Low-voltage power distribution network structure reconstruction and operation control method, system and equipment based on capacity regulating transformer and medium

Abstract

The invention relates to the technical field of power system power distribution network operation and control, and discloses a low-voltage power distribution network structure reconstruction and operation control method, system and device based on a capacity regulating transformer and a medium, and the method comprises the steps: building an optimization model which takes network loss, voltage deviation and switching operation times as targets; and the current non-convex reconstruction problem is converted into a mixed integer second-order cone programming model for calculation through a second-order cone relaxation technology, and efficient solution is carried out by using a commercial solver. And in combination with scene analysis and processing uncertainty, a dynamic reconstruction strategy for collaborative optimization of the transformer gear and the network topology is generated. According to the invention, by introducing the capacity-regulating transformer as a regulation and control means, the optimization space is expanded, the network loss can be reduced more remarkably, the voltage fluctuation can be stabilized, and the switching action can be reduced, so that the operation efficiency generated when the high-proportion renewable energy sources are accessed to the low-voltage power distribution network can be comprehensively improved finally.

Description

Technical Field

[0001] This invention relates to the field of power system distribution network operation and control technology, and in particular to a method, system, equipment and medium for low-voltage distribution network structure reconfiguration and operation control based on a capacity-adjusting transformer. Background Technology

[0002] At present, with the large-scale integration of distributed photovoltaic, wind power and other renewable energy sources into the distribution network, the randomness and volatility inherent in these renewable resources have a significant impact on the distribution network. In particular, they have caused uncertainties in the power flow distribution, voltage stability and network losses of the distribution network. Therefore, it is necessary to restructure the distribution network to minimize the impact of the randomness and volatility inherent in these renewable resources.

[0003] Currently, many traditional distribution network reconfiguration methods are based on static loads and single-objective optimization, which are difficult to adapt to the dynamic operation requirements of active distribution networks. Other existing technologies, such as heuristic search and genetic algorithms, are prone to getting stuck in local optima in terms of multi-objective coordination and real-time performance. Moreover, these algorithms have low computational efficiency when used for distribution network reconfiguration and cannot effectively meet the needs of dynamic network reconfiguration under high-proportion renewable energy access. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method, system, equipment, and medium for low-voltage distribution network structure reconfiguration and operation control based on a variable capacity transformer. It can solve the problems that traditional distribution network reconfiguration methods are difficult to adapt to the dynamic operation requirements of active distribution networks, the shortcomings of existing technologies in multi-objective coordination and real-time performance, and the tendency to fall into local optima and low computational efficiency.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for low-voltage distribution network structure reconfiguration and operation control based on a capacity-adjustable transformer, comprising: A multi-objective optimization model for dynamic reconfiguration of active distribution networks is established. The model aims to minimize the total active power loss, the sum of squared node voltage deviations, and the number of switching operations. The optimization variables include discrete network switch state variables and continuous tap position variables of regulating transformers. The multi-objective optimization model is subjected to convex relaxation treatment, which transforms it into a mixed integer second-order cone programming model, and then the mathematical optimization solver is called to solve it. Based on scenario analysis methods, the uncertainties of load and distributed power output are addressed to generate a set of representative typical operating scenarios; Based on the solution results of the typical operating scenario set, a dynamic reconfiguration strategy for co-optimizing the tap position of the regulating transformer and the distribution network topology is generated, and corresponding regulating transformer adjustment instructions and switch control instructions are output to realize the dynamic reconfiguration of the low-voltage distribution network.

[0007] As a preferred embodiment of the low-voltage distribution network structure reconfiguration and operation control method based on the adjustable-capacity transformer described in this invention, the tap position variable of the adjustable-capacity transformer is limited to a preset lower limit and upper limit.

[0008] As a preferred embodiment of the low-voltage distribution network structure reconfiguration and operation control method based on the adjustable-capacity transformer described in this invention, the convex relaxation process is applied to the branch power constraints in the DistFlow power flow model of the distribution network. By introducing a second-order cone relationship between the square of voltage, the square of current and the branch power, the non-convex constraints are made convex.

