An offshore wind power system fast response frequency optimization scheduling method and device

Abstract

The application discloses a kind of offshore wind power system fast response frequency optimization frequency scheduling method and device, method includes: obtaining the historical offshore wind power output data set of offshore wind power system in same period, select several scene data from historical offshore wind power output data set, clustering analysis method is used to cluster each scene data, determine several clustering output scene data, the optimal scheduling data set of each clustering output scene data is calculated using economic optimization scheduling function, the optimal scheduling data set is applied to offshore wind power system in the period. Visible, the optimal scheduling data of each clustering output scene data is calculated using economic optimization scheduling function, the economic factor under the fast response frequency of offshore wind power participation is fully considered, the optimal scheduling data obtained can improve the response speed of offshore wind power system when system frequency fluctuates, to ensure that offshore wind power system is safely and reliably operated.

Description

Technical Field

[0001] This application relates to the field of offshore wind power, and more specifically, to an optimized scheduling method and apparatus for the fast response frequency of offshore wind power systems. Background Technology

[0002] With the rapid growth of renewable energy, offshore wind power has broad development prospects due to its characteristics of not occupying land resources and high energy efficiency of wind energy resources. Offshore wind farms achieve energy conversion through rotor-driven turbine generators. By integrating the relevant mechanisms of inertial control, it is possible to simulate the kinetic energy exchange of the rotating mass of the offshore wind power system when the frequency changes, thereby achieving rapid frequency response. The purpose of rapid frequency response is to compensate for the shortcomings of existing primary frequency regulation in terms of capacity and speed, and to rapidly inject active power to slow down the rate of frequency decline. By optimizing the scheduling of the offshore wind power system through rapid frequency response services, the offshore wind power system can respond as quickly as possible when the system frequency fluctuates.

[0003] However, most of the current methods for optimizing the scheduling of offshore wind power systems focus on factors of output uncertainty in order to study and analyze the electrical energy products provided by offshore wind farms. They do not consider the economic factors of offshore wind power participating in fast response frequencies. As a result, the scheduling results obtained based on the analysis of output uncertainty factors have low sensitivity to the response of offshore wind power systems when the system frequency fluctuates, and there are operational risks to offshore wind power systems. Summary of the Invention

[0004] In view of the above problems, this application is made to provide an optimized scheduling method and apparatus for the fast response frequency of offshore wind power systems, so as to improve the response speed of offshore wind power systems when the system frequency fluctuates.

[0005] To achieve the above objectives, the following specific solutions are proposed:

[0006] An optimized scheduling method for the fast response frequency of an offshore wind power system includes:

[0007] Obtain historical offshore wind power output datasets for offshore wind power systems during the same period, and select several scenario data from the historical offshore wind power output datasets;

[0008] K-means clustering analysis was used to cluster the data of each scenario to determine several clustered output scenario data. Each clustered output scenario data consisted of output scenario data from multiple time periods.

[0009] The optimal scheduling dataset for each cluster of output scenarios is calculated using an economic optimization scheduling function, which is:

[0010]

[0011] Where N represents the clustered offshore wind power output scenario set composed of all clustered output scenario data, G represents the set of conventional generator units in the offshore wind power system, W represents the set of offshore wind farms in the offshore wind power system, NL represents the set of lines within the offshore wind power system, and T represents the time period set for each clustered output scenario data. Let g be the operating cost of a conventional unit during time period t. Let g be the startup cost of a conventional unit in time period t. The minimum technical output cost of conventional unit g in time period t. For a conventional unit g, the frequency regulation cost during time period t is... The cost of providing a fast response frequency for an offshore wind farm w in time period t. Let g be the frequency regulation capacity of the conventional generating unit during time period t. Reserved frequency regulation capacity for conventional unit g during time period t. The capacity reserved for the fast response frequency of the offshore wind farm w in time period t, where M is the preset network power flow constraint relaxation penalty factor. Let l be the positive power flow relaxation variable for line l. Let l be the reverse power flow relaxation variable. The reduction ratio during clustering for the scenario contributing to the nth cluster;

[0012] The optimal scheduling dataset is applied to offshore wind power systems during the stated period.

[0013] Optionally, the step of calculating the optimal scheduling dataset for each clustered output scenario using an economic optimization scheduling function includes:

[0014] Under the constraints of the basic condition constraint set, the optimal scheduling dataset for each cluster of power output scenarios is calculated using the economic optimization scheduling function. The basic condition constraint set includes power balance constraints, unit power output constraints, unit ramping constraints, and line power flow constraints.

[0015] The power balance constraint is:

[0016]

[0017] in, The predicted load for the offshore wind power system. The power reduction of offshore wind farm w in time period t. Let w be the predicted power generation of the offshore wind farm at time t;

[0018] The unit output constraint is:

[0019]

[0020] in, This represents the lower limit of the output of a conventional unit g in time period t. This represents the upper limit of the output of a conventional unit g during time period t. This represents the start-up and shutdown status of conventional unit g during time period t.

[0021] The unit's ramp-up constraint is:

[0022]

[0023] in, This represents the maximum uphill power of a conventional unit (g). This represents the maximum downhill ramp power of a conventional unit (g).

[0024] The power flow constraint of the line is:

[0025]

[0026] Where D is the set of bus load nodes in the offshore wind power system. Let l be the power flow transmission limit of line l. The generator output power transfer distribution factor of the node where conventional unit g is located on line l is denoted as g. is the generator output power transfer distribution factor from bus load node d to line l.

