A Wind Power Scheduling Method and System Considering Service Quality and Power Margin

Abstract

The present invention discloses a wind power scheduling method and system considering service quality and power margin. The method includes the steps of: obtaining the service quality index of a wind turbine generator set; the service quality index of the wind turbine generator set includes the active power service quality index and the reactive power service quality index; obtaining the active power distribution factor by weighting the active power service quality index and the available active power, then obtaining the active power distribution reference instruction based on the active power distribution factor, and then performing active power scheduling based on the active power distribution reference instruction; obtaining the reactive power / voltage variable gain coefficient by weighting the reactive power service quality index and the available reactive power, then obtaining the reactive power distribution reference instruction based on the reactive power / voltage variable gain coefficient, and then performing active power scheduling based on the reactive power distribution reference instruction. The present invention has the advantages of improving the stable consumption of wind power and the ability of the unit to operate safely, etc.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of wind power, and particularly relates to a wind power scheduling method and system considering service quality and power margin. Background Art

[0002] Due to its cleanliness and economy, wind power has become one of the most popular renewable energies in the world. With the continuous expansion of capacity and scale, the power control of wind farms faces some new challenges. Considering the volatility of wind energy and the complexity of the power flow relationship, the power production of wind turbines in a wind farm should be strategically planned and scheduled. In past research, the power scheduling problem has mainly been divided into two categories: economic power scheduling and power scheduling with electrical constraints. The economic power scheduling problem usually aims at economic indicators such as network loss suppression and power supply. In the power scheduling problem with electrical constraints, wind turbines are coordinated to ensure node voltage safety or system frequency stability. These traditional power scheduling methods can achieve some wind farm-level control objectives. However, they usually ignore the operation safety of the wind turbines themselves, which is the basis and prerequisite of power production.

[0003] In the past decade, a large number of studies on service quality have been carried out in complex electromechanical systems such as aerospace and high-speed railways. However, the service quality assessment and regulation methods for wind farms have not been well studied. A wind turbine is a typical complex electromechanical system containing several key components, such as blades (impellers), nacelles, gearboxes, generators, converters, towers, foundations (floating bodies), etc. In addition, due to the complex and harsh operating conditions, each key component suffers from various forms of failure and malfunction, and each failure may cause the entire wind turbine system to shut down and subsequent high maintenance costs.

[0004] Some studies have focused on the impact of power production on the stability of wind turbines and proposed corresponding regulation methods. However, existing studies usually ignore the impact of reactive power output on the operating state of the units, such as overheating caused by generator and converter overload. Therefore, the research on the comprehensive assessment and precise regulation of the service quality of wind turbines is urgent and practical. Summary of the Invention

[0005] Aiming at the technical problems existing in the prior art, the present invention provides a wind power scheduling method and system considering service quality and power margin, which can improve the ability of wind power to be stably absorbed and the safe operation of wind turbines.

[0006] To solve the above technical problems, the technical solution proposed by the present invention is as follows:

[0007] A wind power scheduling method considering service quality and power margin, comprising the steps of:

[0008] Obtain the service quality indicators of the wind turbine; the service quality of the wind turbine includes the active power service quality indicator and the reactive power service quality indicator;

[0009] Obtain the active power distribution factor by weighting the active power service quality indicator and the available active power, then obtain the active power distribution reference instruction based on the active power distribution factor, and then perform active power scheduling based on the active power distribution reference instruction;

[0010] Obtain the reactive / voltage variable gain coefficient by weighting the reactive power service quality indicator and the available reactive power, then obtain the reactive power distribution reference instruction based on the reactive / voltage variable gain coefficient, and then perform active power scheduling based on the reactive power distribution reference instruction.

[0011] Preferably, the service quality indicators are fairly obtained by establishing a SCADA operating status database, normalizing the modeling, and expert scoring evaluation.

[0012] Preferably, the specific process of establishing the correlation SCADA operating status database is as follows:

[0013] Select representative parameters from the operation monitoring data of the wind farm SCADA system to establish an evaluation system for the service quality of the wind turbine, and establish the SCADA operating status database of active power / reactive power respectively;

[0014] Among them, the operating database corresponding to the active power includes all the underlying evaluation indicators, namely the main drive shaft system, the nacelle and power generation system, the tower system, and the pitch system;

[0015] Among them, the operating database corresponding to the reactive power includes the nacelle and power generation system, specifically the nacelle temperature, the nacelle control cabinet temperature, the generator stator A temperature, the generator stator B temperature, and the inverter coolant temperature.

