Distributed flexible resource power supply scheme generation method and device under extreme disaster

Abstract

The invention discloses a distributed flexible resource power supply scheme generation method and device under an extreme disaster, a storage medium and computer equipment. The method comprises the following steps: constructing a first-stage objective function and a first constraint condition of a target power distribution network; solving the first-stage objective function based on a first constraint condition to obtain an optimal installation position and an optimal installation capacity of each distributed flexible resource in the target power distribution network; according to the optimal installation position and installation capacity of each distributed flexible resource, generating a network topology structure of the target power distribution network, and according to the network topology structure, constructing a second-stage objective function and a second constraint condition of the target power distribution network; and solving the second-stage target function based on a second constraint condition to obtain island division ranges of the network topology of the target power distribution network under the extreme disaster and a load recovery sequence of each island division range, and determining the load recovery sequence of the target power distribution network based on each island division range and the load recovery sequence. And generating a power supply scheme of the distributed flexible resources under the extreme disaster.

Description

Technical Field

[0001] This application relates to the field of power distribution network operation optimization technology, and in particular to a method and apparatus for generating distributed flexible resource power supply schemes under extreme disasters, as well as a storage medium and computer equipment. Background Technology

[0002] With the intensification of global climate change, large-scale power outages in power distribution networks caused by extreme disasters (such as earthquakes, typhoons, and floods) are becoming increasingly frequent, leading to industrial production shutdowns, communication disruptions, and social disorder, resulting in huge economic losses. Traditional power distribution networks rely on centralized power generation and rigid transmission models. Under extreme disasters, power outages due to line faults and equipment damage occur, and the restoration process relies on manual inspection and segmented repair, which is inefficient. In existing technologies, some studies have achieved local power restoration through the coordinated scheduling of distributed photovoltaic and mobile emergency power sources, but these technologies suffer from problems such as limited power source types, uneven spatial and temporal resource allocation, and insufficient coordination of flexible resources, resulting in low reliability of power restoration and weak capacity to guarantee critical loads, making it difficult to meet the needs of rapid power restoration in extreme scenarios.

[0003] To address the challenges of extreme disasters, the concept of a "resilient grid" has been proposed, emphasizing the proactive restoration of loads and the elastic maintenance of the system under fault conditions through the flexible deployment and rapid response capabilities of distributed flexible resources. Distributed flexible resources (such as distributed photovoltaic, wind power, energy storage systems, and controllable loads) have become key to improving the resilience of distribution networks due to their adjustability, modularity, and proximity deployment characteristics. However, existing technologies mostly focus on single resource optimization or static islanding, lacking systematic research on multi-source energy storage interconnection, dynamic load weight allocation, and resource-load coordinated scheduling. This results in low resource utilization efficiency and unclear priority of important load restoration under extreme disasters, failing to achieve a balance between the economy and reliability of power supply restoration. Summary of the Invention

[0004] In view of this, this application provides a method and apparatus for generating distributed flexible resource power supply schemes under extreme disasters, a storage medium, and a computer device. It proposes a two-stage optimization method: first, by constructing a first-stage objective function, the optimal installation location and capacity of distributed flexible resources that meet the first constraint condition are determined; second, based on the constructed second-stage objective function, the islanding range and load restoration sequence that meet the second constraint condition are determined, thereby coordinating the spatiotemporal interaction between various types of distributed flexible resources and loads, prioritizing the restoration of important loads, improving power restoration efficiency and system stability, and facilitating the rapid and reliable power supply of the target distribution network under extreme disasters.

[0005] According to one aspect of this application, a method for generating distributed flexible resource power supply schemes under extreme disasters is provided, comprising:

[0006] Construct a first-stage objective function for the target distribution network and a first constraint condition corresponding to the first-stage objective function, wherein the first-stage objective function is used to determine the location scheme and capacity scheme of distributed flexible resources in the target distribution network;

[0007] Based on the first constraint, the objective function of the first stage is solved by the first optimization algorithm to obtain the optimal installation location and installation capacity of each distributed flexible resource in the target distribution network.

[0008] Based on the optimal installation location and installation capacity of each distributed flexible resource, the network topology of the target distribution network is generated. Based on the network topology of the target distribution network, the second-stage objective function of the target distribution network and the second constraint conditions corresponding to the second-stage objective function are constructed. The second-stage objective function is used to determine the optimal islanding scheme corresponding to the network topology of the target distribution network under extreme disasters.

[0009] Based on the second constraint, the objective function of the second stage is solved by the second optimization algorithm to obtain the island division range of the network topology of the target distribution network under extreme disasters, as well as the load restoration sequence corresponding to each island division range. Based on each island division range and the corresponding load restoration sequence, a power supply scheme for distributed flexible resources under extreme disasters is generated.

[0010] According to another aspect of this application, a distributed flexible resource power supply scheme generation device under extreme disasters is provided, comprising:

[0011] The first construction module is used to construct the first-stage objective function of the target distribution network and the first constraint condition corresponding to the first-stage objective function, wherein the first-stage objective function is used to determine the location scheme and capacity scheme of the distributed flexible resources in the target distribution network.

[0012] The first solution module is used to solve the objective function of the first stage based on the first constraint condition and through the first optimization algorithm to obtain the optimal installation location and installation capacity of each distributed flexible resource in the target distribution network.

[0013] The second construction module is used to generate the network topology of the target distribution network according to the optimal installation location and installation capacity of each distributed flexible resource, and to construct the second-stage objective function of the target distribution network and the second constraint condition corresponding to the second-stage objective function according to the network topology of the target distribution network. The second-stage objective function is used to determine the optimal islanding scheme corresponding to the network topology of the target distribution network under extreme disasters.

[0014] The second solution module is used to solve the objective function of the second stage based on the second constraint conditions using the second optimization algorithm, to obtain the island division range of the network topology of the target distribution network under extreme disasters, and the load restoration sequence corresponding to each island division range, and to generate a power supply scheme for distributed flexible resources under extreme disasters based on each island division range and the corresponding load restoration sequence.

[0015] According to another aspect of this application, a storage medium is provided that stores a computer program thereon, which, when executed by a processor, implements the above-described method for generating distributed flexible resource power supply schemes under extreme disasters.

[0016] According to another aspect of this application, a computer device is provided, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the program to implement the above-described method for generating distributed flexible resource power supply schemes under extreme disasters.

[0017] Based on the above technical solutions, this application provides a method and apparatus for generating distributed flexible resource power supply schemes under extreme disasters, a storage medium, and a computer device. It proposes a two-stage optimization method: first, by constructing a first-stage objective function, the optimal installation location and capacity of distributed flexible resources satisfying the first constraint condition are determined; second, based on the constructed second-stage objective function, the islanding range and load restoration sequence satisfying the second constraint condition are determined, thereby coordinating the spatiotemporal interaction between various types of distributed flexible resources and loads, prioritizing the restoration of important loads, improving power restoration efficiency and system stability, and facilitating rapid and reliable power supply to the target distribution network under extreme disasters.

[0018] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0020] Figure 1 A flowchart illustrating a method for generating a distributed flexible resource power supply scheme under extreme disasters, provided in an embodiment of this application, is shown.

[0021] Figure 2 A flowchart illustrating a method for solving a first-stage objective function according to an embodiment of this application is shown.

[0022] Figure 3 This illustration shows a schematic diagram of a distributed flexible resource power supply scheme generation device under extreme disasters, provided in an embodiment of this application.

[0023] Figure 4 A schematic diagram of the device structure of a computer device provided in an embodiment of this application is shown. Detailed Implementation

[0024] The present application will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in the embodiments of the present application can be combined with each other.

[0025] This embodiment provides a method for generating distributed flexible resource power supply schemes under extreme disasters, such as... Figure 1 As shown, the method includes:

[0026] Step 101: Construct the first-stage objective function of the target distribution network and the first constraint condition corresponding to the first-stage objective function, wherein the first-stage objective function is used to determine the location scheme and capacity scheme of the distributed flexible resources in the target distribution network.

[0027] Step 102: Based on the first constraint, solve the objective function of the first stage using the first optimization algorithm to obtain the optimal installation location and installation capacity of each distributed flexible resource in the target distribution network.

