Source network collaborative planning method and system considering typhoon and wind power capacity credibility

Abstract

The invention provides a source network collaborative planning method and system considering typhoon and wind power capacity credibility, and the method comprises the steps: carrying out the simulation of a typhoon wind field, calculating the theoretical fault rate of a line through employing a line vulnerability curve calculation method under a typhoon disaster, and obtaining the state of each power transmission line of the system; calculating the output power of the wind power plant unit by using the basic information of the power grid, and predicting the output sequence of the wind power plant unit in the typhoon wind field based on the simulated typhoon wind field; obtaining the fault probability of the power transmission line at each moment, generating a typical fault scene, and calculating the wind power credible capacity according to the state of each power transmission line of the system; and constructing a source-network collaborative planning model based on the output sequence of the wind power plant unit and the wind power credible capacity. According to the method, the problems of wind power plant unit output, wind power spinning reserve and line reinforcement planning in typhoon-free weather are solved, and the purposes of considering the load importance level and energy storage access and coordinating resources in the aspects of source-network-load-storage and the like during planning are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of wind power capacity credibility and source network planning, and specifically, to a source network collaborative planning method and system that considers typhoon and wind power capacity credibility, and also to a corresponding computer terminal and computer-readable storage medium. Background Art

[0002] With the introduction of the dual-carbon strategic goals, the traditional power system will continue to transform into a new power system. In this context, extreme natural disasters such as typhoons, which occur infrequently but are extremely destructive, may cause significant losses to the new power system. Due to the deterioration of the global climate environment, the frequency and destructive power of natural disasters are becoming more frequent and severe. It is urgent to enhance the power system's ability to resist extreme natural disasters and reduce the adverse effects of extreme natural disasters on the power system.

[0003] At present, scholars have conducted research on capacity credibility. In the existing literature, for example, Wang Haichao, Lu Zongxiang, and Zhou Shuangxi published "Research on Credibility of Wind Farm Generation Capacity" in "Proceedings of the CSEE" (2005, 25(10): 103-106), which modeled wind power, wind turbines, and wind farms and evaluated wind power capacity credibility through the non-sequential Monte Carlo method; Wang Haiying and Bai Xiaomin published "Evaluation of Power Generation Adequacy and Capacity Credibility of Large-Scale Wind Farms" in "Power System Technology" (2012, 36(06): 200-206), which focused on the potential impact of wind power penetration level and the quality of wind resources on capacity credibility. Ming D and Zhicheng X published an empirical model for evaluating the credibility of wind power capacity in the paper “Empirical Model for Capacity Credit Evaluation of Utility-Scale PVPlant” in IEEE Transactions on Sustainable Energy (2017, 8(1):94-103). The model uses a new artificial neural network algorithm to propose an empirical model for evaluating the credibility of wind power capacity. However, the above studies mainly focus on long time scales, such as one year or one quarter, and there is little research on extreme weather.Scholars have also conducted research on improving the resilience of power systems to disasters. For example, Bagheri, C. Zhao, F. Qiu and J. Wang published "Resilient transmission hardening planning in a high renewable penetration era" in "IEEE Transactions on Power Systems" (2019, 34(2): 873-882), which used load loss as a resilience assessment indicator and adopted reinforcement measures to minimize load losses under extreme weather events; F. Hasanzad and H. Rastegar published "Application of optimal hardening for improving resilience of integrated power and natural gas system in case of earthquake" in "Reliability Engineering & System Safety" (2022, 223, 108476), which enhanced the survivability of the integrated power-natural gas system in earthquake events through reinforcement measures and reduced the combined losses of power and natural gas loads; H. Wang, K. Hou, and J. Zhao, et al. published "Planning-Oriented resilience assessment and control" in "Applied Energy" (2022, 315, 118824). and enhancement of integrated electricity-gas system considering multi-type natural disasters” studied the comprehensive impact of multi-type natural disasters on the transmission network, used the expected load loss as a resilience assessment indicator, and then reduced the expected load loss through reinforcement measures; Jiao Jianing, Liu Lu, Zhang Tianyu et al., published “Multi-stage coordinated receiving power grid resilience enhancement strategy under typhoon disasters” in Automation of Electric Power Systems (2023, 47(12): 9-18), which proposed a receiving power grid resilience enhancement model that comprehensively considers transmission channel reinforcement, unit combination and repair team. This model can ensure the load supply ratio under typhoon disasters, thereby improving system resilience; However, the above studies do not fully consider the high proportion of wind power installed capacity.

[0004] At present, no description or report of similar technology to the present invention has been found, and similar information at home and abroad has not been collected yet. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a source network collaborative planning method and system that considers the credibility of typhoon and wind power capacity, and also provides a corresponding computer terminal and computer-readable storage medium.

[0006] According to one aspect of the present invention, a source-grid collaborative planning method considering the credibility of typhoon and wind power capacity is provided, comprising:

[0007] Provide a typhoon simulation model and simulate the typhoon wind field using basic typhoon information;

[0008] Based on the simulated typhoon wind field, the line vulnerability curve calculation method under typhoon disaster is used to calculate the theoretical line failure rate and obtain the status of each transmission line in the system;

[0009] Provide a wind turbine output model, use the basic information of the power grid to calculate the output power of the wind farm units, and based on the simulated typhoon wind field, predict the output sequence of the wind farm units under the typhoon wind field;

[0010] The theoretical failure rate of the line is used to obtain the failure probability of the transmission line at each moment, and a typical failure scenario is generated;

[0011] Based on the typical fault scenario, the wind power reliable capacity is calculated according to the status of each transmission line of the system;

[0012] Based on the output sequence of the wind farm units and the wind power reliable capacity, a source-network collaborative planning model is constructed for collaborative planning of the source network.

[0013] According to another aspect of the present invention, a source-grid collaborative planning system considering the credibility of typhoon and wind power capacity is provided, comprising:

[0014] The typhoon wind farm and line fault probability module is used to provide a typhoon simulation model, and simulate the typhoon wind farm using the basic typhoon information; based on the simulated typhoon wind farm, the line vulnerability curve calculation method under typhoon disaster is used to calculate the theoretical line failure rate and obtain the status of each transmission line in the system; provide a wind turbine output model, calculate the output power of the wind farm unit using the basic power grid information, and predict the output sequence of the wind farm unit under the typhoon wind farm based on the simulated typhoon wind farm; use the theoretical line failure rate to obtain the failure probability of the transmission line at each time, and generate a typical failure scenario;

[0015] A wind power capacity credibility assessment module, which calculates the wind power credible capacity based on the typical fault scenario and the status of each transmission line of the system;

[0016] A source-network collaborative planning module, which constructs a source-network collaborative planning model based on the output sequence of the wind farm units and the wind power credible capacity, and is used for collaborative planning of the source network.

[0017] According to a third aspect of the present invention, there is provided a computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, can be used to execute any one of the methods described above in the present invention, or to run the system described above in the present invention.

[0018] According to a fourth aspect of the present invention, a computer-readable storage medium stores a computer program thereon, characterized in that when the computer program is executed by a processor, it can be used to execute any of the methods described above in the present invention, or to run the system described above in the present invention.

[0019] Due to the adoption of the above technical solution, the present invention has at least one of the following beneficial effects compared with the prior art:

[0020] The present invention establishes a source-grid collaborative planning model that takes into account the credibility of typhoons and wind power capacity. When conducting source-grid collaborative planning in the initial stage, it not only takes into account the impact of typhoon extreme weather based on load growth and wind power access ratio in ordinary scenarios, but also considers the output of wind farm units under typhoon weather, which reduces the total cost of the planning scheme without considering the output of wind turbine units under typhoon weather by 1.332M$, and avoids system load shedding.

