Coordinated planning method of offshore wind farm and transmission network considering typhoon disaster impact

Abstract

The application provides a kind of offshore wind power and power transmission network collaborative planning method considering typhoon disaster influence, comprising: establishing the multi-scenario distribution robust uncertainty set of offshore wind farm output and power transmission line fault;Based on the multi-scenario distribution robust uncertainty set, the initial model of multi-scenario distribution robust offshore wind power and power transmission network collaborative planning is constructed;The objective function and constraint condition of the initial model are established, the short-term source-grid-load measures are introduced into the initial model, and the final collaborative planning model is obtained, which is used to realize the offshore wind power and power transmission network collaborative planning considering typhoon disaster influence.The application establishes a multi-scenario distribution robust model considering typhoon extreme disaster for offshore wind power and power transmission network collaborative planning problem, and comprehensively considers the multiple uncertainties of offshore wind farm output and power transmission line fault under normal scenario and typhoon extreme disaster scenario, which improves the flexibility of high-proportion new energy power system.

Description

Technical Field

[0001] This invention relates to the field of power system collaborative planning technology, specifically to a collaborative planning method for offshore wind power and power transmission networks that takes into account the impact of typhoon disasters, and also to a corresponding system, computer terminal, and computer-readable storage medium. Background Technology

[0002] In recent years, high-impact, low-probability extreme events such as typhoons, earthquakes, and floods have occurred frequently, posing significant challenges to the safe and stable operation of power systems, and thus, power system resilience has gained widespread attention. The difference between reliability and resilience lies in the fact 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, withstand, absorb, and recover from short-term high-impact, low-probability extreme events. Different types of disasters have different catastrophe-inducing mechanisms and have different impacts on power systems. In the coordinated planning of offshore wind power and transmission networks considering typhoon disasters, two issues are particularly noteworthy. First, compared to normal operating conditions, the randomness, intermittency, and volatility of offshore wind farms are exacerbated under typhoon extreme disaster scenarios, while the probability and uncertainty of transmission line faults increase significantly. Therefore, a key challenge in the coordinated planning of offshore wind power and transmission networks is how to effectively model the uncertainties related to wind farm and transmission network faults in typhoon extreme disaster scenarios. Second, planning is a long-term measure requiring substantial investment. In contrast, typhoon disasters are short-term events. Therefore, a key issue in the coordinated planning of offshore wind power and power transmission networks is how to coordinate planning measures with various short-term resilience enhancement strategies (such as transmission line reinforcement) while maintaining a balance with cost targets in normal scenarios, so as to prevent overly conservative planning schemes and excessive investment.

[0003] Regarding wind farm output modeling, the literature "Optimal resilience enhancement dispatch of a power system with multiple offshore windfarms considering uncertain typhoon parameters" (Liang Y, Lin S, Feng X, Liu M, Su L, Zhang B. Int J Electr Power Energy Syst Jul 2023;153:109337.) uses fuzzy sets based on Wasserstein distance to characterize the uncertainty of wind farm output under typhoon parameters and their influence. The literature "Resilience-based tri-level framework for simultaneous transmission and substation expansion planning considering extreme weather-related events" (IET Gener Transm Distrib. Int J Electr Power Energy Syst Jul 2023;153:109337.) uses fuzzy sets based on Wasserstein distance to characterize the uncertainty of wind farm output under typhoon parameters and their influence. 2020;14(16):3310-3321.) The coordinated planning of power transmission networks and substations takes into account the optimization of power transmission network structure and differentiated load shedding measures to balance economy and flexibility.

[0004] The aforementioned technologies still have the following shortcomings: 1) Existing models typically only address single types of faults (such as high-probability faults or high-loss faults) under extreme typhoon disasters, potentially leading to insufficient systematic assessment of typhoon impacts, possibly underestimating or overestimating the specific impacts of typhoons; 2) They neglect the potentially important role of wind farms in enhancing system resilience, often focusing only on resilience requirements under extreme typhoon disaster scenarios in planning models, failing to consider cost requirements such as wind power consumption under normal scenarios; 3) Existing differentiated reinforcement models often apply a uniform reinforcement level to individual transmission lines, ignoring the varying characteristics of typhoon wind speeds across different line segments. Furthermore, reinforcement models assume that reinforced lines will not experience any faults. However, reinforcement can only reduce the probability of faults, not guarantee that faults will never occur. Although some reinforcement models avoid oversimplification, they fail to simultaneously address reinforcement and planning issues. Summary of the Invention

[0005] To address the aforementioned shortcomings in the prior art, this invention provides a method for coordinated planning of offshore wind power and power transmission networks that takes 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 coordinated planning of offshore wind power and power transmission networks considering the impact of typhoon disasters is provided, comprising:

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

[0008] Based on the multi-scenario sub-Brussels uncertain set, an initial model for collaborative planning of multi-scenario sub-Brussels offshore wind power and power 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 the final collaborative planning model, which is used to realize the collaborative planning of offshore wind power and transmission grid considering the impact of typhoon disasters.

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

[0011] A multi-scenario partial Bruker uncertainty set construction module is used to build multi-scenario partial Bruker uncertainty sets for offshore wind farm output and transmission line faults.

[0012] The collaborative planning model construction module constructs an initial collaborative planning model for offshore wind power and transmission grid based on the multi-scenario sub-Brussels uncertainty set, 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.

[0013] The collaborative planning module utilizes the final collaborative planning model to achieve collaborative planning of offshore wind power and power 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, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it can be used to perform the methods described above in the present invention, or to run 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 having a computer program stored thereon, which, when executed by a processor, can be used to perform the methods described above in the present invention, or to run the system described above in the present invention.