[0027] Optionally, the step of calculating the optimal scheduling dataset for each cluster of output scenario data using an economic optimization scheduling function under the constraints of the basic condition constraint set includes:

[0028] Under the constraints of the basic condition constraint set and the fast response frequency constraint set, the optimal scheduling dataset for each cluster output scenario data is calculated using the economic optimization scheduling function. The fast response frequency constraint set includes the total system inertia constraint.

[0029] The overall inertial constraint of the system is:

[0030]

[0031] in, The total inertia of the offshore wind power system is given. This represents the change in active power of the offshore wind power system when it is subjected to an accident disturbance. For a conventional unit, g is the inertial time constant. For the rated capacity of a conventional unit g, The comprehensive inertia provided for offshore wind farms. , The rated frequency of the offshore wind power system is denoted as .

[0032] Optionally, the fast response frequency constraint set further includes system inertia requirement constraints, which are:

[0033]

[0034] in, This is the preset inertia requirement for the offshore wind power system.

[0035] Optionally, the fast response frequency constraint set further includes system frequency response constraints, which are:

[0036] .

[0037] Optionally, the fast response frequency constraint set further includes a system droop constraint, wherein the system droop constraint is:

[0038]

[0039] in, The dead-time cutoff frequency of conventional unit g is... The operating state of the turbine generator governor of conventional unit g during time period t is given. This represents the operating state of the turbine generator governor of the offshore wind farm w during time period t. This is an indicator variable for whether conventional unit g has a capacity margin during time period t. This is an indicator variable for whether the offshore wind farm w has a capacity margin during time period t. The sag characteristic of the offshore wind power system is as follows:

[0040]

[0041] in, For the sag characteristics of a conventional unit g, The droop characteristics of the offshore wind farm w.

[0042] Optionally, several scenario data points may be selected from the historical offshore wind power output dataset, including:

[0043] For each day in the aforementioned period, calculate the Euclidean distance between the output data of the offshore wind power system on that day and the historical output data of the offshore wind power system on that day in the historical offshore wind power output dataset, and use this distance as the Euclidean distance of the scene data on that historical day.

[0044] From each day of the period, determine the scene data for a number of days whose Euclidean distance is less than a preset Euclidean distance threshold, and obtain the scene data corresponding to the number of days.

[0045] An optimized scheduling device for the fast response frequency of an offshore wind power system, comprising:

[0046] The scene data acquisition unit is used to acquire the historical offshore wind power output dataset of the offshore wind power system in the same period, and select several scene data from the historical offshore wind power output dataset.

[0047] The clustering scenario data determination unit is used to cluster various scenario data using the K-means clustering analysis method to determine several clustered output scenario data. Each clustered output scenario data consists of output scenario data from multiple time periods.

[0048] The scheduling data calculation unit is used to calculate the optimal scheduling dataset for each cluster of output scenario data using an economic optimization scheduling function, wherein the economic optimization scheduling function is:

[0049]

[0050] Where N represents the clustered offshore wind power output scenario set composed of all clustered output scenario data, G represents the set of conventional generator units in the offshore wind power system, W represents the set of offshore wind farms in the offshore wind power system, NL represents the set of lines within the offshore wind power system, and T represents the time period set for each clustered output scenario data. Let g be the operating cost of a conventional unit during time period t. Let g be the startup cost of a conventional unit in time period t. The minimum technical output cost of conventional unit g in time period t. For a conventional unit g, the frequency regulation cost during time period t is... The cost of providing a fast response frequency for an offshore wind farm w in time period t. Let g be the frequency regulation capacity of the conventional generating unit during time period t. Reserved frequency regulation capacity for conventional unit g during time period t. The capacity reserved for the fast response frequency of the offshore wind farm w in time period t, where M is the preset network power flow constraint relaxation penalty factor. Let l be the positive power flow relaxation variable for line l. Let l be the reverse power flow relaxation variable. The reduction ratio during clustering for the scenario contributing to the nth cluster;

[0051] The scheduling data application unit is used to apply the optimal scheduling dataset to the offshore wind power system during the stated period.

[0052] Optionally, the scheduling data calculation unit includes:

[0053] The basic condition constraint calculation unit is used to calculate the optimal scheduling dataset for each cluster of power output scenarios under the constraints of the basic condition constraint set using the economic optimization scheduling function. The basic condition constraint set includes power balance constraints, unit power output constraints, unit ramping constraints, and line power flow constraints.

[0054] The power balance constraint is:

[0055]

[0056] in, The predicted load for the offshore wind power system. The power reduction of offshore wind farm w in time period t. Let w be the predicted power generation of the offshore wind farm at time t;

[0057] The unit output constraint is:

[0058]

[0059] in, This represents the lower limit of the output of a conventional unit g in time period t. This represents the upper limit of the output of a conventional unit g during time period t. This represents the start-up and shutdown status of conventional unit g during time period t.

[0060] The unit's ramp-up constraint is:

[0061]

[0062] in, This represents the maximum uphill power of a conventional unit (g). This represents the maximum downhill ramp power of a conventional unit (g).

[0063] The power flow constraint of the line is:

[0064]

[0065] Where D is the set of bus load nodes in the offshore wind power system. Let l be the power flow transmission limit of line l. Let g be the generator output power transfer distribution factor of the node where conventional unit g is located on line l. is the generator output power transfer distribution factor from bus load node d to line l.

