CN117728400A - A method and terminal for determining power system reserve capacity based on operating scenarios - Google Patents

Abstract

The invention discloses a method and a terminal for determining the reserve capacity of an electric power system based on an operation scene, wherein the method and the terminal utilize historical operation data of the electric power system to cluster to obtain a typical operation scene of the electric power system, and then calculate the typical reserve capacity required by the typical operation scene by taking the lowest reserve cost as a target; screening matched operation scenes from all typical operation scenes aiming at the operation scenes of the period to be determined, inquiring typical standby capacity required by the matched operation scenes, and calculating the standby capacity of the power system under the period to be determined according to the inquired typical standby capacity and the operation data of the period to be determined. In this way, when the standby capacity is determined for the period to be determined, the standby capacity of the power system under the period to be determined can be obtained only by using formula calculation in the follow-up process, the calculated amount of the process is small, and the standby capacity can be quickly and approximately determined.

Description

A method and terminal for determining power system reserve capacity based on operating scenarios

Technical field

The present invention relates to the technical field of power system planning and control, and in particular to a method and terminal for determining power system reserve capacity based on operating scenarios.

Background technique

At present, wind power and photovoltaic power generation have developed into the largest new energy sources, and the proportion of installed capacity and construction scale are still expanding. It is foreseeable that wind power and photovoltaic power generation will become the main power sources in power systems with a high proportion of new energy sources. However, the output of new energy sources is highly volatile, intermittent and uncertain, and the prediction difficulty is much higher than that of traditional loads. With a high proportion of new energy being connected to the power system, large-scale new energy integration has greatly increased the demand for spinning reserve in the power system. The large-scale integration of new energy into the grid on the power generation side has led to a reduction in the proportion of conventional units installed, compressing the ability of conventional units to provide spinning reserve. The shortage of resources for spinning reserve in the power system has been highlighted. The reserve capacity allocation of power systems containing a high proportion of new energy sources is a hot issue in current research and application.

At present, the research and application of reserve capacity allocation of power systems containing high proportion of new energy can be mainly divided into engineering methods, time domain simulation methods and mathematical optimization methods. These existing methods have the following problems:

(1) The existing engineering method is based on experience and reserves a certain percentage of the load as load reserve capacity. This reserve capacity determination method has poor accuracy and does not consider the impact of new energy. In order to ensure the consumption of new energy and operational safety of the power system, the reserve capacity margin is often artificially increased, increasing the demand for already scarce regulatory resources.

(2) The time domain simulation method and mathematical optimization method take into account constraints such as new energy fluctuations, node voltages and line power flows, and combine the power system security of N-1 without power loss and N-1 with power loss to establish reserve capacity. The optimization model is solved using time domain simulation methods and mathematical optimization methods. These methods are computationally complex, lack specialized software support, and need to be simplified in many applications.

In power system planning and power system operation, the installed capacity and output scenarios of new energy power sources often change. For example, in power system power planning, the installed capacity and site selection of new energy power sources are the content of optimization planning; in power system dispatching, changes in meteorological conditions, maintenance, and new new energy installed capacity will all cause changes in output scenarios. In order to cope with the frequent changes in the installed capacity and output scenarios of new energy power sources, there is a need to quickly and approximately determine the reserve capacity in different scenarios to meet the needs of power system optimization planning and power system operation.

Contents of the invention

The technical problem to be solved by the present invention is to provide a method and terminal for determining the reserve capacity of a power system based on operating scenarios, which can be used when the installed capacity and output scenarios of new energy power sources frequently change during power system planning and power system operation. Quickly approximate spare capacity.

In order to solve the above technical problems, the technical solution adopted by the present invention is:

A method for determining power system reserve capacity based on operating scenarios, including steps:

Obtain historical operating data of the power system, and cluster the historical operating data to obtain typical operating scenarios of the power system;

Calculate the typical spare capacity required for the typical operating scenario with the lowest spare cost as the goal;

Filter out matching operating scenarios from all the typical operating scenarios for the operating scenarios for the period to be determined;

Query the typical reserve capacity required to match the operating scenario, and calculate the power system reserve capacity in the to-be-determined period based on the queried typical reserve capacity and the operation data of the to-be-determined period.

In order to solve the above technical problems, another technical solution adopted by the present invention is:

A terminal for determining the reserve capacity of a power system based on operating scenarios, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned one is implemented. Various steps of a method for determining power system reserve capacity based on operating scenarios.

