Power regulation method and device for power distribution network, medium and product

Abstract

This invention discloses a power regulation method, device, medium, and product for distribution networks, comprising: acquiring network topology data, first operating data, and baseline configuration parameters of flexible soft switches in the distribution network; inputting the network topology data, the first operating data, and the baseline configuration parameters into a preset decision model, aiming to minimize the sum of the configuration cost of the flexible soft switches and the line loss cost of the distribution network, and solving the model under preset constraints to obtain a target decision result, wherein the target decision result includes the target location, target capacity configuration, and target power flow allocation scheme of the flexible soft switches, and the constraints include distributed energy utilization constraints, flexible soft switch constraints, and power flow constraints; and using the target decision result to perform power regulation on the distribution network. This invention can improve the accuracy of power regulation in distribution networks.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more particularly to power regulation methods, devices, media, and products for distribution networks. Background Technology

[0002] With the large-scale integration of distributed energy sources such as wind and solar power into the distribution network, the output of distributed energy has natural intermittent and fluctuating characteristics, which can easily cause power supply and demand imbalance in the distribution network, leading to problems such as node voltage exceeding limits and line power overload. In severe cases, it can disrupt the stable distribution of power flow in the distribution network and significantly reduce the safety and reliability of the distribution network operation.

[0003] In existing technologies, power regulation in distribution networks mainly relies on the traditional method of transformer tap changers. However, this method has significant drawbacks: due to the limitations of the hardware itself, this technology can only achieve step-by-step and discrete regulation actions, resulting in slow response speed and low regulation accuracy. It cannot continuously and dynamically regulate the power of the distribution network, making it difficult to adapt to the regulation needs of rapid and frequent power fluctuations under the access of a high proportion of distributed energy. This can easily lead to problems such as unreasonable local power flow and excessive voltage deviation in the distribution network after regulation. It cannot fundamentally solve the problem of distribution network operation stability caused by the fluctuation of distributed energy, resulting in poor accuracy of power regulation in the distribution network. Summary of the Invention

[0004] This invention provides a power regulation method, device, medium, and product for power distribution networks, which can improve the accuracy of power regulation in power distribution networks.

[0005] In a first aspect, an embodiment of the present invention provides a power regulation method for a distribution network, comprising: Acquire network topology data, initial operating data, and baseline configuration parameters of flexible soft switches in the distribution network; The network topology data, the first operating data, and the baseline configuration parameters are input into a preset decision model. The model aims to minimize the sum of the configuration cost of the flexible soft switch and the line loss cost of the distribution network. The model is then solved under preset constraints to obtain the target decision result. The target decision result includes the target location, target capacity configuration, and target power flow allocation scheme of the flexible soft switch. The constraints include distributed energy utilization constraints, flexible soft switch constraints, and power flow constraints. The power regulation of the distribution network is carried out using the target decision results.

[0006] By acquiring network topology data, initial operating data, and baseline configuration parameters of flexible soft switches used for power regulation in the distribution network, multi-dimensional basic data reflecting the physical architecture, real-time operating status, and controllable equipment characteristics of the distribution network can be collected without manual intervention, ensuring the accuracy of power regulation from the data source. By inputting the network topology data, initial operating data, and baseline configuration parameters into a preset decision model, aiming to minimize the sum of the configuration cost of the flexible soft switches and the line loss cost of the distribution network, the location and capacity determination problems of the flexible soft switches and the economic operation problem of the distribution network can be integrated into a unified optimization framework. By optimizing the access location and capacity configuration of the flexible soft switches, they can flexibly adjust power among key nodes in the distribution network, achieving proactive control of power flow distribution. This reduces line losses while fully leveraging the power regulation potential of the flexible soft switches, improving the accuracy of power regulation. This approach also addresses the constraints of distributed energy utilization and flexible power regulation. This paper solves the decision model under the constraints of flexible switching and power flow, obtaining target decision results including the target location, target capacity configuration, and target power flow allocation scheme of the flexible switching. While ensuring the absorption capacity of distributed energy, it optimizes the connection location and capacity configuration of the flexible switching and generates specific power flow allocation schemes. This allows the flexible switching to flexibly adjust the power flow bidirectionally between any two nodes in the distribution network according to the scheme, accurately compensating for power imbalances caused by fluctuations in distributed energy output. This fundamentally solves the control deviation caused by the intermittency of distributed energy, significantly improving the accuracy of the target decision results and further enhancing the accuracy of power control. Using the target decision results for power control of the distribution network, the optimized location, capacity, and power flow allocation schemes can be directly converted into actual control commands, ensuring that the theoretically optimal scheme can be accurately implemented, thereby systematically improving the accuracy of power control in the distribution network. This application can improve the accuracy of power control in the distribution network.

[0007] Furthermore, the step of inputting the network topology data, the first operating data, and the baseline configuration parameters into a preset decision model, with the objective of minimizing the sum of the configuration cost of the flexible soft switch and the line loss cost of the distribution network, and solving the problem under preset constraints using an optimization solver to obtain the target decision result, specifically includes: Several candidate thresholds are obtained by setting the utilization rate threshold in the distributed energy utilization rate constraint; For each of the candidate thresholds, the network topology data, the first running data, and the baseline configuration parameters are input into the preset decision model to call the optimization solver to solve the preset decision model and obtain the candidate location, candidate capacity, candidate power flow allocation scheme, and candidate cost value corresponding to the current candidate threshold. By analyzing each of the candidate site selections, each of the candidate capacities, each of the candidate power flow allocation schemes, and the corresponding candidate cost values, the target site selection, the target capacity configuration, and the target power flow allocation scheme are obtained. The target decision result is obtained based on the target location, the target capacity configuration, and the target power flow allocation scheme.

[0008] By inputting network topology data, initial operational data, and baseline configuration parameters into a pre-defined decision model, and aiming to minimize the sum of the configuration cost of flexible switching devices (FSDs) and the line loss cost of the distribution network, the site selection and capacity determination problems of FSDs and the economic operation problems of the distribution network can be integrated into a unified optimization framework. By optimizing the access location and capacity configuration of FSDs, they can flexibly adjust power between key nodes in the distribution network, achieving proactive control of power flow distribution. While reducing line losses, the power regulation potential of FSDs is fully utilized. The decision model is solved under the constraints of distributed energy utilization rate, FSD constraints, and power flow constraints, yielding target decision results including the target site selection, target capacity configuration, and target power flow allocation scheme for FSDs. Under the premise of ensuring the absorption capacity of distributed energy, the access location and capacity configuration of FSDs can be optimized, and specific power flow allocation schemes can be generated. This allows FSDs to flexibly adjust the power flow bidirectionally between any two nodes in the distribution network according to the scheme, accurately compensating for power imbalances caused by distributed energy output fluctuations. This fundamentally solves the regulation deviation caused by the intermittency of distributed energy, significantly improves the accuracy of target decision results, and further enhances the accuracy of power regulation.

[0009] Furthermore, the process of constructing the decision model specifically includes: Obtain the configuration information of the flexible soft switch, the line loss power of the distribution network, and the second operating data; Based on the configuration information and the line loss power, an objective function is constructed with the goal of minimizing the sum of the configuration cost of the flexible soft switch and the line loss cost of the distribution network. Based on the second running data, the constraints are constructed. The decision model is constructed based on the objective function and the constraints.