[0066] Optionally, the basic condition constraint calculation unit includes:

[0067] The frequency response constraint calculation unit is used to calculate the optimal scheduling dataset for each cluster of output scenario data under the constraints of the basic condition constraint set and the fast response frequency constraint set using an economic optimization scheduling function. The fast response frequency constraint set includes the total system inertia constraint.

[0068] The overall inertial constraint of the system is:

[0069]

[0070] in, The total inertia of the offshore wind power system is given. This represents the change in active power of the offshore wind power system when it is subjected to an accident disturbance. For a conventional unit, g is the inertial time constant. For the rated capacity of a conventional unit g, The comprehensive inertia provided for offshore wind farms. , The rated frequency of the offshore wind power system is denoted as .

[0071] Optionally, the scene data acquisition unit includes:

[0072] The first scenario data acquisition subunit is used to acquire the historical offshore wind power output dataset of the offshore wind power system in the same period. For each day in the period, the Euclidean distance between the output data of the offshore wind power system on that day and the historical output data of the offshore wind power system on that day in the historical offshore wind power output dataset is calculated as the Euclidean distance of the scenario data on that day in the historical period.

[0073] The second scene data acquisition subunit is used to determine the scene data of a number of days from each day of the period whose Euclidean distance is less than a preset Euclidean distance threshold, and obtain a number of scene data corresponding to the number of days.

[0074] By employing the above technical solution, this application obtains historical offshore wind power output datasets for the same period, selects several scenario data from these datasets, and clusters these scenario data using K-means clustering analysis to determine several clustered output scenario data. Each clustered output scenario data consists of output scenario data from multiple time periods. Furthermore, an economic optimization scheduling function is used to calculate the optimal scheduling dataset for each clustered output scenario data, and this optimal scheduling dataset is applied to the offshore wind power system during the stated period. Therefore, calculating the optimal scheduling data for each clustered output scenario data using the economic optimization scheduling function fully considers the economic factors of offshore wind power participation in rapid response frequencies. The obtained optimal scheduling data can improve the response speed of the offshore wind power system during system frequency fluctuations, ensuring the safe and reliable operation of the offshore wind power system. Attached Figure Description

[0075] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0076] Figure 1 This is a schematic diagram illustrating a process for optimizing the scheduling of the rapid response frequency of an offshore wind power system, provided in an embodiment of this application.

[0077] Figure 2 A simplified schematic diagram of a clustering output scenario provided in this application embodiment;

[0078] Figure 3(a) is a schematic diagram of the performance effect of the offshore wind power system provided in the embodiment of this application after optimized scheduling;

[0079] Figure 3(b) is a schematic diagram of another performance effect of the offshore wind power system provided in the embodiment of this application after optimized scheduling;

[0080] Figure 4 A schematic diagram of a device structure for optimizing the scheduling of the rapid response frequency of an offshore wind power system, provided in an embodiment of this application;

[0081] Figure 5 This is a schematic diagram of the structure of a device for optimizing the scheduling of the rapid response frequency of an offshore wind power system, provided as an embodiment of this application. Detailed Implementation

[0082] 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 of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0083] The proposed solution can be implemented based on a terminal with data processing capabilities, such as a computer, server, or cloud platform.

[0084] Next, combined Figure 1 The optimized scheduling method for fast response frequency of offshore wind power systems described in this application may include the following steps:

[0085] Step S110: Obtain the historical offshore wind power output dataset of the offshore wind power system in the same period, and select several scenario data from the historical offshore wind power output dataset.

[0086] Specifically, each scenario data can represent the offshore wind power output data of the offshore wind power system on a certain historical day. The offshore wind power output data can represent the power generation capacity of the offshore wind power system at the optimized time granularity, with the unit being MW. The number of scenario data selected can be selected according to a preset number.

[0087] For example, the period can be selected as winter, and the offshore wind power output data can be selected from the winter historical output data of a certain offshore wind farm from November to January of the following year since its commissioning. Furthermore, 300 daily scene data can be selected through the scene generation process.

[0088] Step S120: Cluster the data of each scenario using K-means clustering analysis to determine several cluster output scenario data.

[0089] Each cluster of output scenario data can be composed of output scenario data from multiple time periods, and the duration of each time period can be customized, such as 15 minutes per time period.

[0090] Specifically, each clustered output scenario data can be selected from various scenario data. Therefore, the clustered output scenario data can also represent the offshore wind power output data for a certain historical day. Thus, the number of clustered output scenario data is much smaller than the number of scenario data. If a time period is 15 minutes, then each clustered output scenario data contains output scenario data from 15 * 4 * 24 = 96 time periods. If a time period is 1 hour, then each clustered output scenario data contains output scenario data from 1 * 24 = 24 time periods.

[0091] For example, as mentioned in step S110, 300 daily scene data points are analyzed using K-means clustering to form 10 cluster centroids, which serve as simplified cluster output scenarios. The probability of occurrence of each cluster output scenario can be determined by the proportion of each cluster output scenario to the total number of scene data points. A schematic diagram of the simplified cluster output scenario is shown below. Figure 2 As shown.

[0092] Step S130: Calculate the optimal scheduling dataset for each cluster of output scenario data using the economic optimization scheduling function.