[0009] As a preferred embodiment of the low-voltage distribution network structure reconfiguration and operation control method based on the adjustable-capacity transformer described in this invention, the objective function of the multi-objective optimization model adopts a weighted sum form, and the three indicators of system network loss, voltage deviation and number of switching operations are linearly combined according to preset weights.

[0010] As a preferred embodiment of the low-voltage distribution network structure reconfiguration and operation control method based on adjustable-capacity transformers described in this invention, in the optimization solution process, a linear decreasing strategy is adopted to dynamically adjust the inertia weight of the optimization algorithm in order to balance the global search and local convergence capabilities.

[0011] As a preferred embodiment of the low-voltage distribution network structure reconfiguration and operation control method based on adjustable-capacity transformers described in this invention, the method employs a Latin hypercube sampling method to generate an initial scenario set covering the uncertainty of load and distributed power output, and then simplifies the initial scenario set using a synchronous back-substitution reduction method, retaining several typical scenarios that have the most representative impact on system operation.

[0012] This preferred scheme employs Latin hypercube sampling to generate the initial scenario set. This ensures uniform sampling across the entire uncertainty range of load and distributed power output, guaranteeing not only the representativeness and comprehensiveness of the samples but also enabling the generated scenarios to effectively cover various possible operational conditions. Simultaneously, the synchronous back-substitution reduction method simplifies the initial scenario set without losing too much important information. This reduces the complexity and computational load in subsequent optimization calculations, thereby improving computational efficiency, while also highlighting the typical scenarios that have the greatest impact on system operation.

[0013] As a preferred embodiment of the low-voltage distribution network structure reconfiguration and operation control method based on adjustable-capacity transformers described in this invention, the optimal solution with the best overall performance is selected from the Pareto optimal solution set obtained by multi-objective optimization using the fuzzy membership function method, and is then used as the final reconfiguration scheme to be executed.

[0014] Secondly, the present invention provides a low-voltage distribution network structure reconfiguration and operation control system based on a capacity-adjustable transformer, comprising: The data acquisition module is used to acquire real-time power distribution network operation data and the current tap position information of the regulating transformer; An optimized calculation module is used to execute the dynamic reconfiguration method and generate a reconfiguration scheme that includes tap changer adjustment instructions for the capacity-adjusting transformer and network switch status control instructions. The control execution module is used to send opening and closing commands to the line switch and tap adjustment commands to the regulating transformer according to the reconfiguration scheme.

[0015] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described above.

[0016] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method described above.

[0017] Compared with existing technologies, the beneficial effects of this invention are that it proposes a low-voltage distribution network structure reconfiguration and operation control method based on adjustable-capacity transformers, establishing an optimization model with network losses, voltage deviations, and the number of switching operations as objectives. Then, the current non-convex reconfiguration problem is transformed into a mixed-integer second-order cone programming model for calculation using second-order cone relaxation techniques, and a commercial solver is used for efficient solution. Uncertainty is addressed through scenario analysis, generating a dynamic reconfiguration strategy that collaboratively optimizes transformer tap positions and network topology. By introducing adjustable-capacity transformers as a control means, this invention expands the optimization space and can more significantly reduce network losses, smooth voltage fluctuations, and reduce switching operations, thereby ultimately comprehensively improving the operational efficiency generated when a high proportion of renewable energy is integrated into the low-voltage distribution network. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a low-voltage distribution network structure reconfiguration and operation control method based on a variable capacity transformer, provided as an embodiment of the present invention.

[0020] Figure 2 This is a comparison diagram of active power loss of branches before and after reconstruction in a low-voltage distribution network structure reconstruction and operation control method based on a variable capacity transformer, provided as an embodiment of the present invention.

