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

Abstract

The application discloses a kind of based on operation scene determination power system reserve capacity method and terminal, utilize the historical operation data of power system clustering, obtain the typical operation scene of power system, then with the minimum target of reserve cost Calculation the typical reserve capacity required by typical operation scene;For the operation scene of period to be determined, filter out matching operation scene from all typical operation scenes, query the typical reserve capacity required by matching operation scene, according to the typical reserve capacity inquired and the operation data of period to be determined, calculate the power system reserve capacity under period to be determined.In this way, when determining reserve capacity for period to be determined, matching operation scene can be directly filtered and corresponding typical reserve capacity is queried, and subsequently only using formula calculation can obtain the power system reserve capacity under period to be determined, the calculation amount of this process is very small, and the reserve capacity can be quickly and approximately determined.

Description

Technical Field

[0001] This invention relates to the field of power system planning and control technology, and in particular to a method and terminal for determining the reserve capacity of a power system based on operating scenarios. Background Technology

[0002] Currently, wind and solar power have become the largest-scale new energy sources, with their installed capacity and construction scale continuing to expand. It is foreseeable that wind and solar power will become the main power sources in power systems with a high proportion of new energy. However, the output of new energy sources is highly volatile, intermittent, and uncertain, making prediction far more difficult than for traditional loads. With a high proportion of new energy integrated into the power system, large-scale grid connection significantly increases the power system's demand for spinning reserve. Large-scale grid connection of new energy on the generation side leads to a decrease in the installed capacity of conventional units, compressing the capacity of conventional units to provide spinning reserve, highlighting the shortage of resources for spinning reserve in the power system. The configuration of reserve capacity in power systems with a high proportion of new energy is currently a hot research and application topic.

[0003] Currently, research and application of reserve capacity configuration in power systems with a high proportion of renewable energy can be mainly divided into engineering methods, time-domain simulation methods, and mathematical optimization methods. These existing methods have the following problems:

[0004] (1) Existing engineering methods are based on experience and reserve a certain percentage of the load as load reserve capacity. This method of determining reserve capacity is inaccurate, does not take into account the impact of new energy sources, and often artificially increases the reserve capacity margin in order to ensure the absorption of new energy sources and the safety of operation of the power system, which increases the demand for already scarce regulation resources.

[0005] (2) Time-domain simulation and mathematical optimization methods consider constraints such as new energy fluctuations, node voltages, and line power flow. They combine the power system security of N-1 without power loss and N-1 with power loss to establish a reserve capacity optimization model, and use time-domain simulation and mathematical optimization methods to solve it. These methods are computationally complex and lack dedicated software support, so simplification is required in many applications.

[0006] In power system planning and operation, the installed capacity and output scenarios of renewable energy sources frequently change. For example, in power system power planning, the installed capacity and site selection of renewable energy sources are part of the optimization planning; in power system dispatching, changes in weather conditions, maintenance, and the addition of new renewable energy capacity all cause changes in output scenarios. To address the frequent changes in the installed capacity and output scenarios of renewable energy sources, methods for quickly and approximately determining reserve capacity under different scenarios are needed to meet the requirements of power system optimization planning and power system operation. Summary of the Invention

[0007] 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 the operating scenario, which can quickly and approximately determine the reserve capacity when the installed capacity and output scenarios of new energy power sources frequently change in power system planning and operation.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0009] A method for determining power system reserve capacity based on operational scenarios includes the following steps:

[0010] Obtain historical operating data of the power system, and cluster the historical operating data to obtain typical operating scenarios of the power system;

[0011] Calculate the typical standby capacity required for the typical operating scenario with the goal of minimizing standby costs;

[0012] For the operational scenario in the time period to be determined, a matching operational scenario is selected from all the typical operational scenarios.

[0013] The typical reserve capacity required for the matching operation scenario is queried, and the power system reserve capacity for the period to be determined is calculated based on the queried typical reserve capacity and the operation data of the period to be determined.

[0014] To solve the above-mentioned technical problems, another technical solution adopted by the present invention is as follows:

[0015] A terminal for determining the reserve capacity of a power system based on an operational scenario includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the various steps of the aforementioned method for determining the reserve capacity of a power system based on an operational scenario.

