A power distribution network self-healing method, system, device and storage medium

Abstract

The application discloses a power distribution network self-healing method, system, device and storage medium, which is started after a fault section is isolated, and comprises the following steps: taking the state of all controllable switches and the scheduling instruction of all controllable distributed resources in the power distribution network as decision variables, taking the maximum load recovery, the minimum network loss and the minimum operation economy as targets, and constructing a power distribution network self-healing optimization model; obtaining the current operation state of the power distribution network, solving the power distribution network self-healing optimization model by using an optimization algorithm, and outputting an optimal solution; and generating a switch operation sequence and the scheduling instruction of each controllable distributed resource based on the optimal solution. The application can quickly seek optimization from a global perspective based on the real-time power grid state, automatically generate an optimal or approximate optimal recovery strategy which comprehensively considers load recovery, economy and safety, and thus significantly improve the power supply elasticity and intelligent level of the power distribution network.

Description

Technical Field

[0001] This invention relates to the field of power system automation and distribution network operation control technology, and in particular to a distribution network self-healing method, system, device and storage medium. Background Technology

[0002] With the large-scale integration of new energy sources such as distributed photovoltaics and energy storage, the topology and power flow of distribution networks are evolving towards bidirectional and multi-source directions, which places higher demands on power restoration after faults. Traditional distribution network fault restoration mainly relies on manual experience or simple heuristic rules, which is time-consuming and difficult to obtain the global optimal solution under complex multi-constraint conditions, which may result in some important loads not being restored or excessive network losses.

[0003] While existing automation solutions have accelerated fault isolation and recovery to some extent, their recovery strategies often struggle to achieve an optimal balance between restoring load capacity, operational economics (such as the number of switching operations and network losses), and system security (such as voltage and power flow exceeding limits) when faced with the randomness of distributed power generation output, load fluctuations, and network topology variability. In particular, how to coordinate the power output of distributed photovoltaic systems and the charging and discharging behavior of energy storage systems, and integrate this with network reconfiguration (switching operations) for optimized implementation, is a core challenge currently facing self-healing control in distribution networks. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a power distribution network self-healing method, system, device, and storage medium. Based on real-time power grid status, it can quickly find the optimal solution from a global perspective and automatically generate the optimal or near-optimal recovery strategy that comprehensively considers load recovery, economy, and security, thereby significantly improving the power supply resilience and intelligence level of the power distribution network.

[0005] Firstly, a self-healing method for distribution networks is provided, which is activated after a faulty section is isolated, including the following steps:

[0006] Using the states of all controllable switches and the scheduling instructions of all controllable distributed resources in the distribution network as decision variables, and aiming at maximizing the recovery load and minimizing network losses and operational economics, a self-healing optimization model for the distribution network is constructed.

[0007] The current operating status of the distribution network is obtained, and the self-healing optimization model of the distribution network is solved using an optimization algorithm to output the optimal solution.

[0008] Based on the optimal solution, generate the switching operation sequence and the scheduling instructions for each controllable distributed resource.

[0009] Furthermore, when solving the self-healing optimization model of the distribution network using optimization algorithms, the following encoding method is adopted:

[0010] The decision variables are encoded using a hybrid coding method, which includes two parts:

[0011] Using binary vectors This represents the state of all controllable switches in the network, corresponding to the network topology gene, as shown below:

[0012] ;

[0013] Where N is the total number of controllable switches; For the first The state of a controllable switch, when When the value equals 1, it indicates that the controllable switch is closed; when... When the value is 0, it indicates that the controllable switch is open;

[0014] Use a vector The scheduling instructions for all controllable distributed resources are represented by the resource scheduling gene, as follows:

[0015] ;

[0016] in, and These are the charging power vector and discharging power vector for all energy storage systems, respectively. and These represent the charging state vector and discharging state vector of all energy storage devices, respectively. This represents the charged state vector for all stored energy.

