Method and device for improving elasticity of power system, equipment and storage medium

Abstract

The embodiment of the invention relates to the technical field of power system optimization scheduling, and provides a method, device and equipment for improving the elasticity of a power system and a storage medium, and the method comprises the steps: obtaining a line fault probability according to historical weather data and power system line damage data, generating a fault scene according to the line fault probability, according to the method, fault scenes of different numbers of damaged lines are clustered, then impact on a power transmission line in extreme weather is used as a weather constraint condition, an active defense two-stage robust optimization model is constructed based on the weather constraint condition, the first stage model is used for minimizing the line active shutdown cost before a disaster occurs, and the second stage model is used for minimizing the line active shutdown cost before the disaster occurs. The second-stage model is used for minimizing the passive load shedding cost when a disaster occurs, and finally the model is solved to obtain an optimal resistance scheme of the electric power system, so that the load shedding amount and economic loss of the electric power system in the extreme weather are effectively reduced, and the elasticity of the electric power system in the extreme weather is improved.

Description

Technical Field

[0001] The embodiments of this specification relate to the technical field of power system optimization and dispatching, and in particular to a method, device, equipment and storage medium for improving the flexibility of a power system. Background Art

[0002] In recent years, wind farms have been increasingly integrated into power systems in a large-scale and centralized manner. However, as global climate conditions become increasingly severe, extreme weather has become more frequent. For example, extreme weather such as typhoons, heavy rains, and hail have caused great damage to power systems in countries around the world. Therefore, the safety of power transmission and distribution in power systems has received increasing attention. It has become a consensus that the potential risks and expected losses of long-term and large-scale power transmission and transformation accidents caused by extreme weather cannot be ignored. Different from conventional climates such as wind, rain, and dryness, and conventional equipment failures such as unstable no-load voltage, transmission line tripping, and busbar flashover removal, extreme weather has the characteristics of low probability, high loss, and strong randomness. In the power transmission and transformation link, the power system not only needs to ensure the reliability of the overall operation of the power grid under the environment of conventional climate and conventional faults, but also needs to ensure continuous and stable power supply of the system under the influence of extreme weather. Introducing elasticity into the power system to form a resilient power grid can prevent extreme weather before it occurs, dispatch when extreme weather occurs, and recover after extreme weather occurs, so that the power system has stronger flexibility, better safety, better power quality, and higher self-healing ability. Therefore, improving the flexibility of the power system has become an inevitable choice for the development of the power system.

[0003] At present, the existing strategies for strengthening the power system before extreme weather occurs, such as building isolation walls and dividing areas, require a lot of manpower and material resources, and some of them may even have negative effects: burying the lines in the ground to resist typhoons is not conducive to subsequent maintenance; after extreme weather occurs, the location of the fault area is generally understood and repaired by receiving feedback from inspection teams or users, which cannot achieve timely repair or replacement of the fault area, delaying the waiting period of users. Therefore, how to effectively and reasonably formulate an emergency system for power system disaster prevention, take effective dispatching measures to prevent disasters, and reduce disaster losses has become the primary problem that needs to be solved in the current research on power system resilience. Summary of the invention

[0004] In response to the above-mentioned problems in the prior art, the purpose of the embodiments of this specification is to provide a method, device, equipment and storage medium for improving the resilience of the power system, which can reasonably dispatch flexibility resources under extreme weather conditions, improve the resilience of the power system, and enhance the active resistance and rapid response capabilities of the lines to extreme weather.

[0005] In order to solve the above technical problems, the specific technical solutions of the embodiments of this specification are as follows:

[0006] On the one hand, an embodiment of the present specification provides a method for improving the resilience of a power system, the method comprising:

[0007] Obtain historical weather data and power system line damage data for the target area;

[0008] Obtaining the probability of line failure under extreme weather conditions based on the historical weather data and the power system line damage data;

[0009] Generate a number of fault scenarios according to the line fault probability, each fault scenario including the number of line breaks and line damage information;

[0010] Clustering the plurality of fault scenarios according to the number of line breaks, and calculating the damage probabilities of scenarios with different numbers of line breaks according to the clustering results and line damage information;

[0011] The impact on the transmission line caused by extreme weather is expressed as a set of discrete uncertainty sets, and the discrete uncertainty sets are used as weather constraints;

[0012] Based on the weather constraints and the damage probability of different line breakage scenarios, a two-stage robust optimization model for active defense is established, wherein the first-stage model is used to minimize the active line shutdown cost before the disaster occurs, and the second-stage model is used to minimize the passive load shedding cost when the disaster occurs;

[0013] The two-stage robust optimization model is solved to obtain the optimal defense solution for the power system.

[0014] Furthermore, obtaining the line failure probability under extreme weather conditions according to the historical weather data and the power system line damage data includes:

[0015] Draw a line vulnerability curve based on the historical weather data and the power system line damage data;

[0016] Fitting the line fragility curve to obtain a line failure rate fitting function;

[0017] Obtaining a line failure rate according to the line failure rate fitting function;

[0018] constructing a line failure probability model according to the line failure rate;

[0019] The line failure probability is obtained according to the failure probability model.

[0020] Furthermore, generating a plurality of fault scenarios according to the line fault probability includes:

[0021] For each line, randomly generate its line state according to the line fault probability, wherein the line state includes normal and fault;

[0022] Combine all lines with fault status to obtain a fault scenario;

[0023] Repeat the above steps until the predetermined number of simulations is reached to obtain several fault scenarios.

[0024] Furthermore, the discrete uncertainty set includes: using the following formula to express the impact of extreme weather on the transmission line:

[0025]

[0026] Among them, U represents a discrete uncertain set, u l represents the decision variable of the discrete uncertainty set, indicates whether the transmission line is impacted during the disaster, l represents the lth transmission line, Z represents the transmission line set in this scenario, L represents the total transmission line set, and μ represents the cardinality budget of the number of damaged lines when the disaster occurs.

[0027] Furthermore, the objective function of the active defense two-stage robust optimization model includes:

[0028]

[0029] Among them, f is the objective function, i g is the g-th generator, N G is the total number of generators, N L is the total number of lines, N is the total number of nodes, T is the total defense time, P i pre is the load reduction before the disaster, P i shed is the load reduction forced during the disaster, C is the cost function, is the power generation of the dispatchable units, is the operating cost of the dispatchable units, are the start-up and shutdown costs of dispatchable units, is the active outage cost of the line, which is calculated based on the damage probability of the different line breakage quantity scenarios, U represents a discrete uncertainty set, u represents a certain uncertain scenario, is the cost coefficient of proactive load reduction before a disaster, Reduce the cost factor for passive loads caused by line damage during disasters;

[0030] The constraints of the active defense two-stage robust optimization model include: weather constraints, unit scheduling constraints, DC power flow constraints, load shedding constraints and node constraints.