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

[0017] This invention constructs a multi-scenario robust uncertainty model for offshore wind farm output and transmission line faults. For the uncertainty of offshore wind farm output, a multi-scenario wind power output uncertainty set based on conditional value of risk is established. This set employs a robust wind power output uncertainty set to handle the uncertainty caused by wind speed prediction errors, while simultaneously using conditional value of risk to quantify the uncertainty of wind turbine faults. This budget uncertainty set is extended to a multi-scenario form to characterize the difference in offshore wind farm output uncertainty under extreme typhoon disasters and normal scenarios. For the uncertainty of transmission line faults, a grid fault uncertainty set based on the 1-norm is constructed to describe the probability distribution characteristics of four types of grid faults (high-probability faults, high-loss faults, cascading faults under extreme typhoon disasters, and fault-free states under normal scenarios). The establishment of these uncertainty sets solves the technical problem of insufficient characterization of wind power output and line fault uncertainties under typhoon disasters, achieving the technical effect of simultaneously characterizing the uncertainty of wind power output intervals and the uncertainty of line fault probability distributions.

[0018] This invention proposes a collaborative planning model for offshore wind power and power transmission networks that coordinates cost and flexibility requirements under both normal and extreme typhoon disaster scenarios. Offshore wind power is treated as a potential resource for enhancing flexibility and is collaboratively planned with the power transmission network. Based on a multi-scenario distributed bar uncertainty set, the proposed collaborative planning model for offshore wind power and power transmission networks presents a three-layer multi-scenario distributed bar optimization structure. Using the worst-case load shedding cost under the typhoon disaster scenario as a flexibility indicator, and the input and operating costs under both extreme typhoon disaster and normal scenarios as economic indicators, the collaborative planning model for offshore wind power and power transmission networks achieves coordination between flexibility and cost through the worst-case output scenario of offshore wind farms and the probability distribution of grid failures. To reduce planned line investment, short-term measures such as preventative unit combination, differentiated load shedding, and differentiated reinforcement are embedded into the planning model. The establishment of this collaborative planning model solves the technical problem that flexible power grid planning cannot simultaneously consider both normal and extreme scenarios, achieving a technical effect of balancing the flexibility and cost of power grid planning schemes.

[0019] This invention constructs a differentiated reinforcement model and integrates it with a collaborative planning model for offshore wind power and power transmission networks to avoid over-investment. First, based on the varying characteristics of typhoon wind speeds, the transmission line is divided into multiple segments, and different levels of reinforcement are applied to each segment. Then, the differentiated reinforcement model is integrated with the collaborative planning model for offshore wind power and power transmission networks to ensure that reinforcement only reduces the probability of fault scenarios, while simultaneously obtaining reinforcement measures and planning decision results. The establishment of this differentiated reinforcement model solves the technical problem that power transmission network reinforcement models struggle to reflect the wind speed differences experienced by long-span lines, achieving the technical effect of improving the feasibility of line reinforcement measures.

[0020] This invention not only considers the output uncertainty of offshore wind farms under both normal and extreme typhoon disaster scenarios, but also simultaneously accounts for the uncertainties of four fault probability distributions under both scenarios. It comprehensively considers the impact of both normal and extreme typhoon disaster scenarios on planning, avoiding over-investment or under-investment leading to load shedding issues caused by extreme typhoon disasters. Furthermore, this invention comprehensively considers transmission network planning, offshore wind farm planning, short-term preventative scheduling based on unit combination, differentiated load shedding, and reinforcement measures, effectively improving resilience while balancing investment scale. Attached Figure Description

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

[0022] Figure 1 This is a flowchart illustrating the workflow of a preferred embodiment of the present invention for a collaborative planning method between offshore wind power and power transmission networks that considers the impact of typhoon disasters.

[0023] Figure 2 This is a schematic diagram of the components of a coordinated planning system for offshore wind power and power transmission networks that takes into account the impact of typhoon disasters in a preferred embodiment of the present invention. Detailed Implementation

[0024] The embodiments of the present invention are described in detail below: These embodiments are implemented based on the technical solution of the present invention, and provide detailed implementation methods and specific operation processes. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

[0025] Existing collaborative planning models for offshore wind power and transmission networks aimed at improving system resilience lack detailed characterization of the uncertainties related to wind farm output and transmission line faults under typhoon disaster scenarios, and tend to be conservative in improving system resilience. To address these limitations, one embodiment of this invention provides a collaborative planning method for offshore wind power and transmission networks that considers the impact of typhoon disasters. This method establishes a multi-scenario distributed bar model considering extreme typhoon disasters for the collaborative planning problem of offshore wind power and transmission networks. It comprehensively considers the multiple uncertainties of offshore wind farm output and transmission line faults under both normal and extreme typhoon disaster scenarios, thereby improving the resilience of high-proportion renewable energy power systems.

[0026] Specifically, such as Figure 1 As shown in the embodiment, the method for coordinated planning of offshore wind power and power transmission networks considering the impact of typhoon disasters may include:

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

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

[0029] S2, based on the multi-scenario sub-Brubar uncertainty set, constructs an initial model for collaborative planning of multi-scenario sub-Brubar offshore wind power and power transmission network;

[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, which is used to realize the collaborative planning of offshore wind power and transmission grid considering the impact of typhoon disasters.

[0031] Furthermore, this 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. Specifically, the worst-case load shedding volume under the extreme disaster scenario of typhoon is used as the resilience indicator, and the planned and expected power generation costs under the extreme disaster and normal scenarios of typhoon are used as the economic indicator.

[0032] In some preferred embodiments, S1 above, which establishes a multi-scenario partial Bruker 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 risk value. This set is used to describe the uncertainty of wind power output under normal and typhoon disaster scenarios.

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

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

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

[0037]

[0038] In the formula, These represent the theoretical output and installed capacity of the wind turbine, respectively; w ci ,w co ,w R These represent the wind turbine cut-in, cut-out, and rated wind power, respectively; w t Let t represent the wind speed at time t; A, B, and C are the conversion coefficients between wind speed and wind turbine power generation.

