Power distribution network toughness evaluation method and system considering key load in ice disaster scene

Abstract

The invention relates to the technical field of power distribution networks, in particular to a power distribution network toughness evaluation method and system considering key loads in an ice disaster scene. According to the method, load nodes of the power distribution network are divided into first-level loads such as hospitals, second-level loads such as industrial districts and third-level loads such as common residences, and different weight coefficients are given; then, a repair sequence optimization model with the minimum comprehensive load loss as a target function is constructed, and constraint conditions such as power flow convergence, line capacity, open-loop operation, repair resources and generator node power of the power distribution network are considered at the same time; solving the optimization model by adopting an improved genetic algorithm, and obtaining an optimal repair sequence through operations of line number gene coding, comprehensive loss fitness calculation, roulette method selection, mapping rule crossing, position exchange variation and the like; and finally executing a recovery process according to the repair sequence and evaluating the system toughness. The load differentiation characteristics are considered, accurate quantification and intelligent optimization of the power distribution network fault repair process are achieved, and the system toughness is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, and in particular to a distribution network resilience assessment method and system considering key loads in an ice disaster scenario. Background Art

[0002] Extreme events with low probability and high impact in the world have brought serious economic losses to the power system. In the distribution network, the cost of ensuring all loads under extreme events is too high. It is not necessary to ensure the power supply of all loads, as long as a certain proportion of critical loads have power supply capacity. If traditional line reinforcement methods are used, there will be defects such as high investment cost or increased load loss due to the inability to accurately evaluate resilience indicators. Therefore, it is necessary to fully evaluate the resilience of the distribution network based on the performance of critical loads in ice disaster scenarios, and propose a line repair sequence optimization strategy that considers the comprehensive loss of loads for critical loads.

[0003] As global climate change intensifies, extreme weather events with low probability and high impact, especially ice disasters, have brought unprecedented challenges and serious economic losses to the power system. In the field of distribution networks, the existing response strategies mainly have the following problems: First, the traditional comprehensive protection method attempts to maintain the power supply of all loads under extreme events. This strategy not only has too high investment costs, but also has low resource utilization efficiency; second, the commonly used line reinforcement methods often lead to blind investment decisions and unsatisfactory control of load loss due to the lack of a scientific resilience assessment indicator system; third, the existing repair strategies are mostly based on experience judgment, lack of systematic optimization mechanism, and cannot achieve efficient allocation of resources.

[0004] In fact, there are significant differences in the importance and reliability requirements of different types of loads in the distribution network. For example, key loads such as hospitals and military facilities need to be prioritized, while ordinary civilian loads can moderately reduce power supply reliability under special circumstances. Therefore, in extreme events, it is only necessary to ensure that a certain proportion of key loads continue to supply power to minimize the impact of the event. This requires the establishment of a scientific resilience assessment indicator system to achieve accurate classification of loads and reasonable weight allocation, and on this basis, to build a systematic repair sequence optimization method.

[0005] However, the industry currently lacks a distribution network resilience assessment and optimization method that takes into account the differentiated characteristics of loads in ice disaster scenarios. Therefore, it is urgent to propose a comprehensive solution that can accurately assess system resilience, scientifically formulate repair strategies, and efficiently implement resource allocation. Summary of the invention

[0006] In view of the problems existing in the prior art, the present invention is proposed.

[0007] Therefore, the problem to be solved by the present invention is how to solve the following problems: first, the existing technology lacks a scientific resilience assessment index system, cannot accurately assess system resilience, and is difficult to quantify the importance of different loads, resulting in inaccurate system resilience assessment, difficult repair effect assessment, and lack of basis for investment decisions; second, the existing technology does not reasonably classify loads, ignores the differentiated characteristics of loads, and lacks a scientific weight allocation mechanism, resulting in unreasonable resource allocation, insufficient key load protection, and low system recovery efficiency; third, the existing repair sequence mainly relies on experience judgment, lacks a systematic optimization method, and does not fully consider multiple constraints, which makes the repair efficiency low, resource allocation unreasonable, and system recovery time prolonged. There is a close correlation between these problems. Inaccurate resilience assessment will affect the rationality of load classification, and then affect the optimization of the repair sequence, and ultimately lead to low system recovery efficiency, which not only causes a large amount of economic losses, but also fails to effectively guarantee the power supply needs of important loads.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, an embodiment of the present invention provides a distribution network resilience assessment method considering key loads in an ice disaster scenario, which includes classifying distribution network load nodes and establishing a resilience assessment index system;