[0010] By acquiring the configuration information of flexible switching devices, the line loss power of the distribution network, and the second operating data, and constructing objective functions and constraints based on these data, a decision model is ultimately obtained. This provides a precise mathematical model foundation for subsequent power regulation optimization, ensuring that the objective function fully covers the configuration economy of flexible switching devices and the operating economy of the distribution network, and that the constraints fully reflect the physical limits and operating rules of the distribution network, thus guaranteeing the accuracy of power regulation at the model level.

[0011] Furthermore, based on the configuration information and the line loss power, the objective function constructed with the goal of minimizing the sum of the configuration cost of the flexible switch and the line loss cost of the distribution network specifically includes: Based on the unit capacity configuration cost, the number of converters, and the service life of the configuration information, the configuration cost of the flexible soft switch is calculated. Based on the line loss power, the obtained loss duration, the number of lines, and the unit line loss electricity price, the line loss cost of the distribution network is calculated. The objective function is constructed based on the configuration cost and the line loss cost.

[0012] By calculating the configuration cost of flexible soft switches based on the unit capacity configuration cost, number of converters, and service life in the configuration information, and calculating the line loss cost of the distribution network based on line loss power, loss duration, number of lines, and unit line loss electricity price, the configuration economy of flexible soft switches and the operation economy of the distribution network can be quantified into a unified objective function. This allows subsequent optimization to reduce line losses while taking into account the configuration cost of flexible soft switches, avoiding distortion of the control scheme due to the one-sided pursuit of a single indicator, and improving the accuracy of power control.

[0013] Furthermore, the process of constructing the constraints based on the second operational data specifically includes: Based on the first node injection power of the photovoltaic equipment and the second node injection power of the wind power station in the second operating data, the distributed energy utilization rate constraint is constructed, wherein the distribution network includes the photovoltaic equipment, the wind power station and the line; Based on the third node injected power of the flexible soft switch in the second operating data, the obtained converter capacity and configuration number of the flexible soft switch, the constraints of the flexible soft switch are constructed. Based on the node voltage, branch current and the third node injected power of the line in the second operating data, the power flow constraint is constructed. The constraint conditions are constructed based on the distributed energy utilization rate constraint, the flexible soft switching constraint, and the power flow constraint.

[0014] This approach, based on the node injection power of photovoltaic equipment and wind power plants, constructs distributed energy utilization constraints to ensure that the distribution network fully absorbs renewable energy during power regulation, avoiding energy waste caused by wind and solar curtailment. Constructing flexible soft-switching constraints based on node injection power, converter capacity, and configuration quantity ensures that the power regulation capability of the flexible soft switches is within their physical limits, avoiding regulation failure due to exceeding equipment capacity. Constructing power flow constraints based on node voltage, branch current, and line node injection power ensures that the voltage and current of the regulated distribution network are within safe ranges, avoiding safety risks caused by voltage exceeding limits or current overload. By integrating these three types of constraints into a complete set of constraints, the decision-making model can seek optimal solutions under multiple physical and operational boundaries, fundamentally guaranteeing the accuracy of the power regulation scheme.

[0015] Furthermore, the construction of the power flow constraint based on the node voltage, branch current, and third node injected power of the line in the second operating data specifically includes: Based on the node voltage, the branch current, and the active and reactive power injection values ​​of the third node, an initial power flow relationship is constructed. By processing the initial power flow relationship using preset relaxation variables, the power flow inequality is obtained; Based on the active power injection value, the reactive power injection value, and the slack variable, a constraint vector is constructed. The power flow constraint is constructed based on the constraint vector and the power flow inequality.

[0016] This method constructs an initial power flow equation based on node voltage, branch current, active power injection value, and reactive power injection value. This accurately describes the physical coupling relationship between voltage, current, and power in the distribution network. By using preset relaxation variables, the initial power flow equation is transformed into a power flow inequality. Furthermore, a constraint vector is constructed based on the active power injection value, reactive power injection value, and relaxation variables, ultimately yielding power flow constraints. This transforms the originally nonlinear power flow equation into a second-order cone form suitable for optimization, significantly reducing the solution complexity while preserving physical accuracy. This ensures that the power control scheme can be accurately solved within an acceptable computation time, improving the real-time performance and accuracy of power control.

[0017] Furthermore, the step of using the decision results to perform power regulation on the distribution network specifically includes: connecting the flexible soft switch to the distribution network according to the target address in the decision results, configuring the capacity of the converter of the flexible soft switch according to the target capacity configuration in the decision results, and performing power distribution control on the flexible soft switch according to the target power flow distribution scheme, so as to use the flexible soft switch to perform power regulation on the distribution network.

[0018] By utilizing the target location from the target decision results to connect the flexible switchgear to the distribution network, it is ensured that the flexible switchgear can play a power regulation role among key nodes. By configuring the capacity of the converter of the flexible switchgear according to the target capacity configuration, it is ensured that the flexible switchgear has sufficient regulation capability to cope with the power fluctuations of distributed energy sources. By controlling the power distribution of the flexible switchgear according to the target power flow distribution scheme, the flexible switchgear can accurately regulate the power of each node in the distribution network according to the optimized scheme, realizing the accurate transformation of the theoretical optimal scheme into actual control actions, thereby systematically improving the accuracy of power regulation in the distribution network.

[0019] Secondly, an embodiment of the present invention provides a power regulation device for a power distribution network, comprising a first module, a second module and a third module; The first module is used to acquire network topology data of the distribution network, first operating data, and baseline configuration parameters of flexible soft switches in the distribution network; The second module is used to input the network topology data, the first operating data, and the baseline configuration parameters into a preset decision model, with the goal of minimizing the sum of the configuration cost of the flexible soft switch and the line loss cost of the distribution network, and to solve the problem under preset constraints to obtain the target decision result. The target decision result includes the target location, target capacity configuration, and target power flow allocation scheme of the flexible soft switch, and the constraints include distributed energy utilization constraints, flexible soft switch constraints, and power flow constraints. The third module is used to perform power regulation on the distribution network using the target decision results.

[0020] This approach, by acquiring network topology data, operational data, and baseline configuration parameters of flexible switches used for power regulation in the distribution network through the first module, allows for the collection of multi-dimensional fundamental data reflecting the physical architecture, real-time operational status, and controllable equipment characteristics of the distribution network without manual intervention, ensuring the accuracy of power regulation from the data source. The second module inputs the network topology data, operational data, and baseline configuration parameters into a pre-defined decision model. With the goal of minimizing the sum of the configuration cost of the flexible switches and the line loss cost of the distribution network, the location and capacity determination of the flexible switches and the economic operation problem of the distribution network can be integrated into a unified optimization framework. By optimizing the access location and capacity configuration of the flexible switches, they can flexibly adjust power among key nodes in the distribution network, achieving proactive control of power flow distribution. This reduces line losses while fully leveraging the power regulation potential of the flexible switches, improving the accuracy of power regulation. This approach is applicable when the utilization rate of distributed energy is approximately... The decision model is solved under constraints of load, flexible soft switching, and power flow, yielding target decision results including target location, target capacity configuration, and target power flow allocation schemes for flexible soft switching. This allows for optimization of the connection location and capacity configuration of flexible soft switching while ensuring the absorption capacity of distributed energy resources. It also generates specific power flow allocation schemes, enabling flexible soft switching to flexibly adjust power flow bidirectionally between any two nodes in the distribution network according to the scheme. This accurately compensates for power imbalances caused by fluctuations in distributed energy output, fundamentally solving the control deviations caused by the intermittency of distributed energy resources, significantly improving the accuracy of target decision results, and further enhancing the accuracy of power control. The third module uses the target decision results to control the power of the distribution network, directly converting the optimized location, capacity, and power flow allocation schemes into actual control commands, ensuring that the theoretically optimal scheme can be accurately implemented, thereby systematically improving the accuracy of power control in the distribution network.

