Primary energy adequacy assessment method, system and device, and storage medium

Abstract

The invention discloses a primary energy adequacy assessment method, system and device and a storage medium, and belongs to the technical field of electric power, and the method comprises the steps: collecting historical supply and demand data in a set region, including the power consumption in the region, the linear power generation amount of primary energy and the adjustable power supply amount; the electricity consumption and the linear generating capacity of the month x are predicted, specifically, centralization processing is carried out on any item of electricity data, linear regression is carried out through the result obtained after centralization processing, the trend relation of the electricity data along with the time change is obtained, and the electricity data of the month x are predicted; obtaining the season item of each electric quantity data in the month x; correcting the result of the preliminary prediction by using the seasonal item of each electric quantity data; and obtaining the adjustable power supply amount of the month x, and carrying out adequacy evaluation on the month x by using all supply and demand data of the month x. The seasonal influence is considered, and the relation between the electric energy demand and the supply capacity in a certain period of time in the future can be evaluated more accurately.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power, and particularly relates to an evaluation method, system, device and storage medium for the adequacy of primary energy. Background Art

[0002] Electric energy within a set area is generated by converting primary energy such as coal, solar energy, water energy, and wind energy. Since electric energy cannot be stored on a large scale, when the electric energy demand within the area exceeds the electric energy generation capacity, it will seriously endanger the stability of the power grid.

[0003] The adequacy of primary energy is mainly used to measure whether the total amount of primary energy that can be converted into electric energy in the future for a period of time can meet the electric energy demand during the same period. If it cannot be met, means such as increasing coal reserves in advance and signing power purchase contracts for external power need to be adopted to increase the total energy; currently, for the evaluation of the adequacy of primary energy, it is difficult to accurately predict the seasonal total amount of primary energy and electricity demand. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides an evaluation method, system, device and storage medium for the adequacy of primary energy, which can more accurately evaluate the relationship between the electric energy demand and the electric energy supply capacity at a certain future time period considering seasonal influences.

[0005] The present invention provides the following technical solutions:

[0006] In the first aspect, an evaluation method for the adequacy of primary energy is provided, including:

[0007] Taking a month as the time unit, collecting historical supply and demand data within a set area, including the electricity consumption, linear power generation of primary energy, and adjustable power supply within the area;

[0008] Predicting the electricity consumption and linear power generation in month x, specifically: for any one of the historical electricity consumption and linear power generation data, performing moving average processing and centering processing on it respectively, using the results after centering processing for linear regression to obtain the trend relationship of each item of electricity data changing with time, and using the trend relationship of each item of electricity data to predict the electricity data of each item in month x respectively; using the historical values and centering processing results of each item of electricity data to obtain the seasonal items of each item of electricity data in month x; using the seasonal items of each item of electricity data to correct the preliminary prediction results;

[0009] Obtaining the adjustable power supply in month x, and using all the supply and demand data in month x to evaluate the adequacy of month x.

[0010] Optionally, the linear power generation of the primary energy includes hydropower generation, solar power generation, wind power generation, nuclear power generation, and natural gas power generation; the adjustable power supply includes externally purchased power and the power generation of unified dispatching coal-fired power plants.

[0011] Optionally, for any one of the historical power consumption and linear power generation data, perform moving average processing and centering processing on it respectively, specifically including:

[0012] For the power consumption data Q, perform a four-term moving average on the power consumption data after the historical i-th month to obtain the average value data for the (i + 1.5)-th month and the average value data for the (i + 2.5)-th month

[0013]

[0014] where x i is the power consumption data for the i-th month, i = 1, 2, 3....12, when i = 12, q i+1 is the power consumption data for January of the next year;

[0015] Perform centering processing on the average value data for the (i + 1.5)-th month and the average value data for the (i + 2.5)-th month to obtain the centering result for the corresponding month;

[0016]

[0017] where is the centering result for the (i + 2)-th month.