[0031] Further, solving the two-stage robust optimization model includes:

[0032] S1: Initialize the upper bound parameter and the lower bound parameter to positive infinity and negative infinity respectively;

[0033] S2: The first stage model in the two-stage robust optimization model is taken as the main problem, and the second stage model is taken as the sub-problem;

[0034] S3: Solve the main problem and use the objective function value of the main problem as a new lower bound parameter;

[0035] S4: Solve the sub-problem, and sum the objective function value of the sub-problem and the objective function value of the main problem as a new upper bound parameter;

[0036] S5: Calculate the absolute value of the difference between the new upper bound parameter and the lower bound parameter, and determine whether the absolute value of the difference is less than a preset threshold;

[0037] S6: If yes, then output the objective function values ​​of the main problem and the sub-problem as the optimal solution;

[0038] S7: If not, generate a new constraint according to the objective function value of the sub-problem and add it to the main problem, and repeat steps S3-S5.

[0039] On the other hand, an embodiment of the present specification provides a device for improving the resilience of a power system, the device comprising:

[0040] An acquisition module is used to obtain historical weather data and power system line damage data of the target area;

[0041] A line failure probability calculation module, used to obtain the line failure probability under extreme weather conditions based on the historical weather data and the power system line damage data;

[0042] A fault scenario generation module, used to generate a number of fault scenarios according to the line fault probability, each fault scenario including the number of disconnections and line damage information;

[0043] A clustering module, configured to cluster the plurality of fault scenarios according to the number of line breaks, and calculate the damage probabilities of scenarios with different numbers of line breaks according to the clustering results and line damage information;

[0044] A weather constraint determination module, used to represent the impact on the transmission line caused by extreme weather as a set of discrete uncertainty sets, and use the discrete uncertainty sets as weather constraint conditions;

[0045] A model building module is used to establish an active defense two-stage robust optimization model based on the weather constraints and the damage probability of different line disconnection quantity scenarios, wherein the first stage model is used to minimize the active line shutdown cost before the disaster occurs, and the second stage model is used to minimize the passive load shedding cost when the disaster occurs;

[0046] The solution module is used to solve the two-stage robust optimization model to obtain the optimal defense solution for the power system.

[0047] On the other hand, an embodiment of the present specification further provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein when the computer program is executed by the processor, the instructions of any one of the above methods are executed.

[0048] On the other hand, an embodiment of the present specification further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor of a computer device, the computer program executes instructions of any one of the above-described methods.

[0049] On the other hand, the embodiments of this specification further provide a computer program product, which, when executed by a processor of a computer device, executes instructions of any one of the above methods.

[0050] By adopting the above-mentioned technical scheme, a method for improving the resilience of the power system provided in the embodiment of this specification can obtain the line failure probability under extreme weather conditions based on historical weather data and power system line damage data, and then use the non-sequential Monte Carlo simulation method to generate fault scenarios according to the line failure probability, and cluster the fault scenarios with different numbers of damaged lines, so that the damage probability of different line break numbers can be obtained according to the clustering results and line damage information. Then, the impact on the transmission line under extreme weather is represented as a set of discrete uncertainty sets, and the discrete uncertainty sets are used as weather constraints. Based on the weather constraints, an active defense two-stage robust optimization model is constructed. The first-stage model is used to minimize the active line shutdown cost before the disaster occurs, and the second-stage model is used to minimize the passive load shedding cost when the disaster occurs. Finally, the model is solved to obtain the optimal defense plan for the power system, which effectively reduces the load shedding and economic losses of the power system in the face of extreme weather, thereby improving the resilience of the power system under extreme weather.

[0051] The above description is only an overview of the technical solutions of some embodiments of this specification. In order to more clearly understand the technical means of some embodiments of this specification, they can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiments of this specification more obvious and easy to understand, the following specifically cites the preferred embodiments and describes them in detail in conjunction with the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 A schematic flow chart of a method for improving the resilience of a power system provided in some embodiments of this specification is shown;

[0054] Figure 2 A schematic diagram of a process for obtaining the probability of line failure under extreme weather conditions according to the historical weather data and the power system line damage data in some embodiments of this specification is shown;

[0055] Figure 3 A schematic diagram of a process of generating several fault scenarios according to the line fault probability in some embodiments of this specification is shown;

[0056] Figure 4 A schematic diagram of a process for solving the two-stage robust optimization model using a column and constraint generation algorithm in some embodiments of this specification is shown;

[0057] Figure 5 A schematic diagram of a simulated typhoon path in some embodiments of this specification is shown;

[0058] Figure 6 A schematic diagram of daily load of the power system before a typhoon disaster in some embodiments of this specification is shown;

[0059] Figure 7 A schematic diagram showing load levels after a line suffers load loss due to a typhoon disaster in some embodiments of this specification;

[0060] Figure 8 A schematic diagram showing a comparison of the elastic performance of power systems under extreme weather conditions in some embodiments of this specification is shown;

[0061] Fig. 9 A schematic diagram of the structure of a device for improving the flexibility of a power system in some embodiments of this specification is shown;

[0062] Fig.10 A schematic diagram of the structure of a computer device in this specification is shown.

[0063] Description of the accompanying symbols:

[0064] 901. Get module;

[0065] 902. Line fault probability calculation module;

[0066] 903. Fault scenario generation module;

[0067] 904, clustering module;

[0068] 905. Weather constraint determination module;

[0069] 906. Model building module;

[0070] 907, solution module;

[0071] 1002. Computer equipment;

[0072] 1004. Processor;

[0073] 1006. Memory;

[0074] 1008. Driving mechanism;

[0075] 1010, input / output module;

[0076] 1012. Input device;

[0077] 1014. Output device;

[0078] 1016. Presentation equipment;

[0079] 1018. Graphical user interface;

[0080] 1020. Network interface;

[0081] 1022. Communication link;

[0082] 1024. Communication bus. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0084] In order to solve the above problems, an embodiment of the present specification provides a method for improving the flexibility of a power system. Figure 1This is a flowchart of a method for improving the resilience of a power system provided in an embodiment of this specification. This specification provides method operation steps as described in the embodiment or flowchart, but may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the system or device product is executed in practice, it can be executed in the order of the method shown in the embodiment or the drawings or in parallel.