[0093] The economic optimization scheduling function is as follows:

[0094]

[0095] Where N represents the clustered offshore wind power output scenario set composed of all clustered output scenario data, G represents the set of conventional generator units in the offshore wind power system, W represents the set of offshore wind farms in the offshore wind power system, NL represents the set of lines within the offshore wind power system, and T represents the time period set for each clustered output scenario data. Let g be the operating cost of a conventional unit during time period t. Let g be the startup cost of a conventional unit in time period t. The minimum technical output cost of conventional unit g in time period t. For a conventional unit g, the frequency regulation cost during time period t is... The cost of providing a fast response frequency for an offshore wind farm w in time period t. Let g be the frequency regulation capacity of the conventional generating unit during time period t. Reserved frequency regulation capacity for conventional unit g during time period t. The capacity reserved for the fast response frequency of the offshore wind farm w in time period t, where M is the preset network power flow constraint relaxation penalty factor. Let l be the positive power flow relaxation variable for line l. Let l be the reverse power flow relaxation variable. The reduction ratio during clustering for the scenario contributing to the nth cluster.

[0096] Specifically, for conventional wind turbines, the economic optimization scheduling function considers the operating cost, start-up cost, minimum technical output cost, and frequency regulation cost of conventional wind turbines. For offshore wind farms, the economic optimization scheduling function considers the cost of providing a fast response frequency.

[0097] Step S140: Apply the optimal scheduling dataset to the offshore wind power system during the period.

[0098] Specifically, the optimal scheduling dataset may include the active power output of each conventional unit in the offshore wind power system at each time period, and the active power output of each offshore wind farm at each time period.

[0099] The optimized scheduling method for the fast response frequency of offshore wind power systems provided in this embodiment obtains historical offshore wind power output datasets for the same period. Several scenario data points are selected from these datasets, and K-means clustering analysis is used to cluster the data points into several clustered output scenarios. Each clustered output scenario consists of output scenario data from multiple time periods. Furthermore, an economic optimization scheduling function is used to calculate the optimal scheduling dataset for each clustered output scenario, and this optimal scheduling dataset is applied to the offshore wind power system during the specified period. Therefore, calculating the optimal scheduling data for each clustered output scenario using the economic optimization scheduling function fully considers the economic factors of offshore wind power participation in fast response frequencies. The obtained optimal scheduling data can improve the response speed of the offshore wind power system when the system frequency fluctuates, ensuring the safe and reliable operation of the offshore wind power system.

[0100] In some embodiments of this application, the process of calculating the optimal scheduling dataset for each cluster output scenario using the economic optimization scheduling function in step S130 is described. This process may include:

[0101] Under the constraints of the basic condition constraint set, the optimal scheduling dataset for each cluster of output scenario data is calculated using the economic optimization scheduling function.

[0102] Specifically, the set of basic condition constraints can include power balance constraints, unit output constraints, unit ramping constraints, and line power flow constraints.

[0103] The power balance constraint is as follows:

[0104]

[0105] in, The predicted load for the offshore wind power system. The power reduction of offshore wind farm w in time period t. Let w be the predicted power generation of the offshore wind farm at time t.

[0106] Specifically, the power generation reduction can represent the power generation reduction caused by wind curtailment due to system safety reasons during the t-th time period of the offshore wind farm w.

[0107] The unit output constraint is:

[0108]

[0109] in, This represents the lower limit of the output of a conventional unit g in time period t. This represents the upper limit of the output of a conventional unit g during time period t. This represents the start-up and shutdown status of a conventional unit g during time period t.

[0110] Specifically, if the state of conventional unit g during time period t is "started", If the conventional unit g is in a stopped state during time period t, .

[0111] The unit's ramp-up constraint is:

[0112]

[0113] in, This represents the maximum uphill power of a conventional unit (g). This represents the maximum downhill ramp power of a conventional unit (g).

[0114] The power flow constraints of the line are:

[0115]

[0116] Where D is the set of bus load nodes in the offshore wind power system. Let l be the power flow transmission limit of line l. Let g be the generator output power transfer distribution factor of the node where conventional unit g is located on line l. is the generator output power transfer distribution factor from bus load node d to line l.

[0117] In some embodiments of this application, the process of calculating the optimal scheduling dataset for each clustered output scenario data under the constraints of the basic condition constraint set, as mentioned in the above embodiments, using an economic optimization scheduling function, is described. This process may include:

[0118] Under the constraints of the basic condition constraint set and the fast response frequency constraint set, the optimal scheduling dataset for each cluster of output scenario data is calculated using the economic optimization scheduling function.

[0119] Specifically, the fast response frequency constraint set includes the system total inertia constraint, the system inertia demand constraint, the system frequency response constraint, and the system droop constraint.

[0120] The total inertia, obtained by combining the inertia provided by the online synchronous resources of the offshore wind power system at each time period with the comprehensive inertia provided by the offshore wind farm, can constrain the offshore wind power system to ensure its safe operation. Therefore, the total system inertia constraint used to constrain the offshore wind power system to ensure its safe operation can be:

[0121]

[0122] in, The total inertia of the offshore wind power system is given. This represents the change in active power of the offshore wind power system when it is subjected to an accident disturbance. For a conventional unit, g is the inertial time constant. For the rated capacity of a conventional unit g, The comprehensive inertia provided for offshore wind farms. , The rated frequency of the offshore wind power system is denoted as .