[0018] Optionally, use the result after centering processing for linear regression to obtain the trend relationship of each power consumption data over time, specifically:

[0019] Q x = A q ·x + B q

[0020] where x is the time in months, Q x is the preliminary predicted power consumption data for month x, A q and B q are two constants obtained through binary linear regression analysis.

[0021] Optionally, use the historical values and centering processing results of each power consumption data to obtain the seasonal term of each power consumption data for month t, specifically:

[0022]

[0023] where The seasonal term for the (e + 2)-th month, q e+2 The power consumption data for the (e + 2)-th month, The centralized result for the (e + 2)-th month, where e = -1, 0, 1,....10. When e = -1, q e The power consumption data for November of the previous year. When e = 0, q e The power consumption data for December of the previous year.

[0024] Optionally, the preliminary prediction result is corrected using the seasonal terms of the power consumption data for each item, specifically:

[0025]

[0026] Among them, The predicted power consumption data for month x, Q x The preliminary predicted power consumption data for month x, The seasonal term for month x.

[0027] Optionally, the adjustable power supply for month x is obtained, and the adequacy of month x is evaluated using all the supply and demand data for month x, specifically including:

[0028] Obtain the power generation M of the unified dispatching coal-fired power plants in month x;

[0029]

[0030] Among them, K is the total current inventory, is the expected incoming coal volume in the future, and mh is the raw coal consumption rate;

[0031] Obtain the purchased power in month x

[0032]

[0033] Among them, G is the purchased power in the same historical period;

[0034] Evaluate the adequacy of month x according to the following formula;

[0035]

[0036] Among them, η is the adequacy coefficient, is the predicted hydropower generation in month x, is the predicted solar power generation in month x, is the predicted wind power generation in month x, is the predicted nuclear power generation in month x, is the predicted natural gas power generation in month x, is the predicted power consumption in month x.

[0037] In a second aspect, an evaluation system for primary energy adequacy is provided, including:

[0038] A data acquisition module, which is used to collect historical supply and demand data within a set area with a monthly time unit, including electricity consumption, linear power generation of primary energy, and adjustable power supply within the area;

[0039] A prediction module, which is used to predict the electricity consumption and linear power generation in month x. Specifically: for any one of the historical electricity consumption and linear power generation data, perform moving average processing and centering processing on it respectively, use the results after centering processing for linear regression to obtain the trend relationship of each electricity data over time, and use the trend relationship of each electricity data to predict the electricity data of month x respectively; use the historical values and centering processing results of each electricity data to obtain the seasonal terms of the electricity data of month x; use the seasonal terms of each electricity data to correct the initially predicted results;

[0040] An evaluation module, which is used to obtain the adjustable power supply in month x and evaluate the adequacy of month x using all supply and demand data in month x.

[0041] In a third aspect, a computer device is provided, including a processor and a memory; wherein, when the processor executes the computer program stored in the memory, the steps of the evaluation method for primary energy adequacy described in any item of the first aspect are implemented.

[0042] In a fourth aspect, a computer-readable storage medium is used to store a computer program; when the computer program is executed by a processor, the steps of the evaluation method for primary energy adequacy described in any item of the first aspect are implemented.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] Starting from historical electricity demand and supply data, the present invention considers the influence of seasonal factors on power supply and demand, quantifies the seasonal influence of electricity consumption and linear power generation of primary energy, more accurately predicts future power supply and demand, enhances the scientific nature of the evaluation, and provides a quantifiable decision-making basis for power system planning; in addition, the centering processing of the present application can improve the sensitivity of trend capture and further improve the timeliness and accuracy of adequacy evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a flowchart of the steps of the evaluation method for primary energy adequacy of the present invention;

[0046] Figure 2 is a block diagram of the data types of the supply and demand data of the present invention;

[0047] Figure 3 It is a structural block diagram of an evaluation system for the adequacy of primary energy of the present invention. Specific embodiments

[0048] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention. It should be noted that the term "including" and any of its variations in the description and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0049] Embodiment 1

[0050] An evaluation method for the adequacy of primary energy includes the following steps:

[0051] S1: Taking a month as the time unit, collect historical supply and demand data within a set area, including the electricity consumption, the linear power generation of primary energy, and the adjustable power supply within the area.