[0028] Step 103: Generate the network topology of the target distribution network based on the optimal installation location and installation capacity of each distributed flexible resource. Based on the network topology of the target distribution network, construct the second-stage objective function of the target distribution network and the second constraint condition corresponding to the second-stage objective function. The second-stage objective function is used to determine the optimal islanding scheme corresponding to the network topology of the target distribution network under extreme disasters.

[0029] Step 104: Based on the second constraint, the objective function of the second stage is solved by the second optimization algorithm to obtain the islanding range of the network topology of the target distribution network under extreme disasters, as well as the load restoration sequence corresponding to each islanding range. Based on each islanding range and the corresponding load restoration sequence, a power supply scheme for distributed flexible resources under extreme disasters is generated.

[0030] This application provides a method for generating a distributed flexible resource power supply scheme under extreme disasters, which can be divided into two steps. The first step is to determine the optimal installation location and capacity of distributed flexible resources (such as distributed photovoltaic units, distributed wind turbine units, and distributed energy storage systems) in the target distribution network. The second step is to divide each distributed flexible resource into islands under extreme disasters based on the optimal installation location and capacity of the distributed flexible resources, obtain the range of each island division and the corresponding load recovery sequence, and finally generate a power supply scheme for distributed flexible resources under extreme disasters.

[0031] Specifically, the first-stage objective function can be constructed. This first-stage objective function may include a network loss objective function and a cost objective function. The network loss objective function is used to limit the impact of the distributed flexible resource layout on tributary transmission efficiency; the cost objective function is used to limit investment costs, maintenance costs, and operating costs. To ensure the feasibility of the distributed flexible resource location and capacity allocation scheme, multi-dimensional constraints, i.e., the first constraints, need to be set. Here, the first constraints can constrain the location and capacity allocation scheme from multiple aspects.

[0032] Subsequently, the first optimization algorithm can be used to solve the objective function of the first stage. During the solution process, the first constraint condition needs to be considered to obtain a suitable location and capacity scheme. The first optimization algorithm can be determined based on actual needs. Finally, the optimal installation location and capacity of each distributed flexible resource are output, providing basic data for subsequent network topology construction.

[0033] Based on the optimal installation location and capacity of each distributed flexible resource, as well as the pre-constructed branch requirements, the network topology of the target distribution network can be generated. Then, based on the network topology, a second-stage objective function can be constructed. Specifically, the second-stage objective function can be an objective function aimed at maximizing load restoration. Taking into account the output of distributed flexible resources and load priorities within the island, priority restoration of critical loads is achieved by dynamically adjusting the island boundaries. For example, primary load nodes such as hospitals and communication base stations are given the highest priority, and their power restoration order takes precedence over ordinary industrial load nodes, ensuring the normal operation of core social functions under extreme disasters.

[0034] The islanding process must satisfy multiple real-time constraints, also known as the second constraint. These second constraints can impose limitations on the islanding scheme from multiple perspectives. Subsequently, a second optimization algorithm can be used to solve the objective function for the second stage. During the solution process, the second constraint must be considered to obtain a suitable islanding scheme. The second optimization algorithm can be determined based on actual needs. Finally, an islanding scheme adapted to extreme disaster scenarios is generated, including the scope of each island and the order of load recovery within each island.

[0035] Finally, based on the solution results of the objective function in the second stage, a power supply scheme for distributed flexible resources under extreme disasters can be generated, clarifying the correspondence between distributed flexible resources and loads within each island and the load recovery sequence. For example, in a typhoon disaster, the generated power supply scheme can prioritize distributed photovoltaic units to power communication base stations, and distributed energy storage systems can release electricity at night to support hospital lighting. Furthermore, the power supply scheme has dynamic adjustment capabilities, updating island boundaries and recovery sequences in real time according to disaster development (such as fault propagation and new resource access), ensuring that the power supply strategy always adapts to changes in system state and maximizes load recovery benefits under extreme disasters. Specifically, the generation frequency of the power supply scheme can be set, and power supply scheme generation events can be triggered periodically according to the generation frequency. Then, based on the changing total available power supply of each distributed flexible resource, the island division range can be redefined multiple times.

[0036] By applying the technical solution of this embodiment, a two-stage optimization method is proposed: First, by constructing a first-stage objective function, the optimal installation location and installation capacity of distributed flexible resources that meet the first constraint condition are determined; second, based on the constructed second-stage objective function, the islanding range and load restoration sequence that meet the second constraint condition are determined, thereby coordinating the spatiotemporal interaction between various types of distributed flexible resources and loads, prioritizing the restoration of important loads, improving power restoration efficiency and system stability, and facilitating the rapid and reliable power supply of the target distribution network under extreme disasters.

[0037] Optionally, in this embodiment, the "construction of the first-stage objective function of the target distribution network" in step 101 includes: obtaining the number of preset branches corresponding to the target distribution network, and constructing a network loss objective function with the goal of minimizing the sum of network losses of each branch based on the number of preset branches and the current and resistance of each branch; constructing a cost objective function with the goal of minimizing the sum of investment cost, maintenance cost and operating cost of the distributed flexible resources; and constructing the first-stage objective function according to the network loss objective function and the cost objective function; the distributed flexible resources include distributed wind turbines, distributed photovoltaic units and distributed energy storage systems; the first constraints corresponding to the first-stage objective function include power flow constraints, distributed wind and solar turbine capacity constraints, candidate node installation capacity constraints, node voltage constraints, branch current constraints and energy storage charging and discharging constraints;

[0038] The power flow constraints are as follows:

[0039]

[0040] in, Let be the active power output at node i, representing the upstream grid, distributed generation, energy storage system charging and discharging power, and conventional load demand at time t. U represents the reactive power output at time t, which includes the upstream power grid at node i, distributed power sources, the charging and discharging power of the energy storage system, and the reactive power output of the conventional load demand. i,t and U j,t G represents the voltages at nodes i and j at time t, respectively. ij and B ij These are the real and imaginary parts of the nodal admittance matrix, respectively, θ ij,t Let N be the phase angle difference between nodes i and j at time t. bus For the set of all nodes;

[0041] The capacity constraints of the distributed wind and solar turbine units are as follows:

[0042]

[0043] Among them, P i WT,rated , These are the rated power of the distributed wind turbine and the distributed photovoltaic unit at nodes i and j, respectively. μ represents the unit maintenance cost of the distributed photovoltaic unit. s S represents the upper limit of the proportion of distributed wind and solar power capacity in the target distribution network. sub The rated capacity of the upstream substation;

[0044] The installation capacity constraints for the candidate nodes are as follows:

[0045]

[0046] Among them, P i DG,rated The rated capacity of the distributed renewable energy installed at node i. and P represents the number of distributed wind turbines installed at node i and the number of distributed photovoltaic units installed at node j, respectively. i DG,max P is the maximum total capacity of distributed power supplies installed at node i. i WT,max and These represent the maximum allowable installation capacity of distributed wind turbines at node i and the maximum allowable installation capacity of distributed photovoltaic units at node j, respectively. and These represent the unit rated capacity of the distributed wind turbine installed at node i and the unit rated capacity of the distributed photovoltaic unit installed at node j, respectively.

[0047] The node voltage constraints are as follows:

[0048] V i min≤V i,t ≤V i max ;

[0049] Among them, V i,t Let V be the voltage value of node i at time t. i min and V i max These are the minimum and maximum voltage values ​​at node i, respectively;

[0050] The branch current constraint conditions are as follows:

[0051]

[0052] Among them, I l,t Let be the branch current of branch l at time t. Let be the maximum branch current flowing through branch l;

[0053] The energy storage charging and discharging constraints are as follows:

[0054]

[0055] in, Let i be the rated power of the distributed energy storage system at time t.

[0056] The charging power of the distributed energy storage system at node i at time t.

