A flexible interconnection device optimization operation method, system, device and medium

Abstract

The application discloses a flexible interconnection device optimal operation method, system, equipment and medium, and the method comprises the steps of obtaining basic data of a power distribution network, constructing a target function and a constraint condition by using the basic data, obtaining a double-layer planning model comprising an upper-layer site selection model and a lower-layer constant-volume operation model according to the target function and the constraint condition, introducing a key constraint in the lower-layer constant-volume operation model, checking the action behavior of each protection device under a preset fault scene, obtaining a checked double-layer planning model, iteratively solving the checked double-layer planning model by using a collaborative evolution intelligent algorithm, obtaining an optimization result, and realizing the optimal operation of the flexible interconnection device, thereby providing a rich selection space for decision makers.

Description

Technical Field

[0001] This invention relates to the field of power system planning and operation technology, and in particular to a method, system, equipment and medium for optimizing the operation of flexible interconnection devices. Background Technology

[0002] Currently, with the deepening of energy transition and the rapid development of active distribution networks (ADNs) characterized by high-proportion distributed generation (DG) penetration and diverse load access, the operating characteristics of distribution networks are becoming increasingly complex. Traditional radial networks face severe challenges in areas such as flexible regulation of tidal flows, stable voltage quality control, and improved power supply reliability. Flexible interconnection devices (SOPs), as a new type of grid equipment based on fully controlled power electronics technology, can replace traditional mechanical tie switches, enabling continuous, rapid, and precise mutual support of active power between feeders. SOPs also provide independent and flexible reactive power support and voltage regulation capabilities. Therefore, SOPs are hailed as a key enabling technology for achieving "flexible interconnection" and "active management" in future distribution networks, possessing significant value in improving renewable energy absorption capacity and enhancing system operating economy and reliability.

[0003] However, while the introduction of Standard Operating Procedures (SOPs) brings significant benefits, it also alters the fundamental characteristics of the distribution network. The core of SOPs lies in their transformation of the distribution network from a passive, single-source radial structure to an active, multi-source coordinated, flexible interconnected structure. This fundamental change in the flexible interconnected structure, especially when the SOP transmission capacity is large, alters the amplitude, phase, and distribution path of short-circuit currents during system faults, significantly impacting traditional relay protection systems configured according to the single-source radial network setting principles. Problems arising include: the boosting current provided by SOPs can extend the range of upstream instantaneous overcurrent protection, causing cascading tripping; or, during adjacent feeder faults, the reverse current provided by SOPs can cause the line protection to lose directionality and maloperate; the shunting effect of SOPs on fault currents can reduce the sensitivity of upstream overcurrent protection, leading to failure to operate during faults at the line's end; and the original time-stepped backup protection sequence can be disrupted. If these problems are not addressed, they directly threaten the safe and stable operation of the distribution network, making the advanced SOP technology a hidden danger to system security.

[0004] However, existing technologies have shortcomings in SOP planning. Most studies only consider conventional operational constraints such as voltage, current, and capacity, failing to accurately model and address the crucial constraint of preventing grid relay protection from maloperating or refusing to operate. Traditional methods treat planning and protection as independent or sequential problems; when planning schemes lead to protection misoperation, they can only passively adjust the scheme or modify protection settings, lacking a mechanism for proactive, collaborative optimization during the planning phase to balance protection performance. For nonlinear, mixed-integer programming problems with complex protection constraints, existing standard genetic algorithms and particle swarm optimization algorithms are prone to getting trapped in local optima, exhibiting poor convergence and making it difficult to obtain high-quality solutions that satisfy multiple strict constraints. Therefore, a method is needed that can proactively and accurately consider relay protection constraints during the planning phase to achieve optimal SOP addressing and capacity determination. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention provides a method, system, device and medium for optimizing the operation of flexible interconnected devices.

[0006] This invention provides a method, system, equipment, and medium for optimizing the operation of flexible interconnected devices to address the problems of insufficient consideration of relay protection constraints, poor coordination between planning and protection, and loose coupling between models and solution methods in existing SOP planning.

[0007] 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 optimizing the operation of a flexible interconnected device, comprising: Acquire basic data of the distribution network, construct objective function and constraints using the basic data, and obtain a two-level planning model including an upper-level location model and a lower-level fixed-capacity operation model based on the objective function and constraints. Key constraints are introduced into the lower-level constant-capacity operation model to verify the action behavior of each protection device under preset fault scenarios, resulting in a verified two-level planning model. The validated bi-level programming model is iteratively solved using a co-evolutionary intelligent algorithm to obtain optimized results, thereby achieving optimized operation of the flexible interconnection device.

