Offshore wind power and power transmission network collaborative planning method considering typhoon disaster influence

Abstract

The invention provides an offshore wind power and power transmission network collaborative planning method considering typhoon disaster influence. The method comprises the following steps: establishing a multi-scene distribution robust uncertainty set of offshore wind power plant output and power transmission line faults; based on the multi-scene distribution robust uncertainty set, constructing a multi-scene distribution robust offshore wind power and power transmission network collaborative planning initial model; and establishing an objective function and constraint conditions of the initial model, and introducing a short-term source-network-load measure into the initial model to obtain a final collaborative planning model which is used for realizing offshore wind power and power transmission network collaborative planning considering typhoon disaster influence. According to the method, the multi-scene distribution robust model considering the typhoon extreme disasters is established to be used for the offshore wind power and power transmission network collaborative planning problem, multiple uncertainties of offshore wind power plant output and power transmission line faults in a normal scene and a typhoon extreme disaster scene are comprehensively considered, and the elasticity of a high-proportion new energy power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of coordinated planning of power systems, and in particular to a method for coordinated planning of offshore wind power and transmission networks taking into account the impact of typhoon disasters, as well as a corresponding system, a computer terminal and a computer-readable storage medium. Background Art

[0002] In recent years, the frequent occurrence of high-impact, low-probability extreme events such as typhoons, earthquakes, and floods has posed significant challenges to the safe and stable operation of power systems, leading to widespread attention on power system resilience. The difference between reliability and resilience is that the former focuses on the system's ability to provide stable power under long-term normal operating conditions, while the latter emphasizes the system's ability to prevent, resist, absorb, and recover from short-term, high-impact, low-probability extreme events. Different types of disasters have different triggering mechanisms and have varying impacts on the power system. Two issues are particularly noteworthy in the coordinated planning of offshore wind power and transmission networks considering typhoon hazards. First, compared to normal operation, under typhoon extreme disaster scenarios, the randomness, intermittency, and volatility of offshore wind farms are exacerbated, while the probability and uncertainty of transmission line failures are significantly increased. Therefore, a key challenge in the coordinated planning of offshore wind power and transmission networks is how to effectively model the uncertainties associated with wind farm and transmission network failures under typhoon extreme disaster scenarios. Second, planning is a long-term measure requiring significant investment. In contrast, typhoon disasters are short-term events. Therefore, a key issue in the coordinated planning of offshore wind power and transmission grids is how to coordinate planning measures with various short-term resilience enhancement strategies (such as transmission line reinforcement) while balancing the cost targets of the normal scenario to prevent overly conservative planning schemes and excessive investment.

[0003] In terms of wind farm output modeling, the paper “Optimal resilience enhancement dispatch of a power system with multiple offshore windfarms considering uncertain typhoon parameters. Int J Electr Power Energy Syst Jul 2023; 153: 109337.” uses a fuzzy set based on Wasserstein distance to characterize the uncertainty of wind farm output under the influence of typhoon parameters and their influence; the paper “Resilience planning method for transmission network and substation coordination considering extreme weather-related events” (M. Shivaie, MK Moghadam, PD Weinsier. Resilience-based tri-level framework for simultaneous transmission and substation expansion planning considering extreme weather-related events. IET Gener Transm Distrib 2020;14(16):3310-3321.) Transmission network structure optimization and differentiated load shedding measures are considered in the coordinated planning of transmission network and substation to balance economy and flexibility.

[0004] The above technologies still have the following deficiencies: 1) Existing models usually only target a single type of failure (such as high-probability failure or high-loss failure) under extreme typhoon disasters, which may lead to insufficient systematic assessment of the impact of typhoons, and may either underestimate or overestimate the specific impact of typhoons; 2) They ignore the important role that wind farms may play in improving system resilience. In planning models, they often only focus on the resilience needs under extreme typhoon disaster scenarios, and fail to take into account the cost requirements such as wind power absorption under normal scenarios; 3) Existing differentiated reinforcement models often use a uniform reinforcement level for a single transmission line, ignoring the changing characteristics of typhoon wind speeds on different line segments. In addition, the reinforcement model assumes that the reinforced line will not fail at all. However, reinforcement can only reduce the probability of failure and cannot ensure that failure will never occur. Although some reinforcement models avoid oversimplification, they fail to coordinate reinforcement and planning issues at the same time. Summary of the Invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for collaborative planning of offshore wind power and transmission networks taking into account the impact of typhoon disasters, and also provides a corresponding system, computer terminal and computer-readable storage medium.

[0006] According to one aspect of the present invention, a method for collaborative planning of offshore wind power and transmission networks taking into account the impact of typhoon disasters is provided, comprising:

[0007] Establish a multi-scenario distributed robust uncertainty set for offshore wind farm output and transmission line faults;

[0008] Based on the multi-scenario distributed robust uncertainty set, an initial model for the coordinated planning of multi-scenario distributed robust offshore wind power and transmission network is constructed;

[0009] The objective function and constraints of the initial model are established, and short-term source-grid-load measures are introduced into the initial model to obtain a final collaborative planning model for realizing the collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters.

[0010] According to a second aspect of the present invention, a system for collaborative planning of offshore wind power and transmission networks taking into account the impact of typhoon disasters is provided, comprising:

[0011] A multi-scenario distributed robust uncertainty set construction module, which is used to establish multi-scenario distributed robust uncertainty sets for offshore wind farm output and transmission line faults;

[0012] a collaborative planning model construction module, which constructs an initial model for collaborative planning of multi-scenario distributed robust offshore wind power and transmission network based on the multi-scenario distributed robust uncertainty set, establishes the objective function and constraints of the initial model, introduces short-term source-grid-load measures into the initial model, and obtains the final collaborative planning model;

[0013] A collaborative planning module, which uses the final collaborative planning model to achieve collaborative planning of offshore wind power and transmission networks taking into account the impact of typhoon disasters.

[0014] According to a third aspect of the present invention, a computer terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the terminal can be used to execute the method described above in the present invention, or to execute the system described above in the present invention.

[0015] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, it can be used to execute the method described above in the present invention, or to run the system described above in the present invention.

[0016] Due to the adoption of the above technical solution, the present invention has at least one of the following beneficial effects compared with the prior art:

[0017] The present invention constructs a multi-scenario robust uncertainty model for offshore wind farm output and transmission line failures. In response to the uncertainty of offshore wind farm output, a multi-scenario wind power output uncertainty set based on conditional risk value is established: wherein, a robust wind power output uncertainty set is used to deal with the uncertainty of wind power output caused by wind speed prediction errors, and conditional risk value is used to quantify the uncertainty of wind turbine failures; the budget uncertainty set is expanded into a multi-scenario form to characterize the difference in offshore wind farm output uncertainty under typhoon extreme disasters and normal scenarios. In response to the uncertainty of transmission line failures, a power grid failure uncertainty set based on the 1 norm is constructed to describe the uncertain probability distribution characteristics of four types of power grid failures (high probability failures, high loss failures, cascading failures under typhoon extreme disaster scenarios, and no-fault states under normal scenarios). The establishment of the above uncertainty set solves the technical problem of insufficient characterization of wind power output and line failure uncertainty under typhoon disasters, and achieves the technical effect of simultaneously characterizing the uncertainty of wind power output intervals and the uncertainty of line failure probability distribution.