[0057] In this embodiment, after distributed wind turbines, distributed photovoltaic units, and distributed energy storage systems are connected to the target distribution network under extreme disasters, power loss will inevitably occur during power transmission due to factors such as line resistance. Reducing network loss can improve system efficiency, reduce operating costs, enhance grid stability, and optimize resource allocation. The objective function for network loss can be expressed as follows:

[0058]

[0059] Among them, F I For system network loss, I j R is the current in branch j. j Let N be the resistance of branch j. x The preset number of branches in the target distribution network;

[0060] Under extreme disasters, reducing costs and optimizing resource allocation can not only accelerate the optimization and upgrading of the energy structure and achieve sustainable energy development, but also enhance the reliability of power supply. With investment, maintenance, and operating costs as the objective, the cost objective function of the target distribution network can be expressed as follows:

[0061] minF C =C inv +C m +C ope ;

[0062] Where, minF C C represents the minimum cost of the target distribution network. inv C m C ope These represent the investment cost, maintenance cost, and operating cost of distributed flexible resources, respectively.

[0063] Taking all factors into consideration, and using the optimal installation location and capacity of distributed wind-solar-storage systems as optimization variables, the objective function for site selection and capacity determination of distributed wind-solar-storage systems (i.e., the first-stage objective function) can be expressed as follows:

[0064] min f=λ I F I +λ C F C ;

[0065] In the formula, λ I and λ C These are the weighting coefficients for the network loss target and the cost target, respectively, and λ I +λ C =1, which can be given according to actual planning needs.

[0066] It should be noted that the output of the aforementioned distributed wind turbines and distributed photovoltaic units can be calculated based on existing output models, and the charging and discharging power of distributed energy storage systems can be calculated based on existing charging and discharging power models, without any limitations here.

[0067] Optionally, in this embodiment, the investment cost calculation formula for the distributed flexible resources is as follows:

[0068]

[0069] in, These represent the investment costs of distributed wind turbines, distributed photovoltaic power plants, and distributed energy storage systems, respectively, where d is the discount rate and n is the investment cost. WT n PV n ESS The full lifecycles of the distributed wind turbine, the distributed photovoltaic generator, and the distributed energy storage system are respectively, c wt and c pv These are the unit investment costs of the distributed generator set and the distributed photovoltaic unit, respectively. and P represents the converter cost and energy storage cost of the distributed energy storage system, respectively. i WT,rated, These are the rated powers of the distributed wind turbine, the distributed photovoltaic generator, and the distributed energy storage system at nodes i, j, and m, respectively. Let m be the installed capacity of the distributed energy storage system at node m.

[0070] The formula for calculating the maintenance cost of the distributed flexible resources is as follows:

[0071]

[0072] in, The maintenance costs are respectively for the distributed wind turbine, the distributed photovoltaic generator, and the distributed energy storage system. and These are the unit maintenance costs for the distributed wind turbine and the distributed photovoltaic unit, respectively. and The fixed maintenance cost and variable maintenance cost of the distributed energy storage system are respectively, h m The number of operating hours of the distributed energy storage system at node m;

[0073] The formula for calculating the operating cost of the distributed flexible resources is as follows:

[0074]

[0075] Among them, C ope C represents the operating cost of the distributed flexible resource. up,t P represents the cost of purchasing electricity from the upper-level power grid at time t. t sub C represents the active power supplied by the upstream power grid to the target distribution network. loss Cost per unit of network loss For the active power loss of branch l, These represent the conventional load demand of node i at time t, the charging power of the distributed energy storage system, the active power output of the distributed photovoltaic unit, and the active power output of the distributed wind turbine, respectively, where Δt is the unit of time, and I l,t R is the current flowing through branch l at time t. l Let Ω be the resistance of branch l, and Ω be the set of load conditions. b This represents the total runtime.

[0076] In the embodiments of this application, optionally, as shown... Figure 2 As shown, step 102 includes:

[0077] Step 102-1: Determine the multiple dimensions corresponding to each particle based on the number of parameters to be solved in the objective function of the first stage.

[0078] Step 102-2: Based on the dimension of each particle, randomly generate a particle set that meets the first constraint condition, wherein the particle set includes multiple target particles, and each target particle includes data of the multiple dimensions.

[0079] Steps 102-3: Based on the multi-dimensional data of each target particle and the objective function of the first stage, calculate the function value of each target particle, and determine the optimal particle from the particle set according to the function value of each target particle.

[0080] Step 102-4: Based on the particle guidance strategy model, update the data of each target particle in the particle set in multiple dimensions to obtain the first update result, and update the first update result of each target particle again according to the preset position update formula to obtain the second update result.

[0081] Steps 102-5 involve recalculating the function value of each target particle based on the second update result of each target particle and the objective function of the first stage, and determining the optimal particle from the particle set based on the function value of each target particle, until the preset conditions are met.

[0082] Step 102-6: Based on the function value corresponding to each optimal particle, determine the final optimal particle from among all optimal particles, and based on the multi-dimensional data of the final optimal particle, determine the optimal installation location and installation capacity corresponding to each distributed flexible resource in the target distribution network.

[0083] The particle guidance strategy model is as follows:

[0084]

[0085] The data for the i-th target particle at the t-th time are multi-dimensional, where τ is the leading coefficient, and g is the leading coefficient. b Data for the current best particle across multiple dimensions. Let f(g) be the fitness of the i-th target particle at the t-th iteration. b ) represents the fitness of the optimal particle, θ is the correction factor, and N max This sets the upper limit for the preset number of iterations;

[0086] The preset position update formula is as follows:

[0087]

[0088] Among them, when hour,

[0089]

[0090] when hour,

[0091]

[0092] Let f() be the multi-dimensional data of the i-th target particle at the (t+1)-th iteration, and let f() be the fitness function. For the new multi-dimensional data of the i-th target particle after mutation and back-learning, ub i lb i Let R3 be a 1×dim vector and follow a uniform distribution of (0.1), d2 be a random number between 0 and 1, and ρ be the upper and lower bounds of the search space. r Let u(t) be the mutation probability, u(t) be the piecewise weight, R1 and R2 be 1×dim vectors and follow a uniform distribution of (0.1), and dim be the number of target particles in the particle set. For the multi-dimensional data of the j-th target particle at the t-th iteration, For the data of the k-th target particle at the t-th iteration, e i Let be the function value of the physical stimulus intensity of the i-th target particle, ρ be the conversion probability, and d1 be a random number between 0 and 1.

[0093] In this embodiment, the location and capacity determination problem of distributed flexible resources is first mapped to a particle-dimensional space. Specifically, the number of dimensions corresponding to each particle is determined based on the number of parameters to be solved in the objective function of the first stage (such as the number of candidate installation nodes and the number of capacity levels of the distributed flexible resources). Each dimension represents a decision variable, and the position of the particle in the multidimensional space corresponds to a set of candidate solutions (location and capacity combinations).

[0094] Based on a defined particle dimension, a set of particles satisfying the first constraint condition is randomly generated. The multidimensional data of each target particle is substituted into the first-stage objective function to calculate the function value. The smaller the function value, the better the distributed flexible resource layout scheme corresponding to that target particle. For example, if the first-stage objective function is to minimize the sum of network loss and cost, the function value can directly reflect the overall benefit of the scheme. Based on the calculation results, the target particle with the smallest function value is selected from the particle set as the current optimal particle, and its position (situation and capacity data) and function value are recorded to provide a benchmark for subsequent iterations.

[0095] Subsequently, based on the particle-guided strategy model, the dimensional data corresponding to each target particle in the particle set is iteratively updated to obtain the first update result. To further improve the quality of the solution, a preset position update formula is introduced to perform a second update on the first update result, resulting in the second update result.

[0096] Substitute the second update result of each target particle into the objective function of the first stage and recalculate the function value. Based on the newly calculated function value, determine the optimal particle again from the particle set. Then, based on the particle-guided strategy model and the preset position update formula, update the data of each target particle in the particle set across multiple dimensions. Repeat the above process until preset conditions are met (such as reaching the maximum number of iterations, the fitness value convergence threshold, or the number of consecutive times the global optimum has not been updated). At this point, for the optimal particle obtained in each round, determine the optimal particle with the smallest function value from these optimal particles, and use it as the global optimal particle. Based on the data of the global optimal particle across multiple dimensions, determine the optimal installation location and installation capacity for each distributed flexible resource. For example, if the data of the global optimal particle across multiple dimensions is "nodes 3, 5, 8 + capacity 200kW, 150kW, 300kW", it means that distributed flexible resources of 200kW, 150kW, and 300kW should be installed at nodes 3, 5, and 8 respectively (further distinctions can be made regarding which distributed flexible resources are being used; this is just an example).

