Offshore wind power AC / DC hybrid power transmission network probability planning method and system considering safety correction

Abstract

The invention relates to the technical field of power system power transmission network planning, in particular to an offshore wind power AC / DC hybrid power transmission network probability planning method and system considering safety correction. The method comprises the following steps: firstly, establishing a correction security constraint optimal power flow model which is suitable for an AC-DC hybrid system and takes RAS into consideration, then, constructing an uncertainty quantification model and a rapid fault screening criterion, establishing wind, load and fault scenes applied to an operation layer, and finally, establishing an offshore power transmission network planning model suitable for large-scale wind power access. A system planner is allowed to assess economic and technical feasibility of a series of potential network and non-network options while considering various uncertainties, and the need to add new lines while considering extensive uncertainties during operation of the system is reduced, providing a balanced choice among cost, flexibility and reliability for the planner.

Description

Technical Field

[0001] The present invention relates to the technical field of power system transmission network planning, and in particular to a probabilistic planning method and system for an offshore wind power AC / DC hybrid transmission network taking safety correction into consideration. Background Art

[0002] With the proposal of China's "dual carbon" goals, new energy power generation has gradually become an important means to achieve energy transformation, and wind power is considered an important source of renewable energy. However, due to the increase in new energy investment and the retirement of thermal power plants, the existing power transmission system is getting closer and closer to its stability limit. The upgrade of the transmission network infrastructure has failed to keep up with the changes in the power generation structure and the growing demand, resulting in a decline in system operating profits.

[0003] The successful integration of wind power, especially offshore wind power resources, relies heavily on transmission network planning. The purpose of Transmission Network Planning (TNP) is to identify an implementable and robust solution to utilize remote wind energy, provide the necessary operational flexibility for changing the generation mix, and improve the resilience of the power system to unexpected events. To date, existing offshore wind energy is usually exported through medium-voltage AC transmission networks, reflecting economic or technical limitations.

[0004] Considering the limited offshore wind power resources and the restrictions on long-distance transmission, AC transmission technology is difficult to apply for a long time. Therefore, when planning offshore wind farms, it is necessary to consider both AC and DC transmission technologies. With the advancement of technology, TNP still has many challenges to be solved. Some of the current challenges are summarized as follows:

[0005] (1) Deterministic and probabilistic planning

[0006] Achieving the goals of TNP requires economic and technical analysis of network extension options. In theory, there are two main categories of system planning methods: deterministic and probabilistic. In engineering industry practice, the choice of transmission paths varies from utility to utility and from region to region. In deterministic methods, the expected future system performance is simulated under the most unfavorable conditions and evaluated for a set of pre-defined fault scenarios. Probabilistic methods consider the same events in deterministic planning criteria, but they are able to evaluate various aspects of planning by providing estimates of the number, duration and frequency of the expected consequences of the investment and quantify these consequences. Therefore, probabilistic methods are more suitable for competitive market environments, because in this case, economic evaluation can be carried out based on the benefits obtained through network extension and compared with any economic losses that market participants may suffer. However, conducting probabilistic TNP through risk analysis is a challenging task, considering the uncertainty in the timing, location and severity of these fault events, not to mention the large number of network elements involved.

[0007] (2) Network and non-network solutions

[0008] In a market liberalization environment, the most economical and feasible options should be implemented, whether they are network solutions or non-network solutions. Generally speaking, the lower the customer's willingness to pay for a reliable supply of electricity, the weaker the economic case for network investment. Therefore, due to economic incentives, localized solutions that include non-network options need to be adopted in planning. Many system operators have begun to use short-term ratings and remedial action schemes (RAS) together as an enhancement option for long-term transmission planning when the cost of network expansion is too high. The measures taken by RAS mainly include but are not limited to power flow control, generator re-sending and load shedding. RAS is becoming increasingly popular in system planning because it is easier and more cost-effective to implement than any other alternatives (such as building major transmission lines or new power plants). In addition, the use of RAS can improve the utilization of existing transmission facilities. Therefore, the TNP process should include the consideration and adoption of alternatives based on RAS safety corrections to more economically postpone or cancel the need for network expansion.

[0009] The above challenges mainly lie in the uncertainty of solving the probabilistic TNP and the operational complexity of considering RAS in the TNP, which hinders the hybrid transmission network from operating flexibly between AC and DC transmission systems and providing reliable services to users at acceptable standards and affordable costs. Summary of the invention

[0010] In view of the problems in the prior art, the present invention provides a probabilistic planning method and system for an offshore wind power AC / DC hybrid transmission network taking into account safety correction. The specific technical solution is as follows:

[0011] A probabilistic planning method for an offshore wind power AC / DC hybrid transmission network considering safety correction comprises the following steps: Step S1, collecting data and setting parameters, including historical load data and probability distribution parameters of the system, wind speed data and probability distribution parameters, system model parameters, and associated costs of supporting components of candidate transmission technologies;

[0012] Step S2, establishing a corrected safety-constrained optimal power flow model considering RAS applicable to an AC / DC hybrid system;

[0013] Step S3, establishing an uncertainty quantification model and an accident screening model, considering multiple uncertainties in modeling, and screening out a set of serious faults for transmission network planning, and establishing wind power generation, load, and fault scenarios applied to the operation layer;

[0014] Step S4, establishing a transmission network expansion planning model, constructing a two-layer objective function, the objective function is decomposed into two layers: an upper-layer investment problem and a lower-layer operation problem; the objective function is subject to the relevant constraints of the corrected safety constraint optimal power flow model;

[0015] Step S5, solving the transmission network expansion planning model to obtain a planning solution.

[0016] Preferably, the system model parameters in step S1 include system topology, line parameters, generator parameters and costs, and the candidate power transmission technology supporting components include AC and DC submarine cables, transformers, reactive power compensation devices or converter stations.