[0008] As a preferred embodiment of the optimized operation method for a flexible interconnected device described in this invention, the construction of the objective function and constraints includes: The objective function should include at least the annual investment cost of the flexible interconnection device, the annual operation and maintenance cost of the flexible interconnection device, and the total active power loss of the distribution network; The constraints must include at least the constraints on flexible interconnection devices, the constraints on distribution network operation, and the constraints on short-circuit current. Based on the objective function and constraints, a two-level planning model is constructed, which includes an upper-level location selection model and a lower-level fixed-capacity operation model.

[0009] As a preferred embodiment of the optimized operation method for a flexible interconnection device according to the present invention, the verification of the action behavior of each protection device under a preset fault scenario includes: When a fault occurs upstream of the flexible interconnect device, check whether an island is formed downstream of the fault point due to the continuous supply of short-circuit current by the flexible interconnect device. When a fault occurs downstream of the flexible interconnection device, check whether the short-circuit current flowing through the downstream protection device exceeds the setting range due to the increase of multiple power sources. When a fault occurs on an adjacent feeder of a flexible interconnect device, verify whether the protection of the adjacent feeder will lose its directionality due to the reverse short-circuit current provided by the flexible interconnect device.

[0010] The beneficial effects of this preferred technical solution are that by verifying the action behavior of the protection device under preset fault scenarios, the reliability and selectivity of the relay protection system after the flexible interconnection device is connected are ensured, effectively avoiding protection maloperation or failure to operate, and ensuring the safe and stable operation of the distribution network.

[0011] As a preferred embodiment of the optimized operation method for a flexible interconnected device according to the present invention, the iterative solution of the verified bi-level programming model using a collaborative evolutionary intelligent algorithm includes: The first optimization algorithm is used to solve the upper-layer addressing model to obtain the optimal addressing scheme for the flexible interconnection device. The second optimization algorithm is used to solve the lower-level fixed-capacity operation model, and the optimal fixed-capacity scheme and operation strategy of the flexible interconnection device are obtained.

[0012] As a preferred embodiment of the flexible interconnection device optimization operation method described in this invention, the method includes: performing a first solution on the upper-layer addressing model using a first optimization algorithm, which includes: Integer encoding is used to represent the addressing scheme consisting of pairs of flexible interconnected device nodes; Multiple populations were established to evolve in parallel to maintain the diversity of the search process; The crossover and mutation probabilities are dynamically adjusted based on the fitness values ​​of individuals in the population. The site selection scheme is passed to the lower-level capacity-determining operation model for capacity determination and operation optimization, and the comprehensive performance score returned by the lower level is used as the fitness value of the site selection scheme.

[0013] The beneficial effects of this preferred technical solution are that by promoting parallel evolution of multiple populations and dynamically adjusting the crossover and mutation probabilities, the diversity of the search can be effectively maintained, ensuring the global optimization capability of the upper-level site selection scheme. At the same time, it can work in conjunction with the lower-level optimization to achieve dual optimization of economy and security.

[0014] As a preferred embodiment of the optimized operation method for a flexible interconnection device according to the present invention, the second solution for the lower-level constant-capacity operation model using a second optimization algorithm includes: Establish an objective function that simultaneously minimizes network losses, voltage deviations, and the investment and maintenance costs of flexible interconnect devices; The refined relay protection constraints are embedded through short-circuit calculation to verify the protection current under each fault. By simulating the social hierarchy and hunting behavior mechanisms of gray wolf populations, multi-objective collaborative optimization of the capacity and operation strategy of flexible interconnection devices is carried out. Output the Pareto optimal solution set that satisfies all operating and protection constraints.

[0015] The beneficial effects of this preferred technical solution are that, through the multi-objective gray wolf optimization algorithm, network loss, voltage deviation and investment cost are minimized, while ensuring that relay protection constraints are met, outputting a Pareto optimal solution set, thereby improving the economic efficiency and safety of operation.

[0016] As a preferred embodiment of the optimized operation method for a flexible interconnected device according to the present invention, it further includes: A dynamic penalty function is constructed, and the penalty function is linearly superimposed with the calculation of system network loss and investment and operation costs of flexible interconnection devices to construct a new comprehensive objective function; During the optimization iteration process, for solutions that do not meet the constraints, the comprehensive objective function value is increased through the corresponding penalty term, which guides the optimization algorithm to eliminate infeasible solutions and converge to the global optimal region that satisfies all constraints.

[0017] In a second aspect, the present invention provides an optimized operation system for flexible interconnected devices, comprising: The model building module is used to acquire basic data of the distribution network, construct objective functions and constraints using the basic data, and obtain a two-level planning model containing an upper-level location model and a lower-level fixed-capacity operation model based on the objective functions and constraints. The verification module is used to introduce key constraints into the lower-level fixed-capacity operation model, verify the action behavior of each protection device under the preset fault scenario, and obtain the verified two-level planning model. The solution module is used to iteratively solve the verified bi-level programming model using a co-evolutionary intelligent algorithm to obtain the optimization result and realize the optimized operation of the flexible interconnection device.

