A method for dispatching a power system taking into account the operation reserve capacity provided by wind turbines

Abstract

The present invention relates to the technical field of power dispatching of electric power systems, and specifically to a method for dispatching electric power systems taking into account the operating reserve capacity provided by wind turbines. The present invention is based on the wind turbine reserve capacity of pitch angle control decisions, takes into account the two-stage random dispatching of wind turbines to provide operating reserve capacity, considers wind speed uncertainty, load uncertainty and random component failures, and determines the optimal day-ahead output-reserve decision based on a random optimization model. According to this process, the reserve capacity of wind turbines taking into account the uncertainty of decision dependence in economic dispatch can be modeled, the wind power reserve capacity can be accurately quantified, and the optimal economic dispatch taking into account the operating risk can be further realized. This method can effectively improve the accuracy of the wind turbine reserve capacity model and thus optimize the output and reserve capacity of conventional units and wind turbines, providing a more reliable basis for power dispatching, reserve planning and other work of the electric power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power system dispatching, and in particular to a method for dispatching a power system taking into account wind turbines providing operating reserve capacity. Background Art

[0002] Driven by the carbon emission targets, wind power grid connection has developed rapidly in recent years. By the end of 2021, my country's wind power grid-connected installed capacity reached 300.15 million kilowatts, doubling from the end of 2016. In the future, wind power will play a leading role in many power systems. In order to maintain the supply and demand balance of system operation, the uncertainty of wind power will lead to an increase in the system's operating reserve demand. This will increase the operating cost of the power system in a high-proportion wind power power system and reduce the benefits of wind power grid connection. Figure 1 As shown in the figure, through the pitch angle control strategy, the wind turbine can be operated at a reduced load to provide spare capacity, thereby reducing the spare cost of conventional units. By modeling and accurately quantifying the wind power reserve capacity, additional operating reserves can be provided for the system to ensure supply and demand balance, improve operational reliability, and reduce total operating costs. Therefore, taking into account the uncertainty of wind power to accurately model the spare capacity of wind turbines and quantify the spare capacity of traditional units and wind turbines in the economic dispatch process is an effective means to improve the economy of wind power-containing power systems from the perspective of dispatch planning, which is of great significance.

[0003] Accurate modeling of wind power reserve capacity helps to achieve the optimal decision of output-reserve in day-ahead economic dispatch and reduce the risk of system load loss. Since wind power output is affected by the uncertainty of wind speed, wind power reserve capacity needs to be determined based on probabilistic criteria. In addition, the pitch angle decision of wind turbine load shedding to provide reserve will affect the coefficient between wind speed and available wind power, thereby affecting the actual wind power output and reserve capacity. Wind power reserve capacity is affected by both wind speed uncertainty and load shedding decision of operators, so the uncertainty of wind power reserve capacity is decision-dependent. Unlike the reserve provided by traditional thermal power and hydropower units, the uncertainty of wind power reserve will lead to unreliable reserve. In actual fault and wind power fluctuation scenarios, insufficient reserve will lead to the risk of load shedding in the system. This makes the traditional reserve quantification criteria based on N-1 criterion or percentage criterion not applicable to the situation where wind power provides reserve. Therefore, in the system economic dispatch, it is necessary to take into account the uncertainty of wind power reserve capacity, conduct joint optimization of output-reserve, and improve system reliability and economy.

[0004] Therefore, when wind power reserve capacity and operation decisions are coupled, how to consider the uncertainty of decision-making dependence, improve the accuracy of the wind turbine reserve capacity model, and then optimize the output and reserve capacity of conventional units and wind turbines, and provide a more reliable basis for power system power dispatching, reserve planning and other work, is a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0005] In view of the deficiencies of the above-mentioned prior art, the present invention provides a power system dispatching method which takes into account the operating reserve capacity provided by wind turbines, which can improve the accuracy of the wind turbine reserve capacity model and thus optimize the output and reserve capacity of conventional units and wind turbines, and provide a more reliable basis for power system power dispatching, reserve planning and other tasks.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] A method for dispatching a power system taking into account the provision of operating reserve capacity by wind turbines comprises the following steps:

[0008] Step 1: Based on the characteristic coefficient of wind turbines, calculate the wind energy utilization coefficient C of each wind turbine at different wind speeds p ;

[0009] Step 2: Based on wind energy utilization coefficient C p The linear function F of wind turbine output to pitch angle at different wind speeds is established by linear fitting. w (β,v);