[0085] It should be noted that the terms "first", "second", etc. in this specification and claims and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of this specification described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0086] Reference Figure 1 As shown, an embodiment of this specification provides a method for improving the resilience of a power system, the method comprising:

[0087] S101: Acquire historical weather data and power system line damage data of the target area;

[0088] S102: Obtaining the probability of line failure under extreme weather conditions according to the historical weather data and the power system line damage data;

[0089] S103: generating a plurality of fault scenarios according to the line fault probability, each fault scenario including the number of disconnections and line damage information;

[0090] S104: clustering the plurality of fault scenarios according to the number of line breaks, and calculating damage probabilities of scenarios with different numbers of line breaks according to the clustering results and line damage information;

[0091] S105: expressing the impact on the transmission line caused by extreme weather as a set of discrete uncertainty sets, and using the discrete uncertainty sets as weather constraints;

[0092] S106: Based on the weather constraints and the damage probabilities of different line-breakage number scenarios, a two-stage active defense robust optimization model is established, wherein the first-stage model is used to minimize the active line shutdown cost before the disaster occurs, and the second-stage model is used to minimize the passive load shedding cost when the disaster occurs;

[0093] S107: Solving the two-stage robust optimization model to obtain an optimal defense solution for the power system.

[0094] By adopting the above-mentioned technical scheme, a method for improving the resilience of the power system provided in the embodiment of this specification can obtain the line failure probability under extreme weather conditions based on historical weather data and power system line damage data, and then use the non-sequential Monte Carlo simulation method to generate fault scenarios according to the line failure probability, and cluster the fault scenarios with different numbers of damaged lines, so that the damage probability of different line break numbers can be obtained according to the clustering results and line damage information. Then, the impact on the transmission line under extreme weather is represented as a set of discrete uncertainty sets, and the discrete uncertainty sets are used as weather constraints. Based on the weather constraints, an active defense two-stage robust optimization model is constructed. The first-stage model is used to minimize the active line shutdown cost before the disaster occurs, and the second-stage model is used to minimize the passive load shedding cost when the disaster occurs. Finally, the model is solved to obtain the optimal defense plan for the power system, which effectively reduces the load shedding and economic losses of the power system in the face of extreme weather, thereby improving the resilience of the power system under extreme weather.

[0095] In step S101, taking typhoon weather as an example, historical weather data may include wind speed, moving path, moving direction, central air pressure, duration, central maximum wind speed and moving speed, etc., and power system line damage data may include damage type, number of damaged lines, degree of damage and economic losses caused by damage, etc., wherein historical weather data can be obtained from meteorological departments, meteorological organizations or professional meteorological websites, and power system line damage data can be obtained from power companies or relevant power grid organizations.

[0096] In some embodiments of this specification, refer to Figure 2 In step S102, the line failure probability under extreme weather conditions is obtained according to the historical weather data and the power system line damage data, including:

[0097] S201: Draw a line vulnerability curve based on the historical weather data and the power system line damage data.

[0098] The line vulnerability curve is a method used to describe and evaluate the vulnerability of power system lines under specific conditions (such as natural disasters such as typhoons). The curve can intuitively show the relationship between the degree of line damage and the typhoon intensity or wind speed under different conditions. In this step, the historical weather data and the power system line damage data are first preprocessed, including removing duplicate, erroneous or invalid data records; the missing data is reasonably estimated or interpolated. The preprocessing can ensure the accuracy and consistency of the data to reduce the impact of incomplete data on subsequent steps. Secondly, the wind speed conditions are classified according to the historical weather data, and the degree of line damage under different typhoon intensities is calculated. The typhoon intensity is used as the horizontal coordinate and the degree of line damage is used as the vertical coordinate. The line vulnerability curve is drawn according to the distribution trend of the data points.

[0099] S202: Fit the line vulnerability curve to obtain a line failure rate fitting function.

[0100] In the embodiments of this specification, according to the existing line fragility curve calculation method, an exponential function is usually used to fit the line fragility curve, and the line failure rate fitting function is obtained as follows:

[0101]

[0102] Where v(t) is the wind speed at time t, λ wind (v(t)) is the failure rate of the line at the wind speed v(t), λ norm is the failure rate under normal weather conditions, and α1, α2, and α3 are fitting coefficients. The nonlinear relationship can be well described by fitting the line vulnerability curve using an exponential function. By adjusting the fitting coefficient of the exponential function, the error between the fitting curve and the actual data can be minimized, and a high-precision fitting result can be obtained, thereby improving the accuracy of assessing line vulnerability and providing good data support for the subsequent two-stage robust optimization model construction.

[0103] High-precision fitting: By adjusting the parameters of the exponential function, the error between the fitting curve and the actual data can be minimized, thereby obtaining a high-precision fitting result, which is crucial for accurately assessing the vulnerability of the line.

[0104] S203: Obtaining a line failure rate according to the line failure rate fitting function.

[0105] The line failure rate under different wind speed conditions can be calculated by formula (1).

[0106] S204: Constructing a line failure probability model according to the line failure rate.

[0107] It can be understood that the line failure rate is an indicator used to express the frequency of line failures. It refers to the ratio of the number of line failures to the total operating time within a specific time period. The line failure probability refers to whether a specific line failure can occur and the probability of its occurrence, which involves the possibility and uncertainty of the failure. Therefore, the failure probability focuses more on the assessment and decision support of the overall risk level of the power system. In order to ensure the reliability of the power system in elasticity analysis, a widely recognized failure probability model should be used. Therefore, it is necessary to associate the two. According to the existing calculation methods of failure probability and failure rate, the line failure probability model is obtained as follows:

[0108]

[0109] Where P represents the line failure probability, λ wind (v(t)) is the failure rate of the line at the wind speed v(t), and T1 is the total time.

[0110] S205: Obtaining line failure probability according to the failure probability model.

[0111] Formula (2) can be used to calculate the probability of power system line failure under different wind speed conditions.

[0112] In some embodiments of this specification, in step S103, a number of fault scenarios are generated using a non-sequential Monte Carlo simulation method. The non-sequential Monte Carlo simulation method simulates various states of the power system through random sampling, and does not need to consider the time sequence. Therefore, a small amount of sampling can be performed at each moment to obtain a relatively accurate result. Figure 3 , the generating of several fault scenarios according to the line fault probability includes:

[0113] S301: For each line, randomly generate its line status according to the line fault probability, and the line status includes normal and faulty;

[0114] S302: Combine all lines whose line status is faulty to obtain a fault scenario;

[0115] S303: Repeat the above steps until a predetermined number of simulations is reached to obtain several fault scenarios.