[0017] Furthermore, Constrained by environmental conditions, the constraint model is expressed as follows:

[0018] ;

[0019] ;

[0020] in, This represents the maximum available output under current irradiance and temperature. Indicates solar irradiance intensity. Indicates photovoltaic conversion efficiency. The light-receiving area of ​​the photovoltaic array. Power temperature coefficient, Actual temperature of photovoltaic cells Standard test temperature;

[0021] and The mutual exclusion constraint is satisfied as follows:

[0022] ;

[0023] in, and Let represent the charging state vector and discharging state vector of all energy storage in the t-th scheduling period, respectively;

[0024] Dynamic changes are subject to the following constraints:

[0025] ;

[0026] in, This represents the state of charge of the energy storage at the beginning of the t-th scheduling period. Rated capacity refers to the maximum total amount of electricity that an energy storage device can store. and These are the charging efficiency and discharging efficiency of energy storage, respectively, reflecting the conversion loss of energy during the charging and discharging process; To optimize the model's time step, i.e. the duration of the scheduling period.

[0027] Furthermore, when solving the self-healing optimization model of the distribution network using optimization algorithms, the initial population is generated using the following method:

[0028] Based on the post-fault distribution network topology, a batch of switching operation sequences is quickly generated using heuristic rules. Candidate values;

[0029] for For each candidate value, through power balancing and security checks, reasonable scheduling instructions are allocated to all controllable distributed resources. Ensure that the schemes represented by individuals meet the following constraints:

[0030] ;

[0031] ;

[0032] in, , , , and These are respectively represented as the real part of the branch current, the real part of the photovoltaic power current flowing into the node, the real part of the battery discharge current, the real part of the battery charging current, and the real part of the node load current. , , , and These are respectively represented as the imaginary part of the branch current, the imaginary part of the photovoltaic power current flowing into the node, the imaginary part of the battery discharge current, the imaginary part of the battery charging current, and the imaginary part of the node load current. This represents the set of all nodes in the distribution network topology that are directly connected to node i through physical branches; It refers to the real component of the branch current flowing from node j to node i in the complex plane. The real component of the branch current flowing from node i to node j in the complex plane refers to the current in the branch. These two parameters are in opposite directions. The imaginary component of the branch current flowing from node j to node i in the complex plane. The imaginary component of the branch current flowing from node i to node j in the complex plane refers to the current in the branch. These two parameters are in opposite directions.

[0033] Each individual that satisfies the above constraints forms a feasible solution, thus constituting the initial population.

[0034] Furthermore, the optimization algorithm used is the artificial immune optimization algorithm, and the solution process is as follows:

[0035] use This represents a complete antibody individual, generating the initial antibody population;

[0036] Construct a comprehensive evaluation function To calculate the antibody affinity, a comprehensive evaluation function is used. It is expressed as follows:

[0037] ;

[0038] Among them, the first item It is the total amount of load restored, which depends on Defined power supply range; second item It's network loss, which needs to be solved through power flow calculation. The calculation requires using... Determined photovoltaic output and net energy storage output As power injection; the third item It is the number of switching actions, directly determined by... The difference from the state before the failure is determined; , and All are weighting coefficients; This represents the set of all loads whose power has been successfully restored. This represents the power regained by the k-th load that was originally de-energized after the implementation of the plan; This indicates the magnitude of the current in the l-th branch of the distribution network after the implementation of the scheme; Let represent the equivalent resistance of the l-th branch; B represents the set of all branches in the distribution network.

[0039] By establishing an artificial immune dynamics model, the self-healing constraints of the system can be solved, namely:

[0040] ;

[0041] in, The concentration of antibody i is represented by , and the probability of the scheme corresponding to antibody i being adopted in the iteration process is also represented by . This represents the affinity conversion coefficient (the rate constant for proposal adoption). The parameter β>0 represents the affinity of antibody i; the parameter β>0 is the network regulation strength coefficient; the parameter This represents the interaction coefficient, and its magnitude is... ,in This represents the estimated network loss for the protocol corresponding to antibody i; parameters Represents the natural extinction rate constant; parameter The penalty coefficient is proportional to the number of switching operations required for the scheme.

[0042] The artificial immune kinetic model is solved iteratively using numerical methods. For each antibody i, the concentration update formula from iteration number t to t+h (where h is the iteration step size) is as follows:

[0043] ;

[0044] Where parameters express ; Initial concentration Set to the same smaller positive value Set the maximum number of iterations. and the iteration termination condition;

[0045] When the iteration terminates, the antibody at its highest concentration is reached. This is the optimal solution.

[0046] Furthermore, in the process of solving the problem using the artificial immune optimization algorithm, the antibody mutation methods are as follows: the network topology gene uses bit flip mutation; the resource scheduling gene uses Gaussian perturbation mutation or polynomial mutation.