[0010] Step 3: According to the linear function F of wind turbine output to pitch angle w (β,v), modeling the output and reserve capacity of wind turbines;

[0011] Step 4: Based on the historical wind speed and load data of the wind farm, the wind speed and load of the previous 24 hours are predicted, and the prediction error of the normal distribution of wind speed is taken into account to generate the real-time 24-hour wind speed scenario and wind speed scenario probability π ws , taking into account the normal distribution of load prediction errors, the real-time 24-hour load scenario and load scenario probability π are generated ds ;

[0012] Step 5: Enumerate the fault scenarios of generator sets and lines in real time for 24 hours, and calculate the probability of fault scenarios in combination with the equipment failure probability FOR

[0013] Step 6: Combine wind speed scenarios, load scenarios, and fault scenarios to obtain all possible system scenarios in real time for 24 hours, and calculate the system scenario probability π s =π ws ·π ds ·π os ;

[0014] Step 7: Based on the system scenarios and system scenario probabilities, a random economic dispatch model of the system is constructed;

[0015] Step 8: Solve the output-reserve decision of the system according to the stochastic economic dispatch model to obtain the total economic dispatch cost taking into account reliability and the day-ahead economic dispatch decision.

[0016] Glossary:

[0017] 24 hours a day refers to one working day before the economic dispatch plan of the power system is formulated based on the historical wind speed and load data forecast of the wind farm and the normal operation of all system components;

[0018] Real-time 24 hours refers to the actual operating working day of the power system. Real-time 24 hours faces a certain system scenario, and real-time operation is adjusted based on the previous decision and system scenario.

[0019] Preferably, in step 1, the wind energy utilization coefficient C of the wind turbine generator set is p for:

[0020]

[0021]

[0022]

[0023] In the formula, the wind energy utilization coefficient C p is the ratio of wind energy converted into available wind power, β is the pitch angle of the wind turbine, λ is the tip speed ratio, R is the radius and angular velocity of the wind turbine blade, ω is the wind turbine rotor speed, v is the wind speed, λ is the tip speed ratio, i are intermediate variables including tip speed ratio λ and pitch angle β.

[0024] Preferably, in step 3, the output of the wind turbine generator set includes the day-ahead dispatched output The reserve capacity of wind turbines includes the maximum reserve capacity on the previous day. Maximum reserve capacity on the day before The upper reserve capacity of the day-ahead dispatch The next reserve capacity for day-ahead dispatch

[0025] Preferably, the maximum upper reserve capacity of wind turbine w at time t is and maximum reserve capacity They are:

[0026]

[0027]

[0028] In the formula, Based on the day-ahead wind speed forecast At minimum pitch angle The maximum available wind power output at that time is Based on the day-ahead wind speed forecast and the pitch angle increases to the pitch angle The wind power will be dispatched to output in the future;

[0029] The upper reserve capacity of wind turbine w dispatched at time t and the next reserve capacity for day-ahead dispatch They are:

[0030]

[0031]

[0032] In the formula, is the minimum pitch angle in scheduling The maximum wind power output at that time; The maximum pitch angle for scheduling increases to The minimum wind power output thereafter; Provides the maximum increment of the upper spare pitch angle for the wind turbine. The maximum reduction in pitch angle that provides a lower reserve for the wind turbine.

[0033] Preferably, the output of wind turbine w in the real-time system scenario s at time t is for:

[0034]

[0035] In the formula, F w is the wind power output conversion function taking into account the cut-in and cut-out wind speeds, is the pitch angle of the wind turbine in the real-time system scenario, is the wind speed in the real-time system scenario; and

[0036] Preferably, in step 4, an integrated moving average autoregressive model is used to predict the wind speed and load for the 24 hours before the day; a sampling method is used to generate a real-time 24-hour wind speed scenario and a wind speed scenario probability π ws ; Use sampling method to calculate the load scenario and load scenario probability π in real time for 24 hours ds .

[0037] Preferably, in step 7, the stochastic economic dispatch model is a two-stage stochastic economic dispatch model, in which the first stage quantifies the output and reserve capacity of wind turbines and conventional units in the previous 24 hours; the second stage calculates the load shedding and wind abandonment of the system based on the real-time 24-hour system scenario and the output and reserve capacity quantified in the first stage.