[0052] The linear power generation of primary energy includes hydropower generation, solar power generation, wind power generation, nuclear power generation, and natural gas power generation; the adjustable power supply includes the purchased electricity and the power generation of the unifiedly regulated coal-fired power plants.

[0053] S2: Predict the electricity consumption and linear power generation in month x.

[0054] S21: For any one of the electricity data in historical electricity consumption and linear power generation, perform moving average processing and centering processing on it respectively.

[0055] Specifically, for the electricity data Q, perform a four-term moving average on the electricity data after the historical month i to obtain the average data for month i + 1.5 and the average data for month i + 2.5

[0056]

[0057] where x i is the electricity data for the i-th month, i = 1, 2, 3....12, and when i = 12, q i+1 is the electricity data for January of the next year;

[0058] Perform centering processing on the average data for month i + 1.5 and the average data for month i + 2.5 to obtain the centering result for the corresponding month;

[0059]

[0060] Among them, is the centralized result of the (i + 2)-th month.

[0061] S22: Perform linear regression using the centralized processing result to obtain the trend relationship of each electricity quantity data over time, and use the trend relationship of each electricity quantity data to predict the electricity quantity data of each month x respectively.

[0062] Q x = A q ·x + B q

[0063] Among them, x is the time in months, and Q x is the preliminary predicted electricity quantity data of month x, and A q and B q are two constants obtained through binary linear regression analysis.

[0064] S23: Use the historical values and centralized processing results of each electricity quantity data to obtain the seasonal terms of each electricity quantity data of month x.

[0065]

[0066] Among them, is the seasonal term of the (e + 2)-th month, q e+2 is the electricity quantity data of the (e + 2)-th month, is the centralized result of the (e + 2)-th month, e = -1, 0, 1,....10. When e = -1, q e is the electricity quantity data of November of the previous year. When e = 0, q e is the electricity quantity data of December of the previous year.

[0067] S24: Use the seasonal terms of each electricity quantity data to correct the preliminary prediction results.

[0068]

[0069] Among them, is the predicted electricity quantity data of month x, Q x is the preliminary predicted electricity quantity data of month x, is the seasonal term of month x.

[0070] More specifically:

[0071] I. Prediction of the total electricity consumption of the whole society

[0072] Perform four-term moving average on the total electricity consumption data of each month of the whole society.

[0073]

[0074] Wherein: is the average value of the total electricity consumption of the whole society in the (i + 1.5)-th month, and y i is the total electricity consumption of the whole society in the i-th month. i = 1, 2, 3....12.

[0075] Further, centralize the result so that it corresponds to the corresponding whole month.

[0076]

[0077] Subtract the centralized result of the corresponding month from the actual monthly total electricity consumption data of the whole society to obtain the seasonal term of the corresponding month, that is, the electricity fluctuation caused by seasonal changes.

[0078]

[0079] Wherein: is the seasonal term in the (i + 2)-th month.

[0080] Perform a binary linear regression on the centralized data items to obtain the changing trend of power generation over time.

[0081] Y = A1·x + B1

[0082] Wherein: Y is the electricity consumption, x is the time, and A1, B1 are constants obtained through binary linear regression.

[0083] The predicted total electricity consumption of the whole society in a future month is the electricity consumption trend plus the seasonal term of the corresponding month.

[0084]

[0085] Wherein: is the predicted total electricity consumption of the whole society in a future month.

[0086] II. Prediction of Hydropower Generation

[0087] Perform a four-term moving average on the monthly hydropower generation data.

[0088]

[0089] Wherein: is the average value of the hydropower generation in the (i + 1.5)-th month, and s i is the hydropower generation in the i-th month. i = 1, 2, 3....12.

[0090] Further, centralize the result so that it corresponds to the corresponding whole month.