[0016] The beneficial effects of this invention are as follows: It utilizes historical operating data of the power system for clustering to obtain typical operating scenarios. Then, it calculates the typical reserve capacity required for each typical operating scenario with the goal of minimizing reserve costs. For the operating scenario in the undetermined time period, it filters out matching operating scenarios from all typical operating scenarios, queries the typical reserve capacity required for the matching operating scenarios, and calculates the power system reserve capacity for the undetermined time period based on the queried typical reserve capacity and the operating data of the undetermined time period. In this way, when determining reserve capacity for the undetermined time period, matching operating scenarios can be directly filtered and the corresponding typical reserve capacity queried. Subsequently, only a formula needs to be used to calculate the power system reserve capacity for the undetermined time period. The computational workload of this process is very small. It can reasonably determine reserved reserves when optimizing power capacity in power system power planning, and can quickly and approximately determine reserve capacity when the installed capacity and output scenarios of new energy power sources frequently change in power system planning and operation. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a method for determining power system reserve capacity based on operational scenarios, according to an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of a terminal for determining the reserve capacity of a power system based on an operational scenario, according to an embodiment of the present invention.

[0019] Label Explanation:

[0020] 1. A terminal for determining the reserve capacity of a power system based on operational scenarios; 2. A memory; 3. A processor. Detailed Implementation

[0021] To explain in detail the technical content, objectives, and effects of the present invention, the following description is provided in conjunction with the embodiments and accompanying drawings.

[0022] Please refer to Figure 1 This invention provides a method for determining the reserve capacity of a power system based on an operational scenario, comprising the following steps:

[0023] Obtain historical operating data of the power system, and cluster the historical operating data to obtain typical operating scenarios of the power system;

[0024] Calculate the typical standby capacity required for the typical operating scenario with the goal of minimizing standby costs;

[0025] For the operational scenario in the time period to be determined, a matching operational scenario is selected from all the typical operational scenarios.

[0026] The typical reserve capacity required for the matching operation scenario is queried, and the power system reserve capacity for the period to be determined is calculated based on the queried typical reserve capacity and the operation data of the period to be determined.

[0027] As described above, the beneficial effects of this invention are as follows: It utilizes historical operating data of the power system for clustering to obtain typical operating scenarios. Then, it calculates the typical reserve capacity required for each typical operating scenario with the goal of minimizing reserve costs. For the operating scenario of the undetermined time period, it filters out matching operating scenarios from all typical operating scenarios, queries the typical reserve capacity required for the matching operating scenarios, and calculates the power system reserve capacity for the undetermined time period based on the queried typical reserve capacity and the operating data of the undetermined time period. In this way, when determining reserve capacity for the undetermined time period, matching operating scenarios can be directly filtered and the corresponding typical reserve capacity queried. Subsequently, only a formula needs to be used to calculate the power system reserve capacity for the undetermined time period. The computational load of this process is very small. It can reasonably determine reserved reserves when optimizing power capacity in power system power planning, and can quickly and approximately determine reserve capacity when the installed capacity and output scenarios of new energy power sources frequently change in power system planning and operation.

[0028] Further, the step of calculating the power system reserve capacity for the period to be determined based on the retrieved typical reserve capacity and the operating data for the period to be determined includes:

[0029] Calculate the power system reserve capacity r for the time period to be determined:

[0030]

[0031] In the formula, r b P represents the typical standby capacity required to match the operating scenario. LD This indicates the load of the operating scenario during the period to be determined; P ws P represents the installed capacity of new energy vehicles for the operating scenario during the undetermined time period; LD,b Indicates the load matching the runtime scenario; P ws,b This indicates the installed capacity of new energy vehicles that matches the operating scenario.

[0032] As described above, since the output, fluctuations, and prediction errors of new energy sources and loads during the same typical period can be expressed by fixed probability distributions, the required reserve capacity of a power system in a given region during the same typical period in different years is only related to the capacity of new energy sources and the capacity of loads. Furthermore, the growth of reserve capacity has an approximately linear relationship with the growth of both new energy source capacity and load capacity. Therefore, the above calculation method can quickly and approximately calculate the reserve capacity of the power system during a given period, reducing the workload of frequent reserve capacity optimization calculations during periods of continuous growth in new energy sources, or eliminating the problems caused by estimating reserve capacity based on experience in engineering projects.

[0033] Furthermore, the acquisition of historical operating data of the power system, and the clustering of the historical operating data to obtain typical operating scenarios of the power system, includes:

[0034] Wind power output, photovoltaic power output, load, season, and meteorological conditions are selected from the historical operating data of the power system as clustering vectors to generate a sample space;

[0035] The samples in the sample space are clustered using a fast clustering method to obtain typical operating scenarios of the power system.