[0039] S112, Establish a power output model for a single wind turbine, represented as follows:

[0040] Let the actual output of the wind turbine be p. wts Fluctuations within the set prediction error range are represented as:

[0041]

[0042] In the formula, Ω WTO S represents the set of uncertain outputs of a single wind turbine; T ,S N These represent typhoon disaster scenarios and normal scenarios, respectively. and These represent scaling factors for the upper and lower limits of wind turbine output in typhoon disaster scenarios and normal scenarios, respectively; This indicates the theoretical output of the wind turbine; p wts and Degree of deviation; Γ represents the budget of the uncertain set; S, W, and 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 (VAT) model is used to quantify the fault models of wind turbines under normal scenarios and extreme typhoon disasters, as follows:

[0044]

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

[0046] Conditional Value at Risk for Wind Turbines in Scenario s Represented as:

[0047]

[0048] In the formula, N represents the number of faulty wind turbines in scenario s; W This indicates the number of wind turbines in wind farm W; Represents the conditional risk value of random variable N in scenario s; The failure probability of m wind turbine units is expressed as:

[0049]

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

[0051] S114, the power output of an offshore wind farm is the sum of the power outputs of all wind turbines that are operating normally. Therefore, the uncertain set Ω of wind power output based on conditional risk value is... WFO Represented as:

[0052]

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

[0054] In some preferred embodiments, the above-mentioned S12, which constructs the uncertainty set of power grid line faults, may further include:

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

[0056]

[0057] In the formula, This indicates that line l is in wind speed w t The probability of failure at that time and the probability of failure throughout the entire typhoon extreme disaster process; wr Indicates the wind speed threshold; This represents the probability of line l failing under normal conditions;

[0058] S122, based on the multiple fault model, constructs a set of high-loss line faults, represented as:

[0059]

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

[0061] S123, randomly select one line from the set of power grid lines with a high probability of failure under extreme typhoon disasters as the initial fault line, and search for the cascading fault lines caused by this initial fault line through fault chain search, represented as:

[0062]

[0063] In the formula, This represents the failure probability of line l in the fault chain c; This indicates the line's rated and maximum permissible transmission capacity;

[0064] S124, Assume the failure probability If they are independent of each other, the overall failure probability of line l is expressed as:

[0065]

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

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

[0068]

[0069] In the formula, Ω GF p represents the set of uncertainties for power grid line faults based on the 1-norm; s (Z s ),p0(Z s ) represent fault scenarios Z respectivelys Actual and theoretical probabilities; Ψ, These 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 This indicates the average number of typhoon days per year; S T ,S N These represent typhoon disaster scenarios and normal scenarios, respectively.

[0070] S126, in Ω GF Introduce auxiliary variable p for terms containing absolute values k (Z s ), represented as:

[0071]

[0072] In some preferred embodiments, S2 and S3 above, based on the multi-scenario distributed bar uncertainty set, construct a multi-scenario distributed bar offshore wind power and transmission grid collaborative planning model; 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 a multi-scenario, distributed planning model of offshore wind power and power transmission network; among which:

[0074] The upper-level model is used to minimize the cost of typhoon extreme disaster prevention measures;

[0075] The intermediate-level model, based on the upper-level model, obtains typhoon extreme disaster prevention measures and is used to obtain the worst-case scenario of offshore wind farm output and line fault probability distribution.

[0076] The lower-level model is used for optimal scheduling in the worst-case scenario;

[0077] S31, based on a 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 objective function for establishing the initial model in step S31 above may further include:

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

[0080]

[0081] In the formula: f1 and f2 represent the objective functions of the upper-level model and the lower-level model, respectively; x l ,x W , These are 0-1 variables, representing the construction status of the power line and offshore wind power, the start-up / shutdown status of the generator set, and the operating status, respectively. These represent the input costs of power lines and offshore wind power, and the start-up and shutdown costs of generators, respectively; C H Indicates the cost of differentiated reinforcement of the line; p gst ,p lst , These represent generator output, line power flow, wind curtailment, and load shedding, respectively. B, G, and L represent generation cost, wind curtailment cost, and load shedding cost for each type of load, respectively. W ,L c ,L e These respectively represent the busbar, generator, offshore wind farm, candidate route, and existing route set; H represents the feasible regions of the upper-level model and the middle-level model, respectively; Ω WFO p represents the feasible region of the mid-level model. s (Z s ) represents fault scenario Z s The actual probability;

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

[0083]

[0084] In some preferred embodiments, the constraints for establishing the initial model in step S31 may further include:

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

[0086] Budget constraints are expressed as follows:

[0087]

[0088] In the formula, Π L ,Π W These represent the total investment limits for power lines and offshore wind farms, respectively.

[0089] Preventative unit combination constraints are used to determine the start-up and shutdown status of units located at different positions in the system in conjunction with the impact of typhoon disasters, and are represented as follows:

[0090]

[0091] In the formula, This indicates that generator set g is in the powered-on state; This represents the minimum start-up and shutdown time of generator set g;

[0092] Differential reinforcement constraints, used to strengthen different segments of transmission lines based on the differences in strong winds experienced at different locations under extreme typhoon conditions, are represented by a piecewise linearized function, expressed as:

[0093]

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

[0095]

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

[0097]

[0098] In the formula, These represent the segmented reinforcement strategy function and the reinforcement cost, respectively. Indicates a range of fractions; This indicates the reinforcement level corresponding to different wind speed ranges; This indicates the cost corresponding to different reinforcement levels; Indicates the maximum wind speed in wind speed segment e; w et Indicates the wind speed experienced by line segment e; h e Indicates the reinforcement level of line segment e; C H Indicates the total reinforcement cost; Indicates the reinforcement cost of line l; L h E l This represents the set of reinforced lines and all segments of line l;

[0099] The 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 offshore wind power and transmission network collaborative planning model. The failure probability of the collaborative planning model is updated after the line is reinforced, and is expressed as:

[0100]

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

[0102]

[0103] In the formula, This indicates the probability of failure after reinforcement of line l; Indicates the reinforcement strength h e The probability of a fault in segment e of line l; This represents the theoretical failure probability after reinforcement.