[0017] Preferably, the step S2 specifically includes the following steps:

[0018] Step S21, establish an objective function of the corrected safety-constrained optimal power flow model considering RAS applicable to the AC / DC hybrid system. The objective function aims to minimize the total power generation cost of the system under normal circumstances. The objective function is as follows:

[0019]

[0020] is the active power generation of the controllable generator g under normal conditions, is the fuel cost coefficient of generator g, N G is the number of generators;

[0021] Step S22, establishing the constraint conditions of the corrected safety-constrained optimal power flow model considering RAS applicable to the AC / DC hybrid system; the specific conditions are as follows:

[0022] (1) Power balance constraint: In an AC / DC hybrid transmission system, all AC and DC buses must satisfy node power balance, which can be expressed by the following active power and reactive power balance equations of the AC and DC buses:

[0023]

[0024] Among them, for each fault k, For active and reactive power generation of wind farms, To reduce the active and reactive power requirements of the load, For the active and reactive output of the converter station, is the vector magnitude of the AC and DC bus voltages at node i, θ is the AC bus voltage angle, The conductance and susceptance of the line between AC node i and node j, is the conductance of the line between DC nodes i and j, is the number of buses in the AC grid and the DC grid; is the active power and reactive power of the generator at AC node i;

[0025] is the active power of the generator at DC node i; are the voltages at the AC and DC nodes j respectively;

[0026] (2) For the converter connected to the AC / DC grid, assuming that converter c is connected to the AC / DC grid, the power transmission and converter loss between the AC / DC grids are modeled as:

[0027]

[0028] Among them, for each fault k, is the loss of converter c, is the power loss coefficient of converter c; is the active power of the AC side converter c; is the active power of the DC side converter c; i c,k is the current of converter c;

[0029] (3) Under any conditions, active and reactive power generation, line flow, and bus voltage should be within their relevant rated values ​​and limits at all times, as shown below:

[0030]

[0031] in, are the maximum and minimum active power generation values ​​of the AC controllable generator g respectively; are the maximum and minimum reactive power generation values ​​of the AC controllable generator g respectively;

[0032] is the maximum and minimum voltage of AC bus i;

[0033] is the maximum and minimum value of the AC line transmission power between node i and node j;

[0034] are the maximum and minimum active power generation values ​​of the DC controllable generator g respectively;

[0035] is the maximum and minimum voltage of DC bus i;

[0036] is the maximum and minimum value of the DC line transmission power between node i and node j;

[0037] are the active power and reactive power of the AC generator g, respectively. is the active power of the DC generator g; (4) Converter station capacity constraint: The power flow through the converter is limited by the current in the converter valve, so the power constraint of the converter station can be expressed as:

[0038]

[0039] in, are the maximum and minimum active power generation values ​​of the AC side converter c respectively; are the maximum and minimum active power generation values ​​of the DC side converter c respectively; are the maximum and minimum reactive power generation values ​​of the AC side converter c respectively;

[0040] (5) Considering power flow control, generator retransmission and load shedding as potential RAS actions, the coupling constraints can be transformed into the response ramp limit and load shedding constraint of the controllable generator and converter station within the fault allowable time k, as follows:

[0041]

[0042] in, They are respectively the change in active power of the AC side converter c, the change in active power of the AC generator g and the change in active power load l in the fault scenario k compared to the normal operation state, are the maximum and minimum values ​​of the active power change of converter station c respectively; are the maximum and minimum values ​​of the active power change of the AC generator g respectively; They are respectively the maximum and minimum changes in the AC active load l.

[0043] Preferably, a unified model of the corrected safety-constrained optimal power flow model considering RAS applicable to AC / DC hybrid systems is established, specifically as follows:

[0044]

[0045] Among them, x k represents the state variable under the kth fault state, u k represents the control variable under the kth fault state, k=0 represents the normal state, Ω k is the set of accidental failure events; g k (x k ,u k ) represents the equality constraint under fault k, h k (x k ,u k ) represents the inequality constraint under fault k, is the operating limit value of the control variable;

[0046] The control variable vector u and state variable x are summarized as:

[0047] u=[Ωp ac,vsc Ωq ac,vsc Ωp ac,gen Ωq ac,gen Ωp ac,load Ωq ac,load ]; (twenty one)

[0048] x=[Ωv ac Ωv dc Ωθ]; (22)

[0049] Ωp ac,vsc ,Ωq ac,vsc are respectively the active output set and reactive output set of the AC side converter; Ωp ac,gen ,Ωq ac,gen are respectively the active output set and reactive output set of the AC side generator; Ωp ac,load ,Ωq ac,load are respectively the active and reactive sets of AC loads; Ωv ac ,Ωv dc , Ωθ are the AC voltage set, DC voltage set and phase angle set of the system nodes respectively.

[0050] Preferably, the step S3 specifically includes the following steps:

[0051] Step S31, constructing an uncertainty quantification model, establishing an uncertainty quantification model for load change, intermittent wind power generation, forced shutdown of generators and transmission line failure, as follows:

[0052] The load change is modeled using the probability distribution function of Gaussian distribution, specifically:

[0053]

[0054] Among them, μ i , σ i is the Gaussian distribution parameter, x load is a load-related random variable;

[0055] The intermittent wind power generation is modeled using the probability distribution function of the Weibull distribution, specifically:

[0056]

[0057] Among them, λ i , i is the Weibull distribution parameter, x wind is a random variable related to wind force;

[0058] Generator forced outages and transmission line failures are modeled using a binomial distribution, specifically:

[0059]

[0060] Among them, θ i , τ i is the parameter in the binomial distribution, x fault is the fault random variable;

[0061] Step S32, fault accident screening, identifying different faults based on the existing "Nk" fault rapid screening method, screening a group of selected faults, counting them into a fault scenario list according to the severity, and determining safety correction measures based on the fault scenarios in the table.

[0062] Preferably, the step S4 specifically includes the following steps:

[0063] Step S41, construct a two-layer objective function, which can be decomposed into two layers, the upper layer investment problem and the lower layer operation problem, as follows:

[0064] minf

[0065]

[0066]

[0067] Among them, N T 、N S 、N K 、N G 、N LThey are the number of time periods, the number of load and wind output scenarios, the number of fault types, the number of AC generators, and the number of loads; is the investment cost of transmission line ij; is the decision variable for the candidate AC transmission line between nodes i and j in period t, 1 indicates the planned line, otherwise 0; is the decision variable for the candidate DC transmission line between nodes i and j in period t, 1 indicates the planned line, otherwise 0; ρ s,t is the accidental probability of normal operation under wind load scenario s during period t; ρ k,s,t is the accidental probability of fault type k under wind load scenario s in period t; d is the discount rate; is the cost of generator g under normal operation of scenario s during period t; They are respectively the power generation cost and load shedding cost of wind-load scenario s under fault k in period t; is the active power of the AC generator g under normal operation of scenario s during period t; is the fuel cost coefficient of generator g; is the active power change of the AC generator g under fault k in scenario s during period t; is the active power reduction of AC load l under fault k in scenario s during period t; Δh k,s is the load shedding duration of wind-load scenario s under fault k; VCR is the customer reliability value cost;

[0068] Step S42, constructing the constraint conditions of the double-layer objective function, including correcting the relevant constraints of the safety-constrained optimal power flow model.