The beneficial effects of the present invention are: clustering historical operating data of the power system to obtain typical operating scenarios of the power system, and then calculating the typical reserve capacity required for the typical operating scenarios with the lowest reserve cost as the goal; targeting the operation of the period to be determined The scenario selects matching operating scenarios from all typical operating scenarios, queries the typical reserve capacity required for matching operating scenarios, and calculates the power system reserve capacity for the to-be-determined period based on the queried typical reserve capacity and the operating data of the period to be determined. In this way, when determining the reserve capacity for the period to be determined, you can directly filter the matching operating scenarios and query the corresponding typical reserve capacity. Subsequently, you only need to use formula calculations to obtain the reserve capacity of the power system for the period to be determined. This The calculation amount of the process is very small, and the reserve can be reasonably determined when optimizing the power supply capacity in the power system power planning. It can quickly adjust the power supply capacity and output scenarios of new energy power sources in the power system planning and operation of the power system when the installed capacity and output scenarios frequently change. Approximately determine spare capacity.

Description of the drawings

Figure 1 is a flow chart of a method for determining the reserve capacity of a power system based on operating scenarios according to an embodiment of the present invention;

Figure 2 is a schematic diagram of a terminal that determines the reserve capacity of a power system based on operating scenarios according to an embodiment of the present invention;

Label description:

1. A terminal that determines the reserve capacity of the power system based on operating scenarios; 2. Memory; 3. Processor.

Detailed ways

In order to describe the technical content, achieved objectives and effects of the present invention in detail, the following description will be made in conjunction with the embodiments and the accompanying drawings.

Referring to Figure 1, an embodiment of the present invention provides a method for determining the reserve capacity of a power system based on operating scenarios, including the steps:

Obtain historical operating data of the power system, and cluster the historical operating data to obtain typical operating scenarios of the power system;

Calculate the typical spare capacity required for the typical operating scenario with the lowest spare cost as the goal;

Filter out matching operating scenarios from all the typical operating scenarios for the operating scenarios for the period to be determined;

Query the typical reserve capacity required to match the operating scenario, and calculate the power system reserve capacity in the to-be-determined period based on the queried typical reserve capacity and the operation data of the to-be-determined period.

As can be seen from the above description, the beneficial effect of the present invention is to use the historical operating data of the power system for clustering to obtain the typical operating scenarios of the power system, and then calculate the typical reserve capacity required for the typical operating scenarios with the lowest reserve cost as the goal; The operation scenario of the period to be determined is to filter out the matching operation scenarios from all typical operation scenarios, query the typical spare capacity required for the matching operation scenario, and calculate the operation data of the period to be determined based on the typical spare capacity queried and the operation data of the period to be determined. Power system reserve capacity. In this way, when determining the reserve capacity for the period to be determined, you can directly filter the matching operating scenarios and query the corresponding typical reserve capacity. Subsequently, you only need to use formula calculations to obtain the reserve capacity of the power system for the period to be determined. This The calculation amount of the process is very small, and the reserved reserve can be reasonably determined when optimizing the power supply capacity in the power system power planning. It can quickly realize the frequent changes in the installed capacity and output scenarios of new energy power sources in the power system planning and power system operation. Approximately determine spare capacity.

Further, calculating the reserve capacity of the power system in the period to be determined based on the queried typical reserve capacity and the operating data of the period to be determined includes:

Calculate the reserve capacity r of the power system during the period to be determined:

In the formula, r _b represents the typical reserve capacity required to match the operating scenario, P _LD represents the load of the operating scenario to be determined during the period; P _ws represents the new energy installed capacity of the operating scenario to be determined during the period; P _LD,b represents the load to match the operating scenario. Load; P _ws,b represents the new energy installed capacity matching the operating scenario.

It can be seen from the above description that since the output, fluctuation, and prediction error of new energy and load during the same typical period can be expressed by fixed probability distributions, the reserve capacity required by a given regional power system in different years during the same typical period is only the sum of the new energy capacity and Load capacity is related, and the growth of reserve capacity has an approximately linear relationship with the growth of new energy capacity and load capacity. Therefore, the above calculation method can quickly and approximately calculate the reserve capacity of the power system in the period to be determined, which reduces the workload of frequent optimization calculations of reserve capacity in the process of continuous growth of new energy sources, or changes the empirical estimation of reserve capacity in engineering. brought about problems.