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

[0105] The intermediate-level model aims to maximize the expected operating cost within the uncertain set of wind power output based on conditional risk value and the feasible region Ω of the intermediate-level model. WFO , China seeks offshore wind farm output p Wst And the worst-case scenario of the actual fault probability distribution of transmission lines, therefore, with Ω WFO , The representative middle-level constraint is represented as:

[0106]

[0107]

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

[0109] Generator rescheduling is represented as:

[0110]

[0111] In the formula, These represent the minimum and maximum output of the generator, respectively.

[0112] Current constraint, represented as:

[0113]

[0114] In the formula, B l , θ represents the line susceptance and maximum transmission capacity, respectively; s(l)t ,θ e(l)t This represents the phase angle between two busbars on the line; M represents a constant. Indicates the fault status of line l;

[0115] Differential load shedding constraints are used to differentiate load shedding in the power system after transmission congestion caused by component failures during typhoons. This prioritizes power supply to critical loads while maintaining system stability. It is expressed as:

[0116]

[0117] In the formula, p bst This indicates the load on busbar b; These represent the proportions of critical loads (first load) and general loads (second load), respectively, and these proportions can be determined through an expert system; L + (b),L - (b) represents the sets of lines for the flow inflow and outflow from target b, respectively. Important loads and general loads are classified according to the power grid company's internal classification of load importance levels.

[0118] By establishing the above constraints, the initial model incorporates preventive unit combination constraints, differentiated load shedding constraints, and differentiated reinforcement constraints to characterize short-term source-grid-load measures, thus obtaining the final collaborative planning model.

[0119] The offshore wind power and transmission grid collaborative planning method considering the impact of typhoon disasters provided in the above embodiments of the present invention first establishes a multi-scenario sub-Browser uncertainty set for offshore wind farm output and transmission line faults: a multi-scenario budget uncertainty set based on conditional risk value to capture the uncertainty of wind turbine output fluctuations and line faults under normal and extreme typhoon disaster scenarios; and constructs a grid fault uncertainty set based on the L1 norm to characterize the uncertainty probability distribution of four types of faults, namely high-probability faults, high-loss faults, cascading faults under extreme typhoon disaster scenarios, and fault-free states under normal scenarios. Subsequently, a multi-scenario sub-Browser collaborative planning model for offshore wind power and transmission grids is constructed: the worst-case load shedding cost under extreme typhoon disaster scenarios is used as an elasticity indicator, and the planned and expected power generation costs under extreme typhoon disaster and normal scenarios are used as economic indicators. This model coordinates system elasticity and cost under the worst-case scenario of uncertainty in offshore wind farm output and grid faults. To further improve the conservatism of the offshore wind power and transmission grid collaborative planning model, short-term source-grid-load measures are introduced into the planning model, including preventive unit combinations, differentiated load shedding, and differentiated reinforcement models. This method comprehensively considers the multiple uncertainties of offshore wind farm output and transmission line faults under normal and extreme typhoon disaster scenarios, in order to improve the resilience of high-proportion renewable energy power systems.

[0120] Based on the same inventive concept, an embodiment of the present invention also provides a collaborative planning system for offshore wind power and power transmission networks that takes into account the impact of typhoon disasters.

[0121] Specifically, such as Figure 2 As shown, the offshore wind power and power grid collaborative planning system considering the impact of typhoon disasters provided in this embodiment may further include:

[0122] A multi-scenario partial Bruker uncertainty set construction module is used to build multi-scenario partial Bruker uncertainty sets for offshore wind farm output and transmission line faults.

[0123] The collaborative planning model construction module is based on the multi-scenario distributed bar uncertainty set. It constructs an initial collaborative planning model for offshore wind power and transmission grid based on the multi-scenario distributed bar uncertainty set, 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] The collaborative planning module utilizes the final collaborative planning model to achieve collaborative planning of offshore wind power and power transmission networks, taking into account the impact of typhoon disasters.

[0125] The specific contents of each functional module constituting the system provided in the above embodiments of the present invention are further described in detail below.

[0126] I. Multi-scenario Partial Brutal Uncertain Set Construction Module, used to construct multi-scenario partial Brutal uncertain sets.

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

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

[0129] 1) Theoretical power output model of wind farm

[0130]

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

[0132] 2) Output model of a single wind turbine

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

[0134]

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

[0136] 3) Risk model for wind turbine failure

[0137] Unlike existing methods that use expected value to describe the failure risk of wind turbines, this method considers the extreme nature of typhoon disaster scenarios and uses a conditional value at risk (VaR) model to quantify the failure models of wind turbines under normal and extreme typhoon disasters:

[0138]

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

[0140] Conditional Value at Risk for Wind Turbines in Scenario s It can be represented as:

[0141]

[0142] in, N represents the number of faulty wind turbines in scenario s; W This indicates the number of wind turbines in wind farm W; Represents the conditional risk value of random variable N in scenario s; The failure probability of m wind turbine units 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 for combination number.

[0145] 4) Uncertain set of offshore wind farm output

[0146] The power output of an offshore wind farm is the sum of the power outputs of all operational wind turbines, and its power output uncertainty set Ω. WFO It can be represented as:

[0147]

[0148] Where, p Wst , These represent the actual and theoretical power output of the wind farm W, respectively.