[0097] This application's embodiments combine global search with local optimization to ensure the optimal layout of distributed flexible resources under complex constraints, providing fundamental support for power restoration under subsequent extreme disasters.

[0098] In addition, θ is the correction factor, with a value between 0 and 1; τ is the leading coefficient, with a value between -2 and 1; ρ is the conversion probability, with a value between 0 and 1; ρ r The value represents the probability of mutation, ranging from 0.01 to 0.1.

[0099] To improve global search capabilities, u(t) is assigned segmented weights:

[0100] In the first half of the search, a combination of logarithmic and exponential weights is used. This ensures that the weights change stably within a specified range and also allows them to adjust adaptively, preventing the population from getting trapped in local optima. The formula is as follows:

[0101]

[0102] In the formula, γ is the adjustment factor; z takes a value of 0.5.

[0103] In the latter half of the search, an exponential weight is used, whose value adaptively decreases with each iteration, thereby improving the optimization accuracy. The formula is as follows:

[0104]

[0105] The value of u(t) is between 0 and 1 in the first half of the search, and between -1 and 1 in the second half of the search.

[0106] It is important to note that when updating the multi-dimensional data of each target particle in the particle ensemble using the particle-guided strategy model, the right side of the model... For the i-th target particle at the t-th time, the data in multiple dimensions are shown on the left. This is the first update result. When updating the first update result using the preset position update formula, the left side of the formula... This is the second update result.

[0107] Furthermore, the function of the physical stimulus intensity mentioned above can be expressed in the form of the butterfly optimization algorithm, as follows:

[0108] e = cI a ;

[0109] Where e and c represent the intensity of the fragrance and the sensory mode, respectively, I represents the intensity of the physical stimulus, and a reflects the change in fragrance absorption. The values ​​of a and c are between 0 and 1.

[0110] Optionally, in this embodiment, the objective function for the second stage is as follows:

[0111]

[0112] Among them, F n To represent the total load to be restored, N1, N2, and N3 are the number of primary, secondary, and tertiary load nodes, respectively, and ω1, ω2, and ω3 are the weighting coefficients for primary, secondary, and tertiary load nodes, respectively. P 1,i,t P 2,i,t P 3,i,t Let μ represent the active power of load node i at time t, representing the active power of load node i at level 1, level 2, and level 3, respectively. 2,i,t μ 3,i,t These are the 0-1 variables representing the load shedding state of secondary load node i and tertiary load node i at time t, respectively. x The duration of the fault.

[0113] In this embodiment, using the partitioning of isolated areas as the optimization variable, a second-stage objective function for distributed flexible resource collaborative scheduling is constructed, with the objective function being to maximize the recovery load. Wherein, μ 2,i,t μ 3,i,tThese are 0-1 variables representing the load reduction status of secondary and tertiary load nodes i at time t, where 1 indicates no load reduction and 0 indicates load reduction. Furthermore, the primary, secondary, and tertiary load nodes can be pre-defined based on their importance level. Specifically, it's possible to first define which common load nodes belong to primary, secondary, and tertiary load nodes, and then automatically identify them based on this setting.

[0114] In this embodiment of the application, optionally, the second constraint condition includes islanding constraint condition and power flow constraint condition considering radial distribution network. The islanding constraint condition includes power constraint within the island, voltage safety constraint, current safety constraint, node connection constraint and island connectivity constraint. The power flow constraint condition considering radial distribution network includes power equality balance constraint, voltage inequality constraint, current inequality constraint and second-order cone constraint.

[0115] The power constraints within the island are as follows:

[0116] P WT +P PV +P ESS ≥P 1,k,t +P 2,k,t +P 3,k,t ;

[0117] Among them, P WT P PV P ESS These represent the output power of distributed wind turbines, distributed photovoltaic units, and distributed energy storage systems, respectively, in P. 1,k,t P 2,k,t P 3,k,t These represent the active power of the primary load node, secondary load node, and tertiary load node k on the island at time t, respectively.

[0118] The voltage safety constraints are as follows:

[0119] U i,min ≤U i,t ≤U i,max ;

[0120] Among them, U i,t Let U be the voltage at node i at time t. i,min and U i,max These are the minimum and maximum voltage values ​​at node i, respectively;

[0121] The current safety constraints are as follows:

[0122] 0≤I ij,t ≤Iij,max ;

[0123] Among them, I ij,t Let I be the current in branch ij at time t. ij,max The maximum current for safe operation of branch ij;

[0124] The node connection constraints are as follows:

[0125]

[0126] Among them, y i,u,t Let N be a 0-1 variable representing whether node i belongs to island u at time t, where n is the total number of nodes. e This represents the total number of isolated islands, and each node can only belong to one isolated island.

[0127] The island connectivity constraints are as follows:

[0128]

[0129] Where, ψ k,u,t Let y be the set of parent nodes of node i in island u at time t. k,u,t Let k be the parent node of node i in island u at time t;

[0130] The power equation balance constraint is as follows:

[0131]

[0132] Where λ(i) is the set of child nodes of node i, γ(i) is the set of parent nodes of node i, and P iy,t and Q iy,t Let P be the active power and reactive power of branch iy at time t, respectively. ji,t and Q ji,t Let P be the active power and reactive power of branch ji at time t, respectively. i,t and Q i,t R represents the active power and reactive power recovered by node i at time t, respectively. ji and X ji These represent the resistance and reactance between branches J1, respectively. P is the square of the current magnitude of branch ij at time t. i,x,t and Q i,x,t Let be the active power and reactive power of load node i at time t, respectively. and Let be the required active power and required reactive power of node i at time t, respectively;

[0133] The voltage inequality constraint is as follows:

[0134]

[0135] Among them, R ij and X ij These represent the resistance and reactance between branches ij, respectively. Let be the square of the voltage magnitude of node j at time t. Let τ be the square of the voltage magnitude of node i at time t. ij,t Let be the voltage regulation parameter of branch ij at time t. and These are the minimum and maximum values ​​of the squared magnitude of the voltage at node j, respectively, where M is a sufficiently large constant, and P... ij,t and Q ij,t Let be the active power and reactive power of branch ij at time t, respectively.

[0136] The current inequality constraint is as follows:

[0137]

[0138] in, The maximum value of the square of the current magnitude of branch ij at time t;

[0139] The second-order cone constraint is as follows:

[0140]

[0141] In this embodiment, y i,u,t Let ψ be a 0-1 variable representing whether node i belongs to island u at time t; 1 if it belongs, 0 if it does not. k,u,t Let be the set of parent nodes of node i in island u at time t. If node i exists in island u, then at least one parent node k of node i must belong to island u for a path to be formed from the power node to node i in the island.

[0142] For a target distribution network with a radial topology, the DistFlow power flow equation is used, and the Big-M method is used to relax the voltage-current inequality constraints.

[0143] The voltage regulation parameters mentioned above can be determined according to actual needs. In the x-th level load node i, x-th level can represent level one, level two, level three, etc.

[0144] In this embodiment of the application, optionally, step 104, "based on the second constraint, solving the objective function of the second stage using the second optimization algorithm to obtain the islanding range of the network topology of the target distribution network under extreme disasters, and the load recovery sequence corresponding to each islanding range," includes: for each distributed flexible resource in the target distribution network, determining the access point of the distributed flexible resource in the network topology of the target distribution network, and simultaneously searching along each branch connected to the access point; determining the power supply feasible region corresponding to the distributed flexible resource based on the load demand of the load nodes in each branch; determining the primary load nodes and secondary load nodes included in the power supply feasible region, and calculating the total load demand of the primary load nodes and the secondary load nodes; and based on the total available power supply corresponding to the distributed flexible resource and the load demand... To determine the total load demand, the search continues along each branch connected to the access point to find new target load nodes. The total load demand is then updated based on these new target load nodes until it is greater than or equal to the total available power supply. Here, the target load node is either a primary load node or a secondary load node. The search endpoint is determined among the branches connected to the access point of the distributed flexible resource, and the path from the search endpoint to the access point is taken as the target path. The initial power supply range of the distributed flexible resource is determined based on each target path. Edge rationalization processing is performed on the initial power supply range corresponding to each distributed flexible resource to obtain the islanding range corresponding to each distributed flexible resource. For each primary and secondary load node within the islanding range, they are sorted according to their corresponding load demand to obtain the load recovery order corresponding to the islanding range.