Further, the method of obtaining historical operating data of the power system and clustering the historical operating data to obtain typical operating scenarios of the power system includes:

Select wind power output, photovoltaic output, load, season and meteorological conditions as clustering vectors from the historical operating data of the power system to generate a sample space;

A fast clustering method is used to cluster the samples in the sample space to obtain typical operating scenarios of the power system.

Further, the calculation of the typical spare capacity required for the typical operating scenario with the lowest spare cost as the goal includes:

Taking the lowest standby cost as the objective function:

In the formula, N represents the total number of nodes in the power grid, T represents the total number of time periods, respectively represent the load positive reserve, negative reserve and accident reserve capacity of unit i in period t, a _i,10 and a _i,30 respectively represent the load reserve and accident reserve prices of unit i in period t.

It can be seen from the above description that by taking the lowest backup cost as the objective function, the typical backup capacity required for typical operating scenarios can be determined based on economic considerations.

Further, selecting matching operating scenarios from all the typical operating scenarios for the operating scenarios for the period to be determined includes:

The typical operating scenario with the shortest distance from the operating scenario in the period to be determined is used as the matching operating scenario:

min{d(x,x _b,i )＝1,2,...,k}

In the formula, x represents the operating scenario for the period to be determined; x _{b, i} represents the i-th typical operating scenario.

It can be seen from the above description that by calculating the distance between the operating scenario of the period to be determined and the typical operating scenario to determine the matching operating scenario, the approximate typical scenario can be quickly matched, thereby improving the efficiency of subsequent reserve capacity estimation.

Referring to Figure 2, another embodiment of the present invention provides a terminal for determining the reserve capacity of a power system based on operating scenarios, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements each step of the above-mentioned method for determining the reserve capacity of a power system based on an operating scenario.

The above-mentioned method and terminal of the present invention for determining the reserve capacity of a power system based on operating scenarios are suitable for quickly and approximately determining the reserve capacity when the installed capacity and output scenarios of new energy sources change frequently during power system planning and power system operation. Capacity is explained below through specific implementation methods:

Embodiment 1

Please refer to Figure 1, a method for determining power system reserve capacity based on operating scenarios, including steps:

S1. Obtain historical operating data of the power system, and cluster the historical operating data to obtain typical operating scenarios of the power system.

Using the historical data of power system operation, select x = {wind power output, photovoltaic output, load, season, meteorological conditions} as the clustering vector to generate a sample space, and then use the fast clustering method to perform clustering to obtain typical characteristics of the power system. Run the scenario. Specifically, it includes the following steps:

(1) Select the number of clustering points k, and use the minimum-maximum principle to determine the initial clustering points and initial classification.

The initial gathering point set is

The idea of using the minimum-maximum principle to determine the initial gathering point is: first select the two samples that are farthest apart among all samples. are the first two initial clustering points; then select the third sample/> Make/> with/> The one with the smallest distance is equal to all the rest and/> The largest of the smaller distances; and so on for iterative calculation until k initial clustering points are determined.

The iterative calculation formula for determining the initial gathering point is:

r＝1,2,...,l

j≠ _1Y

In the formula, Express/> with/> The distance between/> represents x _j and/> The distance between/> l represents the number of initial gathering points determined in the iterative calculation.

The initial classification set is

Determine the initial classification calculation formula as:

In this way, the sample space can be divided into k disjoint categories to form an initial classification set G ⁽⁰⁾ .

(2) Carry out iterative classification calculation until the classification remains unchanged. During the iteration of the mth step (m>0), the cluster point set The generation formula is:

Classification The generation formula is:

S2. Calculate the typical spare capacity required for the typical operating scenario with the lowest spare cost as the goal.

Taking the lowest standby cost as the objective function:

In the formula, N is the total number of nodes in the power grid. To simplify the formula, let node i have units, loads and new energy units participating in the market. If there is no corresponding equipment, set its power to 0; T represents the total number of periods, generally one day It is 96 time periods; represent the positive load reserve, negative reserve, and accident reserve capacity of unit i in period t respectively; a _i,10 and a _i,30 respectively represent the load reserve and accident reserve prices of unit i in period t.