[0016] Preferably, the service quality indicators of the wind turbine are divided into unidirectional indicators and bidirectional indicators; the unidirectional indicators include temperature and tower vibration acceleration; the bidirectional indicators include tower vibration displacement and hub speed;

[0017] The process of normalization is as follows:

[0018] For unidirectional indicators:

[0019]

[0020] Among them, s ij is a certain underlying evaluation data, and k ij is the processed monitoring data, which represents the stability margin of the component; [s ij,min , s ij,max is the normal operating range;

[0021] For two-way indicators:

[0022]

[0023] where s ij is a certain underlying evaluation data, and k ij is the stability margin of a certain component under the influence of this indicator; [s ij,min , s ij,max is the normal operating range; for tower displacement, s ij,min and s ij,max are usually opposite to each other, and its center line s ij,cent is zero; for hub speed, s ij,cent is calculated according to the power curve.

[0024] Preferably, the expert scoring evaluation is specifically as follows:

[0025] First, the normalized underlying evaluation indicators are weighted and summed to:

[0026]

[0027] where k ij is the stability margin of a certain component, and α ij is the weight coefficient of this indicator k ij ; n is the number of types of underlying data in a certain evaluation system;

[0028] The evaluation weight coefficient is obtained by comprehensively considering the severity, occurrence frequency, and maintenance time and cost of various faults of the wind turbine, and scoring the real-time monitoring status variables in the operation database by experts to determine the influence degree of the fault types caused by each underlying evaluation indicator on the service quality of the whole machine.

[0029] Preferably, the specific process for obtaining the active power distribution factor D is as follows:

[0030] D = {d1, d2,..., d n'};

[0031] where

[0032] In the formula, d i is the availability index of the i-th wind turbine; O a，i is the active service quality index of the i-th wind turbine; γ and ε are weight parameters; C i is the rated capacity of the i-th wind turbine; is the available active power of the i-th wind turbine.

[0033] Preferably, the process of obtaining the active power distribution reference instruction based on the active power distribution factor is as follows:

[0034] The active power distribution reference instruction is obtained by proportional distribution based on the active power distribution factor D:

[0035]

[0036] Wherein, is the active power reference value of the i-th wind turbine at time t; is the active power demand instruction of the wind farm from the dispatching center; d i is the availability index of the i-th wind turbine; N is the number of wind turbines in the wind farm.

[0037] Preferably, the process of obtaining the reactive power / voltage gain coefficient by weighting the reactive power service quality index and the available reactive power is as follows:

[0038] The reactive voltage control gain coefficient μ of each wind turbine i is proportional to its reactive power availability index e i Then there is:

[0039] μ i ∝e i

[0040] From the overall perspective of the wind farm, there is:

[0041]

[0042] Wherein, μ i is the reactive voltage control gain of the i-th wind turbine; is the proportionality factor; is the reactive power capacity of the i-th wind turbine; μ is the fixed gain coefficient of the traditional method; N is the number of wind turbines in the wind farm;

[0043] The reactive power gain coefficient of the wind turbine is calculated as:

[0044]

[0045] Wherein, μ i (t) is the reactive voltage control gain of the i-th wind turbine at time t; is the reactive power capacity of the i-th wind turbine; μ is the fixed gain coefficient of the traditional method; N is the number of wind turbines in the wind farm.

[0046] Preferably, the reactive power distribution reference instruction is obtained based on the voltage droop control and the reactive / voltage variable gain coefficient:

[0047]

[0048] Among them, is the reactive power reference value; μ i (t) is the reactive voltage control gain of the i-th wind turbine at time t; Q i,O is the initial reactive power sampling value; V i (t) is the terminal voltage of the i-th wind turbine; V ref is the voltage reference.

[0049] The present invention also discloses a wind power scheduling system considering service quality and power margin, including a memory and a processor connected to each other. A computer program is stored on the memory, and when the computer program is run by the processor, it executes the steps of the method described above.

[0050] Compared with the prior art, the advantages of the present invention are as follows:

[0051] The wind farm power scheduling method of the present invention considering service quality and power margin performs wind farm power scheduling through a power distribution strategy formulated based on the service quality index of wind turbines, and can meet the requirements of regional power grid active power balance, wind farm voltage control, and unit health adjustment simultaneously, which has important scientific significance and application value for improving the stable consumption of wind power and the safe operation ability of units. Brief Description of the Drawings

[0052] Figure 1 It is a schematic diagram of the configuration of the wind farm system in the embodiment of the present invention.