[0018] The present invention proposes a collaborative planning model for offshore wind power and transmission network that coordinates costs and elasticity requirements under normal scenarios and typhoon extreme disaster scenarios. Offshore wind power is regarded as a potential elasticity enhancement resource and is collaboratively planned with the transmission network. Based on the multi-scenario distributed robust uncertainty set, the proposed collaborative planning model for offshore wind power and transmission network presents a three-layer multi-scenario distributed robust optimization structure. Taking the worst-case load shedding cost under the typhoon disaster scenario as the elasticity index, and the investment and operating costs under typhoon extreme disasters and normal scenarios as economic indicators, the collaborative planning model for offshore wind power and transmission network achieves the coordination of elasticity and cost through the worst-case scenario of offshore wind farm output and the probability distribution of grid failure. In order to reduce the investment in planned lines, short-term measures such as preventive unit combination, differentiated load shedding and differentiated reinforcement are embedded in the planning model. The establishment of the above-mentioned collaborative planning model solves the technical problem that it is difficult for elastic grid planning to consider both normal and extreme scenarios at the same time, and achieves the technical effect of balancing the elasticity and cost of the grid planning scheme.

[0019] The present invention constructs a differentiated reinforcement model and integrates it with the offshore wind power and transmission network collaborative planning model to avoid excessive investment. First, according to the different changing characteristics of typhoon wind speed, the transmission line is divided into multiple sections, and different levels of reinforcement are implemented for each section; on this basis, the differentiated reinforcement is integrated with the offshore wind power and transmission network collaborative planning model to ensure that reinforcement only reduces the probability of failure scenarios, while obtaining reinforcement measures and planning decision results. The establishment of the above-mentioned differentiated reinforcement model solves the technical problem that the transmission network reinforcement model is difficult to reflect the wind speed differences experienced by large-span lines, and achieves the technical effect of improving the feasibility of line reinforcement measures.

[0020] This invention not only considers the uncertainty of offshore wind farm output under normal and extreme typhoon disaster scenarios, but also simultaneously accounts for the uncertainty of the four fault probability distributions under normal and extreme typhoon disaster scenarios. This invention comprehensively considers the impact of normal and extreme typhoon disaster scenarios on planning, avoiding load shedding caused by over-investment or under-investment due to extreme typhoon disaster scenarios. This invention comprehensively considers transmission network planning, offshore wind farm planning, short-term preventive scheduling based on unit combinations, differentiated load shedding, and reinforcement measures, effectively improving flexibility while taking into account investment scale. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0022] Figure 1 This is a workflow diagram of a method for collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters in a preferred embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of the components of a coordinated planning system for offshore wind power and transmission network considering the impact of typhoon disasters in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following is a detailed description of an embodiment of the present invention. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process. It should be noted that those skilled in the art may make various modifications and improvements without departing from the scope of the present invention, and these modifications and improvements fall within the scope of protection of the present invention.

[0025] Existing models for coordinating offshore wind power and transmission network planning for resilience improvement lack a detailed characterization of the uncertainties associated with wind farm output and transmission line failures in typhoon disaster scenarios, and tend to be conservative in improving system resilience. To address these limitations, one embodiment of the present invention provides a method for coordinating offshore wind power and transmission network planning that considers the impact of typhoon disasters. This method establishes a multi-scenario distributed robust model that considers typhoon extreme disasters for coordinating offshore wind power and transmission network planning. This method comprehensively considers the multiple uncertainties of offshore wind farm output and transmission line failures in both normal and typhoon extreme disaster scenarios, thereby improving the resilience of a high-proportion renewable energy power system.

[0026] Specifically, if Figure 1 As shown, the method for collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters provided in this embodiment may include:

[0027] S1, establish a multi-scenario distributed robust uncertainty set for offshore wind farm output and transmission line faults.

[0028] Furthermore, the uncertainty set can also include: a multi-scenario wind power output uncertainty set based on conditional risk value, which is used to capture the uncertainty of wind turbine output fluctuations and line failures under normal scenarios and typhoon extreme disaster scenarios; a power grid line failure uncertainty set based on the 1 norm, which is used to characterize the uncertain probability distribution of power grid failures. Power grid line failures can also include: high-probability failures, high-loss failures, cascading failures under typhoon extreme disaster scenarios, and fault-free states under normal scenarios.

[0029] S2, based on the multi-scenario distributed blue-robust uncertainty set, builds the initial model of multi-scenario distributed blue-robust offshore wind power and transmission network coordinated planning;

[0030] S3, establish the objective function and constraints of the initial model, introduce short-term source-grid-load measures into the initial model, and obtain the final collaborative planning model to achieve collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters.

[0031] Furthermore, the collaborative planning model coordinates the resilience and economy of the power system under the worst-case scenario of uncertainty in offshore wind farm output and grid failure through resilience and economic indicators; among them, the worst-case load shedding under the typhoon extreme disaster scenario is used as the resilience indicator, and the planning and expected power generation costs under the typhoon extreme disaster and normal scenarios are used as economic indicators.

[0032] In some preferred embodiments, the above S1, establishing a multi-scenario distributed robust uncertainty set for offshore wind farm output and transmission line faults, may further include:

[0033] S11, construct a multi-scenario wind power output uncertainty set based on conditional value at risk, which is used to describe the uncertainty of wind power output under normal scenarios and typhoon disaster scenarios;

[0034] S12, constructing a power line fault uncertainty set, which uses the 1 norm to characterize the probability distribution of different types of power line faults.