[0038] Preferably, in step 7, the objective function of the stochastic economic dispatch model is:

[0039]

[0040] In the formula, the first term represents the power generation cost of conventional units, is the output of conventional units scheduled in advance, H g is the output cost function of conventional units, g is the number of conventional units, N G is the number of conventional units, t is the specific time, and T is the scheduling period; the second term represents the standby cost of conventional units, The spare capacity of conventional units scheduled in advance in the past day, is the reserve capacity of conventional units scheduled in advance, c GR is the backup cost coefficient of conventional units; the third term represents the backup cost of wind turbines, The reserve capacity of the wind turbines scheduled in advance in the day before. is the reserve capacity of the wind turbines scheduled in advance, c WR is the wind power reserve cost coefficient, N W is the total number of wind turbines; the fourth and fifth items represent the risk cost of real-time scenario rescheduling, where is the load shedding amount of the system node, c dcur is the load shedding cost coefficient, is the wind turbine abandoned air volume, c wcur is the wind curtailment cost coefficient, π s is the scenario probability of system scenario s.

[0041] Preferably, the constraints of the stochastic economic dispatch model include a day-ahead pre-dispatch flow constraint based on a day-ahead wind speed forecast value, and a flow constraint based on a re-dispatch of the system real-time scenario.

[0042] Preferably, in step 8, a stochastically optimized Benders decomposition algorithm is used to solve the system output-reserve; the total economic dispatch cost taking into account reliability and the day-ahead economic dispatch decision include the wind power output, wind power reserve capacity, traditional unit output, and traditional unit reserve capacity in the 24 hours before the day.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 1. The present invention is based on the wind turbine spare capacity of the pitch angle control decision, takes into account the two-stage random scheduling of the wind turbine to provide the operating reserve capacity, considers the uncertainty of wind speed, uncertainty of load and random failure of components, and determines the optimal day-ahead output-reserve decision based on the random optimization model. According to this process, the wind turbine spare capacity taking into account the uncertainty of decision dependence in economic scheduling can be modeled, and the wind power reserve capacity can be accurately quantified. At the same time, the optimal economic scheduling taking into account the operating risk can be further realized, which is of great significance to improving the economy and safety of power system operation. The present invention can model the uncertainty of decision dependence of wind turbines, take into account a variety of uncertainties, and jointly optimize the output-reserve of conventional units and wind turbines in the power system, thereby improving the system operation reliability and the economy of scheduling. It can play a good complementary role to the traditional economic scheduling of power systems that do not take into account the reserve capacity of wind power, and is of great significance to the planning and scheduling of the reserve capacity of wind turbines.

[0045] In summary, this method can effectively improve the accuracy of the wind turbine reserve capacity model and optimize the output and reserve capacity of conventional units and wind turbines, providing a more reliable basis for power system power dispatching, reserve planning and other tasks.

[0046] 2. Since the problem size of the stochastic optimization model increases sharply with the increase in the number of scenarios, Benders decomposition is used to calculate the stochastic economic dispatch model, and the day-ahead and real-time scenarios are solved separately, thereby reducing the problem size and improving computational efficiency.

[0047] 3. This method uses wind turbines to provide backup, which can provide additional backup capacity for the power system and reduce the demand for backup capacity. Compared with the traditional percentage backup quantification criteria, this method considers the uncertainty of wind turbine output and backup decision-making, reduces the risk of load loss, and can improve the reliability of power system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to make the purpose, technical solution and advantages of the invention more clear, the present invention will be further described in detail below with reference to the accompanying drawings, in which:

[0049] Figure 1 It is a schematic diagram of adjusting the wind energy utilization coefficient of a wind turbine generator set in the background technology through pitch angle control;

[0050] Figure 2 is a flow chart in an embodiment;

[0051] Figure 3 A schematic diagram of the spare capacity of a wind turbine generator system based on pitch angle control decision in an embodiment;

[0052] Figure 4 It is a schematic diagram of the working process of the stochastic economic dispatch model in the embodiment;

[0053] Figure 5 A schematic diagram for comparing the backup decision results of conventional units and wind turbine units calculated by the test system using the present invention;

[0054] Figure 6 The figure is a comparative schematic diagram of the output-standby decision results of a conventional unit and a wind turbine unit calculated by the test system using the present invention. DETAILED DESCRIPTION

[0055] The following is a further detailed description through specific implementation methods:

[0056] Example:

[0057] like Figure 2 As shown, this embodiment discloses a method for dispatching a power system taking into account the provision of operating reserve capacity by wind turbines, comprising the following steps:

[0058] Step 1: Based on the characteristic coefficient of wind turbines, calculate the wind energy utilization coefficient C of each wind turbine at different wind speeds p ;

[0059] In specific implementation, the wind energy utilization coefficient C of the wind turbine p for:

[0060]

[0061]

[0062]

[0063] In the formula, the wind energy utilization coefficient C p is the ratio of wind energy converted into available wind power, β is the pitch angle of the wind turbine, λ is the tip speed ratio, R is the radius and angular velocity of the wind turbine blade, ω is the wind turbine rotor speed, v is the wind speed, λ is the tip speed ratio, i are intermediate variables including tip speed ratio λ and pitch angle β.

[0064] Step 2: Based on wind energy utilization coefficient C p The linear function F of wind turbine output to pitch angle at different wind speeds is established by linear fitting. w (β,v);

[0065] Step 3: According to the linear function F of wind turbine output to pitch angle w (β,v), modeling the output and reserve capacity of wind turbines.

[0066] In specific implementation, the output of wind turbines includes the day-ahead dispatch output. The reserve capacity of wind turbines includes the maximum reserve capacity on the previous day. Maximum reserve capacity on the day before The upper reserve capacity of the day-ahead dispatch The next reserve capacity for day-ahead dispatch The reserve capacity of wind turbines based on pitch angle control decision is as follows: Figure 3 shown.

[0067] The maximum upper reserve capacity of wind turbine w at time t and maximum reserve capacity They are:

[0068]

[0069]

[0070] In the formula, Based on the day-ahead wind speed forecast At minimum pitch angle The maximum available wind power output at that time is Based on the day-ahead wind speed forecast The pitch angle increases to The wind power will be dispatched to output in the future;

[0071] The upper reserve capacity of wind turbine w dispatched at time t and the next reserve capacity for day-ahead dispatch They are:

[0072]

[0073]

[0074] In the formula, is the minimum pitch angle in scheduling The maximum wind power output at that time; The maximum pitch angle for scheduling increases to The minimum wind power output thereafter; Provides the maximum increment of the upper spare pitch angle for the wind turbine. The maximum reduction in pitch angle that provides a lower reserve for the wind turbine.

[0075] The output of wind turbine w dispatched in real-time system scenario s at time t for:

[0076]

[0077] In the formula, F w is the wind power output conversion function taking into account the cut-in and cut-out wind speeds, is the pitch angle of the wind turbine in the real-time system scenario, is the wind speed in the real-time system scenario; and

[0078] Step 4: Based on the historical wind speed and load data of the wind farm, the wind speed and load of the previous 24 hours are predicted, and the prediction error of the normal distribution of wind speed is taken into account to generate the real-time 24-hour wind speed scenario and wind speed scenario probability π ws , taking into account the normal distribution of load prediction errors, the real-time 24-hour load scenario and load scenario probability π are generated ds In the specific implementation, the integrated moving average autoregressive model is used to predict the wind speed and load in the 24 hours before the day; the sampling method is used to generate the real-time 24-hour wind speed scenario and wind speed scenario probability π ws ; Use sampling method to calculate the load scenario and load scenario probability π in real time for 24 hours ds .

[0079] Step 5: Enumerate the fault scenarios of generator sets and lines in real time for 24 hours, and calculate the probability of fault scenarios in combination with the equipment failure probability FOR

[0080] Step 6: Combine wind speed scenarios, load scenarios, and fault scenarios to obtain all possible system scenarios in real time for 24 hours, and calculate the system scenario probability π s =π ws ·π ds ·π os ;

[0081] Step 7: Based on the system scenarios and system scenario probabilities, a random economic dispatch model of the system is constructed. The working process of the random economic dispatch model is as follows: Figure 4 The stochastic economic dispatch model is a two-stage stochastic economic dispatch model. The first stage quantifies the output and reserve of wind turbines and conventional units in the previous 24 hours. The second stage calculates the load shedding and wind abandonment of the system based on the real-time 24-hour system scenario and the output and reserve quantified in the first stage.