[0047] Handling infeasible solutions: If a new antibody violates the hard constraints of power distribution network operation during the iteration process, a heuristic rule-based repair strategy is adopted for feasibility processing.

[0048] Secondly, a distribution network self-healing system is provided for activation after a faulty section is isolated, including:

[0049] The optimization model construction module is used to construct a self-healing optimization model for the distribution network, taking the state of all controllable switches and the scheduling instructions of all controllable distributed resources in the distribution network as decision variables, with the goal of maximizing the recovery load and minimizing network losses and operational economy.

[0050] Solution module: Obtains the current operating status of the distribution network, uses optimization algorithms to solve the self-healing optimization model of the distribution network, and outputs the optimal solution;

[0051] The decision generation module is used to generate switch operation sequences and scheduling instructions for each controllable distributed resource based on the optimal solution.

[0052] Thirdly, an electronic device is provided, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory being used to store a computer program, the one or more processors invoking the computer program to cause the electronic device to perform the power distribution network self-healing method as described above.

[0053] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium including a computer program that, when run on an electronic device, causes the electronic device to perform the power distribution network self-healing method as described above.

[0054] Fifthly, a computer program product is provided, the computer program product including a computer program that, when the computer program is run on an electronic device, causes the electronic device to perform the power distribution network self-healing method as described above.

[0055] This invention proposes a self-healing collaborative optimization method, system, equipment, and storage medium for power supply restoration after a distribution network fault occurs and the faulty section is isolated. Unlike existing technologies that rely solely on network topology reconstruction or simply superimpose and independently optimize distributed energy resources and switching operations, the core of this invention lies in breaking through the limitations of traditional isolated optimization and constructing a nonlinear collaborative optimization model that considers deep interaction between power sources, grid, load, and storage.

[0056] Specifically, this invention deeply integrates and models the stochastic output prediction of distributed photovoltaic (PV) power, the dynamic state-of-charge (SOC) balance of energy storage systems, and the network topology reconstruction of the distribution network, rather than simply superimposing commands. This method establishes power flow and self-healing dynamic constraints in the distribution network and utilizes an improved immune algorithm to solve for the globally optimal strategy. In this process, this invention innovatively resolves the strong coupling conflict between "load recovery timeliness" and "system operation safety / economy": namely, the volatility of PV output and the timing characteristics of energy storage charging and discharging directly alter the power distribution of network nodes, thus affecting the feasibility of switching operations and power flow convergence; conversely, changes in topology also constrain the absorption path of distributed resources.

[0057] Therefore, this invention can automatically generate a globally near-optimal recovery strategy that comprehensively considers maximizing load restoration benefits, minimizing network active power losses, and minimizing switching operation costs. This strategy achieves millisecond / second-level collaborative decision-making among the "source-grid-storage" system, significantly improving the power supply resilience and intelligent self-healing level of the distribution network under extreme conditions, and maximizing the rapid transfer of loads and the security of system power supply. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. 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.

[0059] Figure 1 This is a flowchart of the power distribution network self-healing method provided in the embodiments of the present invention;

[0060] Figure 2 This is a diagram of an improved IEEE 33-node test system provided in an embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram showing the voltage changes of each node in the distribution network before and after the self-healing measures are put into operation, provided by an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0063] like Figure 1 As shown, how to coordinate the power output of distributed photovoltaic systems and the charging and discharging behavior of energy storage systems, and integrate them with network reconfiguration (switching operations) for optimization, is a core challenge currently facing the self-healing control of distribution networks. Based on this, the present invention provides a distribution network self-healing method, system, device, and storage medium that can quickly find the optimal solution from a global perspective based on real-time grid conditions, automatically generating an optimal or near-optimal recovery strategy that comprehensively considers load recovery, economy, and security, thereby significantly improving the power supply resilience and intelligence level of the distribution network. The technical solution of the present invention will be described in detail below with reference to specific embodiments.

[0064] This invention provides a self-healing method for power distribution networks, which is activated after a faulty section is isolated, such as... Figure 1As shown, it includes the following steps:

[0065] S1: Using the state of all controllable switches and the scheduling instructions of all controllable distributed resources in the distribution network as decision variables, and aiming at maximizing the recovery load and minimizing network loss and operational economy, a self-healing optimization model for the distribution network is constructed.