[0145] In this embodiment, firstly, the access point of each distributed flexible resource is precisely located within the network topology of the target distribution network. This access point serves as a key node for energy injection and is the starting point for subsequent analysis. For each access point, a search operation is simultaneously performed along each connected branch, using this access point as a reference. During the search process, the load nodes on each branch are thoroughly investigated. Based on the load characteristics and demand parameters of the load nodes, an initial feasible power supply domain corresponding to the distributed flexible resource is constructed. This feasible domain represents the theoretically achievable power supply area of ​​the distributed flexible resource under the current network topology and load distribution conditions, providing a basic scope definition for subsequent precise analysis.

[0146] The initial feasible power supply domain can cover primary, secondary, and tertiary load nodes. Next, within this domain, primary and secondary load nodes are selected. These two types of load nodes, due to their high requirements for power supply reliability and power quality, are of priority in ensuring power supply during disaster scenarios. The load demands of the selected primary and secondary load nodes are cumulatively calculated to obtain the total load demand. This total load demand is then compared with the total available power supply capacity corresponding to the distributed flexible resources. If the current total load demand is less than the total available power supply capacity, it indicates that the distributed flexible resources still have surplus power supply capacity. In this case, a depth search continues along each branch connected to the access point to find new primary or secondary load nodes as target load nodes. Each time a new target load node is identified, its load demand is added to the total load demand for updating. This process continues until the updated total load demand is greater than or equal to the total available power supply capacity, thereby dynamically expanding the power supply range to meet the critical load demands under resource constraints.

[0147] After expanding the power supply range based on critical load demand, the search endpoints on each branch connected to the distributed flexible resource access point are determined. These search endpoints mark the furthest boundary of the power supply range under the current resource allocation and load demand conditions. The paths from the search endpoints to the access point are defined as target paths, which constitute the main channels for power supply to the distributed flexible resource. By combining the target paths, the initial power supply range of the distributed flexible resource can be clearly defined. This range comprehensively considers load demand, resource constraints, and network topology, and represents a relatively reasonable preliminary division of the power supply area.

[0148] Subsequently, the initial power supply range corresponding to each distributed flexible resource undergoes edge rationalization processing. Path optimization algorithms from graph theory and network topology analysis methods are used to smooth and optimize the boundaries of the initial power supply range, eliminating unreasonable node connections and power supply redundancy on the boundaries, improving the rationality and efficiency of the power supply range, and ultimately obtaining the islanding range corresponding to each distributed flexible resource. For the primary and secondary load nodes within each islanding range, they are sorted according to their load demand. A priority sorting algorithm is used, comprehensively considering factors such as the importance and demand of the loads, to determine the load restoration order. This order can guide the power system's recovery operations under extreme disasters, prioritizing the restoration of critical loads and loads with high demand, improving the disaster response capability and power supply restoration efficiency of the entire distribution network.

[0149] Furthermore, as Figure 1 In terms of specific implementation, this application provides a distributed flexible resource power supply scheme generation device under extreme disasters, such as... Figure 3 As shown, the device includes:

[0150] The first construction module is used to construct the first-stage objective function of the target distribution network and the first constraint condition corresponding to the first-stage objective function, wherein the first-stage objective function is used to determine the location scheme and capacity scheme of the distributed flexible resources in the target distribution network.

[0151] The first solution module is used to solve the objective function of the first stage based on the first constraint condition and through the first optimization algorithm to obtain the optimal installation location and installation capacity of each distributed flexible resource in the target distribution network.

[0152] The second construction module is used to generate the network topology of the target distribution network according to the optimal installation location and installation capacity of each distributed flexible resource, and to construct the second-stage objective function of the target distribution network and the second constraint condition corresponding to the second-stage objective function according to the network topology of the target distribution network. The second-stage objective function is used to determine the optimal islanding scheme corresponding to the network topology of the target distribution network under extreme disasters.

[0153] The second solution module is used to solve the objective function of the second stage based on the second constraint conditions using the second optimization algorithm, to obtain the island division range of the network topology of the target distribution network under extreme disasters, and the load restoration sequence corresponding to each island division range, and to generate a power supply scheme for distributed flexible resources under extreme disasters based on each island division range and the corresponding load restoration sequence.

[0154] Optionally, the first building module is configured to:

[0155] Obtain the preset number of branches corresponding to the target distribution network, and based on the preset number of branches, as well as the current and resistance of each branch, construct a network loss objective function with the goal of minimizing the sum of network losses of each branch;

[0156] Construct a cost objective function that aims to minimize the sum of investment costs, maintenance costs, and operating costs of the distributed flexible resources.

[0157] Based on the network loss objective function and the cost objective function, construct the first stage objective function;

[0158] The distributed flexible resources include distributed wind turbines, distributed photovoltaic units, and distributed energy storage systems; the first constraints corresponding to the objective function of the first stage include power flow constraints, distributed wind and solar turbine capacity constraints, candidate node installation capacity constraints, node voltage constraints, branch current constraints, and energy storage charging and discharging constraints.

[0159] The power flow constraints are as follows:

[0160]

[0161] in, Let be the active power output at node i, representing the upstream grid, distributed generation, energy storage system charging and discharging power, and conventional load demand at time t. U represents the reactive power output at time t, which includes the upstream power grid at node i, distributed power sources, the charging and discharging power of the energy storage system, and the reactive power output of the conventional load demand. i,t and U j,t G represents the voltages at nodes i and j at time t, respectively. ij and B ij These are the real and imaginary parts of the nodal admittance matrix, respectively, θ ij,t Let N be the phase angle difference between nodes i and j at time t. bus For the set of all nodes;

[0162] The capacity constraints of the distributed wind and solar turbine units are as follows:

[0163]

[0164] Among them, P i WT,rated , These are the rated power of the distributed wind turbine and the distributed photovoltaic unit at nodes i and j, respectively. μ represents the unit maintenance cost of the distributed photovoltaic unit. s S represents the upper limit of the proportion of distributed wind and solar power capacity in the target distribution network. sub The rated capacity of the upstream substation;

[0165] The installation capacity constraints for the candidate nodes are as follows:

[0166]

[0167] Among them, P i DG,rated The rated capacity of the distributed renewable energy installed at node i. and P represents the number of distributed wind turbines installed at node i and the number of distributed photovoltaic units installed at node j, respectively. i DG,max P is the maximum total capacity of distributed power supplies installed at node i. i WT,max and These represent the maximum allowable installation capacity of distributed wind turbines at node i and the maximum allowable installation capacity of distributed photovoltaic units at node j, respectively. and These represent the unit rated capacity of the distributed wind turbine installed at node i and the unit rated capacity of the distributed photovoltaic unit installed at node j, respectively.

[0168] The node voltage constraints are as follows:

[0169] V i min ≤V i,t ≤V i max ;

[0170] Among them, V i,t Let V be the voltage value of node i at time t. i min and V i max These are the minimum and maximum voltage values ​​at node i, respectively;

[0171] The branch current constraint conditions are as follows:

[0172] |I l,t |≤I l max ;

[0173] Among them, I l,t Let I be the branch current of branch l at time t. l max Let be the maximum branch current flowing through branch l;

[0174] The energy storage charging and discharging constraints are as follows:

[0175]

[0176] in, Let i be the rated power of the distributed energy storage system at time t. The charging power of the distributed energy storage system at node i at time t.

[0177] Optionally, the investment cost calculation formula for the distributed flexible resources is as follows:

[0178]

[0179] in, These represent the investment costs of distributed wind turbines, distributed photovoltaic power plants, and distributed energy storage systems, respectively, where d is the discount rate and n is the investment cost. WT n PV n ESS The full lifecycles of the distributed wind turbine, the distributed photovoltaic generator, and the distributed energy storage system are respectively, c wt and c pvThese are the unit investment costs of the distributed generator set and the distributed photovoltaic unit, respectively. and P represents the converter cost and energy storage cost of the distributed energy storage system, respectively. i WT,rated , These are the rated powers of the distributed wind turbine, the distributed photovoltaic generator, and the distributed energy storage system at nodes i, j, and m, respectively. Let m be the installed capacity of the distributed energy storage system at node m.

