A risk assessment method for electricity spot trading

Abstract

The present invention relates to the field of power trading risk assessment, and specifically to a power spot trading risk assessment method, comprising the following steps: S1, obtaining photovoltaic impact information and wind power impact information of the area where the target power spot supplier is located; S2, calculating the total predicted power generation of the target power generator in the target time period based on the photovoltaic impact information and the wind power impact information; S3, obtaining the power consumption impact information and historical power consumption information of the power grid; S4, calculating the first total power consumption of the power grid in the target time period based on the power consumption impact information and the historical power consumption information; the present invention takes into account that the common power grid power consumption calculation method only calculates the individual effects of season, GDP growth rate, industry type and population density on the total power consumption, and does not take into account the mutual influence between these factors, resulting in an inaccurate calculation result of the power grid power consumption, so four coupling influence coefficients are introduced, and the accuracy of the total power generation of the power grid finally calculated is higher.

Description

Technical Field

[0001] The present invention relates to the field of power transaction risk assessment, and in particular to a power spot transaction risk assessment method. Background Art

[0002] Electricity spot trading refers to electric energy products traded in real time in the electricity market. Buyers and sellers trade at market prices based on the current (or short-term) electricity supply and demand situation, usually involving electricity delivery the next day or the same day. The risk of electricity spot trading is directly related to the power generation of the electricity spot supplier. If the electricity spot supplier is unable to provide the electricity spot volume required by the contract, there is a direct risk of default. The power generation forecast method of electricity spot suppliers that rely on clean energy is generally based on the single influence of factors such as wind power or sunlight intensity, without considering the mutual influence between these factors, resulting in low final calculation accuracy. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the present invention provides a method for risk assessment of electricity spot trading, which solves the technical problems in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A method for risk assessment of electricity spot trading, comprising the following steps:

[0006] S1. Obtain photovoltaic power impact information and wind power impact information in the area where the target electricity spot supplier is located;

[0007] Photoelectric impact information includes: solar irradiance and solar incident angle at each moment in the target time period;

[0008] Wind power impact information includes: atmospheric pressure and wind speed at each moment in the target time period;

[0009] S2. Calculate the total predicted power generation of the target power generation party in the target time period based on the photovoltaic impact information and wind power impact information. ;

[0010] S3. Obtaining the power consumption impact information and historical power consumption information of the power grid;

[0011] S4. Calculate the first total power consumption of the power grid in the target time period based on the power consumption impact information and the historical power consumption information. ;

[0012] S5. Based on the total predicted power generation and the first total electricity consumption Calculate transaction risk value .

[0013] Furthermore, in step S2, the following steps are specifically included:

[0014] S21. Obtain temperature information at each moment in the target time period in the region where the target electricity spot supplier is located;

[0015] S22. Calculate the actual air density based on the temperature information and photovoltaic efficiency impact value ;

[0016] S23. Calculate the first coupling term based on photovoltaic impact information and wind power impact information and the second coupling term ;

[0017] S24, according to the actual air density , Photovoltaic efficiency impact value , the first coupling term and the second coupling term Calculate the predicted amount of photovoltaic power generation and wind power generation forecasts ;

[0018] S25, based on the predicted amount of photovoltaic power generation and wind power generation forecasts Calculate total forecast power generation , and its calculation formula is:

[0019] ;

[0020] Where, It represents the total predicted power generation of the target power generator in the target time period.

[0021] Furthermore, in step S22, the actual air density The calculation formula is:

[0022] ;

[0023] Where, represents the air density at the tth moment; represents the atmospheric pressure at the tth moment; represents the specific gas constant; represents the temperature information at the t-th moment; in the present invention, is 287.05;

[0024] Photovoltaic efficiency impact value The calculation formula is:

[0025] ;

[0026] Where, represents the actual efficiency of the photovoltaic panel at the tth moment; Indicates the standard efficiency of photovoltaic panels under standard test conditions; Indicates the temperature coefficient of photovoltaic efficiency; represents the solar irradiance at the tth moment; Indicates the rated operating temperature of the photovoltaic panel.