[0066] S2: Obtain the current operating status of the distribution network, use optimization algorithms to solve the self-healing optimization model of the distribution network, and output the optimal solution;

[0067] S3: Generate switching operation sequences and scheduling instructions for each controllable distributed resource based on the optimal solution.

[0068] In this embodiment, the solution of the distribution network self-healing optimization model using an artificial immune optimization algorithm is taken as an example. This algorithm treats power loss load as an antigen and encodes candidate self-healing schemes as antibodies. By simulating the dynamic evolution process of the immune system, it quickly finds the optimal strategy from a global perspective and outputs the optimal strategy that includes the switching operation sequence, photovoltaic setpoint, and energy storage dispatch instructions. The specific process will be described below.

[0069] (1) Antibody encoding: Each antibody represents a complete self-healing scheme and is characterized by a hybrid encoding method. Its gene structure consists of two parts: network topology gene and resource scheduling gene.

[0070] Use a binary vector (i.e., network topology genes) represent the state of all controllable switches (connecting switches and sectionalizing switches) in the network:

[0071] ;

[0072] Where N is the total number of controllable switches; For the first The state of a controllable switch, when When the value equals 1, it indicates that the controllable switch is closed; when... When the value is 0, it indicates that the controllable switch is open.

[0073] Use a vector (i.e., the resource scheduling gene) represents the scheduling instructions for all controllable distributed resources, as follows:

[0074] ;

[0075] in, For all controllable photovoltaic power output vectors, Let M be the output of the Mth controllable photovoltaic (PV) power generation, where M is the total number of controllable PV power generation units. The controllable PV power generation is constrained by environmental conditions, and the constraint model is expressed as follows:

[0076] ;

[0077] ;

[0078] in, This represents the maximum available output under current irradiance and temperature. Indicates solar irradiance intensity. Indicates photovoltaic conversion efficiency. The light-receiving area of ​​the photovoltaic array. Power temperature coefficient, Actual temperature of photovoltaic cells Standard test temperature;

[0079] and These are the charging power vector and discharging power vector for all energy storage systems, respectively. and Let represent the charging state vector and discharging state vector of all energy storage, respectively, and the mutual exclusion constraints that they must satisfy are as follows:

[0080] ;

[0081] in, and Let represent the charging state vector and discharging state vector of all energy storage in the t-th scheduling period, respectively;

[0082] Let be the charge state vector for all energy storage, which is affected by charging and discharging power, and its dynamic changes follow the constraints below:

[0083] ;

[0084] in, This represents the state of charge of the energy storage at the beginning of the t-th scheduling period. Rated capacity refers to the maximum total amount of electricity that an energy storage device can store. and These are the charging efficiency and discharging efficiency of energy storage, respectively, reflecting the conversion loss of energy during the charging and discharging process; To optimize the model's time step, i.e. the duration of the scheduling period (or the time interval between two adjacent scheduling moments).

[0085] In this embodiment, SOC is explicitly defined as a component of the decision vector, based on the following technical considerations:

[0086] Energy storage charging and discharging operations , The feasibility of this is limited by the current state of the State of Charge (SOC). For example, if the SOC is close to its lower limit, then a discharge command... Not executable; charging command if already close to the limit. Not executable. Using SOC as a decision variable allows the optimization algorithm to automatically verify and satisfy the requirements during the optimization process. Physical boundary constraints, and These are the lower and upper limits, respectively.

[0087] Therefore, this embodiment will... and , They are collectively defined as decision variables, and a nonlinear programming model incorporating dynamic physical constraints of energy storage is constructed. This setup is not a simple parameter calculation, but rather reflects an advanced modeling capability for the complex dynamic characteristics of energy storage systems, enabling the optimization algorithm to solve for the globally optimal self-healing recovery strategy while satisfying the energy storage safety boundary.

[0088] In addition, the charging and discharging power of energy storage must meet upper and lower limits.