[0082] The objective function of the stochastic economic dispatch model is:

[0083]

[0084] In the formula, the first term represents the power generation cost of conventional units, is the output of conventional units scheduled in advance, H g is the output cost function of conventional units, g is the number of conventional units, N G is the number of conventional units, t is the specific time, and T is the scheduling period; the second term represents the standby cost of conventional units, The spare capacity of conventional units scheduled in advance in the day before, is the reserve capacity of conventional units scheduled in advance, c GRis the backup cost coefficient of conventional units; the third term represents the backup cost of wind turbines, The reserve capacity of the wind turbines scheduled in advance in the day before. is the reserve capacity of the wind turbines scheduled in advance, c WR is the wind power reserve cost coefficient, N W is the total number of wind turbines; the fourth and fifth items represent the risk cost of real-time scenario rescheduling, where is the load shedding amount of the system node, c dcur is the load shedding cost coefficient, is the wind turbine abandoned air volume, c wcur is the wind curtailment cost coefficient, π s is the scenario probability of system scenario s.

[0085] The constraints of the stochastic economic dispatch model include the pre-dispatch power flow constraint based on the day-ahead wind speed forecast value, and the power flow constraint based on the system real-time scenario re-dispatch. In specific implementation, the constraints include wind turbine output and reserve capacity constraints taking into account pitch angle decisions, as well as conventional unit output and reserve capacity constraints, power balance constraints, phase angle constraints, and line transmission capacity constraints.

[0086] Step 8: According to the stochastic economic dispatch model, the stochastic optimized Benders decomposition algorithm is used to solve the output-reserve decision of the system, and the total cost of economic dispatch considering reliability and the day-ahead economic dispatch decision are obtained. The total cost of economic dispatch considering reliability and the day-ahead economic dispatch decision include the wind power output, wind power reserve capacity, traditional unit output, and traditional unit reserve capacity in the 24 hours before the day.

[0087] Since the problem size of the stochastic optimization model increases sharply with the increase of the number of scenarios, Benders decomposition is used to calculate the stochastic economic dispatch model, and the day-ahead and real-time scenarios are solved separately, thereby reducing the problem size and improving computational efficiency.

[0088] The present invention is based on the wind turbine spare capacity of the pitch angle control decision, takes into account the two-stage random scheduling of the wind turbine to provide the operating spare capacity, considers the wind speed uncertainty, load uncertainty and component random failure, and determines the optimal day-ahead output-reserve decision based on the random optimization model. According to this process, the wind turbine spare capacity taking into account the uncertainty of decision dependence in economic scheduling can be modeled, and the wind power reserve capacity can be accurately quantified. At the same time, the optimal economic scheduling taking into account the operating risk can be further realized, which is of great significance to improving the economy and safety of power system operation. The present invention can model the uncertainty of decision dependence of wind turbines, take into account multiple uncertainties, and jointly optimize the output-reserve of conventional units and wind turbines in the power system, thereby improving the system operation reliability and the economy of scheduling. It can play a good complementary role in the traditional economic scheduling of power systems that do not take into account the wind power reserve capacity, and is of great significance to the planning and scheduling of taking into account the reserve capacity of wind turbines. In addition, this method uses wind turbines to provide backup to provide additional backup capacity for the power system, while reducing the demand for backup capacity; compared with the traditional percentage backup quantification criteria, considering the uncertainty of wind turbine output and backup decision-making, reducing the risk of load loss, and improving the reliability of power system operation. This method can effectively improve the accuracy of the wind turbine backup capacity model and optimize the output and backup of conventional units and wind turbines, providing a more reliable basis for power system power dispatching, backup planning and other work.

[0089] The power system economic dispatch method proposed in the present invention that takes into account the wind turbines providing operating reserve capacity is applied to the improved IEEE-RBTS. In order to reflect the characteristics of the high-proportion wind power power system, it is assumed that there are wind turbines in the improved system, and a 10MW conventional unit at node 1 and a 20MW conventional unit at node 2 are replaced by 105MW wind turbines respectively.

[0090] In order to illustrate the advantages of the present invention, the following four cases are used for illustration based on the above-mentioned IEEE-RBTS.

[0091] Case 1: Using the deterministic percentage reserve criterion, excluding wind power reserve capacity;

[0092] Case 2: Using deterministic percentage reserve criteria, including wind power reserve capacity;

[0093] Case 3: Using the probabilistic reserve criterion, taking into account wind speed, load uncertainty and random component failures, excluding wind power reserve capacity;

[0094] Case 4: Using the probabilistic reserve criterion, the method proposed in the present invention is adopted to consider wind speed, load uncertainty and random component failures, including wind power reserve capacity.

[0095] Table 1 shows the total cost of these four dispatch cases, the output cost and standby cost of conventional units, the standby cost of wind turbine units, and the load shedding and wind abandonment costs.