[0180] The formula for calculating the maintenance cost of the distributed flexible resources is as follows:

[0181]

[0182] in, The maintenance costs are respectively for the distributed wind turbine, the distributed photovoltaic generator, and the distributed energy storage system. and These are the unit maintenance costs for the distributed wind turbine and the distributed photovoltaic unit, respectively. and The fixed maintenance cost and variable maintenance cost of the distributed energy storage system are respectively, h m The number of operating hours of the distributed energy storage system at node m;

[0183] The formula for calculating the operating cost of the distributed flexible resources is as follows:

[0184]

[0185] Among them, C ope C represents the operating cost of the distributed flexible resource. up,t P represents the cost of purchasing electricity from the upper-level power grid at time t. t sub C represents the active power supplied by the upstream power grid to the target distribution network. loss Cost per unit of network loss For the active power loss of branch l, These represent the conventional load demand of node i at time t, the charging power of the distributed energy storage system, the active power output of the distributed photovoltaic unit, and the active power output of the distributed wind turbine, respectively, where Δt is the unit of time, and I l,t R is the current flowing through branch l at time t. l The resistance of branch l is Ω. l For the load condition set, Ω b This represents the total runtime.

[0186] Optionally, the first solution module is used for:

[0187] Based on the number of parameters to be solved in the objective function of the first stage, determine the multiple dimensions corresponding to each particle;

[0188] Based on the dimension of each particle, a set of particles that meet the first constraint condition is randomly generated, wherein the set of particles includes multiple target particles, and each target particle includes data of the multiple dimensions;

[0189] Based on the data of each target particle in multiple dimensions and the objective function of the first stage, the function value of each target particle is calculated, and the optimal particle is determined from the particle set according to the function value of each target particle.

[0190] Based on the particle-guided strategy model, the data of each target particle in the particle set in multiple dimensions are updated to obtain the first update result. Then, according to the preset position update formula, the first update result of each target particle is updated again to obtain the second update result.

[0191] Based on the second update result of each target particle and the objective function of the first stage, the function value of each target particle is calculated again, and the optimal particle is determined again from the particle set according to the function value of each target particle, until the preset condition is met.

[0192] Based on the function value corresponding to each optimal particle, the final optimal particle is determined from among all the optimal particles, and based on the multi-dimensional data of the final optimal particle, the optimal installation location and installation capacity corresponding to each distributed flexible resource in the target distribution network are determined.

[0193] The particle guidance strategy model is as follows:

[0194]

[0195] The data for the i-th target particle at the t-th time are multi-dimensional, where τ is the leading coefficient, and g is the leading coefficient. b Data for the current best particle across multiple dimensions. Let f(g) be the fitness of the i-th target particle at the t-th iteration. b ) represents the fitness of the optimal particle, θ is the correction factor, and N max This sets the upper limit for the preset number of iterations;

[0196] The preset position update formula is as follows:

[0197]

[0198] Among them, when hour,

[0199]

[0200] when hour,

[0201]

[0202] Let f() be the multi-dimensional data of the i-th target particle at the (t+1)-th iteration, and let f() be the fitness function. For the new multi-dimensional data of the i-th target particle after mutation and back-learning, ub i lb i Let R3 be a 1×dim vector and follow a uniform distribution of (0.1), d2 be a random number between 0 and 1, and ρ be the upper and lower bounds of the search space. r Let u(t) be the mutation probability, u(t) be the piecewise weight, R1 and R2 be 1×dim vectors and follow a uniform distribution of (0.1), and dim be the number of target particles in the particle set. For the multi-dimensional data of the j-th target particle at the t-th iteration, For the data of the k-th target particle at the t-th iteration, e i Let be the function value of the physical stimulus intensity of the i-th target particle, ρ be the conversion probability, and d1 be a random number between 0 and 1.

[0203] Optionally, the objective function for the second stage is as follows:

[0204]

[0205] Among them, F n To represent the total load to be restored, N1, N2, and N3 are the number of primary, secondary, and tertiary load nodes, respectively, and ω1, ω2, and ω3 are the weighting coefficients for primary, secondary, and tertiary load nodes, respectively. P 1,i,t P 2,i,t P 3,i,t Let μ represent the active power of load node i at time t, representing the active power of load node i at level 1, level 2, and level 3, respectively. 2,i,t μ 3,i,t These are the 0-1 variables representing the load shedding state of secondary load node i and tertiary load node i at time t, respectively. x The duration of the fault.

[0206] Optionally, the second constraint includes islanding constraint and power flow constraint considering the radial distribution network. The islanding constraint includes power constraints within the island, voltage safety constraints, current safety constraints, node connection constraints, and island connectivity constraints. The power flow constraint considering the radial distribution network includes power equality balance constraints, voltage inequality constraints, current inequality constraints, and second-order cone constraints.

[0207] The power constraints within the island are as follows:

[0208] P WT +P PV +P ESS ≥P 1,k,t +P 2,k,t +P 3,k,t ;

[0209] Among them, P WT P PV P ESS These represent the output power of distributed wind turbines, distributed photovoltaic units, and distributed energy storage systems, respectively, in P. 1,k,t P 2,k,t P 3,k,t These represent the active power of the primary load node, secondary load node, and tertiary load node k on the island at time t, respectively.

[0210] The voltage safety constraints are as follows:

[0211] U i,min ≤U i,t ≤U i,max ;

[0212] Among them, U i,t Let U be the voltage at node i at time t. i,min and U i,max These are the minimum and maximum voltage values ​​at node i, respectively;

[0213] The current safety constraints are as follows:

[0214] 0≤I ij,t ≤I ij,max ;

[0215] Among them, I ij,t Let I be the current in branch ij at time t. ij,max The maximum current for safe operation of branch ij;

[0216] The node connection constraints are as follows:

[0217]

[0218] Among them, y i,u,tLet N be a 0-1 variable representing whether node i belongs to island u at time t, where n is the total number of nodes. e This represents the total number of isolated islands, and each node can only belong to one isolated island.

[0219] The island connectivity constraints are as follows:

[0220]

[0221] Where, ψ k,u,t Let y be the set of parent nodes of node i in island u at time t. k,u,t Let k be the parent node of node i in island u at time t;

[0222] The power equation balance constraint is as follows:

[0223]

[0224] Where λ(i) is the set of child nodes of node i, γ(i) is the set of parent nodes of node i, and P iy,t and Q iy,t Let P be the active power and reactive power of branch iy at time t, respectively. ji,t and Q ji,t Let P be the active power and reactive power of branch ji at time t, respectively. i,t and Q i,t R represents the active power and reactive power recovered by node i at time t, respectively. ji and X ji These represent the resistance and reactance between branches J1, respectively. P is the square of the current magnitude of branch ij at time t. i,x,t and Q i,x,t Let be the active power and reactive power of load node i at time t, respectively. and Let be the required active power and required reactive power of node i at time t, respectively;

[0225] The voltage inequality constraint is as follows:

[0226]

[0227] Among them, R ij and X ij These represent the resistance and reactance between branches ij, respectively. Let be the square of the voltage magnitude of node j at time t. Let τ be the square of the voltage magnitude of node i at time t. ij,t Let be the voltage regulation parameter of branch ij at time t. and These are the minimum and maximum values ​​of the squared magnitude of the voltage at node j, respectively, where M is a sufficiently large constant, and P... ij,t and Q ij,t Let be the active power and reactive power of branch ij at time t, respectively.

[0228] The current inequality constraint is as follows:

[0229]

[0230] in, The maximum value of the square of the current magnitude of branch ij at time t;

[0231] The second-order cone constraint is as follows:

[0232]

[0233] Optionally, the second solver module is used for:

[0234] For each distributed flexible resource in the target distribution network, the access point of the distributed flexible resource is determined in the network topology of the target distribution network, and a search is performed simultaneously along each branch connected to the access point. Based on the load demand of the load nodes in each branch, the power supply feasible region corresponding to the distributed flexible resource is determined.

