A method and device for dispatching a power system of an offshore wind farm under a typhoon, and a medium

Abstract

The application discloses a typhoon offshore wind farm power system scheduling method, device and medium, wherein the scheduling method comprises the following steps: generating a typical scenario set by analyzing typhoon operation information; combining the latitude and longitude coordinates of typhoon path points, and obtaining a predicted value sequence of wind speed and wind power; establishing a risk index of offshore wind turbine operation, an operation constraint and a target function of expected income, and constructing an offshore wind power optimal output model; establishing a target function of total power system operation cost and an operation constraint group, and constructing an optimization model; solving the two models to obtain an optimal solution, and scheduling the power system of the offshore wind farm by taking the optimal solution as the power system scheduling method. The application provides a typhoon offshore wind farm power system scheduling method, device and medium, and the feasibility of the power grid scheduling method during the typhoon is improved by considering the wind power uncertainty caused by the typhoon forecast error and optimizing the output process of the offshore wind power.

Description

Technical Field

[0001] This invention relates to the field of power system disaster prevention and mitigation technology, and in particular to a power system dispatching method, device and medium for offshore wind farms during typhoons. Background Technology

[0002] In recent years, my country's offshore wind power has developed rapidly, with installed capacity increasing year by year. Wind power output exhibits strong randomness and volatility, and the impact of offshore wind power integration into the power system cannot be ignored. Furthermore, complex and changeable offshore weather conditions can adversely affect both offshore wind farms and the power system. Therefore, how to dispatch power systems containing large-scale offshore wind power during typhoons is a crucial issue that must be considered. The output level of offshore wind farms is strongly correlated with wind speed. Current publicly available grid dispatching methods during typhoons do not consider the uncertainty in wind power output caused by typhoon forecast errors. Moreover, existing technologies directly formulate dispatching methods based on the predicted output of offshore wind farms without optimizing the output process, leading to low feasibility of grid dispatching methods during typhoons. Summary of the Invention

[0003] This invention provides a power system dispatching method, device, and medium for offshore wind farms during typhoons, to solve the problems that existing power grid dispatching methods during typhoons do not consider the uncertainty of wind power caused by typhoon forecast errors and do not optimize the output process of offshore wind power.

[0004] This invention provides a power system dispatching method for offshore wind farms during typhoons, comprising:

[0005] Historical typhoon operation information and offshore wind farm operation information are obtained. By analyzing the typhoon operation information, a typical scenario set is generated based on a clustering method. Combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, the wind speed and wind power at the offshore wind farms in the typical scenario set are predicted to obtain wind speed prediction value sequences and wind power prediction value sequences.

[0006] Based on the offshore wind farm operation information, wind speed prediction sequence, and wind power prediction sequence, risk indicators and first operation constraints for offshore wind turbine operation are established, along with a first objective function for the expected revenue of the offshore wind farm. An optimal output model for offshore wind power is constructed using the risk indicators, first operation constraints, and first objective function. Based on the offshore wind farm operation information, a second objective function for the total operating cost of the power system in the offshore wind farm and a second set of operation constraints for the offshore wind farm are established. A multi-day optimization model for the power system is constructed based on the second objective function and the second set of operation constraints.

[0007] Solving the offshore wind power optimal output model and the power system multi-day optimization model yields the first optimal solution for the start-up and shutdown plan of thermal power units and pumped storage units during typhoons, as well as the second optimal solution for the baseline operation plan of thermal power units. The first and second optimal solutions are then used as power system dispatching methods to dispatch the power system of the offshore wind farm.

[0008] This invention predicts wind speed and wind power, obtaining wind speed and wind power prediction sequences. These sequences fully consider the uncertainties in wind speed and wind power caused by typhoon forecast errors, enhancing the accuracy of the power system dispatching method. Through the optimal output model for offshore wind power, it comprehensively considers the risk indicators of offshore wind turbine operation, the expected returns of offshore wind farms, and the operating status of offshore wind turbines, achieving optimal overall output. Through the multi-day optimization model of the power system, it considers operating costs and constraints from multiple perspectives, optimizing the power system. By using the optimal solutions obtained from solving the optimal output model for offshore wind power and the multi-day optimization model of the power system as the power system dispatching method for offshore wind farms, the output process of offshore wind power can be optimized. Compared to existing technologies, this invention considers the uncertainty in wind power caused by typhoon forecast errors and optimizes the output process of offshore wind power.

[0009] As a preferred approach, by analyzing the typhoon operation information, a set of typical scenarios is generated based on a clustering method, specifically:

[0010] Based on the predicted and actual typhoon paths in the typhoon operation information, the relative error between the predicted and actual values ​​is calculated. The step size of the typhoon path or direction forecast error is selected. Using the typhoon path and angle errors of all historical sampling points in the typhoon operation information, prediction error histograms of typhoon path and direction forecast errors are plotted respectively. Based on the prediction error histograms, an initial fitting distribution index corresponding to the typhoon path or direction forecast error is established.

[0011] The parameters in the initial fitting distribution index are determined by the maximum likelihood estimation method to obtain the final fitting distribution index, and an error distribution model is constructed based on the final fitting distribution index.

[0012] Based on the error distribution model and typhoon operation information, a set of typical scenarios is generated using clustering methods.

[0013] This preferred solution constructs an error distribution model using the forecast and actual values ​​of the typhoon path. This model can fully consider the data uncertainty caused by typhoon forecast errors, providing typical data support for the subsequent construction of typical scenario sets.

[0014] As a preferred approach, based on the error distribution model and typhoon operational information, a typical scenario set is generated using a clustering method, specifically:

[0015] Based on the error distribution model and typhoon forecast information in the typhoon operation information, a first set of typhoon path and direction prediction error sequences is generated using the Monte Carlo method. The first set of typhoon path and direction prediction error sequences is then clustered using a clustering method to generate a second set of typhoon path and direction prediction error sequences. Each sequence in the second set of typhoon path and direction prediction error sequences is taken as a typical scenario of typhoon forecast error, thus obtaining the typical scenario set.

[0016] This preferred scheme generates a typical scenario set based on the error distribution model and typhoon forecast information through clustering. This typical scenario set is a typical summary of historical typhoon data. As a set of typical examples, the typical scenario set can provide strong data support for subsequent data prediction and calculation.

[0017] As a preferred approach, by combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, the wind speed and wind power at the offshore wind farms in the typical scenario set are predicted, resulting in a wind speed prediction sequence and a wind power prediction sequence, specifically:

[0018] Combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, the wind speed prediction method under the typhoon conditions is used to predict the wind speed at each wind turbine in each classic scenario of the offshore wind farm in the typical scenario set, and the wind speed prediction value sequence is obtained.

[0019] Combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, and based on the historical wind speed and corresponding power output of each wind turbine in the offshore wind farm operation information, the least squares method is used to fit the wind speed and power output curves of each wind turbine; the power output of each wind turbine is calculated based on the wind speed and power output curves to obtain the wind power prediction value sequence.

[0020] This preferred scheme predicts wind speed and wind power, obtaining wind speed prediction sequences and wind power prediction sequences. The two prediction sequences fully consider the uncertainties in wind speed and wind power caused by typhoon forecast errors, thus enhancing the reliability of the power system dispatching method of this invention.