[0096] Table 1

[0097]

[0098] Figure 5 and Figure 6 The results of these four scheduling cases are listed from the perspectives of backup configuration and load shedding in actual scenarios.

[0099] Analysis Table 1 and Figure 5 The following conclusions can be drawn:

[0100] (1) Both Case 1 and Case 2 are based on the deterministic percentage reserve criterion. The required reserve capacity determined by the system is the same, but Case 2 uses more wind power reserve.

[0101] (2) Case 3 and Case 4 are based on probabilistic criteria. Due to the consideration of multiple uncertainties, the actual backup demand of the system is higher than that of Case 1 and Case 2.

[0102] (3) In case 3, at times 1-4 and 24, the output of conventional units is limited by the output lower limit and cannot provide sufficient backup; while in case 4, sufficient backup can be provided after using wind power backup capacity.

[0103] (4) Case 4 has the lowest total cost, which means that the economic dispatch method proposed in the present invention is more economical than the conventional method. This is because the reserve capacity of wind power is used and the optimal reserve capacity is optimized by comprehensively considering various uncertainties.

[0104] Figure 6 It is the simulation result of unit output and system load shedding under a real-time system scenario. Figure 6 The following conclusions can be drawn:

[0105] (1) The load shedding in Case 1 and Case 2 is relatively high, indicating that the conventional deterministic percentage reserve criterion is not applicable to future power systems with a high proportion of renewable energy. At the same time, Case 2 also shows that in order to avoid load shedding, the deterministic reserve criterion is not applicable to the situation where wind power reserve capacity is used.

[0106] (2) The actual load shedding in Case 3 and Case 4 is the smallest. This is because the system provides a higher reserve than in Case 1 and Case 2, which can ensure the reliability of system operation.

[0107] (4) Case 4 has the lowest total cost and the least load shedding, which means that the economic dispatch method proposed in the present invention can improve the economy compared with the conventional method while ensuring the reliability of system operation.

[0108] The present invention can model the uncertainty of decision-making dependence of wind turbines, take into account various uncertainties, jointly optimize the output-reserve of conventional units and wind turbines in the power system, and improve the reliability of system operation and the economy of dispatch. It can play a good supplementary role for the traditional economic dispatch of power systems that do not take into account the reserve capacity of wind power, and is of great significance for the planning and dispatch of wind turbine reserve capacity.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A method for dispatching a power system taking into account the provision of operating reserve capacity by wind turbines, characterized in that: The following steps are involved: Step 1: Based on the characteristic coefficient of wind turbines, calculate the wind energy utilization coefficient C of each wind turbine at different wind speeds p ; Step 2: Based on wind energy utilization coefficient C p The linear function F of wind turbine output to pitch angle at different wind speeds is established by linear fitting. w (β,v); Step 3: According to the linear function F of wind turbine output to pitch angle w (β,v), modeling the output and reserve capacity of wind turbines; Step 4: Based on the historical wind speed and load data of the wind farm, the wind speed and load of the previous 24 hours are predicted, and the prediction error of the normal distribution of wind speed is taken into account to generate the real-time 24-hour wind speed scenario and wind speed scenario probability π ws , taking into account the normal distribution of load prediction errors, the real-time 24-hour load scenario and load scenario probability π are generated ds ; Step 5: Enumerate the fault scenarios of generator sets and lines in real time for 24 hours, and calculate the probability of fault scenarios in combination with the equipment failure probability FOR Step 6: Combine wind speed scenarios, load scenarios, and fault scenarios to obtain all possible system scenarios in real time for 24 hours, and calculate the system scenario probability π s =π ws ·π ds ·π os ; Step 7: Based on the system scenarios and system scenario probabilities, a stochastic economic dispatch model of the system is constructed; the objective function of the stochastic economic dispatch model is: In the formula, the first term represents the power generation cost of conventional units, is the output of conventional units scheduled in advance, H g is the output cost function of conventional units, g is the number of conventional units, N G is the number of conventional units, t is the specific time, and T is the scheduling period; the second term represents the standby cost of conventional units, The spare capacity of conventional units scheduled in advance in the day before, is the reserve capacity of conventional units scheduled in advance, c GR is the backup cost coefficient of conventional units; the third term represents the backup cost of wind turbines, The reserve capacity of the wind turbines scheduled in advance in the day before. is the reserve capacity of the wind turbines scheduled in advance, c WR is the wind power reserve cost coefficient, N W is the total number of wind turbines; the fourth and fifth items represent the risk cost of real-time scenario rescheduling, where is the load shedding amount of the system node, c dcur is the load shedding cost coefficient, is the wind turbine abandoned air volume, c wcur is the wind curtailment cost coefficient, π s is the scenario probability of system scenario s; Step 8: Solve the output-reserve decision of the system according to the stochastic economic dispatch model to obtain the total economic dispatch cost taking into account reliability and the day-ahead economic dispatch decision.