[0018] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of the aforementioned optimized operation method for a flexible interconnect device.

[0019] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the aforementioned method for optimizing the operation of a flexible interconnect device.

[0020] Compared with existing technologies, the beneficial effects of this invention are as follows: The method of this invention embeds protection constraints into the design of the optimization model and algorithm. Through the embedded optimization model and algorithm, the planning scheme and protection performance of this invention are actively coordinated, rather than relying on post-event verification. Active coordination improves the scientific rigor and efficiency of the overall decision-making process. During modeling, the method of this invention considers the short-circuit current characteristics exhibited by the SOP as an inverter power supply during fault conditions. This invention also considers the impact of different types of faults, such as three-phase and two-phase faults, on protection, making the constraint model in this invention closer to actual engineering conditions. The proposed two-layer co-evolutionary framework decomposes the complex mixed-integer nonlinear multi-objective problem into more manageable sub-problems. This invention employs an improved multi-population genetic algorithm and a multi-objective gray wolf optimization algorithm, combined with a dynamic penalty and repair mechanism. The dynamic penalty and repair mechanism enhances the global search capability and convergence speed of the improved multi-population genetic algorithm and multi-objective gray wolf optimization algorithm in this invention. The improved multi-population genetic algorithm and multi-objective gray wolf optimization algorithm of this invention can effectively handle strict protection constraints. This invention provides an optimal solution set, not a single solution. The optimal solution set of this invention demonstrates the trade-offs between different performance indicators and provides a rich selection space. This invention effectively solves the problems of poor coordination between planning and protection and insufficient optimization accuracy in the prior art. Attached Figure Description

[0021] 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.

[0022] Figure 1 A schematic diagram of the overall process logic of an optimized operation method for a flexible interconnected device provided in one embodiment of the present invention; Figure 2 A diagram showing the connection location in a power distribution network for a standard operating procedure (SOP) of an optimized operation method for a flexible interconnection device according to an embodiment of the present invention. Figure 3 This is a SOP (Standard Operating Procedure) flow chart for optimizing the operation of a flexible interconnected device, provided as an embodiment of the present invention. Detailed Implementation

[0023] 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.

[0024] Example 1, referring to Figures 1-3 As an embodiment of the present invention, a method for optimizing the operation of a flexible interconnected device is provided, comprising: S100: Obtain basic data of the distribution network, construct objective function and constraints using the basic data, and obtain a two-level planning model including upper-level location model and lower-level fixed-capacity operation model based on the objective function and constraints. S200: Introduce key constraints in the lower-level constant-capacity operation model to verify the action behavior of each protection device under the preset fault scenario, and obtain the verified two-level planning model. In one optional embodiment, the verification can be based on electromagnetic transient simulation. A power distribution network model is built in electromagnetic transient simulation software, a preset fault scenario is set, the simulation is run, the current and voltage changes of each protection device are recorded when the fault occurs, and the operating time and operating current of the protection device are analyzed to see if they meet the setting value requirements. If the protection device is found to be maloperating or refusing to operate, the protection setting value is adjusted or the SOP operation strategy is optimized. In another optional embodiment, the verification can also be based on real-time digital simulation (RTDS). A distribution network model is built in RTDS, and the actual protection device is connected to the RTDS system to form a closed-loop test environment. Preset fault scenarios are triggered, the actual action behavior of the protection device is observed, and the action time and action current of the protection device are analyzed to see if they meet the setting requirements. If abnormal action of the protection device is found, the protection setting value is adjusted or the SOP operation strategy is optimized. In this embodiment of the invention, the verification focuses on the three-stage current protection widely deployed in distribution networks. After the distributed generation (DG) is connected to the distribution network, the system changes from single-source power supply to multi-source power supply, and the magnitude and direction of the short-circuit current will change accordingly during a fault.

[0025] S300: Iteratively solves the verified bi-level programming model using a co-evolutionary intelligent algorithm to obtain optimized results and achieve optimized operation of the flexible interconnection device.

[0026] Specifically, such as Figure 2As shown, the flexible multi-state switch is connected to two independent nodes in the distribution network via two sets of voltage source converter (VSC) units, and is electrically isolated through DC-side capacitors. In this topology, DC bus capacitor isolation enables each VSC's AC side to have independent decoupling control capabilities, allowing for dynamic power flow balance between feeders through active power coordination. Under fault conditions, the DC capacitors provide rapid dynamic reactive power support, and, in conjunction with the coordinated adjustment of the VSC output voltage amplitude and frequency, ensure the continuity of power supply to non-faulty areas. The overall algorithm of this invention is a nested loop. The outer loop (upper GA) generates an addressing scheme, the inner loop (lower MOGWO) performs capacity determination and multi-objective optimization on the scheme, and feeds back the results. Through co-evolution, the globally optimal addressing-capacity combination is finally found.