[0089] (2) Generation of a high-quality initial antibody population: A complete antibody individual can be represented as When generating the initial antibody population, a batch of switching operation sequences is first quickly generated based on the post-fault distribution network topology using heuristic rules (such as "prioritize restoring important load feeders" and "restoring nearby feeders"). The candidate values ​​are then determined; for each candidate topology, reasonable setpoints are allocated to all controllable distributed resources through power balancing and security checks to ensure that the scheme represented by the antibody meets the following constraints:

[0090] ;

[0091] ;

[0092] in, , , , and These are respectively represented as the real part of the branch current, the real part of the photovoltaic power current flowing into the node, the real part of the battery discharge current, the real part of the battery charging current, and the real part of the node load current. , , , and These are respectively represented as the imaginary part of the branch current, the imaginary part of the photovoltaic power current flowing into the node, the imaginary part of the battery discharge current, the imaginary part of the battery charging current, and the imaginary part of the node load current. This represents the set of all nodes in the distribution network topology that are directly connected to node i through physical branches; It refers to the real component of the branch current flowing from node j to node i in the complex plane. The real component of the branch current flowing from node i to node j in the complex plane refers to the current in the branch. These two parameters are in opposite directions. The imaginary component of the branch current flowing from node j to node i in the complex plane. The two parameters, i.e., the imaginary component of the branch current flowing from node i to node j in the complex plane, are opposite in direction. When the antibody-represented scheme self-heals and satisfies the above constraints, a feasible solution is formed, thus constructing the initial antibody population. , This represents the initial antibody population size (i.e., the total number of initial antibodies). This process ensures that the optimization search begins from a high-quality feasible solution space.

[0093] (3) Global parallel optimization based on artificial immune dynamics: The quality of the antibody (scheme), i.e. its affinity, will be evaluated through a comprehensive evaluation function. To calculate this, the function is directly related to the above-mentioned encoded variables, as shown below:

[0094] ;

[0095] Among them, the first item It is the total amount of load restored, which depends on Defined power supply range; second item It's network loss, which needs to be solved through power flow calculation. The calculation requires using... Determined photovoltaic output and net energy storage output As power injection; the third item It is the number of switching actions, directly determined by... The difference from the state before the failure is determined; , and These are the weight coefficients for each objective; This represents the set of all loads whose power has been successfully restored. This represents the power regained by the k-th load that was originally de-energized after the implementation of the plan; This indicates the current magnitude of each branch in the distribution network after the implementation of the plan; Let B represent the inherent physical parameters of the distribution network lines; let B represent the set of all branches in the distribution network. Through this encoding method and objective function (comprehensive evaluation function), the complex self-healing problem of the power grid is transformed into a mathematical optimization problem that can be directly handled by intelligent optimization algorithms such as artificial immune systems.

[0096] (4) Optimal strategy extraction and output: By establishing an artificial immune dynamics model, the self-healing constraints of the system are solved, i.e.:

[0097] ;

[0098] in, The concentration of antibody i is represented by , and the probability of the scheme corresponding to antibody i being adopted in the iteration process is also represented by . This represents the affinity conversion coefficient (the rate constant for proposal adoption). The parameter β>0 represents the affinity of antibody i; the parameter β>0 is the network regulation strength coefficient; the parameter This represents the interaction coefficient, and its magnitude is... ,in This represents the estimated network loss for the protocol corresponding to antibody i; parameters Represents the natural extinction rate constant; parameter This is a penalty coefficient, proportional to the number of switching operations required for the scheme.

[0099] Artificial immune dynamics models are a continuous mathematical abstraction of traditional discrete immune operators, in which... Representing cloning and proliferation, antibodies with higher affinity exhibit a faster concentration growth rate. The larger the value, the more likely it is that superior individuals are replicated in the population. Representing mutation and inhibition, this item describes the interaction between antibodies. When there are too many similar antibodies (of the same kind) in the population (i.e., the concentration is too high), this item will generate negative feedback, prompting the antibodies to mutate or die naturally in order to maintain diversity.

[0100] The encoding method in this embodiment is a hybrid encoding (binary + real number), and different encoding types require different mutation strategies. Meanwhile, the distribution network reconfiguration problem has hard constraints (such as radial operation and no islanding), necessitating the handling of infeasible solutions.

[0101] Regarding the mutation method: Since this embodiment uses hybrid encoding, the mutation operation needs to be performed according to the gene type. For the network topology gene: bit flip mutation is used, that is, with a very small probability. Changing 0 to 1 or 1 to 0 alters the switching state. Resource scheduling mechanism: Employs Gaussian perturbation or polynomial mutation, which involves adding small random perturbations near the current power setpoint to explore optimal power allocation.

