Power system insurance and supply risk assessment method considering typical meteorological years of extreme weather

Abstract

The invention discloses a power system insurance and supply risk assessment method considering an extreme weather typical meteorological year, and the method comprises the steps: 1, obtaining historical meteorological data of a target region, generating a typical meteorological year through employing a Sandia method, and improving the typical meteorological year through employing a quantile increment mapping method, and obtaining the extreme weather typical meteorological year; 2, performing scene division on historical meteorological data in a typical meteorological year of extreme weather by adopting a K-means clustering algorithm, using an obtained clustering result to train a denoising variation auto-encoder model, generating a typical weather scene set and calculating the probability of a typical meteorological scene; 3, by taking the minimum load shedding capacity and the minimum wind and light abandoning capacity of the power system as targets, constructing a supply guarantee operation model of the power system; and 4, calculating an insurance supply risk assessment index of the power system. According to the method, supply insurance risk assessment is carried out on the typical meteorological year of the extreme weather, and supply insurance weak links of the novel power system with high new energy permeability under the extreme weather are described by calculating supply insurance risk indexes under the typical scene of the extreme weather.

Description

Technical Field

[0001] The present invention relates to the field of power system risk assessment, and in particular to a method for assessing power system supply security risks in a typical meteorological year taking into account extreme weather. Background Art

[0002] As extreme weather events become increasingly frequent, current typical meteorological year models overlook rare but significant extreme weather events (such as heat waves, cold snaps, and strong winds). Incorporating these into modeling can help improve the reliability of power system design and assessment under extreme conditions. Existing research often uses multi-year observational data or typical meteorological years as meteorological input to assess power system performance under conventional weather conditions. However, this lacks coverage of extreme weather scenarios and makes it difficult to reflect system weaknesses and potential risks under the penetration of high-energy renewable energy. A method is urgently needed that combines the construction of a typical meteorological year for extreme weather with multi-scenario supply assurance risk assessment. Summary of the Invention

[0003] The purpose of this invention is to address the shortcomings of the existing technology and provide a power system supply risk assessment method taking into account a typical meteorological year with extreme weather, so as to accurately quantify the power system supply risk in extreme weather scenarios under the background of high-energy penetration, thereby improving the operational reliability and safety of the power grid under extreme weather conditions.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] The method for assessing power system supply security risk in a typical meteorological year taking into account extreme weather conditions is characterized in that it is performed according to the following steps:

[0006] Step 1: Obtain historical meteorological data for the target area for at least N years. Use the Sandia method to select a representative typical meteorological month from the N years corresponding to each month to form a typical meteorological year. Then, use the quantile increment mapping method to improve the typical meteorological year to obtain the extreme weather typical meteorological year.

[0007] Step 2: Use the K-means clustering algorithm to divide the historical meteorological data under the typical meteorological year of extreme weather into scenes, obtain the clustering results under different extreme weather types, and use them to train the denoising variational autoencoder DVAE model to generate a set of typical extreme weather scenes. , thus calculating any typical extreme weather scenario Probability ;

[0008] Step 3: Build a power supply guarantee operation model for the power system with the goal of minimizing load shedding and wind and solar curtailment.

[0009] Step 4: Based on the guaranteed supply operation model, calculate the power system's typical extreme weather scenario The risk assessment indicators for power supply guarantee under the 2020 Regulations include: load shedding risk assessment indicators and wind and solar curtailment risk assessment indicators;

[0010] The method for assessing power system supply security risk in a typical meteorological year taking into account extreme weather conditions according to the present invention is also characterized in that step 1 is performed as follows;

[0011] Step 1.1, construct a typical meteorological year;

[0012] Step 1.1.1: For any meteorological parameter data in the historical meteorological data of more than N years The different values of are sorted in ascending order according to each month of each year, and the data of any meteorological parameter after sorting is calculated using formula (1) Cumulative distribution function in any month ;

[0013] (1)

[0014] In formula (1), is the total number of meteorological parameter data; After sorting Meteorological parameter data, After sorting Meteorological parameter data;

[0015] Step 1.1.2: Calculate any meteorological parameter data using formula (2) In some of N years Year Filkenstein-Schafer statistics under the month ;

[0016] (2)