[0069] Preferably, the step S5 specifically includes the following steps:

[0070] Step S51, determining whether the capacitive current safety margin of the AC power transmission technology is feasible according to the AC cable length and voltage level; if the AC cable is feasible, determining the planned technology as a candidate for AC / DC hybrid technology; if not feasible, determining it as a candidate for DC technology, and initializing evolutionary calculation to randomly generate an initial population for the decision variables of the power transmission technology;

[0071] Step S52, based on the generated candidate transmission network planning scheme, according to the line distance and system configuration, obtain the investment cost of the supporting technical scheme related to the transmission technology decision;

[0072] Step S53, using the "Nk" criterion to troubleshoot component faults, determine the severity of the faults, add them to the fault scenario list according to the severity, generate a fault ranking table, and determine the fault types to be applied to the operation layer optimization;

[0073] Step S54, using Monte Carlo sampling according to the uncertainty quantification model to generate values ​​of load, wind power and component failure random variables, thereby generating load, wind power and failure scenarios;

[0074] Step S55, based on the generated load, wind, and fault scenarios, for each scenario, the operation layer calculation of the lower layer of the two-layer objective function is performed; if any network constraints are violated, RAS actions are applied in the following order: power flow control, generator rescheduling, and load shedding, and the operation cost and RAS cost are calculated;

[0075] Step S56, verifying the power transmission technical solution through time domain simulation, conducting power system security assessment, and determining whether it meets the stability criteria. If not, adding the planning solution to the infeasible solution, and continuing to generate a new population for iteration; if it meets, directly generating a new population for iteration;

[0076] Step S57, determine whether the maximum number of iterations has been reached, if yes, go to the next step S58, otherwise, go back to step S52, and generate a new solution by evolutionary calculation;

[0077] Step S58, outputting the final transmission network planning solution.

[0078] A probabilistic planning system for an offshore wind power AC / DC hybrid transmission network considering safety correction, applied to the method described, comprising:

[0079] Data preparation module, used to collect data and set parameters, including historical load data and probability distribution parameters of the system, wind speed data and probability distribution parameters, system model parameters, and associated costs of candidate transmission technology supporting components;

[0080] The power flow modeling module is used to establish a corrected safety-constrained optimal power flow model considering RAS for AC / DC hybrid systems. The quantitative screening module is used to establish uncertainty quantification models and accident screening models, consider multiple uncertainties to model, and screen out severe fault sets for transmission network planning, and establish wind power generation, load, and fault scenarios for the operating layer. The planning modeling module is used to establish a transmission network expansion planning model and construct a two-layer objective function, which is decomposed into two layers:

[0081] The upper layer investment problem and the lower layer operation problem; the objective function is subject to the relevant constraints of the corrected safety constrained optimal power flow model;

[0082] The model solving module is used to solve the transmission network expansion planning model and obtain a planning solution.

[0083] A computer-readable storage medium, the computer-readable storage medium comprising a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the probabilistic planning method for an offshore wind power AC / DC hybrid transmission network taking into account safety correction.

[0084] A processor is used to run a program, wherein when the program is run, the probabilistic planning method for an offshore wind power AC / DC hybrid transmission network taking safety correction into consideration is executed.

[0085] Compared with the prior art, the present invention has the following beneficial effects:

[0086] The present invention proposes a probabilistic planning method and system for an offshore wind power AC / DC hybrid transmission network considering safety correction, which is used to assist power system planning operators in probabilistic planning of offshore transmission schemes and consider safety correction schemes. The planning problem is decomposed into an upper-level network expansion problem that integrates investment decision variables and several lower-level sub-problems that examine the feasibility and optimality of the solution from the upper level, and the optimal transmission technology design for offshore wind power integration is selected from the aspects of cost, flexibility and reliability. First, a Corrective Security-Constrained Optimal Power Flow (C-SCOPF) model considering RAS for AC / DC hybrid systems is established. Then, an uncertainty quantification model and a fast fault screening criterion are constructed to establish wind, load and fault scenarios applied to the operating layer. Finally, an offshore transmission network planning model suitable for large-scale wind power access is established, allowing system planners to evaluate the economic and technical feasibility of a series of potential network and non-network options under various uncertainties, and reduce the need to add new lines during system operation considering extensive uncertainties, providing planners with options to balance cost, flexibility and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0088] Figure 1 The figure is a flow chart of the method of the present invention.

[0089] Figure 2 The present invention is a specific flow chart of the method.

[0090] Figure 3 Schematic diagram of the topological structure of three different transmission technology solutions. Figure 3(a) is a schematic diagram of HVAC topology; Figure 3 (b) is a schematic diagram of HVDC topology; Figure 3 (c) is a schematic diagram of the MTDC topology structure.

[0091] Figure 4 A single-line diagram is connected for a test example in a specific embodiment.

[0092] Figure 5 It is a system principle diagram of the present invention. DETAILED DESCRIPTION

[0093] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0094] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0095] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0096] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0097] Embodiment 1:

[0098] like Figure 1 As shown, this embodiment provides a probabilistic planning method for an offshore wind power AC / DC hybrid transmission network considering safety correction, which essentially constructs an upper-level planning problem for determining the transmission technology and network line type within a given planning period, and several lower-level problems for checking the feasibility and optimality of solutions considering different RAS. It includes the following steps:

[0099] Step S1, collect data and set parameters, including the system's historical load data and probability distribution parameters, wind speed data and probability distribution parameters, system model parameters, and associated costs of candidate power transmission technology supporting components. System model parameters include system topology, line parameters, generator parameters and costs, and candidate power transmission technology supporting components include AC and DC submarine cables, transformers, reactive power compensation devices or converter stations.

[0100] Step S2, establish a corrective security-constrained optimal power flow model considering RAS applicable to AC / DC hybrid systems. It is used to fully utilize the power flow control capability of the DC transmission system. RAS detects abnormal conditions in the power system and takes predetermined / pre-designed actions to prevent these conditions from escalating into system-wide disturbances. After any component outage, such as during maintenance or after a forced outage, system operators apply stricter restrictions within a shorter adjustment period to anticipate the impact of further fault events. This requires the re-dispatch of generators and dispatchable loads, and load shedding is required when there are no other alternatives.