[0021] As a preferred embodiment, risk indicators and first operational constraints for offshore wind turbine operation are established based on the offshore wind farm operation information, wind speed prediction sequence, and wind power prediction sequence, along with a first objective function for the expected revenue of the offshore wind farm, specifically:

[0022] The first wind speed of the first wind turbine is obtained at the first moment based on the wind speed prediction value sequence. The failure rate of the first wind turbine is constructed based on the first wind speed. The risk state continuous operation time of the first wind turbine is constructed based on the offshore wind farm operation information. The risk index of the offshore wind turbine operation is established through the failure rate and the risk state continuous operation time.

[0023] Based on the wind power prediction sequence and the offshore wind farm operation information, the first operating constraint of the offshore wind turbine is established.

[0024] Based on the risk indicators of the offshore wind turbine operation and the operational information of the offshore wind farm, a first objective function for the expected revenue of the offshore wind farm is established.

[0025] The risk indicators for offshore wind turbine operation constructed by this preferred scheme can comprehensively consider the failure rate and continuous operation time of the wind turbine under risk conditions. The first operating constraint can consider the operating status of the offshore wind turbine, and the first objective function can systematically consider the revenue of the offshore wind farm. This ensures that the optimal output model of offshore wind power generated on this basis will take into account these three aspects to achieve the goal of optimal output of offshore wind power.

[0026] As a preferred embodiment, a second objective function for the total operating cost of the power system in the offshore wind farm and a second set of operating constraints for the offshore wind farm are established based on the aforementioned offshore wind farm operation information, specifically as follows:

[0027] Based on the offshore wind farm operation information, the costs of pumped storage units, thermal power units, wind curtailment penalty costs, energy storage operation costs, and load shedding losses for the first scenario in the typical scenario set are calculated. Combining the costs of pumped storage units, thermal power units, wind curtailment penalty costs, energy storage operation costs, and load shedding losses, a second objective function is established based on the probability of the first scenario.

[0028] Based on the offshore wind farm operation information, the following constraints are established: thermal power unit operation constraints, ramping constraints, pumped storage unit operation constraints, reservoir capacity constraints, offshore wind power operation constraints, battery energy storage operation constraints, system power balance and reserve constraints, and network constraints. These constraints constitute the second set of operation constraints for the offshore wind farm.

[0029] The second objective function constructed by this preferred scheme can consider operating costs from multiple perspectives of power system operation, and the second set of operating constraints can consider constraints from multiple aspects such as thermal power units, pumped storage units, and reservoir capacity. This ensures that the multi-day optimization model of the power system generated on this basis will take into account these two perspectives and achieve the goal of optimizing the power system.

[0030] As a preferred option, offshore wind power operation constraints are established based on the offshore wind farm operation information, specifically as follows:

[0031] Based on the active power output of the first wind turbine in the first offshore wind farm at the second moment in the first scenario, and the number of wind turbines in the first offshore wind farm, the total power output of the first offshore wind farm is calculated. The offshore wind power operation constraint is constructed by making the total power output of the first offshore wind farm less than or equal to the maximum value of the predicted value sequence of wind power.

[0032] As a preferred embodiment, by analyzing the typhoon operation information and generating a typical scenario set based on a clustering method, the method further includes:

[0033] The predicted latitude and longitude coordinates of the typhoon path points in the typhoon operation information are corrected to obtain the corrected latitude and longitude coordinates of the typhoon path points.

[0034] This preferred solution reduces data errors caused by the predicted latitude and longitude coordinates of typhoon path points by correcting the predicted coordinates.

[0035] This invention provides a power system dispatching device for offshore wind farms during typhoons, comprising:

[0036] The scenario and prediction value acquisition module is used to acquire historical typhoon operation information and offshore wind farm operation information. By analyzing the typhoon operation information, a typical scenario set is generated according to the clustering method. Combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, the wind speed and wind power at the offshore wind farms in the typical scenario set are predicted to obtain wind speed prediction value sequence and wind power prediction value sequence.

[0037] The optimization model construction module is used to establish risk indicators and first operating constraints for offshore wind turbine operation, as well as a first objective function for the expected revenue of the offshore wind farm, based on the offshore wind farm operation information, wind speed prediction sequence, and wind power prediction sequence. It then constructs an optimal output model for offshore wind power based on the risk indicators, first operating constraints, and first objective function. Furthermore, it establishes a second objective function for the total operating cost of the power system in the offshore wind farm and a second set of operating constraints for the offshore wind farm based on the offshore wind farm operation information. Finally, it constructs a multi-day optimization model for the power system based on the second objective function and the second set of operating constraints.

[0038] The application module solves the optimal output model of offshore wind power and the multi-day optimization model of the power system to obtain the first optimal solution of the start-up and shutdown plan of thermal power units and pumped storage units during typhoons, as well as the second optimal solution of the baseline operation plan of thermal power units. The first and second optimal solutions are used as the power system dispatching method to dispatch the power system of the offshore wind farm.

[0039] As a preferred embodiment, the scene and predicted value acquisition module includes:

[0040] The first construction unit of the typical scenario set calculates the relative error between the forecast and actual values ​​of the typhoon path based on the forecast and actual values ​​of the typhoon path in the typhoon operation information, selects the step size of the typhoon path or direction forecast error, and plots the prediction error histograms of the typhoon path and direction forecast errors using the typhoon path and angle errors of all historical sampling points in the typhoon operation information. Based on the prediction error histograms, an initial fitting distribution index corresponding to the typhoon path or direction forecast error is established.

[0041] The parameters in the initial fitting distribution index are determined by the maximum likelihood estimation method to obtain the final fitting distribution index, and an error distribution model is constructed based on the final fitting distribution index.

[0042] Based on the error distribution model and typhoon operation information, a set of typical scenarios is generated using clustering methods;

[0043] The second construction unit of the typical scenario set is used to generate a first set of typhoon path and direction prediction error sequences by means of the error distribution model and typhoon forecast information in the typhoon operation information, and to cluster the first set of typhoon path and direction prediction error sequences by means of the clustering method to generate a second set of typhoon path and direction prediction error sequences. Each sequence in the second set of typhoon path and direction prediction error sequences is taken as a typical scenario of typhoon forecast error to obtain the typical scenario set.

[0044] The correction unit is used to correct the predicted latitude and longitude coordinates of the typhoon path points in the typhoon operation information to obtain the corrected latitude and longitude coordinates of the typhoon path points.

[0045] The sequence value acquisition unit is used to combine the latitude and longitude coordinates of the typhoon path point in the typhoon operation information and use a preset typhoon condition wind speed prediction method to predict the wind speed at each wind turbine in each classic scenario of the offshore wind farm in the typical scenario set, so as to obtain the wind speed prediction value sequence.

[0046] Combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, and based on the historical wind speed and corresponding power output of each wind turbine in the offshore wind farm operation information, the least squares method is used to fit the wind speed and power output curves of each wind turbine; the power output of each wind turbine is calculated based on the wind speed and power output curves to obtain the wind power prediction value sequence.