[0017] In formula (2), yes In N years Monthly cumulative distribution function; It's someone Year Under the Moon The cumulative distribution function of

[0018] Step 1.1.3: Obtain a Year Weighted sum of the next month , thus obtaining the weighted sum of each month in N years, and selecting the year corresponding to the minimum weighted sum of each month in N years to form a typical meteorological year;

[0019] (3)

[0020] In formula (3), It is meteorological parameter data The weight factor of is the total number of meteorological parameter data;

[0021] Step 1.2: Construct a typical meteorological year for extreme weather;

[0022] Step 1.2.1. Calculate the cumulative distribution function of any meteorological parameter data x in any month of a typical meteorological year and the cumulative distribution function of the corresponding month in N years ;

[0023] Step 1.2.2: Calculate any meteorological parameter data using formula (4) Quantile ;

[0024] (4)

[0025] In formula (4), It is a meteorological parameter quantiles in a typical meteorological year;

[0026] Step 1.2.3: Calculate any meteorological parameter data using equations (5) and (6) Cumulative distribution function for any month in a typical meteorological year median Corresponding typical values And the cumulative distribution function of the corresponding month in N years median Corresponding typical values ;

[0027] (5)

[0028] (6)

[0029] In formula (5)-formula (6), -1 represents the inverse operation of the cumulative distribution function;

[0030] Step 1.2.4: Calculate any meteorological parameter data using formula (7) Offset in a typical meteorological year ;

[0031] (7)

[0032] Step 1.2.5: Calculate any meteorological parameter data using formula (8) Incremental mapping results ;

[0033] (8)

[0034] Step 1.2.6: Replace each meteorological parameter data in the typical meteorological year with the corresponding incremental mapping result to obtain the typical meteorological year for extreme weather.

[0035] Furthermore, the step 3 is performed as follows:

[0036] Step 3.1: Establish the objective function of the supply guarantee operation model according to formula (10) ;

[0037] (10)

[0038] In formula (10), For load Load shedding capacity; For photovoltaic power stations The amount of abandoned light; For wind power plants The amount of wind curtailment; is the load set, For photovoltaic power station collection, Assemble for photovoltaic power station;

[0039] Step 3.2: Establish a wind power output model according to formula (11);

[0040] (11)

[0041] In formula (11), For wind power plants The actual output power; For wind power plants wind speed; For wind power plants Rated power of wind power; For wind power plants Rated wind speed; For wind power plants Wind power cut-in wind speed; For wind power plants Wind power cut-out wind speed;

[0042] Step 3.3: Establish the photovoltaic output model according to formula (12);

[0043] P pv = P STC η S T C G C G S T C [ 1 − γ ( θ − θ S T C ) ] (12)

[0044] In formula (12), For photovoltaic power stations The actual output power; For photovoltaic power stations Rated power of power generation; It is a photovoltaic power station The efficiency of photovoltaic modules; For photovoltaic power stations The actual irradiance; For photovoltaic power stations Rated irradiation; For photovoltaic power stations The actual temperature; For photovoltaic power stations Temperature at rated power; is the temperature coefficient;

[0045] Step 3.4: Establish the power balance constraint of the power system according to formula (13);

[0046] (13)

[0047] In formula (13), Assemble for the generator set; For generators contribution; For wind power plants The actual output; For wind power plants The actual output; For load Power;

[0048] Step 3.5: Establish conventional unit constraints according to formula (14);

[0049] (14)

[0050] In formula (14), and Generator Minimum and maximum active output;

[0051] Step 3.6: Establish wind power output constraints, photovoltaic output constraints, and load shedding constraints according to formula (15);

[0052] (15)

[0053] In formula (15), For wind power plants The original output value; For photovoltaic power stations The original output value; For load The original load size.

[0054] Furthermore, step 4 is performed as follows;

[0055] Step 4.1: Establish load shedding risk assessment index according to formula (16) ;

[0056] (16)

[0057] In formula (16), For load Typical scenarios in any extreme weather Load shedding under For load Typical scenarios in any extreme weather The original load under

[0058] Step 4.2: Establish the wind curtailment risk assessment index according to formula (17) ;

[0059] (17)

[0060] In formula (17), For wind farms Typical scenarios in any extreme weather The amount of wind curtailment under For wind farms Typical scenarios in any extreme weather The original output value under ;

[0061] Step 4.3: Establish the risk assessment index of abandoned light according to formula (18) ;

[0062] (18)

[0063] In formula (18), For photovoltaic power stations Typical scenarios in any extreme weather The amount of wind curtailment under For photovoltaic power stations Typical scenarios in any extreme weather The original output value under .