[0021] The present invention adopts preventive reinforcement measures in the early planning stage, which can avoid the load shedding penalty cost of 96.203M$, reduce the total cost of the planning scheme, greatly enhance the power system's ability to resist typhoon weather, and reduce the overall system loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0023] Figure 1 This is a workflow diagram of a source-grid collaborative planning method considering typhoon and wind power capacity credibility in a preferred embodiment of the present invention;

[0024] Figure 2 A schematic diagram of the components of a source-grid collaborative planning system that considers the credibility of typhoon and wind power capacity in a preferred embodiment of the present invention;

[0025] Figure 3 A schematic diagram of line fault probability in a preferred embodiment of the present invention;

[0026] Figure 4This is a schematic diagram of wind farm unit output in a preferred embodiment of the present invention;

[0027] Figure 5 A vulnerability curve diagram of power system components in a preferred embodiment of the present invention;

[0028] Figure 6 This is a power characteristic curve diagram of a wind turbine generator set in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following is a detailed description of the embodiments of the present invention: This embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation method and a specific operation process are given. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

[0030] In view of the problem that the prior art does not involve the joint consideration of load growth and wind farm unit output, wind power rotating reserve capacity and line reinforcement planning under typhoon weather in the planning model, an embodiment of the present invention provides a source-grid collaborative planning method that considers the credibility of typhoons and wind power capacity. The method fully considers the output of wind farm units under typhoon weather and avoids system load shedding. By taking preventive reinforcement measures in the early stage of planning, the load shedding penalty cost is avoided, the total cost of the planning scheme is reduced, the power system's ability to resist typhoon weather is greatly improved, the overall system loss is reduced, and the problem that the existing scheme does not involve wind farm unit output, wind power rotating reserve and line reinforcement planning under typhoon weather is effectively solved, so as to achieve the purpose of considering the load importance level and energy storage access, and coordinating various resources such as source-grid-load-storage during planning.

[0031] Specifically, Figure 1 As shown, the source-network collaborative planning method considering the credibility of typhoon and wind power capacity provided in this embodiment may include the following operations:

[0032] S1, provides a typhoon simulation model, uses the basic information of typhoon to simulate the typhoon wind field;

[0033] S2, based on the simulated typhoon wind field, using the line vulnerability curve calculation method under typhoon disasters, calculate the line theoretical failure rate and obtain the status of each transmission line in the system;

[0034] S3, providing a wind turbine output model, using basic grid information to calculate the output power of wind farm units, and based on the simulated typhoon wind field, predicting the output sequence of wind farm units under the typhoon wind field;

[0035] S4, using the line theoretical failure rate to obtain the failure probability of the transmission line at each moment and generate a typical failure scenario;

[0036] S5, based on the typical fault scenario and according to the status of each transmission line of the system, calculating the wind power reliable capacity;

[0037] S6, based on the output sequence of the wind farm units and the wind power reliable capacity, construct a source-network collaborative planning model for collaborative planning of various aspects of source-network resources.

[0038] In some preferred implementations, the above S1, providing a typhoon simulation model, using the basic typhoon information to simulate the typhoon wind field, may further include the following operations:

[0039] The basic information of the typhoon includes: the initial pressure of the typhoon, the path of the typhoon and the moving speed;

[0040] The provision of a typhoon simulation model uses basic typhoon information to simulate the typhoon wind field, including:

[0041] The Batts wind field model is used to model the typhoon, and the coordinates of each bus node in the power system are established to obtain the real-time distance between the typhoon center and each component.

[0042] Usually, after a typhoon makes landfall, the energy it contains gradually decays due to the friction and resistance caused by vegetation, buildings and other facilities on land, and the central pressure gradually increases over time. The pressure difference with the atmosphere decreases accordingly, and the typhoon intensity continues to weaken until it disappears. The decay law of the central pressure difference over time can be described by the exponential decay principle. ：

[0043] Δp(t)=Δp(t ldf )exp(-a d (tt ldf ))t>t ldf (1)

[0044]

[0045] Where: Δp(t) is the central pressure difference at time t; t ldf is the time when the typhoon landed; Δp(t ldf ) is the central pressure difference at the time of typhoon landfall; a d is the decay constant; where v(t ldf ) and R max (t ldf ) are the typhoon moving speed and maximum wind speed radius at the time of landfall; a d0 and a d1 are the decay constants obtained from historical data; ε ad is a normally distributed error with zero mean.

[0046] According to the central pressure difference of the typhoon at time t, the maximum gradient wind speed at time t can be further calculated.

[0047]

[0048] Where: k is a coefficient determined by the latitude of the region, and is generally between 6.93 and 6.97.

[0049] According to the Batts model, the maximum wind speed of a typhoon at time t It can be obtained based on the maximum gradient wind speed and typhoon moving speed at that moment:

[0050]

[0051] Where: v(t) is the typhoon moving speed at time t.

[0052] Taking into account the temporal and spatial evolution characteristics of typhoons, it is necessary to model the coordinates of the typhoon center. The calculation formula is as follows:

[0053]

[0054] Where: (x cm ,y cm ) are the coordinates of the center position of the typhoon at the time of landfall; θ is the angle between the typhoon's moving path and the coastline.

[0055] The maximum wind speed radius of a typhoon is R max The mapping relationship between (t) and the typhoon center pressure difference Δp(t) can be expressed as:

[0056]

[0057] Where: is a random error; b and c are constants.

[0058] Finally, the wind speed of the element at any position at any time during the typhoon can be obtained:

[0059]

[0060] Where: v c (t) is the wind speed at the component position at any time t; d(t) is the distance between the typhoon center and the component position at time t, which can be calculated by the following formula:

[0061]

[0062] Where: (x cm ,y cm ) is the component position coordinate, which is given by the initial example data.

[0063] In some preferred implementations, the above S2, based on the simulated typhoon wind field, uses the line vulnerability curve calculation method under typhoon disaster to calculate the line theoretical failure rate and obtain the status of each transmission line of the system, and can further include the following operations:

[0064] According to the tower fragility curve, Figure 5 As shown in Figure 2, the mapping relationship between tower failure probability and wind speed can be expressed as:

[0065]

[0066] Where: v m,k (t i ) is t i The wind speed at the kth tower of the mth line at the moment; v d,t and v ex are the tower design wind speed and the ultimate wind speed; γ is the constant coefficient; λ m,k (t i ) is t i The probability of failure of the kth tower on the mth line at time.

[0067] The cumulative failure probability of the kth tower of the mth transmission line during the T period of time when the typhoon lasts can be expressed by the following formula:

[0068]

[0069] Where: N is the total number of short time periods, Δt is the time interval of each short time period.

[0070] Similarly, the line vulnerability curve can be characterized as:

[0071]

[0072] Where: v m,l (t i ) is t i The wind speed at the location of the lth line section of the mth line at the moment; v d,line is the line design wind speed; α and β are constant coefficients; Δl is the length of each line segment; λ m,l (t i ) is t i The failure probability of the lth line section of the mth line at time.

[0073] The cumulative failure probability of the lth line section of the mth transmission corridor during the typhoon period T can be expressed as follows:

[0074]

[0075] Since the towers and line sections in the power transmission system are connected in series, any failure in any of the components will cause the transmission line to be disconnected and lose its normal operating ability. Moreover, each component exists independently and is not affected by other components. Therefore, according to the failure probability equivalent model of the series system, the comprehensive failure probability of the towers and all line sections can be equivalent to the failure probability of the mth transmission line. Then the cumulative failure probability of the mth transmission line under a typhoon can be expressed as:

[0076]

[0077] Where: K and L represent the total number of towers and line sections of the mth transmission line respectively.

[0078] In some preferred implementations, the above S3 provides a wind turbine output model, calculates the output power of the wind farm units using basic grid information, and predicts the output sequence of the wind farm units under the typhoon wind farm based on the simulated typhoon wind farm, and may further include the following operations:

[0079] The basic information of the power grid includes: system generators, lines, loads and wind power output data;

[0080] The wind turbine output model is provided, the output power of the wind farm units is calculated by using the basic information of the power grid, and the output sequence of the wind farm units under the typhoon wind farm is predicted based on the simulated typhoon wind farm, including:

[0081] In general, the typical relationship between wind power output and wind speed can be approximated by Figure 6 The curve shown in the figure indicates that in typhoon weather, when the real-time wind speed is within the normal operating range of the wind turbine, wind energy can be converted into electrical energy. If the wind speed exceeds the cut-out wind speed of the unit, the wind turbine will be stopped to avoid damage to the physical structure of the unit such as the fan blades under strong winds.