[0027] It should be noted that this invention constructs a two-layer planning model and introduces key protection constraints, and uses a co-evolutionary intelligent algorithm for optimization, thereby realizing the optimal location, capacity setting and operation strategy of flexible interconnection devices. This significantly improves the economy, operation performance and relay protection reliability of the distribution network, and effectively solves the problems of poor coordination between planning and protection and insufficient optimization accuracy in the prior art.

[0028] In this embodiment of the invention, step S100 includes the following sub-steps A1-A3; In A1: the objective function should include at least the annual investment cost of the flexible interconnection device, the annual operation and maintenance cost of the flexible interconnection device, and the total active power loss of the distribution network; In A2: the constraints must include at least the constraints of flexible interconnection devices, distribution network operation constraints, and short-circuit current constraints; In A3: Based on the objective function and constraints, a two-level planning model is constructed, which includes an upper-level location selection model and a lower-level fixed-capacity operation model.

[0029] In an alternative embodiment, the constraints may include voltage stability constraints, defining upper and lower limits for node voltages in the model, checking whether the voltage is within the set allowable range for each node, and adjusting the reactive power output of the flexible interconnection device (SOP) or changing the operation strategy of the distribution network if the node voltage exceeds the range. In another optional embodiment, the constraint can be a power balance constraint, which calculates the supply and demand difference of active power and reactive power of all nodes in the distribution network to ensure that the power supply and demand of all nodes in the system are balanced, that is, the sum of active power and reactive power is zero. If a power imbalance occurs, the balance is achieved by adjusting the power output of the SOP or optimizing the operation strategy of the distributed power source. In this embodiment of the invention, the constraints include SOP constraints, distribution network operation constraints, and short-circuit current constraints; Specifically, if the SOP is equivalent to a back-to-back voltage source converter, its steady-state model can be described as being at the connection point. Inject controllable active power , For losses and reactive power The connection of SOP enables power transfer, reactive power support, and voltage regulation between feeders.

[0030] The objective function, including the annual investment cost at SOP, is expressed as: The annual operating and maintenance cost of SOP is expressed as follows: The total active power loss of the distribution network is expressed as: in, For SOP capacity, For the number of SOPs, For service life, , for SOP at any node The active power and active power loss at the location, for Time Branch The current flowing through, These include SOP investment costs, maintenance costs, and network losses. These are network loss and SOP loss costs, respectively.

[0031] The constraints include SOP constraints, where the SOP capacity constraint is expressed as: SOP active power constraint is expressed as: The loss constraint is expressed as: in, , , Each is a SOP (Standard Operating Procedure) The active power, total capacity, and active power loss of the transmission at each end. This is the SOP loss factor.

[0032] The operating constraints of the distribution network are expressed as follows: The active and reactive power balance constraint is expressed as: Ohm's law constraint is expressed as: The power constraint at the beginning of the branch is expressed as: System security constraints are represented as follows: in, For nodes voltage, For flow through branch road Active and reactive power, For nodes The sum of injected active power, , These are the upper and lower limits of the system's allowable voltage. This is the maximum current allowed to flow through the branch.

[0033] The short-circuit current constraint is expressed as: in, For flow protection device Fault current, For protection devices The I-th segment setting value.

[0034] It should be noted that, through system modeling and problem analysis, the impact of SOP access on the distribution network is fully considered. A multi-objective optimization model and refined constraints are constructed, which include economy, operation performance and safety, to provide a scientific basis for subsequent optimization solutions and ensure the comprehensiveness and feasibility of the optimization scheme.

[0035] In this embodiment of the invention, step S200 includes the following sub-steps B1-B3; In B1: When a fault occurs upstream of the flexible interconnect device, check whether an island is formed downstream of the fault point due to the continuous supply of short-circuit current by the flexible interconnect device; In B2: When a fault occurs downstream of the flexible interconnection device, check whether the short-circuit current flowing through the downstream protection device exceeds the setting range due to the increase of multiple power sources. In B3: When a fault occurs on an adjacent feeder of the flexible interconnection device, verify whether the adjacent feeder protection will lose its directionality due to the reverse short-circuit current provided by the flexible interconnection device.

[0036] In embodiments of the present invention, such as Figure 3 The diagram illustrates the impact of SOP (Standard Operating Procedure) access on relay protection.

[0037] Specifically, when the upstream of the SOP During a fault, all protection devices can operate, but after S3 operates, an island is formed downstream and the fault point is always subject to the short-circuit current provided by SOP. When SOP downstream During a fault, the short-circuit current flowing through S4 is supplied by the system's main power supply and DG. The increase in short-circuit current may increase the protection distance of S4 and cause it to lose its selectivity. When SOP adjacent feeder During a fault, SOP provides a reverse short-circuit current to the adjacent feeder, causing S3 and S4 to malfunction without directionality. Simultaneously, the increased current flowing through S1 may cause S1 to malfunction.