[0101] Given the rapid controllability of Voltage Source Converters (VSCs), DC power systems can provide an impact on power flows in a shorter time scale. With the increasing development of DC grids, RAS becomes a key tool because it controls power flows to provide acceptable control actions. In fact, the ability to control power flows is one of the advantages of DC transmission. In the present invention, power flow control, generator re-dispatch and load reduction are considered as potential RAS actions that can help balance demand and supply after one or more system elements are out of service.

[0102] Step S2 specifically includes the following steps:

[0103] Step S21, establish an objective function of the corrected safety-constrained optimal power flow model considering RAS applicable to the AC / DC hybrid system. The objective function aims to minimize the total power generation cost of the system under normal circumstances. The objective function is as follows:

[0104]

[0105] is the active power generation of the controllable generator g under normal conditions, is the fuel cost coefficient of generator g, N G is the number of generators;

[0106] Step S22, establishing the constraint conditions of the corrected safety-constrained optimal power flow model considering RAS applicable to the AC / DC hybrid system; the specific conditions are as follows:

[0107] (1) Power balance constraint: In an AC / DC hybrid transmission system, all AC and DC buses must satisfy node power balance, which can be expressed by the following active power and reactive power balance equations of the AC and DC buses:

[0108]

[0109]

[0110] Among them, for each fault k, For active and reactive power generation of wind farms, To reduce the active and reactive power requirements of the load, For the active and reactive output of the converter station, is the vector magnitude of the AC and DC bus voltages at node i, θ is the AC bus voltage angle, The conductance and susceptance of the line between AC node i and node j, is the conductance of the line between DC nodes i and j, is the number of buses in the AC grid and the DC grid; is the active power and reactive power of the generator at AC node i;

[0111] is the active power of the generator at DC node i; are the voltages at the AC and DC nodes j respectively;

[0112] (2) For the converter connected to the AC / DC grid, assuming that converter c is connected to the AC / DC grid, the power transmission and converter loss between the AC / DC grids are modeled as:

[0113]

[0114] Among them, for each fault k, is the loss of converter c, is the power loss coefficient of converter c; is the active power of the AC side converter c; is the active power of the DC side converter c; i c,k is the current of converter c;

[0115] (3) In addition to the above equality constraints, the inequality constraint set defines the requirements for the optimal operation state of the power system, that is, in any state, the active and reactive power generation, line flow, and bus voltage should be within their relevant rated values ​​and limits at any time, as shown in the following:

[0116]

[0117]

[0118] in, are the maximum and minimum active power generation values ​​of the AC controllable generator g respectively; are the maximum and minimum reactive power generation values ​​of the AC controllable generator g respectively;

[0119] is the maximum and minimum voltage of AC bus i;

[0120] is the maximum and minimum value of the AC line transmission power between node i and node j;

[0121] are the maximum and minimum active power generation values ​​of the DC controllable generator g respectively;

[0122] is the maximum and minimum voltage of DC bus i;

[0123] is the maximum and minimum value of the DC line transmission power between node i and node j;

[0124] are the active power and reactive power of the AC generator g, respectively. is the active power of the DC generator g;

[0125] (4) Converter station capacity constraint: The interface between the onshore and offshore elements of the transmission system needs to be robust to facilitate power flow. The power flow through the converter is limited by the current in the converter valve, so the power constraint of the converter station can be expressed as:

[0126]

[0127] in, are the maximum and minimum active power generation values ​​of the AC side converter c respectively; are the maximum and minimum active power generation values ​​of the DC side converter c respectively; are the maximum and minimum reactive power generation values ​​of the AC side converter c respectively;

[0128] (5) For each fault, the difference in control behavior between the base case and the post-fault state should be within a certain coupling range. Considering power flow control, generator retransmission and load shedding as potential RAS actions, the coupling constraints can be transformed into the response ramp limit and load shedding constraint of the controllable generators and converter stations within the fault allowable time k, as follows:

[0129]

[0130]

[0131] in, They are respectively the change in active power of the AC side converter c, the change in active power of the AC generator g and the change in active power load l in the fault scenario k compared to the normal operation state, are the maximum and minimum values ​​of the active power change of converter station c respectively; are the maximum and minimum values ​​of the active power change of the AC generator g respectively; are the maximum and minimum changes of the AC active load l. For each accidental fault event, all constraints must be satisfied.

[0132] A unified model of the corrected security-constrained optimal power flow model considering RAS applicable to AC / DC hybrid systems is established as follows:

[0133]

[0134] Among them, x k represents the state variable under the kth fault state, u k represents the control variable under the kth fault state, k=0 represents the normal state, Ω k is the set of accidental failure events; g k (x k ,u k ) represents the equality constraint under fault k, h k (x k ,u k ) represents the inequality constraint under fault k, The equality and inequality constraints are the operating limits on the control variables; each equality and inequality constraint defines several constraints that must be satisfied in the optimization problem.

[0135] The control variable vector u and state variable x are summarized as:

[0136] u=[Ωp ac,vsc Ωq ac,vsc Ωp ac,gen Ωq ac,gen Ωp ac,load Ωq ac,load ]; (twenty one)

[0137] x=[Ωv ac Ωv dc Ωθ]; (22)

[0138] Ωp ac,vsc ,Ωq ac,vsc are respectively the active output set and reactive output set of the AC side converter; Ωp ac,gen ,Ωq ac,gen are respectively the active output set and reactive output set of the AC side generator; Ωpac,load ,Ωq ac,load are respectively the active and reactive sets of AC loads; Ωv ac ,Ωv dc , Ωθ are the AC voltage set, DC voltage set and phase angle set of the system nodes respectively.

[0139] The proposed C-SCOPF is a nonlinear optimization implemented on AMPL and solved using the commercial solver IPOPT. The solution obtained from C-SCOPF, which can be calculated before considering uncertainties, provides an indicator of the base case operating cost for the TNP model.

[0140] Step S3, establish an uncertainty quantification model and an accident screening model, consider multiple uncertainty impacts for modeling, and screen out a set of serious faults for transmission network planning, and establish wind power generation, load, and fault scenarios applied to the operation layer.

[0141] Step S3 specifically includes the following steps:

[0142] Step S31, constructing an uncertainty quantification model. One of the key challenges of transmission network planning is how to explicitly represent the extensive uncertainty associated with the operation of the power system, which adds complexity to the formulation of existing planning models. At the transmission network planning level, the sources of uncertainty faced by decision makers mainly include system demand, variable generation, component outage, demand-side control, government policies, etc. The present invention considers establishing uncertainty quantification models for load changes, intermittent wind power generation, forced outage of generators, and transmission line failures.