[0091]

[0092] Subtract the centralized result of the corresponding month from the actual monthly hydropower generation data to obtain the seasonal term for the corresponding month, that is, the power fluctuation caused by seasonal changes.

[0093]

[0094] In the formula: is the seasonal term for the (i + 2)-th month.

[0095] Perform a binary linear regression on the centralized data items to obtain the changing trend of power generation over time.

[0096] S = A2·x + B2

[0097] In the formula: S is the hydropower generation, x is the time, and A2 and B2 are constants obtained through binary linear regression.

[0098] The predicted hydropower generation for a future month is the power generation trend plus the seasonal term for the corresponding month.

[0099]

[0100] In the formula: is the predicted hydropower generation for a future month.

[0101] III. Solar power generation prediction;

[0102] Perform a four-term moving average on the monthly solar power generation data.

[0103]

[0104] In the formula: is the average solar power generation for the (i + 1.5)-th month, and t i is the solar power generation for the i-th month. i = 1, 2, 3....12.

[0105] Further centralize the result to make it correspond to the corresponding whole month.

[0106]

[0107] Subtract the centralized result of the corresponding month from the actual monthly solar power generation data to obtain the seasonal term for the corresponding month, that is, the power fluctuation caused by seasonal changes.

[0108]

[0109] In the formula: is the seasonal term for the (i + 2)-th month.

[0110] Perform binary linear regression on the centralized data items to obtain the changing trend of power generation over time.

[0111] T = A3·x + B3

[0112] Where: T is the solar power generation, x is the time, and A3, B3 are constants obtained through binary linear regression.

[0113] The predicted solar power generation for a future month is the power generation trend plus the seasonal term for the corresponding month.

[0114]

[0115] Where: is the predicted solar power generation for a future month.

[0116] IV. Wind power generation prediction;

[0117] Perform a four-term moving average on the wind power generation data for each month.

[0118]

[0119] Where: is the average wind power generation at the (i + 1.5)-th month, and f i is the wind power generation at the i-th month. i = 1, 2, 3....12.

[0120] Further centralize the result to make it correspond to the corresponding whole month.

[0121]

[0122] Subtract the centralized result of the corresponding month from the actual monthly wind power generation data to obtain the seasonal term for the corresponding month, that is, the power fluctuation caused by seasonal changes.

[0123]

[0124] Where: is the seasonal term for the (i + 2)-th month.

[0125] Perform binary linear regression on the centralized data items to obtain the changing trend of power generation over time.

[0126] F = A4·x + B4

[0127] Where: F is the wind power generation, x is the time, and A4, B4 are constants obtained through binary linear regression.

[0128] The predicted wind power generation for a future month is the power generation trend plus the seasonal term for the corresponding month.

[0129]

[0130] In the formula: is the predicted wind power generation for a certain month in the future.

[0131] V. Prediction of nuclear power generation;

[0132] Perform a four-term moving average on the nuclear power generation data for each month.

[0133]

[0134] In the formula: is the average nuclear power generation for the (i + 1.5)-th month, h i is the nuclear power generation for the i-th month. i = 1, 2, 3....12.

[0135] Further centralize the results to make them correspond to the corresponding whole months.

[0136]

[0137] Subtract the centralized result of the corresponding month from the actual monthly nuclear power generation data to obtain the seasonal term for the corresponding month, that is, the power fluctuation caused by seasonal changes.

[0138]

[0139] In the formula: is the seasonal term for the (i + 2)-th month.

[0140] Perform a binary linear regression on the centralized data items to obtain the changing trend of power generation over time.

[0141] H = A5·x + B5

[0142] In the formula: H is the nuclear power generation, x is the time, and A5, B5 are constants obtained through binary linear regression.

[0143] The predicted nuclear power generation for a certain month in the future is the power generation trend plus the seasonal term for the corresponding month.

[0144]

[0145] In the formula: is the predicted nuclear power generation for a certain month in the future.