[0064] An electronic device of the present invention includes a memory and a processor, and is characterized in that the memory is used to store a program that supports the processor to execute the power system power supply risk assessment method, and the processor is configured to execute the program stored in the memory.

[0065] The present invention provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium. The computer program is characterized in that when the computer program is executed by a processor, the steps of the power system supply guarantee risk assessment method are executed.

[0066] Compared with the prior art, the beneficial effects of the present invention are embodied in:

[0067] 1. This invention introduces the quantile increment mapping method to correct the deviation of the traditional typical meteorological year, constructing an extreme weather typical meteorological year that is more consistent with the characteristics of extreme weather events, significantly improving the coverage and accuracy of meteorological input for extreme events;

[0068] 2. This invention combines the K-means clustering method with a denoising variational autoencoder to perform scene division and generation on multidimensional meteorological data. This not only ensures the typicality and diversity of the generated scenes, but also simultaneously calculates the probability of occurrence of each scene, overcoming the technical bottleneck of a single scene extraction method and difficult probability assessment.

[0069] 3. Based on the goal of minimizing load shedding and wind and solar power abandonment, the present invention constructs a supply guarantee operation model, which can directly output load shedding risk indicators and wind and solar power abandonment risk indicators under multi-scenario simulation. It can not only accurately identify the weak links of high-energy energy penetration in the power system under extreme weather conditions, but also provide a reliable decision-making basis for power grid operation scheduling and planning and design. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a flow chart of the power system supply risk assessment method considering a typical meteorological year with extreme weather conditions according to the present invention;

[0071] Figure 2 This is an example diagram of temperature, wind speed and radiation data for a certain month in a typical meteorological year TMY and a typical meteorological year XTMY with extreme weather, generated by the power system supply guarantee risk assessment method for a typical meteorological year with extreme weather in the present invention. DETAILED DESCRIPTION

[0072] In this embodiment, a method for assessing the power system supply risk in a typical meteorological year with extreme weather is to assess the supply risk in a typical meteorological year with extreme weather, and to characterize the weak links of the new power system with high new energy penetration under extreme weather by calculating the supply risk index under the typical scenario of extreme years. Specifically, Figure 1 As shown, the generation method is carried out in the following steps:

[0073] Step 1: Obtain historical meteorological data for the target area for at least N years. Use the Sandia method to select a representative typical meteorological month from the N years corresponding to each month to form a typical meteorological year. Then, use the quantile increment mapping method to improve the typical meteorological year to obtain the extreme weather typical meteorological year.

[0074] Step 1.1, construct a typical meteorological year;

[0075] Step 1.1.1: For any meteorological parameter data in the historical meteorological data of more than N years The different values of are sorted in ascending order according to each month of each year, and the data of any meteorological parameter after sorting is calculated using formula (1) Cumulative distribution function in any month ;

[0076] (1)

[0077] In formula (1), is the total number of meteorological parameter data; After sorting Meteorological parameter data, After sorting Meteorological parameter data.

[0078] Step 1.1.2: Calculate any meteorological parameter data using formula (2) In some of N years Year Filkenstein-Schafer statistics under the month ;

[0079] (2)

[0080] In formula (2), yes In N years Monthly cumulative distribution function; It's someone Year Under the Moon The cumulative distribution function of .

[0081] Step 1.1.3: Obtain a Year Weighted sum of the next month , thus obtaining the weighted sum of each month in N years, and selecting the year corresponding to the minimum weighted sum of each month in N years to form a typical meteorological year;

[0082] (3)

[0083] In formula (3), It is meteorological parameter data The weight factor of is the total number of meteorological parameter data.

[0084] Step 1.2: Construct a typical meteorological year for extreme weather;

[0085] Step 1.2.1. Calculate the cumulative distribution function of any meteorological parameter data x in any month of a typical meteorological year and the cumulative distribution function of the corresponding month in N years ;

[0086] Step 1.2.2: Calculate any meteorological parameter data using formula (4) Quantile ;

[0087] (4)

[0088] In formula (4), It is a meteorological parameter Quantile in a typical meteorological year.

