reference node based on the voltage sensitivity; Calculating the modularity of the node and the reference node based on the edge weight and a preset modularity calculation formula; The determining the edge weight between the node and the reference node based on the voltage sensitivity includes: The edge weight between the node and the reference node is calculated based on the following formula: Among them, A ij is the edge weight between node i and benchmark node j, S PU,ij is the voltage sensitivity between node i and reference node j under active power, S QU,ij is the voltage sensitivity between node i and reference node j under reactive power.

2. The method for dynamic partitioning of distribution system control based on dynamic source-load matching according to claim 1 is characterized in that: The determining of the plurality of nodes as reference nodes based on the power supply history data and preset screening conditions includes: Based on the power supply history data, several nodes having large power demands and / or large power returned to the upper power grid are determined as reference nodes.

3. The method for dynamic partitioning of power distribution system control based on dynamic source-load matching according to claim 1, characterized in that: Determining the voltage sensitivity of each of the nodes based on the parameter data of each of the nodes includes: The voltage sensitivity of each of the nodes is based on the following formula: ΔU=S PU ΔP+S QU ΔQ Among them, ΔU is the voltage variation of the node, ΔP is the injected active power deviation matrix of the node, ΔQ is the reactive power deviation matrix of the node, S PU is the voltage sensitivity under active power, S QU is the voltage sensitivity under reactive power.

4. The method for dynamic partitioning of distribution system control based on dynamic source-load matching according to claim 3 is characterized in that: The calculating the modularity of the node and the reference node based on the edge weight and a preset modularity calculation formula includes: The modularity of the node and the benchmark node is calculated based on the following formula: Where ρ is the modularity, m is the sum of all the edge weights in the network, and k i represents the sum of the edge weights of the edges connected to node i, k j represents the sum of the edge weights of the edges connected to the j reference node, δ(i,j) represents whether the node i and the reference node j are in the same area. If they are in the same area, δ(i,j) = 1, otherwise δ(i,j) = 0.

5. The method for dynamic partitioning of distribution system control based on dynamic source-load matching according to claim 1 is characterized in that: The dividing the initial partition corresponding to the reference node based on the modularity and the reference node includes: The nodes whose modularity is within a preset module range are divided into the same partitions as the corresponding reference nodes to obtain initial partitions corresponding to the number of the reference nodes.

6. A dynamic partitioning device for power distribution system control based on dynamic source-load matching, characterized in that: include: An acquisition module is used to obtain parameter data and power supply history data of each node in the distribution system control area; A reference module, configured to determine a plurality of the nodes as reference nodes based on the power supply history data and preset screening conditions; a determination module, configured to determine the modularity of the node and the reference node based on parameter data of each node; a partitioning module, configured to partition an initial partition corresponding to the reference node based on the modularity and the reference node; A calculation module, configured to calculate the reactive power margin and source-load active power balance of each of the initial partitions based on the parameter data of each of the nodes; A constraint module determines whether the source-load matching degree of each of the initial partitions, which is composed of the modularity, the reactive power margin and the source-load active power balance, meets the preset control constraint conditions; An adjustment module, configured to adjust each of the initial partitions using a genetic algorithm to obtain a target partition that satisfies the regulation constraint conditions when the regulation constraint conditions are not satisfied; The determining the modularity of the node and the reference node based on the parameter data of each node includes: determining a voltage sensitivity of each of the nodes based on parameter data of each of the nodes; determining an edge weight between the node and the reference node based on the voltage sensitivity; Calculating the modularity of the node and the reference node based on the edge weight and a preset modularity calculation formula; The determining the edge weight between the node and the reference node based on the voltage sensitivity includes: The edge weight between the node and the reference node is calculated based on the following formula: Among them, A ij is the edge weight between node i and benchmark node j, S PU,ij is the voltage sensitivity between node i and reference node j under active power, S QU,ij is the voltage sensitivity between node i and reference node j under reactive power.

7. A dynamic partitioning device for power distribution system control based on dynamic source-load matching, characterized in that: The device comprises: one or more processors; a storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the dynamic partitioning method for power distribution system control as described in any one of claims 1-5.

8. A storage medium containing computer-executable instructions, characterized in that: When executed by a computer processor, the computer executable instructions are used to execute the dynamic partitioning method for distribution system control based on dynamic source-load matching as described in any one of claims 1 to 5.

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