2. The method for dispatching a power system taking into account the provision of operating reserve capacity by wind turbines as claimed in claim 1, characterized in that: In step 1, the wind energy utilization coefficient C of the wind turbine is p for: In the formula, the wind energy utilization coefficient C p is the ratio of wind energy converted into available wind power, β is the pitch angle of the wind turbine, λ is the tip speed ratio, R is the radius and angular velocity of the wind turbine blade, ω is the wind turbine rotor speed, v is the wind speed, λ is the tip speed ratio, i are intermediate variables including tip speed ratio λ and pitch angle β.

3. The method for dispatching a power system taking into account the provision of operating reserve capacity by wind turbines as claimed in claim 2, characterized in that: In step 3, the output of the wind turbine includes the day-ahead dispatch output The reserve capacity of wind turbines includes the maximum reserve capacity on the previous day. Maximum reserve capacity on the day before The upper reserve capacity of the day-ahead dispatch The next reserve capacity for day-ahead dispatch 4. The method for dispatching a power system taking into account the provision of operating reserve capacity by wind turbines as claimed in claim 3, characterized in that: The maximum upper reserve capacity of wind turbine w at time t and maximum reserve capacity They are: In the formula, Based on the day-ahead wind speed forecast At minimum pitch angle The maximum available wind power output at that time is Based on the day-ahead wind speed forecast and the pitch angle increases to the pitch angle The wind power will be dispatched to output in the future; The upper reserve capacity of wind turbine w dispatched at time t and the next reserve capacity for day-ahead dispatch They are: In the formula, is the minimum pitch angle in scheduling The maximum wind power output at that time; The maximum pitch angle for scheduling increases to The minimum wind power output thereafter; Provides the maximum increment of the upper spare pitch angle for the wind turbine. The maximum reduction in pitch angle that provides a lower reserve for the wind turbine.

5. The method for dispatching a power system taking into account the provision of operating reserve capacity by wind turbines as claimed in claim 4, characterized in that: The output of wind turbine w dispatched in real-time system scenario s at time t for: In the formula, F w is the wind power output conversion function taking into account the cut-in and cut-out wind speeds, is the pitch angle of the wind turbine in the real-time system scenario, is the wind speed in the real-time system scenario; and 6. The method for dispatching a power system taking into account the provision of operating reserve capacity by wind turbines as claimed in claim 5, characterized in that: In step 4, the integrated moving average autoregressive model is used to predict the wind speed and load for the 24 hours before the day; the sampling method is used to generate the real-time 24-hour wind speed scenario and wind speed scenario probability π ws ; Use sampling method to calculate the load scenario and load scenario probability π in real time for 24 hours ds .

7. The method for dispatching a power system taking into account the provision of operating reserve capacity by wind turbines as claimed in claim 6, characterized in that: In step 7, the stochastic economic dispatch model is a two-stage stochastic economic dispatch model. The first stage quantifies the output and reserve capacity of wind turbines and conventional units in the previous 24 hours. The second stage calculates the load shedding and wind abandonment of the system based on the real-time 24-hour system scenario and the output and reserve capacity quantified in the first stage.

8. The method for dispatching a power system taking into account wind turbines providing operating reserve capacity as claimed in claim 7, characterized in that: The constraints of the stochastic economic dispatch model include the day-ahead pre-dispatch flow constraint based on the day-ahead wind speed forecast value, and the flow constraint based on the re-dispatch of the system real-time scenario.

9. The method for dispatching a power system taking into account the provision of operating reserve capacity by wind turbines as claimed in claim 8, characterized in that: In step 8, the system output-reserve is solved by using the stochastic optimization Benders decomposition algorithm; the total economic dispatch cost taking into account reliability and the day-ahead economic dispatch decision include the wind power output, wind power reserve capacity, traditional unit output, and traditional unit reserve capacity in the 24 hours before the day.

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