[0035] In some preferred embodiments, the above S11, constructing a multi-scenario wind power output uncertainty set based on conditional value at risk, may further include:

[0036] S111, establish the theoretical output model of the wind farm, expressed as:

[0037]

[0038] Where, Respectively represent the theoretical output and installed capacity of wind turbines; w ci ,w co ,w R Respectively represent wind turbine cut-in, cut-out and rated wind power; w t represents the wind speed at time t; A, B, C are the conversion coefficients between wind speed and wind turbine power generation;

[0039] S112, establish a single wind turbine output model, expressed as:

[0040] Assume that the actual output of wind turbine p wts Fluctuates within the set forecast error range, expressed as:

[0041]

[0042] Where, Ω WTO represents the uncertainty set of output of a single wind turbine; S T ,S N represent typhoon disaster scenario and normal scenario respectively; and Represent the scaling coefficients of the upper and lower limits of wind turbine output in typhoon disaster scenarios and normal scenarios respectively; Indicates the theoretical output of the fan; Indicates p wts and The degree of deviation; Γ represents the budget of the uncertainty set; S, W, T represent the scenario set, wind turbine set and scheduling time set respectively;

[0043] S113, considering the extreme nature of typhoon disaster scenarios, the conditional value at risk model is used to quantify the wind turbine failure model under normal scenarios and typhoon extreme disasters, which can be expressed as:

[0044]

[0045] Where N CVaR represents the conditional value at risk; E(·) represents the expectation; ρ, P represent the probability density function and cumulative probability distribution function of the random variable N respectively; β represents the confidence level; N VaR represents the conditional value at risk of the random variable N; N max Represents the maximum value of a discrete variable; n represents the upper limit of a given discrete variable;

[0046] Conditional risk value of wind turbines in scenario s Expressed as:

[0047]

[0048] Where, Indicates the number of faulty wind turbines in scenario s; N W represents the number of wind turbines in the wind farm W; represents the conditional value at risk of the random variable N in scenario s; represents the failure probability of m wind turbines, which is expressed as:

[0049]

[0050] in, represent the failure probability of wind turbine w in scenario s and normal state respectively; γ represents the failure scaling factor; C(N W ,m) represents the symbol of the combination number;

[0051] S114, the output of offshore wind farm is the sum of the outputs of all wind turbines that can operate normally, then the wind power output uncertainty set Ω based on conditional value at risk is WFO Expressed as:

[0052]

[0053] in, They represent the actual output and theoretical output of the wind farm W respectively.

[0054] In some preferred implementations, the above S12, constructing the grid line fault uncertainty set, may further include:

[0055] S121, establish a power grid line model with high probability of failure under typhoon extreme disasters, expressed as:

[0056]

[0057] Where, Indicates that line l has a wind speed of w t The failure probability at the time of the typhoon and the failure probability during the entire typhoon extreme disaster process;r Indicates the wind speed threshold; represents the failure probability of line l in the normal scenario;

[0058] S122, construct a high-loss line fault set based on the multiple fault model, expressed as:

[0059]

[0060] Among them, p gt ,p lt ,ΔD bt Respectively represent the generator output, line flow and load shedding; G1, G2 represent the feasible domain of decision variables before and after the fault; Ω f represents a set of multiple faults; z l Indicates line fault status; N L Indicates the number of lines; K max Indicates the maximum number of faulty lines;

[0061] In step S123, a line is randomly selected from the set of power grid lines with high probability of failure under typhoon extreme disasters as the initial fault line, and the chain fault lines caused by the initial fault line are searched through the fault chain, which is expressed as:

[0062]

[0063] Where, represents the failure probability of line l in fault chain c; Indicates the rated and allowed maximum transmission capacity of the line;

[0064] S124, set the failure probability If they are independent of each other, the comprehensive failure probability of line l can be expressed as:

[0065]

[0066] Where C represents the initial fault set; N c Indicates the number of fault chains; represents the failure probability of line l under all fault chain scenarios;

[0067] S125 uses the 1-norm to characterize the deviation between the actual probability distribution of the fault and the theoretical probability distribution, which is expressed as:

[0068]

[0069] Where, Ω GF represents the uncertain set of power line faults based on the 1 norm; p s (Z s ),p0(Z s ) represent the fault scenario Zs The actual and theoretical probability of ;Ψ, They represent the total magnitude of the failure rate deviation, the lower limit coefficient of the failure rate deviation, and the upper limit coefficient of the failure rate deviation respectively; T D represents the average number of typhoon days per year; S T ,S N represent typhoon disaster scenario and normal scenario respectively;

[0070] S126, in Ω GF The term containing absolute value introduces auxiliary variable p k (Z s ), expressed as:

[0071]

[0072] In some preferred embodiments, the above S2 and S3 construct a multi-scenario distributed robust offshore wind power and transmission network collaborative planning model based on a multi-scenario distributed robust uncertainty set; establish the objective function and constraints of the initial model, introduce short-term source-grid-load measures into the initial model, and obtain the final collaborative planning model, which may further include:

[0073] S21: Establish a three-layer structure for the coordinated planning model of offshore wind power and transmission network in multiple scenarios;

[0074] The upper-level model is used to minimize the cost of preventive measures against extreme typhoon disasters;

[0075] The middle-level model obtains a plan for preventing typhoon extreme disasters based on the upper-level model, which is used to obtain the worst-case scenario of offshore wind farm output and line failure probability distribution;

[0076] The lower-level model is used to optimize scheduling under the worst-case scenario;

[0077] S31, based on the three-layer structure, establishes the objective function and constraints of the initial model, introduces short-term source-grid-load measures into the initial model, and obtains the final collaborative planning model.

[0078] In some preferred embodiments, the above S31, establishing the objective function of the initial model, may further include:

[0079] The objective function of establishing the initial model for the coordinated planning of multi-scenario distributed offshore wind power and transmission network is:

[0080]

[0081] Where: f1, f2 represent the objective functions of the upper model and the lower model respectively; x l ,x W , are 0-1 variables, representing the construction status of the line and offshore wind power, the start / stop and operation status of the generator set respectively; are the line and offshore wind power investment costs, generator startup and shutdown costs respectively; C H represents the differentiated reinforcement cost of the line; p gst ,p lst , Respectively represent generator output, line flow, wind curtailment, and load shedding; Respectively represent the power generation cost, wind curtailment cost, and the cost of removing various types of loads; B, G, L W ,L c ,L e They represent busbar, generator, offshore wind farm, candidate line and existing line set respectively; H represents the feasible domain of the upper model and the middle model respectively; Ω WFO represents the feasible domain of the middle-level model; p s (Z s ) indicates fault scenario Z s The actual probability of

[0082] In the above formula, the expected value of load shedding under the worst-case scenario of offshore wind farm output and line failure probability distribution is used as the elasticity index, and f2 is further rewritten as:

[0083]

[0084] In some preferred embodiments, the above S31, establishing the constraint conditions of the initial model, may further include:

[0085] S311, establish upper-level constraints, including:

[0086] The input budget constraint is expressed as:

[0087]

[0088] Where, π L ,Π W They represent the total investment limits for transmission lines and offshore wind farms respectively;

[0089] Preventive unit commitment constraints are used to determine the start and stop status of units at different locations in the system based on the impact of typhoon disasters, and are expressed as:

[0090]

[0091] Where, Indicates that the generator set g is in the starting state; Indicates the minimum start-up and shutdown time of generator set g;

[0092] Differentiated reinforcement constraints are used to strengthen different sections of the transmission line based on the differences in strong winds at different locations under extreme typhoon disasters. A piecewise linearization function is used to characterize the piecewise differentiated reinforcement, which is expressed as:

[0093]