[0123] Understandably, to ensure the safe operation of offshore wind power systems, it is necessary to control the frequency variation rate of offshore wind power systems. Controlled at the safety threshold Within this timeframe, time can be allocated to allow for subsequent frequency adjustment responses to synchronize resources, preventing frequency drops from exceeding the operating threshold of the low-frequency load shedding device.

[0124] To ensure the inertial response of synchronous resources and the overall inertia provided by offshore wind farms meet inertial requirements and guarantee the safety of offshore wind power systems, system inertial requirement constraints can be imposed on the offshore wind power system. These constraints can be:

[0125]

[0126] in, This is the preset inertia requirement for the offshore wind power system.

[0127] To ensure sufficient frequency response handling by each conventional turbine and each offshore wind farm in an offshore wind power system, a system frequency response constraint can be imposed on the offshore wind power system. The system frequency response constraint can be:

[0128]

[0129] in, It can represent the maximum response frequency of an offshore wind power system.

[0130] To address the droop settings and governor response requirements of conventional turbine units and offshore wind farms, system droop constraints can be applied to offshore wind power systems. These constraints can be:

[0131]

[0132] in, The dead-time cutoff frequency of conventional unit g is... The operating state of the turbine generator governor of conventional unit g during time period t is given. This represents the operating state of the turbine generator governor of the offshore wind farm w during time period t. This is an indicator variable for whether conventional unit g has a capacity margin during time period t. This is an indicator variable for whether the offshore wind farm w has a capacity margin during time period t. The sag characteristic of the offshore wind power system is as follows:

[0133]

[0134] in, For the sag characteristics of a conventional unit g, The droop characteristics of the offshore wind farm w.

[0135] The offshore wind power system was simulated using power grid simulation technology. The optimal scheduling dataset obtained by the economic optimization scheduling function was applied to the simulated offshore wind power system. The comprehensive inertial supply of the offshore wind farm increased the system inertial value by about 13%, as shown in Figure 3(a). This enabled the system to respond quickly when the system frequency fluctuated, which helped to buy time for the subsequent primary and secondary frequency regulation actions and ensure the safe and reliable operation of the system.

[0136] In addition, Figure 3(b) shows that when the system is subjected to the same disturbance and the offshore wind power penetration rate is high, whether the offshore wind farm provides fast frequency response service will have a significant impact on the frequency change rate of the system. Fast frequency response can effectively reduce the frequency change rate and prevent low-frequency load shedding.

[0137] The optimized scheduling method for fast response frequency of offshore wind power systems provided in this embodiment fully considers the risk of low inertia of offshore wind power systems under the new power system. By constraining the process, it ensures that the relevant inertia requirements are met and the frequency response is sufficient, which is conducive to ensuring the safe and reliable operation of the system.

[0138] In some embodiments of this application, the process of selecting several scenario data from the historical offshore wind power output dataset mentioned in the above embodiments is described, and this process may include:

[0139] S1. For each day in the period, calculate the Euclidean distance between the output data of the offshore wind power system on that day and the historical output data of the offshore wind power system on that day in the historical offshore wind power output dataset, and use this distance as the Euclidean distance of the scene data on that day.

[0140] Specifically, when calculating Euclidean distance, the factors considered in the daily scene data can include relevant characteristics such as wind speed, wind direction, temperature, and humidity.

[0141] S2. From the days of the period, determine the scene data of a number of days whose Euclidean distance is less than a preset Euclidean distance threshold, and obtain the scene data corresponding to the number of days.

[0142] In addition to calculating the Euclidean distance between the current day and historical days and determining the scene data based on the Euclidean distance, several scene data can also be selected from the historical offshore wind power output dataset through methods such as Latin hypercube sampling, Monte Carlo method, autoregressive-moving average model (ARMA model) method, and nonparametric probabilistic prediction method.

[0143] The following describes the optimized scheduling device for realizing the fast response frequency of offshore wind power systems provided in the embodiments of this application. The optimized scheduling device for realizing the fast response frequency of offshore wind power systems described below can be referred to in correspondence with the optimized scheduling method for realizing the fast response frequency of offshore wind power systems described above.

[0144] See Figure 4 , Figure 4 This is a schematic diagram of an optimized scheduling device for realizing rapid response frequency of an offshore wind power system, as disclosed in an embodiment of this application.

[0145] like Figure 4 As shown, the device may include:

[0146] Scene data acquisition unit 11 is used to acquire the historical offshore wind power output dataset of the offshore wind power system in the same period, and select several scene data from the historical offshore wind power output dataset;

[0147] Clustering scenario data determination unit 12 is used to cluster various scenario data using K-means clustering analysis to determine several clustered output scenario data. Each clustered output scenario data consists of output scenario data from multiple time periods.

[0148] The scheduling data calculation unit 13 is used to calculate the optimal scheduling dataset for each cluster of output scenario data using an economic optimization scheduling function, wherein the economic optimization scheduling function is:

[0149]

[0150] Where N represents the clustered offshore wind power output scenario set composed of all clustered output scenario data, G represents the set of conventional generator units in the offshore wind power system, W represents the set of offshore wind farms in the offshore wind power system, NL represents the set of lines within the offshore wind power system, and T represents the time period set for each clustered output scenario data. Let g be the operating cost of a conventional unit during time period t. Let g be the startup cost of a conventional unit in time period t. The minimum technical output cost of conventional unit g in time period t. For a conventional unit g, the frequency regulation cost during time period t is... The cost of providing a fast response frequency for an offshore wind farm w in time period t. Let g be the frequency regulation capacity of the conventional generating unit during time period t. Reserved frequency regulation capacity for conventional unit g during time period t. The capacity reserved for the fast response frequency of the offshore wind farm w in time period t, where M is the preset network power flow constraint relaxation penalty factor. Let l be the positive power flow relaxation variable for line l. Let l be the reverse power flow relaxation variable. The reduction ratio during clustering for the scenario contributing to the nth cluster;

[0151] The scheduling data application unit 14 is used to apply the optimal scheduling dataset to the offshore wind power system during the said period.