[0021] Thirdly, another embodiment of the present invention provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus where the computer-readable storage medium is located to perform a power regulation method for a power distribution network.

[0022] Fourthly, another embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements a power regulation method for a power distribution network. Attached Figure Description

[0023] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating one embodiment of a power regulation method for a power distribution network provided in this application; Figure 2 This is a flowchart illustrating steps S201 to S204 provided in this application; Figure 3 This is a flowchart illustrating steps S301 to S304 provided in this application; Figure 4 This is a schematic diagram of the topology of the flexible soft switch connected to the active distribution network provided in this application; Figure 5 This is a schematic diagram of the IEEE 33-node system topology for accessing distributed energy sources provided in this application; Figure 6 This is a schematic diagram of the load, photovoltaic and wind power curves of the distribution network at different time periods provided in this application; Figure 7 This is a schematic diagram of the flexible soft switch addressing and capacity optimization scheme provided in this application; Figure 8 This is the system topology provided in this application after configuring the flexible soft switch when the threshold is 0.6; Figure 9 This is a schematic diagram of the maximum node voltage variation curves corresponding to the 24 time periods provided in this application; Figure 10 This is a schematic diagram of the maximum line current value variation curves corresponding to the 24 time periods provided in this application; Figure 11 This is a schematic diagram illustrating the changes in the utilization rate of distributed energy provided in this application; Figure 12 This is a schematic diagram illustrating the changes in overall cost before and after optimization provided in this application; Figure 13 This is a schematic diagram of the structure of a power regulation device for a power distribution network provided in this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0027] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0030] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0031] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this application according to the specific circumstances.

[0032] In the power system sector, the intermittency and volatility of distributed energy resources can easily lead to imbalances in power regulation of the distribution network, reducing operational safety and reliability. Existing transformer tap changers used for power regulation have fundamental flaws: they can only achieve step-by-step adjustment, resulting in slow response speed, low accuracy, and an inability to continuously and dynamically regulate. This makes them ill-suited to the rapidly fluctuating power demands of a high proportion of distributed energy resources, leading to problems such as unreasonable power flow and excessive voltage deviations even after regulation, resulting in poor accuracy of power regulation in the distribution network.

[0033] See Figure 1 In order to improve the accuracy of power regulation in distribution networks, an embodiment of the present invention provides a power regulation method for distribution networks, including steps S101 to S103. Step S101: Obtain the network topology data, first operating data, and baseline configuration parameters of the flexible soft switch in the distribution network; In some embodiments, acquiring network topology data of the distribution network, first operating data, and baseline configuration parameters of the flexible soft switch in the distribution network specifically includes: reading network topology data of the distribution network from the distribution network management system, including the number of nodes in the distribution network, node numbers, branch connection relationships between nodes, resistance and reactance values ​​of each branch, and load type of each node; acquiring first operating data from the dispatch automation system, including load power values ​​of each node in each time period, photovoltaic equipment injection power values ​​of each node in each time period, wind power station equipment injection power values ​​of each node in each time period, node voltage values ​​of each node in each time period, and branch current values ​​of each branch in each time period; and reading baseline configuration parameters from the equipment parameter configuration file of the flexible soft switch, including the alternative access node location of the flexible soft switch, unit capacity configuration cost, preset service life, and maintenance cost coefficient.

[0034] Step S102: Input the network topology data, the first operating data, and the baseline configuration parameters into a preset decision model. With the goal of minimizing the sum of the configuration cost of the flexible soft switch and the line loss cost of the distribution network, solve the problem under preset constraints to obtain the target decision result. The target decision result includes the target location, target capacity configuration, and target power flow allocation scheme of the flexible soft switch. The constraints include distributed energy utilization constraints, flexible soft switch constraints, and power flow constraints. See Figure 2 In some embodiments, the process of constructing the decision model specifically includes steps S201 to S204; Step S201: Obtain the configuration information of the flexible soft switch, the line loss power of the distribution network, and the second operating data; In some embodiments, obtaining the configuration information of the flexible switch, the line loss power of the distribution network, and the second operating data specifically includes: reading the configuration information of the flexible switch from the equipment parameter configuration file of the flexible switch, including the unit capacity configuration cost of the flexible switch, the total number of converters, the preset service life, the alternative access addresses, and the maximum allowed number of configurations; obtaining the line loss power of the distribution network from the historical operating database, including the line loss power values ​​of each branch at each time period; and obtaining the second operating data from the dispatch automation system, including historical distribution network node load data, photovoltaic power output data, wind power output data, allowable upper and lower limits of node voltage, allowable upper and lower limits of line current, and impedance parameters of each branch.

[0035] Step S202: Based on the configuration information and the line loss power, construct an objective function that aims to minimize the sum of the configuration cost of the flexible soft switch and the line loss cost of the distribution network. In some embodiments, constructing an objective function based on the configuration information and the line loss power, with the goal of minimizing the sum of the configuration cost of the flexible soft switch and the line loss cost of the distribution network, specifically includes: calculating the configuration cost of the flexible soft switch based on the unit capacity configuration cost, the number of converters, and the service life of the configuration information; calculating the line loss cost of the distribution network based on the line loss power, the obtained loss duration, the number of lines, and the unit line loss electricity price; and constructing the objective function based on the configuration cost and the line loss cost. Specifically, based on the unit capacity configuration cost of flexible soft switches, the total number of converters, and the service life, the annual configuration cost of flexible soft switches is calculated. This configuration cost reflects the average annual investment cost of flexible soft switches throughout their entire service life. Based on the line loss power values ​​of each branch at each time period, combined with the total number of operating days in a year, the number of time periods per day, and the unit line loss electricity price, the annual line loss cost of the distribution network is calculated. This line loss cost reflects the annual operating cost of the distribution network due to power transmission losses. Minimizing the sum of the configuration cost of flexible soft switches and the line loss cost is defined as the objective function, so that the optimization process can reduce the operating line loss of the distribution network while taking into account the configuration economy of flexible soft switches.

[0036] In some embodiments, the formula for constructing an objective function based on the configuration information and the line loss power, with the goal of minimizing the sum of the configuration cost of the flexible switch and the line loss cost of the distribution network, specifically includes: Objective function: ; In the formula, C I To calculate the configuration cost of flexible soft switches over one year; To maintain cost coefficient; C L The cost of line losses for the distribution network for one year; Formula for calculating the configuration cost of flexible soft switches: ; In the formula, c I The unit capacity configuration cost for flexible soft switches; N The number of converters within the flexible soft-switching circuit; For flexible soft switching i The capacity of each converter; n This refers to the service life; i For converter index; Formula for calculating the cost of power distribution network line losses: ; In the formula, for t Time-of-day branch i Line loss power; The duration of loss for each time period; n t The number of time periods in a day; n b The number of distribution network lines; The unit line loss electricity price; i For route indexing; t This is a time-period index.