[0143] Probabilistic methods usually use a probability distribution function (PDF) to simulate one / part or all of the parameters. The present invention performs uncertainty modeling and probability distribution research on system load and renewable energy output based on the characteristics of system load and renewable energy output. Load and wind speed are modeled using Gaussian distribution and Weibull distribution. Random load is generated based on the assumption that all loads independently obey Gaussian distribution from a long-term perspective, where a given demand level is used as the mean. The corresponding PDF of the Gaussian distribution is given in formula (23). The wind power generation mode will generate uncertainty about power flow, and the Weibull distribution is used to model the uncertainty of wind.

[0144] The details are as follows:

[0145] The load change is modeled using the probability distribution function of Gaussian distribution, specifically:

[0146]

[0147] Among them, μi , σ i is the Gaussian distribution parameter, x load is a load-related random variable;

[0148] The intermittent wind power generation is modeled using the probability distribution function of the Weibull distribution, specifically:

[0149]

[0150] Among them, λ i , i is the Weibull distribution parameter, x wind is a random variable related to wind force.

[0151] The power system flow can also change significantly with planned or unplanned outages of system components. Multiple levels of contingency are considered, and a variety of contingencies can be selected based on the operating criteria. Generator forced outages and transmission line failures are modeled using a binomial distribution, specifically:

[0152]

[0153] Among them, θ i , τ i is the parameter in the binomial distribution, x fault is a fault random variable.

[0154] Step S32, fault accident screening, based on the existing "Nk" fault fast screening method to identify different faults, screen a group of selected faults, count them into the fault scenario list according to the severity, and determine safety corrective measures based on the fault scenarios in the table. According to the existing transmission planning standards, the typical "N-1" or "N-2" criteria are adopted, which checks all reliable single / multi-component faults to ensure that the power supply has sufficient redundancy and will not be interrupted when the components in the network fail. In order to improve the calculation efficiency, the present invention is based on the existing "Nk" fault fast screening method to identify the emergency limits that may cause the network load or bus voltage to exceed the limit under different fault combinations. The main purpose is to screen a group of selected faults and determine any accident that belongs to one of the following fault consequences: potential steady-state voltage instability, meeting any interlocking tripping standards, and exceeding any emergency or normal thermal overvoltage limits. It is counted into the fault scenario list according to the severity, and safety corrective measures are determined based on the fault scenarios in the table.

[0155] Step S4, establish a transmission network expansion planning model, construct a two-layer objective function, and the objective function is decomposed into two layers: an upper investment problem and a lower operation problem; the objective function is subject to the relevant constraints of the corrected safety constraint optimal power flow model.

[0156] A transmission network extension planning model is established to conduct a detailed evaluation of the current TNP mechanism to determine how to design the offshore transmission network in a range of scenarios and compare different technologies where appropriate, assessing the benefits of the onshore transmission network, especially in terms of cost, flexibility and resilience. Through technical and economic analysis, the development of standards for offshore transmission grid extension for offshore wind farms can select the best design option.

[0157] The basic components of a TNP optimization model are a set of decision variables, a set of constraints, and an objective function. In the context of transmission planning, the main decision variables correspond to different lines and different types of investment options. The objective function in transmission network planning is defined as the sum of the net present value cost of the capital cost of the transmission network investment plus the operating cost during the planning period.

[0158] Step S4 specifically includes the following steps:

[0159] Step S41, construct a two-layer objective function, which can be decomposed into two layers, the upper layer investment problem and the lower layer operation problem, as follows:

[0160] minf

[0161]

[0162]

[0163] Among them, N T 、N S 、N K 、N G 、N L They are the number of time periods, the number of load and wind output scenarios, the number of fault types, the number of AC generators, and the number of loads; is the investment cost of transmission line ij; is the decision variable for the candidate AC transmission line between nodes i and j in period t, 1 indicates the planned line, otherwise 0; is the decision variable for the candidate DC transmission line between nodes i and j in period t, 1 indicates the planned line, otherwise 0; ρ s,t is the accidental probability of normal operation under wind load scenario s during period t; ρ k,s,t is the accidental probability of fault type k under wind load scenario s in period t; d is the discount rate; is the cost of generator g under normal operation of scenario s during period t; They are respectively the power generation cost and load shedding cost of wind-load scenario s under fault k in period t; is the active power of the AC generator g under normal operation of scenario s during period t; is the fuel cost coefficient of generator g; is the active power change of the AC generator g under fault k in scenario s during period t; is the active power reduction of AC load l under fault k in scenario s during period t; Δh k,s is the load shedding duration of wind-load scenario s under fault k; VCR (value of customer reliability, VCR) is the customer reliability value cost.

[0164] The objective function includes three cost-related terms: (1) The first term gives the total investment cost of the offshore transmission system network solution. The investment consists of AC and DC submarine cables, transformers, reactive compensation devices or converter stations. The cost of each component depends on its voltage level and capacity. (2) The second term is the total expected operating cost, which is the sum of the fuel costs of the generators in the normal scenario. (3) The third term is the total expected cost of control actions in the accident scenario, including generator output adjustment and load shedding. Although a large amount of additional renewable energy capacity has been committed, there is still a risk of load shedding after an accident. In the planning process, VCR is used to evaluate the economic benefits of transmission network planning projects. It represents the range of customers' willingness to pay for reliable power supply. All costs in the objective function are discounted to present value.

[0165] Step S42, constructing constraints for the two-layer objective function, including the relevant constraints of the optimal power flow model for correcting security constraints. The constraint set includes transmission construction constraints, power flow constraints, generation scheduling constraints and power balance. In order to consider the planning period and sub-period, the variables have a time-related superscript t in all constraints. Because with the expansion of suitable connection interfaces, significant changes in offshore wind power capacity will also bring changes in the power flow of the entire network. These constraints establish power flow capacity limits for candidate lines.

[0166] Step S5, solving the transmission network expansion planning model to obtain a planning solution. Due to the non-convex nature of the proposed TNP model, a customized evolutionary algorithm is applied to solve the optimization problem.