[0027] Furthermore, in step S23, the first coupling term The calculation formula is:

[0028] ;

[0029] Where, Indicates the suppression coefficient of photovoltaic output on wind power; Indicates the maximum possible irradiance;

[0030] The second coupling term The calculation formula is:

[0031] ;

[0032] Where, represents the mechanical restraint coefficient; represents the wind speed at the tth moment; Indicates the fan cut-out wind speed; Indicates the attenuation coefficient of dust on power generation; It indicates the amount of dust deposition per unit wind speed increase; Indicates the critical wind speed for sand emission; express function; represents the base of natural logarithms; Indicates the wind speed heat dissipation coefficient; Represents the basic heat dissipation constant.

[0033] Furthermore, in step S24, the photovoltaic power generation prediction amount The calculation formula is:

[0034] ;

[0035] Where, Represents the total effective area of ​​the photovoltaic panels; Indicates the total number of moments in the target time period; represents the comprehensive system loss; represents the incident angle of sunlight at the tth moment; Indicates a time interval;

[0036] Wind power generation forecast The calculation formula is:

[0037] ;

[0038] Where, represents the fan swept area; represents the wind energy utilization coefficient; Indicates the mechanical and electrical efficiency of the fan.

[0039] Furthermore, in step S3, the electricity consumption impact information includes: season, GDP growth rate, industry type and population density; the historical electricity consumption information represents the total daily electricity consumption of the power grid.

[0040] Furthermore, in step S4, the following steps are specifically included:

[0041] S41. Set a sliding window with a length of 1 year in the historical electricity consumption information. Each time the sliding window slides once, a training sample is obtained. The total electricity consumption L days after each training sample is used as a sample label.

[0042] S42. Train the long short-term memory network using the training samples and sample labels to obtain a target model;

[0043] S43: Input the total electricity consumption per day in the past year into the target model and output the second total electricity consumption for the target time period. ;

[0044] S44, the second total power consumption is calculated based on the power consumption impact information. Perform correction to obtain the first total power consumption .

[0045] Furthermore, in step S44, the following steps are specifically included:

[0046] S441. Calculate a first coupling influence coefficient based on power consumption influence information , and its calculation formula is:

[0047] ;

[0048] Where, represents the seasonal coefficient; represents the GDP growth rate; represents the first interaction weight parameter;

[0049] S442. Calculate the second coupling influence coefficient , and its calculation formula is:

[0050] ;

[0051] Where, represents the industrial structure coefficient; represents the second interaction weight parameter;

[0052] S443. Calculate the third coupling influence coefficient , and its calculation formula is:

[0053] ;

[0054] Where, represents the third interaction weight parameter;

[0055] S444. Calculate the fourth coupling influence coefficient , and its calculation formula is:

[0056] ;

[0057] Where, represents the population density coefficient; represents the fourth interaction weight parameter;

[0058] S445, according to the first coupling influence coefficient , the second coupling influence coefficient , the third coupling influence coefficient and the fourth coupling influence coefficient Total electricity consumption for the second Perform correction to obtain the first total power consumption .

[0059] Furthermore, in step S445, the first total power consumption The calculation formula is:

[0060] ;

[0061] Where, 、 、 and Respectively express about The first, second, third and fourth empirical coefficients of .

[0062] Furthermore, in step S5, the transaction risk value The calculation formula is:

[0063] ;

[0064] Where, Represents the electricity spot trading volume.

[0065] Compared with the existing technology, the present invention provides a method for risk assessment of electricity spot trading, which has the following beneficial effects:

[0066] 1. The present invention introduces the first coupling term and the second coupling term to quantify the comprehensive impact of meteorological conditions on photovoltaic power generation and wind power generation. Compared with common calculation methods, the prediction accuracy of power generation is higher, which facilitates the subsequent calculation of the risk value of electricity spot trading based on it.