[0082] The output power of wind farm units is determined by the real-time wind speed at the location, and its calculation formula is:

[0083]

[0084] Where: P r is the rated power of the wind turbine; V in 、V out 、V r are the cut-in wind speed, cut-out wind speed and rated wind speed of the fan respectively; the calculation formula of constants A, B and C is:

[0085]

[0086] 1) When 0≤v <v inWhen the actual wind speed is lower than the cut-in wind speed of the wind turbine, the torque generated by the low wind speed on the fan blades cannot make the wind turbine rotate. At this time, the wind turbine is in a stationary state and the output power of the unit is zero.

[0087] 2) When v in ≤v <v r When the actual wind speed is higher than the cut-in wind speed of the wind turbine but lower than the rated wind speed, the output power of the wind turbine is positively correlated with the actual wind speed, that is, as the wind speed increases, the output of the turbine increases.

[0088] 3) When v r ≤v <v out When the actual wind speed is between the rated wind speed and the cut-out wind speed of the wind turbine, the wind turbine is always in full power state due to the limitations of the material and physical properties of the wind turbine.

[0089] 4) When v ≥ v out When the actual wind speed is greater than the cut-out wind speed of the wind turbine, the wind turbine will stop rotating and be disconnected from the power system to prevent the fan blades from being damaged by excessive wind speed.

[0090] The wind power output is reconstructed using the Markov chain-Monte Carlo method, and the calculation process is as follows:

[0091] (1) Determine the variable state: First, select the appropriate state number based on the historical output data of the wind farm, which can be divided according to the wind power output distribution. Then, based on the numerical value in the wind farm output sequence, divide the original wind farm output sequence into corresponding state intervals to ensure that the wind power output data is converted into discrete state points.

[0092] (2) Constructing a state transfer matrix: Construct an N×N state transfer matrix P to represent the transfer characteristics between different state points. N represents the number of selected states. Each element p in the state transfer matrix mn It means that when the current state is m, the probability that the wind power output changes to state n in the next hour can be expressed as:

[0093] p mn =P(X t =n|X t-1 =m)(16)

[0094] On this basis, the multi-step transfer state matrix is ​​defined as:

[0095]

[0096] The multi-step state transition matrix represents the matrix of probability values ​​of transitioning from a certain state to the remaining states after k unit times.

[0097] (3) Construct the cumulative transfer matrix Psum , whose first column is all zero, and starting from the second column, each element is the sum of the elements of the first j columns of the corresponding i-th row in the state transfer matrix, which is specifically represented as:

[0098]

[0099] (4) Generate a random state sequence: Based on the constructed cumulative transfer state matrix, a random number u that obeys a uniform distribution of 0-1 is randomly generated through the Monte Carlo sampling method. The number of states in the next hour is determined by comparing the element values ​​in the cumulative transfer state matrix. This step is then repeated until a sequence that meets the time scale requirements is generated.

[0100] (5) Reconstructing the wind power output time series: Since the random state sequence obtained according to the cumulative transfer state matrix is ​​discrete, it needs to be converted into a time series wind power output sequence. The state sequence is converted by the following formula:

[0101] X=X j,min +u(X j,max -X j,min )(19)

[0102] Where: X is the predicted wind power output value at that moment, X j,min ,X j,max are the minimum and maximum values ​​of the partition interval where state j is located.

[0103] Following the above steps, the predicted wind power output series of any time scale can be obtained.

[0104] In some preferred implementations, the above S4, using the line theoretical failure rate to obtain the failure probability of the transmission line at each moment and generate a typical failure scenario, may further include the following operations:

[0105] S41, using the line theoretical failure rate to obtain the failure probability of the transmission line at each moment, and using the Monte Carlo method to obtain multiple failure scenarios;

[0106] S42: clustering the multiple fault scenarios using a K-medoid clustering algorithm to obtain a typical fault scenario.

[0107] In some preferred implementations, the above S5, based on the typical fault scenario and according to the status of each transmission line of the system, calculates the wind power reliable capacity, and may further include the following operations:

[0108] When evaluating the reliability of typhoon scenarios, S51 uses the total energy not supplied (TENS) of the power system during the typhoon as the system reliability indicator. This indicator and EENS both count the amount of load loss in the system within a certain period, but the difference lies in the length of the statistical period.

[0109] During typhoons, TENS can be obtained based on the optimal load shedding model of DC power flow:

[0110] Set the objective function as:

[0111]

[0112] The set constraints include: node power balance constraints, line flow constraints, line transmission capacity constraints, generator output constraints and load shedding constraints. The specific expressions are:

[0113] Node power balance constraints:

[0114]

[0115] Where: N l represents the set of transmission lines; P l(mn),t represents the power flow through the transmission line l at time t (with nodes m and n at both ends); d t represents the load of the system at time t.

[0116] Line flow constraints:

[0117]

[0118] Where: B represents the correlation matrix; θ m,t ,θ n,t Represent the phase angles of nodes m and n respectively.

[0119] Line transmission capacity constraints:

[0120]

[0121] Where: P l max ,P l min They are the upper and lower limits of the line transmission capacity respectively.

[0122] Generator output constraints:

[0123]

[0124] Where: They are the upper and lower limits of the generator set output power respectively.

[0125] Load shedding constraints:

[0126]

[0127] The TENS calculation formula of the power system during the typhoon period is as follows:

[0128]

[0129] Where: DNS(t k ) is t k The unsupplied demand of the system at a certain time can be calculated by the most efficient load shedding model in equations (20) to (26); n is the length of the calculation time;

[0130] S51, based on the typical fault scenario and according to the status of each transmission line in the system, the scenario shortage load is calculated by optimal load shedding, and the system load shedding at that moment is obtained by weighted calculation through the index and probability of each scenario, and the reliability index of the transmission system is calculated;

[0131] S52, remove the wind power in the system and add equivalent units, calculate the reliability index of the new power system, and adjust the capacity of the equivalent units so that the reliability indexes of the two systems before and after the wind power is removed are within the set error range. At this time, the capacity of the equivalent units is the reliable capacity of the wind power.

[0132] In some preferred implementations, the above S6, constructing a source-grid collaborative planning model based on the output sequence of the wind farm units and the wind power reliable capacity, may further include the following operations:

[0133] S61, adding line reinforcement variables to the source network collaborative planning model, setting the objective function of the source network collaborative planning model to minimize the total cost, then the objective function is expressed as:

[0134] f total =F L +F G +F H +F fuel +F op +F wc +F rc (27)

[0135] In the formula, F total is the total cost, F L is the line construction cost, F G is the power source investment cost, F H is the line reinforcement cost, F fuel is the fuel cost of the generator set, F op is the generator start-up and shutdown cost, F wc is the penalty cost for wind curtailment, F rcPenalty costs for load shedding;

[0136] The calculation formula for each cost is:

[0137]

[0138] Where N cl Represents the set of lines to be built; C l represents the cost of constructing line l; x l represents the route planning decision variable, when x l =1 indicates that the line will be built. l =0 means no construction; N cg Represents the set of generators to be built; C g represents the construction cost of the generator set g; x g represents the decision variable for generator planning, when x g =1 means the unit is put into construction. g =0 means no construction; C h represents the line reinforcement cost; h l represents the line reinforcement decision variable, h l =1 means the line is reinforced, otherwise it is not reinforced. π s represents the probability of scene s occurring, and the probability value of each scene is the ratio of the number of scenes of this type to the total number of scenes; S represents the set of all scenes; T represents the set of all moments in scene s; N G Represents the collection of all generating units, including those that have been built and those to be built; represents the unit power generation cost of the generator set g; p g,s,t It represents the active power output by the generator set at time t in scenario s; Respectively represent the startup and shutdown costs of generator set g; y g,s,t ,z g,s,t They represent the start-up and shutdown actions of the generator set g at time t in scenario s; N w represents the set of all wind farms; C w represents the unit wind curtailment penalty cost; represents the abandoned wind power of the wind turbine at time t under scenario s; C d represents the unit load shedding penalty cost; represents the load shedding amount of the power system at time t under scenario s;

[0139] S62, setting conventional constraints of the source network collaborative planning model, including:

[0140]

[0141] y g,s,t -z g,s,t=v g,s,t -v g,s,t-1 (36)

[0142]