[0149] (2) Set of power grid line faults

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

[0151] 1) Set of high-probability faulty lines

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

[0153]

[0154] Among them, w r Wind speed threshold; This represents the probability of line l failing under normal conditions; This indicates that line l is in wind speed w t The probability of failure at that time and the probability of failure throughout the entire typhoon extreme disaster process.

[0155] 2) High-loss line set

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

[0157]

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

[0159] 3) The set of lines that cause cascading failures

[0160] In the case of typhoon extreme disasters, a line is randomly selected from the set of lines with high failure probability as the initial fault line, and then the cascading fault lines caused by the fault chain are searched.

[0161]

[0162] in, This represents the failure probability of line l in the fault chain c; This indicates the line's rated and maximum permissible transmission capacity.

[0163] Considering that wind speed and cascading faults both affect the probability of line failure, we assume... If they are independent of each other, the overall failure probability of line l can be expressed as:

[0164]

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

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

[0167] Considering the deviation between the actual and theoretical probability distributions of faults, and combining the idea of ​​sub-Bruker optimization, the 1-norm is used to characterize this deviation:

[0168]

[0169] Ω GF p represents the set of uncertainties for line faults based on the 1-norm; s (Z s ),p0(Z s ) represent fault scenarios Z respectively s Actual and theoretical probabilities; Ψ, These 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 This represents the average number of typhoon days per year. To improve the solution efficiency of the planning model, Ω... GF Terms containing absolute values ​​are introduced by introducing an auxiliary variable p. k (Z s This can be represented as:

[0170]

[0171] II. Collaborative Planning Model Construction Module, used to establish multi-scenario distributed bar programming models for the collaboration between offshore wind power and power transmission networks, oriented towards resilience enhancement.

[0172] The proposed multi-scenario distributed bar programming model for offshore wind power and power transmission grid coordination with resilience enhancement has a three-layer structure. The upper-layer model aims to minimize the cost 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, based on the investment, reinforcement, and unit combination schemes obtained from the upper-layer model, is used to find the worst-case offshore wind farm output and line fault probability distribution scenarios. These scenarios maximize the expected value of system operating 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] Where f1 and f2 represent the objective functions of the upper and lower layer models, respectively; x l ,x W , These are 0-1 variables, representing the construction status of the power line and offshore wind power, the start-up / shutdown status of the generator set, and the operating status, respectively. These represent the input costs of power lines and offshore wind power, and the start-up and shutdown costs of generators, respectively; C H Indicates the cost of differentiated reinforcement of the line; p gst ,p lst , These represent generator output, line power flow, wind curtailment, and load shedding, respectively. Represents generation cost, wind curtailment cost, and load shedding cost for various types of loads; B, G, L W ,L c ,L e These respectively represent the busbar, generator, offshore wind farm, candidate route, and existing route set; H represents the feasible regions of the upper-level and middle-level models, respectively; Ω WFO This represents the feasible region of the mid-level model.

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

[0177]

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

[0179] (2) Constraints

[0180] 1) Upper-level constraints

[0181] a. Budgetary constraints

[0182]

[0183] Π L ,Π W These represent the total investment limits for power lines and offshore wind farms, respectively.

[0184] b. Preventive unit combination constraints

[0185]

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

[0187] c. Differentiated reinforcement constraints

[0188] Differential reinforcement aims to strengthen different sections of transmission lines based on the varying degrees of strong winds experienced at different locations during extreme typhoon disasters.

[0189] First, a set of lines to be reinforced is constructed. Lines located in the set of uncertain faults are selected as lines to be reinforced. Considering the cost constraints of reinforcement, lines with a high probability of failure are chosen to form the set Π of lines to be reinforced. H .

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

[0191]

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

[0193]

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

[0195]

[0196] These represent the segmented reinforcement strategy function and the reinforcement cost, respectively. Indicates a range of fractions; This indicates the reinforcement level corresponding to different wind speed ranges; This indicates the cost corresponding to different reinforcement levels; Indicates the maximum wind speed in wind speed segment e; w et Indicates the wind speed experienced by line segment e; h e Indicates the reinforcement level of line segment e; C H Indicates the total reinforcement cost; Indicates the reinforcement cost of line l; L h E l This represents the set of reinforced lines and all segments of line l.

[0197] Finally, update the reinforced line fault uncertainty set Ω. GF The differentiated reinforcement model (21)-(23) is integrated into the multi-scenario distributed bar programming model for the coordination of offshore wind power and power transmission network. Unlike the existing methods that set the reinforced line as no longer prone to failure, this method considers reducing the line failure probability after reinforcement. Therefore, the failure probability will be updated after the line is reinforced.

[0198]

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

[0200]

[0201] Indicates the reinforcement strength h e The probability of a fault in segment e of line l; This indicates the probability of failure after reinforcement of line l; This represents the theoretical failure probability after reinforcement.

[0202] 2) Mid-level constraints

[0203] The intermediate-level model aims to maximize the expected operating cost within the uncertain set Ω. WFO , China seeks offshore wind farm output p Wst And the worst-case scenario of the actual fault probability distribution of transmission lines. Therefore, the intermediate constraint is the uncertain set Ω. WFO , Considering that the intermediate-level model only searches for the worst-case probability distribution p of the transmission line after obtaining the hardening strategy. s (Z s Therefore, the uncertainty set of transmission line faults needs to be updated in the intermediate-level model. Instead of still using the line fault probability set Ω before reinforcement GF Failure probability is adopted Ω WFO , The representative mid-level constraint can be expressed as:

[0204]

[0205] 3) Lower-level constraints

[0206] 1) Generator rescheduling

[0207]

[0208] These represent the minimum and maximum output of the generator, respectively.

[0209] 2) Current constraints

[0210]

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

[0212] 3) Differentiated load shedding and other constraints

[0213]

[0214] p bstThis indicates the load on busbar b; These represent the proportions of critical loads and general loads, respectively; L + (b),L - (b) represents the set of routes for tidal current inflow and outflow from target b, respectively.