[0047] As a preferred embodiment, the optimization model construction module includes:

[0048] The first model parameter acquisition unit is used to obtain the first wind speed of the first wind turbine at the first moment based on the wind speed prediction value sequence, construct the failure rate of the first wind turbine based on the first wind speed, construct the risk state continuous operation time of the first wind turbine based on the offshore wind farm operation information, and establish the risk index of the offshore wind turbine operation through the failure rate and the risk state continuous operation time.

[0049] Based on the wind power prediction sequence and the offshore wind farm operation information, the first operating constraint of the offshore wind turbine is established.

[0050] Based on the risk indicators of the offshore wind turbine operation and the offshore wind farm operation information, a first objective function for the expected revenue of the offshore wind farm is established.

[0051] The second model parameter acquisition unit is used to calculate the costs of pumped storage units, thermal power units, wind curtailment penalty costs, energy storage operation costs, and load shedding loss costs for the first scenario in the typical scenario set based on the offshore wind farm operation information. Combining the costs of pumped storage units, thermal power units, wind curtailment penalty costs, energy storage operation costs, and load shedding loss costs, the second objective function is established based on the probability of the first scenario.

[0052] Based on the offshore wind farm operation information, the following constraints are established: thermal power unit operation constraints, ramping constraints, pumped storage unit operation constraints, reservoir capacity constraints, offshore wind power operation constraints, battery energy storage operation constraints, system power balance and reserve constraints, and network constraints. These constraints constitute the second set of operation constraints for the offshore wind farm.

[0053] The third model parameter acquisition unit is used to calculate the total output of the first offshore wind farm based on the active power output of the first wind turbine in the first offshore wind farm at the second time in the first scenario and the number of wind turbines in the first offshore wind farm. The offshore wind farm operation constraints are constructed by making the total output of the first offshore wind farm less than or equal to the maximum value of the predicted value sequence of wind power.

[0054] The present invention provides a storage medium storing a computer program, which is called and executed by a computer to realize the power system scheduling method for an offshore wind farm under typhoon conditions as described above. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating a power system dispatching method for an offshore wind farm during a typhoon, provided by an embodiment of the present invention.

[0056] Figure 2 This is a comparison chart of the power output of offshore wind power before and after optimization, provided in an embodiment of the present invention.

[0057] Figure 3 This is a schematic diagram of the structure of a power system dispatching device for an offshore wind farm during a typhoon, provided in an embodiment of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0059] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more.

[0060] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

[0061] Please see Figure 1 The present invention provides a power system dispatching method for offshore wind farms during typhoons, comprising S1 to S3:

[0062] S1. Obtain historical typhoon operation information and offshore wind farm operation information. By analyzing the typhoon operation information, generate a typical scenario set based on the clustering method. Combine the latitude and longitude coordinates of the typhoon path points in the typhoon operation information to predict the wind speed and wind power at the offshore wind farms in the typical scenario set, and obtain the wind speed prediction value sequence and the wind power prediction value sequence.

[0063] In this embodiment of the invention, S1 includes S1.1 to S1.3:

[0064] S1.1 Based on the forecast and actual values ​​of the typhoon path in the typhoon operation information, calculate the relative error between the forecast and actual values, and select the step size δ for the typhoon path or direction forecast error. l Using the typhoon path and angle errors of all historical sampling points in the typhoon operation information, we plotted the prediction error histograms of the typhoon path and direction forecast errors respectively, and established the initial fitting distribution index corresponding to the typhoon path or direction forecast error based on the prediction error histograms.

[0065] The initial fit distribution index is:

[0066]

[0067] Where N is the prediction error step size δ for the selected path or direction. l The number of prediction error histogram intervals H corresponding to the time i To determine the height of the prediction error histogram, φ(A) i ) is the error distribution model.

[0068] The parameters in the initial fitted distribution index are determined by the maximum likelihood estimation method, resulting in the final fitted distribution index. An error distribution model φ(A) is then constructed based on the final fitted distribution index. i );

[0069] According to the error distribution model φ(A) i Information on typhoon operations and clustering methods are used to generate a set of typical scenarios.

[0070] S1.2, According to the error distribution model φ(A) i The typhoon forecast information in the typhoon operation information is used to generate the first set of typhoon path and direction prediction error sequences using the Monte Carlo method. The first set of typhoon path and direction prediction error sequences is then clustered using a clustering method to generate the second set of typhoon path and direction prediction error sequences. Each sequence in the second set of typhoon path and direction prediction error sequences is taken as a typical scenario of typhoon forecast error, resulting in the typical scenario set Ω.

[0071] S1.3 First, the predicted latitude and longitude coordinates of the typhoon path points in the typhoon operation information are corrected to obtain the corrected longitude and latitude coordinates of the typhoon path points.

[0072] The corrected longitude and latitude coordinates of the typhoon path are as follows:

[0073]

[0074]

[0075] Among them, R e The radius of the Earth (can be taken as 6371.39 km). and ψ represents the typhoon track forecast error and angle forecast error at all times under any typical scenario s in the typical scenario set Ω. i and φ i These are the predicted longitude and latitude coordinates of the i-th sampling point in the typhoon forecast information, respectively.

[0076] Then, combining the latitude and longitude coordinates of the typhoon path points in the corrected typhoon operation information, the wind speed prediction method under the typhoon conditions is used to predict the wind speed at each wind turbine in each classic scenario of the offshore wind farm in the typical scenario set Ω, and obtain the wind speed prediction value sequence.

[0077] By combining the latitude and longitude coordinates of the typhoon path points in the corrected typhoon operation information, and based on the historical wind speed and corresponding power output of each wind turbine in the offshore wind farm operation information, the least squares method is used to fit the wind speed and power output curves of each wind turbine. Based on the wind speed and power output curves, the power output of each wind turbine in each classic scenario in the typical scenario set Ω is calculated to obtain the wind power prediction value sequence.

[0078] This embodiment constructs an error distribution model using the predicted and actual values ​​of typhoon paths. This model fully considers the data uncertainty caused by typhoon forecast errors, providing typical data support for the subsequent construction of a typical scenario set. Based on the error distribution model and typhoon forecast information, a typical scenario set is generated through clustering. This typical scenario set is a typical summary of historical typhoon data, serving as a set of examples that provides strong data support for subsequent data prediction and calculation. By correcting the predicted latitude and longitude coordinates of typhoon path points, the data error caused by these coordinates can be reduced, making the generated wind speed and wind power prediction sequences more accurate. The wind speed and wind power prediction sequences fully consider the uncertainties in wind speed and wind power caused by typhoon forecast errors, enhancing the reliability of the power system dispatching method of this invention.

[0079] S2. Based on the offshore wind farm operation information, wind speed prediction sequence, and wind power prediction sequence, establish risk indicators and first operation constraints for offshore wind turbine operation, as well as the first objective function for the expected revenue of the offshore wind farm. Construct an optimal output model for offshore wind power using the risk indicators, first operation constraints, and first objective function. Based on the offshore wind farm operation information, establish a second objective function for the total operating cost of the power system in the offshore wind farm and a second set of operation constraints for the offshore wind farm. Construct a multi-day optimization model for the power system based on the second objective function and the second set of operation constraints.