[0037] By calculating the configuration cost of flexible soft switches based on the unit capacity configuration cost, number of converters, and service life in the configuration information, and calculating the line loss cost of the distribution network based on line loss power, loss duration, number of lines, and unit line loss electricity price, the configuration economy of flexible soft switches and the operation economy of the distribution network can be quantified into a unified objective function. This allows subsequent optimization to reduce line losses while taking into account the configuration cost of flexible soft switches, avoiding distortion of the control scheme due to the one-sided pursuit of a single indicator, and improving the accuracy of power control.

[0038] Step S203: Based on the second running data, the constraint conditions are constructed. See Figure 3 In some embodiments, the construction of the constraint conditions based on the second running data specifically includes steps S301 to S304; Step S301: Based on the first node injection power of the photovoltaic equipment and the second node injection power of the wind power station in the second operating data, the distributed energy utilization rate constraint is constructed, wherein the distribution network includes the photovoltaic equipment, the wind power station and the line; In some embodiments, the distributed energy utilization rate constraint is constructed based on the first node injection power of the photovoltaic equipment and the second node injection power of the wind power station in the second operating data. The distribution network includes the photovoltaic equipment, the wind power station, and the transmission line. Specifically, the constraint includes: extracting the predicted available photovoltaic power and the predicted available wind power of each node in each time period from the second operating data, and calculating the sum of the predicted available photovoltaic power and the predicted available wind power of all nodes in each time period as the total predicted available amount; obtaining the first node injection power of the photovoltaic equipment and the second node injection power of the wind power station from the second operating data as decision variables, and calculating the sum of the photovoltaic output and the wind power output of all nodes in each time period as the total absorption amount expression; defining the ratio of the total absorption amount expression to the total predicted available amount as the distributed energy utilization rate, and setting that the utilization rate must be greater than or equal to a preset utilization rate threshold, thereby constructing the distributed energy utilization rate constraint.

[0039] It should be noted that the distributed energy utilization rate constraint can constrain the values ​​of photovoltaic and wind power output during the optimization solution process, ensuring that the distribution network can absorb a sufficient proportion of distributed energy generation in actual operation, and avoiding energy waste caused by wind and solar curtailment.

[0040] In some embodiments, the distributed energy utilization constraint is constructed based on the first node injection power of the photovoltaic equipment and the second node injection power of the wind power station in the second operating data. The distribution network includes relevant formulas for the photovoltaic equipment, the wind power station, and the transmission lines, specifically including: Formula for calculating the utilization rate of distributed energy: ; In the formula, They are respectively in t Time period i The predicted available power of photovoltaic power and the predicted available power of wind power corresponding to the nodes; and They are respectively in t Periodic photovoltaic equipment and wind power plant injection i Active power of a node; i For node indexing; t Indexed by time period; N The number of time periods in a day; n The number of nodes; Distributed energy utilization constraints: ; In the formula, For distributed energy utilization rate; This is the utilization threshold.

[0041] It should be noted that, through The (Epsilon) constraint method, used to construct distributed energy utilization constraints, ensures a minimum level of distributed energy absorption while optimizing the overall system cost. Subsequent power regulation can then be implemented through... Calculate the corresponding optimal cost by taking different values, as... The increase in the size of distributed energy sources improves their utilization rate, but may lead to increased costs. Therefore, it is also possible to draw... The Pareto frontier between cost and other factors analyzes different The trade-off between economic efficiency and environmental friendliness under different values ​​is considered, and then the best decision is selected based on specific needs.

[0042] Step S302: Based on the third node injection power of the flexible soft switch in the second operating data, the obtained converter capacity and configuration number of the flexible soft switch, the constraints of the flexible soft switch are constructed. In some embodiments, the flexible soft switch constraints are constructed based on the third node injected power of the flexible soft switch in the second operating data, the obtained converter capacity and configuration number of the flexible soft switch, specifically including: Please refer to Figure 4 , Figure 4This is a schematic diagram of the flexible soft switch connected to an active distribution network topology provided in this application. The distribution network receives electrical energy from the transmission network through high-voltage / medium-voltage substations (HV / MV) and distributes the energy to loads in different areas via multiple feeders. The two feeders shown in the diagram connect different power supply areas containing distributed generation and loads, respectively. The two feeders are interconnected through a flexible soft switch (SOP). The flexible soft switch adopts a back-to-back voltage source converter (B2BVSC) structure, consisting of two converters connected back-to-back, and connected to designated nodes on the two feeders, thereby establishing a controllable power exchange channel between the two power supply areas. This topology allows the flexible soft switch to bidirectionally regulate active and reactive power between the two feeders, realizing power flow transfer and load balancing between feeders, while providing power fluctuation compensation for distributed energy sources connected to the feeders. Based on this topology feature, the injected power value of the flexible soft switch at its connection node (i.e., the injected power at the third node) is extracted from the second operating data, including active power. The injected and reactive power injected values ​​are read from the equipment parameter configuration file of the flexible soft switch, including the rated capacity and maximum configuration quantity of the converters on both sides. Based on this data, three constraints are constructed for the flexible soft switch: power balance constraint, capacity constraint, and configuration quantity constraint. The power balance constraint requires that the sum of the active power injected into the network by the converters on both sides of the flexible soft switch is zero, to reflect the basic characteristic of the flexible soft switch as a power transfer device, that is, the active power absorbed from one node is equal to the active power released to another node, ensuring that it only transfers power during operation without generating net injection or net absorption of active power. The capacity constraint requires that the square root of the sum of the square of the active power and the square of the reactive power injected into the network by each converter does not exceed the rated capacity of the converter on that side, to ensure that the flexible soft switch does not exceed its physical limits during operation and to ensure safe operation of the equipment. The configuration quantity constraint requires that the total number of actual configurations of the flexible soft switch in all alternative addresses does not exceed the preset maximum number of configurations, to avoid excessive investment costs due to excessive configuration.

[0043] In some embodiments, based on the third node injected power of the flexible soft switch in the second operating data, the obtained converter capacity and configuration number of the flexible soft switch, a relevant formula for the constraint of the flexible soft switch is constructed, specifically including: Flexible soft-switching constraints: Power balance constraints: ; In the formula, i and j The node numbers for the distribution network connected to both sides of the SOP; and They represent t Active power injected into the network by the converters on both sides of the SOP during the time period; Capacity constraints: ; In the formula, i and j The node numbers for the distribution network connected to both sides of the SOP; , , and They represent t Active and reactive power injected into the network by the converters on both sides of the SOP during the time period; and These represent the capacities of the converters on both sides of the SOP; Configuration quantity constraints: ; In the formula, Let be a binary variable, representing the first... i Should each alternative address be configured with a Standard Operating Procedure (SOP)? n L Number of alternative addresses; n s This is the maximum number of SOPs that can be configured.