[0038] It should be noted that, through detailed analysis of the impact of SOP access on relay protection, problems such as the continuous islanding short-circuit current caused by upstream faults, the loss of protection selectivity caused by the increase of short-circuit current caused by downstream faults, and the protection maloperation caused by the reverse current of adjacent feeder faults were identified. This provides a scientific basis for optimizing SOP planning and protection configuration, and significantly improves the safety and reliability of the distribution network.

[0039] In this embodiment of the invention, step S300 includes the following sub-steps C1-C2; In C1: The first optimization algorithm is used to solve the upper-layer addressing model to obtain the optimal addressing scheme for the flexible interconnection device; In C2: The second optimization algorithm is used to solve the lower-level fixed-capacity operation model to obtain the optimal fixed-capacity scheme and operation strategy of the flexible interconnection device.

[0040] In an optional embodiment, the first optimization algorithm can be a particle swarm optimization algorithm. Initialize the particle swarm, with each particle representing a possible location scheme. Calculate the fitness value of each particle, evaluate its economy and safety, update the velocity and position of each particle, adjust it according to the individual optimal position and the global optimal position, pass the updated location scheme to the lower-level model for sizing and running optimization, and update the particle fitness value according to the comprehensive performance score fed back from the lower level. Repeat the steps until the iteration termination condition is met, and output the optimal location scheme. In another optional embodiment, the first optimization algorithm can also be a differential evolution algorithm. Initialize the population, with each individual representing a possible location scheme. Perform a differential operation on each individual in the population to generate a differential vector. Perform a cross operation between the differential vector and the target individual to generate a trial vector. Calculate the fitness value of the trial vector and evaluate its economy and safety. If the fitness of the trial vector is better than that of the target individual, replace the target individual with the trial vector. Pass the updated location scheme to the lower-level model for capacity sizing and runtime optimization. Update the fitness value of the individual based on the comprehensive performance score fed back from the lower level. Repeat the steps until the iteration termination condition is met and output the optimal location scheme. In this embodiment of the invention, the first optimization algorithm includes an adaptive genetic algorithm; Specifically, the upper layer focuses on global location optimization for the Standard Operating Procedure (SOP), employing an improved multi-population adaptive genetic algorithm. Node pairs are represented by integer encoding, and multi-population parallel evolution maintains search diversity. Exploration and development are achieved through adaptive crossover and mutation probability balancing. The core innovation lies in its collaborative evaluation mechanism: the fitness of location schemes is not directly calculated; instead, each scheme is passed to the lower layer for multi-objective optimization and safety verification, and the comprehensive performance score fed back from the lower layer serves as the basis for evolution.

[0041] It should be noted that this method ensures that the site selection results, based on topological feasibility, naturally possess the potential for synergistic optimization of economy and security.

[0042] In an optional embodiment, the second optimization algorithm can be a non-dominated sorting genetic algorithm. This algorithm generates an initial population, where each individual represents a possible SOP (Standard Operating Procedure) size and running strategy. Multiple objective function values ​​are calculated for each individual, and a non-dominated sort is performed. Individuals are selected based on the non-dominated sort and crowding level to generate a new population. Crossover and mutation operations are performed on the new population to generate offspring. The parent and offspring populations are merged, and the non-dominated sort and crowding level are recalculated to select the next generation. This process is repeated until a termination condition is met, outputting a Pareto optimal solution set, which represents the optimal size and running strategy that satisfies all constraints. In an optional embodiment, the second optimization algorithm can also be a multi-objective particle swarm optimization algorithm, generating an initial particle swarm where each particle represents a possible SOP (Standard Operating Procedure) sizing and running strategy. Multiple objective function values ​​are calculated for each particle, and the particles are non-dominatedly sorted to determine the Pareto rank of each particle. The individual optimal position and global optimal position of each particle are updated, and the velocity and position of each particle are updated based on these positions. Constraint checks are performed on the position of each particle, and the steps are repeated until a termination condition is met, outputting the Pareto optimal solution set, i.e., the optimal sizing and running strategy that satisfies all constraints. In this embodiment of the invention, the second optimization algorithm includes the multi-objective gray wolf optimization algorithm; Specifically, the Gray Wolf Optimization Algorithm is a swarm intelligence optimization algorithm constructed by simulating the social hierarchy and hunting behavior mechanisms of gray wolf populations. Due to its simple structure, few parameters, and strong global search capability, this algorithm is widely used in various complex optimization problems. Existing research shows that it outperforms traditional methods such as particle swarm optimization and genetic algorithms in both convergence speed and optimization accuracy. This invention uses this algorithm as its core framework to construct a multi-objective Gray Wolf Optimization Algorithm capable of simultaneously handling multiple conflicting objectives.