[0021] Figure 3 This is a diagram showing the node voltage distribution before and after reconstruction of a low-voltage distribution network structure reconstruction and operation control method based on a variable-capacity transformer, provided as an embodiment of the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0023] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for low-voltage distribution network structure reconfiguration and operation control based on a capacity-adjustable transformer, including: This invention provides a method that can effectively solve the problems mentioned above. The following will describe in detail how to implement the low-voltage distribution network structure reconfiguration and operation control method based on the adjustable capacity transformer, using multiple embodiments. Figure 1 A flowchart illustrating a low-voltage distribution network structure reconfiguration and operation control method based on a capacity-adjustable transformer is shown, including: S101. Establish a multi-objective optimization model for dynamic reconfiguration of active distribution networks. The model aims to minimize the total active power loss of the system, the sum of squares of node voltage deviations, and the number of switching operations. The optimization variables include discrete network switch state variables and continuous tap position variables of regulating transformers. In this embodiment of the invention, the tap position variable of the adjustable transformer is limited to a preset lower limit and upper limit.

[0024] Specifically, the tap position variable of the adjustable transformer. Satisfy constraints: ,in and These are the lower and upper limits of the tap position, respectively.

[0025] In this embodiment of the invention, the objective function of the multi-objective optimization model adopts a weighted sum form, which linearly combines the three indicators of system network loss, voltage deviation and number of switching operations according to preset weights.

[0026] It should be noted that with the large-scale integration of distributed photovoltaic and wind power and other renewable energy sources into low-voltage distribution networks, the randomness and volatility of their output lead to frequent changes in power flow direction, increased risk of node voltage exceeding limits, and a significant increase in network losses. If only a single objective, such as minimizing network losses, is used for reconfiguration, it may cause voltage quality deterioration or frequent operation of switching equipment, affecting system stability and equipment reliability. Therefore, it is necessary to take minimizing the total active power loss of the system, minimizing the sum of squares of node voltage deviations, and minimizing the number of switching operations as optimization objectives. For example, minimizing network losses can reduce operating costs, minimizing the sum of squares of voltage deviations can ensure that the voltage of the entire network is within a safe and qualified range, and minimizing the number of switching operations can reduce mechanical wear and avoid transient disturbances caused by frequent switching. The synergistic optimization of these three objectives can effectively cope with the operational challenges brought about by the integration of a high proportion of renewable energy sources and achieve efficient, stable, and long-life operation of the distribution network in an uncertain environment.

[0027] In an optional implementation, a multi-objective optimization model can be established with the objectives of minimizing the total active power loss of the system, minimizing the sum of squares of node voltage deviations, and minimizing the number of switching operations. For example, the total active power loss of the system can be quantified by the sum of the active power losses of each branch; the sum of squares of node voltage deviations can be calculated by summing the squares of the differences between the actual voltage and the rated voltage of each node; and the number of switching operations can be statistically calculated by summing the XOR results of the current switching state and the switching state at the previous moment. The optimization variables include discrete network switch state variables and continuous tap position variables of the adjustable transformer, for example... The network switch status variable takes the value 0 or 1, which indicates that the corresponding tie switch or sectionalizing switch is in the open or closed state, respectively. The tap position variable of the regulating transformer is a continuous variable but is restricted to the preset lower limit and upper limit of the tap position. For example, if the lower limit of the tap position is 1 and the upper limit is 5, then the tap position variable can only take the value within this integer range. The objective function uses a weighted sum to linearly combine the three indicators according to preset weights. For example, the weight of network loss is set to 0.5, the weight of voltage deviation is set to 0.3, and the weight of the number of switch operations is set to 0.2, so as to reflect the emphasis of each objective in different operating stages.

[0028] Among them, the tap position variable of the adjustable capacity transformer refers to the tap position control parameter used by the adjustable capacity transformer to adjust the output voltage; the network switch status variable refers to the opening and closing status of the sectionalizing switch and tie switch used to change the topology in the distribution network; and the sum of squares of node voltage deviations refers to the sum of the squares of the deviations of the voltage of all load nodes from their rated values, which is used to measure the voltage quality level of the entire network.