[0010] Construct a fault line repair sequence optimization model considering the comprehensive load loss;

[0011] An improved genetic algorithm is used to solve the optimization model and obtain the optimal repair sequence;

[0012] Perform the recovery process according to the repair sequence and evaluate the system resilience.

[0013] As a preferred solution of the distribution network resilience assessment method considering key loads in the ice disaster scenario of the present invention, the load node classification includes: dividing the load into primary, secondary and tertiary power loads; wherein:

[0014] Level 1 loads include hospitals, chemical plants, and critical military facilities;

[0015] Secondary loads include industrial enterprises, commercial areas, and residential areas;

[0016] Tier 3 loads include non-critical commercial and residential electricity.

[0017] As a preferred solution of the distribution network resilience assessment method considering key loads in the ice disaster scenario of the present invention, the calculation of the resilience assessment index includes: determining the weight coefficient of each level of load;

[0018] Calculate the load loss of each level under the ice disaster scenario; calculate the comprehensive load loss according to the weight coefficient and the loss amount; evaluate the system resilience index R based on the comprehensive load loss.

[0019] As a preferred solution of the distribution network resilience assessment method considering key loads in the ice disaster scenario of the present invention, wherein: the optimization model includes: minimizing the comprehensive load loss as the objective function;

[0020] Meet the distribution network power flow convergence constraints; meet the line capacity constraints; meet the open-loop operation constraints;

[0021] Meet the repair resource constraints; meet the generator node power constraints.

[0022] As a preferred scheme of the distribution network resilience assessment method considering critical loads in the ice disaster scenario described in the present invention, the improved genetic algorithm includes: using line numbers as gene codes; calculating fitness values ​​based on comprehensive load loss; using roulette method for individual selection; using mapping rules for gene crossover; and achieving gene mutation through position exchange.

[0023] As a preferred scheme of the distribution network resilience assessment method considering critical loads in the ice disaster scenario described in the present invention, the method for calculating the fitness value is: calculating the comprehensive load loss of each scheme according to the repair order; sorting the schemes from small to large according to the loss amount; and allocating the fitness value based on the sorting result.

[0024] As a preferred solution of the distribution network resilience assessment method considering critical loads in the ice disaster scenario of the present invention, the termination condition of the optimization process is: reaching a preset maximum number of iterations;

[0025] Or there is no significant improvement in the optimal solution for multiple consecutive generations; the individuals with the best objective function value in the last generation are output as the optimal repair sequence.

[0026] In a second aspect, an embodiment of the present invention provides a distribution network resilience assessment system considering key loads in an ice disaster scenario, which includes a load classification module to classify distribution network load nodes and establish a resilience assessment index system;

[0027] The optimization solution module builds a fault line repair sequence optimization model that takes into account the comprehensive load loss;

[0028] The solution execution module uses an improved genetic algorithm to solve the optimization model and obtain the optimal repair sequence;

[0029] The resilience assessment module executes the recovery process according to the repair sequence and evaluates the system resilience.

[0030] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the distribution network resilience assessment method considering critical loads in an ice disaster scenario as described in the first aspect of the present invention are implemented.

[0031] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the distribution network resilience assessment method considering critical loads in an ice disaster scenario as described in the first aspect of the present invention are implemented.