[0094] The segmented differentiated reinforcement cost is expressed as:

[0095]

[0096] The total cost limit of differentiated reinforcement is expressed as:

[0097]

[0098] Where, Represent the segmented reinforcement strategy function and reinforcement cost respectively; Indicates the score interval; Indicates the reinforcement level corresponding to different wind speed ranges; Indicates the cost corresponding to different reinforcement levels; represents the maximum wind speed in wind speed segment e; w et Indicates the wind speed of line segment e; h e Indicates the reinforcement level of line segment e; C H represents the total reinforcement cost; represents the reinforcement cost of line l; L h ,E l represents the set of reinforced lines and all segments of line l;

[0099] Updated and reinforced power grid line fault uncertainty set Ω GF The differentiated reinforcement models (21) to (23) are integrated into the multi-scenario distributed robust offshore wind power and transmission network collaborative planning model. The failure probability of the collaborative planning model is updated after the line is reinforced, which is expressed as:

[0100]

[0101] The failure scenario probability is updated using the following formula:

[0102]

[0103] Where, represents the failure probability of line l after reinforcement; Indicates the reinforcement strength h e The probability of failure of section e of the lower line l; represents the theoretical failure probability after reinforcement;

[0104] S312, establish mid-level constraints, including:

[0105] The middle-level model aims to maximize the expected operating cost. Based on the uncertainty set of wind power output and the feasible domain Ω of the middle-level model, the middle-level model is constructed. WFO , Find the offshore wind farm output p Wst and the worst scenario of the actual fault probability distribution of the transmission line, so Ω WFO , The middle-level constraints represented by are expressed as:

[0106]

[0107]

[0108] S313: Establish lower-level constraints, including:

[0109] Generator redispatch, expressed as:

[0110]

[0111] Where, Represent the minimum and maximum output of the generator respectively.

[0112] The power flow constraint is expressed as:

[0113]

[0114] Where B l , Represent line susceptance and maximum transmission capacity respectively; θ s(l)t ,θ e(l)t Indicates the phase angle of two busbars of the line; M indicates a constant; Indicates the fault status of line l;

[0115] Differentiated load shedding constraints are used to implement differentiated load shedding in the power system after transmission congestion caused by component failures under the influence of typhoon disasters. This ensures that important loads are given priority power supply while maintaining system stability. It is expressed as:

[0116]

[0117] Where p bst represents the load of busbar b; They represent the proportion of important load (first load) and general load (second load), respectively, and the proportion can be determined by the expert system; L + (b),L - (b) represents the set of lines where the flow flows into and out of target b, respectively. Among them, important loads and general loads are divided according to the load importance level set by the power grid company.

[0118] By establishing the above constraints, the initial model introduced preventive unit commitment constraints, differentiated load shedding constraints, and differentiated reinforcement constraints to represent short-term source-grid-load measures, resulting in the final collaborative planning model.

[0119] The above-mentioned embodiment of the present invention provides a method for collaborative planning of offshore wind power and transmission networks that considers the impact of typhoon disasters. First, a multi-scenario distributed robust uncertainty set is established for offshore wind farm output and transmission line faults. A multi-scenario budget uncertainty set based on conditional value at risk captures the uncertainty of wind turbine output fluctuations and line faults under normal and extreme typhoon disaster scenarios. A 1-norm-based grid fault uncertainty set is constructed to characterize the uncertain probability distributions of four types of faults: high-probability faults, high-loss faults, cascading faults under extreme typhoon disaster scenarios, and a fault-free state under normal scenarios. Subsequently, a multi-scenario distributed robust offshore wind power and transmission network collaborative planning model is constructed. The worst-case load shedding cost under extreme typhoon disaster scenarios is used as a resilience indicator, and the planned and expected power generation costs under extreme typhoon disaster and normal scenarios are used as economic indicators. This model balances system resilience and cost under the worst-case scenario of offshore wind farm output and grid fault uncertainty. To further improve the conservatism of the offshore wind power and transmission network collaborative planning model, short-term source-grid-load measures are introduced into the planning model, including preventive unit commitment, differentiated load shedding, and a differentiated reinforcement model. This method comprehensively considers the multiple uncertainties of offshore wind farm output and transmission line failures under normal scenarios and typhoon extreme disaster scenarios to improve the resilience of high-proportion renewable energy power systems.

[0120] Based on the same inventive concept, an embodiment of the present invention further provides a coordinated planning system for offshore wind power and transmission network taking into account the impact of typhoon disasters.

[0121] Specifically, if Figure 2 As shown, the offshore wind power and transmission network coordinated planning system considering the impact of typhoon disasters provided by this embodiment may further include:

[0122] A multi-scenario distributed robust uncertainty set construction module, which is used to establish multi-scenario distributed robust uncertainty sets for offshore wind farm output and transmission line faults;

[0123] The collaborative planning model construction module builds an initial model for the collaborative planning of offshore wind power and transmission networks based on a multi-scenario distributed robust uncertainty set. It also establishes the objective function and constraints of the initial model and introduces short-term source-grid-load measures into the initial model to obtain the final collaborative planning model.

[0124] Collaborative planning module, which uses the final collaborative planning model to realize the collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters.

[0125] The specific contents of the functional modules constituting the system provided by the above embodiment of the present invention are further described in detail below.

[0126] 1. Multi-scenario distributed robust uncertainty set construction module, used to construct multi-scenario distributed robust uncertainty sets

[0127] (1) Uncertain set of wind power output based on conditional value at risk

[0128] In order to describe the uncertainty of wind power output under normal scenarios and typhoon disaster scenarios, a robust uncertainty set based on multi-scenario conditional value at risk is constructed.

[0129] 1) Wind farm theoretical output model

[0130]

[0131] in, Respectively represent the theoretical output and installed capacity of wind turbines; w ci ,w co ,w R Respectively represent wind turbine cut-in, cut-out and rated wind power; w t represents the wind speed at time t; A, B, and C are the conversion coefficients between wind speed and wind turbine power generation.

[0132] 2) Single wind turbine output model

[0133] Assuming the actual output of wind turbine p wts Fluctuations within a certain range of forecast error can be expressed as:

[0134]

[0135] Among them, Ω WTO represents the uncertainty set of output of a single wind turbine; S T ,S N represent typhoon disaster scenario and normal scenario respectively; and Represent the scaling coefficients of the upper and lower limits of wind turbine output in typhoon disaster scenarios and normal scenarios respectively; Indicates p wts and The degree of deviation; Γ represents the budget of the uncertainty set; S, W, and T represent the scenario set, wind turbine set, and scheduling time set, respectively.