[0102] Regarding the handling of infeasible solutions: During the iteration process, if a new antibody violates the hard constraints of distribution network operation (such as forming an electrical island or a network loop), it must be made feasible. This embodiment adopts a heuristic rule-based repair strategy: Topology repair: If a loop occurs, switches on non-critical load feeders are disconnected first; if an island occurs, the nearest tie switch is forcibly closed to restore power supply; Power correction: If a dispatch command causes node voltage to exceed limits, the output of distributed power sources is scaled proportionally. After the above processing, it is ensured that all antibodies entering the concentration update formula are feasible solutions that satisfy the physical constraints.

[0103] The artificial immune kinetic model is solved iteratively using numerical methods. For each antibody i, the concentration update formula from iteration number t to t+h (where h is the iteration step size) is as follows:

[0104] ;

[0105] Where parameters express In this model, the rate of change of antibody concentration mainly depends on the current concentration state of each antibody and the properties of the protocol itself, and does not directly depend on time t itself; therefore, this function belongs to an autonomous system. However, in numerical solutions, to conform to the standard differential equation form... Here, the time variable t is preserved in the function definition. For example, regarding the expression... Meaning: This is an intermediate calculation step in the solution process; where h is the iteration step size. The slope is calculated at the current moment; this expression means that after advancing half an iteration step in time, the current state plus half the increment is used as a temporary state, and the rate of change at the midpoint is recalculated by substituting it into the dynamic model, thereby improving the accuracy and stability of numerical integration.

[0106] initial concentration Set to the same smaller positive value:

[0107] ;

[0108] Set the maximum number of iterations The value is 100. The iteration terminates when the system reaches a stable state, i.e., the iteration termination condition is met.

[0109] ;

[0110] Where parameters This represents the concentration change threshold, which is assumed to be 10 in this example. -4 When the iteration terminates, the antibody at its highest concentration is... The corresponding self-healing solution is the global approximate optimal solution found by the algorithm that achieves the best balance among load recovery, economy, and operability.

[0111] This embodiment does not simply apply the standard immune algorithm, but rather improves it at the application level for the specific multi-objective optimization problem of 'distribution network self-healing'. Customized coding: A hybrid coding system integrating network topology and resource scheduling is designed, transforming the complex network reconstruction problem into a machine-solvable vector optimization problem. Dynamic constraint integration: Complex physical constraints such as power flow calculation and power balance are directly embedded into the dynamic evolution equation of antibody concentration (e.g., through penalty terms or feasibility processing), improving the algorithm's convergence efficiency.

[0112] The above embodiments provide a distribution network self-healing method that, based on real-time grid status, coordinates the output of distributed photovoltaics and the charging and discharging behavior of energy storage systems, and integrates them with network reconfiguration (switching operations) for integrated optimization. It quickly seeks optimization from a global perspective and automatically generates the optimal or near-optimal recovery strategy that comprehensively considers load recovery, economy, and security, thereby significantly improving the power supply resilience and intelligence level of the distribution network to achieve rapid load transfer and maximize system power supply security.

[0113] This invention also provides a distribution network self-healing system for activation after a faulty section is isolated, comprising:

[0114] The optimization model construction module is used to construct a self-healing optimization model for the distribution network, taking the state of all controllable switches and the scheduling instructions of all controllable distributed resources in the distribution network as decision variables, with the goal of maximizing the recovery load and minimizing network losses and operational economy.

[0115] Solution module: Obtains the current operating status of the distribution network, uses optimization algorithms to solve the self-healing optimization model of the distribution network, and outputs the optimal solution;

[0116] The decision generation module is used to generate switch operation sequences and scheduling instructions for each controllable distributed resource based on the optimal solution.

[0117] It should be understood that the functional unit modules in the various embodiments of the present invention can be concentrated in one processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated into one unit module, and can be implemented in hardware or software.

[0118] This invention also provides an electronic device, which includes one or more processors and a memory; the memory is coupled to the one or more processors and is used to store a computer program, wherein the one or more processors call the computer program to cause the electronic device to perform the power distribution network self-healing method as described above.

[0119] This invention also provides a computer-readable storage medium comprising a computer program that, when executed on an electronic device, causes the electronic device to perform the power distribution network self-healing method as described above.

[0120] This invention also provides a computer program product, which includes a computer program that, when run on an electronic device, causes the electronic device to perform the power distribution network self-healing method as described above.

[0121] The effects of the technical solution of the present invention will be further illustrated below with a simulation example.