[0215] By establishing the above constraints, the collaborative planning model introduces preventative unit combination constraints, differentiated load shedding constraints, and differentiated reinforcement constraints to characterize short-term source-grid-load measures. Specifically: the preventative unit combination constraint is used to determine the start-up and shutdown status of units located at different positions in the system based on the impact of typhoon disasters; the differentiated reinforcement constraint is used to strengthen different sections of transmission lines based on the differences in strong winds experienced at different locations under extreme typhoon disasters; and the differentiated load shedding constraint is used to differentiate load shedding in the power system after transmission congestion caused by component failures under the impact of typhoon disasters, ensuring priority power supply to important loads while maintaining system stability.

[0216] III. Collaborative Planning Module: The collaborative planning model constructed using the collaborative planning model construction module enables collaborative planning of offshore wind power and power transmission networks, taking into account the impact of typhoon disasters.

[0217] It should be noted that the steps in the method provided by the present invention can be implemented using the corresponding components in the system. Those skilled in the art can refer to the technical solution of the system to implement the steps 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, which will not be elaborated here.

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

[0219] In this specific application example, an improved IEEE 30-node simulation system is used for verification. This system includes 41 existing power plants and 8 conventional power plants, with a total generating capacity of 24,000 MW, and integrates 3,600 MW of offshore wind power. The system's peak load is 21,000 MW. Nodes 3, 12, 13, 14, 15, 18, and 23 can be used to integrate offshore wind power, with each node having an integration capacity of 1,200 MW. A simulated typhoon makes landfall at node 23 with a moving speed of 15 km / h and an initial maximum wind speed of 52 m / s. Assume a single wind turbine has a capacity of 10 MW, and each offshore wind farm is configured with N wind turbines. W The value is 120, with a confidence level β of 99%. In the power grid fault uncertainty set, an average of 20 typhoon days per year are considered, i.e., T... D =20. Since the uncertainty in line failure rate mainly stems from wind speed prediction errors, in the typhoon day scenario, k... E Below, the failure rate uncertainty factor is taken. The values ​​are 0.8 and 1.2 respectively. In the differentiated reinforcement model strategy 1, the wind speed range is divided into segments from 27 m / s to 53 m / s, with each segment having a step size of 4 m / s. For example, the first segment... The speed range is 27 m / s to 31 m / s. Table 1 shows a comparison of the results from different collaborative planning models for offshore wind power and power transmission networks.

[0220] Table 1 Comparison of results from different collaborative planning models for offshore wind power and power transmission networks

[0221]

[0222] The verification through this specific application example shows that the technical solution provided by the above embodiments of the present invention has the following implementation effects:

[0223] Effectiveness of Multi-Scenario Setup. Compared to the single-scenario model, which focuses only on either a network overload scenario or a typhoon disaster scenario, the proposed multi-scenario model exhibits lower total cost, indicating that simultaneously considering both network overload and typhoon disaster scenarios better balances 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 to C1, the economic indicator increased by 217 million yuan, while the resilience indicator decreased by 28,988 MWh, indicating that the planning scheme focusing only on the network overload scenario will suffer losses exceeding the reduced grid construction costs in the event of a severe typhoon disaster. Compared to C2, the economic indicator decreased by 19 million yuan, and the resilience indicator decreased by 4,083 MWh, indicating that the planning scheme considering only the typhoon disaster scenario is less cost-efficient and overly conservative.

[0224] The effectiveness of robust configuration is discussed. By comparing the results with the multi-scenario robust model C3, the proposed model C5 provides a more cost-effective planning scheme. For a 30-node system, the robust model achieves a resilience index of 19439 MWh and an economic index of 47.698 billion yuan. Compared with the proposed model, the economic index increases by 0.69 billion yuan, while the resilience index decreases to 4304 MWh. The robust model tends to deploy more lines to cope with high-loss failures.

[0225] The effectiveness of conditional value-at-risk (VAT) settings. Compared to models that do not consider VAT, the proposed model C5 achieves a higher-cost planning scheme, indicating that ignoring turbine failure risk easily underestimates the impact of typhoon disasters and overestimates offshore wind power output under typhoon disaster scenarios. Specifically, compared to C5, C4 increases input costs and wind curtailment costs by RMB 0.21 billion and RMB 0.02 billion, respectively, to improve offshore wind power absorption capacity. However, due to more offshore wind power participating in load supply, generation costs and load reduction decrease by RMB 0.18 billion and 7946 MWh, respectively, ultimately reducing the total cost by RMB 0.16 billion. Although VAT settings lead to higher input costs in planning schemes, their main function is to objectively reflect turbine failure risks under typhoon disaster scenarios, making the planning model closer to reality, rather than merely reducing the model's conservatism.

[0226] An embodiment of the present invention also provides a computer terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it can be used to execute the method of any of the above embodiments of the present invention, or to run the system of any of the above embodiments of the present invention.

[0227] Optionally, the memory is used to store programs; the memory may include volatile memory, such as random-access memory (RAM), such as static random-access memory (SRAM), double data rate synchronous dynamic random-access memory (DDR SDRAM), etc.; the memory may also include non-volatile memory, such as flash memory. The memory is used to store computer programs (such as application programs, functional modules, etc. that implement the above methods), computer instructions, etc., and the aforementioned computer programs, computer instructions, etc., can be partitioned and stored in one or more memories. Furthermore, the aforementioned computer programs, computer instructions, data, etc., can be accessed by the processor.

[0228] A processor is used to execute computer programs stored in memory to implement the various steps of the methods or various modules of the systems involved in the above embodiments. For details, please refer to the relevant descriptions in the preceding method and system embodiments.