[0116] Specifically, it is assumed that the line status can be represented by a set of two-dimensional [0,1] variables, where 0 represents a normal line and 1 represents a line failure. The state s of the i-th line is i It is expressed as:

[0117]

[0118] Among them, s i represents the state of the i-th line, Ri is a random number between [0,1], P i is the failure probability of the i-th line. When the generated random number R i Less than or equal to the line failure probability P i When , it indicates that the line status of the line in the current simulation cycle is faulty, traverse all the lines in the power system, obtain the status of each line according to formula (3), and combine all the lines with faulty status into a fault scenario. Repeat the above steps until the predetermined number of simulations is reached to obtain several different fault scenarios. In order to ensure that the generated fault scenario is closer to the actual situation, the number of simulations is set to 10000 in the embodiment of this specification.

[0119] In step S104, the generated fault scenarios are clustered according to different numbers of line breaks, so that fault scenarios with the same number of line breaks are clustered into one category. The damage probability of scenarios with different numbers of line breaks can be calculated based on the clustering results and line damage information. The damage probability can be used to know which lines are more likely to be damaged, so that when the active defense two-stage robust optimization model is subsequently constructed, the lines to be actively shut down can be selected based on the damage probability.

[0120] In step S105, the impact on the transmission line caused by extreme weather is represented as a set of discrete uncertainty sets, which can be represented by the following formula:

[0121]

[0122] Among them, U represents a discrete uncertain set, u l represents the decision variable of the discrete uncertain set, indicates whether the transmission line is impacted during the disaster, μ represents the cardinality budget of the number of damaged lines when the disaster occurs, l represents the lth transmission line, Z represents the transmission line set in this scenario, and L represents the total transmission line set.

[0123] In step S106, before the disaster, by flexibly adjusting the dispatchable resources (such as units), a two-stage robust optimization model of active defense of the power system based on source-grid-load coordination is constructed, wherein the first stage is the unit dispatching cost and the active line shutdown cost, which meets the weather constraints, unit dispatching constraints, transmission line constraints, power balance constraints, start-stop cost constraints and operation reserve requirements, etc. The second stage is the passive load shedding cost when the disaster occurs, which meets the flow constraints, load shedding constraints, node constraints and power balance constraints, etc. The model objective function is established as follows:

[0124]

[0125] Among them, f is the objective function, i g is the g-th generator, N G is the total number of generators, NL is the total number of lines, N is the total number of nodes, T is the total defense time, P i pre is the load reduction before the disaster, P i shed is the load reduction forced during the disaster, C is the cost function, is the power generation of the dispatchable units, is the operating cost of the dispatchable units, are the start-up and shutdown costs of dispatchable units, is the active outage cost of the line, which is calculated based on the damage probability of the different line breakage quantity scenarios, U represents a discrete uncertainty set, u represents a certain uncertain scenario, is the cost coefficient of proactive load reduction before a disaster, The cost factor for reducing passive loads caused by line damage during disasters.

[0126] As can be seen from formula (5), the objective function is expressed as a three-layer defense-attack-defense model. The first layer of the defense model is expressed as an outer layer min problem (i.e., the first to third items in the objective function), and its goal is to select an active defense method that can minimize the disaster loss; the second layer of the attack model is expressed as an outer layer max problem, that is, to find the worst scenario that may occur in the system under a certain number of line breaks, and obtain the specific fault line; the third layer of the defense model is expressed as an inner layer min problem, that is, to ensure that the passive load shedding cost caused by line outage is minimized.

[0127] The constraints of the above objective function include:

[0128] (1) Unit dispatch constraints

[0129]

[0130]

[0131] Among them, formula (6) is the power balance constraint; formula (7) is the unit hot standby constraint; formula (8) is the unit output constraint; formula (9) is the unit ramp rate constraint; formulas (10) and (11) are the unit minimum shutdown and startup time constraints respectively; formulas (12) and (13) represent the unit startup and shutdown cost constraints. In each formula, N G is the total number of generators, is the output of dispatchable generator set i at time t, P d,t is the active power consumed by the load at time t, N is the total number of nodes, represents the state of unit i at time t, Represents the power-on state, otherwise it is the power-off state, P i,max , P i,minare the upper and lower limits of the generator output, ρ represents the hot standby coefficient, R d Indicates the lower limit of the unit climbing rate, R u Indicates the upper limit of the unit climbing rate, T shut represents the minimum shutdown time of the unit, k represents the kth moment, represents the state of unit i at time k, T open Indicates the minimum start time of the unit. are the start-up and shutdown costs of each generator set, H i represents the minimum startup cost of the unit, J i Indicates the minimum shutdown cost of the unit. In the active defense two-stage robust optimization model, the unit scheduling cost will be automatically optimized with the production of the worst scenario and participate in subsequent calculations as the cost of the power supply side.

[0132] (2) DC power flow constraints

[0133]

[0134] in, Indicates whether the line will be shut down in advance. It means the line has been shut down ahead of schedule. Indicates that the line has been suspended in advance, P l,min Indicates the minimum line capacity, P l,t represents the active power on line l, P l,max Represents the maximum capacity of the line. DC power flow constraints are used to evaluate the system power flow distribution and cost under different scheduling schemes. By considering DC power flow constraints, an optimal scheduling scheme that meets the dual goals of economy and safety can be formulated.

[0135] (3) Node active load reduction constraints

[0136]

[0137]

[0138] Among them, formula (15) is the power balance constraint, which reduces the generator output by actively reducing the load, formula (16) is the active load reduction constraint of each node, ensuring that the load reduced by each node is not greater than the total load carried by the node, and formula (17) is the active load reduction constraint of all nodes. G is the total number of generators, N is the total number of nodes, i g is the g-th generator, P i,t represents the power generation of generator set i at time t, P d,t is the load of each node before the disaster, represents the pre-load reduction at time t before the disaster, and γ is the active load reduction ratio before the disaster. Since the load has been informed to the majority of users before the active shedding, the power outage cost caused is much smaller than the economic loss caused by the typhoon disaster.