[0143] Where N l represents the set of built transmission lines; P l(mn),s,t represents the power flow through the nodes m and n at both ends of the transmission line l at time t in scenario s; p w,s,t represents the output power of the wind farm unit at time t in scenario s; d s,t represents the load of the system at time t in scenario s, and the variable v g,s,t Indicates the start and stop status of the unit, v g,s,t =1 means the unit is in the on state, otherwise it is in the off state, UT g Indicates the minimum start-up time of the generator set g; DT g Indicates the minimum downtime of the generator set g; U g,u Indicates the startup duration of the generator set at the start time of each scenario; U g,d represents the downtime duration of the generator set at the start time of each scenario, σ g,u Represents the ramp rate of the generator set g; σ g,d represents the downhill rate of the generator set g, are the upper and lower limits of the generator set output power, respectively. Indicates the maximum output power of the generator set; S w Represents the total installed capacity of the wind farm; R s Indicates the total reserve rate of the power system; D max represents the maximum load of the power system in the planned year, S R is the spinning reserve capacity of the power system, S w,t is the wind power reserve capacity at time t under the typhoon, which is the difference between the wind power output under the typhoon wind farm and the wind power credible capacity under the typhoon wind farm;

[0144] S63, setting additional constraints for the source network collaborative planning model, including:

[0145]

[0146] (h+i l,s -hi l,s ) l min ≤P l(mn),s,t ≤(h+i l,s -hi l,s ) l max (49)

[0147] P l min x l (h+i l,s -hi l,s )≤P l(mn),s,t ≤P l max x l (h+i l,s -hi l,s ) (50)

[0148] Where B represents the correlation matrix; θ m,s,t ,θ n,s,t They represent the phase angles of nodes m and n at both ends of the line, respectively; h represents the line reinforcement decision variable; when h = 1, the line takes reinforcement measures, otherwise it does not; i l,s Indicates whether the line is affected by the typhoon. After the typhoon simulation is completed, the line status is input as a definite value. l max ,P l min are the upper and lower limits of the line transmission capacity, respectively, h+i l,s -hi l,s It represents the combination used to ensure that the power flow constraint depends on whether the line is reinforced and whether it is affected by typhoon;

[0149] S64, the Big M method is used to characterize equations (47) to (50), and the converted expression is:

[0150] |P l(mn),s,t -B l(mn),s,t (θ m,s,t -θ n,s,t )|≤M(1-(h+i l,s -hi l,s )) (51)

[0151] |P l(mn),s,t -B l(mn),s,t (θ m,s,t -θ n,s,t )|≤M(1-x l (h+i l,s -hi l,s )) (52)

[0152] The transformation of formula (52) is:

[0153] |P l(mn),s,t -B l(mn),s,t (θ m,s,t -θ n,s,t )|≤M(1-(x l h(1-i l,s )+x l il,s )) (53)

[0154] In order to linearize x l h term, here we introduce a 0-1 variable u l =x l h, then:

[0155]

[0156] |P l(mn),s,t -B l(mn),s,t (θ m,s,t -θ n,s,t )|≤M(1-(u l -u l i l,s +x l i l,s )) (55)

[0157] Convert equation (50) into the following expression:

[0158] P l min (u l -u l i l,s +x l i l,s )≤P l(mn),s,t ≤P l max (u l -u l i l,s +x l i l,s ) (56)

[0159] S65, constrains integer decision variables, including:

[0160]

[0161] h≤x l (58)

[0162]

[0163] Based on the same inventive concept, an embodiment of the present invention further provides a source-grid collaborative planning system that takes into account the credibility of typhoons and wind power capacity.

[0164] Specifically, Figure 2 As shown, the source-network collaborative planning system considering the credibility of typhoon and wind power capacity provided by this embodiment may include the following modules:

[0165] The typhoon wind farm and line fault probability module is used to provide a typhoon simulation model, and simulate the typhoon wind farm using the basic typhoon information; based on the simulated typhoon wind farm, the line vulnerability curve calculation method under typhoon disaster is used to calculate the theoretical line failure rate and obtain the status of each transmission line in the system; a wind turbine output model is provided, and the output power of the wind farm unit is calculated using the basic information of the power grid, and the output sequence of the wind farm unit under the typhoon wind farm is predicted based on the simulated typhoon wind farm; the line theoretical failure rate is used to obtain the failure probability of the transmission line at each time, and generate a typical failure scenario;

[0166] A wind power capacity credibility assessment module, which calculates the wind power credible capacity based on the typical fault scenario and the status of each transmission line of the system;

[0167] A source-network collaborative planning module, which constructs a source-network collaborative planning model based on the output sequence of the wind farm units and the wind power credible capacity, and is used for collaborative planning of the source network.

[0168] In some preferred embodiments, the above system may further include a data module, an input module and an output module; wherein:

[0169] The data module is used to store corresponding data;

[0170] The input module is used to obtain relevant data from the data module;

[0171] The output module is used to output corresponding results.

[0172] The specific working contents of each of the above functional modules are further described in detail below.

[0173] (1) Data module

[0174] It is used to store basic information of power grid and typhoon; basic information of typhoon includes initial pressure, path, speed, etc. Basic information of power grid includes system generator, line, load and wind power output data, among which load data is the load size of each node in the system, system generator data includes parameters such as unit installed capacity, output upper and lower limits, unit status, etc. Line data includes parameters such as transmission network topology, line length, line admittance, line capacity, etc.

[0175] (2) Input module

[0176] It is used to obtain relevant data from the data module, including basic typhoon information, system generators, lines, loads and wind power output data, and present them in a standard format that can be called by the program to facilitate data calculation.

[0177] (3) Typhoon wind field and line failure probability module

[0178] (I) Calculation of component failure probability and wind turbine output.

[0179] The Batts model, a classic typhoon simulation model proposed by Huajun Z, Lin C, Shuhan Y, et al. in "Spatial-Temporal Reliability and Damage Assessment of Transmission Networks Under Hurricanes" published in "IEEE Transactions on Smart Grid" (2020, 11 (2)), is used for typhoon simulation. Typhoons can cause damage to components such as overhead lines and towers. In the embodiment of the present invention, the overhead lines are taken as typical damaged components under typhoon disasters, and the theoretical failure rate of the line is calculated using the line vulnerability curve under typhoon disasters proposed in "Study on Resilience Assessment and Improvement Measures of Transmission Systems under Extreme Meteorological Disasters" published by Tang Wenhu, Yang Yihao, Li Yajing, et al. in "Proceedings of the CSEE" (2020, 40 (07): 2244-2254+2403). The output power of the wind farm unit is determined by the real-time wind speed at the location. The wind turbine output model of "Multi-time scale defense dispatch strategy for large power grids with high penetration rate wind power considering typhoons" published by He Jun, Zhang Bokai, Gan Deshu, et al. in "High Voltage Technology" (2023, 49(04): 1724-1734) is adopted.

[0180] (II) Generation of typical fault scenarios.

[0181] The failure probability of the transmission line at each moment is obtained according to the line theoretical failure rate. Then the typical failure scenario is generated by Monte Carlo simulation method.

[0182] (iii) Forming linear equation constraints that can be embedded in the planning model.

[0183] At the same time, it forms power system node power balance constraints, generator set start-up and shutdown logic constraints, start-up and shutdown mutual exclusion constraints, minimum start-up and shutdown time constraints, unit climbing / downhill rate constraints, generator set output upper and lower limit constraints, system total reserve constraints, system rotating reserve constraints, wind abandonment constraints, load shedding constraints, existing transmission line flow constraints, to-be-built line flow constraints, existing line transmission capacity constraints, to-be-built line transmission capacity constraints, generator operating status, line construction, reinforcement and other integer decision variable constraints.

[0184] (4) Wind power capacity credibility assessment module

[0185] Based on the typical fault scenario obtained in (3), according to the status of each transmission line in the system, the optimal load shedding is used to calculate the load shortage in the scenario, and the system load shedding at that moment is obtained by weighted calculation of the index and probability of each scenario, and the reliability index of the transmission system is calculated. The wind power in the system is removed and the equivalent unit is added. The reliability index of the new power system is calculated, and the capacity of the equivalent unit is adjusted so that the reliability index of the two systems before and after the wind power is removed is within a certain error range. At this time, the capacity of the equivalent unit is the reliable capacity of the wind power.