[0089] Step 1.2.3: Calculate any meteorological parameter data using equations (5) and (6) Cumulative distribution function for any month in a typical meteorological year median Corresponding typical values And the cumulative distribution function of the corresponding month in N years median Corresponding typical values ;

[0090] (5)

[0091] (6)

[0092] In equations (5) and (6), -1 represents the inverse operation of the cumulative distribution function.

[0093] Step 1.2.4: Use formula (7) to calculate the offset of any meteorological parameter data x in a typical meteorological year. ;

[0094] (7)

[0095] Step 1.2.5: Calculate the incremental mapping result of any meteorological parameter data x using formula (8) ;

[0096] (8)

[0097] Step 1.2.6: Replace each meteorological parameter data in the typical meteorological year with the corresponding incremental mapping result to obtain a typical meteorological year for extreme weather. The typical meteorological year TMY obtained using the Sandia method can only represent the average year and lacks the description of extreme years (especially in the supply guarantee scenario). The quantile incremental mapping (QDM) method can take into account the relative changes in the original data. The improved XTMY using QDM contains more accurate multi-year extreme and typical weather information. The meteorological data of TMY and XTMY for a certain month are as follows: Figure 2 shown.

[0098] Step 2: Use the K-means clustering algorithm to divide the historical meteorological data under the typical meteorological year of extreme weather into scenes, obtain the clustering results under different extreme weather types, and use them to train the denoising variational autoencoder DVAE model to generate a set of typical extreme weather scenes. , thus calculating any typical extreme weather scenario Probability .

[0099] By injecting noise into latent variables and optimizing denoising criteria, DVAE can construct a scene generation model with good robust performance, significantly improving the efficiency and accuracy of extreme weather scene generation, ensuring that the scene generation results are both in line with physical laws and statistically reliable. Use scenario It is expressed as the ratio of the number of days with extreme weather in a typical meteorological year to the total number of days with extreme weather in a typical meteorological year.

[0100] Step 3: Build a power supply guarantee operation model for the power system with the goal of minimizing load shedding and wind and solar curtailment.

[0101] Step 3.1: Establish the objective function of the supply guarantee operation model according to formula (10) ;

[0102] (10)

[0103] In formula (10), For load Load shedding capacity; For photovoltaic power stations The amount of abandoned light; For wind power plants The amount of wind curtailment; is the load set, For photovoltaic power station collection, Assembled for photovoltaic power station.

[0104] Step 3.2: Establish a wind power output model according to formula (11);

[0105] (11)

[0106] In formula (11), For wind power plants The actual output power; For wind power plants wind speed; For wind power plants wind power rated power; For wind power plants Rated wind speed; For wind power plants Wind power cut-in wind speed; For wind power plants The wind power cut-out wind speed.

[0107] Step 3.3: Establish the photovoltaic output model according to formula (12);

[0108] P pv = P STC η S T C G C G S T C [ 1 − γ ( θ − θ S T C ) ] (12)

[0109] In formula (12), For photovoltaic power stations The actual output power; For photovoltaic power stations Rated power of power generation; It is a photovoltaic power station The efficiency of photovoltaic modules; For photovoltaic power stations The actual irradiance; For photovoltaic power stations Rated irradiation; For photovoltaic power stations The actual temperature; For photovoltaic power stations Temperature at rated power; is the temperature coefficient, with a typical value of 0.0045 / °C.

[0110] Step 3.4: Establish the power balance constraint of the power system according to formula (13);

[0111] (13)

[0112] In formula (13), Assemble for the generator set; For generators contribution; For wind power plants The actual output; For wind power plants The actual output; For load power.

[0113] Step 3.5: Establish conventional unit constraints according to formula (14);

[0114] (14)

[0115] In formula (14), and Generator Minimum and maximum active output.

[0116] Step 3.6: Establish wind power output constraints, photovoltaic output constraints, and load shedding constraints according to formula (15);

[0117] (15)

[0118] In formula (15), For wind power plants The original output value; For photovoltaic power stations The original output value; For load The original load size.