[0139] (4) Node passive load reduction constraints

[0140]

[0141] Among them, i g is the g-th generator, N G is the total number of generators, N is the total number of nodes, P ig,t is the output of dispatchable generator set i at time t, represents the pre-cut load at time t before the disaster, represents the amount of forced load reduction at time t during the disaster. In the node constraints, the reduction amount of each node should not be greater than the difference between the total load before the disaster and the active load reduction. In the process of solving the objective function, the algorithm will automatically select the worst scenario and optimize the system cost. Finally, through the objective function (5) and constraints (6)-(19), the model of active protection of the power system against extreme disasters under source-grid-load coordination is obtained. The source-grid-load coordination represents the two-stage robust optimization model of active protection. Active protection under extreme weather conditions is achieved from the power supply side, line side and load side respectively. The power supply side is mainly achieved by pre-dispatching generators, the line side is mainly achieved by actively shutting down easily damaged lines, and the load side is mainly achieved by actively reducing loads.

[0142] In step S107, the embodiment of this specification adopts a column and constraint generation algorithm to solve the two-stage robust optimization model, referring to Figure 4 , the solution process includes:

[0143] S1: Initialize the upper bound parameter and the lower bound parameter to positive infinity and negative infinity respectively;

[0144] S2: The first stage model in the two-stage robust optimization model is taken as the main problem, and the second stage model is taken as the sub-problem;

[0145] S3: Solve the main problem and use the objective function value of the main problem as a new lower bound parameter;

[0146] S4: Solve the sub-problem, and sum the objective function value of the sub-problem and the objective function value of the main problem as a new upper bound parameter;

[0147] S5: Calculate the absolute value of the difference between the new upper bound parameter and the lower bound parameter, and determine whether the absolute value of the difference is less than a preset threshold;

[0148] S6: If yes, then output the objective function values ​​of the main problem and the sub-problem as the optimal solution;

[0149] S7: If not, generate a new constraint according to the objective function value of the sub-problem and add it to the main problem, and repeat steps S3-S5.

[0150] Specifically, the column and constraint generation algorithm is an iterative method for solving complex optimization problems. The upper and lower bound parameters in the column and constraint generation algorithm are key indicators used to evaluate the quality of the current solution and drive the algorithm to converge to the optimal solution during the algorithm iteration process. The upper bound parameter refers to the objective function value of a feasible solution found for the optimization problem currently under consideration during the algorithm iteration process. This value is the best solution currently known, and it represents an upper limit of the optimal objective function value that the algorithm may achieve. In each iteration, if a better feasible solution is obtained by solving the subproblem or the main problem, and the objective function value of the solution is less than the current upper bound parameter, the upper bound parameter is updated to the objective function value of the solution. The lower bound parameter refers to the objective function value obtained by solving the main problem during the algorithm iteration process. This value is a lower limit of the minimum objective function value that the currently known optimal solution may achieve. In each iteration, by solving the main problem and adding new columns (i.e., latent variables) or constraints, the solution of the main problem may change, thereby updating the lower bound parameter. If the new lower bound parameter is greater than the previous upper bound parameter, it means that a global optimal solution has been found and the algorithm can terminate.

[0151] The column and constraint generation algorithm is used to solve the two-stage robust optimization model. The model is divided into the main problem (i.e., the first-stage model) and the sub-problem (i.e., the second-stage model). In the main problem, an active defense strategy is formulated and the total cost of the power system is optimized. The obtained decision variables are passed to the sub-problem as known quantities. Its simplified form is expressed as follows:

[0152] (1) Objective function

[0153]

[0154] (2) Constraints

[0155]

[0156]

[0157] θ (1,1) =0 (31)

[0158] Among them, η is a set of cutting planes, x lis the optimal solution of a set of sub-problems under uncertain scenarios, and is passed to the main problem as a known quantity, and then the lower bound of the algorithm is updated by solving the main problem. Equations (23)-(28) only contain the decision variables of the first stage; the decision variables of the first and second stages in equations (29)-(32) affect each other and constitute a complete constraint. In the above equations, i g is the g-th generator, N G is the total number of generators, N L is the total number of lines, N is the total number of nodes, T is the total defense time, P i pre is the load reduction before the disaster, P i shed Forced to reduce load during disasters, is the operating cost of the dispatchable units, are the start-up and shutdown costs of dispatchable units, is the active outage cost of the line, which is calculated based on the damage probability of the different line breakage quantity scenarios, U represents a discrete uncertainty set, u represents a certain uncertain scenario, is the cost coefficient of proactive load reduction before a disaster, is the cost factor for reducing the passive load caused by line damage during the disaster, T shut represents the minimum shutdown time of the unit, k represents the kth moment, represents the state of unit i at time k, T open Indicates the minimum start time of the unit. are the start-up and shutdown costs of each generator set, H i represents the minimum startup cost of the unit, J i Represents the minimum unit shutdown cost. d,t is the load of each node before the disaster, represents the pre-load reduction at time t before the disaster, γ is the active load reduction ratio before the disaster, C g is the generator correlation coefficient, C l is the load correlation coefficient, S'·X·S is the power angle correlation coefficient, θ i,t is the line power angle, ρ is the hot standby coefficient, θ (1,1) is the balanced node power angle, P i,max , P i,min They are the upper and lower limits of the generator output respectively.

[0159] Find the "worst" scenario under a given defense strategy in the subproblem, regenerate the scenario constraints and add them to the main problem, solve the main problem and reformulate the defense strategy. Since the subproblem is a bilinear programming problem and it is expressed as a max-min model, it is converted into a single-layer model using the dual method, and the big M method is used to process the bilinear part, and then the MIP solver is used to solve it. Its simplified form is expressed as follows:

[0160] (1) Objective function

[0161]

[0162] (2) Constraints

[0163]

[0164] C l '·λ1+C l '·λ 61 +C l '·λ 62 ≤10 5 (36)

[0165] (-S'·X·S)'·λ1+[1,0,...,0] 1×(N-1) ·λ4+(X·S)'·λ 51 +(X·S)'·λ 52 =0 (37)

[0166] -u l ·M≤r1,r1≤u l ·M (38)

[0167] λ 51 -(1-u l )·M≤r1,r1≤λ 51 +(1-u l )·M (39)

[0168] -u l ·M≤r2,r2≤u l ·M (40)

[0169] λ 52 -(1-u l )·M≤r2,r2≤λ 52 +(1-u l )·M (41)

[0170] λ2≤0,λ 31 ≥,λ 32 ≤0,λ 51 ≥0,λ 52 ≤0,λ61 ≥0,λ 62 ≤0 (42)