[0044] Step S303: Based on the node voltage, branch current and the third node injected power of the line in the second operating data, the power flow constraint is constructed. In some embodiments, constructing the power flow constraint based on the node voltage, branch current, and third node injected power of the line in the second operating data specifically includes: constructing an initial power flow relationship based on the active power injection value and reactive power injection value of the node voltage, branch current, and third node injected power; processing the initial power flow relationship using preset relaxation variables to obtain a power flow inequality; constructing a constraint vector based on the active power injection value, the reactive power injection value, and the relaxation variables; and constructing the power flow constraint based on the constraint vector and the power flow inequality. Specifically, based on node voltage, branch current, and the injected active and reactive power values, an initial power flow equation describing the physical coupling relationship between voltage, current, and power is constructed. Pre-defined current square relaxation variables and voltage square relaxation variables are introduced to replace the complex square terms in the original equation with newly defined relaxation variables, thus relaxing the original strict equality relationship into an inequality relationship, resulting in the power flow inequality: the sum of the squares of the injected active power and reactive power is less than or equal to the product of the squares of the current and voltage relaxation variables. Based on twice the injected active power value, twice the injected reactive power value, and the difference between the squares of the current and voltage relaxation variables, a constraint vector describing the coupling relationship between power flow and relaxation variables is constructed. The second norm of the constraint vector being less than or equal to the sum of the squares of the current and voltage relaxation variables is used as the final power flow constraint.

[0045] It should be noted that the initial power flow relationship originates from Ohm's law and the definition of power in circuit theory, reflecting the inherent connection between various electrical quantities in the distribution network. Specifically, the square of the branch current is equal to the sum of the squares of the active power and reactive power injected into the nodes at both ends of the branch, divided by the square of the node voltage. Since this relationship is a nonlinear equation, directly using it as a constraint will lead to a difficult-to-solve nonlinear programming problem with extremely high complexity, especially in large-scale distribution networks or multi-time-period optimization scenarios, where the solution efficiency will be insufficient for practical applications. Therefore, this invention employs a second-order cone relaxation method for the initial power flow. The initial power flow relationship is transformed to obtain the power flow inequality, which is then transformed into a standard second-order cone form for efficient solution. The power flow inequality is mathematically easier to handle than the initial power flow relationship, but still retains the core constraints of the original physical relationship. Through the above transformation, the originally nonlinear power flow equation is transformed into a convex second-order cone constraint form. While strictly preserving the accuracy of the original physical relationship, the entire optimization problem is transformed into an efficient solution of a mixed-integer second-order cone programming problem, which greatly reduces the solution complexity and ensures that the power regulation scheme can be accurately solved within an acceptable computation time.

[0046] In some embodiments, the process of constructing the relevant formula for the power flow constraint based on the node voltage, branch current, and third node injected power of the line in the second operating data specifically includes: Initial power flow relationship: ; The process of constructing the power-flow inequality: ; ; ; Constraint vector: ; Power flow constraints: ; In the formula, for t Time period i The voltage at the node; They are respectively t Time period nodes i The active power injection value and reactive power injection value; For nodes i and nodes j The current in the branch between; Let the square of the current be a relaxation variable; Let be the voltage squared relaxation variable.

[0047] This method constructs an initial power flow equation based on node voltage, branch current, active power injection value, and reactive power injection value. This accurately describes the physical coupling relationship between voltage, current, and power in the distribution network. By using preset relaxation variables, the initial power flow equation is transformed into a power flow inequality. Furthermore, a constraint vector is constructed based on the active power injection value, reactive power injection value, and relaxation variables, ultimately yielding power flow constraints. This transforms the originally nonlinear power flow equation into a second-order cone form suitable for optimization, significantly reducing the solution complexity while preserving physical accuracy. This ensures that the power control scheme can be accurately solved within an acceptable computation time, improving the real-time performance and accuracy of power control.

[0048] Step S304: Based on the distributed energy utilization rate constraint, the flexible soft switching constraint, and the power flow constraint, the constraint conditions are constructed.

[0049] In some embodiments, the constraint conditions are constructed based on the distributed energy utilization rate constraint, the flexible soft-switching constraint, and the power flow constraint. Specifically, this includes: integrating the constructed distributed energy utilization rate constraint, flexible soft-switching constraint, and power flow constraint, and constructing node power balance constraints, node voltage constraints, line current constraints, distributed energy output constraints, and line power flow constraints. The node power balance constraint requires that the active and reactive power injected into each node be balanced with the load demand of that node to ensure power conservation. The node voltage constraint requires that the voltage amplitude of each node always remain within a preset safe upper and lower limit range to avoid equipment insulation damage due to excessive voltage or equipment malfunction due to excessive voltage. The line current constraint requires that the current amplitude of each branch always remain within a preset safe upper and lower limit range to avoid excessive current. Safety accidents such as line overheating, insulation aging, and even fires caused by overload; distributed energy output constraints require that photovoltaic and wind power outputs not exceed their predicted available power and not be lower than zero, ensuring that the optimization results conform to the actual power generation capacity of distributed energy and avoiding infeasible dispatch schemes that exceed the actual power generation capacity; based on Ohm's law and Kirchhoff's voltage law in circuit theory, a line flow constraint describing the physical relationship between the node voltages, branch currents, and branch impedances at both ends of each branch in the distribution network is constructed, that is, for each branch, the sum of the squares of the voltages at both ends of the nodes minus the sum of the squares of the branch resistance and the squares of the reactance multiplied by the square of the branch current, and then minus the sum of twice the product of the branch resistance and the active power injected into the node and the product of the branch reactance and the reactive power injected into the node, must be equal to zero; all the above constraints are integrated together to form the final constraints.

[0050] In some embodiments, based on the distributed energy utilization rate constraint, the flexible soft-switching constraint, and the power flow constraint, the relevant formulas for the constraint conditions are constructed, specifically including: Node power balance constraints: ; In the formula, They represent in t Time period lines, SOP, photovoltaic, wind power plant injection i Active power of a node; express t Time period i The load on the node; Node voltage constraints and line current constraints: ; In the formula, These represent the upper and lower limits of the node voltage, respectively. These represent the upper and lower limits of the line current, respectively. for t Time periodi The voltage at the node; For nodes i and nodes j The current in the branch between; Distributed energy output constraints: ; In the formula, They are respectively in t Time period i The predicted available power of photovoltaic power and the predicted available power of wind power corresponding to the nodes; and They are respectively in t Periodic photovoltaic equipment and wind power plant injection i Active power of a node; Power flow constraints: ; In the formula, They are respectively t Time period i Nodes and j The voltage at the node; They are nodes i and nodes j The resistance, reactance, and current of the branches between them; They are respectively t Time period nodes i The active power injection value and reactive power injection value.

[0051] This approach, based on the node injection power of photovoltaic equipment and wind power plants, constructs distributed energy utilization constraints to ensure that the distribution network fully absorbs renewable energy during power regulation, avoiding energy waste caused by wind and solar curtailment. Constructing flexible soft-switching constraints based on node injection power, converter capacity, and configuration quantity ensures that the power regulation capability of the flexible soft switches is within their physical limits, avoiding regulation failure due to exceeding equipment capacity. Constructing power flow constraints based on node voltage, branch current, and line node injection power ensures that the voltage and current of the regulated distribution network are within safe ranges, avoiding safety risks caused by voltage exceeding limits or current overload. By integrating these three types of constraints into a complete set of constraints, the decision-making model can seek optimal solutions under multiple physical and operational boundaries, fundamentally guaranteeing the accuracy of the power regulation scheme.