[0080] In this embodiment of the invention, S2 includes S2.1 to S2.4:

[0081] S2.1 First, based on the wind speed prediction value sequence, the first wind speed of the first wind turbine is obtained at the first moment, and the failure rate of the first wind turbine is constructed based on the first wind speed; based on the offshore wind farm operation information, the risk state continuous operation time of the first wind turbine is constructed, and the risk index of offshore wind turbine operation is established through the failure rate and the risk state continuous operation time.

[0082] The failure rate, the duration of operation under risk conditions, and the risk indicators are as follows:

[0083]

[0084] J 2,i (t)=max{0,J 2,i (t-1)+s i,t};

[0085]

[0086] Where w is the weighting coefficient (a real number greater than 0 and less than 1), T d v represents the duration of the typhoon (which can take different values ​​depending on the duration of the typhoon's impact). r Let v be the wind speed at the turning point (taken as 15 m / s). i (t) represents the wind speed at point i of the wind turbine at time t (obtained from the wind speed prediction sequence); b is a parameter representing the degree to which the failure probability is affected by wind speed (taken as 0.15), p normal s represents the failure probability of a wind turbine under normal conditions. i,t Let J be the state variable of wind turbine i at time t (value 1 if operating in a risky state, and -5 otherwise). 2,i (t-1) represents the duration of the risk state at the previous moment, and exp represents an exponential function with the natural constant e as the base.

[0087] Then, based on the wind power prediction sequence and the offshore wind farm operation information, the first operating constraint of the offshore wind turbine is established.

[0088] The first operational constraint is:

[0089]

[0090]

[0091]

[0092] in, This refers to the expected output state of the wind turbine based on wind speed. and The minimum, predicted, and optimized reported output values ​​of wind turbine m in the i-th offshore wind farm at time t are given. It is obtained from the wind power prediction value sequence. and Let $\frac{1}{2}$ be the cut-in wind speed and cut-out wind speed of wind turbine $m$ in the $i$-th offshore wind farm. Let v be the operating state variable of wind turbine m in the i-th offshore wind farm at time t. i,m,t Let m be the wind speed at the i-th offshore wind farm turbine at time t.

[0093] Secondly, based on the risk indicators of offshore wind turbine operation and combined with the operation information of offshore wind farms, a first objective function for the expected returns of offshore wind farms is established.

[0094] The first objective function is:

[0095]

[0096] For parameters have:

[0097]

[0098]

[0099]

[0100] in, and Let N represent the power generation revenue, tripping cost, and risk cost of the m-th wind turbine in the i-th offshore wind farm at time t, respectively, where T is the length of the scheduling time window; pl and c represents the number of wind turbines in one offshore wind farm and the number of wind turbines in another offshore wind farm. price c wf C off and C repair These include the offshore wind power transmission price, risk cost conversion factor, turbine tripping cost, and expected maintenance cost. Let m be the active power of the i-th offshore wind farm turbine at time t. and J represents the operating state variables of the i-th offshore wind farm turbine m at time t and (t-1), respectively. i (t) represents the risk indicator for the operation of offshore wind turbines, T wf Let Δt be the time from when wind turbine m fails to when it is repaired. Let m be the predicted output value of the i-th offshore wind farm turbine at time k. Let m be the operating state variable of the i-th offshore wind farm turbine m at time t.

[0101] Finally, an optimal power output model for offshore wind power is constructed using risk indicators, the first operational constraint, and the first objective function.

[0102] S2.2. Based on the offshore wind farm operation information, calculate the costs of pumped storage units (including start-up and shutdown costs and operating costs), thermal power units (including start-up and shutdown costs and operating costs), wind curtailment penalty costs, energy storage operating costs, and load shedding loss costs for the first typical scenario. Combine the costs of pumped storage units, thermal power units, wind curtailment penalty costs, energy storage operating costs, and load shedding loss costs, and establish a second objective function based on the probability of the first scenario.

[0103] The second objective function is:

[0104]

[0105] Where Ω is the set of typical scenes, s is any typical scene in the set of typical scenes Ω, and ω s Let be the probability that typical scenario s may occur. and These are the start-up and shutdown costs for pumped storage units and thermal power units that were dispatched several days prior. and These are, respectively, the operating costs of pumped storage units participating in multi-day dispatch under typical scenario s, the operating costs of thermal power units, the cost of wind curtailment penalty, the operating costs of energy storage, and the cost of load shedding loss; Let be the number of wind turbines in the i-th offshore wind farm;

[0106] For parameters and have:

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114] Where NP, NG, NB, and ND represent the number of pumped storage units, thermal power units, battery energy storage units, and load nodes participating in the dispatch operation, respectively. pl and C represents the number of wind turbines in one offshore wind farm and the number of wind turbines in another offshore wind farm. pu C pd C gu and C gd These are the costs of starting up and stopping the pumps of pumped storage units and thermal power units, respectively. and Let $t$ be the start-up and stop-up variables of the pump of the i-th pumped storage unit at time $t$, the operating state variables of the thermal power unit $k$, and the start-up and stop-up variables of the thermal power unit $k$. and These are the pumping cost conversion factor and the power generation cost conversion factor for pumped storage unit i, respectively. and These are, respectively, the pumping power and generating power of pumped-storage units at time t under typical scenario s, the generating power of thermal power units, the reported output and actual output of wind turbines, the charging power and generating power of battery energy storage, and the preventive load shedding amount at load node d; a k b k and c k All are cost coefficients for thermal power unit k; C w π c and π d These are, respectively, the cost coefficient for wind curtailment penalty, the unit operating cost of battery energy storage equivalent lifespan loss during charging, and the unit operating cost of battery energy storage equivalent lifespan loss during discharging; c load The preventive load shedding penalty cost coefficient is T, where T is the length of the scheduling time window and Δt is the scheduling time interval.

[0115] S2.3. Based on the operation information of offshore wind farms, establish the following constraints: operation constraints for thermal power units, ramping constraints, pumped storage unit operation constraints, reservoir capacity constraints, offshore wind power operation constraints, battery energy storage operation constraints, system power balance and reserve constraints, and network constraints. These constraints constitute the second set of operation constraints for offshore wind farms.

[0116] First, the operating constraints of thermal power units are:

[0117]

[0118]

[0119]

[0120]

[0121] in, and These are the lower and upper limits of the active power output of the k-th thermal power unit, respectively. Let be the power generation capacity of the k-th thermal power unit at time t under a typical scenario s. Let T be the time that the k-th thermal power unit has been running continuously at time t. g This refers to the minimum allowable continuous operation time for thermal power units. and Let be the state variables, start-up variables, and stop variables of thermal power unit k at time t, respectively. Let be the state variable of thermal power unit k at time (t-1).

[0122] Second, the climbing constraint is:

[0123]

[0124] Among them, R D,k R U,k Let be the maximum downhill and uphill gradient rates of the k-th thermal power unit, respectively, and Δt be the dispatch time interval. and Let be the start-up and stop-down variables of thermal power unit k at time t, respectively. and Let K be the minimum shutdown power and startup power of the k-th thermal power unit. and These represent the power generation of the k-th and (k-1)-th thermal power units at time t under typical scenario s.