[0067] 2. The present invention takes into account that the common methods for calculating power grid electricity consumption only calculate the separate effects of season, GDP growth rate, industry type and population density on total electricity consumption, but does not take into account the mutual influence between these factors, resulting in inaccurate calculation results of power grid electricity consumption. Therefore, four coupling influence coefficients are introduced. The final calculated total power generation of the power grid is more accurate, so the final calculated transaction risk value is also more accurate, which facilitates the power spot supplier to choose a response strategy based on the risk value. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0069] Figure 1 This is a schematic diagram of a method for assessing electricity spot trading risks according to the present invention. DETAILED DESCRIPTION

[0070] To make the above-mentioned objectives, features, and advantages of the present invention more clearly understood, the present invention is further described below in detail with reference to the accompanying drawings and specific embodiments. This will enable a full understanding of how this application uses technical means to solve technical problems and achieve technical effects, and to implement the invention accordingly.

[0071] Those skilled in the art will appreciate that all or part of the steps in the following embodiments can be accomplished by instructing related hardware through a program. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] Spot electricity refers to electric energy products traded in real time in the electricity market. Buyers and sellers trade at market prices based on current (or short-term) electricity supply and demand conditions, typically involving electricity delivery the next day or the same day. Unlike long-term contracts (such as annual or monthly contracts), spot electricity trading is closer to the actual time of electricity consumption, and prices fluctuate frequently with supply and demand fluctuations. Some spot electricity suppliers rely on renewable energy sources such as wind and solar power. This highly weather-dependent forecasting method is severely affected by the weather, and even short-term forecasts can have significant errors. For example, a photovoltaic output forecast of 80% for the next day may only reach 50% on cloudy days, forcing power generators to purchase spot electricity at high prices to make up for the shortfall. In addition, transmission congestion or network failures may cause localized electricity prices to deviate from system electricity prices, affecting transaction execution. For this reason, please refer to the relevant information. Figure 1 As shown, the present invention proposes a method for risk assessment of electricity spot trading, comprising the following steps:

[0073] S1. Obtain photovoltaic and wind power impact information for the target spot power supplier's region. Specifically, for a spot power supplier that relies on clean energy for power generation, wind and sunlight conditions are important factors affecting power generation. After the spot power supplier and the demander sign a contract, wind and sunlight conditions are important factors in determining whether the spot power supplier can fulfill the contract. Therefore, in step S1, the photovoltaic impact information includes: solar irradiance and solar incidence angle at each moment in the target time period.

[0074] Wind power impact information includes: atmospheric pressure and wind speed at each moment in the target time period; specifically, photovoltaic impact information and wind power impact information can be directly obtained from the forecast information for the target period published by the meteorological station in the area where the target electricity spot supplier is located.

[0075] S2. Calculate the total predicted power generation of the target power generation party in the target time period based on the photovoltaic impact information and wind power impact information. ; Specifically, the common method of predicting photovoltaic power generation only considers the separate effects of information such as solar irradiance, solar incidence angle, and working parameters of photovoltaic panels on photovoltaic power generation; similarly, wind power generation is predicted based on information such as air density, wind speed, and working parameters of wind turbines, but the above prediction method does not take into account that photovoltaic power generation and wind power generation will also be affected by other factors, for example: on a windless sunny day, because the high-pressure system suppresses air flow, high irradiance is often accompanied by low wind speed, at this time the photovoltaic output is more and the wind output is less; strong winds are usually caused by low-pressure systems, which are often accompanied by cloud cover, at this time the wind output is more and the photovoltaic output is less, etc. From the above content, it can be seen that in step S2, the following steps are specifically included:

[0076] S21. Obtain temperature information at each moment in the target time period in the region where the target electricity spot supplier is located;

[0077] S22. Calculate the actual air density based on the temperature information and photovoltaic efficiency impact value Specifically, in step S22, the actual air density The calculation formula is:

[0078] ;

[0079] Where, represents the air density at the tth moment; represents the atmospheric pressure at the tth moment; represents the specific gas constant; represents the temperature information at the t-th moment; in the present invention, is 287.05;

[0080] Photovoltaic efficiency impact value The calculation formula is:

[0081] ;

[0082] Where, represents the actual efficiency of the photovoltaic panel at the tth moment; Indicates the standard efficiency of photovoltaic panels under standard test conditions; Indicates the temperature coefficient of photovoltaic efficiency; represents the solar irradiance at the tth moment; Indicates the rated operating temperature of the photovoltaic panel; in the present invention, It is -0.004.