[0186] (5) Module for constructing a source-grid collaborative planning model considering the output sequence of wind farm units and wind power reliable capacity under typhoon wind farms

[0187] In terms of short-term operation, considering the impact of typhoon weather, reinforcement variables are added to the source-grid collaborative planning model to pre-reinforce some transmission lines to reduce the risk of line disconnection during disasters, so as to ensure sustainable power supply of the power system. The objective function is to minimize the total cost, including line construction cost, generator construction cost, line reinforcement cost, generator fuel cost, generator start-up and shutdown cost, wind abandonment penalty cost, and load shedding penalty cost, which is specifically expressed as shown in formula (60).

[0188] f total =F L +F G +F H +F fuel +F op +F wc +F rc (60)

[0189] In the formula, F total is the total cost, F L is the line construction cost, F G is the power source investment cost, F H is the line reinforcement cost, F fuel is the fuel cost of the generator set, F op is the generator start-up and shutdown cost, F wc is the penalty cost for wind curtailment, F rc is the load shedding penalty cost. The specific costs are shown in equations (61) to (67).

[0190]

[0191] Where N cl Represents the set of lines to be built; C l represents the cost of constructing line 1; x l represents the route planning decision variable, when x l =1 indicates that the line is being built. l =0 means no construction.cg Represents the set of generator sets to be built; C g represents the construction cost of the generator set g; x g represents the decision variable for generator planning, when x g =1 means the unit is put into construction. g =0 means no investment. h represents the line reinforcement cost; h l represents the line reinforcement decision variable, h l =1 means the line is reinforced, otherwise it is not reinforced. π s represents the probability of scene s occurring, and the probability value of each scene is the ratio of the number of scenes of this type to the total number of scenes; S represents the set of all scenes; T represents the set of all moments in scene s; N G Represents the collection of all generating units, including those that have been built and those to be built; represents the unit power generation cost of the generator set g; p g,s,t It represents the active power output by the generator set at time t in scenario s. Respectively represent the startup and shutdown costs of generator set g; y g,s,t ,z g,s,t They represent the start-up and shutdown actions of the generator set g at time t in scenario s. w represents the set of all wind farms; C w represents the unit wind curtailment penalty cost; represents the abandoned wind power of the wind turbine at time t under scenario s. d represents the unit load shedding penalty cost; It represents the load shedding amount of the power system at time t under scenario s.

[0192] The model also includes some constraints, such as power system node power balance constraints, generator start-stop logic constraints, generator start-stop mutual exclusion constraints (since the generator can only have one operating state at the same time, this constraint needs to be added), generator minimum start-stop time constraints, generator climbing / downhill rate constraints, generator output upper and lower limit constraints, system total reserve constraints, system spinning reserve constraints, wind abandonment constraints, and load shedding constraints, as shown in equations (68) to (79), respectively.

[0193]

[0194] y g,s,t -z g,s,t =v g,s,t -v g,s,t-1 (69)

[0195]

[0196] Where N l represents the set of built transmission lines; P l(mn),s,t represents the power flow through the transmission line l at time t in scenario s (with nodes m and n at both ends respectively); p w,s,t Table d is the output power of the wind farm unit at time t in scenario s; s,t represents the load of the system at time t in scenario s, and the variable v g,s,t Indicates the start and stop status of the unit, v g,s,t =1 means the unit is in the on state, otherwise it is in the off state, UT g Indicates the minimum start-up time of the generator set g; DT g Indicates the minimum downtime of the generator set g; U g,u Indicates the startup duration of the generator set at the start time of each scenario; U g,d represents the downtime duration of the generator set at the start time of each scenario, σ g,u Represents the ramp rate of the generator set g; σ g,d represents the downhill rate of the generator set g, are the upper and lower limits of the generator set output power, respectively. Indicates the maximum output power of the generator set; S w Represents the total installed capacity of the wind farm; R s Indicates the total reserve rate of the power system, usually taken as 20%; D max represents the maximum load of the power system in the planned year, S R is the spinning reserve capacity of the power system, generally 3% to 5% of the system load at that moment, S w,t is the wind power reserve capacity at time t under the typhoon. In the embodiment of the present invention, the difference between the wind power output under the typhoon and the wind power credible capacity under the typhoon scenario is taken as the wind power reserve capacity.

[0197] Under normal weather conditions, ignoring the small probability of unexpected events, the line will always be in normal operation. l,s =1, thus ensuring the result is 1; similarly, in typhoon weather, the operation status of the line can be obtained by simulating the operation of the typhoon. l,s =1, same as above, otherwise h+i l,s -hi l,s The result will be determined by h. If h = 1, the line is reinforced and the line can operate normally. Otherwise, the line is in a faulty state. Therefore, it is necessary to add several constraints, such as the power flow constraints of existing transmission lines, the power flow constraints of the lines to be built, the transmission capacity constraints of existing lines, and the transmission capacity constraints of the lines to be built. The formulas are shown in formulas (80) and (81).

[0198]

[0199] (h+i l,s -hi l,s ) l min ≤P l(mn),s,t ≤(h+i l,s -hi l,s ) l max (82)

[0200] P l min x l (h+i l,s -hi l,s )≤P l(mn),s,t ≤P l max x l (h+i l,s -hi l,s ) (83)

[0201] Where B represents the correlation matrix; θ m,s,t ,θ n,s,t Respectively represent the phase angle of nodes m and n, h represents the line reinforcement decision variable, when h = 1, the line takes reinforcement measures, otherwise it is not reinforced; i l,s Indicates whether the line is affected by the typhoon. After the typhoon simulation is completed, the line status is input as a definite value. l max ,P l min are the upper and lower limits of the line transmission capacity, respectively, h+i l,s -hi l,s Ensuring power flow constraints depends on a combination of whether the line is reinforced and whether it is affected by typhoons.

[0202] Since the constraints of equations (80) to (83) are nonlinear, they cannot be solved directly. Here, the big M method is used to characterize them, and the converted expressions are as follows:

[0203] |P l(mn),s,t -B l(mn),s,t (θ m,s,t -θ n,s,t )|≤M(1-(h+i l,s -hi l,s )) (84)

[0204] |P l(mn),s,t -B l(mn),s,t (θ m,s,t -θ n,s,t )|≤M(1-x l (h+i l,s -hi l,s )) (85)

[0205] Then the transformation of equation (85) can be written as:

[0206] |P l(mn),s,t -B l(mn),s,t (θ m,s,t -θ n,s,t )|≤M(1-(x l h(1-i l,s )+x l i l,s )) (86)

[0207] In order to linearize x l and h terms, where a 0-1 variable u is introduced l =x l h:

[0208]

[0209] |P l(mn),s,t -B l(mn),s,t (θ m,s,t -θ n,s,t )|≤M(1-(u l -u l i l,s +x l i l,s )) (88)

[0210] The same method is used to process equation (83) and convert it into the following expression:

[0211] P l min (u l -u l i l,s +x l i l,s )≤P l(mn),s,t ≤P l max (u l -u l i l,s +x l i l,s ) (89)

[0212] Finally, it is also necessary to constrain integer decision variables such as generator operating status, line construction, and reinforcement, as shown in the following equations (90) to (92):

[0213]

[0214] h≤x l (91)

[0215]

[0216] The constructed source-grid collaborative planning model takes into account load growth, wind farm unit output under typhoon weather, wind power rotating reserve and line reinforcement planning goals. The objective function is to minimize the total cost, including line construction cost, generator unit construction cost, line reinforcement cost, generator unit fuel cost, generator unit start-up and shutdown cost, wind abandonment penalty cost, and load shedding penalty cost.

[0217] (6) Solution module

[0218] The YALMIP toolkit and Gurobi solver are used to solve the source-grid collaborative planning model with a solution accuracy of 0.5%. The line construction cost, generator set construction cost, line reinforcement cost, generator set fuel cost, generator set start-up and shutdown cost, wind abandonment penalty cost, load shedding penalty cost, new lines, new units, and reinforced lines are obtained.

[0219] (7) Output module

[0220] Output transmission line failure probability, wind farm unit output sequence, typical failure scenarios, source network planning model and other results.