[0125] Third, the operating constraints of pumped-storage units are:

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132]

[0133]

[0134] in, Let be the operating variables of the pumping and power generation states of the i-th pumped storage unit at time t. and Let T be the starting variable of the pump of the i-th pumped storage unit and the stopping variable of the k-th thermal power unit at time t. e T b These represent the start and end times of scheduling within a day, N. p The maximum number of times the storage pump unit is allowed to start and stop within a day; Let T be the continuous operating time of the i-th pumped-storage unit at time t, in either pumping or power generation mode. p T pg These are the minimum allowable continuous operation times for pumped-storage units in pumping and power generation states, respectively. and These are the lower and upper limits of the rated pumping power and power generation power of the i-th pumped storage unit, respectively. This represents the pumping power of the pumped storage unit at time t under a typical scenario s.

[0135] Fourth, the storage capacity constraint is:

[0136]

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] in, These represent the reservoir capacities of the upper and lower reservoirs of the pumped-storage power station P at time t under a typical scenario s. These represent the reservoir capacity of the pumped storage power station P at time t under typical scenario s, at the start and end of the scheduling process, respectively, and c. g,P c p,P These are the conversion coefficients for pumping power (P), power generation, and flow rate of the pumped storage power station, respectively. These represent the pumping power and generating power of pumped-storage unit i at time t under typical scenario s, η g,P η p,P These represent the power generation efficiency and pumping efficiency of unit P in a pumped storage power station, respectively, ρ0, g, and h. P These are the density of water, gravitational acceleration, and the average head difference between the upper and lower reservoirs of the pumped storage power station P, respectively. and These represent the upper and lower limits of the upper reservoir capacity and the upper and lower limits of the lower reservoir capacity of the pumped storage power station P, respectively. Pmin δ Pmax Δt represents the lower and upper limits of the reservoir capacity change of pumped storage power station P (upper and lower) within a single scheduling day, respectively, and Δt is the scheduling time interval.

[0144] Fifth, the operational constraints for offshore wind power are:

[0145]

[0146] For parameters have:

[0147]

[0148] in, Let m be the active power output of wind turbine m in the i-th offshore wind farm at time t under typical scenario s. This represents the maximum value in the predicted sequence of wind power output. This represents the total output of the i-th offshore wind farm at time t under a typical scenario s. Let represent the number of wind turbines in the i-th offshore wind farm.

[0149] Sixth, the operational constraints of battery energy storage are:

[0150]

[0151]

[0152]

[0153] E n,0 SOCn min≤E n,t,s ≤E n,0 SOC n max;

[0154]

[0155]

[0156] Among them, E n,t,s Let n be the battery energy storage value at time t under a typical scenario s. These represent the battery energy storage n values ​​at the start and end of the scheduling process, respectively, under typical scenario s. These represent the charging power and generating power of the battery storage at time t under a typical scenario s, ξ. n η is the self-discharge rate of the battery energy storage system. c,n ,η d,n These are the charging and discharging efficiencies of battery energy storage, respectively. These represent the upper limits of the charging and discharging power of the battery energy storage n, and the SOC (State of Charge). n min, SOC n max represents the upper and lower limits of the battery's state of charge, respectively, allowing it to operate at its maximum and minimum charge levels. n,0 The rated capacity of the battery energy storage device. These represent the battery energy storage charging and discharging operation state variables at time t under a typical scenario s, respectively, with Δt being the scheduling time interval.

[0157] Seventh, the system power balance and reserve constraints are as follows:

[0158]

[0159]

[0160]

[0161]

[0162] For parameters and have:

[0163]

[0164]

[0165]

[0166]

[0167]

[0168] in, and These are the on-spinning reserves that thermal power units, pumped storage units, and battery energy storage can provide, respectively. β represents the downstream spinning reserve that thermal power units and battery energy storage can provide, respectively. L β W These represent the system load, the spinning reserve factor required for offshore wind power, and NW, NG, NB, ND, and N, respectively. ph These represent the total number of wind turbine units, the number of thermal power units under dispatch, the number of battery energy storage units, the number of load nodes, and the number of pumped storage power stations, respectively. pl and These represent the number of wind turbines in one offshore wind farm and the number of wind turbine units in another offshore wind farm. and These represent the pumping power and generating power of pumped-storage units at time t under typical scenario s, the generating power of thermal power units, the reported output and actual output of wind turbines, the charging power and generating power of battery energy storage, and the preventive load shedding; R D,k R U,k These represent the maximum downhill and uphill gradient rates of the k-th thermal power unit, respectively. These represent the maximum and minimum power generation of thermal power units at time t under typical scenario s, the maximum power generation of pumped storage units, and the maximum and minimum power generation of battery energy storage. These represent the state variables of thermal power unit k at time t under typical scenario s, the operating variables of pumped storage unit i in terms of power generation state, the operating state variables of battery energy storage charging state, and the operating state variables of battery energy storage at time t. This represents the predicted load at load node d.

[0169] Eighth, the network constraints are:

[0170]

[0171]

[0172]

[0173]

[0174]

[0175] Where, θ i,t,s θ i min and θ i maxLet θ represent the phase angle, minimum phase angle, and maximum phase angle of node i at time t in typical scenario s, respectively; δ is the equilibrium node number; θ δ,t,s and θ j,t,s Let X be the phase angles of nodes δ and j at time t in a typical scenario s; ij Let be the reactance of line ij; Let be the transmission power of line l at time t under typical scenario s; These represent the minimum and maximum transmission power of the line under normal operating conditions, respectively; δ is the slack node number; K L K G K P K W K B and K D These are the node-branch, node-generator, node-pumped storage station, node-wind farm, node-battery storage, and node-load correlation matrices, respectively. (The node-branch matrix is ​​defined as 1 for a branch pointing to a node and -1 for a branch moving away from a node; the other correlation matrices are defined as having a value of 1 if the element is directly connected to a node and a value of 0 if it is not directly connected.) This is the line transmission power vector (with dimensions NL×1) at time t under a typical scenario s. and These are the generator power generation vector, pumped storage power generation vector, pumped storage power generation vector, offshore wind farm power generation vector, battery energy storage discharge power vector, battery energy storage charging power vector, load vector, and load shedding vector (these are column vectors arranged in ascending order of the subscripts of the power system components represented by each vector).

[0176] S2.4 Construct a multi-day optimization model for the power system based on the second objective function and the second set of operating constraints.

[0177] The risk index for offshore wind turbine operation constructed in this embodiment can comprehensively consider the failure rate and continuous operation time of the wind turbine under risk conditions. The first operating constraint can consider the operating status of the offshore wind turbine, and the first objective function can systematically consider the revenue of the offshore wind farm. This ensures that the optimal output model of offshore wind power generated on this basis takes into account these three perspectives to achieve the goal of optimal output of offshore wind power. The second objective function can consider the operating cost from multiple perspectives of power system operation, and the second set of operating constraints can consider the constraints of thermal power units, pumped storage units, and reservoir capacity. This ensures that the multi-day optimization model of the power system generated on this basis takes into account these two perspectives to achieve the goal of optimizing the power system.