[0053] Figure 2 It is a schematic diagram of the evaluation system of the service quality of wind turbines in the embodiment of the present invention.

[0054] Figure 3 It is a control flowchart of the power distribution method in the embodiment of the present invention. Detailed Embodiment

[0055] The following further describes the present invention in combination with the drawings of the specification and specific embodiments.

[0056] The wind farm power scheduling method of the embodiment of the present invention considering service quality and power margin specifically includes the steps of:

[0057] Obtain the service quality index of wind turbines; the service quality index of wind turbines includes the active power service quality index and the reactive power service quality index; the service quality index is fairly obtained through establishing a correlation SCADA operation status database, normalized modeling, and expert scoring evaluation;

[0058] Perform wind farm power scheduling based on the power distribution strategy formulated based on the obtained service quality index of wind turbines, which specifically includes two parts: active power distribution and reactive power distribution;

[0059] Among them, active power distribution is carried out based on the active power distribution reference instruction; the active power distribution reference instruction is obtained by proportionally distributing based on the active power distribution factor, and the active power distribution factor is obtained by weighting the active service quality index and the available active power;

[0060] Among them, active power distribution is carried out based on the reactive power distribution reference instruction; the reactive power distribution reference instruction is obtained based on the reactive power / voltage variable gain coefficient; the reactive power / voltage variable gain coefficient is obtained by weighting the reactive service quality index and the available reactive power.

[0061] The wind farm power scheduling method considering service quality and power margin of the present invention performs wind farm power scheduling through a power distribution strategy formulated based on the service quality index of wind turbines, and can meet the requirements of regional power grid active power balance, wind farm voltage control and unit health adjustment simultaneously, which has important scientific significance and application value for improving the stable consumption of wind power and the safe operation ability of units.

[0062] In specific applications, such as Figure 1 shown, the present invention adopts a radial wind turbine farm group system, which is configured to include 24 5-megawatt wind turbines, a 33 / 0.9 KV wind power export transformer, a 155 / 33 KV medium-voltage bus transformer, a 380 / 155 KV high-voltage bus transformer and an external power grid. The system layout is as follows: every 8 5MW wind turbines are connected to a power feeder at a distance of 2 km, and three power feeders converge to the medium-voltage bus and are transmitted to the external power grid.

[0063] In a specific embodiment, the specific process of establishing the correlation SCADA operation status database is as follows:

[0064] The operation monitoring data of the wind farm SCADA system is the underlying evaluation index of the service quality index. There are many index parameters that affect the health status of wind turbines, and these indexes reflect the unit operation status from different degrees, different aspects and different levels. In order to comprehensively and truly reflect the health status of wind turbines, based on field research, the information such as the structure, fault mode and mechanism of wind turbines, and operation and maintenance logs are comprehensively summarized and analyzed, and selected as Figure 2The representative parameters shown are used to establish the evaluation system S for the service quality of wind turbines, denoted as S = {S1, S2, S3, S4}. Specifically, the evaluation system S includes (S1) the main drive shaft system, (S2) the nacelle and power generation system, (S3) the tower system, and (S4) the pitch system; each object layer element contains n indicators, that is, Si = {si1, si2,... sij}, where sij is the jth evaluation indicator of the ith object layer element. Among them, (S1) the main drive shaft system includes (s11) hub speed, (s12) hub temperature, (s13) hub control cabinet temperature, (s14) main shaft A temperature, (s15) main shaft B temperature; (S2) the nacelle and power generation system includes (s21) nacelle temperature, (s22) nacelle control cabinet temperature, (s23) generator stator A temperature, (s24) generator stator B temperature, (s25) frequency converter coolant temperature; (S3) the tower system includes (s31) tower vibration axial displacement, (s32) tower vibration axial acceleration, (s33) tower vibration radial displacement, (s34) tower vibration radial acceleration; (S4) the pitch system includes (s41) pitch motor temperature A, (s42) pitch motor temperature B, (s43) pitch motor temperature C, (s44) pitch battery maximum temperature.