[0122] This simulation example uses PSCAD to verify the effectiveness of self-healing control in a distribution network considering the stochasticity of distributed renewable energy generation. To evaluate the effectiveness of the proposed self-healing control strategy in a distribution network containing distributed power sources, a simulation test environment was built based on the PSCAD simulation platform. The simulation hardware configuration is as follows: Intel Core i7 processor (3.2GHz), 64GB of RAM, and Windows 11 operating system. The network model used in the simulation is an improved IEEE 33-node distribution test system. To simulate a high proportion of renewable energy integration, distributed photovoltaic power generation units (marked as PV1 and PV2 in the figure) and their supporting battery energy storage systems were connected at nodes 5 and 16 respectively, thus forming the following... Figure 2 The improved test system topology is shown.

[0123] After applying the self-healing method for distribution networks provided by this invention, simulations were performed to obtain the voltage changes at each node in the distribution network before and after the self-healing measures were put into operation, such as... Figure 3 As shown in the figure, the horizontal axis represents the electrical node numbers from the beginning to the end of the system, and the vertical axis represents the corresponding voltage amplitude (per unit value). From the overall trend, under the traditional operating mode without self-healing control (blue dotted line), the system voltage shows a significant monotonic decreasing trend along the feeder direction, and at multiple nodes, the voltage has dropped to near the allowable lower limit of 0.95 pu, indicating that the system voltage level is too low and there is a risk of voltage exceeding the limit. In contrast, after implementing the self-healing control strategy proposed in this invention (red square line), the overall system voltage level is significantly improved and stabilized within the acceptable range of 0.95 pu to 1.0 pu. Crucially, after introducing the coordinated regulation of distributed power sources and energy storage systems, the voltage curve becomes smoother, effectively suppressing the gradual voltage drop phenomenon in traditional distribution networks, demonstrating the positive role of the proposed strategy in maintaining system voltage stability and improving power supply quality.

[0124] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0125] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, electronic devices, storage media, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0126] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0127] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0128] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0129] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A self-healing method for a power distribution network, characterized in that, When the faulty section is isolated and the system is started, the following steps are included: Using the states of all controllable switches and the scheduling instructions of all controllable distributed resources in the distribution network as decision variables, and aiming at maximizing the recovery load and minimizing network losses and operational economics, a self-healing optimization model for the distribution network is constructed. The current operating status of the distribution network is obtained, and the self-healing optimization model of the distribution network is solved using an optimization algorithm to output the optimal solution. Based on the optimal solution, generate the switching operation sequence and the scheduling instructions for each controllable distributed resource.

2. The self-healing method for power distribution networks according to claim 1, characterized in that, When solving the self-healing optimization model of the distribution network using optimization algorithms, the following encoding method is adopted: The decision variables are encoded using a hybrid coding method, which includes two parts: Using binary vectors This represents the state of all controllable switches in the network, corresponding to the network topology gene, as shown below: ； Where N is the total number of controllable switches; For the first The state of a controllable switch, when When the value equals 1, it indicates that the controllable switch is closed; when... When the value is 0, it indicates that the controllable switch is open; Use a vector The scheduling instructions for all controllable distributed resources are represented by the resource scheduling gene, as follows: ； in, For all controllable photovoltaic power output vectors, and These are the charging power vector and discharging power vector for all energy storage systems, respectively. and These represent the charging state vector and discharging state vector of all energy storage devices, respectively. This represents the charged state vector for all stored energy.

3. The self-healing method for power distribution networks according to claim 2, characterized in that, Constrained by environmental conditions, the constraint model is expressed as follows: ； ； in, This represents the maximum available output under current irradiance and temperature. Indicates solar irradiance intensity. Indicates photovoltaic conversion efficiency. The light-receiving area of ​​the photovoltaic array. Power temperature coefficient, Actual temperature of photovoltaic cells Standard test temperature; and The mutual exclusion constraint is satisfied as follows: ； in, and Let represent the charging state vector and discharging state vector of all energy storage in the t-th scheduling period, respectively; Dynamic changes are subject to the following constraints: ； in, This represents the state of charge of the energy storage at the beginning of the t-th scheduling period. Rated capacity refers to the maximum total amount of electricity that an energy storage device can store. and These are the charging efficiency and discharging efficiency of energy storage, respectively, reflecting the conversion loss of energy during the charging and discharging process; To optimize the model's time step, i.e. the duration of the scheduling period.