[0178] S3. Solve the optimal output model of offshore wind power and the multi-day optimization model of the power system to obtain the first optimal solution of the start-up and shutdown plan of thermal power units and pumped storage units during typhoons, as well as the second optimal solution of the baseline operation plan of thermal power units. Use the first and second optimal solutions as the power system dispatching method to dispatch the power system of the offshore wind farm.

[0179] In this embodiment of the invention, firstly, the optimal output model for offshore wind power and the multi-day optimization model of the power system are solved to obtain the start-up and shutdown plans for thermal power units and pumped storage units during typhoons. Pumped storage unit pumping and power generation capacity Thermal power unit power generation Actual output of offshore wind turbines Energy storage charging and discharging power The optimal solution.

[0180] Then, the baseline operating plan for the thermal power unit is obtained based on the generating capacity of the thermal power unit;

[0181] The baseline operating plan for thermal power units is as follows:

[0182]

[0183] in, Let Ω be the power generation capacity of the thermal power unit, s be any typical scenario in the typical scenario set Ω, and ω be the power output of the thermal power unit. s This represents the probability that a typical scenario s may occur.

[0184] Finally, the start-up and shutdown plans for thermal power units and pumped storage units during the typhoon will be released. The optimal solution and the baseline operation plan of thermal power units. The optimal solution is used as a power system dispatching method to dispatch the power system of offshore wind farms.

[0185] For an explanation of the embodiments of the present invention, please refer to [link / reference]. Figure 2 An embodiment of the present invention provides a comparison chart of the power output of offshore wind power before and after optimization. It can be seen that after 250 time points (the interval between each two time points is 15 minutes), the active power output of the optimized offshore wind farm is always maintained at a stable high level, and there is no phenomenon of unstable power output or low power output.

[0186] In general, the embodiments of the present invention have the following beneficial effects:

[0187] This invention, through wind speed and wind power prediction, obtains wind speed and wind power prediction sequences. These two sequences fully consider the uncertainties in wind speed and wind power caused by typhoon forecast errors, enhancing the accuracy of the power system dispatching method. The optimal output model for offshore wind power comprehensively considers the risk indicators of offshore wind turbine operation, the expected returns of offshore wind farms, and the operating status of offshore wind turbines, achieving optimal overall output. The multi-day power system optimization model considers operating costs and constraints from multiple perspectives, optimizing the power system. By using the optimal solutions obtained from solving the optimal output model for offshore wind power and the multi-day power system optimization model as the power system dispatching method for offshore wind farms, the output process of offshore wind power can be optimized. Compared to existing technologies, this invention considers the uncertainty in wind power caused by typhoon forecast errors and optimizes the output process of offshore wind power.

[0188] Please see Figure 3 An embodiment of the present invention provides a power system dispatching device for an offshore wind farm during a typhoon, comprising:

[0189] The scenario and prediction value acquisition module 10 is used to acquire historical typhoon operation information and offshore wind farm operation information. By analyzing the typhoon operation information, a typical scenario set is generated according to the clustering method. Combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, the wind speed and wind power at the offshore wind farm in the typical scenario set are predicted to obtain the wind speed prediction value sequence and the wind power prediction value sequence.

[0190] The optimization model construction module 20 is used to establish risk indicators and first operating constraints for offshore wind turbine operation based on offshore wind farm operation information, wind speed prediction sequence, and wind power prediction sequence, as well as a first objective function for the expected revenue of offshore wind farm. The optimal output model of offshore wind power is constructed from the risk indicators, first operating constraints, and first objective function. The module also establishes a second objective function for the total operating cost of the power system in the offshore wind farm and a second set of operating constraints for the offshore wind farm based on the offshore wind farm operation information. The module further constructs a multi-day optimization model of the power system based on the second objective function and the second set of operating constraints.

[0191] Application module 30 solves the optimal output model of offshore wind power and the multi-day optimization model of the power system, and obtains the first optimal solution of the start-up and shutdown plan of thermal power units and pumped storage units during typhoons, as well as the second optimal solution of the baseline operation plan of thermal power units. The first and second optimal solutions are used as the power system dispatching method to dispatch the power system of the offshore wind farm.

[0192] In one embodiment, the scene and predicted value acquisition module 10 further includes:

[0193] The first construction unit of the typical scenario set calculates the relative error between the forecast and actual values ​​of the typhoon path based on the forecast and actual values ​​of the typhoon path in the typhoon operation information. It selects the step size of the typhoon path or direction forecast error and uses the typhoon path and angle errors of all historical sampling points in the typhoon operation information to draw the prediction error histograms of the typhoon path and direction forecast errors respectively. Based on the prediction error histograms, it establishes the initial fitting distribution index corresponding to the typhoon path or direction forecast error.

[0194] The parameters in the initial fitted distribution index are determined by the maximum likelihood estimation method, and the final fitted distribution index is obtained. An error distribution model is then constructed based on the final fitted distribution index.

[0195] Based on the error distribution model and typhoon operation information, a set of typical scenarios is generated using clustering methods;

[0196] The second construction unit of the typical scenario set is used to generate the first set of typhoon path and direction prediction error sequences by using the Monte Carlo method based on the error distribution model and typhoon forecast information in the typhoon operation information. The first set of typhoon path and direction prediction error sequences is then clustered by the clustering method to generate the second set of typhoon path and direction prediction error sequences. Each sequence in the second set of typhoon path and direction prediction error sequences is taken as a typical scenario of typhoon forecast error, thus obtaining the typical scenario set.

[0197] The correction unit is used to correct the predicted latitude and longitude coordinates of the typhoon path points in the typhoon operation information, so as to obtain the corrected latitude and longitude coordinates of the typhoon path points.

[0198] The sequence value acquisition unit is used to combine the latitude and longitude coordinates of the typhoon path point in the typhoon operation information and use the preset wind speed prediction method under typhoon conditions to predict the wind speed at each wind turbine in each classic scenario of the offshore wind farm in the typical scenario set, and obtain the wind speed prediction value sequence.

[0199] By combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, and based on the historical wind speed and corresponding power output of each wind turbine in the offshore wind farm operation information, the least squares method is used to fit the wind speed and power output curves of each wind turbine; the power output of each wind turbine is calculated based on the wind speed and power output curves, and a sequence of predicted wind power values ​​is obtained.

[0200] In one embodiment, the optimization model building module 20 further includes:

[0201] The first model parameter acquisition unit is used to obtain the first wind speed of the first wind turbine at the first moment based on the wind speed prediction value sequence, construct the failure rate of the first wind turbine based on the first wind speed, construct the risk state continuous operation time of the first wind turbine based on the offshore wind farm operation information, and establish the risk index of offshore wind turbine operation through the failure rate and risk state continuous operation time.

[0202] Based on the wind power forecast sequence and combined with the offshore wind farm operation information, the first operating constraint of offshore wind turbines is established.

[0203] Based on the risk indicators of offshore wind turbine operation and combined with offshore wind farm operation information, a first objective function for the expected return of offshore wind farms is established.