[0235] The primary and secondary load nodes included in the power supply feasible domain are identified, and the total load demand of the primary and secondary load nodes is calculated. Based on the total available power supply corresponding to the distributed flexible resources and the total load demand, the search continues along each branch connected to the access point to find a new target load node. The total load demand is updated based on the new target load node until the updated total load demand is greater than or equal to the total available power supply. The target load node is either a primary load node or a secondary load node.

[0236] The search endpoint in each branch connected to the access point of the distributed flexible resource is determined, and the path from the search endpoint to the access point is taken as the target path. The initial power supply range of the distributed flexible resource is determined based on each target path.

[0237] Edge rationalization processing is performed on the initial power supply range corresponding to each distributed flexible resource to obtain the island division range corresponding to each distributed flexible resource. For the primary load nodes and secondary load nodes within each island division range, they are sorted according to the load demand corresponding to the load nodes to obtain the load recovery order corresponding to the island division range.

[0238] It should be noted that other corresponding descriptions of the functional units involved in the distributed flexible resource power supply scheme generation device under extreme disasters provided in this application embodiment can be found in the following references. Figures 1 to 2 The corresponding descriptions in the method will not be repeated here.

[0239] This application also provides a computer device, which may specifically be a personal computer, a server, a network device, etc. Figure 4 As shown, the computer device includes a bus, a processor, memory, and a communication interface, and may also include an input / output interface and a display device. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores location information. The network interface allows communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the various method embodiments.

[0240] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0241] In one embodiment, a computer-readable storage medium is provided, which may be non-volatile or volatile, having stored thereon a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0242] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0243] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0244] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0245] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0246] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for generating distributed flexible resource power supply schemes under extreme disasters, characterized in that, include: Construct a first-stage objective function for the target distribution network and a first constraint condition corresponding to the first-stage objective function, wherein the first-stage objective function is used to determine the location scheme and capacity scheme of distributed flexible resources in the target distribution network; Based on the first constraint, the objective function of the first stage is solved by the first optimization algorithm to obtain the optimal installation location and installation capacity of each distributed flexible resource in the target distribution network. Based on the optimal installation location and installation capacity of each distributed flexible resource, the network topology of the target distribution network is generated. Based on the network topology of the target distribution network, the second-stage objective function of the target distribution network and the second constraint conditions corresponding to the second-stage objective function are constructed. The second-stage objective function is used to determine the optimal islanding scheme corresponding to the network topology of the target distribution network under extreme disasters. Based on the second constraint, the objective function of the second stage is solved by the second optimization algorithm to obtain the island division range of the network topology of the target distribution network under extreme disasters, as well as the load restoration sequence corresponding to each island division range. Based on each island division range and the corresponding load restoration sequence, a power supply scheme for distributed flexible resources under extreme disasters is generated.

2. The method according to claim 1, characterized in that, The first-stage objective function for constructing the target distribution network includes: Obtain the preset number of branches corresponding to the target distribution network, and based on the preset number of branches, as well as the current and resistance of each branch, construct a network loss objective function with the goal of minimizing the sum of network losses of each branch; Construct a cost objective function that aims to minimize the sum of investment costs, maintenance costs, and operating costs of the distributed flexible resources. Based on the network loss objective function and the cost objective function, construct the first stage objective function; The distributed flexible resources include distributed wind turbines, distributed photovoltaic units, and distributed energy storage systems; the first constraints corresponding to the objective function of the first stage include power flow constraints, distributed wind and solar turbine capacity constraints, candidate node installation capacity constraints, node voltage constraints, branch current constraints, and energy storage charging and discharging constraints. The power flow constraints are as follows: in, Let be the active power output at node i, representing the upstream grid, distributed generation, energy storage system charging and discharging power, and conventional load demand at time t. U represents the reactive power output at time t, which includes the upstream power grid at node i, distributed power sources, the charging and discharging power of the energy storage system, and the reactive power output of the conventional load demand. i,t and U j,t G represents the voltages at nodes i and j at time t, respectively. ij and B ij These are the real and imaginary parts of the nodal admittance matrix, respectively, θ ij,t Let N be the phase angle difference between nodes i and j at time t. bus For the set of all nodes; The capacity constraints of the distributed wind and solar turbine units are as follows: in, These are the rated power of the distributed wind turbine and the distributed photovoltaic unit at nodes i and j, respectively. μ represents the unit maintenance cost of the distributed photovoltaic unit. s S represents the upper limit of the proportion of distributed wind and solar power capacity in the target distribution network. sub The rated capacity of the upstream substation; The installation capacity constraints for the candidate nodes are as follows: Among them, P i DG,rated The rated capacity of the distributed renewable energy installed at node i. and P represents the number of distributed wind turbines installed at node i and the number of distributed photovoltaic units installed at node j, respectively. i DG,max P is the maximum total capacity of distributed power supplies installed at node i. i WT,max and These represent the maximum allowable installation capacity of distributed wind turbines at node i and the maximum allowable installation capacity of distributed photovoltaic units at node j, respectively. and These represent the unit rated capacity of the distributed wind turbine installed at node i and the unit rated capacity of the distributed photovoltaic unit installed at node j, respectively. The node voltage constraints are as follows: In i min ≤V i,t ≤V i max ; Among them, V i,t Let V be the voltage value of node i at time t. i min and V i max These are the minimum and maximum voltage values ​​at node i, respectively; The branch current constraint conditions are as follows: Among them, I l,t Let be the branch current of branch l at time t. Let be the maximum branch current flowing through branch l; The energy storage charging and discharging constraints are as follows: in, Let i be the rated power of the distributed energy storage system at time t. The charging power of the distributed energy storage system at node i at time t.

3. The method according to claim 2, characterized in that, The investment cost calculation formula for the distributed flexible resources is as follows: in, These represent the investment costs of distributed wind turbines, distributed photovoltaic power plants, and distributed energy storage systems, respectively, where d is the discount rate and n is the investment cost. WT n PV n ESS The full lifecycles of the distributed wind turbine, the distributed photovoltaic generator, and the distributed energy storage system are respectively, c wt and c pv These are the unit investment costs of the distributed generator set and the distributed photovoltaic unit, respectively. and P represents the converter cost and energy storage cost of the distributed energy storage system, respectively. i WT,rated , These are the rated powers of the distributed wind turbine, the distributed photovoltaic generator, and the distributed energy storage system at nodes i, j, and m, respectively. Let m be the installed capacity of the distributed energy storage system at node m. The formula for calculating the maintenance cost of the distributed flexible resources is as follows: in, The maintenance costs are respectively for the distributed wind turbine, the distributed photovoltaic generator, and the distributed energy storage system. and These are the unit maintenance costs for the distributed wind turbine and the distributed photovoltaic unit, respectively. and The fixed maintenance cost and variable maintenance cost of the distributed energy storage system are respectively, h m The number of operating hours of the distributed energy storage system at node m; The formula for calculating the operating cost of the distributed flexible resources is as follows: Among them, C ope C represents the operating cost of the distributed flexible resource. up,t P represents the cost of purchasing electricity from the upper-level power grid at time t. t sub C represents the active power supplied by the upstream power grid to the target distribution network. loss Cost per unit of network loss For the active power loss of branch l, These represent the conventional load demand of node i at time t, the charging power of the distributed energy storage system, the active power output of the distributed photovoltaic unit, and the active power output of the distributed wind turbine, respectively, where Δt is the unit of time, and I l,t R is the current flowing through branch l at time t. l The resistance of branch l is Ω. l For the load condition set, Ω b This represents the total runtime.