[0136] 3) Risk model of wind turbine failure

[0137] Different from the existing method of using expected value to describe the failure risk of wind turbines, this paper considers the extreme nature of typhoon disaster scenarios and uses the conditional value at risk model to quantify the wind turbine failure model under normal scenarios and typhoon extreme disasters:

[0138]

[0139] Among them, N CVaR represents the conditional value at risk; E(·) represents the expectation; ρ, P represent the probability density function and cumulative probability distribution function of the random variable N respectively; β represents the confidence level; N VaR represents the conditional value at risk of the random variable N; N max Represents the maximum value of a discrete variable; n represents the upper limit of a given discrete variable;

[0140] Conditional risk value of wind turbines in scenario s It can be expressed as:

[0141]

[0142] in, Indicates the number of faulty wind turbines in scenario s; N W represents the number of wind turbines in the wind farm W; represents the conditional value at risk of the random variable N in scenario s; represents the failure probability of m wind turbines, which can be expressed as:

[0143]

[0144] in, represent the failure probability of wind turbine w in scenario s and normal state respectively; γ represents the failure rate scaling factor; C(N W ,m) represents the symbol of the combination number.

[0145] 4) Uncertainty of Offshore Wind Farm Output

[0146] The output of an offshore wind farm is the sum of the outputs of all wind turbines that can operate normally, and its output uncertainty set Ω WFO It can be expressed as:

[0147]

[0148] Among them, p Wst , They represent the actual output and theoretical output of the wind farm W respectively.

[0149] (2) Grid line fault collection

[0150] The 1-norm is used to characterize the probability distribution of four different types of power line faults.

[0151] 1) High probability fault line collection

[0152] The power grid line model with high probability of failure under typhoon extreme disasters can be expressed as:

[0153]

[0154] Among them, w r Wind speed threshold; represents the failure probability of line l in the normal scenario; Indicates that line l has a wind speed of w t The failure probability at that time and the failure probability during the entire typhoon extreme disaster process.

[0155] 2) High-loss line collection

[0156] Constructing a high-loss line fault set based on the multiple fault model:

[0157]

[0158] Among them, p gt ,p lt ,ΔD bt Respectively represent the generator output, line flow and load shedding; G1, G2 represent the feasible domain of decision variables before and after the fault; Ω f represents a set of multiple faults; z l Indicates line fault status; N L Indicates the number of lines; K max Indicates the maximum number of faulty lines.

[0159] 3) Collection of lines that cause cascading failures

[0160] A line is randomly selected from the set of lines with high failure probability under typhoon extreme disasters as the initial fault line, and then the chain fault lines caused by it are searched through the fault chain.

[0161]

[0162] in, represents the failure probability of line l in fault chain c; Indicates the rated and allowed maximum transmission capacity of the line.

[0163] Considering that both wind speed and cascading failures can affect the failure probability of the line, assuming If they are independent of each other, the comprehensive failure probability of line l can be expressed as:

[0164]

[0165] Where C represents the initial fault set; N c represents the number of fault chains; l F represents the failure probability of line l under all fault chain scenarios.

[0166] 4) Uncertain set of line faults under 1 norm

[0167] Considering the deviation between the actual probability distribution of faults and the theoretical probability distribution, combined with the idea of distributed robust optimization, the 1-norm is used to characterize the deviation:

[0168]

[0169] Ω GF represents the uncertain set of line faults based on the 1 norm; p s (Z s ),p0(Z s ) represent the fault scenario Z s The actual and theoretical probability of ;Ψ, They represent the total magnitude of the failure probability deviation, the lower limit coefficient of the failure rate deviation, and the upper limit coefficient of the failure rate deviation respectively; T D Indicates the average number of typhoon days per year. In order to improve the efficiency of solving the planning model, Ω GF The terms containing absolute values are obtained by introducing auxiliary variables p k (Z s ) can be expressed as:

[0170]

[0171] 2. Collaborative planning model construction module, used to establish a multi-scenario distributed robust planning model for offshore wind power and transmission network collaboration to improve resilience

[0172] The proposed multi-scenario distributed robust planning model for offshore wind power and transmission network coordination for resilience improvement has a three-layer structure. The upper-layer model aims to minimize the costs of three typhoon extreme disaster prevention measures, including offshore wind power and transmission line investment, differentiated reinforcement and preventive unit combination; the middle-layer model is based on the investment, reinforcement and unit combination schemes obtained by the upper-layer model to find the worst-case offshore wind farm output and line failure probability distribution scenarios. These scenarios will maximize the expected value of system operation costs under normal and typhoon extreme scenarios; the lower-layer model performs optimal scheduling under the worst-case scenario.

[0173] (1) Objective function

[0174]

[0175] Among them, f1 and f2 represent the objective functions of the upper and lower models respectively; x l ,x W , are 0-1 variables, representing the construction status of the line and offshore wind power, the start / stop and operation status of the generator set respectively; are the line and offshore wind power investment costs, generator startup and shutdown costs respectively; C H represents the differentiated reinforcement cost of the line; p gst ,p lst , Respectively represent generator output, line flow, wind curtailment, and load shedding; Indicates the cost of power generation, wind curtailment cost, and the cost of removing various types of loads; B, G, L W ,L c ,L e They represent busbar, generator, offshore wind farm, candidate line and existing line set respectively; H represents the feasible domain of the upper and middle level models respectively; Ω WFO Represents the feasible region of the middle-level model.

[0176] In the above formula, taking the worst-case line failure probability distribution and the expected value of load shedding under the offshore wind farm output scenario as elasticity indicators, f2 can be further rewritten as:

[0177]

[0178] The advantage of rewriting the above formula is that it can take into account the system resilience under typhoon extreme disaster scenarios and the system costs under typhoon and normal scenarios in the planning model at the same time.

[0179] (2) Constraints

[0180] 1) Upper-level constraints

[0181] a. Input budget constraints

[0182]

[0183] Π L ,Π W Represent the total investment limits of transmission lines and offshore wind farms respectively.

[0184] b. Preventive unit commitment constraints

[0185]

[0186] Indicates that the generator g is in the on state; Indicates the minimum start-up and shutdown time of generator set g.

[0187] c. Differentiated reinforcement constraints

[0188] Differentiated reinforcement aims to strengthen different sections of the transmission lines based on the differences in strong winds experienced by different locations during extreme typhoon disasters.

[0189] First, a set of lines to be reinforced is constructed. The lines in the fault uncertainty set are considered as lines to be reinforced. Considering the reinforcement cost limit, the lines with high failure probability are selected to form the set of lines to be reinforced Π H .

[0190] Then, differentiated segmented reinforcement strategies are designed and different reinforcement costs are calculated. Typically, one reinforcement level can cope with typhoons of varying intensities. Therefore, segmented differentiated reinforcement can be represented by a piecewise linearization function as follows:

[0191]

[0192] The segmented differentiated reinforcement cost can be expressed as:

[0193]

[0194] The total reinforcement cost limit can be expressed as:

[0195]

[0196] Represent the segmented reinforcement strategy function and reinforcement cost respectively; Indicates the score interval; Indicates the reinforcement level corresponding to different wind speed ranges; Indicates the cost corresponding to different reinforcement levels; represents the maximum wind speed in wind speed segment e; w et Indicates the wind speed of line segment e; h e Indicates the reinforcement level of line segment e; C H represents the total reinforcement cost; represents the reinforcement cost of line l; L h ,E l Represents the set of reinforced lines and all segments of line l.