[0152] Optionally, the scheduling data calculation unit includes:

[0153] The basic condition constraint calculation unit is used to calculate the optimal scheduling dataset for each cluster of power output scenarios under the constraints of the basic condition constraint set using the economic optimization scheduling function. The basic condition constraint set includes power balance constraints, unit power output constraints, unit ramping constraints, and line power flow constraints.

[0154] The power balance constraint is:

[0155]

[0156] in, The predicted load for the offshore wind power system. The power reduction of offshore wind farm w in time period t. Let w be the predicted power generation of the offshore wind farm at time t;

[0157] The unit output constraint is:

[0158]

[0159] in, This represents the lower limit of the output of a conventional unit g in time period t. This represents the upper limit of the output of a conventional unit g during time period t. This represents the start-up and shutdown status of conventional unit g during time period t.

[0160] The unit's ramp-up constraint is:

[0161]

[0162] in, This represents the maximum uphill power of a conventional unit (g). This represents the maximum downhill ramp power of a conventional unit (g).

[0163] The power flow constraint of the line is:

[0164]

[0165] Where D is the set of bus load nodes in the offshore wind power system. Let l be the power flow transmission limit of line l. Let g be the generator output power transfer distribution factor of the node where conventional unit g is located on line l. is the generator output power transfer distribution factor from bus load node d to line l.

[0166] Optionally, the basic condition constraint calculation unit includes:

[0167] The frequency response constraint calculation unit is used to calculate the optimal scheduling dataset for each cluster of output scenario data under the constraints of the basic condition constraint set and the fast response frequency constraint set using an economic optimization scheduling function. The fast response frequency constraint set includes the total system inertia constraint.

[0168] The overall inertial constraint of the system is:

[0169]

[0170] in, The total inertia of the offshore wind power system is given. This represents the change in active power of the offshore wind power system when it is subjected to an accident disturbance. For a conventional unit, g is the inertial time constant. For the rated capacity of a conventional unit g, The comprehensive inertia provided for offshore wind farms. , The rated frequency of the offshore wind power system is denoted as .

[0171] Optionally, the scene data acquisition unit includes:

[0172] The first scenario data acquisition subunit is used to acquire the historical offshore wind power output dataset of the offshore wind power system in the same period. For each day in the period, the Euclidean distance between the output data of the offshore wind power system on that day and the historical output data of the offshore wind power system on that day in the historical offshore wind power output dataset is calculated as the Euclidean distance of the scenario data on that day in the historical period.

[0173] The second scene data acquisition subunit is used to determine the scene data of a number of days from each day of the period whose Euclidean distance is less than a preset Euclidean distance threshold, and obtain a number of scene data corresponding to the number of days.

[0174] The optimized scheduling device for the fast response frequency of offshore wind power systems provided in this application embodiment can be applied to devices such as terminals (mobile phones, computers, etc.) for optimized scheduling of the fast response frequency of offshore wind power systems. Optionally, Figure 5 This diagram shows the hardware structure of the equipment for optimizing the scheduling of fast response frequencies in offshore wind power systems. (Refer to...) Figure 5 The hardware structure of the equipment for the optimized scheduling of the fast response frequency of the offshore wind power system may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4.

[0175] In this embodiment of the application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4;

[0176] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0177] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0178] The memory stores a program, which the processor can call. The program is used for:

[0179] Obtain historical offshore wind power output datasets for offshore wind power systems during the same period, and select several scenario data from the historical offshore wind power output datasets;

[0180] K-means clustering analysis was used to cluster the data of each scenario to determine several clustered output scenario data. Each clustered output scenario data consisted of output scenario data from multiple time periods.

[0181] The optimal scheduling dataset for each cluster of output scenarios is calculated using an economic optimization scheduling function, which is:

[0182]

[0183] Where N represents the clustered offshore wind power output scenario set composed of all clustered output scenario data, G represents the set of conventional generator units in the offshore wind power system, W represents the set of offshore wind farms in the offshore wind power system, NL represents the set of lines within the offshore wind power system, and T represents the time period set for each clustered output scenario data. Let g be the operating cost of a conventional unit during time period t. Let g be the startup cost of a conventional unit in time period t. The minimum technical output cost of conventional unit g in time period t. For a conventional unit g, the frequency regulation cost during time period t is... The cost of providing a fast response frequency for an offshore wind farm w in time period t. Let g be the frequency regulation capacity of the conventional generating unit during time period t. Reserved frequency regulation capacity for conventional unit g during time period t. The capacity reserved for the fast response frequency of the offshore wind farm w in time period t, where M is the preset network power flow constraint relaxation penalty factor. Let l be the positive power flow relaxation variable for line l. Let l be the reverse power flow relaxation variable. The reduction ratio during clustering for the scenario contributing to the nth cluster;

[0184] The optimal scheduling dataset is applied to offshore wind power systems during the stated period.