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

[0230] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, can be used to perform the method of any of the above embodiments of the present invention, or to run the system of any 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 place to another. Storage media can be any available medium accessible to a general-purpose or special-purpose computer. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Alternatively, the ASIC can reside in a user device. Of course, the processor and storage medium can also exist as discrete components in a communication device.

[0232] The offshore wind power and transmission network collaborative planning method and system considering the impact of typhoon disasters provided in the above embodiments of the present invention can not only consider the output uncertainty of offshore wind farms under normal scenarios and extreme typhoon disaster scenarios, but also simultaneously take into account the uncertainties of four fault probability distributions under both normal and extreme typhoon disaster scenarios. This method and system can comprehensively consider the impact of normal scenarios and extreme typhoon disaster scenarios on planning, avoiding load shedding problems caused by over-investment or under-investment during extreme typhoon 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, effectively improving resilience while taking into account the scale of investment.

[0233] Any matters not covered in the above embodiments of the present invention are well-known in the art.

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

Claims

1. A method for coordinated planning of offshore wind power and power transmission networks considering the impact of typhoon disasters, characterized in that, include: Establish a multi-scenario distributed Brussels uncertainty set for offshore wind farm output and transmission line faults; Based on the multi-scenario sub-Brussels uncertain set, an initial model for collaborative planning of multi-scenario sub-Brussels offshore wind power and power 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 the final collaborative planning model, which is used to realize the collaborative planning of offshore wind power and transmission grid considering the impact of typhoon disasters. in: Based on the multi-scenario sub-Brussels bar uncertainty set, the constructed multi-scenario sub-Brussels bar offshore wind power and transmission network collaborative planning initial model includes a three-layer structure; wherein, the upper-layer model is used to minimize the cost of typhoon extreme disaster prevention measures; the middle-layer model obtains typhoon extreme disaster prevention measures based on the upper-layer model, and is used to obtain the worst-case scenario of offshore wind farm output and line fault probability distribution; the lower-layer model is used to perform optimal scheduling under the worst-case scenario. Based on the three-layer structure, the objective function of the initial model is established, and preventive unit combination constraints, differentiated load shedding constraints, and differentiated reinforcement constraints, which characterize short-term source-grid-load measures, are introduced to obtain the final collaborative planning model. Among them, the differentiated reinforcement constraints divide the transmission line into multiple segments according to the different variation characteristics of typhoon wind speed, and implement different levels of reinforcement treatment on each segment. The establishment of a multi-scenario distributed uncertainty set for offshore wind farm output and transmission line faults includes: Construct a multi-scenario wind power output uncertainty set based on conditional risk value. This set is used to describe the uncertainty of wind power output under normal and typhoon disaster scenarios. Construct an uncertainty set of power grid line faults, and use the 1 norm to characterize the probability distribution of different types of power grid line faults; The construction of a multi-scenario wind power output uncertainty set based on conditional value of risk includes: The theoretical power output model of a wind farm is established, expressed as follows: (1) In the formula, These represent the theoretical output and installed capacity of the wind turbine, respectively. These represent the fan's cut-in, cut-out, and rated wind speed, respectively. express t Wind speed at all times; This is the conversion coefficient between wind speed and wind turbine power generation; A power output model for a single wind turbine is established, represented as follows: Assume the actual output of the wind turbine Fluctuations within the set prediction error range are represented as: (2) In the formula, This represents the set of uncertain output power of a single wind turbine unit; These represent typhoon disaster scenarios and normal scenarios, respectively. and These represent scaling factors for the upper and lower limits of wind turbine output in typhoon disaster scenarios and normal scenarios, respectively; This indicates the theoretical output of the wind turbine; express and Degree of deviation; Represents the budget of an uncertain set; These represent the set of wind turbine units and the set of scheduling times, respectively. This represents a combination of typhoon disaster scenarios and normal scenarios. ; Considering the extreme nature of typhoon disaster scenarios, the conditional value-at-risk (VAT) model is used to quantify the fault models of wind turbines under normal scenarios and extreme typhoon disasters, as follows: (3) In the formula, Represents conditional risk value; Expressing expectations; Representing random variables respectively N The probability density function and cumulative probability distribution function; Indicates confidence level; Represents random variables N Conditional risk value; Represents the maximum value of a discrete variable; n This represents the upper limit of a given discrete variable; Wind turbine in the scene s Conditional Value at Risk Represented as: (4) In the formula, Representing a scene s , s ∈ The number of wind turbine units that failed; Indicates wind farm W Number of medium-sized wind turbine units; Representing a scene s , s ∈ random variables N Conditional risk value; express m The failure probability of a wind turbine unit is expressed as: (5) in, Indicates wind turbine w In the scene s , s ∈ The probability of failure is as follows; Indicates wind turbine w Failure probability under normal conditions; This represents the failure rate scaling factor; Symbols representing combinations; If the power output of an offshore wind farm is the sum of the outputs of all wind turbines that are operating normally, then the uncertain set of wind power output based on conditional risk value is... Represented as: (6) in, They represent wind farms W The actual output and theoretical output; The construction of the power grid line fault uncertainty set includes: A power grid line model with a high probability of failure under extreme typhoon disasters is established, represented as follows: (7) In the formula, Indicates the line l wind speed The probability of failure at that time and the probability of failure throughout the entire typhoon extreme disaster process; Indicates the wind speed threshold; Indicates the line l Failure probability under normal scenarios; The high-loss line fault set is constructed based on the multiple fault model and represented as follows: (8) in, These represent generator output, line power flow, and load shedding, respectively. This represents the feasible region of the decision variables before and after the failure. Represents a set of multiple faults; Indicates the status of a line fault; Indicates the number of lines; Indicates the maximum number of faulty lines; From the set of power grid lines with a high probability of failure under extreme typhoon disasters, one line is randomly selected as the initial fault line. The chain of faults triggered by this initial fault line is then searched, represented as follows: (9) In the formula, Indicating in the fault chain c Central route l The probability of failure; This indicates the line's rated and maximum permissible transmission capacity; Assume the failure probability If they are independent of each other, then the lines l The overall failure probability is expressed as: (10) In the formula, Represents the initial set of faults; Indicates the number of faulty chains; Indicates the circuit under all fault chain scenarios. l The probability of failure; The deviation between the actual probability distribution and the theoretical probability distribution of faults is characterized by the 1-norm, expressed as: (11) In the formula, Let f(x) represent the set of uncertainties for power grid line faults based on the 1-norm. These represent the fault scenarios respectively. Actual and theoretical probabilities; These 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. This indicates the average number of typhoon days per year; These represent typhoon disaster scenarios and normal scenarios, respectively. exist Introducing auxiliary variables for terms containing absolute values , is represented as: (12)。 