The constraints are:

In the formula, p _i,t represents the output of unit i in period t; w _i,t represents the planned power of new energy unit i on node i in period t; d _i,t represents the planned power of new energy unit i on node i in period t. Bus load prediction power; Respectively represent the maximum output and minimum output of unit i in period t;/> Respectively represent the maximum capacity of load positive reserve, load negative reserve, and accident reserve available to unit i in period t;/> Indicates the maximum climbing rate of unit i;/> Indicates the maximum landslide rate of unit i; u _i,t indicates the start and stop status of unit i in period t, 0 indicates that the unit is shut down, and 1 indicates that the unit is on; Indicates the minimum continuous startup time and minimum continuous shutdown time of the unit;/> Indicates the time unit i has been continuously powered on and the time it has been continuously shut down during period t;/> represents the power flow limit of line or section l, G _li represents the transfer distribution factor of node i to line or section l; G _ji represents the power transfer distribution factor of node i to section j of inter-regional AC tie line;/> Indicates the planned power of tie line section j in period t.

In a power grid with a high proportion of new energy connected, the deviation of new energy forecasting is more significant than that of load forecasting, so the new energy forecasting error is mainly considered. The new energy output interval description method formed by the satisfaction probability of non-emergency reserve (SPN) is used, that is, when the new energy unit fluctuates within the output interval, the load reserve can be guaranteed to be sufficient and is not restricted by network security constraints. , in order to meet the requirements of the SPN index, the power balance is maintained through the participation adjustment coefficient τ of the unit load reserve in each period. Then after the load backup call, the following constraints need to be met:

In the formula, Indicates the output of unit i after the backup call; Kai indicates the relationship between area a and generating unit i. When a contains generating unit i, Kai = 1, otherwise, Kai = 0; τ _i,t indicates that unit i is in Load reserve adjustment coefficient for period t; W _a,t represents the overall forecast deviation of new energy;/> Indicates the actual output of the new energy unit.

S3: Filter out matching operating scenarios from all the typical operating scenarios for the operating scenarios in the period to be determined.

Find the typical operating scenario with the shortest distance from the operating scenario in the period to be determined, and obtain the matching operating scenario. That is, min{d(x,x _b,i ),i=1,2,...,k}, where x represents the operating scenario for the period to be determined; x _b,i represents the i-th typical operating scenario.

S4. Query the typical reserve capacity required to match the operating scenario, and calculate the power system reserve capacity in the to-be-determined period based on the queried typical reserve capacity and the operation data of the to-be-determined period.

Assuming that one of the operating goals of the power system is to minimize the curtailment rate of new energy, P _LD >P _LD,b , P _ws ≥P _ws,b , then the calculation of the reserve capacity of the operating scenario in the period to be determined is determined by both load growth and new energy growth. , can be approximately expressed as:

In the formula, r represents the reserve capacity of the operating scenario during the period to be determined, r _b represents the typical reserve capacity required to match the operating scenario, P _LD represents the load of the operating scenario during the period to be determined; P _ws represents the new energy installed capacity of the operating scenario during the period to be determined. ;P _LD,b represents the load matching the operating scenario; P _ws,b represents the new energy installed capacity matching the operating scenario.

The determined reserve capacity of the operating scenario for the period to be determined can then be added to the power balance constraints of the operational simulation of the power system flexibility power planning:

Among them, the basic principle of using the power system reserve capacity of typical operating scenarios to estimate the power system reserve capacity of the operating scenario to be determined in the period to be determined based on the selected predictions is:

In a power system containing a high proportion of new energy, the output of the same wind farm in the same typical period in different years obeys the same probability distribution, and the fitting parameters of its probability distribution in different years vary within a small range; the same wind farm in the same typical period The output fluctuations at the same time scale in different years all obey the T_location distribution, and the fitting parameters of the T_location distribution in different years vary within a small range.

In a power system containing a high proportion of new energy, the output of the same photovoltaic field in the same typical period in different years obeys the same probability distribution, and the fitting parameters of its probability distribution in different years vary within a small range; the same photovoltaic field in the same typical period The output fluctuations at the same time scale in different years all obey the T_location distribution, and the fitting parameters of the T_location distribution in different years vary within a small range.

In a power system containing a high proportion of new energy, a given area contains several wind farms and photovoltaic farms. Due to the spatiotemporal correlation between electric fields, their mutual correlation coefficients are mainly limited by meteorological conditions and geographical location. This spatiotemporal correlation can be modeled using the Copula function. Therefore, the output and fluctuation of new energy in different years during the same typical period in a given region can be expressed by fixed probability distributions, that is, the output and fluctuation of new energy are predictable. The impact of its new energy output, fluctuations and forecast errors on the reserve capacity required by the regional power system in different years is similar.

Load magnitudes and fluctuations within a given area are predictable. The impact of load size, fluctuations and prediction errors on the reserve capacity required by the regional power system in different years is also similar.