[0146] VI. Regression analysis of natural gas power generation;

[0147] Perform a four-term moving average on the natural gas power generation data for each month.

[0148]

[0149] Wherein: is the average natural gas power generation in the (i + 1.5)-th month, and r i is the natural gas power generation in the i-th month. i = 1, 2, 3....12.

[0150] Further, centralize the result so that it corresponds to the corresponding whole month.

[0151]

[0152] Subtract the centralized result of the corresponding month from the actual monthly natural gas power generation data to obtain the seasonal term of the corresponding month, that is, the power fluctuation caused by seasonal changes.

[0153]

[0154] Wherein: is the seasonal term in the (i + 2)-th month.

[0155] Perform binary linear regression on the centralized data items to obtain the changing trend of power generation over time.

[0156] R = A6·x + B6

[0157] Wherein: R is the natural gas power generation, x is the time, and A6, B6 are constants obtained through binary linear regression.

[0158] The predicted natural gas power generation for a future month is the power generation trend plus the seasonal term of the corresponding month.

[0159]

[0160] Wherein: is the predicted natural gas power generation for a future month.

[0161] S3: Obtain the adjustable power supply for month x.

[0162] Specifically, S31: Obtain the power generation M of the unified regulated coal-fired power plants in month x;

[0163]

[0164] Wherein, K is the current total inventory, is the predicted future coal arrival volume, and mh is the raw coal consumption rate;

[0165] S32: Obtain the externally purchased power in month x

[0166]

[0167] Wherein, G is the externally purchased power in the same historical period.

[0168] Of course, in some other embodiments, the adjustable power supply can be obtained according to the set values of regional characteristics and expert experience.

[0169] S4: Use all the supply and demand data of month x to evaluate the adequacy of month x.

[0170] Evaluate the adequacy of month x according to the following formula;

[0171]

[0172] where η is the adequacy coefficient, is the predicted hydropower generation of month x, is the predicted solar power generation of month x, is the predicted wind power generation of month x, is the predicted nuclear power generation of month x, is the predicted natural gas power generation of month x, is the predicted electricity consumption of month x.

[0173] η>1 represents an abundance of primary energy, and η<1 represents a shortage of primary energy.

[0174] Embodiment 2

[0175] Taking the actual data of a certain province as an example, the steps of the energy adequacy evaluation method are as follows:

[0176] (1) Collect historical data

[0177] The historical data for the past 12 months is shown in the following table:

[0178] Table 1 Historical supply and demand data

[0179]

[0180] The current inventory of the unified dispatching coal-fired power plants is 13 million tons.

[0181] (2) Regression analysis of the electricity consumption of the whole society

[0182] According to the historical data, the changing trend of electricity consumption over time is Y = 19.224*x + 635.37, and the seasonal term of electricity consumption is shown in Table 2.

[0183] Table 2 shows the seasonal term of electricity consumption

[0184]

[0185]

[0186] (3) Regression analysis of hydropower generation

[0187] The changing trend of power generation over time obtained from historical data is S = 0.0054 * x + 2.504

[0188] Table 3 shows the seasonal terms of hydropower generation

[0189]

[0190] (4) Regression analysis of solar power generation

[0191] The changing trend of power generation over time obtained from historical data is T = 1.3918 * x + 47.601

[0192] Table 4 shows the seasonal terms of solar power generation

[0193]

[0194]

[0195] (5) Regression analysis of wind power generation

[0196] The changing trend of power generation over time obtained from historical data is F = -0.5471 * x + 46.808

[0197] Table 5 shows the seasonal terms of wind power generation

[0198]

[0199] (6) Regression analysis of nuclear power generation

[0200] The changing trend of power generation over time obtained from historical data is H = -0.3482 * x + 45.367

[0201] Table 6 shows the seasonal terms of nuclear power generation

[0202]

[0203]

[0204] (7) Regression analysis of natural gas power generation

[0205] The changing trend of power generation over time obtained from historical data is

[0206] R = 0.8759 * x + 18.844

[0207] Table 7 shows the seasonal terms of natural gas power generation

[0208]

[0209] (8) Analysis of power generation of unified dispatching coal-fired power plants

[0210] The monthly estimated coal intake is 16 million tons, the standard coal consumption rate for power supply is 507 g / kWh, and the electricity generation capacity is (13 + 16) / 507*100 = 57.2 billion kWh.