[0036] Furthermore, the calculation of the typical standby capacity required for the typical operating scenario with the goal of minimizing standby costs includes:

[0037] The objective function is to minimize the reserve cost.

[0038]

[0039] In the formula, N represents the total number of nodes in the power grid, and T represents the total number of time periods. These represent the positive reserve, negative reserve, and emergency reserve capacity of unit i during time period t, respectively. i,10 a i,30 These represent the load reserve and emergency reserve prices for unit i during time period t, respectively.

[0040] As described above, by taking the lowest standby cost as the objective function, the typical standby capacity required for typical operating scenarios can be determined while considering economic factors.

[0041] Furthermore, the step of selecting matching operating scenarios from all the typical operating scenarios for the operating scenario of the time period to be determined includes:

[0042] The typical operating scenario that is closest to the operating scenario of the time period to be determined will be selected as the matching operating scenario:

[0043] min{d(x,x b，i )=1,2,...,k}

[0044] In the formula, x represents the operational scenario for the time period to be determined; x b,i This represents the i-th typical operating scenario.

[0045] As described above, by calculating the distance between the operating scenario of the time period to be determined and the typical operating scenario, the matching operating scenario can be determined quickly, thereby improving the efficiency of subsequent reserve capacity estimation.

[0046] Please refer to Figure 2 Another embodiment of the present invention provides a terminal for determining the reserve capacity of a power system based on an operating scenario, 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, it implements the various steps of the above-described method for determining the reserve capacity of a power system based on an operating scenario.

[0047] The method and terminal for determining power system reserve capacity based on operating scenarios described above are applicable to situations where the installed capacity and output scenarios of new energy power sources frequently change during power system planning and operation, enabling rapid and approximate determination of reserve capacity. The following detailed implementation methods illustrate this:

[0048] Example 1

[0049] Please refer to Figure 1 A method for determining power system reserve capacity based on operational scenarios, comprising the following steps:

[0050] S1. Obtain historical operating data of the power system, and cluster the historical operating data to obtain typical operating scenarios of the power system.

[0051] Using historical data from power system operation, x = {wind power output, photovoltaic power output, load, season, and meteorological conditions} is selected as the clustering vector to generate a sample space. Then, a fast clustering method is used for clustering to obtain typical operating scenarios of the power system. The specific steps include the following:

[0052] (1) Select the number of cluster points k, and use the minimum-maximum principle to determine the initial cluster points and initial classification.

[0053] The initial set of cluster points is

[0054] The approach to determining the initial cluster point using the minimax principle is: first select the two samples that are furthest apart from each other among all samples. The first two are the initial cluster points; then the third sample is selected. Make and The one with the smallest distance is equal to all the others. The largest of the smaller distances; and so on, iteratively calculating until k initial cluster points are determined.

[0055] The iterative calculation formula for determining the initial cluster point is:

[0056]

[0057] r = 1, 2, ..., l

[0058] j≠1 Y

[0059] In the formula, express and The distance between them x represents j and The distance between them l represents the number of initial cluster points determined in the iterative calculation.

[0060] The initial classification set is

[0061] The initial classification calculation formula is determined as follows:

[0062]

[0063] This divides the sample space into k disjoint classes, forming the initial classification set G. (0) .

[0064] (2) Perform iterative classification calculations until the classification remains unchanged, where the set of cluster points is calculated in the m-th iteration (m>0). The formula for generating it is:

[0065]

[0066] Classification The formula for generating it is:

[0067]

[0068] S2. Calculate the typical standby capacity required for the typical operating scenario with the goal of minimizing standby costs.

[0069] The objective function is to minimize the reserve cost.

[0070]

[0071] In the formula, N represents the total number of nodes in the power grid. To simplify the formula, node i is assumed to have generating units, loads, and new energy generating units participating in the market. If there is no corresponding equipment, its power is set to 0. T represents the total number of time periods, which is generally 96 time periods per day. These represent the positive reserve, negative reserve, and emergency reserve capacity of unit i during time period t, respectively; a i,10 a i,30 These represent the load reserve and emergency reserve prices for unit i during time period t, respectively.