Since the output, fluctuation, and prediction error of new energy sources and loads in the same typical period can be expressed by fixed probability distributions, the reserve capacity required by a given regional power system in different years during the same typical period is only related to the new energy capacity and load capacity, and , the growth of spare capacity has an approximately linear relationship with the growth of new energy capacity and load capacity. Under the condition of high proportion of new energy, this approximate linear relationship can be expressed as:

But subject to the following conditions:

(1) In order to achieve low-carbon operation, minimizing the power curtailment rate of new energy sources is often one of the power system operation goals. Under this goal, new energy output is prioritized to meet load demand, and the adjustment tasks are mostly completed by thermal power, hydropower, and energy storage. However, when the predicted output of new energy is greater than the load, the output of new energy will be limited by the load and will be curtailed, thus affecting the error of new energy prediction and the reserve capacity required for changes in new energy output. Under the condition of high proportion of new energy, the main performance is the speed of growth of new energy capacity and load capacity, that is, the size of (P _ws /P _ws,b ) and (P _LD /P _LD,b ).

(2) According to national standards, the load reserve capacity should not be less than (2% ~ 5%) P _LD .

Generally speaking, through the above technical solutions conceived in this embodiment, the following beneficial effects can be achieved:

(1) Compared with existing methods, this embodiment provides a rapid prediction method for power system reserve capacity. It does not require iterative calculations and the calculation amount is very small. It can reasonably determine the reserved reserve when optimizing power supply capacity in power system power planning. , and implant the determined reserve into the iterative calculation of optimizing the power supply capacity to obtain a more reasonable power supply capacity optimization result.

(2) Compared with existing methods, this embodiment can provide a fast and more accurate method for scheduling to determine reserve reserves as new energy sources continue to grow. It reduces the workload of frequent backup capacity optimization calculations in the continuous growth of new energy sources, or changes the problems caused by empirically estimating backup capacity in engineering.

(3) Compared with existing methods, this embodiment can quickly and approximately determine the reserve capacity by time period, which can reduce the demand for adjustment capacity. It is important to change the flexibility resource shortage problem under the condition that a high proportion of new energy is connected to the power system. effect.

Embodiment 2

Referring to Figure 2, a terminal 1 for determining the reserve capacity of a power system based on operating scenarios includes a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. The processor 3. When the computer program is executed, each step of the method for determining the reserve capacity of the power system based on the operating scenario in Embodiment 1 is implemented.

In summary, the present invention provides a method and terminal for determining the reserve capacity of a power system based on operating scenarios. It uses historical operating data of the power system for clustering to obtain typical operating scenarios of the power system, and then aims to minimize the reserve cost. Calculate the typical spare capacity required for typical operating scenarios; filter out matching operating scenarios from all typical operating scenarios for the operating scenario for the period to be determined, query the typical spare capacity required for matching operating scenarios, and based on the queried typical spare capacity and the to-be-determined Determine the operating data for the period and calculate the reserve capacity of the power system for the period to be determined. In this way, when determining the reserve capacity for the period to be determined, you can directly filter the matching operating scenarios and query the corresponding typical reserve capacity. Subsequently, you only need to use formula calculations to obtain the reserve capacity of the power system for the period to be determined. This The calculation amount of the process is very small, and the reserve can be reasonably determined when optimizing the power supply capacity in the power system power planning. It can quickly adjust the power supply capacity and output scenarios of new energy power sources in the power system planning and operation of the power system when the installed capacity and output scenarios frequently change. Approximately determine spare capacity.

The above are only embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent transformations made using the contents of the description and drawings of the present invention, or directly or indirectly applied in related technical fields, are equally included in within the scope of patent protection of this invention.

Claims (10)