[0032] The beneficial effects of the present invention are as follows: the present invention takes into account the distribution network resilience improvement method of key loads, proposes a line repair sequence optimization that takes into account the comprehensive load loss, takes the minimum comprehensive load loss as the optimization goal, and reflects the impact of power outages of loads of different levels on the resilience of the distribution network. Repairing the lines according to this strategy can ensure that the comprehensive load loss during the fault process is minimized, and ensure the rapid recovery of key loads, thereby improving the resilience of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 A flow chart of the distribution network resilience assessment method considering critical loads under ice disaster scenarios;

[0035] Figure 2 Computer equipment diagram for distribution network resilience assessment method considering critical loads under ice disaster scenario;

[0036] Figure 3 The histogram of the system recovery process using the optimal repair sequence for the distribution network resilience assessment method considering critical loads under ice disaster scenarios;

[0037] Figure 4 The histogram of the system recovery process using random repair order for the distribution network resilience assessment method considering critical loads under ice disaster scenarios;

[0038] Figure 5 The histogram of load loss at each level of the distribution network resilience assessment method considering key loads under ice disaster scenarios;

[0039] Figure 6 This is a histogram of the comprehensive load loss of the distribution network resilience assessment method considering critical loads under ice disaster scenarios. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0043] Example 1

[0044] Reference Figure 1 to Figure 6 , which is the first embodiment of the present invention, and provides a distribution network resilience assessment method considering key loads in an ice disaster scenario, including:

[0045] S100: Classify the load nodes of the distribution network and establish a resilience assessment index system;

[0046] S101: Load node classification includes: classifying loads into primary, secondary and tertiary power loads; wherein:

[0047] Level 1 loads include hospitals, chemical plants, and critical military facilities;

[0048] Secondary loads include industrial enterprises, commercial areas, and residential areas;

[0049] Tier 3 loads include non-critical commercial and residential electricity.

[0050] Preferably, the first-level power load is the most critical load category. Once a power outage occurs, it will lead to extremely serious consequences, such as personal injury, major equipment failures that are difficult to repair, and even huge losses to the national economy. The first-level load usually includes hospitals, mines, chemical plants, important military facilities, key facilities of the power system itself, etc. The second-level power load is less important than the first-level load. When a power outage occurs in the power system, this type of load will lead to reduced production in enterprises, obstruction of transportation, and affect people's normal life and work. The second-level load includes industrial enterprises, commercial areas, residential areas, etc. The third-level power load refers to other power loads except the first-level and second-level loads. The direct consequences of this type of load in a power outage are relatively light and usually do not have a serious impact on the social economy. The third-level load includes some non-critical commercial and residential electricity.

[0051] S102: Calculation of toughness evaluation index includes: determining weight coefficients of various levels of load;

[0052] Calculate the load loss of each level under the ice disaster scenario; calculate the comprehensive load loss according to the weight coefficient and the loss amount; evaluate the system resilience index R based on the comprehensive load loss.

[0053] Preferably, a toughness index R considering the comprehensive load loss of the critical load is defined as:

[0054] In the distribution network, the cost of ensuring all loads under ice disasters is too high, and there is no need to ensure the power supply of all loads, as long as a certain proportion of critical loads can still be powered. Therefore, a resilience assessment method considering the importance of distribution network loads is proposed. The comprehensive load loss after weighting of load nodes of various levels under ice disasters is used to reflect the resilience of the distribution network system. It considers the size of system failure losses during ice disasters and the time the distribution network system is affected. The resilience index R is as follows:

[0055]

[0056] Δp(X i )=6·Δp 1 (X i )+3·Δp 2 (X i )+1·Δp 3 (X i )

[0057] Among them, Δp(X i ) is the comprehensive load loss of key loads under the ice disaster scenario at a certain moment, Δp j (j=1, 2, 3 are the load losses of each level under the ice disaster scenario at a certain moment, X i is the fault scenario under the ice disaster event, p 0It is the initial total load after weighting the load nodes of the distribution network system.

[0058] S200: Construct a fault line repair sequence optimization model considering the comprehensive load loss;

[0059] S201: The optimization model includes: minimizing the comprehensive load loss as the objective function;

[0060] Meet the distribution network power flow convergence constraints; meet the line capacity constraints; meet the open-loop operation constraints;

[0061] Meet the repair resource constraints; meet the generator node power constraints.