[0072] The constraints are:

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085] In the formula, p i,t This indicates the output of unit i during time period t; w i,t d represents the planned power output of renewable energy unit i at node i during time period t; i,t This represents the predicted bus load power at node i during time period t. These represent the maximum and minimum output of unit i during time period t, respectively; These represent the maximum available positive load reserve, negative load reserve, and emergency reserve capacity of unit i during time period t, respectively. This represents the maximum ramp rate of unit i; Indicates the maximum landslide rate of unit i; u i,t This indicates the start / stop status of unit i during time period t, where 0 indicates unit is off and 1 indicates unit is on. Indicates the minimum continuous start-up time and minimum continuous downtime of the unit; This indicates the duration of continuous operation and continuous shutdown of unit i during time period t; G represents the power flow limit of a line or section l. l-i G represents the transfer distribution factor of node i to line or section l; j-i This represents the power transfer distribution factor of node i to section j of the inter-regional AC link line; This indicates the planned power of the connecting line section j during time period t.

[0086] In power grids with a high proportion of renewable energy integration, renewable energy forecasting errors are more significant than load forecasting errors, so renewable energy forecasting errors are the primary consideration. A renewable energy output range description method based on the satisfaction probability of non-emergency reserve (SPN) is adopted. This means that when renewable energy units fluctuate within their output range, load reserves are guaranteed to be sufficient and unrestricted by network security constraints, thus meeting the SPN requirements. Power balance is maintained through the load reserve participation adjustment coefficient τ in each time period. After load reserve is invoked, the following constraints must be met:

[0087]

[0088]

[0089]

[0090]

[0091]

[0092] In the formula, This indicates the output of generator unit i after it is called up from standby; Kai represents the relationship between region a and generator unit i. When region a contains generator unit i, Kai = 1; otherwise, Kai = 0; τ i,t W represents the load reserve adjustment factor of unit i during time period t; a,t This indicates an overall forecast deviation for new energy sources; This indicates the actual output of the new energy generating unit.

[0093] S3. Select matching operating scenarios from all the typical operating scenarios for the operating scenario of the time period to be determined.

[0094] Find the typical running scenario that is closest to the running scenario of the time period to be determined, and obtain the matching running scenario. That is, min{d(x,x b,i ), i=1,2,…,k}, where x represents the operating scenario for the time period to be determined; x b,i This represents the i-th typical operating scenario.

[0095] S4. Query the typical reserve capacity required for the matching operation scenario, and calculate the power system reserve capacity for the period to be determined based on the queried typical reserve capacity and the operation data of the period to be determined.

[0096] Assuming one of the goals of power system operation is to minimize the curtailment rate of renewable energy, P LD >P LD,b P ws ≥P ws,b Therefore, the reserve capacity for calculating the operating scenario in the undetermined time period is jointly determined by load growth and new energy growth, and can be approximated as:

[0097]

[0098] In the formula, r represents the standby capacity of the operating scenario for the time period to be determined. b P represents the typical standby capacity required to match the operating scenario. LD This indicates the load of the operating scenario during the period to be determined; P ws P represents the installed capacity of new energy vehicles for the operating scenario during the undetermined time period; LD,b Indicates the load matching the runtime scenario; P ws,b This indicates the installed capacity of new energy vehicles that matches the operating scenario.

[0099] Subsequently, the reserve capacity for the determined operating scenarios in the undetermined time period can be added to the power balance constraints of the power system flexibility power planning operation simulation:

[0100]

[0101] The basic principle for estimating the power system reserve capacity for a given period based on the selected predicted reserve capacity for typical operating scenarios is as follows:

[0102] In a power system with a high proportion of new energy, the output of the same wind farm in different years during the same typical period follows the same probability distribution, and the fitting parameters of the probability distribution in different years vary within a very small range; the output fluctuations of the same wind farm in different years during the same typical period all follow the T_location distribution, and the fitting parameters of the T_location distribution in different years vary within a very small range.

[0103] In power systems with a high proportion of new energy sources, the output of the same photovoltaic power plant in different years during the same typical period follows the same probability distribution, and the fitting parameters of the probability distribution in different years vary within a very small range; the output fluctuations of the same photovoltaic power plant in different years during the same typical period all follow the T_location distribution, and the fitting parameters of the T_location distribution in different years vary within a very small range.