[0171] Among them, equations (32)-(33) are the objective functions after dualization, π is the dual variable, equation (34) is the constraint condition in the uncertainty set in the subproblem, and equations (35)-(42) are the constraint conditions after dualization. By optimizing the subproblem, the upper bound of the column and constraint production algorithm is updated, and a scenario is obtained. The new variables and new constraints in the scenario are added to the main problem. By iteratively solving the main problem and subproblems, the robust optimal solution of the power system under typhoon disasters is obtained. In the above equations, U represents a discrete uncertainty set, u represents an uncertain scenario, and x represents a cost loss before dualization. represents the possible cost loss set before the duality, T is the total defense time, is the cost factor for reducing passive loads caused by line damage during disasters, P i shed Forced to reduce load during disasters, represents the possible cost loss set after duality, C g is the generator correlation coefficient, C l is the load correlation coefficient, S'·X·S is the power angle correlation coefficient, θ i,t is the line power angle, ρ is the hot standby coefficient, θ (1,1) is the balanced node power angle, P d,t is the load of each node before the disaster, λ1 represents the dual variable and has no practical meaning, N G is the total number of generators, represents the start and stop status of generator set i at time t, P i,max , P i,min are the upper and lower limits of generator output, P l,max , P l,min are the upper and lower limits of line capacity, u l,t It indicates whether line l is disconnected at time t, and M represents a very large number.

[0172] The two-stage optimization model in the embodiment of this specification is tested using the IEEE-30 node main grid system. The system includes 30 nodes, 6 generator sets, 41 transmission lines, and simulates the typhoon path as follows: Figure 5 As shown, the typhoon landed on line 12 at 0 o'clock and moved inland along lines 12-14-24-26-32-36. The actual load of a certain area for 24 hours is used as simulation data. The total installed capacity of the system is 5124.9MW, the peak load is 3967.6MW, and the base capacity is 1000MW.

[0173] Assuming that the typhoon lasts for 8 hours, the time before the typhoon lands is defined as the pre-disaster time, when the system takes measures to enter the prevention state. The time after the typhoon leaves is defined as the post-disaster time, when the system quickly adjusts to the recovery state. The time is divided according to 24 hours a day. Assuming that the first 8 hours are the pre-disaster stage, the system operates normally at this time, the middle 8 hours are the typhoon's continuous action stage, and the last 8 hours are the post-disaster stage, when the system begins to recover in an orderly manner. The present invention takes into account the two stages of pre-disaster and disaster, pre-dispatches available resources, reduces the unit output, and helps the power system to actively resist typhoon disasters from the source. Without considering the evolution process of chain failures in the power system, all damaged lines meet the following constraints:

[0174] (1) During the typhoon period, the line repair is not considered. That is, in 8-16 hours, the line that was damaged at the previous moment must be damaged at the next moment, that is, it satisfies:

[0175]

[0176] (2) Pre-load shedding is only carried out before the typhoon enters the country. Load shedding caused by line damage after the typhoon enters the country is forced load shedding, and its cost coefficient is much higher than the active load shedding before the disaster, which meets the following requirements:

[0177] Active load shedding:

[0178]

[0179] Passive load shedding:

[0180]

[0181] (3) Assuming that power generation will not be directly affected by the typhoon, only the reduction in generator output caused by load loss is considered. Active load shedding before the disaster occurs before the typhoon arrives, that is, from 1 to 8 seconds. Since the cut-off load should not be restored, the total load cut off at each moment should be greater than or equal to the previous moment; from 9 to 16 seconds, active load shedding is no longer considered, only the load loss caused by line damage is considered, the active load shedding value remains unchanged, and the load loss value is greater than or equal to the previous moment. During the multiple periods of time when the load is cut off, economic losses will be caused to the system. Although the load loss cost coefficients in the two stages are different, their cost losses are expressed as the sum of the products of the load shedding (loss) amount and the load shedding (loss) cost coefficient at each moment. It is brought into the two-stage robust optimization model, that is: the first stage is before the typhoon disaster occurs, the system operates normally with load. At this time, a series of active defense measures are taken to reduce the unit output, so that the system load interruption cost is minimized after the typhoon damages the line in the second stage. The two stages are coordinated and optimized to form a system pre-disaster active disaster defense strategy. Here, taking the number of line breaks as an example, it is assumed that the convergence accuracy of the algorithm is 0.05.

[0182] Figure 6This is the per-unit value of the load carried by each node when the system is operating normally before the typhoon arrives, that is, a schematic diagram of the system's daily load. The different colored bars represent the output levels of different generators. From 9 am to 9 pm, there is basically a peak in electricity consumption, and except for the third generator which was shut down for the first five hours, the rest of the units were all in operation throughout the whole process. The output of each generator was basically the same within 24 hours, without a large fluctuation.

[0183] Figure 7 The load level after the line suffered a load loss due to the typhoon disaster. The bar charts of different colors represent the output of different generators. It can be seen from the figure that before the typhoon arrived, each generator set was working normally. At this time, a small amount of flexible load was shut down through active prevention, but the overall load level did not change much. Only one unit was shut down at 6 o'clock, reducing the output of a few units. After the typhoon arrived at 8 o'clock, the load began to be reduced a lot, and the load level reached the lowest at 12 o'clock, from 3820.6MW under normal circumstances to 1420.6MW; from 11 to 14 o'clock, the load per unit value was lower than 2p.u., resulting in a large number of interrupted loads, and the power outage was extremely serious. At this time, only two units were still in working condition, and the economic losses were very huge; after the typhoon ended, through the implementation of repair work and the re-dispatch of generator sets, the load level gradually increased, the units resumed output in an orderly manner, and the system gradually tended to be stable. Figure 8 This is a schematic diagram comparing the resilience performance of power systems under extreme weather conditions. Figure 8 It can be seen that the active defense two-stage robust optimization model in the embodiment of this specification has improved elastic performance to a certain extent compared with the traditional power system that does not adopt the active defense model.