[0197] Finally, the reinforced line fault uncertainty set Ω is updated GF The differentiated reinforcement models (21)-(23) are integrated into the multi-scenario distributed robust planning model for the coordination of offshore wind power and transmission grid. Unlike the existing method that sets the reinforced line as no longer faulty, this paper considers reducing the probability of line failure after reinforcement. Therefore, after the line is reinforced, its failure probability will be updated, that is,

[0198]

[0199] The failure scenario probability is updated using the following formula:

[0200]

[0201] Indicates the reinforcement strength h e The probability of failure of section e of the lower line l; represents the failure probability of line l after reinforcement; Represents the theoretical failure probability after reinforcement.

[0202] 2) Middle-level constraints

[0203] The middle-level model aims to maximize the expected operating cost in the uncertain set Ω WFO , Find the offshore wind farm output p Wst and the worst-case scenario of the actual failure probability distribution of the transmission line. Therefore, the middle-level constraint is the uncertainty set Ω WFO , Considering that the middle-level model only finds the worst probability distribution p of the transmission line after the reinforcement strategy is obtained s (Z s ), so the transmission line fault uncertainty set needs to be updated in the middle-level model Instead of still using the line failure probability set Ω before reinforcement GF , the failure probability is Ω WFO , The middle-level constraints represented by can be expressed as:

[0204]

[0205] 3) Lower-level constraints

[0206] 1) Generator rescheduling

[0207]

[0208] Respectively represent the minimum and maximum output of the generator.

[0209] 2) Power flow constraints

[0210]

[0211] B l , Represent line susceptance and maximum transmission capacity respectively; θ s(l)t ,θ e(l)t Indicates the phase angle of two busbars of the line; M indicates a constant; Indicates the fault status of line 1.

[0212] 3) Differentiated load shedding and other constraints

[0213]

[0214] p bstrepresents the load of busbar b; Respectively represent the proportion of important load and general load; L + (b),L - (b) represents the set of lines for flow inflow and outflow from target b respectively.

[0215] By establishing these constraints, the collaborative planning model introduces preventive unit commitment constraints, differentiated load shedding constraints, and differentiated reinforcement constraints to represent short-term source-grid-load measures. The preventive unit commitment constraint determines the start-up and shutdown status of units at different locations in the system, taking into account the impact of typhoon disasters. The differentiated reinforcement constraint strengthens different sections of the transmission line, taking into account the differences in strong winds experienced by different locations during extreme typhoon disasters. The differentiated load shedding constraint implements differentiated load shedding in the power system after transmission congestion caused by component failures under the influence of typhoon disasters, ensuring priority power supply to critical loads while maintaining system stability.

[0216] 3. Collaborative planning module, using the collaborative planning model constructed by the collaborative planning model construction module to achieve collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters.

[0217] It should be noted that the steps in the method provided by the present invention can be implemented by using the corresponding components in the system. Those skilled in the art can refer to the technical solution of the system to implement the step flow of the method, and can also refer to the technical solution of the method to implement the composition of the system. That is, the embodiments in the system and the embodiments in the method can be understood as preferred examples of each other, and will not be elaborated here.

[0218] The technical solution provided by the above embodiment of the present invention is further described in detail below with reference to a specific application example.

[0219] In this specific application example, an improved IEEE 30-node calculation system is used for verification. The system has 41 existing power plants and 8 conventional power plants with a total generating capacity of 24,000MW, access to 3,600MW of offshore wind power, and a system peak load of 21,000MW. Nodes 3, 12, 13, 14, 15, 18, and 23 can be used to access offshore wind power, and the access capacity of each node is 1,200MW. The simulated typhoon landed at node 23, with a moving speed of 15km / h and an initial maximum wind speed of 52m / s. Assuming that the capacity of a single wind turbine is 10MW, the number of wind turbines configured in each offshore wind farm is N. W is 120, and the confidence level β is 99%. In the grid fault uncertainty set, consider an average of 20 typhoon days per year, that is, T D = 20. Since the uncertainty of line failure rate mainly comes from the wind speed prediction error, in the typhoon day scenario k E Next, take the failure rate uncertainty factor In the differentiated reinforcement model strategy 1, the wind speed interval starts from 27m / s and ends at 53m / s, and is divided into segments with a step size of 4m / s. For example, in the first interval The results of different offshore wind power and transmission network collaborative planning models are shown in Table 1.

[0220] Table 1 Comparison of results of different offshore wind power and transmission network coordinated planning models

[0221]

[0222] Through verification of this specific application example, it can be seen that the technical solution provided by the above embodiment of the present invention has the following implementation effects:

[0223] Effectiveness of multi-scenario setting. Compared with the single-scenario model, which only focuses on either the network overload scenario or the typhoon disaster scenario, the proposed multi-scenario model shows a lower total cost, indicating that considering both the network overload scenario and the typhoon disaster scenario can better coordinate resilience and cost. Taking C1, C2 and C5 as examples, in C5, the economic indicator is 47.629 billion yuan and the resilience indicator (load reduction) is 23,743 MWh. Compared with C1, the economic indicator increased by 217 million yuan, while the resilience indicator decreased by 28,988 MWh, indicating that the planning scheme that only focuses on the network overload scenario will suffer losses that exceed the reduced grid construction costs when encountering severe typhoon disasters; compared with C2, the economic indicator decreased by 19 million yuan and the resilience indicator decreased by 4,083 MWh, indicating that the planning scheme that only considers the typhoon disaster scenario is less cost-effective and too conservative.

[0224] The effectiveness of the distributed robust setting was demonstrated. Compared with the results of the multi-scenario robust model C3, the proposed model C5 provides a more cost-effective planning solution. For a 30-bus system, the robust model achieved a resilience index of 19,439 MWh and an economic index of 47.698 billion yuan. Compared with the proposed model, the economic index increased by 69 million yuan, while the resilience index decreased by 4,304 MWh. The robust model tends to deploy more lines to cope with high-loss faults.

[0225] The effectiveness of the conditional value at risk setting. Compared with the model that did not consider the conditional value at risk, the proposed model C5 obtained a more costly planning scheme, indicating that ignoring the risk of wind turbine failure tends to underestimate the impact of typhoon disasters and overestimate the offshore wind power output under typhoon disaster scenarios. Specifically, compared with C5, the investment cost and wind power curtailment cost of C4 increased by RMB 21 million and RMB 2 million, respectively, to improve the offshore wind power absorption capacity. However, due to the participation of more offshore wind power in load supply, the power generation cost and load reduction amount decreased by RMB 18 million and 7946MWh, respectively, and the total cost was ultimately reduced by RMB 16 million. Although the conditional value at risk setting makes the planning scheme more expensive, its main role is to objectively reflect the risk of wind turbine failure under typhoon disaster scenarios, making the planning model closer to reality, rather than simply reducing the model's conservatism.