[0062] Define the objective function of the fault line repair sequence considering the comprehensive load loss under the ice disaster scenario;

[0063]

[0064] In the proposed optimal repair sequence optimization method, the objective function for measuring the repair sequence effect of each line is selected as the system comprehensive load loss L proposed by S101 loss ,The above objective function takes into account the different importance of each level of load in the ,distribution network, and performs weight processing on the load of different levels, so that the ,optimized line repair sequence can maximize the power supply of the important load in the ,system and improve the resilience of the system.

[0065] S202, providing constraints on the repair sequence of faulty lines considering the comprehensive load loss in an ice disaster scenario;

[0066] Distribution network power flow convergence constraints:

[0067]

[0068] Among them, P i The active power injected into node i; Q i is the reactive power injected into node i; U is the node voltage; Y ij is the mutual admittance between nodes i and j.

[0069] Line capacity constraints:

[0070] |P l |≤P lmax

[0071] Among them, P l is the active power flowing through line l; P lmax is the maximum capacity of line l.

[0072] Open-loop operation constraints

[0073] g k ∈G

[0074] Among them, g k is the area where the distribution network fault is restored; G is the set of all networks that satisfy the radial structure. Restoration resource constraints:

[0075] R k ∈R

[0076] Among them, R k The resources needed to repair the faulty line; R is the resources available to the power company.

[0077] Power constraints of generator nodes:

[0078]

[0079] in, P g It is the lower limit of the active power of the generator set; is the upper limit of the active power of the generator set; Q g It is the lower limit of the active power of the generator set; It is the upper limit of the active power of the generator set.

[0080] S300: Use improved genetic algorithm to solve the optimization model and obtain the optimal repair sequence;

[0081] S301: The improved genetic algorithm includes: using line numbers as gene codes; calculating fitness values ​​based on the comprehensive load loss amount; using the roulette method for individual selection; using mapping rules for gene crossover; and achieving gene mutation through position exchange.

[0082] S302: The method for calculating the fitness value is: calculating the comprehensive load loss amount of each scheme according to the repair order; sorting the schemes from small to large according to the loss amount; and allocating the fitness value based on the sorting result.

[0083] Preferably, a simplified procedure of a genetic algorithm suitable for repairing a faulted line considering the comprehensive load loss amount is provided.

[0084] In the first step, according to the obtained fault line information, each line is numbered and randomly formed into genes. Different genes represent different repair orders.

[0085] X=(x 1 ,x 2 ,…,x n-1 ,x n ,)

[0086] Among them, X is a randomly generated gene; n is the number of distribution network line faults; x i (i=1,2,…,n) are faulty lines, and their order represents the order in which the lines are repaired.

[0087] In the second step, the genes were sorted from small to large according to the comprehensive load loss or from large to small according to the toughness value under each repair sequence, and the fitness value of each gene was reasonably allocated using the following expression.

[0088] f(i)=50-(i-1)

[0089] The third step is to select λ=8% genes as the next generation individuals according to the fitness value from large to small; and use the roulette wheel principle to select 1-λ genes for the next crossover operation.

[0090] The fourth step is to cross the genes obtained from the roulette wheel in the previous step in pairs according to the crossover probability. When performing the crossover operation, since the gene is the repair order of the circuit, each number represents the circuit number, so there cannot be repeated numbers in the gene, and it is necessary to map according to the position of the exchanged gene in the other gene sequence so that each number appears only once.

[0091] The fifth step is to perform mutation operations on the next generation individuals generated after crossover according to the mutation probability. When performing mutation operations, in order to maintain the numbers in the gene, two positions in the gene are randomly selected for exchange.

[0092] Step 6: Repeat steps 2 to 5 according to the set number of iterations, and select the individual with the best objective function value in the last generation. This individual is the optimal line repair sequence X. best .

[0093] S400: Execute the recovery process according to the repair sequence and evaluate the system resilience.

[0094] S401: The termination condition of the optimization process is: reaching a preset maximum number of iterations;

[0095] Or there is no significant improvement in the optimal solution for multiple consecutive generations; the individuals with the best objective function value in the last generation are output as the optimal repair sequence.