[0185] Optionally, the refined and extended functions of the program can be found in the description above.

[0186] This application embodiment also provides a storage medium that can store a program suitable for execution by a processor, the program being used for:

[0187] Obtain historical offshore wind power output datasets for offshore wind power systems during the same period, and select several scenario data from the historical offshore wind power output datasets;

[0188] K-means clustering analysis was used to cluster the data of each scenario to determine several clustered output scenario data. Each clustered output scenario data consisted of output scenario data from multiple time periods.

[0189] The optimal scheduling dataset for each cluster of output scenarios is calculated using an economic optimization scheduling function, which is:

[0190]

[0191] Where N represents the clustered offshore wind power output scenario set composed of all clustered output scenario data, G represents the set of conventional generator units in the offshore wind power system, W represents the set of offshore wind farms in the offshore wind power system, NL represents the set of lines within the offshore wind power system, and T represents the time period set for each clustered output scenario data. Let g be the operating cost of a conventional unit during time period t. Let g be the startup cost of a conventional unit in time period t. The minimum technical output cost of conventional unit g in time period t. For a conventional unit g, the frequency regulation cost during time period t is... The cost of providing a fast response frequency for an offshore wind farm w in time period t. Let g be the frequency regulation capacity of the conventional generating unit during time period t. Reserved frequency regulation capacity for conventional unit g during time period t. The capacity reserved for the fast response frequency of the offshore wind farm w in time period t, where M is the preset network power flow constraint relaxation penalty factor. Let l be the positive power flow relaxation variable for line l. Let l be the reverse power flow relaxation variable. The reduction ratio during clustering for the scenario contributing to the nth cluster;

[0192] The optimal scheduling dataset is applied to offshore wind power systems during the stated period.

[0193] Optionally, the refined and extended functions of the program can be found in the description above.

[0194] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0195] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0196] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An optimized scheduling method for the fast response frequency of an offshore wind power system, characterized in that, include: Obtain historical offshore wind power output datasets for offshore wind power systems during the same period, and select several scenario data from the historical offshore wind power output datasets; K-means clustering analysis was used to cluster the data of each scenario to determine several clustered output scenario data. Each clustered output scenario data consisted of output scenario data from multiple time periods. The optimal scheduling dataset for each cluster of output scenarios is calculated using an economic optimization scheduling function, which is: ； Where N represents the clustered offshore wind power output scenario set composed of all clustered output scenario data, G represents the set of conventional generator units in the offshore wind power system, W represents the set of offshore wind farms in the offshore wind power system, NL represents the set of lines within the offshore wind power system, and T represents the time period set for each clustered output scenario data. Let g be the operating cost of a conventional unit during time period t. Let g be the startup cost of a conventional unit in time period t. The minimum technical output cost of conventional unit g in time period t. For a conventional unit g, the frequency regulation cost during time period t is... The cost of providing a fast response frequency for an offshore wind farm w in time period t. Let g be the frequency regulation capacity of the conventional generating unit during time period t. Reserved frequency regulation capacity for conventional unit g during time period t. The capacity reserved for the fast response frequency of the offshore wind farm w in time period t, where M is the preset network power flow constraint relaxation penalty factor. Let l be the positive power flow relaxation variable for line l. Let l be the reverse power flow relaxation variable. The reduction ratio during clustering for the scenario contributing to the nth cluster; The optimal scheduling dataset is applied to offshore wind power systems during the stated period.

2. The method according to claim 1, characterized in that, The calculation of the optimal scheduling dataset for each clustered output scenario using the economic optimization scheduling function includes: Under the constraints of the basic condition constraint set, the optimal scheduling dataset for each cluster of power output scenarios is calculated using the economic optimization scheduling function. The basic condition constraint set includes power balance constraints, unit power output constraints, unit ramping constraints, and line power flow constraints. The power balance constraint is: ； in, The predicted load for the offshore wind power system. The power reduction of offshore wind farm w in time period t. Let w be the predicted power generation of the offshore wind farm at time t; The unit output constraint is: ； in, This represents the lower limit of the output of a conventional unit g in time period t. This represents the upper limit of the output of a conventional unit g during time period t. This represents the start-up and shutdown status of conventional unit g during time period t. The unit's ramp-up constraint is: ； in, This represents the maximum uphill power of a conventional unit (g). This represents the maximum downhill ramp power of a conventional unit (g). The power flow constraint of the line is: ； Where D is the set of bus load nodes in the offshore wind power system. Let l be the power flow transmission limit of line l. The generator output power transfer distribution factor of the node where conventional unit g is located on line l is denoted as g. is the generator output power transfer distribution factor from bus load node d to line l.

3. The method according to claim 2, characterized in that, The process of calculating the optimal scheduling dataset for each cluster of output scenarios using an economic optimization scheduling function under the constraints of the basic condition constraint set includes: Under the constraints of the basic condition constraint set and the fast response frequency constraint set, the optimal scheduling dataset for each cluster output scenario data is calculated using the economic optimization scheduling function. The fast response frequency constraint set includes the total system inertia constraint. The overall inertial constraint of the system is: ； in, The total inertia of the offshore wind power system is given. This represents the change in active power of the offshore wind power system when it is subjected to an accident disturbance. For a conventional unit, g is the inertial time constant. For the rated capacity of a conventional unit g, The comprehensive inertia provided for offshore wind farms. The frequency variation rate of the offshore wind power system The safety threshold, , The rated frequency of the offshore wind power system is denoted as .