[0184] Since load interruption will not only lead to power shortage, but also cause national economic losses, the cost of load interruption is much greater than the cost of power generation. The embodiment of this specification is based on the annual data of the power balance sheet of previous years, and takes the ratio of gross national product (100 million yuan) to total power energy consumption (100 million kilowatt-hours) as the basis for the value of load interruption cost. The load interruption cost coefficient is 13 yuan / degree, that is, 13,000 yuan / MWh, and the active load shedding cost coefficient before the disaster is 5,000 yuan / MWh. Assuming that the active load shedding before the disaster does not exceed 10% of the total system load, the unit output cost is based on, and the unit start-up and shutdown cost is based on 400,000 yuan / time. Find the "worst" scenario under different values ​​of the number of disconnections. The algorithm converges after 4 iterations and meets the preset accuracy. Table 1 shows the solution results for 24 hours during the disaster period. From the analysis, it can be seen that with the increase in the number of iterations, the simulated disaster scenarios become more and more serious, the active load reduction before the disaster and the passive load loss during the disaster both increase, and the unit output decreases accordingly. Since our goal is to find the worst scenario of any four random combinations of lines 12 / 14 / 24 / 26 / 32 / 36, the most serious fault is when the {14, 24, 26, 32}th line on the path is broken. The direct economic loss caused by the disaster at this time is as high as 200 million yuan, and the total system cost after coordinated active defense before the disaster is as high as 222 million yuan.

[0185] Table 1

[0186]

[0187] For different values ​​of η, the two-stage robust analysis method is used to find the worst scenarios under different numbers of line breaks and the optimization solutions under the corresponding scenarios, and the total system cost is calculated. Table 2 compares the probability of occurrence, the worst scenario and the system cost for different numbers of line breaks. The total cost of active defense is the sum of the pre-dispatching cost and load interruption cost considered by the system in this chapter. The load interruption cost is the passive load loss cost caused by extreme disasters. The total cost of the traditional system is the load loss cost of the system without taking any defensive measures after the typhoon arrives without considering the active defense of the power system.

[0188] Table 2

[0189]

[0190] From the horizontal comparison in Table 2, it can be seen that with the improvement of the redundancy of line breaks, the system first actively reduces the load, and the cost of active defense before the disaster continues to increase; as the disaster continues to deteriorate, when the number of line breaks is greater than or equal to 4, the active load reduction before the disaster can no longer meet the system operation requirements, and the cost of passive load loss increases significantly. From the vertical comparison in the table, it can be seen that the two-stage robust model considering the active defense of the power system source-grid-load side before the disaster has reduced the total cost by tens of millions of yuan compared with the model that only considers the loss of load during the disaster. Therefore, it can be proved that the feasibility of the two-stage robust optimization model of active defense in this manual can be proved, and it is very necessary for the system to actively defend before the disaster.

[0191] 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, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. And the acquisition, storage, use, and processing of data in the technical solutions described in the embodiments of this application are in compliance with relevant regulations.

[0192] Based on the above-mentioned method for improving the resilience of the power system, the embodiments of this specification also provide a device for improving the resilience of the power system. The device may include a system (including a distributed system), software (application), module, component, server, client, etc. using the method described in the embodiments of this specification and combined with the necessary implementation hardware. Based on the same innovative concept, the device in one or more embodiments provided in the embodiments of this specification is as described in the following embodiments. Since the implementation scheme and method of the device to solve the problem are similar, the implementation of the specific device in the embodiments of this specification can refer to the implementation of the aforementioned method, and the repetitions will not be repeated. As used below, the term "unit" or "module" can be a combination of software and / or hardware that implements predetermined functions. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceived.

[0193] Specifically, Fig. 9 This is a schematic diagram of a module structure of an embodiment of a device for improving the flexibility of a power system provided in an embodiment of this specification, referring to Fig. 9 As shown, an apparatus for improving the flexibility of a power system provided in an embodiment of this specification includes:

[0194] An acquisition module 901 is used to acquire historical weather data and power system line damage data of a target area;

[0195] A line failure probability calculation module 902 is used to obtain the line failure probability under extreme weather conditions based on the historical weather data and the power system line damage data;

[0196] A fault scenario generating module 903 is used to generate a number of fault scenarios according to the line fault probability, each fault scenario including the number of disconnections and line damage information;

[0197] A clustering module 904 is used to cluster the plurality of fault scenarios according to the number of line breaks, and obtain damage probabilities of scenarios with different numbers of line breaks according to the clustering results and line damage information;

[0198] A weather constraint determination module 905 is used to represent the impact on the transmission line caused by extreme weather as a set of discrete uncertainty sets, and use the discrete uncertainty sets as weather constraint conditions;

[0199] A model building module 906 is used to establish an active defense two-stage robust optimization model based on the weather constraints and the damage probability of different line disconnection quantity scenarios, wherein the first stage model is used to minimize the active line shutdown cost before the disaster occurs, and the second stage model is used to minimize the passive load shedding cost when the disaster occurs;

[0200] The solution module 907 is used to solve the two-stage robust optimization model to obtain the optimal defense solution for the power system.

[0201] The beneficial effects obtained by the device provided in the embodiments of this specification are consistent with the beneficial effects obtained by the above method and will not be repeated here.

[0202] Reference Fig.10 As shown, based on the above-mentioned method for improving the resilience of the power system, a computer device 1002 is also provided in an embodiment of this specification, wherein the above-mentioned method is run on the computer device 1002. The computer device 1002 may include one or more processors 1004, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. The computer device 1002 may also include any memory 1006, which is used to store any kind of information such as code, settings, data, etc. Non-limitingly, for example, the memory 1006 may include any one or more combinations of the following: any type of RAM, any type of ROM, flash memory device, hard disk, optical disk, etc. More generally, any memory may use any technology to store information. Further, any memory may provide volatile or non-volatile retention of information. Further, any memory may represent a fixed or removable component of the computer device 1002. In one case, when the processor 1004 executes an associated instruction stored in any memory or combination of memories, the computer device 1002 may perform any operation of the associated instruction. The computer device 1002 also includes one or more drive mechanisms 1008 for interacting with any storage, such as a hard disk drive mechanism, an optical disk drive mechanism, etc.

[0203] The computer device 1002 may also include an input / output module 1010 (I / O) for receiving various inputs (via input devices 1012) and for providing various outputs (via output devices 1014). A specific output mechanism may include a presentation device 1016 and an associated graphical user interface (GUI) 1018. In other embodiments, the input / output module 1010 (I / O), the input device 1012, and the output device 1014 may not be included, and the computer device 1002 may be used as a computer device in a network. The computer device 1002 may also include one or more network interfaces 1020 for exchanging data with other devices via one or more communication links 1022. One or more communication buses 1024 couple the components described above together.

[0204] The communication link 1022 may be implemented in any manner, for example, through a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 1022 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc. governed by any protocol or combination of protocols.