[0052] Step S204: Based on the objective function and the constraints, the decision model is constructed. In some embodiments, the decision model is constructed based on the objective function and the constraints, specifically including: combining the objective function constructed in step S202 and the constraints constructed in step S203 to form a complete preset decision model.

[0053] By acquiring the configuration information of flexible switching devices, the line loss power of the distribution network, and the second operating data, and constructing objective functions and constraints based on these data, a decision model is ultimately obtained. This provides a precise mathematical model foundation for subsequent power regulation optimization, ensuring that the objective function fully covers the configuration economy of flexible switching devices and the operating economy of the distribution network, and that the constraints fully reflect the physical limits and operating rules of the distribution network, thus guaranteeing the accuracy of power regulation at the model level.

[0054] In some embodiments, the step of inputting the network topology data, the first operating data, and the baseline configuration parameters into a preset decision model, with the goal of minimizing the sum of the configuration cost of the flexible soft switch and the line loss cost of the distribution network, and solving the model under preset constraints using an optimization solver to obtain the target decision result, specifically includes: setting a utilization rate threshold in the distributed energy utilization rate constraint to obtain several candidate thresholds; for each candidate threshold, inputting the network topology data, the first operating data, and the baseline configuration parameters into the preset decision model to call the optimization solver to solve the preset decision model, obtaining candidate site selection, candidate capacity, candidate power flow allocation scheme, and candidate cost value corresponding to the current candidate threshold; analyzing each candidate site selection, each candidate capacity, each candidate power flow allocation scheme, and the corresponding candidate cost value to obtain the target site selection, the target capacity configuration, and the target power flow allocation scheme; and obtaining the target decision result based on the target site selection, the target capacity configuration, and the target power flow allocation scheme. Specifically, based on actual operational needs and the distributed energy penetration rate of the distribution network, a utilization threshold is set in the distributed energy utilization constraint. Multiple different thresholds are selected as candidate values ​​(e.g., values ​​can be taken from 0.6 to 1.0 in steps of 0.05), resulting in several candidate thresholds. For each candidate threshold, it is substituted into the decision model as the utilization threshold in the distributed energy utilization constraint. The acquired network topology data, initial operational data, and baseline configuration parameters are also input into the decision model. An optimization solver (such as the Gurobi solver) is then called to solve the decision model. The optimization solver employs a hybrid algorithm combining branch and bound and interior point methods to efficiently solve the mixed-integer second-order cone programming problem, obtaining candidate site selection methods corresponding to the current candidate threshold. The objective function is used to determine the target location, capacity configuration, and power flow allocation schemes, as well as the corresponding candidate cost values. After solving all candidate thresholds, a set of candidate decision results is obtained, including candidate location schemes, capacity configuration schemes, power flow allocation schemes, and cost values ​​under different utilization rate requirements. These candidate decision results are analyzed, and a Pareto front curve is plotted with the utilization rate threshold as the horizontal axis and the candidate cost value as the vertical axis. The cost trend is observed as the threshold increases. Based on actual operating requirements, an optimal compromise point is selected from the Pareto front curve, and the corresponding location, capacity configuration, and power flow allocation schemes are determined as the target location, target capacity configuration, and target power flow allocation schemes, respectively. These three data are used as the final target decision results.

[0055] By inputting network topology data, initial operational data, and baseline configuration parameters into a pre-defined decision model, and aiming to minimize the sum of the configuration cost of flexible switching devices (FSDs) and the line loss cost of the distribution network, the site selection and capacity determination problems of FSDs and the economic operation problems of the distribution network can be integrated into a unified optimization framework. By optimizing the access location and capacity configuration of FSDs, they can flexibly adjust power between key nodes in the distribution network, achieving proactive control of power flow distribution. While reducing line losses, the power regulation potential of FSDs is fully utilized. The decision model is solved under the constraints of distributed energy utilization rate, FSD constraints, and power flow constraints, yielding target decision results including the target site selection, target capacity configuration, and target power flow allocation scheme for FSDs. Under the premise of ensuring the absorption capacity of distributed energy, the access location and capacity configuration of FSDs can be optimized, and specific power flow allocation schemes can be generated. This allows FSDs to flexibly adjust the power flow bidirectionally between any two nodes in the distribution network according to the scheme, accurately compensating for power imbalances caused by distributed energy output fluctuations. This fundamentally solves the regulation deviation caused by the intermittency of distributed energy, significantly improves the accuracy of target decision results, and further enhances the accuracy of power regulation.

[0056] Step S103: Use the target decision results to perform power regulation on the distribution network; In some embodiments, the power regulation of the distribution network using the decision results specifically includes: connecting the flexible soft switch to the distribution network according to the target addressing in the decision results, configuring the converter of the flexible soft switch according to the target capacity configuration in the decision results, and performing power distribution control on the flexible soft switch according to the target power flow distribution scheme, so as to use the flexible soft switch to regulate the power of the distribution network. Specifically, based on the target location information from the target decision results, the specific access node location of the flexible switch in the distribution network is determined, i.e., it is clear between which two nodes the flexible switch should be installed. Based on the target capacity configuration information, the capacity parameters of each converter inside the flexible switch are set and adjusted. The rated capacity parameter of each converter is configured to the target capacity value through the equipment control system to ensure that the flexible switch has sufficient power regulation capability to cope with the power fluctuations of distributed energy. Based on the target power flow allocation scheme, a power regulation command is generated. This command specifies the active power and reactive power values ​​that the converters on both sides of the flexible switch should inject into the distribution network at different times. The command is sent to the local controller of the flexible switch. The controller adjusts the firing angle and modulation ratio of the converter in real time according to the command, controlling it to output the corresponding active power and reactive power according to the scheme requirements, ultimately realizing the power regulation of the distribution network using the flexible switch.

[0057] By utilizing the target location from the target decision results to connect the flexible switchgear to the distribution network, it is ensured that the flexible switchgear can play a power regulation role among key nodes. By configuring the capacity of the converter of the flexible switchgear according to the target capacity configuration, it is ensured that the flexible switchgear has sufficient regulation capability to cope with the power fluctuations of distributed energy sources. By controlling the power distribution of the flexible switchgear according to the target power flow distribution scheme, the flexible switchgear can accurately regulate the power of each node in the distribution network according to the optimized scheme, realizing the accurate transformation of the theoretical optimal scheme into actual control actions, thereby systematically improving the accuracy of power regulation in the distribution network.