[0167] Step S5 specifically includes the following steps:

[0168] Step S51, determining whether the capacitive current safety margin of the AC power transmission technology is feasible according to the AC cable length and voltage level; if the AC cable is feasible, determining the planned technology as a candidate for AC / DC hybrid technology; if not feasible, determining it as a candidate for DC technology, and initializing evolutionary calculation to randomly generate an initial population for the decision variables of the power transmission technology;

[0169] Step S52, based on the generated candidate transmission network planning scheme, according to the line distance and system configuration, obtain the investment cost of the supporting technical scheme related to the transmission technology decision;

[0170] Step S53, using the "N-2" criterion to troubleshoot component faults, determine the severity of the faults, add them to the fault scenario list according to the severity, generate a fault ranking table, and determine the fault types to be applied to the operation layer optimization;

[0171] Step S54, using Monte Carlo sampling according to the uncertainty quantification model in step S31, generating values ​​of load, wind power and component failure random variables, thereby generating load, wind power and failure scenarios;

[0172] Step S55, based on the generated load, wind, and fault scenarios, for each scenario, the operation layer calculation of the lower layer of the two-layer objective function is performed; if any network constraints are violated, RAS actions are applied in the following order: power flow control, generator rescheduling, and load shedding, and the operation cost and RAS cost are calculated;

[0173] Step S56, verifying the power transmission technical solution through time domain simulation, conducting power system security assessment, and determining whether it meets the stability criteria. If not, adding the planning solution to the infeasible solution, and continuing to generate a new population for iteration; if it meets, directly generating a new population for iteration;

[0174] Step S57, determine whether the maximum number of iterations has been reached, if yes, go to the next step S58, otherwise, go back to step S52, and generate a new solution by evolutionary calculation;

[0175] Step S58, outputting the final transmission network planning solution.

[0176] This example specifically illustrates how the proposed wind power integrated offshore transmission network probabilistic planning technology considering AC / DC hybrid transmission can evaluate the benefits of different expansion schemes of offshore wind farms. The proposed invention aims to determine the offshore transmission system for large-scale wind power access.

[0177] The specific steps are as follows:

[0178] Step 1: Read the basic data of the modified IEEE 14-node test system, including: historical load data and probability distribution parameters of the system, wind speed data and probability distribution parameters, system model (system topology, line parameters, generator parameters and costs), and associated costs of candidate transmission technology supporting components (AC / DC submarine cables, transformers, reactive power compensation devices or converter stations);

[0179] Step 2: Construct the C-SCOPF model of the operation layer;

[0180] Step 3: Establish multiple uncertainty quantification models and design a list of TNP fault types;

[0181] Step 4: Establish a TNP model that includes upper-level investment and lower-level operation;

[0182] Step 5: Construct a TNP solution process based on evolutionary computation;

[0183] Step 6: Obtain the final transmission network expansion solution results and analyze and compare them with the conventional TNP method.

[0184] First, the combined application of different planning methods (deterministic methods and probabilistic methods, network methods and non-network methods) is compared and analyzed, as shown in Table 1.

[0185] Table 1 Comparison of characteristics of different planning technologies

[0186]

[0187] As attached Figure 3 As shown in the figure, there are three different types of transmission system configurations: High Voltage Alternating Current (HVAC), High Voltage Direct Current (HVDC), and Multi-Terminal HVDC (MTDC). The HVAC solution commonly used in large offshore wind farms is composed of an offshore substation (step-up transformer) connected to the local medium-voltage wind farm internal power grid, long-distance submarine cables, and onshore substations (step-up and step-down transformers). The DC configuration requires a rectifier and filters at both ends, which greatly increases the cost of the entire transmission system. However, depending on the transmission voltage level and the number of connections, the DC configuration presents a variety of design options, including HVDC and MTDC.

[0188] TNP can provide system planners with the following analyses: 1) which transmission technologies will be used; 2) what is the optimal routing of the transmission lines; 3) what is the rating of each transmission line; and 4) what are the ratings of the transformers and / or converter stations.

[0189] The specific implementation method is tested and explained on a modified IEEE 14-node test system, which consists of 14 nodes, 20 lines and 5 generators. The single-line diagram is shown in the attached figure. Figure 4 Two potential wind farm sites with high capacity for reliable power generation are shown in the attached Figure 4 shown.

[0190] The two offshore wind farms consist of multiple doubly-fed induction generator wind turbines, each with a rated power of 3.0 and 2.0 MW. The historical wind speed data is derived from meteorological measurements. There are a total of 5 years of time series data, which are fitted using the Weibull distribution. Since offshore wind power can be integrated in a point-to-point or multi-terminal manner via AC or DC links, it is necessary to select a suitable transmission configuration.

[0191] The simulation results are shown in Table 2. It should be noted that the base case operating costs and RAS costs are equivalent annual costs. The cost of the offshore transmission network depends on four main factors: the cost of cables, transformers, reactive compensation devices and / or converter stations. The cost of DC transmission systems makes it difficult to compete with AC technology in pure economic terms, especially the high cost of converter stations. The two wind power projects are close enough to the onshore network that DC transmission is not required, so DC technology does not bring any advantages in this case.

[0192] Table 2 Comparison of different planning technology solutions

[0193]

[0194] As shown in Table 2, the cost of method 1 is always higher than that of method 2 and the method proposed in the invention. This is because the deterministic method only evaluates system performance under the most unfavorable conditions and a set of preset fault conditions, completely ignoring uncertainty. The probabilistic method also considers events in the deterministic method, but it can evaluate various aspects of planning by introducing uncertainty and take full advantage of economies of scale in transmission investment. In addition, the adoption of RAS safety corrections is considered in the embodiments because their implementation is more cost-effective than building new transmission infrastructure and can improve the utilization of existing facilities. By adopting a safety correction scheme, the power supply reliability and system safety performance can be greatly improved in the operating scenario, which not only brings greater economic benefits but also stimulates the flexibility of new control methods of the power system.

[0195] Embodiment 2:

[0196] like Figure 5 As shown, based on the same inventive concept as that of Example 1, this embodiment provides a probabilistic planning system for an offshore wind power AC / DC hybrid transmission network taking safety correction into consideration, which is applied to the method described, and includes:

[0197] Data preparation module, used to collect data and set parameters, including historical load data and probability distribution parameters of the system, wind speed data and probability distribution parameters, system model parameters, and associated costs of candidate transmission technology supporting components;

[0198] The power flow modeling module is used to establish a corrected safety-constrained optimal power flow model considering RAS for AC / DC hybrid systems. The quantitative screening module is used to establish uncertainty quantification models and accident screening models, consider multiple uncertainties to model, and screen out severe fault sets for transmission network planning, and establish wind power generation, load, and fault scenarios for the operating layer. The planning modeling module is used to establish a transmission network expansion planning model and construct a two-layer objective function, which is decomposed into two layers:

[0199] The upper layer investment problem and the lower layer operation problem; the objective function is subject to the relevant constraints of the corrected safety constrained optimal power flow model;

[0200] The model solving module is used to solve the transmission network expansion planning model and obtain a planning solution.