[0104] In a power system with a high proportion of renewable energy, a given region contains several wind farms and photovoltaic (PV) farms. Due to the spatiotemporal correlation between these farms, their cross-correlation coefficients are primarily limited by meteorological conditions and geographical location. This spatiotemporal correlation can be modeled using a Copula function. Therefore, the renewable energy output and fluctuations in different years within a given region during the same typical period can be represented by a fixed probability distribution, meaning that renewable energy output and fluctuations are predictable. The impact of renewable energy output, fluctuations, and their prediction errors on the required reserve capacity of the power system in this region in different years is similar.

[0105] Load size and fluctuations within a given region are predictable. The impact of load size, fluctuations, and prediction errors on the required reserve capacity of the power system in that region is also similar in different years.

[0106] Since the output, fluctuations, and prediction errors of new energy sources and loads during the same typical period can be described 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 capacity of new energy sources and the capacity of loads. Furthermore, the growth of reserve capacity has an approximately linear relationship with the growth of both new energy source capacity and load capacity. Under conditions of a high proportion of new energy sources, this approximately linear relationship can be expressed as:

[0107]

[0108] However, it is subject to the following conditions:

[0109] (1) To achieve low-carbon operation, minimizing the curtailment rate of renewable energy is often one of the operational goals of the power system. Under this goal, renewable energy output is prioritized to meet load demand, and regulation tasks are mostly completed by thermal power, hydropower, and energy storage. However, when the predicted renewable energy output exceeds the load, renewable energy output will be curtailed due to load limitations, thus affecting the renewable energy prediction error and the reserve capacity required for changes in renewable energy output. Under high-proportion renewable energy conditions, this is mainly reflected in the speed of renewable energy capacity growth and load capacity growth, i.e., (P ws / P ws,b ) and (P LD / P LD,b The size of ).

[0110] (2) According to national standards, the load reserve capacity should not be less than (2% to 5%)P. LD .

[0111] In summary, the above technical solutions conceived in this embodiment can achieve the following beneficial effects:

[0112] (1) Compared with existing methods, this embodiment provides a method for rapid prediction of power system reserve capacity. It does not require iterative calculation and has a very small amount of computation. It can reasonably determine the reserved reserve when optimizing the power supply capacity in the power supply planning of the power system, and embed the determined reserved reserve into the iterative calculation of the optimized power supply capacity to obtain a more reasonable power supply capacity optimization result.

[0113] (2) Compared with existing methods, this embodiment provides a fast and more accurate method for determining reserve capacity during the continuous growth of new energy sources. This reduces the workload of frequently performing reserve capacity optimization calculations during the continuous growth of new energy sources, or eliminates the problems caused by estimating reserve capacity based on experience in engineering projects.

[0114] (3) Compared with existing methods, this embodiment can quickly and approximately determine the reserve capacity in different time periods, which can reduce the demand for regulation capacity and play an important role in changing the shortage of flexibility resources under the condition of high proportion of new energy access to the power system.

[0115] Example 2

[0116] Please refer to Figure 2 A terminal 1 for determining the reserve capacity of a power system based on an operating scenario includes a memory 2, a processor 3, and a computer program stored on the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, it implements the various steps of the method for determining the reserve capacity of a power system based on an operating scenario according to Embodiment 1.

[0117] In summary, this invention provides a method and terminal for determining power system reserve capacity based on operational scenarios. It utilizes historical operational data of the power system for clustering to obtain typical operational scenarios. Then, it calculates the typical reserve capacity required for each typical operational scenario with the goal of minimizing reserve costs. For operational scenarios in a time period to be determined, it filters out matching operational scenarios from all typical operational scenarios, queries the typical reserve capacity required for the matching operational scenarios, and calculates the power system reserve capacity for that time period based on the queried typical reserve capacity and the operational data of the time period to be determined. In this way, when determining reserve capacity for a time period to be determined, matching operational scenarios can be directly filtered and the corresponding typical reserve capacity queried. Subsequent calculations using formulas are sufficient to obtain the power system reserve capacity for that time period. This process involves minimal computation and can reasonably determine reserved reserves when optimizing power capacity in power system planning. It can also quickly and approximately determine reserve capacity when the installed capacity and output scenarios of new energy power sources frequently change during power system planning and operation.