[0058] For example, to verify the effectiveness and correctness of the power regulation method for distribution networks proposed in this invention, simulation analysis was performed based on an IEEE 33-node distribution network. See [link to relevant documentation]. Figure 5 , Figure 5 This is a schematic diagram of the IEEE 33-node system topology for accessing distributed energy resources provided in this application, where photovoltaic equipment access nodes are 7, 10, 24, and 27, and wind power access nodes are 13 and 30; optimized configuration of flexible soft switching is considered for five candidate locations: node pairs 12-22, 25-29, 8-21, 9-15, and 18-33; see [link to relevant documentation]. Figure 6 , Figure 6 This is a schematic diagram of the load, photovoltaic and wind power curves of the distribution network at different time periods provided in this application. It shows the typical daily load curve, photovoltaic output curve and wind power output curve of the distribution network. The simulation example parameters are shown in Table 1, where the service life of the flexible soft switch is 20 years, the unit capacity configuration cost is 1000 yuan / kVA, the maintenance cost coefficient is 0.2, the electricity price is 0.5 yuan / kWh, and the maximum number of flexible soft switches is 3. Table 1 Example Parameters The decision model constructed in this invention was modeled using the YALMIP toolkit (an optimization modeling toolbox based on MATLAB), and solved using the Gurobi 12.0 solver; different utilization thresholds were considered in the distributed energy utilization efficiency constraints. Based on the given settings, the location and sizing optimization schemes for flexible soft switches are obtained, as follows: Figure 7 As shown, Figure 7 This is a schematic diagram of the flexible soft switch addressing and capacity optimization scheme provided in this application, including sub-diagram a representing the addressing of the left node of the flexible soft switch and sub-diagram b representing the addressing of the right node of the flexible soft switch; from Figure 7 It can be seen that, in the same Under this condition, only three alternative addresses have non-zero capacity configurations, which is consistent with the preset constraint that the maximum number of configurations is 3. Furthermore, a comparison of sub-figures a and b shows that the capacity configurations of the converters at both ends of the flexible soft switch are different, indicating that this invention sets capacity as a continuously optimized variable, effectively avoiding the problem of capacity configuration redundancy. When the value is 0.6, the system topology after configuring the flexible soft switch is as follows: Figure 8 As shown, others The topology under these values ​​can be derived in a similar way; To verify the effectiveness of this invention in improving the stability and reliability of power distribution networks, please refer to [link / reference needed]. Figure 9 and Figure 10 , Figure 9 This is a schematic diagram of the maximum node voltage variation curves corresponding to the 24 time periods provided in this application. Figure 10 This is a schematic diagram of the maximum line current variation curves for 24 time periods provided in this application, which can be used to compare the system reliability performance before and after optimization. Simulation results show that, under the baseline scheme (full power mode) without the introduction of flexible soft switching and power flow optimization, some lines experienced current over-limit phenomena, leading to a decrease in system reliability. However, after adopting the scheme of this invention, regardless of... Regardless of the value, through the optimized configuration of flexible soft switches and the power flow optimization of distributed energy, all line currents remain within the allowable range, and the voltage of each node does not exceed the limit. This shows that the present invention can effectively alleviate the reliability risks brought about by the large-scale access of new energy sources and significantly improve the stability and reliability of the distribution network operation. To verify the synergistic optimization effect of this invention on the distributed energy absorption capacity and overall cost of the distribution network, please refer to [link / reference]. Figure 11 and Figure 12 , Figure 11 This is a schematic diagram illustrating the changes in distributed energy utilization rates provided in this application, showing different... Changes in distributed energy utilization rate under various values Figure 12 This is a schematic diagram illustrating the changes in overall cost before and after optimization, as provided in this application, showing the different... A comparison of the optimized overall cost and the original line loss cost under the given value; from Figure 11 It can be seen that, with The increase in the value of the distributed energy source leads to a corresponding increase in its utilization rate, indicating that this invention can effectively enhance the distribution network's ability to absorb distributed energy sources and reduce dependence on traditional energy sources. Figure 12 It can be seen that when When the value is in the range of 0.6 to 0.95, the optimized overall cost is significantly lower than the unoptimized line loss cost, indicating that the present invention can effectively reduce the operating cost of the distribution network while maintaining a high distributed energy absorption rate. When the value is 1, the distributed energy output reaches its maximum and cannot be further adjusted. At this point, system stability can only be maintained by adjusting the flexible switching mechanism, resulting in a higher overall system cost than before optimization. Therefore, by selecting a suitable value... By adjusting the values, this invention can reduce the overall cost of the distribution network and effectively improve the utilization rate of distributed energy resources while ensuring the stable operation of the system, thereby improving the accuracy of power regulation in the distribution network.

[0059] By acquiring network topology data, initial operating data, and baseline configuration parameters of flexible soft switches used for power regulation in the distribution network, multi-dimensional basic data reflecting the physical architecture, real-time operating status, and controllable equipment characteristics of the distribution network can be collected without manual intervention, ensuring the accuracy of power regulation from the data source. By inputting the network topology data, initial operating data, and baseline configuration parameters into a preset decision model, aiming to minimize the sum of the configuration cost of the flexible soft switches and the line loss cost of the distribution network, the location and capacity determination problems of the flexible soft switches and the economic operation problem of the distribution network can be integrated into a unified optimization framework. By optimizing the access location and capacity configuration of the flexible soft switches, they can flexibly adjust power among key nodes in the distribution network, achieving proactive control of power flow distribution. This reduces line losses while fully leveraging the power regulation potential of the flexible soft switches, improving the accuracy of power regulation. This approach also addresses the constraints of distributed energy utilization and flexible power regulation. This paper solves the decision model under the constraints of flexible switching and power flow, obtaining target decision results including the target location, target capacity configuration, and target power flow allocation scheme of the flexible switching. While ensuring the absorption capacity of distributed energy, it optimizes the connection location and capacity configuration of the flexible switching and generates specific power flow allocation schemes. This allows the flexible switching to flexibly adjust the power flow bidirectionally between any two nodes in the distribution network according to the scheme, accurately compensating for power imbalances caused by fluctuations in distributed energy output. This fundamentally solves the control deviation caused by the intermittency of distributed energy, significantly improving the accuracy of the target decision results and further enhancing the accuracy of power control. Using the target decision results for power control of the distribution network, the optimized location, capacity, and power flow allocation schemes can be directly converted into actual control commands, ensuring that the theoretically optimal scheme can be accurately implemented, thereby systematically improving the accuracy of power control in the distribution network. This application can improve the accuracy of power control in the distribution network.

[0060] See Figure 13 Based on the above method embodiments, corresponding device embodiments are provided; One embodiment of the present invention provides a power regulation device for a power distribution network, including a first module 100, a second module 200 and a third module 300; The first module 100 is used to acquire network topology data of the distribution network, first operating data, and baseline configuration parameters of flexible soft switches in the distribution network; The second module 200 is used to input the network topology data, the first operating data and the baseline configuration parameters into a preset decision model, with the goal of minimizing the sum of the configuration cost of the flexible soft switch and the line loss cost of the distribution network, and to solve the problem under preset constraints to obtain the target decision result. The target decision result includes the target location, target capacity configuration and target power flow allocation scheme of the flexible soft switch, and the constraints include distributed energy utilization constraints, flexible soft switch constraints and power flow constraints. The third module 300 is used to perform power regulation on the distribution network using the target decision results.