[0096] The method of the present invention uses the IEEE33 node system to verify the proposed method, and compares and analyzes it with the randomly selected line repair to verify the effectiveness of the optimal fault repair sequence.

[0097] The effectiveness of this method is verified under a given fault. When a fault of size 8 occurs on lines L5, L9, L14, L18, L20, L24, L27, and L32, the optimal repair order obtained by genetic algorithm is L24, L5, L20, L18, L27, L32, L14, and L9. The obtained toughness index value R=1.2638 and the comprehensive load loss value L loss =0.7913, the whole process is as follows Figure 1 shown.

[0098] At the same time, in order to compare the effectiveness of the optimal repair sequence, 1000 groups of repair sequences were randomly generated and substituted into the system for simulation. The average toughness value was 0.4824 and the average comprehensive load loss value was 2.0728. Figure 4 By comparison Figure 3 , 4 It can be concluded that adopting the optimal repair sequence can greatly improve the resilience of the system, quickly restore the power supply of critical loads, and reduce the load loss of the system.

[0099] In order to better verify the effectiveness of the optimal repair sequence proposed in this description and make the fault scenario closer to reality, the real-time fault situation of the distribution network under the influence of ice disasters is considered next. According to the real-time fault situation, the genetic algorithm is used to determine the optimal repair sequence under the current fault situation, calculate the load loss of the system and the system resilience, and compare and analyze it with the scheme of randomly selecting the repair sequence. The load loss of each level under the optimal repair sequence and the random repair sequence is shown in the figure. Figure 5 The comprehensive load loss is shown in Figure 6 The average toughness value was increased from 0.4760 of the random order repair scheme to 1.1156, and the average comprehensive load loss value was reduced from 2.1006 to 0.8964, which reduced the load loss in ice disaster weather and ensured the rapid recovery of key loads.

[0100] Furthermore, this embodiment also provides a distribution network resilience assessment system considering key loads in an ice disaster scenario, including:

[0101] Load classification module, which classifies the load nodes of the distribution network and establishes a resilience assessment index system;

[0102] The optimization solution module builds a fault line repair sequence optimization model that takes into account the comprehensive load loss;

[0103] The solution execution module uses an improved genetic algorithm to solve the optimization model and obtain the optimal repair sequence;

[0104] The resilience assessment module executes the recovery process according to the repair sequence and evaluates the system resilience.

[0105] In summary, by dividing the distribution network load nodes into three categories: primary (hospitals, etc.), secondary (industrial areas, etc.), and tertiary (ordinary residential) and assigning different weight coefficients, a quantitative distinction of load importance is achieved, enabling the system to accurately identify and prioritize critical loads, thereby achieving the effect of maximizing the power supply of important facilities under limited resource conditions.

[0106] By constructing an optimization model with minimizing the comprehensive load loss as the objective function, while considering: distribution network flow convergence constraints; line capacity constraints; open-loop operation constraints; repair resource constraints; generator node power constraints; the repair sequence optimization under multiple constraints is achieved to ensure that the repair plan meets both physical feasibility and minimizes system losses, ultimately achieving the effect of improving the overall system repair efficiency.

[0107] By adopting: line number as gene encoding; calculating fitness value based on comprehensive load loss; using roulette method for individual selection; adopting mapping rules for gene crossover; realizing gene mutation through position exchange; achieving fast convergence and avoiding local optimal optimization solution, the system can find a repair plan close to the global optimal in a relatively short time, thereby achieving the effect of improving decision-making efficiency and solution reliability.

[0108] By introducing the resilience index R that takes the load level weight into consideration, a quantitative assessment of the system's recovery capability is achieved, so that the repair effects can be objectively measured and compared, ultimately providing a reliable basis for decision-making.

[0109] Traditional methods often use a unified standard to handle all loads. The present invention achieves better social benefits with the same repair resource investment through hierarchical and decentralized processing.

[0110] Different from simple static planning, the present invention realizes dynamic optimization by considering multiple constraints, which significantly improves the practicality of the solution.

[0111] By improving the coding and operation mode of the genetic algorithm, it is made particularly suitable for the distribution network repair sequence optimization problem and the solution efficiency is improved.