[0205] Corresponding to Figures 1 to 4 In addition to the method shown, an embodiment of the specification also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are executed.

[0206] The embodiment of the present specification also provides a computer-readable instruction, wherein when the processor executes the instruction, the program therein causes the processor to execute the following Figures 1 to 4 The method shown.

[0207] The embodiments of the present specification also provide a computer program product, including at least one instruction or at least one program, wherein the at least one instruction or the at least one program is loaded and executed by a processor to implement the following Figures 1 to 4 The method shown.

[0208] It should be understood that in the various embodiments of this specification, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this specification.

[0209] It should also be understood that in the embodiments of this specification, the term "and / or" is only a description of the association relationship of the associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this specification generally indicates that the associated objects before and after are in an "or" relationship.

[0210] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this specification can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this specification.

[0211] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0212] In the several embodiments provided in this specification, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0213] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of this specification.

[0214] In addition, each functional unit in each embodiment of this specification may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0215] If the integrated unit 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 this specification is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of this specification. 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 and other media that can store program codes.

[0216] Specific embodiments are used in this specification to illustrate the principles and implementation methods of this specification. The description of the above embodiments is only used to help understand the methods and core ideas of this specification. At the same time, for those skilled in the art, according to the ideas of this specification, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on this specification.

Claims

1. A method for improving the flexibility of a power system, characterized in that: The method comprises: Obtain historical weather data and power system line damage data for the target area; Obtaining the probability of line failure under extreme weather conditions based on the historical weather data and the power system line damage data; Generate a number of fault scenarios according to the line fault probability, each fault scenario including the number of line breaks and line damage information; Clustering the plurality of fault scenarios according to the number of line breaks, and calculating the damage probabilities of scenarios with different numbers of line breaks according to the clustering results and line damage information; The impact on the transmission line caused by extreme weather is expressed as a set of discrete uncertainty sets, and the discrete uncertainty sets are used as weather constraints; Based on the weather constraints and the damage probability of different line breakage scenarios, a two-stage robust optimization model for active defense is established, wherein the first-stage model is used to minimize the active line shutdown cost before the disaster occurs, and the second-stage model is used to minimize the passive load shedding cost when the disaster occurs; The two-stage robust optimization model is solved to obtain the optimal defense solution for the power system.

2. The method according to claim 1, characterized in that The obtaining of the line failure probability under extreme weather conditions according to the historical weather data and the power system line damage data includes: Draw a line vulnerability curve based on the historical weather data and the power system line damage data; Fitting the line fragility curve to obtain a line failure rate fitting function; Obtaining a line failure rate according to the line failure rate fitting function; constructing a line failure probability model according to the line failure rate; The line failure probability is obtained according to the failure probability model.

3. The method according to claim 1, characterized in that The generating of a plurality of fault scenarios according to the line fault probability comprises: For each line, randomly generate its line state according to the line fault probability, wherein the line state includes normal and fault; Combine all lines with fault status to obtain a fault scenario; Repeat the above steps until the predetermined number of simulations is reached to obtain several fault scenarios.

4. The method according to claim 1, characterized in that: The discrete uncertainty set includes: using the following formula to express the impact of extreme weather on the transmission line: Among them, U represents a discrete uncertain set, u l represents the decision variable of the discrete uncertainty set, indicates whether the transmission line is impacted during the disaster, l represents the lth transmission line, Z represents the transmission line set in this scenario, L represents the total transmission line set, and μ represents the cardinality budget of the number of damaged lines when the disaster occurs.

5. The method according to claim 1, characterized in that: The objective function of the active defense two-stage robust optimization model includes: Among them, f is the objective function, i g is the g-th generator, N G is the total number of generators, N L is the total number of lines, N is the total number of nodes, T is the total defense time, To reduce load before disasters, is the load reduction forced during the disaster, C is the cost function, is the power generation of the dispatchable units, is the operating cost of the dispatchable units, are the start-up and shutdown costs of dispatchable units, is the active outage cost of the line, which is calculated based on the damage probability of the different line breakage quantity scenarios, U represents a discrete uncertainty set, u represents a certain uncertain scenario, is the cost coefficient of proactive load reduction before a disaster, The cost factor for reducing passive loads caused by line damage during disasters; The constraints of the active defense two-stage robust optimization model include: weather constraints, unit scheduling constraints, DC power flow constraints, load shedding constraints and node constraints.

6. The method according to claim 1, characterized in that The solving of the two-stage robust optimization model includes: S1: Initialize the upper bound parameter and the lower bound parameter to positive infinity and negative infinity respectively; S2: The first stage model in the two-stage robust optimization model is taken as the main problem, and the second stage model is taken as the sub-problem; S3: Solve the main problem and use the objective function value of the main problem as a new lower bound parameter; S4: Solve the sub-problem, and sum the objective function value of the sub-problem and the objective function value of the main problem as a new upper bound parameter; S5: Calculate the absolute value of the difference between the new upper bound parameter and the lower bound parameter, and determine whether the absolute value of the difference is less than a preset threshold; S6: If yes, then output the objective function values ​​of the main problem and the sub-problem as the optimal solution; S7: If not, generate a new constraint according to the objective function value of the sub-problem and add it to the main problem, and repeat steps S3-S5.

7. A device for improving the flexibility of a power system, characterized in that: The device comprises: An acquisition module is used to obtain historical weather data and power system line damage data of the target area; A line failure probability calculation module, used to obtain the line failure probability under extreme weather conditions based on the historical weather data and the power system line damage data; A fault scenario generation module, used to generate a number of fault scenarios according to the line fault probability, each fault scenario including the number of disconnections and line damage information; A clustering module, configured to cluster the plurality of fault scenarios according to the number of line breaks, and calculate the damage probabilities of scenarios with different numbers of line breaks according to the clustering results and line damage information; A weather constraint determination module, used to represent the impact on the transmission line caused by extreme weather as a set of discrete uncertainty sets, and use the discrete uncertainty sets as weather constraint conditions; A model building module is used to establish an active defense two-stage robust optimization model based on the weather constraints and the damage probability of different line disconnection quantity scenarios, wherein the first stage model is used to minimize the active line shutdown cost before the disaster occurs, and the second stage model is used to minimize the passive load shedding cost when the disaster occurs; The solution module is used to solve the two-stage robust optimization model to obtain the optimal defense solution for the power system.

8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The method comprises at least one instruction or at least one program, wherein the at least one instruction or the at least one program is loaded and executed by a processor to implement the method according to any one of claims 1 to 6.

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