[0118] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent modifications made based on the content of the present invention specification and drawings, or direct or indirect applications in related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for determining the reserve capacity of a power system based on operational scenarios, characterized in that, Including the following 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 standby capacity required for the typical operating scenario with the goal of minimizing standby costs; For the operational scenario in the time period to be determined, a matching operational scenario is selected from all the typical operational scenarios. Query the typical reserve capacity required for the matching operation scenario, and calculate the power system reserve capacity for the time period to be determined based on the queried typical reserve capacity and the operation data of the time period to be determined. The step of calculating the power system reserve capacity for the period to be determined based on the retrieved typical reserve capacity and the operating data for the period to be determined includes: Calculate the power system reserve capacity r for the time period to be determined: In the formula, r b This represents the typical standby capacity required to match the operating scenario. P LD This indicates the load of the operating scenario during the period to be determined; P ws This indicates the installed capacity of new energy vehicles for the operating scenario during the period to be determined. P LD,b This indicates the load matching the running scenario; P ws,b This indicates the installed capacity of new energy vehicles that matches the operating scenario.

2. The method for determining power system reserve capacity based on operational scenarios according to claim 1, characterized in that, The process of acquiring historical operating data of the power system and clustering the historical operating data to obtain typical operating scenarios of the power system includes: Wind power output, photovoltaic power output, load, season, and meteorological conditions are selected from the historical operating data of the power system as clustering vectors to generate a sample space; The samples in the sample space are clustered using a fast clustering method to obtain typical operating scenarios of the power system.

3. The method for determining power system reserve capacity based on operational scenarios according to claim 1, characterized in that, The calculation of the typical standby capacity required for the typical operating scenario with the goal of minimizing standby costs includes: The objective function is to minimize the reserve cost. In the formula, N Indicates the total number of generating units. T Indicates the total number of time periods. , , They represent the generating units. i exist t The positive reserve, negative reserve, and emergency reserve capacity for each time period. , They represent the generating units. i During the period t The price of load reserve and emergency reserve.

4. The method for determining power system reserve capacity based on operational scenarios according to claim 1, characterized in that, The step of selecting matching operating scenarios from all the typical operating scenarios for the operating scenario of the time period to be determined includes: The typical operating scenario that is closest to the operating scenario of the time period to be determined will be selected as the matching operating scenario: In the formula, x represents the operating scenario for the time period to be determined; Indicates the first i A typical operating scenario.

5. A terminal for determining the reserve capacity of a power system based on an operational scenario, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it performs the following 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 standby capacity required for the typical operating scenario with the goal of minimizing standby costs; For the operational scenario in the time period to be determined, a matching operational scenario is selected from all the typical operational scenarios. Query the typical reserve capacity required for the matching operation scenario, and calculate the power system reserve capacity for the time period to be determined based on the queried typical reserve capacity and the operation data of the time period to be determined. The step of calculating the power system reserve capacity for the period to be determined based on the retrieved typical reserve capacity and the operating data for the period to be determined includes: Calculate the power system reserve capacity r for the time period to be determined: In the formula, r b This represents the typical standby capacity required to match the operating scenario. P LD This indicates the load of the operating scenario during the period to be determined; P ws This indicates the installed capacity of new energy vehicles for the operating scenario during the period to be determined. P LD,b This indicates the load matching the running scenario; P ws,b This indicates the installed capacity of new energy vehicles that matches the operating scenario.

6. A terminal for determining power system reserve capacity based on operational scenarios according to claim 5, characterized in that, The process of acquiring historical operating data of the power system and clustering the historical operating data to obtain typical operating scenarios of the power system includes: Wind power output, photovoltaic power output, load, season, and meteorological conditions are selected from the historical operating data of the power system as clustering vectors to generate a sample space; The samples in the sample space are clustered using a fast clustering method to obtain typical operating scenarios of the power system.

7. A terminal for determining power system reserve capacity based on operating scenarios according to claim 5, characterized in that, The calculation of the typical standby capacity required for the typical operating scenario with the goal of minimizing standby costs includes: The objective function is to minimize the reserve cost. In the formula, N Indicates the total number of generating units. T Indicates the total number of time periods. , , They represent the generating units. i exist t The positive reserve, negative reserve, and emergency reserve capacity for each time period. , They represent the generating units. i During the period t The price of load reserve and emergency reserve.

8. A terminal for determining power system reserve capacity based on operating scenarios according to claim 5, characterized in that, The step of selecting matching operating scenarios from all the typical operating scenarios for the operating scenario of the time period to be determined includes: The typical operating scenario that is closest to the operating scenario of the time period to be determined will be selected as the matching operating scenario: In the formula, x represents the operating scenario for the time period to be determined; Indicates the first i A typical operating scenario.

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