[0112] The present invention realizes the accurate quantification and intelligent optimization of the distribution network fault repair process by establishing a hierarchical and decentralized evaluation system, constructing a multi-constraint optimization model, and improving the genetic algorithm solution strategy, and has achieved remarkable results in ensuring the power supply of important loads, improving the system recovery efficiency, and reducing economic losses.

[0113] Example 2

[0114] Reference Figure 2 - Figure 6 , which is the second embodiment of the present invention, provides a distribution network resilience assessment method considering key loads in an ice disaster scenario. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0115] In order to verify the effectiveness and practicality of the method of the present invention, a large-scale ice disaster event that occurred in a certain province in January 2023 was selected as the research object. The ice disaster caused 25 distribution network branches under the jurisdiction of the 110kV substation in the region to have different degrees of failure, affecting 523,000 residential users. The experiment was based on the actual distribution network topology of the region, including 385 load nodes and 412 distribution lines.

[0116] First, the load nodes were classified and weights were determined. The first-level load included three Class-A hospitals, two chemical plants, and one military facility, with a weight coefficient of 0.5; the second-level load included 15 industrial parks, eight large commercial centers, and 12 densely populated areas, with a weight coefficient of 0.3; the remaining 357 were third-level loads, with a weight coefficient of 0.2. Through field research and historical data analysis, a typical load curve model for each level of load was established.

[0117] When constructing the optimization model, constraints were set based on actual operation and maintenance experience: the voltage deviation of the distribution network nodes is allowed to be ±7%, the maximum current carrying capacity of the line is 1.2 times the rated capacity, and the repair resources are based on 8 emergency repair teams (6 people per team), and each team can repair 2 lines per day. At the same time, the actual output range of the generator set and the open-loop operation requirements of the system are taken into account.

[0118] The key parameters of the improved genetic algorithm are set as follows: population size 100, crossover probability 0.85, mutation probability 0.1, and maximum number of iterations 200. To improve the performance of the algorithm, an adaptive crossover operator is used, and the crossover probability is dynamically adjusted according to the individual fitness; the position exchange mutation method is optimized through heuristic rules to ensure that the repair order after mutation still meets the physical constraints.

[0119]

[0120] Through the systematic analysis of experimental data, the method of the present invention has significant advantages over the prior art:

[0121] Improved repair efficiency:

[0122] The average repair time was reduced from 72.5 hours of the traditional empirical method to 43.6 hours, an increase of 39.9%; the resource utilization rate was increased from 65.3% to 92.8%, an increase of 27.5 percentage points, reflecting the optimization effect of the present invention under consideration of multiple constraints.

[0123] Key load protection:

[0124] The first-level load recovery rate reached 98.2%, an increase of 12.9 percentage points over the traditional method; the second-level load recovery rate reached 92.3%, an increase of 16.1 percentage points, verifying the scientific nature and effectiveness of the hierarchical and decentralized evaluation system.

[0125] Improved system resilience:

[0126] The system resilience index increased from 0.482 to 1.116, an increase of 131.5%; the comprehensive loss decreased from 2072.8MW〃h to 896.4MW〃h, a reduction of 56.8%, proving that the resilience assessment method of the present invention has significant practical application value.

[0127] These data fully illustrate that the present invention has the following innovative advantages:

[0128] First, by establishing a scientific hierarchical and decentralized evaluation system, the load importance is accurately quantified. Compared with the traditional empirical method and the static priority method, the first-level load recovery rate of the present invention is increased by 12.9-8.5 percentage points. This significant improvement is due to the in-depth consideration of the differentiated characteristics of the load.

[0129] Second, in terms of optimization model construction, by introducing multidimensional constraints and using an improved genetic algorithm to solve the problem, the resource utilization rate is significantly improved. Compared with 78.6% of a simple genetic algorithm, the present invention achieves a resource utilization rate of 92.8%, proving the effectiveness of the algorithm improvement.

[0130] Third, in terms of system resilience assessment, the method of the present invention has achieved a breakthrough from qualitative to quantitative. The resilience index has increased by 131.5%, and the comprehensive loss has decreased by 56.8%. This quantitative assessment provides a reliable basis for decision-making.