[0211] (9) Analysis of purchased electricity

[0212] The estimated value of purchased electricity is directly taken from the historical same period value.

[0213] Table 8 shows the historical same period values of purchased electricity.

[0214]

[0215]

[0216] (10) Adequacy evaluation

[0217] Estimate the adequacy of primary energy supply in April 2025.

[0218] The electricity demand is: 19.224*14 + 635.37 + 9.2725 = 91.37 billion kWh.

[0219] Hydropower supply: 0.0054*14 + 2.504 + 0.15 = 2.73 billion kWh.

[0220] Solar power supply: 1.3918*14 + 47.601 + 16.65 = 83.73 billion kWh.

[0221] Wind power supply: -0.5471*14 + 46.808 - 6.19 = 32.95 billion kWh.

[0222] Coal-fired thermal power supply: 57.2 billion kWh

[0223] Nuclear power supply: -0.3482*14 + 45.367 + 1.24 = 41.73 billion kWh.

[0224] Gas power generation supply: 0.8759*14 + 18.844 + 1.41 = 32.52 billion kWh.

[0225] Purchased power supply: 10.963 billion kWh.

[0226] The adequacy is

[0227] (2.73 + 83.73 + 32.95 + 57.2 + 41.73 + 32.52 + 10.96) / 91.37 = 0.96.

[0228] Example 3

[0229] An evaluation system for the adequacy of primary energy, comprising:

[0230] A data acquisition module, which is used to collect historical supply and demand data within a set area on a monthly basis, including electricity consumption, linear power generation of primary energy, and adjustable power supply within the area;

[0231] A prediction module, which is used to predict the electricity consumption and linear power generation in month x. Specifically: for any one of the historical electricity consumption and linear power generation data, perform moving average processing and centering processing on it respectively, use the result after centering processing for linear regression, obtain the trend relationship of each electricity data over time, and use the trend relationship of each electricity data to predict the electricity data of month x respectively; use the historical values and centering processing results of each electricity data to obtain the seasonal terms of the electricity data of month x; use the seasonal terms of each electricity data to correct the preliminary prediction results;

[0232] An evaluation module, which is used to obtain the adjustable power supply in month x and evaluate the adequacy of month x using all the supply and demand data in month x.

[0233] For a more specific process of the above method, reference can be made to the corresponding content disclosed in the foregoing embodiments, and details will not be elaborated here.

[0234] Embodiment 4

[0235] The present invention provides a computer device, including a processor and a memory; wherein, when the processor executes the computer program stored in the memory, the steps of the above method for evaluating the adequacy of primary energy are implemented.

[0236] For a more specific process of the above method, reference can be made to the corresponding content disclosed in the foregoing embodiments, and details will not be elaborated here.

[0237] Embodiment 5

[0238] The present invention provides a computer-readable storage medium for storing a computer program; when the computer program is executed by a processor, the steps of the above method for evaluating the adequacy of primary energy are implemented.

[0239] For a more specific process of the above method, reference can be made to the corresponding content disclosed in the foregoing embodiments, and details will not be elaborated here.

[0240] In this specification, the various embodiments are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the various embodiments can be referred to each other. For the systems, devices, and storage media disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0241] Those skilled in the art can clearly understand that the technologies in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solutions in the embodiments of the present invention, in essence, or the parts that contribute to the prior art can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of the present invention.

[0242] The above are only the preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present invention should be regarded as within the protection scope of the present invention.