[0029] It should be noted that S101 establishes a multi-objective optimization model for the dynamic reconfiguration of the active distribution network, with the optimization objectives being to minimize the total active power loss of the system, the sum of squares of node voltage deviations, and the number of switching operations. The optimization variables include discrete network switch state variables and continuous tap position variables of the regulating transformer. This step provides a unified mathematical expression framework for all subsequent optimizations and controls. For example, by explicitly including the tap position of the regulating transformer as a continuous control variable in the model, the optimization space that traditionally relies solely on switch reconfiguration is expanded, enabling voltage regulation and topology adjustment to be carried out in a coordinated manner. This lays the structural foundation for the convex relaxation treatment in S102 and provides the variables and objectives for generating the coordinated control strategy in S104.

[0030] S102, perform convex relaxation on the multi-objective optimization model, transform it into a mixed integer second-order cone programming model, and call the mathematical optimization solver to solve it; In one alternative implementation, the multi-objective optimization model can be solved by performing convex relaxation on the multi-objective optimization model, transforming it into a mixed integer second-order cone programming model, and then calling a mathematical optimization solver for solving it. Alternatively, a hybrid algorithm combining genetic algorithm and particle swarm optimization algorithm can be used.

[0031] In an optional implementation, a hybrid algorithm combining genetic algorithm and particle swarm optimization can be used to solve the multi-objective optimization model. For example, during the initialization phase, a population of feasible solutions that satisfy the radial topology constraints of the distribution network is generated, where each individual encodes discrete switch state variables and continuous tap position variables of the regulating transformer. Subsequently, during the evolutionary process, selection, crossover, and mutation operations of the genetic algorithm are first performed to maintain population diversity and to conduct a global search. For example, an environment selection mechanism based on non-dominated sorting and congestion distance is used to screen Pareto frontier individuals. Then, the high-quality individuals of the current generation are used as the initial particles of the particle swarm optimization algorithm. The velocity update formula of the particle swarm is used to guide individuals to move towards local and global optima. For example, a linear decreasing strategy of inertia weight is introduced during velocity update to balance exploration and development capabilities. After position update, individuals that violate voltage limits or topological connectivity constraints are repaired to ensure that all solutions are feasible. Finally, through multiple generations of iteration, a high-quality Pareto optimal solution set covering the three objectives of network loss, voltage deviation, and number of switching operations is obtained.

[0032] Among them, the hybrid algorithm combining genetic algorithm and particle swarm optimization refers to a multi-objective optimization solution strategy that combines the advantages of the strong global search capability of genetic algorithm and the fast local convergence speed of particle swarm optimization. It is used to deal with the dynamic reconfiguration problem of distribution network containing discrete and continuous mixed variables, multiple constraints, and non-convex characteristics.

[0033] In this embodiment of the invention, the multi-objective optimization model is subjected to convex relaxation processing, which transforms it into a mixed integer second-order cone programming model, and then a mathematical optimization solver is called to solve it.

[0034] In this embodiment of the invention, the convex relaxation process is applied to the branch power constraints in the DistFlow power flow model of the distribution network. By introducing a second-order cone relationship between the square of the voltage, the square of the current and the branch power, the non-convex constraints are made convex.

[0035] In this embodiment of the invention, during the optimization process, a linear decreasing strategy is used to dynamically adjust the inertia weight of the optimization algorithm in order to balance the global search and local convergence capabilities.

[0036] Second-order cone-convex relaxation is applied to the branch power constraints in the DistFlow power flow equations of the distribution network. The relaxed constraint form is as follows: in, For the active and reactive power of the branch circuit, The square of the branch current, The square of the node voltage.

[0037] In an optional implementation, the objective function of the mixed-integer second-order cone programming model is in the form of a weighted sum: in, For system network loss, For voltage deviation, This refers to the number of switch operations. These are the weighting coefficients.

[0038] In one alternative implementation, inertia weight Adopt a linear decreasing strategy: in, and These are the initial and final inertia weights, respectively. This represents the current iteration number. This represents the maximum number of iterations.