4. The method according to claim 1, characterized in that, Based on the first constraint, the first optimization algorithm is used to solve the objective function of the first stage to obtain the optimal installation location and installation capacity for each distributed flexible resource in the target distribution network, including: Based on the number of parameters to be solved in the objective function of the first stage, determine the multiple dimensions corresponding to each particle; Based on the dimension of each particle, a set of particles that meet the first constraint condition is randomly generated, wherein the set of particles includes multiple target particles, and each target particle includes data of the multiple dimensions; Based on the data of each target particle in multiple dimensions and the objective function of the first stage, the function value of each target particle is calculated, and the optimal particle is determined from the particle set according to the function value of each target particle. Based on the particle-guided strategy model, the data of each target particle in the particle set in multiple dimensions are updated to obtain the first update result. Then, according to the preset position update formula, the first update result of each target particle is updated again to obtain the second update result. Based on the second update result of each target particle and the objective function of the first stage, the function value of each target particle is calculated again, and the optimal particle is determined again from the particle set according to the function value of each target particle, until the preset condition is met. Based on the function value corresponding to each optimal particle, the final optimal particle is determined from among all the optimal particles, and based on the multi-dimensional data of the final optimal particle, the optimal installation location and installation capacity corresponding to each distributed flexible resource in the target distribution network are determined. The particle guidance strategy model is as follows: The data for the i-th target particle at the t-th time are multi-dimensional, where τ is the leading coefficient, and g is the leading coefficient. b Data for the current best particle across multiple dimensions. Let f(g) be the fitness of the i-th target particle at the t-th iteration. b ) represents the fitness of the optimal particle, θ is the correction factor, and N max This sets the upper limit for the preset number of iterations; The preset position update formula is as follows: Among them, when hour, when hour, Let f() be the multi-dimensional data of the i-th target particle at the (t+1)-th iteration, and let f() be the fitness function. For the new multi-dimensional data of the i-th target particle after mutation and back-learning, ub i lb i Let R3 be a 1×dim vector and follow a uniform distribution of (0.1), d2 be a random number between 0 and 1, and ρ be the upper and lower bounds of the search space. r Let u(t) be the mutation probability, u(t) be the piecewise weight, R1 and R2 be 1×dim vectors and follow a uniform distribution of (0.1), and dim be the number of target particles in the particle set. For the multi-dimensional data of the j-th target particle at the t-th iteration, For the data of the k-th target particle at the t-th iteration, e i Let be the function value of the physical stimulus intensity of the i-th target particle, ρ be the conversion probability, and d1 be a random number between 0 and 1.

5. The method according to claim 1, characterized in that, The objective function for the second stage is as follows: Among them, F n To represent the total load to be restored, N1, N2, and N3 are the number of primary, secondary, and tertiary load nodes, respectively, and ω1, ω2, and ω3 are the weighting coefficients for primary, secondary, and tertiary load nodes, respectively. P 1,i,t P 2,i,t P 3,i,t Let μ represent the active power of load node i at time t, representing the active power of load node i at level 1, level 2, and level 3, respectively. 2,i,t μ 3,i,t These are the 0-1 variables representing the load shedding state of secondary load node i and tertiary load node i at time t, respectively. x The duration of the fault.

6. The method according to claim 5, characterized in that, The second constraint includes islanding constraint and power flow constraint considering radial distribution network. The islanding constraint includes power constraint within the island, voltage safety constraint, current safety constraint, node connection constraint and island connectivity constraint. The power flow constraint considering radial distribution network includes power equality balance constraint, voltage inequality constraint, current inequality constraint and second-order cone constraint. The power constraints within the island are as follows: P WT +P PV +P ESS ≥P 1,k,t +P 2,k,t +P 3,k,t ; Among them, P WT P PV P ESS These represent the output power of distributed wind turbines, distributed photovoltaic units, and distributed energy storage systems, respectively, in P. 1,k,t P 2,k,t P 3,k,t These represent the active power of the primary load node, secondary load node, and tertiary load node k on the island at time t, respectively. The voltage safety constraints are as follows: IN i,min ≤U i,t ≤U i,max ; Among them, U i,t Let U be the voltage at node i at time t. i,min and U i,max These are the minimum and maximum voltage values ​​at node i, respectively; The current safety constraints are as follows: 0≤I ij,t ≤I ij,max ; Among them, I ij,t Let I be the current in branch ij at time t. ij,max The maximum current for safe operation of branch ij; The node connection constraints are as follows: Among them, y i,u,t Let N be a 0-1 variable representing whether node i belongs to island u at time t, where n is the total number of nodes. e This represents the total number of isolated islands, and each node can only belong to one isolated island. The island connectivity constraints are as follows: Where, ψ k,u,t Let y be the set of parent nodes of node i in island u at time t. k,u,t Let k be the parent node of node i in island u at time t; The power equation balance constraint is as follows: Where λ(i) is the set of child nodes of node i, γ(i) is the set of parent nodes of node i, and P iy,t and Q iy,t Let P be the active power and reactive power of branch iy at time t, respectively. ji,t and Q ji,t Let P be the active power and reactive power of branch ji at time t, respectively. i,t and Q i,t R represents the active power and reactive power recovered by node i at time t, respectively. ji and X ji These represent the resistance and reactance between branches J1, respectively. P is the square of the current magnitude of branch ij at time t. i,x,t and Q i,x,t Let be the active power and reactive power of load node i at time t, respectively. and Let be the required active power and required reactive power of node i at time t, respectively; The voltage inequality constraint is as follows: Among them, R ij and X ij These represent the resistance and reactance between branches ij, respectively. Let be the square of the voltage magnitude of node j at time t. Let τ be the square of the voltage magnitude of node i at time t. ij,t Let be the voltage regulation parameter of branch ij at time t. and These are the minimum and maximum values ​​of the squared magnitude of the voltage at node j, respectively, where M is a sufficiently large constant, and P... ij,t and Q ij,t Let be the active power and reactive power of branch ij at time t, respectively. The current inequality constraint is as follows: in, The maximum value of the square of the current magnitude of branch ij at time t; The second-order cone constraint is as follows:

7. The method according to claim 5 or 6, characterized in that, Based on the second constraint, the second optimization algorithm is used to solve the objective function of the second stage to obtain the islanding range of the network topology of the target distribution network under extreme disasters, and the load restoration sequence corresponding to each islanding range, including: For each distributed flexible resource in the target distribution network, the access point of the distributed flexible resource is determined in the network topology of the target distribution network, and a search is performed simultaneously along each branch connected to the access point. Based on the load demand of the load nodes in each branch, the power supply feasible region corresponding to the distributed flexible resource is determined. The primary and secondary load nodes included in the power supply feasible domain are identified, and the total load demand of the primary and secondary load nodes is calculated. Based on the total available power supply corresponding to the distributed flexible resources and the total load demand, the search continues along each branch connected to the access point to find a new target load node. The total load demand is updated based on the new target load node until the updated total load demand is greater than or equal to the total available power supply. The target load node is either a primary load node or a secondary load node. The search endpoint in each branch connected to the access point of the distributed flexible resource is determined, and the path from the search endpoint to the access point is taken as the target path. The initial power supply range of the distributed flexible resource is determined based on each target path. Edge rationalization processing is performed on the initial power supply range corresponding to each distributed flexible resource to obtain the island division range corresponding to each distributed flexible resource. For the primary load nodes and secondary load nodes within each island division range, they are sorted according to the load demand corresponding to the load nodes to obtain the load recovery order corresponding to the island division range.

8. A device for generating distributed flexible resource power supply schemes under extreme disasters, characterized in that, include: The first construction module is used to construct the first-stage objective function of the target distribution network and the first constraint condition corresponding to the first-stage objective function, wherein the first-stage objective function is used to determine the location scheme and capacity scheme of the distributed flexible resources in the target distribution network. The first solution module is used to solve the objective function of the first stage based on the first constraint condition and through the first optimization algorithm to obtain the optimal installation location and installation capacity of each distributed flexible resource in the target distribution network. The second construction module is used to generate the network topology of the target distribution network according to the optimal installation location and installation capacity of each distributed flexible resource, and to construct the second-stage objective function of the target distribution network and the second constraint condition corresponding to the second-stage objective function according to the network topology of the target distribution network. The second-stage objective function is used to determine the optimal islanding scheme corresponding to the network topology of the target distribution network under extreme disasters. The second solution module is used to solve the objective function of the second stage based on the second constraint conditions using the second optimization algorithm, to obtain the island division range of the network topology of the target distribution network under extreme disasters, and the load restoration sequence corresponding to each island division range, and to generate a power supply scheme for distributed flexible resources under extreme disasters based on each island division range and the corresponding load restoration sequence.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

10. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 7.

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