[0065] Furthermore, by establishing a SCADA operating status database related to active power / reactive power respectively; the main task of a wind turbine is power production, so the cumulative loss caused by active power output is the main factor for equipment deterioration and faults, and almost all operating monitoring data is related to active power output. In Figure 2 , the set of underlying judgment factors directly related to active power can be denoted as Sa = {S1, S2, S3, S4}, that is, (S1) the main drive shaft system, (S2) the nacelle and power generation system, (S3) the tower system, and (S4) the pitch system.

[0066] On the other hand, due to the requirements of power balance and voltage control, the wind turbine executes the reactive power output command, resulting in changes in the operating state. Since it does not directly participate in power production, the impact of reactive power on the operating state is mainly reflected in the temperature change of the power generation system. In Figure 2 , the set of underlying judgment factors directly related to reactive power can be denoted as Sre = {S2}, that is, (S2) the nacelle and power generation system, and the main contents are (s21) nacelle temperature, (s22) nacelle control cabinet temperature, (s23) generator stator A temperature, (s24) generator stator B temperature, and (s25) frequency converter coolant temperature. These data can be collected through the SCADA system, and the real-time stability of the wind turbine can be judged through them.

[0067] Specifically, the evaluation system S can be divided into unidirectional indicators and bidirectional indicators. Among them, the unidirectional indicators include temperature and tower vibration acceleration. The bidirectional indicators include tower vibration displacement and hub speed.

[0068] In order to judge the stability margin of each monitoring data within a unified value range, it is necessary to normalize them according to their threshold ranges. For unidirectional indicators,

[0069]

[0070] where s ij is a certain underlying evaluation data, and k ij is the processed monitoring data, which represents the stability margin of this component. [s ij，min， s ij，max is the normal operating range.

[0071] For bidirectional indicators,

[0072]

[0073] where s ij is a certain underlying evaluation data, and k ij is the stability margin of a certain component under the influence of this indicator; [s ij，min , s ij，max is the normal operating range; for tower displacement, s ij，min and s ij，max are usually opposite to each other, and its center line s ij，cent is zero; for hub speed, s ij，cent can be calculated according to the power curve.

[0074] Furthermore, the service quality of the wind turbine is fairly evaluated by the expert scoring method, specifically:

[0075] First of all, the normalized underlying evaluation indicators are weighted and summed to:

[0076]

[0077] where k ij is the stability margin of a certain component, and α ij is the weight coefficient of this indicator k ij ; n is the number of types of underlying data in a certain evaluation system;

[0078] The evaluation weight coefficient is obtained by comprehensively considering factors such as the severity, occurrence frequency, maintenance time, and cost of various faults of wind turbines. By means of expert scoring, the real-time monitoring status variables in the operation database are scored to determine the degree of influence of the fault types that may be caused by each underlying evaluation index on the service quality of the whole machine. The detailed scoring rules are as follows:

[0079]

[0080] For severity, level I represents simple operation instability, where operation parameters exceed the allowable values, with little impact on equipment safety and can be solved through routine maintenance;

[0081] Level II represents minor faults that affect the power output of the fan;

[0082] Level III represents serious faults at the component level, such as gear wear, etc.;

[0083] Level IV represents serious faults that will cause the whole machine to shut down.

[0084] For maintenance time and cost, level I represents short downtime duration and cheap spare parts; level II represents more maintenance cost and duration; level III represents serious faults that require the invocation of a large number of operation and maintenance resources (such as operation and maintenance vessels and personnel); for the failure rate, levels I-II represent occasional occurrence and frequent occurrence respectively.

[0085] By analyzing the influence degree of various fault forms, the weight coefficients of each underlying evaluation index can be obtained. For example, excessive axial displacement of the tower may cause the wind turbine to collapse. The severity, maintenance cost, and failure rate of this accident are rated as IV, III, and I respectively. Therefore, the weight coefficient of k 31 is 2.3. Through this method, the service quality indicators O a and O re related to active power and reactive power can be obtained respectively.

[0086] As Figure 3 shown, the active power distribution factor is obtained by weighting the active power-related service quality indicator O a ={O a，1 , O a，2 , …, O a，n'} and the available active power. The total power demand instruction of the dispatching center is expected to be proportionally distributed to wind turbines with different stability margins based on the service quality indicator. In addition, due to the wake effect and terrain differences, the wind energy captured by wind turbines at different positions is different, which leads to different available active powers. In order to improve resource utilization and avoid potential fault risks at the same time, the service quality indicator O aThe available active power is combined to generate the availability index D = {d1, d2,..., d n'}, where:

[0087]

[0088] In the above formula, d i is the availability index of the i-th wind turbine; O a，i is the active power related service quality index of the i-th wind turbine; γ and ε are weight parameters; C i is the rated capacity of the i-th wind turbine; is the available active power of the i-th wind turbine.