[0083] S23. Calculate the first coupling term based on photovoltaic impact information and wind power impact information and the second coupling term Specifically, in step S23, the first coupling term The calculation formula is:

[0084] ;

[0085] Where, Indicates the suppression coefficient of photovoltaic output on wind power; Indicates the maximum possible irradiance; in the present invention, is 0.3; is 1000; it should be noted that when , Otherwise, calculate according to the original formula;

[0086] The second coupling term The calculation formula is:

[0087] ;

[0088] Where, represents the mechanical restraint coefficient; represents the wind speed at the tth moment; Indicates the fan cut-out wind speed; Indicates the attenuation coefficient of dust on power generation; It indicates the amount of dust deposition per unit wind speed increase; Indicates the critical wind speed for sand emission; express function; represents the base of natural logarithms; Indicates the wind speed heat dissipation coefficient; represents the basic heat dissipation constant; in the present invention, is 0.2; Obtained according to the parameters provided by the fan manufacturer; is 0.13; is 0.05; is 1.3; It is 800.

[0089] S24, according to the actual air density , Photovoltaic efficiency impact value , the first coupling term and the second coupling term Calculate the predicted amount of photovoltaic power generation and wind power generation forecasts ;

[0090] Specifically, in step S24, the photovoltaic power generation forecast The calculation formula is:

[0091] ;

[0092] Where, Represents the total effective area of ​​the photovoltaic panels; Indicates the total number of moments in the target time period; Represents the comprehensive system loss; represents the incident angle of sunlight at the tth moment; Indicates a time interval; in the present invention, Obtained based on data provided by the manufacturer; is 24; is 0.82; is 1;

[0093] Wind power generation forecast The calculation formula is:

[0094] ;

[0095] Where, represents the fan swept area; represents the wind energy utilization coefficient; Indicates the mechanical and electrical efficiency of the fan; in the present invention, 、 and All were obtained based on information provided by the manufacturer.

[0096] S25, based on the predicted amount of photovoltaic power generation and wind power generation forecasts Calculate total predicted power generation , and its calculation formula is:

[0097] ;

[0098] Where, It represents the total predicted power generation of the target power generator in the target time period.

[0099] Since traditional calculation methods for wind power generation and photovoltaic power generation only consider the impact of single variables such as solar irradiance and wind speed on power generation, and ignore the interaction between meteorological conditions, resulting in inaccurate calculation of the final total predicted power generation, the first coupling term and the second coupling term are introduced in step S2 of the present invention to quantify the comprehensive impact of meteorological conditions on photovoltaic power generation and wind power generation. Compared with common calculation methods, the prediction accuracy of power generation is higher, so that the risk value of electricity spot trading can be calculated based on it in the later stage.

[0100] S3. Obtain the power consumption impact information and historical power consumption information of the power grid; specifically, in step S3, the power consumption impact information includes: season, GDP growth rate, industry type and population density; the historical power consumption information represents the total power consumption of the power grid in history.

[0101] S4. Calculate the first total power consumption of the power grid in the target time period based on the power consumption impact information and the historical power consumption information. Specifically, if the transmission line capacity of the power grid is insufficient, a "node electricity price" (such as the PJM market in the United States) may form in the congested area, which is significantly different from the system average price. This will cause the power spot supplier to access the grid at the low-price node in the congested area, but sell electricity at the high-price node, resulting in profit compression. In addition, the power grid needs to balance power generation and load in real time. If the spot trading power causes the local voltage or frequency to exceed the limit, it may be forced to adjust by the dispatching agency, resulting in additional costs. Therefore, in step S4, the following steps are specifically included:

[0102] S41. Set a sliding window of 1 year in the historical electricity consumption information. Each time the sliding window slides, a training sample is obtained. The total electricity consumption L days after each training sample is used as the sample label. Specifically, L represents the time interval from the current time period to the target time period.