[0061] This approach, by acquiring network topology data, operational data, and baseline configuration parameters of flexible switches used for power regulation in the distribution network through the first module, allows for the collection of multi-dimensional fundamental data reflecting the physical architecture, real-time operational status, and controllable equipment characteristics of the distribution network without manual intervention, ensuring the accuracy of power regulation from the data source. The second module inputs the network topology data, operational data, and baseline configuration parameters into a pre-defined decision model. With the goal of minimizing the sum of the configuration cost of the flexible switches and the line loss cost of the distribution network, the location and capacity determination of the flexible switches and the economic operation problem of the distribution network can be integrated into a unified optimization framework. By optimizing the access location and capacity configuration of the flexible switches, they can flexibly adjust power among key nodes in the distribution network, achieving proactive control of power flow distribution. This reduces line losses while fully leveraging the power regulation potential of the flexible switches, improving the accuracy of power regulation. This approach is applicable when the utilization rate of distributed energy is approximately... The decision model is solved under constraints of load, flexible soft switching, and power flow, yielding target decision results including target location, target capacity configuration, and target power flow allocation schemes for flexible soft switching. This allows for optimization of the connection location and capacity configuration of flexible soft switching while ensuring the absorption capacity of distributed energy resources. It also generates specific power flow allocation schemes, enabling flexible soft switching to flexibly adjust power flow bidirectionally between any two nodes in the distribution network according to the scheme. This accurately compensates for power imbalances caused by fluctuations in distributed energy output, fundamentally solving the control deviations caused by the intermittency of distributed energy resources, significantly improving the accuracy of target decision results, and further enhancing the accuracy of power control. The third module uses the target decision results to control the power of the distribution network, directly converting the optimized location, capacity, and power flow allocation schemes into actual control commands, ensuring that the theoretically optimal scheme can be accurately implemented, thereby systematically improving the accuracy of power control in the distribution network.

[0062] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the power regulation method for a power distribution network provided by any of the above-described method embodiments of the present invention.

[0063] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0064] Based on the above-described embodiment of a power regulation method for a power distribution network, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a power regulation method for a power distribution network according to any embodiment of the present invention.

[0065] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0066] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0067] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0068] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute a power regulation method for a power distribution network as described in any of the above-described method embodiments of the present invention.

[0069] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0070] Based on the above-described method embodiments, another embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements a power regulation method for a power distribution network.

[0071] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A power regulation method for a power distribution network, characterized in that, include: Acquire network topology data, initial operating data, and baseline configuration parameters of flexible soft switches in the distribution network; The network topology data, the first operating data, and the baseline configuration parameters are input into a preset decision model. The model aims to minimize the sum of the configuration cost of the flexible soft switch and the line loss cost of the distribution network. The model is then solved under preset constraints to obtain the target decision result. The target decision result includes the target location, target capacity configuration, and target power flow allocation scheme of the flexible soft switch. The constraints include distributed energy utilization constraints, flexible soft switch constraints, and power flow constraints. The power regulation of the distribution network is carried out using the target decision results.

2. The power regulation method for a distribution network as described in claim 1, characterized in that, The process involves inputting the network topology data, the first operational data, and the baseline configuration parameters into a preset decision model. The objective is to minimize the sum of the configuration cost of the flexible soft switch and the line loss cost of the distribution network. An optimization solver is used to solve the model under preset constraints to obtain the target decision result. Specifically, this includes: Several candidate thresholds are obtained by setting the utilization rate threshold in the distributed energy utilization rate constraint; For each of the candidate thresholds, the network topology data, the first running data, and the baseline configuration parameters are input into the preset decision model to call the optimization solver to solve the preset decision model and obtain the candidate location, candidate capacity, candidate power flow allocation scheme, and candidate cost value corresponding to the current candidate threshold. By analyzing each of the candidate site selections, each of the candidate capacities, each of the candidate power flow allocation schemes, and the corresponding candidate cost values, the target site selection, the target capacity configuration, and the target power flow allocation scheme are obtained. The target decision result is obtained based on the target location, the target capacity configuration, and the target power flow allocation scheme.

3. The power regulation method for a distribution network as described in claim 1, characterized in that, The process of constructing the decision model specifically includes: Obtain the configuration information of the flexible soft switch, the line loss power of the distribution network, and the second operating data; Based on the configuration information and the line loss power, an objective function is constructed with the goal of minimizing the sum of the configuration cost of the flexible soft switch and the line loss cost of the distribution network. Based on the second running data, the constraints are constructed. The decision model is constructed based on the objective function and the constraints.

4. The power regulation method for a distribution network as described in claim 3, characterized in that, Based on the configuration information and the line loss power, an objective function is constructed with the goal of minimizing the sum of the configuration cost of the flexible switch and the line loss cost of the distribution network. Specifically, this includes: Based on the unit capacity configuration cost, the number of converters, and the service life of the configuration information, the configuration cost of the flexible soft switch is calculated. Based on the line loss power, the obtained loss duration, the number of lines, and the unit line loss electricity price, the line loss cost of the distribution network is calculated. The objective function is constructed based on the configuration cost and the line loss cost.

5. The power regulation method for a distribution network as described in claim 3, characterized in that, The constraint conditions constructed based on the second operational data specifically include: Based on the first node injection power of the photovoltaic equipment and the second node injection power of the wind power station in the second operating data, the distributed energy utilization rate constraint is constructed, wherein the distribution network includes the photovoltaic equipment, the wind power station and the line; Based on the third node injected power of the flexible soft switch in the second operating data, the obtained converter capacity and configuration number of the flexible soft switch, the constraints of the flexible soft switch are constructed. Based on the node voltage, branch current and the third node injected power of the line in the second operating data, the power flow constraint is constructed. The constraint conditions are constructed based on the distributed energy utilization rate constraint, the flexible soft switching constraint, and the power flow constraint.

6. The power regulation method for a distribution network as described in claim 5, characterized in that, The power flow constraint is constructed based on the node voltage, branch current, and third node injected power of the line in the second operating data, specifically including: Based on the node voltage, the branch current, and the active and reactive power injection values ​​of the third node, an initial power flow relationship is constructed. By processing the initial power flow relationship using preset relaxation variables, the power flow inequality is obtained; Based on the active power injection value, the reactive power injection value, and the slack variable, a constraint vector is constructed. The power flow constraint is constructed based on the constraint vector and the power flow inequality.

7. The power regulation method for a distribution network as described in claim 1, characterized in that, The method of using the decision results to regulate the power distribution network specifically includes: connecting the flexible soft switch to the distribution network according to the target address in the decision results, configuring the capacity of the converter of the flexible soft switch according to the target capacity configuration in the decision results, and controlling the power distribution of the flexible soft switch according to the target power flow distribution scheme, so as to use the flexible soft switch to regulate the power distribution network.

8. A power regulation device for a power distribution network, characterized in that, It includes Module 1, Module 2, and Module 3; The first module is used to acquire network topology data of the distribution network, first operating data, and baseline configuration parameters of flexible soft switches in the distribution network; The second module is used to input the network topology data, the first operating data, and the baseline configuration parameters into a preset decision model, with the goal of minimizing the sum of the configuration cost of the flexible soft switch and the line loss cost of the distribution network, and to solve the problem under preset constraints to obtain the target decision result. The target decision result includes the target location, target capacity configuration, and target power flow allocation scheme of the flexible soft switch, and the constraints include distributed energy utilization constraints, flexible soft switch constraints, and power flow constraints. The third module is used to perform power regulation on the distribution network using the target decision results.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus containing the computer-readable storage medium to perform the power regulation method for a power distribution network as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the communication device, the power regulation method of the distribution network as described in any one of claims 1 to 7 is implemented.

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