[0226] An embodiment of the present invention further provides a computer terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor can be used to execute any one of the methods described above in the embodiments of the present invention, or to execute any one of the systems described above in the embodiments of the present invention.

[0227] Optionally, the memory is used to store programs; the memory may include volatile memory (English: volatile memory), such as random-access memory (English: random-access memory, abbreviated: RAM), such as static random-access memory (English: static random-access memory, abbreviated: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviated: DDR SDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory is used to store computer programs (such as applications, functional modules, etc. that implement the above-mentioned methods), computer instructions, etc. The above-mentioned computer programs, computer instructions, etc. can be partitioned and stored in one or more memories. In addition, the above-mentioned computer programs, computer instructions, data, etc. can be called by the processor.

[0228] The processor is configured to execute the computer program stored in the memory to implement the various steps of the method or various modules of the system involved in the above embodiments. For details, please refer to the relevant descriptions in the above method and system embodiments.

[0229] The processor and memory can be independent structures or integrated structures. When the processor and memory are independent structures, the memory and processor can be coupled via a bus.

[0230] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it can be used to execute any method of the above embodiments of the present invention, or to run any system of the above embodiments of the present invention.

[0231] Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transfer of computer programs from one location to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. Alternatively, the ASIC can be located in a user device. Of course, the processor and storage medium can also exist as discrete components in a communication device.

[0232] The above-mentioned embodiments of the present invention provide a method and system for collaborative planning of offshore wind power and transmission networks that considers the impact of typhoon disasters. This method and system can not only consider the uncertainty of offshore wind farm output under normal scenarios and typhoon extreme disaster scenarios, but also simultaneously consider the uncertainty of the four types of fault probability distribution under normal scenarios and typhoon extreme disaster scenarios. This method and system can comprehensively consider the impact of normal scenarios and typhoon extreme disaster scenarios on planning, and avoid load shedding problems caused by over-investment or under-investment due to typhoon extreme disaster scenarios. This method and system comprehensively consider transmission network planning, offshore wind farm planning, short-term preventive scheduling based on unit combination, differentiated load shedding and reinforcement measures, and can effectively improve flexibility while taking into account the scale of investment.

[0233] Matters not mentioned in the above embodiments of the present invention are well known in the art.

[0234] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various variations or modifications within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A method for collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters, characterized in that: include: Establish a multi-scenario distributed robust uncertainty set for offshore wind farm output and transmission line faults; Based on the multi-scenario distributed robust uncertainty set, an initial model for the coordinated planning of multi-scenario distributed robust offshore wind power and transmission network is constructed; The objective function and constraints of the initial model are established, and short-term source-grid-load measures are introduced into the initial model to obtain a final collaborative planning model for realizing the collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters.

2. The method for collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters according to claim 1 is characterized in that: The establishment of a multi-scenario distributed robust uncertainty set for offshore wind farm output and transmission line faults includes: Construct a multi-scenario wind power output uncertainty set based on conditional value at risk, which is used to describe the uncertainty of wind power output under normal scenarios and typhoon disaster scenarios; An uncertain set of power grid line faults is constructed, which uses the 1-norm to characterize the probability distribution of different types of power grid line faults.

3. The method for collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters according to claim 2 is characterized in that: The construction of a multi-scenario wind power output uncertainty set based on conditional value at risk includes: The theoretical output model of the wind farm is established and expressed as: Where, Respectively represent the theoretical output and installed capacity of wind turbines; w ci ,w co ,w R They represent wind turbine cut-in, cut-out and rated wind power respectively; wt represents wind speed at time t; A, B, C are conversion coefficients between wind speed and wind turbine power generation; The output model of a single wind turbine is established and expressed as: Assume that the actual output of wind turbine p wts Fluctuates within the set forecast error range, expressed as: Where, Ω WTO represents the uncertainty set of output of a single wind turbine; S T ,S N represent typhoon disaster scenario and normal scenario respectively; and Represent the scaling coefficients of the upper and lower limits of wind turbine output in typhoon disaster scenarios and normal scenarios respectively; Indicates the theoretical output of the fan; Indicates p wts and The degree of deviation; Γ represents the budget of the uncertainty set; S, W, T represent the scenario set, wind turbine set and scheduling time set respectively; Considering the extreme nature of typhoon disaster scenarios, the conditional value at risk model is used to quantify the wind turbine failure model under normal scenarios and typhoon extreme disasters, which can be expressed as: Where N CVaR represents the conditional value at risk; E(·) represents the expectation; ρ, P represent the probability density function and cumulative probability distribution function of the random variable N respectively; β represents the confidence level; N VaR represents the conditional value at risk of the random variable N; N max Represents the maximum value of a discrete variable; n represents the upper limit of a given discrete variable; Conditional risk value of wind turbines in scenario s Expressed as: Where, Indicates the number of faulty wind turbines in scenario s; N W represents the number of wind turbines in the wind farm W; represents the conditional value at risk of the random variable N in scenario s; represents the failure probability of m wind turbines, which is expressed as: in, represent the failure probability of wind turbine w in scenario s and normal state respectively; γ represents the failure rate scaling factor; C(N W ,m) represents the symbol of the combination number; The output of an offshore wind farm is the sum of the outputs of all wind turbines that can operate normally. The uncertainty set of wind power output based on conditional value at risk is Ω. WFO Expressed as: in, They represent the actual output and theoretical output of the wind farm W respectively.

4. The method for collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters according to claim 2 is characterized in that: The constructing of the power grid line fault uncertainty set includes: A power grid line model with high probability of failure under typhoon extreme disasters is established, which is expressed as: Where, Indicates that line l has a wind speed of w t The failure probability at the time of the typhoon and the failure probability during the entire typhoon extreme disaster process; r Indicates the wind speed threshold; represents the failure probability of line l in the normal scenario; Based on the multiple fault model, a high-loss line fault set is constructed, which is expressed as: Among them, p gt ,p lt ,ΔD bt Respectively represent the generator output, line flow and load shedding; G1, G2 represent the feasible domain of decision variables before and after the fault; Ω f represents a set of multiple faults; z l Indicates line fault status; N L Indicates the number of lines; K max Indicates the maximum number of faulty lines; A line is randomly selected from the set of power grid lines with high probability of failure under typhoon extreme disasters as the initial fault line, and the chain fault lines caused by the initial fault line are searched through the fault chain, which can be expressed as: Where, represents the failure probability of line l in fault chain c; Indicates the rated and allowed maximum transmission capacity of the line; Assume the failure probability If they are independent of each other, the comprehensive failure probability of line l can be expressed as: Where C represents the initial fault set; N c Indicates the number of fault chains; represents the failure probability of line l under all fault chain scenarios; The 1-norm is used to characterize the deviation between the actual probability distribution of faults and the theoretical probability distribution, which is expressed as: Where, Ω GF represents the uncertain set of power line faults based on the 1 norm; p s (Z s ),p0(Z s ) represent the fault scenario Z s The actual and theoretical probability of ;Ψ, They represent the total magnitude of the failure rate deviation, the lower limit coefficient of the failure rate deviation, and the upper limit coefficient of the failure rate deviation respectively; T D represents the average number of typhoon days per year; S T ,S N represent typhoon disaster scenario and normal scenario respectively; In Ω GF The term containing absolute value introduces auxiliary variable p k (Z s ), expressed as:

5. The method for collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters according to claim 1 is characterized in that: The method comprises: constructing an initial model for collaborative planning of multi-scenario distributed robust offshore wind power and transmission network based on the multi-scenario distributed robust uncertainty set; establishing an objective function and constraints of the initial model, introducing short-term source-grid-load measures into the initial model, and obtaining a final collaborative planning model, including: A three-layer structure of the initial model for the coordinated planning of offshore wind power and transmission network in multiple scenarios is established; The upper-level model is used to minimize the cost of preventive measures against extreme typhoon disasters; The middle-level model obtains a typhoon extreme disaster prevention measure plan based on the upper-level model, which is used to obtain the worst-case scenario of offshore wind farm output and line failure probability distribution; The lower model is used to perform optimal scheduling under the worst-case scenario; Based on the three-layer structure, the objective function and constraint conditions of the initial model are established, and short-term source-grid-load measures are introduced into the initial model to obtain the final collaborative planning model.

6. The method for collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters according to claim 5 is characterized in that: Establishing the objective function of the initial model includes: The objective function of establishing the initial model for the coordinated planning of multi-scenario distributed offshore wind power and transmission network is: Where: f1, f2 represent the objective functions of the upper model and the lower model respectively; x l ,x W , are 0-1 variables, representing the construction status of the line and offshore wind power, the start / stop and operation status of the generator set respectively; are the line and offshore wind power investment costs, generator startup and shutdown costs respectively; C H represents the differentiated reinforcement cost of the line; p gst ,p lst , Respectively represent generator output, line flow, wind curtailment, and load shedding; Respectively represent the power generation cost, wind curtailment cost, and the cost of removing various types of loads; B, G, L W ,L c ,L e They represent busbar, generator, offshore wind farm, candidate line and existing line set respectively; H represents the feasible domain of the upper model and the middle model respectively; Ω WFO represents the feasible domain of the middle-level model; p s (Z s ) indicates fault scenario Z s The actual probability of In the above formula, the expected value of load shedding under the worst-case scenario of offshore wind farm output and line failure probability distribution is used as the elasticity index, and f2 is further rewritten as:

7. The method for collaborative planning of offshore wind power and transmission network considering the impact of typhoon disasters according to claim 5, characterized in that: Establishing constraints for the initial model and introducing short-term source-grid-load measures into the initial model includes: The constraints for establishing the initial model for the coordinated planning of offshore wind power and transmission grid in multiple scenarios include: Establish upper-level constraints, including: The input budget constraint is expressed as: Where, π L ,Π W They represent the total investment limits for transmission lines and offshore wind farms respectively; The preventive unit commitment constraint is expressed as: Where, Indicates that the generator set g is in the starting state; Indicates the minimum start-up and shutdown time of generator set g; Differentiated reinforcement constraints are characterized by piecewise linearization functions, which can be expressed as: The segmented differentiated reinforcement cost is expressed as: The total cost limit of differentiated reinforcement is expressed as: Where, Represent the segmented reinforcement strategy function and reinforcement cost respectively; Indicates the score interval; Indicates the reinforcement level corresponding to different wind speed ranges; Indicates the cost corresponding to different reinforcement levels; represents the maximum wind speed in wind speed segment e; w et Indicates the wind speed of line segment e; h e Indicates the reinforcement level of line segment e; C H represents the total reinforcement cost; represents the reinforcement cost of line l; L h ,E l represents the set of reinforced lines and all segments of line l; Updated and reinforced power grid line fault uncertainty set Ω GF The differentiated reinforcement constraints (21) to (23) are integrated into the initial model of coordinated planning of offshore wind power and transmission network under multiple scenarios. The failure probability of the initial model is updated after the line is reinforced, which is expressed as: Update the failure scenario probability, expressed as: Where, represents the failure probability of line l after reinforcement; Indicates the reinforcement strength h e The probability of failure of section e of the lower line l; represents the theoretical failure probability after reinforcement; Establish mid-level constraints, including: The middle-level model aims to maximize the expected operating cost. Based on the uncertainty set of wind power output and the feasible domain Ω of the middle-level model, the middle-level model is constructed. WFO , Find the offshore wind farm output p Wst and the worst scenario of the actual fault probability distribution of the transmission line, so Ω WFO , The middle-level constraints represented by are expressed as: Establish lower-level constraints, including: Generator redispatch, expressed as: Where, Respectively represent the minimum and maximum output of the generator; The power flow constraint is expressed as: Where B l , Represent line susceptance and maximum transmission capacity respectively; θ s(l)t ,θ e(l)t Indicates the phase angle of two busbars of the line; M indicates a constant; Indicates the fault status of line l; Differentiated load shedding constraint is expressed as: Where p bst represents the load of busbar b; Respectively represent the proportion of important load and general load; L + (b),L - (b) represents the set of lines for the flow to and from target b; By establishing the above constraints, the initial model introduces preventive unit commitment constraints, differentiated load shedding constraints, and differentiated reinforcement constraints that characterize short-term source-grid-load measures, thereby obtaining the final collaborative planning model.

8. A coordinated planning system for offshore wind power and transmission network considering the impact of typhoon disasters, characterized by: include: A multi-scenario distributed robust uncertainty set construction module, which is used to establish multi-scenario distributed robust uncertainty sets for offshore wind farm output and transmission line faults; a collaborative planning model construction module, which constructs an initial model for collaborative planning of multi-scenario distributed robust offshore wind power and transmission network based on the multi-scenario distributed robust uncertainty set, establishes the objective function and constraints of the initial model, introduces short-term source-grid-load measures into the initial model, and obtains the final collaborative planning model; A collaborative planning module, which uses the final collaborative planning model to achieve collaborative planning of offshore wind power and transmission networks taking into account the impact of typhoon disasters.

9. A computer terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When executing the computer program, the processor can be used to perform the method according to any one of claims 1 to 7, or run the system according to claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can be used to perform the method according to any one of claims 1 to 7, or to run the system according to claim 8.

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