[0204] The second model parameter acquisition unit is used to calculate the costs of pumped storage units, thermal power units, wind curtailment penalty costs, energy storage operation costs, and load shedding loss costs for the first scenario in the typical scenario set based on the offshore wind farm operation information. Combining the costs of pumped storage units, thermal power units, wind curtailment penalty costs, energy storage operation costs, and load shedding loss costs, a second objective function is established based on the probability of the first scenario.

[0205] Based on the operational information of offshore wind farms, the following operational constraints are established: thermal power unit operation constraints, ramping constraints, pumped storage unit operation constraints, reservoir capacity constraints, offshore wind power operation constraints, battery energy storage operation constraints, system power balance and reserve constraints, and network constraints. These constraints constitute the second set of operational constraints for offshore wind farms.

[0206] The third model parameter acquisition unit is used to calculate the total output of the first offshore wind farm based on the active power output of the first wind turbine in the first offshore wind farm at the second time in the first scenario and the number of wind turbines in the first offshore wind farm. The offshore wind power operation constraints are constructed by making the total output of the first offshore wind farm less than or equal to the maximum value of the predicted value sequence of wind power.

[0207] This device predicts wind speed and wind power, obtaining wind speed and wind power prediction sequences. These sequences fully consider the uncertainties in wind speed and wind power caused by typhoon forecast errors, enhancing the accuracy of the power system dispatching method. Through the optimal output model for offshore wind power, it comprehensively considers the risk indicators of offshore wind turbine operation, the expected returns of offshore wind farms, and the operating status of offshore wind turbines, achieving optimal overall output. Through the multi-day optimization model of the power system, it considers operating costs and constraints from multiple perspectives, optimizing the power system. By using the optimal solutions obtained from solving the optimal output model for offshore wind power and the multi-day optimization model of the power system as the power system dispatching method for offshore wind farms, the goal of optimizing the output process of offshore wind power can be achieved. Compared to existing technologies, this device can consider the uncertainty in wind power caused by typhoon forecast errors and optimize the output process of offshore wind power.

[0208] Accordingly, this embodiment of the invention also provides a computer-readable storage medium, which includes a stored computer program, wherein, when the computer program is running, it controls the device where the computer-readable storage medium is located to execute the power system dispatching method for an offshore wind farm under a typhoon.

[0209] One method for power system dispatching of an offshore wind farm during a typhoon, if implemented as a software functional unit and used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0210] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A power system dispatching method for offshore wind farms during typhoons, characterized in that, include: Historical typhoon operation information and offshore wind farm operation information are obtained. By analyzing the typhoon operation information, a typical scenario set is generated based on a clustering method. Combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, the wind speed and wind power at the offshore wind farms in the typical scenario set are predicted to obtain wind speed prediction value sequences and wind power prediction value sequences. Based on the offshore wind farm operation information, wind speed prediction sequence, and wind power prediction sequence, risk indicators and first operation constraints for offshore wind turbine operation are established, along with a first objective function for the expected revenue of the offshore wind farm. An optimal output model for offshore wind power is constructed using the risk indicators, first operation constraints, and first objective function. Based on the offshore wind farm operation information, a second objective function for the total operating cost of the power system in the offshore wind farm and a second set of operation constraints for the offshore wind farm are established. A multi-day optimization model for the power system is constructed based on the second objective function and the second set of operation constraints. Solving the offshore wind power optimal output model and the power system multi-day optimization model, we obtain the first optimal solution for the start-up and shutdown plan of thermal power units and pumped storage units during typhoons, as well as the second optimal solution for the baseline operation plan of thermal power units. The first and second optimal solutions are used as the power system dispatching method to dispatch the power system of the offshore wind farm. The process of analyzing the typhoon's operational information and generating a set of typical scenarios based on clustering methods is as follows: Based on the predicted and actual typhoon paths in the typhoon operation information, the relative error between the predicted and actual values ​​is calculated. The step size of the typhoon path or direction forecast error is selected. Using the typhoon path and angle errors of all historical sampling points in the typhoon operation information, prediction error histograms of typhoon path and direction forecast errors are plotted respectively. Based on the prediction error histograms, an initial fitting distribution index corresponding to the typhoon path or direction forecast error is established. The parameters in the initial fitting distribution index are determined by the maximum likelihood estimation method to obtain the final fitting distribution index, and an error distribution model is constructed based on the final fitting distribution index. Based on the error distribution model and typhoon operation information, a set of typical scenarios is generated using clustering methods; The step of generating a typical scenario set based on the error distribution model and typhoon operation information using a clustering method is as follows: Based on the error distribution model and typhoon forecast information in the typhoon operation information, a first set of typhoon path and direction prediction error sequences is generated using the Monte Carlo method. The first set of typhoon path and direction prediction error sequences is then clustered using a clustering method to generate a second set of typhoon path and direction prediction error sequences. Each sequence in the second set of typhoon path and direction prediction error sequences is taken as a typical scenario of typhoon forecast error, thus obtaining the typical scenario set.

2. The power system dispatching method for an offshore wind farm under typhoon conditions as described in claim 1, characterized in that, By combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, the wind speed and wind power at the offshore wind farms in the typical scenario set are predicted, resulting in wind speed prediction sequences and wind power prediction sequences, specifically: Combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, the wind speed prediction method under the typhoon conditions is used to predict the wind speed at each wind turbine in each classic scenario of the offshore wind farm in the typical scenario set, and the wind speed prediction value sequence is obtained. Combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, and based on the historical wind speed and corresponding power output of each wind turbine in the offshore wind farm operation information, the least squares method is used to fit the wind speed and power output curves of each wind turbine; the power output of each wind turbine is calculated based on the wind speed and power output curves to obtain the wind power prediction value sequence.

3. The power system dispatching method for an offshore wind farm under typhoon conditions as described in claim 1, characterized in that, Based on the offshore wind farm operation information, wind speed forecast sequence, and wind power forecast sequence, risk indicators and first operational constraints for offshore wind turbine operation are established, along with a first objective function for the expected revenue of the offshore wind farm, specifically: The first wind speed of the first wind turbine is obtained at the first moment based on the wind speed prediction value sequence. The failure rate of the first wind turbine is constructed based on the first wind speed. The risk state continuous operation time of the first wind turbine is constructed based on the offshore wind farm operation information. The risk index of the offshore wind turbine operation is established through the failure rate and the risk state continuous operation time. Based on the wind power prediction sequence and the offshore wind farm operation information, the first operating constraint of the offshore wind turbine is established. Based on the risk indicators of the offshore wind turbine operation and the operational information of the offshore wind farm, a first objective function for the expected revenue of the offshore wind farm is established.