[0039] It should be noted that S102 performs convex relaxation on the multi-objective optimization model, transforming it into a mixed-integer second-order cone programming model, and then calls a mathematical optimization solver to solve it. This step significantly improves the solvability and computational efficiency of the model. For example, by relaxing the non-convex power constraints in the DistFlow power flow model into a second-order cone form, the original non-convex mixed-integer problem is transformed into a convex form that can be efficiently processed by commercial solvers such as Gurobi. This ensures that high-quality solutions can be obtained quickly in the multiple typical scenarios generated by S103, avoiding the problem that traditional heuristic algorithms are prone to getting trapped in local optima or having excessively long solution times, and providing reliable computational support for subsequent batch solutions of multiple scenarios.

[0040] S103, based on scenario analysis methods, addresses the uncertainties in load and distributed power output, generating a set of representative typical operating scenarios; It should be noted that distributed photovoltaic (PV) output is highly volatile due to meteorological factors such as irradiance and temperature, while load demand varies on intraday and seasonal scales depending on user behavior. If a single deterministic scenario is directly used for reconfiguration decisions, it will fail to reflect the risks and diversity in actual operation, leading to performance degradation or even failure of the optimization scheme in real-world environments. Therefore, it is necessary to first generate an initial scenario set covering the entire range in the probability distribution space of load and distributed power sources through Latin hypercube sampling to ensure that different output levels and load combinations are fully represented. Then, redundant or weakly representative scenarios are eliminated through synchronous back-substitution reduction methods, retaining the typical scenarios that have the most significant impact on system network losses, voltage overruns, and topology adjustments. For example, after refining 10 typical scenarios from a set of 500 initial scenarios, the computational complexity of the optimization model is significantly reduced, while still maintaining the ability to capture key features of the uncertainty distribution, thereby supporting the generation of robust and adaptable dynamic reconfiguration strategies.

[0041] Among them, the set of operating scenarios refers to the specific combinations that may be achieved by load and distributed power output under uncertain conditions.

[0042] In this embodiment of the invention, an initial scenario set with uncertainties in coverage load and distributed power output is generated using the Latin hypercube sampling method. Then, the initial scenario set is simplified using the synchronous back-substitution reduction method, retaining several typical scenarios that have the most representative impact on system operation.

[0043] In one optional implementation, a Latin hypercube sampling method can be used to generate an initial scenario set covering the uncertainties of load and distributed generation output. For example, for the load power of each node in the distribution network and the output of distributed generation such as photovoltaic and wind power, their probability distribution models are established respectively. The load follows a normal distribution, and the photovoltaic output follows a Beta distribution. Latin hypercube sampling is used to uniformly divide the interval on the cumulative distribution function of each variable and randomly sample to ensure that the entire value range of each variable is fully covered and the samples have low correlation, thereby generating a high-dimensional uncertainty scenario set containing hundreds of initial scenarios. Then, the initial scenario set is simplified by a synchronous back-substitution reduction method. For example, using the probability distance between scenarios as a metric, the two closest scenarios are iteratively merged and replaced with their probability-weighted average. At the same time, the probability weights of the remaining scenarios are updated until a preset number of typical scenarios are retained. These typical scenarios can effectively approximate the original uncertainty distribution in terms of statistical characteristics and have a significant impact on the formulation of system network loss, voltage over-limit risk, and reconfiguration strategy.

[0044] Among them, the Latin hypercube sampling method refers to a stratified random sampling technique that can achieve efficient coverage of the joint distribution of multidimensional random variables with a small number of samples. The synchronous back-substitution reduction method refers to a scene reduction technique that reduces the number of scenes while maintaining the statistical characteristics of the original scene set through stepwise clustering and substitution operations. Typical scenes refer to uncertain realization samples that are representative and influential on the system's operating status and optimization decisions after reduction.

[0045] Specifically, Latin hypercube sampling is used to generate the initial scene, and synchronous back-substitution reduction is used to reduce the scene. The objective function is: in, Let be the probability of scenario s. Let be the probabilistic distance between scene s and the reserved scene set.