[0201] Embodiment 3:

[0202] Based on the same inventive concept as Example 1, this embodiment provides a computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the probabilistic planning method for offshore wind power AC / DC hybrid transmission network considering safety correction.

[0203] Embodiment 4:

[0204] Based on the same inventive concept as Example 1, this embodiment provides a processor, characterized in that the processor is used to run a program, wherein the program executes the probabilistic planning method for offshore wind power AC / DC hybrid transmission network considering safety correction when running.

[0205] Those of ordinary skill in the art will appreciate that the modules of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0206] In the embodiments provided by the present invention, it should be understood that the division of modules is only a logical function division, and there may be other division methods in actual implementation, for example, multiple modules can be combined into one module, one module can be split into multiple modules, or some features can be ignored, etc.

[0207] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of software functional modules.

[0208] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nlyMemory), random access memory (RAM, RandomAccessMemory), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program codes.

[0209] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A probabilistic planning method for offshore wind power AC / DC hybrid transmission network considering safety correction, characterized in that: The following steps are involved: Step S1, collecting data and setting parameters, including historical load data and probability distribution parameters of the system, wind speed data and probability distribution parameters, system model parameters, and associated costs of candidate transmission technology supporting components; Step S2, establishing a corrected safety-constrained optimal power flow model considering RAS applicable to an AC / DC hybrid system; Step S3, establishing an uncertainty quantification model and an accident screening model, considering multiple uncertainties in modeling, and screening out a set of serious faults for transmission network planning, and establishing wind power generation, load, and fault scenarios applied to the operation layer; Step S4, establishing a transmission network expansion planning model, constructing a two-layer objective function, the objective function is decomposed into two layers: an upper-layer investment problem and a lower-layer operation problem; the objective function is subject to the relevant constraints of the corrected safety constraint optimal power flow model; Step S5, solving the transmission network expansion planning model to obtain a planning solution.

2. A probabilistic planning method for offshore wind power AC / DC hybrid transmission network considering safety correction according to claim 1, characterized in that: The system model parameters in step S1 include system topology, line parameters, generator parameters and costs, and the candidate power transmission technology supporting components include AC and DC submarine cables, transformers, reactive power compensation devices or converter stations.

3. A probabilistic planning method for offshore wind power AC / DC hybrid transmission network considering safety correction according to claim 1, characterized in that: The step S2 specifically includes the following steps: Step S21, establish an objective function of the corrected safety-constrained optimal power flow model considering RAS applicable to the AC / DC hybrid system. The objective function aims to minimize the total power generation cost of the system under normal circumstances. The objective function is as follows: is the active power generation of the controllable generator g under normal conditions, is the fuel cost coefficient of generator g, N G is the number of generators; Step S22, establishing the constraint conditions of the corrected safety-constrained optimal power flow model considering RAS applicable to the AC / DC hybrid system; the specific conditions are as follows: (1) Power balance constraint: In an AC / DC hybrid transmission system, all AC and DC buses must satisfy node power balance, which can be expressed by the following active power and reactive power balance equations of the AC and DC buses: Among them, for each fault k, For active and reactive power generation of wind farms, To reduce the active and reactive power requirements of the load, For the active and reactive output of the converter station, is the vector magnitude of the AC and DC bus voltages at node i, θ is the AC bus voltage angle, The conductance and susceptance of the line between AC node i and node j, is the conductance of the line between DC nodes i and j, is the number of buses in the AC grid and the DC grid; is the active power and reactive power of the generator at AC node i; is the active power of the generator at DC node i; are the voltages of the AC and DC nodes j respectively; (2) For the converter connected to the AC / DC grid, assuming that converter c is connected to the AC / DC grid, the power transmission and converter loss between the AC / DC grids are modeled as: Among them, for each fault k, is the loss of converter c, is the power loss coefficient of converter c; is the active power of the AC side converter c; is the active power of the DC side converter c; i c,k is the current of converter c; (3) Under any conditions, active and reactive power generation, line flow, and bus voltage should be within their relevant rated values ​​and limits at all times, as shown below: in, are the maximum and minimum active power generation values ​​of the AC controllable generator g respectively; are the maximum and minimum reactive power generation values ​​of the AC controllable generator g respectively; is the maximum and minimum voltage of AC bus i; is the maximum and minimum value of the AC line transmission power between node i and node j; are the maximum and minimum active power generation values ​​of the DC controllable generator g respectively; is the maximum and minimum voltage of DC bus i; is the maximum and minimum value of the DC line transmission power between node i and node j; are the active power and reactive power of the AC generator g, respectively. is the active power of the DC generator g; (4) Converter station capacity constraint: The power flow through the converter is limited by the current in the converter valve. Therefore, the power constraint of the converter station can be expressed as: in, are the maximum and minimum active power generation values ​​of the AC side converter c respectively; are the maximum and minimum active power generation values ​​of the DC side converter c respectively; are the maximum and minimum reactive power generation values ​​of the AC side converter c respectively; (5) Considering power flow control, generator retransmission and load shedding as potential RAS actions, the coupling constraints can be transformed into the response ramp limit and load shedding constraint of the controllable generator and converter station within the fault allowable time k, as follows: in, They are respectively the change in active power of the AC side converter c, the change in active power of the AC generator g and the change in active power load l in the fault scenario k compared to the normal operation state, are the maximum and minimum values ​​of the active power change of converter station c respectively; are the maximum and minimum values ​​of the active power change of the AC generator g respectively; They are respectively the maximum and minimum changes in the AC active load l.