[0043] Given the SOP installation location determined by the upper-level genetic algorithm, the SOP capacity to be optimized and the operating power at each time period are set as optimization variables. The algorithm simultaneously minimizes three objectives: total system active power loss, node voltage deviation, and SOP-related investment and operating costs. Ensuring correct operation of relay protection is also a core constraint.

[0044] The algorithm simulates the social hierarchy within a gray wolf population, categorizing current candidate solutions into different levels based on their quality to guide the search direction of the entire population. By simulating the behavior of gray wolves surrounding, chasing, and attacking prey, the position of each candidate solution is updated. A dynamic penalty function is introduced into the algorithm to output a Pareto optimal solution set.

[0045] It should be noted that by mimicking social hunting behavior in nature for intelligent search and integrating dynamic punishment and repair mechanisms to strictly ensure relay protection safety constraints, the multi-objective collaborative optimization problem of SOP capacity setting and operation strategy is solved efficiently and reliably.

[0046] In this embodiment of the invention, after completing steps C1-C2, step S300 also includes steps C3-C6; In C3: Integer encoding is used to represent the addressing scheme consisting of pairs of flexible interconnect device nodes; In C4: Multiple populations are established to evolve in parallel to maintain the diversity of the search process; In C5: The crossover and mutation probabilities are dynamically adjusted based on the fitness values ​​of individuals in the population; In C6: The location scheme is passed to the lower-level capacity-determining operation model for capacity determination and operation optimization, and the comprehensive performance score returned by the lower level is used as the fitness value of the location scheme.

[0047] In this embodiment of the invention, the crossover probability and mutation probability are dynamically adjusted according to the distribution of individual fitness in the population, so that the algorithm parameters can be adaptively updated with the evolution process and fitness changes, thereby enhancing its global search capability and convergence robustness.

[0048] Genetic algorithms achieve population evolution through three steps: selection, crossover, and mutation, where the crossover probability... With the probability of mutation The setting of is a key parameter affecting the algorithm's convergence behavior and optimization performance. The adaptive genetic algorithm used is specifically designed for... and Dynamic adjustments are implemented, and the specific mechanism is as follows. Within the framework of this model, which aims to minimize fitness values, individuals with fitness values ​​lower than the population average are assigned a lower fitness score. and To protect the structural stability of excellent solutions, ensuring their high probability of being preserved to the next generation; conversely, for individuals with fitness above average, a higher fitness is assigned. and This promotes the replacement and renewal of individuals. Simultaneously, the algorithm sets a non-zero baseline crossover rate for the individual with the best fitness (i.e., the smallest value) in the population. With the rate of variation This avoids high-quality individuals from falling into evolutionary stagnation due to zero probability of operation, and also increases their chances of participating in crossover and mutation, thereby maintaining population diversity and enhancing the algorithm's global exploration capability. The crossover probability and mutation probability are expressed as follows: in, This is the minimum fitness value in the population. The average fitness value of the current population. The individual with the lower fitness value among the two individuals to be crossed. The fitness value of the individual to be mutated. , These are the initially set baseline crossover rate and baseline mutation rate, respectively. .

[0049] It should be noted that by dynamically adjusting the crossover and mutation probabilities, the evolutionary process is flexibly controlled based on the differences in individual fitness. Low-fitness individuals have their mutation probability increased to promote updates, while high-fitness individuals have their mutation probability reduced to maintain superior characteristics. Simultaneously, setting a non-zero probability for the optimal individual avoids evolutionary stagnation, significantly enhancing the global search capability and convergence robustness of the genetic algorithm, and improving optimization accuracy and efficiency.

[0050] In this embodiment of the invention, after completing steps C3-C6, step S300 also includes steps C7-C10; In C7: Establish an objective function that simultaneously minimizes network losses, voltage deviations, and the investment and maintenance costs of flexible interconnect devices; In C8: Refined relay protection constraints are embedded through short-circuit calculations to verify the protection current under each fault; In C9: Multi-objective collaborative optimization of the capacity and operation strategy of flexible interconnection devices is carried out by simulating the social hierarchy and hunting behavior mechanism of gray wolf populations; In C10: Output the Pareto optimal solution set that satisfies all operating and protection constraints.

[0051] In this embodiment of the invention, to verify the effectiveness of the multi-objective gray wolf optimization algorithm of the present invention in solving multi-objective optimization problems, a standard multi-objective test function is used, specifically expressed as follows: The lower layer is responsible for optimizing SOP capacity and operating strategies at a given location, employing a multi-objective gray wolf optimization algorithm that integrates dynamic constraint processing mechanisms. The model simultaneously minimizes network losses, voltage deviations, and SOP investment and maintenance costs, and embeds refined relay protection constraints through a short-circuit calculation module: verifying whether the protection current meets selectivity requirements under various fault conditions. The algorithm efficiently handles complex constraints through dynamic penalty functions and feasible solution repair strategies, ultimately outputting the optimal solution set under the premise of strictly ensuring the safety of protection actions, achieving a multi-dimensional trade-off between operational economy, power quality, and safety.