[0221] It should be noted that the steps in the method provided by the present invention can be implemented by using the corresponding components in the system, and those skilled in the art can refer to the technical solution of the system to implement the step flow of the method, and can also refer to the technical solution of the method to implement the composition of the system, that is, the embodiments in the system and the embodiments in the method can be understood as preferred examples of each other, which will not be elaborated here.

[0222] The implementation of the technical solution provided in the above embodiment of the present invention and the obtained effect are further verified and analyzed in conjunction with a specific application example.

[0223] In this specific application example, an improved IEEE RTS-24 node system is used for model verification and analysis, and the IEEE RTS-24 node system is projected onto a 140km×350km map. The basic data of the system are set as follows: the system load increases to 3705MW, and wind farms with an installed capacity of 400MW are connected at nodes 9, 13, and 20 respectively. The unit penalty cost of load shedding is 1000$ / (MW·h), the unit wind abandonment cost is 40$ / (MW·h), the system total reserve is set to 20%, the rotating reserve is set to 5% of the load, the line reinforcement cost is set to 15% of the investment and construction cost, and the typhoon path is selected as 40° north-west, the initial pressure is 960 hPa, the travel speed is set to 25km / h, and the typhoon path is assumed to be a straight line. According to the typhoon model and the transmission line probability model, the failure probability of the transmission line can be obtained as follows: Figure 3 As shown. Figure 3 It can be seen that the line failure probability presents a "double peak" characteristic. In addition, the lines near the typhoon path are severely affected and have a higher failure probability. Reinforcement measures can be taken for this type of line before the typhoon arrives to reduce its failure probability.

[0224] Table 1 Credibility of wind power capacity under typhoon weather

[0225]

[0226] It can be seen from Table 1 that the reliability of wind power capacity varies due to the different locations of wind farms. Figure 4 As shown in the figure, it can be seen that in typhoon weather, wind farms far away from the typhoon center can be in full power state for a long time. At this time, a certain capacity of wind power can be set as a reserve in typhoon weather according to the wind power capacity credibility assessment results and wind power forecast output, so as to improve the utilization rate of wind power and reduce the phenomenon of wind abandonment in typhoon weather.

[0227] Taking into account the strong volatility of wind turbine output in short-term typhoon weather, this paper adds wind power spinning reserve to the system spinning reserve, and uses the difference between the predicted output of the wind farm and the credible capacity of wind power under typhoon as the wind power spinning reserve capacity to cope with the large fluctuations in wind power output.

[0228] In order to compare and analyze the impact of wind turbines connected to the grid and not connected to the grid in a typhoon scenario, two schemes are set as follows:

[0229] Option 1: Consider the output of wind farm units during typhoons and set up wind power backup;

[0230] Option 2: Do not consider the output of wind farm groups during typhoons.

[0231] Table 2 Results of source-grid collaborative planning considering typhoon and wind power capacity credibility

[0232]

[0233]

[0234] From the results in Table 2, we can see that after considering the typhoon scenario, in order to meet the system load demand, the system needs to reinforce 7 lines, namely lines 1-3, 1-5, 2-4, 2-6, 3-9, 15-16, and 15-21. The total reinforcement cost is 1.56M$, the total planning cost is 36.039M$, the unit start-up and shutdown cost is 0.518M$, and the system wind abandonment cost is 0M$. Figure 3And take node 4 as an example for analysis: Node 4 has two power supply lines, lines 2-4 and 4-9, and both have a high probability of failure under typhoons. If the line is disconnected, it is very easy to cause system load shedding. Based on the reinforcement cost, the planning scheme chooses to reinforce the low-cost line on the premise of meeting the node load demand. By comparing Scheme 1 and Scheme 2, it can be analyzed that if wind power output is not considered in typhoon weather, the total investment cost will increase by 1.332M$, the total installed capacity of the unit will increase by 32MW, and the line reinforcement cost will increase by 0.886M$.

[0235] In order to analyze the impact of line reinforcement on the planning scheme, the planning results of the scheme without taking reinforcement measures are calculated here:

[0236] Table 3 Results of source network collaborative planning without considering line reinforcement

[0237]

[0238] The results in Table 3 show that if line reinforcement is not considered in the typhoon scenario and only the construction of lines is considered, load shedding will still occur when 8 new lines are built, with a load shedding cost of 96.203M$ and a grid investment cost of 9.46M$. The total cost increases by 100.519M$ compared with Scheme 1. Therefore, it is necessary to consider extreme weather such as typhoons in the initial planning stage and take certain reinforcement measures for individual lines or towers, which can effectively avoid tower collapse and line disconnection and system load shedding under typhoon disasters.

[0239] An embodiment of the present invention further provides a computer terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor can be used to execute any method of the above-mentioned embodiments of the present invention, or to run any system of the above-mentioned embodiments of the present invention.

[0240] Optionally, the memory is used to store programs; the memory may include volatile memory (English: volatile memory), such as random-access memory (English: random-access memory, abbreviated: RAM), such as static random-access memory (English: static random-access memory, abbreviated: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviated: DDR SDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory is used to store computer programs (such as applications, functional modules, etc. that implement the above method), computer instructions, etc., and the above computer programs, computer instructions, etc. can be partitioned and stored in one or more memories. And the above computer programs, computer instructions, data, etc. can be called by the processor.

[0241] The above-mentioned computer programs, computer instructions, etc. may be stored in one or more memories in partitions, and the above-mentioned computer programs, computer instructions, data, etc. may be called by a processor.

[0242] The processor is used to execute the computer program stored in the memory to implement the various steps of the method or various modules of the system involved in the above embodiments. For details, please refer to the relevant descriptions in the above method and system embodiments.

[0243] The processor and the memory may be independent structures or integrated structures. When the processor and the memory are independent structures, the memory and the processor may be coupled and connected via a bus.

[0244] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it can be used to execute any method of the above embodiments of the present invention, or to run any system of the above embodiments of the present invention.

[0245] Among them, computer-readable media include computer storage media and communication media, wherein the communication media include any media that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general or special-purpose computer. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a user device. Of course, the processor and the storage medium can also be present in a communication device as discrete components.

[0246] The source-network collaborative planning method and system considering the credibility of typhoon and wind power capacity provided by the above-mentioned embodiment of the present invention, by inputting the basic information of the typhoon, constructing a simulated typhoon disaster model, obtaining the line failure probability, and thus predicting the output sequence of the wind farm units, the output power of the wind farm units is determined by the real-time wind speed at the location. The failure probability of the transmission line at each moment is obtained according to the theoretical failure rate of the line, a large number of failure scenarios are obtained by the Monte Carlo method, and the K-medoid clustering algorithm is adopted to obtain the typical failure scenario. Based on the obtained typical failure scenario, according to the status of each transmission line in the system, the optimal load shedding is used to calculate the shortage load in the scenario, remove the wind power in the system and add the equivalent unit, obtain the reliable capacity of wind power, and form a linear equation constraint that can be embedded in the planning model; based on the output sequence of the wind farm units and the reliable capacity of wind power, a source-network collaborative planning model is constructed for collaborative planning of the source network, and the planning investment cost, typhoon model, line failure probability and other results are output. Compared with the existing technology, it solves the problems of wind farm unit output, wind power rotating standby and line reinforcement planning in typhoon weather, fully considers the load importance level and energy storage access, and achieves the purpose of coordinating various resources such as source-grid-load-storage during planning.

[0247] The source-grid collaborative planning method and system that considers the credibility of typhoons and wind power capacity provided by the above-mentioned embodiments of the present invention reduces the total cost of the planning scheme that considers the output of wind farm units under typhoon weather by 1.332M$ compared with the planning scheme that does not consider the output of wind turbine units under typhoon weather, and avoids system load shedding; taking preventive reinforcement measures in the initial planning stage can avoid the load shedding penalty cost of 96.203M$, reduce the total cost of the planning scheme, greatly enhance the power system's ability to resist typhoon weather, and reduce the overall system loss.

[0248] All matters not covered in the above embodiments of the present invention are well known in the art.