[0089] The power reference command is proportionally allocated based on the availability index D:

[0090]

[0091] where, is the active power reference value of the i-th wind turbine at time t; is the active power demand command of the dispatching center for the wind farm; d i is the availability index of the i-th wind turbine; N is the number of wind turbines in the wind farm.

[0092] The reactive power reference is calculated by voltage droop control, and the voltage droop control expression can be described as:

[0093] Q i (t) = Q i，0 + μ(V i (t) - V ref )

[0094] where, Q i (t) and Q i，0 are the reactive power of the i-th wind turbine at time t and the initial time respectively; V i (t) is the terminal voltage of the i-th wind turbine; V ref is the voltage reference, generally taking a value of 1 p.u.; μ is the gain coefficient.

[0095] The reactive / voltage variable gain coefficient is obtained by weighting the reactive power related service quality index and the available reactive power. The reactive power availability index of the wind turbine can be written as:

[0096]

[0097] In the above formula, e i is the reactive power availability index of the i-th wind turbine; is the reactive power capacity of the i-th wind turbine; γ and ε are weight parameters; C iis the rated capacity of the i-th wind turbine.

[0098] The reactive voltage control gain μ of each wind turbine i is proportional to its reactive power availability index e i and there is:

[0099] μ i ∝ e i

[0100] From the overall perspective of the wind farm, the present invention has the same reactive power response performance as the traditional droop method, and there is:

[0101]

[0102] where μ i is the reactive voltage control gain of the i-th wind turbine; is the proportionality factor; is the reactive power capacity of the i-th wind turbine; μ is the fixed gain coefficient of the traditional method; N is the number of wind turbines in the wind farm.

[0103] Therefore, the reactive power gain of the wind turbine can be calculated as:

[0104]

[0105] where μ i (t) is the reactive voltage control gain of the i-th wind turbine at time t; is the reactive power capacity of the i-th wind turbine; μ is the fixed gain coefficient of the traditional method; N is the number of wind turbines in the wind farm.

[0106] The reactive power distribution reference command is obtained based on voltage droop control and the reactive / voltage variable gain coefficient:

[0107]

[0108] where is the reactive power reference value; μ i (t) is the reactive voltage control gain of the i-th wind turbine at time t; Qi,0 is the initial reactive power sampling value; V i (t) is the terminal voltage of the i-th wind turbine; V ref is the voltage reference.

[0109] An embodiment of the present invention provides a computer program product, including a computer program, and when the computer program is run by a processor, it executes the steps of the method as described above. An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, it executes the steps of the method as described above. An embodiment of the present invention also discloses a computer device, including a memory and a processor connected to each other, a computer program is stored on the memory, and when the computer program is run by the processor, it executes the steps of the method as described above.

[0110] The present invention realizes all or part of the processes in the above-described embodiment methods, and can also be completed by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-described method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable storage medium includes: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. The memory is used to store computer programs and / or modules. The processor realizes various functions by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. The memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices, etc.

[0111] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present invention should be regarded as within the protection scope of the present invention.

Claims

1. A wind power scheduling method considering service quality and power margin, characterized in that Including the steps: Obtain the service quality indicators of the wind turbine generator set; the service quality indicators of the wind turbine generator set include the active power service quality indicator and the reactive power service quality indicator; Based on the active power service quality indicator and the available active power, obtain the active power distribution factor by weighting, then obtain the active power distribution reference instruction based on the active power distribution factor, and then perform active power scheduling based on the active power distribution reference instruction; Based on the reactive power service quality indicator and the available reactive power, obtain the reactive power / voltage variable gain coefficient by weighting, then obtain the reactive power distribution reference instruction based on the reactive power / voltage variable gain coefficient, and then perform active power scheduling based on the reactive power distribution reference instruction; The specific process of obtaining the active power distribution factor D is: D = {d1, d2, …, d n ,}; Among them where d i is the availability index of the i-th fan; O a,i is the active power service quality index of the i-th fan; γ and ε are weight parameters; C i is the rated capacity of the i-th fan; is the available active power of the i-th fan.