2. The method for coordinated planning of offshore wind power and power transmission networks considering the impact of typhoon disasters according to claim 1, characterized in that, The objective function for establishing the initial model includes: The objective function for establishing the initial model of collaborative planning between multi-scenario distributed offshore wind power and power transmission network is: (13) (14) (15) In the formula: Let these represent the objective functions of the upper-level model and the lower-level model, respectively. These are 0-1 variables, representing the construction status of the power line and offshore wind power, the start-up / shutdown status of the generator set, and the operating status, respectively. These represent the costs of power line and offshore wind power input, and the costs of generator start-up and shutdown, respectively. This indicates the cost of differentiated reinforcement of the line; These represent generator output, line power flow, wind curtailment, and two different load shedding parameters, respectively. These represent the costs of power generation, wind curtailment, and load shedding for two different types of loads, respectively. These respectively represent the busbar, generator, offshore wind farm, candidate route, and existing route set; These represent the feasible regions of the upper-level model and the middle-level model, respectively. This represents the feasible region of the mid-level model; Indicates the fault scenario The actual probability; In the above formula, the expected value of load shedding under the worst-case scenario of offshore wind farm output and line fault probability distribution is used as an elasticity index. f 2 can be further rewritten as: (16)。 3. The method for coordinated planning of offshore wind power and power transmission networks considering the impact of typhoon disasters according to claim 1, characterized in that, Establish constraints for the initial model and introduce short-term source-grid-load measures into the initial model, including: The constraints for establishing the initial model of collaborative planning between offshore wind power and power transmission grid in multiple scenarios include: Establish upper-level constraints, including: Budget constraints are expressed as follows: (17) In the formula, These represent the total investment limits for power lines and offshore wind farms, respectively. Preventive unit combination constraints are expressed as: (18) In the formula, This indicates that generator set g is in the powered-on state; This represents the minimum start-up and shutdown time of generator set g; Differential hardening constraints are characterized by a piecewise linearized function, expressed as: (19) The segmented differentiated reinforcement cost is expressed as: (20) The total cost limit for differentiated reinforcement is expressed as follows: (21) In the formula, These represent the segmented reinforcement strategy function and the reinforcement cost, respectively. Indicates a range of fractions; This indicates the reinforcement level corresponding to different wind speed ranges; This indicates the cost corresponding to different reinforcement levels; Indicates wind speed range e Maximum wind speed in the middle; Indicates line segment e The wind speed received; Indicates line segment e The reinforcement level; C H Indicates the total reinforcement cost; Indicates the line l The cost of reinforcement; Indicates a collection of reinforced lines and lines l All segments; Updated and reinforced power grid line fault uncertainty set The differentiated reinforcement constraints (19)~(21) are integrated into the initial model of multi-scenario distributed offshore wind power and power transmission network collaborative planning. The failure probability of the initial model is updated after the line is reinforced, and is expressed as: (22) The probability of updating the failure scenario is represented as: (23) In the formula, Indicates the line l The probability of failure after reinforcement; Indicates the reinforcement strength Downline l of e Segment failure probability; This represents the theoretical failure probability after reinforcement. Establish mid-level constraints, including: The intermediate-level model aims to maximize the expected operating cost within the uncertain set of wind power output based on conditional risk value and the feasible region of the intermediate-level model. China seeks to contribute to offshore wind farms And the worst-case scenario of the actual fault probability distribution of transmission lines, therefore, with The representative middle-level constraint is represented as: (24) (25) Establish lower-level constraints, including: Generator rescheduling is represented as: (26) In the formula, These represent the minimum and maximum output of the generator, respectively. Current constraint, represented as: (27) (28) (29) (30) (31) In the formula, These represent the line susceptance and maximum transmission capacity, respectively. Indicates the phase angle between two busbar segments of the line; Represents a constant; Indicates the fault status of line l; Differential load shedding constraints are expressed as: (32) (33) (34) (35) (36) In the formula, Indicates busbar b The load; These represent the proportions of critical loads and general loads, respectively. These represent the destinations of the tidal current inflow and outflow, respectively. b A collection of routes; By establishing the above constraints, the initial model incorporates preventive unit combination constraints, differentiated load shedding constraints, and differentiated reinforcement constraints that characterize short-term source-grid-load measures, resulting in the final collaborative planning model.

4. A collaborative planning system for offshore wind power and power transmission networks considering the impact of typhoon disasters, used to implement the method described in any one of claims 1-3, characterized in that, include: A multi-scenario partial Bruker uncertainty set construction module is used to build multi-scenario partial Bruker uncertainty sets for offshore wind farm output and transmission line faults. The collaborative planning model construction module constructs an initial collaborative planning model for offshore wind power and transmission grid based on the multi-scenario sub-Brussels uncertainty set, 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. The collaborative planning module utilizes the final collaborative planning model to achieve collaborative planning of offshore wind power and power transmission networks, taking into account the impact of typhoon disasters.

5. A computer terminal, 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, it can be used to perform the method of any one of claims 1-3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program can be used to perform the method of any one of claims 1-3.

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