4. A probabilistic planning method for offshore wind power AC / DC hybrid transmission network considering safety correction according to claim 1, characterized in that: A unified model of the corrected security-constrained optimal power flow model considering RAS applicable to AC / DC hybrid systems is established as follows: Among them, x k represents the state variable under the kth fault state, u k represents the control variable under the kth fault state, k=0 represents the normal state, Ω k is the set of accidental failure events; g k (x k ,u k ) represents the equality constraint under fault k, h k (x k ,u k ) represents the inequality constraint under fault k, is the operating limit value of the control variable; The control variable vector u and state variable x are summarized as: u=[Ωp ac,vsc Ωq ac,vsc Ωp ac,gen Ωq ac,gen Ωp ac,load Ωq ac,load ]; (21) x=[Ωv ac Oh dc Oh]; (22) Ωp ac,vsc ,Ωq ac,vsc are respectively the active output set and reactive output set of the AC side converter; Ωp ac,gen ,Ωq ac,gen are respectively the active output set and reactive output set of the AC side generator; Ωp ac,load ,Ωq ac,load are respectively the active and reactive sets of AC loads; Ωv ac ,Ωv dc , Ωθ are the AC voltage set, DC voltage set and phase angle set of the system nodes respectively.

5. The method for probabilistic planning of an offshore wind power AC / DC hybrid transmission network considering safety correction according to claim 1, characterized in that: The step S3 specifically comprises the following steps: Step S31, constructing an uncertainty quantification model, establishing an uncertainty quantification model for load change, intermittent wind power generation, forced shutdown of generators and transmission line failure, as follows: The load change is modeled using the probability distribution function of Gaussian distribution, specifically: Among them, μ i , σ i is the Gaussian distribution parameter, x load is a load-related random variable; The intermittent wind power generation is modeled using the probability distribution function of the Weibull distribution, specifically: Among them, λ i , i is the Weibull distribution parameter, x wind is a random variable related to wind force; Generator forced outages and transmission line failures are modeled using a binomial distribution, specifically: Among them, θ i , τ i is the parameter in the binomial distribution, x fault is the fault random variable; Step S32, fault accident screening, identifying different faults based on the existing "Nk" fault rapid screening method, screening a group of selected faults, counting them into a fault scenario list according to the severity, and determining safety correction measures based on the fault scenarios in the table.

6. A probabilistic planning method for offshore wind power AC / DC hybrid transmission network considering safety correction according to claim 1, characterized in that: The step S4 specifically comprises the following steps: Step S41, construct a two-layer objective function, which can be decomposed into two layers, the upper layer investment problem and the lower layer operation problem, as follows: minf Among them, N T 、N S 、N K 、N G 、N L They are the number of time periods, the number of load and wind output scenarios, the number of fault types, the number of AC generators, and the number of loads; is the investment cost of transmission line ij; is the decision variable for the candidate AC transmission line between nodes i and j in period t, 1 indicates the planned line, otherwise 0; is the decision variable for the candidate DC transmission line between nodes i and j in period t, 1 indicates the planned line, otherwise 0; ρ s,t is the accidental probability of normal operation under wind load scenario s during period t; ρ k,s,t is the accidental probability of fault type k under wind load scenario s in period t; d is the discount rate; is the cost of generator g under normal operation of scenario s during period t; They are respectively the power generation cost and load shedding cost of wind-load scenario s under fault k in period t; is the active power of the AC generator g under normal operation of scenario s during period t; is the fuel cost coefficient of generator g; is the active power change of the AC generator g under fault k in scenario s during period t; is the active power reduction of AC load l under fault k in scenario s during period t; Δh k,s is the load shedding duration of wind-load scenario s under fault k; VCR is the customer reliability value cost; Step S42, constructing the constraint conditions of the double-layer objective function, including correcting the relevant constraints of the safety-constrained optimal power flow model.

7. A probabilistic planning method for offshore wind power AC / DC hybrid transmission network considering safety correction according to claim 1, characterized in that: The step S5 specifically includes the following steps: Step S51, determining whether the capacitive current safety margin of the AC power transmission technology is feasible according to the AC cable length and voltage level; if the AC cable is feasible, determining the planned technology as a candidate for AC / DC hybrid technology; if not feasible, determining it as a candidate for DC technology, and initializing evolutionary calculation to randomly generate an initial population for the decision variables of the power transmission technology; Step S52, based on the generated candidate transmission network planning scheme, according to the line distance and system configuration, obtain the investment cost of the supporting technical scheme related to the transmission technology decision; Step S53, using the "Nk" criterion to troubleshoot component faults, determine the severity of the faults, add them to the fault scenario list according to the severity, generate a fault ranking table, and determine the fault types to be applied to the operation layer optimization; Step S54, using Monte Carlo sampling according to the uncertainty quantification model to generate values ​​of load, wind power and component failure random variables, thereby generating load, wind power and failure scenarios; Step S55, based on the generated load, wind, and fault scenarios, for each scenario, the operation layer calculation of the lower layer of the two-layer objective function is performed; if any network constraints are violated, RAS actions are applied in the following order: power flow control, generator rescheduling, and load shedding, and the operation cost and RAS cost are calculated; Step S56, verifying the power transmission technical solution through time domain simulation, conducting power system security assessment, and determining whether it meets the stability criteria. If not, adding the planning solution to the infeasible solution, and continuing to generate a new population for iteration; if it meets, directly generating a new population for iteration; Step S57, determine whether the maximum number of iterations has been reached, if yes, go to the next step S58, otherwise, go back to step S52, and generate a new solution by evolutionary calculation; Step S58, outputting the final transmission network planning solution.

8. A probabilistic planning system for offshore wind power AC / DC hybrid transmission network considering safety correction, characterized in that: The method applied to any one of claims 1 to 7, comprising: Data preparation module, used to collect data and set parameters, including historical load data and probability distribution parameters of the system, wind speed data and probability distribution parameters, system model parameters, and associated costs of candidate transmission technology supporting components; The power flow modeling module is used to establish a corrected safety-constrained optimal power flow model considering RAS for AC / DC hybrid systems; the quantitative screening module is used to establish uncertainty quantification models and accident screening models, consider multiple uncertainties to model, and screen out serious fault sets for transmission network planning, and establish wind power generation, load, and fault scenarios for the operation layer; the planning modeling module is used to establish a transmission network expansion planning model and construct a two-layer objective function, which is decomposed into two layers: upper-layer investment problems and lower-layer operation problems; the objective function is subject to the relevant constraints of the corrected safety-constrained optimal power flow model; The model solving module is used to solve the transmission network expansion planning model and obtain a planning solution.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the probabilistic planning method for offshore wind power AC / DC hybrid transmission network considering safety correction as described in any one of claims 1 to 7.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the probabilistic planning method for offshore wind power AC / DC hybrid transmission network considering safety correction as described in any one of claims 1 to 7.

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