4. The self-healing method for power distribution networks according to claim 1, characterized in that, When solving the self-healing optimization model of the distribution network using optimization algorithms, the initial population is generated using the following method: Based on the post-fault distribution network topology, a batch of switching operation sequences is quickly generated using heuristic rules. Candidate values; for For each candidate value, through power balancing and security checks, reasonable scheduling instructions are allocated to all controllable distributed resources. Ensure that the schemes represented by individuals meet the following constraints: ； ； in, , , , and These are respectively represented as the real part of the branch current, the real part of the photovoltaic power current flowing into the node, the real part of the battery discharge current, the real part of the battery charging current, and the real part of the node load current. , , , and These are respectively represented as the imaginary part of the branch current, the imaginary part of the photovoltaic power current flowing into the node, the imaginary part of the battery discharge current, the imaginary part of the battery charging current, and the imaginary part of the node load current. This represents the set of all nodes in the distribution network topology that are directly connected to node i through physical branches; Each individual that satisfies the above constraints forms a feasible solution, thus constituting the initial population.

5. The self-healing method for power distribution networks according to claim 2, characterized in that, The optimization algorithm used is the artificial immune optimization algorithm, and the solution process is as follows: use This represents a complete antibody individual, generating the initial antibody population; Construct a comprehensive evaluation function To calculate the antibody affinity, a comprehensive evaluation function is used. It is expressed as follows: ； Among them, the first item It is the total amount of load restored, which depends on Defined power supply range; second item It's network loss, which needs to be solved through power flow calculation. The calculation requires using... Determined photovoltaic output and net energy storage output As power injection; the third item It is the number of switching actions, directly determined by... The difference from the state before the failure is determined; , and All are weighting coefficients; This represents the set of all loads whose power has been successfully restored. This represents the power regained by the k-th load that was originally de-energized after the implementation of the plan; This indicates the magnitude of the current in the l-th branch of the distribution network after the implementation of the scheme; B represents the equivalent resistance of the l-th branch in the distribution network; B represents the set of all branches in the distribution network. By establishing an artificial immune dynamics model, the self-healing constraints of the system can be solved, namely: ； in, The concentration of antibody i is represented by , and the probability of the scheme corresponding to antibody i being adopted in the iteration process is also represented by . Indicates the affinity conversion coefficient; The parameter β>0 represents the affinity of antibody i; the parameter β>0 is the network regulation strength coefficient; the parameter This represents the interaction coefficient, and its magnitude is... ,in This represents the estimated network loss for the protocol corresponding to antibody i; parameters Represents the natural extinction rate constant; parameter The penalty coefficient is proportional to the number of switching operations required for the scheme. The artificial immune kinetic model is solved iteratively using numerical methods. For each antibody i, the concentration update formula from iteration number t to t+h is as follows: ； Where parameters express h is the iteration step size; the initial concentration Set to the same smaller positive value Set the maximum number of iterations. and the iteration termination condition; When the iteration terminates, the antibody at its highest concentration is reached. This is the optimal solution.

6. The self-healing method for power distribution networks according to claim 5, characterized in that, In the process of solving the problem using the artificial immune optimization algorithm, the antibody mutation methods are as follows: the network topology gene uses bit flip mutation; the resource scheduling gene uses Gaussian perturbation mutation or polynomial mutation. Handling infeasible solutions: If a new antibody violates the hard constraints of power distribution network operation during the iteration process, a heuristic rule-based repair strategy is adopted for feasibility processing.

7. A self-healing system for a power distribution network, characterized in that, Used for startup after a faulty section has been isolated, including: The optimization model construction module is used to construct a self-healing optimization model for the distribution network, taking the state of all controllable switches and the scheduling instructions of all controllable distributed resources in the distribution network as decision variables, with the goal of maximizing the recovery load and minimizing network losses and operational economy. Solution module: Obtains the current operating status of the distribution network, uses optimization algorithms to solve the self-healing optimization model of the distribution network, and outputs the optimal solution; The decision generation module is used to generate switch operation sequences and scheduling instructions for each controllable distributed resource based on the optimal solution.

8. An electronic device, characterized in that, The electronic device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store a computer program, and the one or more processors invoke the computer program to cause the electronic device to perform the power distribution network self-healing method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a computer program that, when run on an electronic device, causes the electronic device to perform the power distribution network self-healing method as described in any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when run on an electronic device, causes the electronic device to perform the power distribution network self-healing method as described in any one of claims 1-6.

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