[0249] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art may make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A source-network collaborative planning method considering the credibility of typhoon and wind power capacity, characterized in that: include: Provide a typhoon simulation model and simulate the typhoon wind field using basic typhoon information; Based on the simulated typhoon wind field, the line vulnerability curve calculation method under typhoon disaster is used to calculate the theoretical line failure rate and obtain the status of each transmission line in the system; Provide a wind turbine output model, use the basic information of the power grid to calculate the output power of the wind farm units, and based on the simulated typhoon wind field, predict the output sequence of the wind farm units under the typhoon wind field; The theoretical failure rate of the line is used to obtain the failure probability of the transmission line at each moment, and a typical failure scenario is generated; Based on the typical fault scenario, the wind power reliable capacity is calculated according to the status of each transmission line of the system; Based on the output sequence of the wind farm units and the wind power reliable capacity, a source-network collaborative planning model is constructed for collaborative planning of the source network.

2. The source-network collaborative planning method considering the credibility of typhoon and wind power capacity according to claim 1 is characterized in that: The basic information of the typhoon includes: the initial pressure of the typhoon, the path of the typhoon and the moving speed; The provision of a typhoon simulation model uses basic typhoon information to simulate the typhoon wind field, including: The Batts wind field model is used to model the typhoon, and the coordinates of each bus node in the power system are established to calculate the real-time distance between the typhoon center and each component of the power system and simulate the typhoon wind field; among which: The decay law of the typhoon center pressure difference over time is described by the exponential decay principle: Δp(t)=Δp(t ldf )exp(-a d (t-t ldf )) t>t ldf (1) Where: Δp(t) is the central pressure difference at time t; t ldf is the time when the typhoon landed; Δp(t ldf ) is the central pressure difference at the time of typhoon landfall; a d is the decay constant; where v(t ldf ) and R max (t ldf ) are the typhoon moving speed and maximum wind speed radius at the time of landfall; a d0 and a d1 are the decay constants obtained from historical data; ε ad is a normally distributed error with zero mean; According to the central pressure difference of the typhoon at time t, calculate the maximum gradient wind speed at time t for: Where: k is a coefficient determined by the latitude of the region; According to the Batts model, the maximum wind speed of a typhoon at time t According to the maximum gradient wind speed and typhoon moving speed at that moment: Where: v(t) is the typhoon moving speed at time t; Taking into account the temporal and spatial evolution characteristics of typhoons, the typhoon center coordinates are modeled and the calculation formula is as follows: Where: (x cm ,y cm ) is the coordinate of the center position of the typhoon at the time of landing; θ is the angle between the typhoon's moving path and the coastline; Typhoon maximum wind speed radius R max The mapping relationship between (t) and the typhoon center pressure difference Δp(t) is expressed as: Where: is a random error; b and c are constants; Finally, the wind speed of the element at any position at any time during the typhoon is obtained: Where: v c (t) is the wind speed at the component position at any time t; d(t) is the distance between the typhoon center and the component position at time t, which is calculated by the following formula: Where: (x cm ,y cm ) is the component position coordinate, which is given by the initial example data.

3. The source-network collaborative planning method considering the credibility of typhoon and wind power capacity according to claim 1 is characterized in that: The method of calculating the line vulnerability curve under typhoon disaster based on the simulated typhoon wind field is used to calculate the theoretical line failure rate and obtain the status of each transmission line of the system, including: According to the tower vulnerability curve, the mapping relationship between tower failure probability and wind speed is constructed as follows: Where: v m,k (t i ) is t i The wind speed at the kth tower of the mth line at the moment; v d,t and v ex are the tower design wind speed and the ultimate wind speed; γ is the constant coefficient; λ m,k (t i ) is t i The probability of failure of the kth tower of the mth line at time; The cumulative failure probability of the kth tower of the mth transmission line during the T period of time when the typhoon lasts is expressed as: Where: N is the total number of short time periods, Δt is the time of each short time interval; Similarly, the line vulnerability curve is characterized as: Where: v m,l (t i ) is t i The wind speed at the location of the lth line section of the mth line at the moment; v d,line is the line design wind speed; Δ and β are constant coefficients; Δl is the length of each line segment; λ m,l (t i ) is t i The failure probability of the lth line section of the mth line at time; The cumulative failure probability of the lth line section of the mth transmission corridor during the typhoon period T is expressed as: According to the fault probability equivalent model of the series system, the failure probability of the mth transmission line is equivalently expressed by the comprehensive failure probability of the tower and all line sections. Then the cumulative failure probability of the mth transmission line under a typhoon is expressed as: Where: K and L represent the total number of towers and line sections of the mth transmission line respectively.

4. The source-network collaborative planning method considering the credibility of typhoon and wind power capacity according to claim 1 is characterized in that: The basic information of the power grid includes: system generators, lines, loads and wind power output data; The wind turbine output model is provided, the output power of the wind farm units is calculated by using the basic information of the power grid, and the output sequence of the wind farm units under the typhoon wind farm is predicted based on the simulated typhoon wind farm, including: The output power of wind farm units is determined by the real-time wind speed at the location, and its calculation formula is: Where: P r is the rated power of the wind turbine; V in 、V out 、V r are the cut-in wind speed, cut-out wind speed and rated wind speed of the fan respectively; the calculation formula of constants A, B and C is: When 0≤v <v in When the actual wind speed is lower than the cut-in wind speed of the wind turbine, the torque generated by the low wind speed on the fan blades cannot make the wind turbine rotate. At this time, the wind turbine is in a stationary state and the output power of the unit is zero. When v in ≤v <v r When the actual wind speed is higher than the cut-in wind speed of the wind turbine but lower than the rated wind speed, the output power of the wind turbine is positively correlated with the actual wind speed, that is, as the wind speed increases, the output of the turbine increases; When v r vv <v out When the actual wind speed is between the rated wind speed and the cut-out wind speed of the wind turbine, the wind turbine is always in full power state due to the limitation of the material and physical characteristics of the wind turbine. When v ≥ v out When the actual wind speed is greater than the cut-out wind speed of the wind turbine, the wind turbine will stop rotating and be disconnected from the power system to prevent the fan blades from being damaged by excessive wind speed. The wind power output is reconstructed using the Markov chain-Monte Carlo method, including: Firstly, the appropriate state number is selected according to the historical output data of the wind farm, and the wind power output distribution is divided. Then, the original wind farm output sequence is divided into corresponding state intervals based on the numerical values ​​in the wind farm output sequence, so as to ensure that the wind power output data is converted into discrete state points and determine the variable state. Construct an N×N state transfer matrix P to characterize the transfer characteristics between different state points. N represents the number of selected states. Each element p in the state transfer matrix mn It indicates the probability that the wind power output will change to state n in the next hour when the current state is m, expressed as: p mn =P(X t =n|X t-1 =m)(16) On this basis, the multi-step transfer state matrix is ​​defined as: The multi-step state transfer matrix represents the matrix of probability values ​​of transitioning from a certain state to the remaining states after k unit time, and constructs the state transfer matrix; Construct the cumulative transfer matrix P sum , whose first column is all zero, and starting from the second column, each element is the sum of the elements of the first j columns of the corresponding i-th row in the state transfer matrix, which is specifically represented as: Based on the constructed cumulative transfer state matrix, a random number u that obeys a 0-1 uniform distribution is randomly generated by the Monte Carlo sampling method, and the number of states in the next hour is determined by comparing the element values ​​in the cumulative transfer state matrix. Then this step is repeated until a sequence that meets the time scale requirements is generated, and a random state sequence is obtained: The state sequence is converted by the following formula: X=X j,min +u(X j,max -X j,min )(19) Where: X is the predicted wind power output value at that moment, X j,min ,X j,max are the minimum and maximum values ​​of the divided interval where state j is located, respectively, and the wind power output time series is reconstructed to obtain the predicted wind power output series of any time scale.

5. The source-network collaborative planning method considering the credibility of typhoon and wind power capacity according to claim 1 is characterized in that: The theoretical failure rate of the line is used to obtain the failure probability of the transmission line at each moment, and a typical failure scenario is generated, including: The failure probability of the transmission line at each moment is obtained by using the theoretical failure rate of the line, and multiple failure scenarios are obtained by using the Monte Carlo method; The K-medoid clustering algorithm is used to cluster the multiple fault scenarios to obtain typical fault scenarios.