[0119] Step 4: Based on the guaranteed supply operation model, calculate the power system's typical extreme weather scenario The risk assessment indicators for power supply guarantee under the 2020 Regulations include: load shedding risk assessment indicators and wind and solar curtailment risk assessment indicators;

[0120] Step 4.1: Establish load shedding risk assessment index according to formula (16) ;

[0121] (16)

[0122] In formula (16), For load Typical scenarios in any extreme weather Load shedding under For load Typical scenarios in any extreme weather The original load under.

[0123] Step 4.2: Establish the wind curtailment risk assessment index according to formula (17) ;

[0124] (17)

[0125] In formula (17), For wind farms Typical scenarios in any extreme weather The amount of wind curtailment under For wind farms Typical scenarios in any extreme weather The original output value under .

[0126] Step 4.3: Establish the risk assessment index of abandoned light according to formula (18) ;

[0127] (18)

[0128] In formula (18), For photovoltaic power stations Typical scenarios in any extreme weather The amount of wind curtailment under For photovoltaic power stations Typical scenarios in any extreme weather The original output value under .

[0129] This invention is applicable to power systems with high penetration rates of new energy. It can accurately assess the power system's supply risks under extreme weather conditions and identify weak links in the power system. The assessment results can provide support for grid risk response and long-term planning and design.

[0130] In this embodiment, an electronic device includes a memory and a processor, wherein the memory is used to store a program that supports the processor to execute the above method, and the processor is configured to execute the program stored in the memory.

[0131] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are executed.

[0132] Example:

[0133] This example uses ten years of measured historical meteorological data of a province, including temperature, wind speed and radiation, and the meteorological data of a month in a typical meteorological year TMY generated by the Sandia method and a typical meteorological year XTMY of extreme weather generated by the quantile increment mapping method proposed in this invention. Figure 2 As shown, Figure 2 Figure (a) in the middle shows the hourly temperature data for a month in a typical meteorological year and extreme weather. Figure 2 Figure (b) shows the hourly wind speed data for a month in a typical meteorological year and extreme weather. Figure 2 Figure (c) in the middle shows the hourly radiation data for a certain month in a typical meteorological year and a typical meteorological year with extreme weather.

Claims

1. A method for assessing power system supply security risk in a typical meteorological year considering extreme weather conditions, characterized in that: It is done in the following steps; Step 1: Obtain historical meteorological data for the target area for at least N years. Use the Sandia method to select a representative typical meteorological month from the N years corresponding to each month to form a typical meteorological year. Then, use the quantile increment mapping method to improve the typical meteorological year to obtain the extreme weather typical meteorological year. Step 2: Use the K-means clustering algorithm to divide the historical meteorological data under the typical meteorological year of extreme weather into scenes, obtain the clustering results under different extreme weather types, and use them to train the denoising variational autoencoder DVAE model to generate a set of typical extreme weather scenes. , thus calculating any typical extreme weather scenario Probability ; Step 3: Build a power supply guarantee operation model for the power system with the goal of minimizing load shedding and wind and solar curtailment. Step 4: Based on the guaranteed supply operation model, calculate the power system's typical extreme weather scenario The supply guarantee risk assessment indicators under the 2020 Energy Strategies Framework include: load shedding risk assessment indicators and wind and solar power abandonment risk assessment indicators.

2. The method for assessing power system supply security risk in a typical meteorological year taking into account extreme weather according to claim 1, characterized in that: The step 1 is carried out as follows: Step 1.1, construct a typical meteorological year; Step 1.1.1: For any meteorological parameter data in the historical meteorological data of more than N years The different values of are sorted in ascending order according to each month of each year, and the data of any meteorological parameter after sorting is calculated using formula (1) Cumulative distribution function in any month ; (1) In formula (1), is the total number of meteorological parameter data; After sorting Meteorological parameter data, After sorting Meteorological parameter data; Step 1.1.2: Calculate any meteorological parameter data using formula (2) In some of N years Year Filkenstein-Schafer statistics under the month ; (2) In formula (2), yes In N years Monthly cumulative distribution function; It's someone Year Under the Moon The cumulative distribution function of Step 1.1.3: Obtain a Year Weighted sum of the next month , thus obtaining the weighted sum of each month in N years, and selecting the year corresponding to the minimum weighted sum of each month in N years to form a typical meteorological year; (3) In formula (3), It is meteorological parameter data The weight factor of is the total number of meteorological parameter data; Step 1.2: Construct a typical meteorological year for extreme weather; Step 1.2.