[0046] It should be noted that S103 uses scenario analysis to handle the uncertainty of load and distributed power output, generating a representative set of typical operating scenarios. This step effectively characterizes the operational risks and diversity brought about by the high proportion of renewable energy access. For example, by using Latin hypercube sampling and synchronous back-substitution reduction, the number of scenarios is significantly reduced while ensuring full coverage of uncertainty. This allows the optimization model in S102 to solve multiple typical operating conditions with limited computing resources, avoiding the failure of the reconfiguration strategy in actual operation due to ignoring uncertainty, and providing robust and representative decision input for S104.

[0047] S104 generates a dynamic reconfiguration strategy for the tap position of the regulating transformer and the distribution network topology based on the solution results of a typical operating scenario set, and outputs the corresponding regulating transformer adjustment command and switch control command to realize the dynamic reconfiguration of the low-voltage distribution network.

[0048] In an optional implementation, the optimal combination of network switch states and tap position configuration of the regulating transformer can be extracted based on the optimization results corresponding to each scenario in the set of typical operating scenarios. For example, in a high-load, low-PV-output scenario, the optimization results indicate that the tie switches 7 and 14 are closed, the sectionalizing switches 9 and 32 are opened, and the tap position of the regulating transformer is adjusted to the 4th position to increase the terminal voltage. In a low-load, high-PV-output scenario, the optimization results indicate that the tie switch 7 is opened, the sectionalizing switch 32 is closed, and the tap position is lowered to the 2nd position to suppress voltage over-limit.

[0049] In an optional implementation, the solutions for all typical scenarios can be comprehensively analyzed to identify the switching actions and gear settings that consistently occur in multiple high-probability scenarios, forming a unified dynamic reconfiguration strategy that takes into account different operating conditions. For example, if the tap changer needs to be set to gear 3 and switch 37 needs to be disconnected in scenarios with a probability weight of over 80%, this configuration is included in the final execution scheme. Then, the comprehensive membership degree of each solution is calculated using the fuzzy membership function method from the Pareto optimal solution set obtained from multi-objective optimization, and the solution with the largest membership degree is selected as the final reconfiguration scheme. This scheme explicitly specifies the opening and closing state of each controllable switch and the target gear of the regulating transformer. The control execution module generates specific regulating transformer adjustment instructions and switch control instructions accordingly. For example, it sends an adjustment instruction of "switch to gear 3" to the regulating transformer, and at the same time sends opening and closing instructions such as "disconnect switch 9 and close switch 14" to the distribution automation terminal, realizing the coordinated dynamic adjustment of the low-voltage distribution network topology and voltage regulation capability.

[0050] Among them, the dynamic reconfiguration strategy refers to a coordinated operation scheme that includes network topology adjustment and voltage regulation equipment control, which is optimized and calculated based on typical uncertainty scenarios. The capacity regulating transformer adjustment command refers to the control signal that specifies the tap changer of the capacity regulating transformer to switch to a specific tap. The switch control command refers to the control signal that performs closing or opening operations on the sectionalizing switch or tie switch in the distribution network.

[0051] In this embodiment of the invention, from the Pareto optimal solution set obtained by multi-objective optimization, the fuzzy membership function method is used to select the compromise solution with the best overall performance as the final reconstruction scheme.

[0052] Specifically, the fuzzy membership function method is used to select the compromise optimal solution from the Pareto solution set, and the membership degree of each objective function is... The calculation is as follows: in, and Let be the maximum and minimum values ​​of the m-th objective function in the Pareto solution set, respectively; the optimal solution is the comprehensive membership degree. The largest solution.

[0053] It should be noted that S104 generates a dynamic reconfiguration strategy for the tap position of the regulating transformer and the distribution network topology based on the solution results of a set of typical operating scenarios, and outputs the corresponding regulating transformer adjustment instructions and switch control instructions. This step transforms the mathematical optimization results of the previous steps into executable physical control actions, laying the foundation for subsequent execution steps.