1. A method for determining the reserve capacity of a power system based on operating scenarios, characterized by including the steps: Obtain historical operating data of the power system, and cluster the historical operating data to obtain typical operating scenarios of the power system; Calculate the typical spare capacity required for the typical operating scenario with the lowest spare cost as the goal; Filter out matching operating scenarios from all the typical operating scenarios for the operating scenarios for the period to be determined; Query the typical reserve capacity required to match the operating scenario, and calculate the power system reserve capacity in the to-be-determined period based on the queried typical reserve capacity and the operation data of the to-be-determined period. 2. A method for determining power system reserve capacity based on operating scenarios according to claim 1, characterized in that, according to the queried typical reserve capacity and the operating data of the period to be determined, the calculation to be determined is The reserve capacity of the power system during the period includes: Calculate the reserve capacity r of the power system during the period to be determined: In the formula, r _b represents the typical reserve capacity required to match the operating scenario, P _LD represents the load of the operating scenario to be determined during the period; P _ws represents the new energy installed capacity of the operating scenario to be determined during the period; P _LD,b represents the load to match the operating scenario. Load; P _ws,b represents the new energy installed capacity matching the operating scenario. 3. A method for determining the reserve capacity of a power system based on operating scenarios according to claim 1, characterized in that: obtaining historical operating data of the power system, and clustering the historical operating data to obtain typical operating data of the power system. Running scenarios include: Select wind power output, photovoltaic output, load, season and meteorological conditions as clustering vectors from the historical operating data of the power system to generate a sample space; A fast clustering method is used to cluster the samples in the sample space to obtain typical operating scenarios of the power system. 4. A method for determining the reserve capacity of a power system based on operating scenarios according to claim 1, characterized in that the calculation of the typical reserve capacity required for the typical operating scenario with the lowest reserve cost as the goal includes: Taking the lowest standby cost as the objective function: In the formula, N represents the total number of nodes in the power grid, T represents the total number of time periods, respectively represent the load positive reserve, negative reserve and accident reserve capacity of unit i in period t, a _i,10 and a _i,30 respectively represent the load reserve and accident reserve prices of unit i in period t. 5. A method for determining the reserve capacity of a power system based on operating scenarios according to claim 1, characterized in that screening out matching operating scenarios from all typical operating scenarios for the operating scenarios for the period to be determined includes: The typical operating scenario with the shortest distance from the operating scenario in the period to be determined is used as the matching operating scenario: min{d(x,x _b,i )＝1,2,...,k} In the formula, x represents the operating scenario for the period to be determined; x _{b, i} represents the i-th typical operating scenario. 6. A terminal for determining the reserve capacity of a power system based on operating scenarios, including a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program Perform the following steps during the program: Obtain historical operating data of the power system, and cluster the historical operating data to obtain typical operating scenarios of the power system; Calculate the typical spare capacity required for the typical operating scenario with the lowest spare cost as the goal; Filter out matching operating scenarios from all the typical operating scenarios for the operating scenarios for the period to be determined; Query the typical reserve capacity required to match the operating scenario, and calculate the power system reserve capacity in the to-be-determined period based on the queried typical reserve capacity and the operation data of the to-be-determined period. 7. A terminal for determining power system reserve capacity based on operating scenarios according to claim 6, characterized in that the calculation of the to-be-determined reserve capacity is based on the queried typical reserve capacity and the operating data of the to-be-determined period. The reserve capacity of the power system during the period includes: Calculate the reserve capacity r of the power system during the period to be determined: In the formula, r _b represents the typical reserve capacity required to match the operating scenario, P _LD represents the load of the operating scenario to be determined during the period; P _ws represents the new energy installed capacity of the operating scenario to be determined during the period; P _LD,b represents the load to match the operating scenario. Load; P _ws,b represents the new energy installed capacity matching the operating scenario. 8. A terminal for determining the reserve capacity of a power system based on operating scenarios according to claim 6, characterized in that the historical operation data of the power system are obtained, and the historical operation data are clustered to obtain typical data of the power system. Running scenarios include: Select wind power output, photovoltaic output, load, season and meteorological conditions as clustering vectors from the historical operating data of the power system to generate a sample space; A fast clustering method is used to cluster the samples in the sample space to obtain typical operating scenarios of the power system. 9. A terminal for determining the reserve capacity of a power system based on operating scenarios according to claim 6, wherein the calculation of the typical reserve capacity required for the typical operating scenario with the lowest reserve cost as the goal includes: Taking the lowest standby cost as the objective function: In the formula, N represents the total number of nodes in the power grid, T represents the total number of time periods, respectively represent the load positive reserve, negative reserve and accident reserve capacity of unit i in period t, a _i,10 and a _i,30 respectively represent the load reserve and accident reserve prices of unit i in period t. 10. A terminal for determining the reserve capacity of a power system based on operating scenarios according to claim 6, wherein the operating scenarios for the period to be determined are selected from all the typical operating scenarios to select matching operating scenarios including: The typical operating scenario with the shortest distance from the operating scenario in the period to be determined is used as the matching operating scenario: min{d(x,x _b,i ),i=1,2,...,k} In the formula, x represents the operating scenario for the period to be determined; x _{b, i} represents the i-th typical operating scenario.