1. Calculate the cumulative distribution function of any meteorological parameter data x in any month of a typical meteorological year and the cumulative distribution function of the corresponding month in N years ; Step 1.2.2: Calculate any meteorological parameter data using formula (4) Quantile ; (4) In formula (4), It is a meteorological parameter quantiles in a typical meteorological year; Step 1.2.3: Calculate any meteorological parameter data using equations (5) and (6) Cumulative distribution function for any month in a typical meteorological year median Corresponding typical values And the cumulative distribution function of the corresponding month in N years median Corresponding typical values ; (5) (6) In formula (5)-formula (6), -1 represents the inverse operation of the cumulative distribution function; Step 1.2.4: Calculate any meteorological parameter data using formula (7) Offset in a typical meteorological year ; (7) Step 1.2.5: Calculate any meteorological parameter data using formula (8) Incremental mapping results ; (8) Step 1.2.6: Replace each meteorological parameter data in the typical meteorological year with the corresponding incremental mapping result to obtain the typical meteorological year for extreme weather.

3. The method for assessing power system supply security risk in a typical meteorological year taking into account extreme weather conditions according to claim 2, characterized in that: The step 3 is carried out as follows: Step 3.1: Establish the objective function of the supply guarantee operation model according to formula (10) ; (10) In formula (10), For load Load shedding capacity; For photovoltaic power stations The amount of abandoned light; For wind power plants The amount of wind curtailment; is the load set, For photovoltaic power station collection, Assemble for photovoltaic power station; Step 3.2: Establish a wind power output model according to formula (11); (11) In formula (11), For wind power plants The actual output power; For wind power plants wind speed; For wind power plants Rated power of wind power; For wind power plants Rated wind speed; For wind power plants Wind power cut-in wind speed; For wind power plants Wind power cut-out wind speed; Step 3.3: Establish the photovoltaic output model according to formula (12); (12) In formula (12), For photovoltaic power stations The actual output power; For photovoltaic power stations Rated power of power generation; It is a photovoltaic power station The efficiency of photovoltaic modules; For photovoltaic power stations The actual irradiance; For photovoltaic power stations Rated irradiation; For photovoltaic power stations The actual temperature; For photovoltaic power stations Temperature at rated power; is the temperature coefficient; Step 3.4: Establish the power balance constraint of the power system according to formula (13); (13) In formula (13), Assemble for the generator set; For generators contribution; For wind power plants The actual output; For wind power plants The actual output; For load Power; Step 3.5: Establish conventional unit constraints according to formula (14); (14) In formula (14), and Generator Minimum and maximum active output; Step 3.6: Establish wind power output constraints, photovoltaic output constraints, and load shedding constraints according to formula (15); (15) In formula (15), For wind power plants The original output value; For photovoltaic power stations The original output value; For load The original load size.

4. The method for assessing power system supply security risk in a typical meteorological year taking into account extreme weather conditions according to claim 3, characterized in that: Step 4 is carried out as follows; Step 4.1: Establish load shedding risk assessment index according to formula (16) ; (16) In formula (16), For load Typical scenarios in any extreme weather Load shedding under For load Typical scenarios in any extreme weather The original load under Step 4.2: Establish the wind curtailment risk assessment index according to formula (17) ; (17) In formula (17), For wind farms Typical scenarios in any extreme weather The amount of wind curtailment under For wind farms Typical scenarios in any extreme weather The original output value under ; Step 4.3: Establish the risk assessment index of abandoned light according to formula (18) ; (18) In formula (18), For photovoltaic power stations Typical scenarios in any extreme weather The amount of wind curtailment under For photovoltaic power stations Typical scenarios in any extreme weather The original output value under .

5. An electronic device comprising a memory and a processor, characterized in that: The memory is used to store a program that supports the processor to execute the power system power supply guarantee risk assessment method according to any one of claims 1 to 4, and the processor is configured to execute the program stored in the memory.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the power system supply guarantee risk assessment method according to any one of claims 1 to 4 are executed.

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