6. The source-network collaborative planning method considering the credibility of typhoon and wind power capacity according to claim 1 is characterized in that: The calculating of wind power reliable capacity based on the typical fault scenario and according to the status of each transmission line of the system includes: The total unsupplied electricity of the power system during the typhoon is used as a reliability index for evaluating the reliability of the typhoon scenario, and the total unsupplied electricity TENS is obtained based on the optimal load shedding model of the DC power flow; Construct an optimal load shedding model for DC power flow, including Set the objective function as: The set constraints include: node power balance constraint, line flow constraint, line transmission capacity constraint, generator output constraint and load shedding constraint, where the constraint expressions are: Node power balance constraints: Where: N l represents the set of transmission lines; P l(mn),t represents the power flow through the transmission line l at time t (with nodes m and n at both ends); d t represents the load of the system at time t; Line flow constraints: Where: B represents the correlation matrix; θ m,t ,θ n,t Respectively represent the phase angles of nodes m and n; Line transmission capacity constraints: Where: P l max ,P l min are the upper and lower limits of the line transmission capacity respectively; Generator output constraints: Where: They are the upper and lower limits of the generator set output power respectively; Load shedding constraints: The calculation formula for the total unsupplied electricity TENS of the power system during the typhoon is as follows: Where: DNS(t k ) is t k The unsupplied demand of the system at that time can be calculated by the optimal load shedding model of equations (20) to (26); n is the length of the calculation time; Based on the typical fault scenario, according to the status of each transmission line in the system, the scenario shortage load is calculated through optimal load shedding, and the system load shedding at that moment is obtained through weighted calculation of the index and probability of each scenario, and the reliability index of the transmission system is calculated; Remove wind power from the system and add equivalent units, calculate the reliability index of the new power system, and adjust the capacity of the equivalent units so that the reliability index of the two systems before and after the wind power is removed is within the set error range. At this time, the capacity of the equivalent units is the reliable capacity of wind power.

7. The source-network collaborative planning method considering the credibility of typhoon and wind power capacity according to claim 1 is characterized in that: The constructing of a source-grid collaborative planning model based on the output sequence of the wind farm units and the wind power reliable capacity includes: Add line reinforcement variables to the source network collaborative planning model, and set the objective function of the source network collaborative planning model to minimize the total cost. The objective function is expressed as: minF total =F L +F G +F H +F fuel +F op +F wc +F rc (27) In the formula, F total is the total cost, F L is the line construction cost, F G is the power source investment cost, F H is the line reinforcement cost, F fuel is the fuel cost of the generator set, F op is the generator start-up and shutdown cost, F wc is the penalty cost for wind curtailment, F rc Penalty costs for load shedding; The calculation formula for each cost is: Where N cl Represents the set of lines to be built; C l represents the cost of constructing line l; x l represents the route planning decision variable, when x l =1 indicates that the line is being built. l =0 means no construction; N cg Represents the set of generator sets to be built; C g represents the construction cost of the generator set g; x g represents the decision variable for generator planning, when x g =1 means the unit is put into construction. g =0 means no construction; C h represents the line reinforcement cost; h l represents the line reinforcement decision variable, h l =1 means the line is reinforced, otherwise it is not reinforced; π s represents the probability of scene s occurring, and the probability value of each scene is the ratio of the number of scenes of this type to the total number of scenes; S represents the set of all scenes; T represents the set of all moments in scene s; N G Represents the collection of all generating units, including those that have been built and those to be built; represents the unit power generation cost of the generator set g; p g,s,t It represents the active power output by the generator set at time t in scenario s; Respectively represent the startup and shutdown costs of generator set g; y g,s,t ,z g,s,t They represent the start-up and shutdown actions of the generator set g at time t in scenario s; N w represents the set of all wind farms; C w represents the unit wind curtailment penalty cost; represents the abandoned wind power of the wind turbine at time t under scenario s; C d represents the unit load shedding penalty cost; represents the load shedding amount of the power system at time t under scenario s; Set general constraints for the source network collaborative planning model, including: y g,s,t -z g,s,t =v g,s,t -v g,s,t-1 (36) Where N l represents the set of built transmission lines; P l(mn),s,t represents the power flow through the nodes m and n at both ends of the transmission line l at time t in scenario s; p w,s,t represents the output power of the wind farm unit at time t in scenario s; d s,t represents the load of the system at time t in scenario s, and the variable v g,s,t Indicates the start and stop status of the unit, v g,s,t =1 means the unit is in the on state, otherwise it is in the off state, UT g Indicates the minimum start-up time of the generator set g; DT g Indicates the minimum downtime of the generator set g; U g,u Indicates the startup duration of the generator set at the start time of each scenario; U g,d represents the downtime duration of the generator set at the start time of each scenario, σ g,u Represents the ramp rate of the generator set g; σ g，d represents the downhill rate of the generator set g, are the upper and lower limits of the generator set output power, respectively. Indicates the maximum output power of the generator set; S w Represents the total installed capacity of the wind farm; R s Indicates the total reserve rate of the power system; D max represents the maximum load of the power system in the planned year, S R is the spinning reserve capacity of the power system, S w，t is the wind power reserve capacity at time t under the typhoon, which is the difference between the wind power output under the typhoon wind farm and the wind power credible capacity under the typhoon wind farm; Set additional constraints for the source network collaborative planning model, including: (h+i l，s -hi l，s )P l min ≤P l(mn)，s，t ≤(h+i l，s -hi l，s )P l max (49) P l min x l (h+i l，s -hi l，s )≤P l(mn)，s，t ≤P l max x l (h+i l，s -hi l，s ) (50) Where B represents the correlation matrix; θ m，s，t ,θ n，s，t They represent the phase angles of nodes m and n at both ends of the line, respectively; h represents the line reinforcement decision variable; when h = 1, the line takes reinforcement measures, otherwise it does not; i l，s Indicates whether the line is affected by the typhoon. After the typhoon simulation is completed, the line status is input as a definite value. l max , P l min are the upper and lower limits of the line transmission capacity, respectively, h+i l，s -hi l，s It represents the combination used to ensure that the power flow constraint depends on whether the line is reinforced and whether it is affected by typhoon; The Big M method is used to characterize equations (47) to (50), and the converted expressions are: |P l(mn)，s，t -B l(mn)，s，t (θ m，s，t -θ n，s，t )|≤M(1-(h+i l，s -hi l，s )) (51) |P l(mn)，s，t -B l(mn)，s，t (θ m，s，t -θ n，s，t )|≤M(1-x l (h+i l，s -hi l，s )) (52) The transformation of formula (52) is: |P l(mn)，s，t -B l(mn)，s，t (θ m，s，t -θ n，s，t )|≤M(1-(x l h(1-i l，s )+x l i l，s )) (53) In order to linearize x l h term, here we introduce a 0-1 variable u l =x l h, then: |P l(mn)，s，t -B l(mn)，s，t (θ m，s，t -θ n，s，t )|≤M(1-(u l -u l i l，s +x l i l，s )) (55) Convert equation (50) into the following expression: P l min (u l -u l i l，s +x l i l，s )≤P l(mn)，s，t ≤P l max (u l -u l i l，s +x l i l，s ) (56) Constraints are placed on integer decision variables, including: h≤x l (58) 8. A source-network collaborative planning system considering the credibility of typhoon and wind power capacity, characterized in that: include: The typhoon wind field and line fault probability module is used to provide a typhoon simulation model and simulate the typhoon wind field using the basic typhoon information; Based on the simulated typhoon wind field, the line vulnerability curve calculation method under typhoon disaster is used to calculate the theoretical line failure rate and obtain the status of each transmission line in the system; Provide a wind turbine output model, use the basic information of the power grid to calculate the output power of the wind farm units, and based on the simulated typhoon wind farm, predict the output sequence of the wind farm units under the typhoon wind farm; use the theoretical failure rate of the line to obtain the failure probability of the transmission line at each time, and generate a typical failure scenario; A wind power capacity credibility assessment module, which calculates the wind power credible capacity based on the typical fault scenario and the status of each transmission line of the system; A source-network collaborative planning module, which constructs a source-network collaborative planning model based on the output sequence of the wind farm units and the wind power credible capacity, and is used for collaborative planning of the source network.

9. 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 described in any one of claims 1 to 7, or to run the system described in claim 8.

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

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