[0103] S42. Train the long short-term memory network using the training samples and sample labels to obtain a target model;

[0104] S43: Input the total electricity consumption per day in the past year into the target model and output the second total electricity consumption for the target time period. ;

[0105] S44, the second total power consumption is calculated based on the power consumption impact information. Perform correction to obtain the first total power consumption Specifically, the use of the long short-term memory network to predict the total power consumption in the target time period only takes into account the cyclical change trend of power grid power consumption, and does not take into account the changes in GDP growth rate, industry type and population density. Therefore, in step S44, the following steps are specifically included:

[0106] S441. Calculate a first coupling influence coefficient based on power consumption influence information , and its calculation formula is:

[0107] ;

[0108] Where, represents the seasonal coefficient; represents the GDP growth rate; represents the first interaction weight parameter; in the present invention, is the percentage of temperature deviation from the historical mean; if the GDP growth rate is 5%, then is 0.05; is 0.12;

[0109] S442. Calculate the second coupling influence coefficient , and its calculation formula is:

[0110] ;

[0111] Where, represents the industrial structure coefficient; represents the second interaction weight parameter; in the present invention, , the benchmark value is 0.6; is 0.05;

[0112] S443. Calculate the third coupling influence coefficient , and its calculation formula is:

[0113] ;

[0114] Where, represents the third interaction weight parameter; in the present invention, is 0.08;

[0115] S444. Calculate the fourth coupling influence coefficient , and its calculation formula is:

[0116] ;

[0117] Where, represents the population density coefficient; represents the fourth interaction weight parameter; in the present invention, The base density is 2000; is 0.06;

[0118] S445, according to the first coupling influence coefficient , the second coupling influence coefficient , the third coupling influence coefficient and the fourth coupling influence coefficient Total electricity consumption for the second Perform correction to obtain the first total power consumption Specifically, since the common method of correcting electricity consumption only considers the individual effects of season, GDP growth rate, industry type and population density on total electricity consumption, it does not consider the mutual influence between these factors. For example, during the high temperature in summer, the electricity consumption of the tertiary industry (such as commercial air conditioning) surges, but the secondary industry (such as factories) may reduce production due to high temperature, resulting in a decrease in electricity consumption; high GDP regions are usually densely populated and have a high proportion of the tertiary industry, resulting in a higher electricity demand per unit population (such as dense office buildings and data centers). Therefore, in step S445, the first total electricity consumption The calculation formula is:

[0119] ;

[0120] Where, 、 、 and Respectively express about The first, second, third and fourth empirical coefficients of; In the present invention, 、 、 and 0.1, 0.11, 0.06 and 0.003 respectively;

[0121] S5. Based on the total predicted power generation and the first total electricity consumption Calculate transaction risk value Specifically, in step S5, the transaction risk value The calculation formula is:

[0122] ;

[0123] Where, Represents the electricity spot trading volume.

[0124] In step S4 of the present invention, considering that the common method for calculating power grid electricity consumption only calculates the individual effects of season, GDP growth rate, industry type and population density on total electricity consumption, and does not take into account the mutual influence between these factors, resulting in the calculation result of power grid electricity consumption not being accurate enough, four coupling influence coefficients are introduced, and the accuracy of the total power generation of the power grid finally calculated is higher, so the accuracy of the transaction risk value finally calculated is also higher, which is convenient for the power spot supplier to select a response strategy according to the risk value.