4. The power system dispatching method for an offshore wind farm under typhoon conditions as described in claim 1, characterized in that, Based on the aforementioned offshore wind farm operation information, a second objective function for the total operating cost of the power system in the offshore wind farm and a second set of operating constraints for the offshore wind farm are established, specifically as follows: Based on the offshore wind farm operation information, the costs of pumped storage units, thermal power units, wind curtailment penalty costs, energy storage operation costs, and load shedding losses for the first scenario in the typical scenario set are calculated. Combining the costs of pumped storage units, thermal power units, wind curtailment penalty costs, energy storage operation costs, and load shedding losses, a second objective function is established based on the probability of the first scenario. Based on the offshore wind farm operation information, the following constraints are established: thermal power unit operation constraints, ramping constraints, pumped storage unit operation constraints, reservoir capacity constraints, offshore wind power operation constraints, battery energy storage operation constraints, system power balance and reserve constraints, and network constraints. These constraints constitute the second set of operation constraints for the offshore wind farm.

5. A power system dispatching method for an offshore wind farm under typhoon conditions as described in claim 4, characterized in that, Offshore wind farm operation constraints are established based on the aforementioned offshore wind farm operation information, specifically as follows: Based on the active power output of the first wind turbine in the first offshore wind farm at the second moment in the first scenario, and the number of wind turbines in the first offshore wind farm, the total power output of the first offshore wind farm is calculated. The offshore wind power operation constraint is constructed by making the total power output of the first offshore wind farm less than or equal to the maximum value of the predicted value sequence of wind power.

6. The power system dispatching method for an offshore wind farm under typhoon conditions as described in claim 1, characterized in that, By analyzing the typhoon operation information and generating a typical scenario set based on clustering methods, the method also includes: The predicted latitude and longitude coordinates of the typhoon path points in the typhoon operation information are corrected to obtain the corrected latitude and longitude coordinates of the typhoon path points.

7. A power system dispatching device for an offshore wind farm during a typhoon, characterized in that, include: The scenario and prediction value acquisition module is used to acquire historical typhoon operation information and offshore wind farm operation information. By analyzing the typhoon operation information, a typical scenario set is generated according to the clustering method. Combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, the wind speed and wind power at the offshore wind farms in the typical scenario set are predicted to obtain wind speed prediction value sequence and wind power prediction value sequence. The optimization model construction module is used to establish risk indicators and first operating constraints for offshore wind turbine operation, as well as a first objective function for the expected revenue of the offshore wind farm, based on the offshore wind farm operation information, wind speed prediction sequence, and wind power prediction sequence. It then constructs an optimal output model for offshore wind power based on the risk indicators, first operating constraints, and first objective function. Furthermore, it establishes a second objective function for the total operating cost of the power system in the offshore wind farm and a second set of operating constraints for the offshore wind farm based on the offshore wind farm operation information. Finally, it constructs a multi-day optimization model for the power system based on the second objective function and the second set of operating constraints. The application module solves the offshore wind power optimal output model and the power system multi-day optimization model to obtain the first optimal solution for the start-up and shutdown plan of thermal power units and pumped storage units during typhoons, as well as the second optimal solution for the baseline operation plan of thermal power units. The first and second optimal solutions are used as the power system dispatching method to dispatch the power system of the offshore wind farm. The scenario and predicted value acquisition module includes: The first construction unit of the typical scenario set calculates the relative error between the forecast and actual values ​​of the typhoon path based on the forecast and actual values ​​of the typhoon path in the typhoon operation information, selects the step size of the typhoon path or direction forecast error, and plots the prediction error histograms of the typhoon path and direction forecast errors using the typhoon path and angle errors of all historical sampling points in the typhoon operation information. Based on the prediction error histograms, an initial fitting distribution index corresponding to the typhoon path or direction forecast error is established. The parameters in the initial fitting distribution index are determined by the maximum likelihood estimation method to obtain the final fitting distribution index, and an error distribution model is constructed based on the final fitting distribution index. Based on the error distribution model and typhoon operation information, a set of typical scenarios is generated using clustering methods; The second construction unit of the typical scenario set is used to generate a first set of typhoon path and direction prediction error sequences using the Monte Carlo method based on the error distribution model and typhoon forecast information in the typhoon operation information, and to cluster the first set of typhoon path and direction prediction error sequences using a clustering method to generate a second set of typhoon path and direction prediction error sequences. Each sequence in the second set of typhoon path and direction prediction error sequences is taken as a typical scenario of typhoon forecast error to obtain the typical scenario set.

8. A power system dispatching device for an offshore wind farm under typhoon conditions according to claim 7, characterized in that, The scenario and predicted value acquisition module also includes: The correction unit is used to correct the predicted latitude and longitude coordinates of the typhoon path points in the typhoon operation information to obtain the corrected latitude and longitude coordinates of the typhoon path points. The sequence value acquisition unit is used to combine the latitude and longitude coordinates of the typhoon path point in the typhoon operation information and use a preset typhoon condition wind speed prediction method to predict the wind speed at each wind turbine in each classic scenario of the offshore wind farm in the typical scenario set, so as to obtain the wind speed prediction value sequence. Combining the latitude and longitude coordinates of the typhoon path points in the typhoon operation information, and based on the historical wind speed and corresponding power output of each wind turbine in the offshore wind farm operation information, the least squares method is used to fit the wind speed and power output curves of each wind turbine; the power output of each wind turbine is calculated based on the wind speed and power output curves to obtain the wind power prediction value sequence.

9. A power system dispatching device for an offshore wind farm under typhoon conditions according to claim 7, characterized in that, The optimization model construction module includes: The first model parameter acquisition unit is used to obtain the first wind speed of the first wind turbine at the first moment based on the wind speed prediction value sequence, construct the failure rate of the first wind turbine based on the first wind speed, construct the risk state continuous operation time of the first wind turbine based on the offshore wind farm operation information, and establish the risk index of the offshore wind turbine operation through the failure rate and the risk state continuous operation time. Based on the wind power prediction sequence and the offshore wind farm operation information, the first operating constraint of the offshore wind turbine is established. Based on the risk indicators of the offshore wind turbine operation and the offshore wind farm operation information, a first objective function for the expected revenue of the offshore wind farm is established. The second model parameter acquisition unit is used to calculate the costs of pumped storage units, thermal power units, wind curtailment penalty costs, energy storage operation costs, and load shedding loss costs for the first scenario in the typical scenario set based on the offshore wind farm operation information. Combining the costs of pumped storage units, thermal power units, wind curtailment penalty costs, energy storage operation costs, and load shedding loss costs, the second objective function is established based on the probability of the first scenario. Based on the offshore wind farm operation information, the following constraints are established: thermal power unit operation constraints, ramping constraints, pumped storage unit operation constraints, reservoir capacity constraints, offshore wind power operation constraints, battery energy storage operation constraints, system power balance and reserve constraints, and network constraints. These constraints constitute the second set of operation constraints for the offshore wind farm. The third model parameter acquisition unit is used to calculate the total output of the first offshore wind farm based on the active power output of the first wind turbine in the first offshore wind farm at the second time in the first scenario and the number of wind turbines in the first offshore wind farm. The offshore wind farm operation constraints are constructed by making the total output of the first offshore wind farm less than or equal to the maximum value of the predicted value sequence of wind power.

10. A storage medium, characterized in that, The storage medium stores a computer program, which is called and executed by a computer to implement the power system dispatching method for offshore wind farms under typhoon conditions as described in any one of claims 1 to 6.

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