[0052] In this embodiment of the invention, after completing steps C7-C10, step S300 above also includes steps C11-C12; In C11: Construct a dynamic penalty function, and linearly superimpose the penalty function with the calculation of system network loss and investment and operation costs of flexible interconnection devices to construct a new comprehensive objective function; In C12: During the optimization iteration process, for solutions that do not meet the constraints, the comprehensive objective function value is increased through the corresponding penalty term, which guides the optimization algorithm to eliminate infeasible solutions and converge to the global optimal region that satisfies all constraints.

[0053] In this embodiment of the invention, the inequality constraints are handled by incorporating a penalty function into the objective function. The node voltage penalty function is expressed as: in, for Node voltage over-limit penalty function, for Node voltage over-limit penalty factor, .

[0054] The branch current penalty function is expressed as: in, for Branch overload penalty function, for Node voltage over-limit penalty factor, .

[0055] The short-circuit current penalty function is expressed as: in, for Branch over-limit penalty function, for Node voltage over-limit penalty factor, .

[0056] A new comprehensive objective function is constructed by linearly superimposing the three penalty function terms with the calculation formulas for system network loss and SOP investment. During the optimization calculation process, if a set of solutions fails to meet the constraints, its corresponding penalty term will cause the objective function value to increase sharply.

[0057] It should be noted that this mechanism ensures that infeasible solutions are naturally eliminated during the iterative optimization process due to their poor fitness, thereby guiding the algorithm to converge to the global optimal solution that satisfies all constraints.

[0058] The above is an illustrative scheme of a flexible interconnect device optimization operation method according to this embodiment. It should be noted that the technical solution of this flexible interconnect device optimization operation system and the technical solution of the aforementioned flexible interconnect device optimization operation method belong to the same concept. Details not described in detail in the technical solution of the flexible interconnect device optimization operation system in this embodiment can be found in the description of the technical solution of the aforementioned flexible interconnect device optimization operation method.

[0059] The flexible interconnect device optimized operation system in this embodiment includes: The model building module is used to acquire basic data of the distribution network, construct objective functions and constraints using the basic data, and obtain a two-level planning model containing an upper-level location model and a lower-level fixed-capacity operation model based on the objective functions and constraints. The verification module is used to introduce key constraints into the lower-level fixed-capacity operation model, verify the action behavior of each protection device under the preset fault scenario, and obtain the verified two-level planning model. The solution module is used to iteratively solve the verified bi-level programming model using a co-evolutionary intelligent algorithm to obtain the optimization result and realize the optimized operation of the flexible interconnection device.

[0060] This embodiment also provides a computer device suitable for optimizing the operation of flexible interconnect devices, including: The memory and processor are used to store computer-executable instructions and execute the computer-executable instructions to implement an optimized operation method for a flexible interconnected device as proposed in the above embodiments.

[0061] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a method for optimizing the operation of a flexible interconnect device as proposed in the above embodiments.

[0062] The storage medium proposed in this embodiment and the method for optimizing the operation of flexible interconnect devices proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0063] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computing device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0064] Example 2, referring to Table 1, differs from the first example and provides a verification test of a method for optimizing the operation of a flexible interconnected device, verifying and explaining the technical effects used in this method.

[0065] The optimization results of the method of the present invention and the traditional method are compared in Table 1.

[0066] Table 1 Comparison of optimization results between the inventive method and the conventional method

[0067] As shown in Table 1, this invention slightly increases computation time and investment costs, but in return, it achieves a qualitative leap in system security and planning efficiency. Due to the introduction of refined two-layer iteration and real-time protection verification, the average optimization time increased from 85.2 seconds to 127.5 seconds, an increase of approximately 50%.

[0068] To ensure absolute reliability of the protection, the annual comprehensive cost increased slightly by 2.9% to RMB 1.632 million. However, the core benefits of this invention are extremely significant, with the protection constraint satisfaction rate increasing dramatically from 40% in traditional methods to 95%, essentially eliminating the risk of false activation and refusal to activate the protection.

[0069] More importantly, the initial planning solution is a feasible solution, requiring no subsequent adjustments. This completely changes the cumbersome iterative model of traditional methods, reducing the average of 3.2 post-planning adjustments to zero. This marks a paradigm shift from passive verification to proactive embedded security. Although it slightly increases upfront calculations and investment, it greatly improves the reliability of power grid operation and fundamentally shortens the overall planning cycle, resulting in significant comprehensive benefits.

[0070] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not 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.