2. The wind power scheduling method considering service quality and power margin according to claim 1, characterized in that The service quality indicators of the wind turbine generator set are fairly obtained through establishing the SCADA operation status database, normalization modeling and expert scoring evaluation.

3. The wind power scheduling method considering service quality and power margin according to claim 2, characterized in that The specific process of establishing the relevant SCADA operation status database is: Select representative parameters from the operation monitoring data of the wind farm SCADA system to establish an evaluation system for the service quality of the wind turbine generator set, and establish the SCADA operation status database of active power / reactive power respectively; The operation database corresponding to the active power includes all the underlying evaluation indicators, namely the main drive shaft system, the nacelle and power generation system, the tower system and the pitch system; The operation database corresponding to the reactive power includes the nacelle and power generation system, specifically the nacelle temperature, the nacelle control cabinet temperature, the generator stator A temperature, the generator stator B temperature and the frequency converter coolant temperature.

4. The wind power scheduling method considering service quality and power margin according to claim 3, characterized in that The service quality indicators of the wind turbine generator set are divided into unidirectional indicators and bidirectional indicators; the unidirectional indicators include temperature and tower vibration acceleration; the bidirectional indicators include tower vibration displacement and hub speed; For the unidirectional indicators, perform normalization processing: Among them, s ij is a certain underlying evaluation data; [s ij,min , s ij,max is the normal operating range; For the bidirectional indicators, perform normalization processing: Among them, s ij is a certain underlying evaluation data, and k ij is the stability margin of a certain component; [s ij,min , s iJ,max is the normal operating range; for the tower displacement, s ij,min and s ij,max are opposite to each other, and its center line s ij,cent is zero; for the hub speed, s ij,cent is calculated according to the power curve.

5. The wind power scheduling method considering service quality and power margin according to claim 4, characterized in that The specific content of the expert scoring evaluation is: First, the normalized underlying evaluation indicators are weighted and summed to obtain: Among them, α ij is the weight coefficient of index k ij ; n is the number of underlying data types in a certain evaluation system The evaluation weight coefficient is determined by comprehensively considering the severity, occurrence frequency, maintenance time and cost of various faults of the wind turbine generator set, and scoring the real-time monitoring status variables in the operation database in the form of expert scoring to determine the influence degree of the fault types caused by each underlying evaluation indicator on the overall service quality of the machine.

6. The wind power scheduling method considering service quality and power margin according to claim 1, characterized in that The process of obtaining the active power distribution reference instruction based on the active power distribution factor is: The active power distribution reference instruction is obtained by proportional distribution based on the active power distribution factor D; Among them, is the active power reference value of the i-th wind turbine at time t; is the active power demand instruction of the dispatching center for the wind farm; d i is the availability index of the i-th wind turbine; N is the number of wind turbines in the wind farm.

7. The wind power scheduling method considering service quality and power margin according to any one of claims 1-5, characterized in that The process of obtaining the reactive power / voltage gain coefficient based on the reactive power service quality indicator and the available reactive power is: The reactive voltage control gain coefficient μ of each fan i is proportional to its reactive power availability index e i and thus: μ i ∝e i From the overall perspective of the wind farm: Among them, μ i is the reactive voltage control gain of the i-th wind turbine; is the scaling factor; is the reactive power capacity of the i-th wind turbine; μ is the fixed gain coefficient of the traditional method; N is the number of wind turbines in the wind farm; The reactive power gain coefficient of the wind turbine generator set is calculated as: Among them, μ i (t) is the reactive voltage control gain of the i-th wind turbine at time t; is the reactive power capacity of the i-th wind turbine; μ is the fixed gain coefficient of the traditional method; N is the number of wind turbines in the wind farm.

8. The wind power scheduling method considering service quality and power margin according to claim 7, characterized in that Based on the voltage droop control and the reactive power / voltage variable gain coefficient, obtain the reactive power distribution reference instruction; Among them, is the reactive power reference value; μ i (t) is the reactive voltage control gain of the i-th wind turbine at time t; Q i,0 is the initial reactive power sampling value; V i (t) is the terminal voltage of the i-th wind turbine; V ref is the voltage reference.

9. A wind power scheduling system considering service quality and power margin, comprising a memory and a processor connected to each other, wherein a computer program is stored on the memory, and is characterized in that, When the computer program is run by the processor, it executes the steps of the method described in any one of claims 1-8.

Images