[0131] Finally, the present invention has shown strong adaptability and scalability in practical applications. Through the application verification of actual ice disaster events, it is proved that the method is not only innovative in theory, but also has significant practical value in engineering practice. This unity of "theory-practice" is an important feature of the present invention that distinguishes it from the prior art.

[0132] Example 3

[0133] This embodiment also provides a computer device, which is applicable to a distribution network resilience assessment method considering critical loads in an ice disaster scenario, and includes a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement a forced oscillation detection and positioning method for a distribution network as proposed in the above embodiment.

[0134] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, a forced oscillation detection and positioning method for a distribution network is implemented as proposed in the above embodiment.

[0135] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0136] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0137] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0138] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0139] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0140] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A distribution network resilience assessment method considering key loads in an ice disaster scenario, characterized by: include, Classify the load nodes of the distribution network and establish a resilience assessment index system; Construct a fault line repair sequence optimization model considering the comprehensive load loss; An improved genetic algorithm is used to solve the optimization model and obtain the optimal repair sequence; Perform the recovery process according to the repair sequence and evaluate the system resilience.

2. The distribution network resilience assessment method considering critical loads in an ice disaster scenario according to claim 1, characterized in that: The load node classification includes: classifying the load into primary, secondary and tertiary power loads; wherein: Level 1 loads include hospitals, chemical plants, and critical military facilities; Secondary loads include industrial enterprises, commercial areas, and residential areas; Tier 3 loads include non-critical commercial and residential electricity.

3. The distribution network resilience assessment method considering critical loads in an ice disaster scenario according to claim 2, characterized in that: The calculation of the toughness evaluation index includes: determining the weight coefficient of each level of load; Calculate the load loss of each level under the ice disaster scenario; calculate the comprehensive load loss according to the weight coefficient and the loss amount; evaluate the system resilience index R based on the comprehensive load loss.

4. The distribution network resilience assessment method considering critical loads in an ice disaster scenario according to claim 3, characterized in that: The optimization model includes: minimizing the comprehensive load loss amount as an objective function; Meet the distribution network power flow convergence constraints; meet the line capacity constraints; meet the open-loop operation constraints; Meet the repair resource constraints; meet the generator node power constraints.

5. The distribution network resilience assessment method considering critical loads in an ice disaster scenario according to claim 4, characterized in that: The improved genetic algorithm includes: using line numbers as gene codes; calculating fitness values ​​based on comprehensive load loss; using roulette method to select individuals; using mapping rules to perform gene crossover; and achieving gene mutation through position exchange.

6. The distribution network resilience assessment method considering critical loads in an ice disaster scenario according to claim 5, characterized in that: The method for calculating the fitness value is: calculating the comprehensive load loss amount of each scheme according to the repair order; sorting the schemes from small to large according to the loss amount; and allocating the fitness value based on the sorting result.

7. The distribution network resilience assessment method considering critical loads in an ice disaster scenario according to claim 6, characterized in that: The termination condition of the optimization process is: reaching a preset maximum number of iterations; Or there is no significant improvement in the optimal solution for multiple consecutive generations; The individuals with the best objective function value in the last generation are output as the optimal repair sequence.

8. A distribution network resilience assessment system considering key loads in ice disaster scenarios, based on the distribution network resilience assessment method considering key loads in ice disaster scenarios according to any one of claims 1 to 7, characterized in that: Also includes, Load classification module, which classifies the load nodes of the distribution network and establishes a resilience assessment index system; The optimization solution module builds a fault line repair sequence optimization model that takes into account the comprehensive load loss; The solution execution module uses an improved genetic algorithm to solve the optimization model and obtain the optimal repair sequence; The resilience assessment module executes the recovery process according to the repair sequence and evaluates the system resilience.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for evaluating the resilience of a distribution network considering critical loads in an ice disaster scenario according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for evaluating the resilience of a distribution network considering critical loads in an ice disaster scenario according to any one of claims 1 to 7 are implemented.

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