[0054] Example 2, by Figure 2 As shown in the simulation results below, the trend and level of network loss changes in the distribution network before and after network reconfiguration are as follows: Before reconfiguration, the branch network loss was relatively high, and the fluctuation was quite obvious throughout the observation period, reaching a maximum of about 52kW. This indicates that the power flow distribution under the original operation mode was not reasonable, and the light or heavy load phenomenon of some lines was prominent during certain periods. After reconfiguration, the network loss decreased significantly and remained at a low level overall, with the maximum value not exceeding 27kW. Moreover, the curve was flat and the fluctuation was significantly reduced, indicating that the load distribution was effectively balanced by optimizing the switch combination, reducing redundant power transmission, and improving the economic efficiency and stability of system operation.

[0055] Depend on Figure 3 As shown in the voltage comparison results of key nodes in the figure below, network reconfiguration significantly improves the voltage level of the distribution network: Before reconfiguration, the voltages of nodes 21, 24, 27, 30, and 33 were generally low, with the lowest being approximately 0.92 pu, which was close to or below the lower limit of safe operation, posing a risk of voltage exceeding the limit; after reconfiguration, the voltages of all nodes were effectively raised, all recovering to above 0.95 pu, and the voltage quality was significantly improved. This indicates that the proposed reconfiguration strategy optimizes the reactive power flow distribution, enhances voltage support capability, and effectively alleviates the voltage drop problem at the end of the line.

[0056] Table 1 Performance Comparison Before and After Reconstruction

[0057] As shown in Table 1, the proposed distribution network reconfiguration method significantly improves system performance, as evidenced by the simulation results. Regarding network losses, the total active power loss decreased from 202.52 kW to 139.19 kW after reconfiguration, a reduction of 31.27%, demonstrating significant energy savings. In terms of voltage quality, the lowest voltage point before reconfiguration was node 18, with a voltage of 0.914 pu, significantly below the safe operating limit. After reconfiguration, the lowest voltage point shifted to node 32, increasing the voltage to 0.938 pu, effectively preventing voltage exceedance issues. Simultaneously, the maximum node voltage deviation decreased from 8.6% to 6.2%, improving the uniformity of voltage distribution across the entire network. The optimized network topology was achieved by disconnecting switches 7, 14, 9, 32, and 37, indicating that this switch combination effectively balances power flow distribution, reduces losses, and improves voltage levels.

[0058] Therefore, this invention employs the aforementioned method for low-voltage distribution network structure reconfiguration and operation control based on adjustable-capacity transformers. First, an optimization model is established with network losses, voltage deviations, and the number of switching operations as objectives. Second, the non-convex reconfiguration problem is transformed into a mixed-integer second-order cone programming model using second-order cone relaxation techniques, and a commercial solver is used for efficient solution. Simultaneously, scenario analysis is incorporated to handle uncertainties. Finally, a dynamic reconfiguration strategy that collaboratively optimizes transformer tap positions and network topology is generated. By introducing adjustable-capacity transformers as a key control mechanism, this invention effectively expands the optimization space, significantly reduces network losses, smooths voltage fluctuations, reduces switching operations, and comprehensively improves the operational efficiency of low-voltage distribution networks with a high proportion of renewable energy access.

[0059] Example 3: This example also provides a low-voltage distribution network structure reconfiguration and operation control system based on a capacity-adjustable transformer, including: The data acquisition module is used to acquire real-time power distribution network operation data and the current tap position information of the regulating transformer; The optimization calculation module is used to execute the dynamic reconfiguration method and generate a reconfiguration scheme that includes tap changer adjustment instructions for the capacity-adjusting transformer and network switch status control instructions. The control execution module is used to send opening and closing commands to the line switches and tap adjustment commands to the regulating transformer according to the reconfiguration scheme.

[0060] The above-mentioned unit modules can be embedded in the processor of the electronic device in hardware form or independent of it, or they can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0061] This embodiment also provides an electronic device, which can be a terminal. The electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a method for reconfiguration and operation control of a low-voltage distribution network structure based on a variable-capacity transformer. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0062] This embodiment also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, it performs the following steps: A multi-objective optimization model for dynamic reconfiguration of active distribution networks is established. The model aims to minimize the total active power loss, the sum of squared node voltage deviations, and the number of switching operations. The optimization variables include discrete network switch state variables and continuous tap position variables of regulating transformers. The multi-objective optimization model is subjected to convex relaxation treatment, which transforms it into a mixed-integer second-order cone programming model, and then the mathematical optimization solver is called to solve it. Based on scenario analysis methods, the uncertainties of load and distributed power output are addressed to generate a set of representative typical operating scenarios; Based on the solution results of typical operating scenarios, a dynamic reconfiguration strategy for co-optimizing the tap position of the regulating transformer and the topology of the distribution network is generated, and the corresponding regulating transformer adjustment command and switch control command are output to realize the dynamic reconfiguration of the low-voltage distribution network.