Claims

1. A method for optimizing the operation of a flexible interconnected device, characterized in that, include: Acquire basic data of the distribution network, construct objective function and constraints using the basic data, and obtain a two-level planning model including an upper-level location model and a lower-level fixed-capacity operation model based on the objective function and constraints. Key constraints are introduced into the lower-level constant-capacity operation model to verify the action behavior of each protection device under preset fault scenarios, resulting in a verified two-level planning model. The validated bi-level programming model is iteratively solved using a co-evolutionary intelligent algorithm to obtain optimized results, thereby achieving optimized operation of the flexible interconnection device.

2. The method for optimizing the operation of a flexible interconnected device as described in claim 1, characterized in that, Constructing the objective function and constraints includes: The objective function should include at least the annual investment cost of the flexible interconnection device, the annual operation and maintenance cost of the flexible interconnection device, and the total active power loss of the distribution network; The constraints must include at least the constraints on flexible interconnection devices, the constraints on distribution network operation, and the constraints on short-circuit current. Based on the objective function and constraints, a two-level planning model is constructed, which includes an upper-level location selection model and a lower-level fixed-capacity operation model.

3. The method for optimizing the operation of a flexible interconnected device as described in claim 2, characterized in that, Verification of the action behavior of each protection device under preset fault scenarios includes: When a fault occurs upstream of the flexible interconnect device, check whether an island is formed downstream of the fault point due to the continuous supply of short-circuit current by the flexible interconnect device. When a fault occurs downstream of the flexible interconnection device, check whether the short-circuit current flowing through the downstream protection device exceeds the setting range due to the increase of multiple power sources. When a fault occurs on an adjacent feeder of a flexible interconnect device, verify whether the protection of the adjacent feeder will lose its directionality due to the reverse short-circuit current provided by the flexible interconnect device.

4. The method for optimizing the operation of a flexible interconnected device as described in claim 3, characterized in that, The iterative solution of the validated bilevel programming model using a co-evolutionary intelligent algorithm includes: The first optimization algorithm is used to solve the upper-layer addressing model to obtain the optimal addressing scheme for the flexible interconnection device. The second optimization algorithm is used to solve the lower-level fixed-capacity operation model, and the optimal fixed-capacity scheme and operation strategy of the flexible interconnection device are obtained.

5. The method for optimizing the operation of a flexible interconnected device as described in claim 4, characterized in that, The first solution to the upper-level addressing model using the first optimization algorithm includes: Integer encoding is used to represent the addressing scheme consisting of pairs of flexible interconnected device nodes; Multiple populations were established to evolve in parallel to maintain the diversity of the search process; The crossover and mutation probabilities are dynamically adjusted based on the fitness values ​​of individuals in the population. The site selection scheme is passed to the lower-level capacity-determining operation model for capacity determination and operation optimization, and the comprehensive performance score returned by the lower level is used as the fitness value of the site selection scheme.

6. A method for optimizing the operation of a flexible interconnected device as described in claim 4 or 5, characterized in that, The second optimization algorithm is used to solve the lower-level constant-capacity operation model in a second way, including: Establish an objective function that simultaneously minimizes network losses, voltage deviations, and the investment and maintenance costs of flexible interconnect devices; The refined relay protection constraints are embedded through short-circuit calculation to verify the protection current under each fault. By simulating the social hierarchy and hunting behavior mechanisms of gray wolf populations, multi-objective collaborative optimization of the capacity and operation strategy of flexible interconnection devices is carried out. Output the Pareto optimal solution set that satisfies all operating and protection constraints.

7. The method for optimizing the operation of a flexible interconnected device as described in claim 6, characterized in that, Also includes: A dynamic penalty function is constructed, and the penalty function is linearly superimposed with the calculation of system network loss and investment and operation costs of flexible interconnection devices to construct a new comprehensive objective function; During the optimization iteration process, for solutions that do not meet the constraints, the comprehensive objective function value is increased through the corresponding penalty term, which guides the optimization algorithm to eliminate infeasible solutions and converge to the global optimal region that satisfies all constraints.

8. A flexible interconnect device optimized operation system, employing the flexible interconnect device optimized operation method as described in any one of claims 1 to 7, characterized in that, include: The model building module is used to acquire basic data of the distribution network, construct objective functions and constraints using the basic data, and obtain a two-level planning model containing an upper-level location model and a lower-level fixed-capacity operation model based on the objective functions and constraints. The verification module is used to introduce key constraints into the lower-level fixed-capacity operation model, verify the action behavior of each protection device under the preset fault scenario, and obtain the verified two-level planning model. The solution module is used to iteratively solve the verified bi-level programming model using a co-evolutionary intelligent algorithm to obtain the optimization result and realize the optimized operation of the flexible interconnection device.

9. A computer device, characterized in that, include: 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 optimized operation method of a flexible interconnected device according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the steps of the optimized operation method of a flexible interconnected device according to any one of claims 1 to 7.

Images