4. The method according to claim 3, characterized in that, The fast response frequency constraint set also includes system inertia requirement constraints, which are as follows: ； in, This is the preset inertia requirement for the offshore wind power system.

5. The method according to claim 4, characterized in that, The fast response frequency constraint set also includes system frequency response constraints, which are: 。 6. The method according to claim 5, characterized in that, The fast response frequency constraint set also includes system droop constraints, which are: ； in, The dead-time cutoff frequency of conventional unit g is... The operating state of the turbine generator governor of conventional unit g during time period t is given. This represents the operating state of the turbine generator governor of the offshore wind farm w during time period t. This is an indicator variable for whether conventional unit g has a capacity margin during time period t. This is an indicator variable for whether the offshore wind farm w has a capacity margin during time period t. The sag characteristic of the offshore wind power system is as follows: ； in, For the sag characteristics of a conventional unit g, The droop characteristics of the offshore wind farm w.

7. The method according to any one of claims 1-6, characterized in that, Several scenario data points were selected from the historical offshore wind power output dataset, including: For each day in the aforementioned period, calculate the Euclidean distance between the output data of the offshore wind power system on that day and the historical output data of the offshore wind power system on that day in the historical offshore wind power output dataset, and use this distance as the Euclidean distance of the scene data on that historical day. From each day of the period, determine the scene data for a number of days whose Euclidean distance is less than a preset Euclidean distance threshold, and obtain the scene data corresponding to the number of days.

8. An optimized scheduling device for the fast response frequency of an offshore wind power system, characterized in that, include: The scene data acquisition unit is used to acquire the historical offshore wind power output dataset of the offshore wind power system in the same period, and select several scene data from the historical offshore wind power output dataset. The clustering scenario data determination unit is used to cluster various scenario data using the K-means clustering analysis method to determine several clustered output scenario data. Each clustered output scenario data consists of output scenario data from multiple time periods. The scheduling data calculation unit is used to calculate the optimal scheduling dataset for each cluster of output scenario data using an economic optimization scheduling function, wherein the economic optimization scheduling function is: ； Where N represents the clustered offshore wind power output scenario set composed of all clustered output scenario data, G represents the set of conventional generator units in the offshore wind power system, W represents the set of offshore wind farms in the offshore wind power system, NL represents the set of lines within the offshore wind power system, and T represents the time period set for each clustered output scenario data. Let g be the operating cost of a conventional unit during time period t. Let g be the startup cost of a conventional unit in time period t. The minimum technical output cost of conventional unit g in time period t. For a conventional unit g, the frequency regulation cost during time period t is... The cost of providing a fast response frequency for an offshore wind farm w in time period t. Let g be the frequency regulation capacity of the conventional generating unit during time period t. Reserved frequency regulation capacity for conventional unit g during time period t. The capacity reserved for the fast response frequency of the offshore wind farm w in time period t, where M is the preset network power flow constraint relaxation penalty factor. Let l be the positive power flow relaxation variable for line l. Let l be the reverse power flow relaxation variable. The reduction ratio during clustering for the scenario contributing to the nth cluster; The scheduling data application unit is used to apply the optimal scheduling dataset to the offshore wind power system during the stated period.

9. The apparatus according to claim 8, characterized in that, The scheduling data calculation unit includes: The basic condition constraint calculation unit is used to calculate the optimal scheduling dataset for each cluster of power output scenarios under the constraints of the basic condition constraint set using the economic optimization scheduling function. The basic condition constraint set includes power balance constraints, unit power output constraints, unit ramping constraints, and line power flow constraints. The power balance constraint is: ； in, The predicted load for the offshore wind power system. The power reduction of offshore wind farm w in time period t. Let w be the predicted power generation of the offshore wind farm at time t; The unit output constraint is: ； in, This represents the lower limit of the output of a conventional unit g in time period t. This represents the upper limit of the output of a conventional unit g during time period t. This represents the start-up and shutdown status of conventional unit g during time period t. The unit's ramp-up constraint is: ； in, This represents the maximum uphill power of a conventional unit (g). This represents the maximum downhill ramp power of a conventional unit (g). The power flow constraint of the line is: ； Where D is the set of bus load nodes in the offshore wind power system. Let l be the power flow transmission limit of line l. The generator output power transfer distribution factor of the node where conventional unit g is located on line l is denoted as g. is the generator output power transfer distribution factor from bus load node d to line l.

10. The apparatus according to claim 9, characterized in that, The basic condition constraint calculation unit includes: The frequency response constraint calculation unit is used to calculate the optimal scheduling dataset for each cluster of output scenario data under the constraints of the basic condition constraint set and the fast response frequency constraint set using an economic optimization scheduling function. The fast response frequency constraint set includes the total system inertia constraint. The overall inertial constraint of the system is: ； in, The total inertia of the offshore wind power system is given. This represents the change in active power of the offshore wind power system when it is subjected to an accident disturbance. For a conventional unit, g is the inertial time constant. For the rated capacity of a conventional unit g, The comprehensive inertia provided for offshore wind farms. The frequency variation rate of the offshore wind power system The safety threshold, , The rated frequency of the offshore wind power system is denoted as .

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