[0063] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0064] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0065] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for low-voltage distribution network structure reconfiguration and operation control based on a variable-capacity transformer, characterized in that, include: A multi-objective optimization model for dynamic reconfiguration of active distribution networks is established. The model aims to minimize the total active power loss, the sum of squared node voltage deviations, and the number of switching operations. The optimization variables include discrete network switch state variables and continuous tap position variables of regulating transformers. The multi-objective optimization model is subjected to convex relaxation treatment, which transforms it into a mixed integer second-order cone programming model, and then the mathematical optimization solver is called to solve it. Based on scenario analysis methods, the uncertainties of load and distributed power output are addressed to generate a set of representative typical operating scenarios; Based on the solution results of the typical operating scenario set, a dynamic reconfiguration strategy for co-optimizing the tap position of the regulating transformer and the distribution network topology is generated, and corresponding regulating transformer adjustment instructions and switch control instructions are output to realize the dynamic reconfiguration of the low-voltage distribution network.

2. The method for low-voltage distribution network structure reconfiguration and operation control based on a variable-capacity transformer as described in claim 1, characterized in that, The tap position variable of the adjustable transformer is limited to a preset lower limit and upper limit.

3. The method for low-voltage distribution network structure reconfiguration and operation control based on a variable-capacity transformer as described in claim 2, characterized in that, The convex relaxation process is applied to the branch power constraints in the DistFlow power flow model of the distribution network. By introducing a second-order cone relationship between the square of voltage, the square of current and the branch power, the non-convex constraints are made convex.

4. The method for low-voltage distribution network structure reconfiguration and operation control based on a variable-capacity transformer as described in claim 3, characterized in that, The objective function of the multi-objective optimization model adopts a weighted sum form, which linearly combines the three indicators of system network loss, voltage deviation and number of switching operations according to preset weights.

5. The method for low-voltage distribution network structure reconfiguration and operation control based on a variable-capacity transformer as described in claim 4, characterized in that, During the optimization process, a linear decreasing strategy is used to dynamically adjust the inertia weight of the optimization algorithm in order to balance the global search and local convergence capabilities.

6. The method for low-voltage distribution network structure reconfiguration and operation control based on a variable-capacity transformer as described in claim 5, characterized in that, The Latin hypercube sampling method is used to generate an initial scenario set with uncertainties in coverage load and distributed power output. Then, the initial scenario set is simplified by the synchronous back-substitution reduction method, retaining several typical scenarios that have the most representative impact on system operation.

7. The method for low-voltage distribution network structure reconfiguration and operation control based on a variable-capacity transformer as described in claim 6, characterized in that, From the Pareto optimal solution set obtained from multi-objective optimization, the fuzzy membership function method is used to select the compromise solution with the best overall performance as the final reconstruction scheme.

8. A low-voltage distribution network structure reconfiguration and operation control system based on a variable-capacity transformer, using the method described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to acquire real-time power distribution network operation data and the current tap position information of the regulating transformer; An optimized calculation module is used to execute the dynamic reconfiguration method and generate a reconfiguration scheme that includes tap changer adjustment instructions for the capacity-adjusting transformer and network switch status control instructions. The control execution module is used to send opening and closing commands to the line switch and tap adjustment commands to the regulating transformer according to the reconfiguration scheme.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the low-voltage distribution network structure reconfiguration and operation control method based on a variable-capacity transformer as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the low-voltage distribution network structure reconfiguration and operation control method based on the adjustable-capacity transformer according to any one of claims 1 to 7.

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