[0125] The above embodiments provide a detailed introduction to the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for assessing the risk of electricity spot trading, characterized in that: The following steps are involved: S1. Obtain photovoltaic power impact information and wind power impact information in the area where the target electricity spot supplier is located; Photoelectric impact information includes: solar irradiance and solar incident angle at each moment in the target time period; Wind power impact information includes: atmospheric pressure and wind speed at each moment in the target time period; S2. Calculate the total predicted power generation of the target power generation party in the target time period based on the photovoltaic impact information and wind power impact information. ; In step S2, the following steps are specifically included: S21. Obtain temperature information at each moment in the target time period in the region where the target electricity spot supplier is located; S22. Calculate the actual air density based on the temperature information and photovoltaic efficiency impact value ; S23. Calculate the first coupling term based on photovoltaic impact information and wind power impact information and the second coupling term ; In step S23, the first coupling term The calculation formula is: ； Where, Indicates the suppression coefficient of photovoltaic output on wind power; Indicates the maximum possible irradiance; The second coupling term The calculation formula is: ； Where, represents the mechanical restraint coefficient; represents the wind speed at the tth moment; Indicates the fan cut-out wind speed; Indicates the attenuation coefficient of dust on power generation; It indicates the amount of dust deposition per unit wind speed increase; Indicates the critical wind speed for sand emission; express function; represents the base of natural logarithms; Indicates the wind speed heat dissipation coefficient; represents the basic heat dissipation constant; S24, according to the actual air density , Photovoltaic efficiency impact value , the first coupling term and the second coupling term Calculate the predicted amount of photovoltaic power generation and wind power generation forecasts ; In step S24, the photovoltaic power generation prediction amount The calculation formula is: ； Where, Represents the total effective area of ​​the photovoltaic panels; Indicates the total number of moments in the target time period; Represents the comprehensive system loss; represents the incident angle of sunlight at the tth moment; Indicates a time interval; Wind power generation forecast The calculation formula is: ； Where, represents the fan swept area; represents the wind energy utilization coefficient; Indicates the mechanical and electrical efficiency of the fan; S25, based on the predicted amount of photovoltaic power generation and wind power generation forecasts Calculate total forecast power generation Calculate total forecast power generation ； Where, It represents the total predicted power generation of the target power generator in the target time period; S3. Obtaining the power consumption impact information and historical power consumption information of the power grid; S4. Calculate the first total power consumption of the power grid in the target time period based on the power consumption impact information and the historical power consumption information. ; S5. Based on the total predicted power generation and the first total electricity consumption Calculate transaction risk value .

2. The method for assessing the risk of electricity spot trading according to claim 1, characterized in that: In step S22, the actual air density The calculation formula is: ； Where, represents the air density at the tth moment; represents the atmospheric pressure at the tth moment; represents the specific gas constant; Represents the temperature information at the tth moment; Photovoltaic efficiency impact value The calculation formula is: ； Where, represents the actual efficiency of the photovoltaic panel at the tth moment; Indicates the standard efficiency of photovoltaic panels under standard test conditions; Indicates the temperature coefficient of photovoltaic efficiency; represents the solar irradiance at the tth moment; Indicates the rated operating temperature of the photovoltaic panel.

3. The method for assessing the risk of electricity spot trading according to claim 1, characterized in that: In step S3, the electricity consumption influencing information includes: season, GDP growth rate, industry type and population density; the historical electricity consumption information represents the total daily electricity consumption of the power grid.

4. The method for assessing the risk of electricity spot trading according to claim 1, characterized in that: In step S4, the following steps are specifically included: S41. Set a sliding window with a length of 1 year in the historical electricity consumption information. Each time the sliding window slides, a training sample is obtained. The total electricity consumption L days after each training sample is used as the sample label. S42. Train the long short-term memory network using the training samples and sample labels to obtain a target model; S43: Input the total electricity consumption per day in the past year into the target model and output the second total electricity consumption for the target time period. ; S44, the second total power consumption is calculated based on the power consumption impact information. Perform correction to obtain the first total power consumption .

5. The method for assessing the risk of electricity spot trading according to claim 4, characterized in that: In step S44, the following steps are specifically included: S441. Calculate a first coupling influence coefficient based on power consumption influence information , and its calculation formula is: ； Where, represents the seasonal coefficient; represents the GDP growth rate; represents the first interaction weight parameter; S442. Calculate the second coupling influence coefficient , and its calculation formula is: ； Where, represents the industrial structure coefficient; represents the second interaction weight parameter; S443. Calculate the third coupling influence coefficient , and its calculation formula is: ； Where, represents the third interaction weight parameter; S444. Calculate the fourth coupling influence coefficient , and its calculation formula is: ； Where, represents the population density coefficient; represents the fourth interaction weight parameter; S445, according to the first coupling influence coefficient , the second coupling influence coefficient , the third coupling influence coefficient and the fourth coupling influence coefficient Total electricity consumption for the second Perform correction to obtain the first total power consumption .

6. The method for assessing the risk of electricity spot trading according to claim 5, characterized in that: In step S445, the first total power consumption The calculation formula is: ； Where, 、 、 and Respectively express about The first, second, third and fourth empirical coefficients of .

7. The method for assessing the risk of electricity spot trading according to claim 1, characterized in that: In step S5, the transaction risk value The calculation formula is: ； Where, Represents the electricity spot trading volume.

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