Claims

1. A method for evaluating the adequacy of primary energy, characterized in that, include: Collect historical supply and demand data within a set area on a monthly basis, including electricity consumption, linear power generation of primary energy, and adjustable power supply within the area; Predicting electricity consumption and linear power generation for month x: Specifically, for any item of historical electricity consumption and linear power generation, perform sliding average processing and centering processing on it, use the centering result to perform linear regression to obtain the trend relationship of each item of electricity data over time, and use the trend relationship of each item of electricity data to predict each item of electricity data for month x; Using the historical values of various power data and the results of centralized processing, the seasonal terms of various power data for month x are obtained; the seasonal terms of various power data are used to revise the preliminary prediction results; Obtain the adjustable power supply for month x and use all supply and demand data for month x to assess its adequacy.

2. The evaluation method of primary energy abundance according to claim 1, wherein The linear power generation of the primary energy includes hydropower generation, solar power generation, wind power generation, nuclear power generation and natural gas power generation; the adjustable power supply includes purchased electricity and power generation of centrally-controlled coal-fired power plants.

3. The evaluation method for the adequacy of primary energy according to claim 1, characterized in that The sliding average processing and centralization processing are respectively performed on any one of the electricity data of the historical electricity consumption and the linear power generation, specifically including: For the electricity data Q, perform four sliding averages on the electricity data after month i to obtain the mean data for month i+1.5 and the average data of i+2.5 months where x i is the power consumption data for the i-th month, i = 1, 2, 3....12, and when i = 12, q i+1 is the power consumption data for January of the next year; The mean data for the (i + 1.5)th month and the mean data for the (i + 2.5)th month are centralized to obtain the centralized results for the corresponding months; in, is the centralized result for month i+2.

4. The evaluation method for the adequacy of primary energy according to claim 1, characterized in that The linear regression is performed on the results after the centralization process to obtain the trend relationship of various power data over time, specifically: Q x = A q ·x + B q Among them, x is the time in months, Q x is the preliminary forecast electricity data for month x, A q and B q are two constants obtained through binary linear regression analysis.

5. The evaluation method for the adequacy of primary energy according to claim 1, characterized in that, The seasonal items of the various electricity data in month t are obtained by using the historical values of the various electricity data and the centralized processing results, specifically: Among them, is the seasonal term for the (e + 2)-th month, q e+2 is the power consumption data for the (e + 2)-th month, is the centering result for the (e + 2)-th month, where e = -1, 0, 1,....

10. When e = -1, q e is the power consumption data for November of the previous year. When e = 0, q e is the power consumption data for December of the previous year.

6. The method for evaluating primary energy abundance according to claim 1, wherein: The seasonal items of various electricity data are used to correct the preliminary prediction results, specifically: Among them, The electricity quantity data predicted for month x, Q x is the preliminary predicted electricity quantity data for month x, is the seasonal term for month x.

7. The method for evaluating primary energy abundance according to claim 2, wherein: The method of obtaining the adjustable power supply in month x and evaluating the adequacy of power supply in month x using all supply and demand data in month x specifically includes: Get the power generation M of the centralized coal-fired power plant in month x; Among them, K is the current total inventory, is the expected amount of coal in the future, mh is the raw coal consumption rate; Obtain the purchased electricity quantity for month x Among them, G is the amount of electricity purchased during the same period in history; The adequacy assessment for month x is performed according to the following formula; Among them, η is the sufficiency coefficient, Forecasted hydroelectric power generation for month x, Forecasted solar power generation for month x, is the predicted wind power generation for month x, Forecasted nuclear power generation for month x, Forecasted natural gas electricity generation for month x, Forecasted electricity consumption for month x.

8. An evaluation system for the adequacy of primary energy, characterized in that, include: The data acquisition module is used to collect historical supply and demand data within a set area on a monthly basis, including electricity consumption, linear power generation of primary energy, and adjustable power supply within the area; The prediction module is used to predict the electricity consumption and linear power generation in month x. Specifically, it performs sliding average processing and centering processing on any one of the electricity data of historical electricity consumption and linear power generation, and uses the results of the centering processing to perform linear regression to obtain the trend relationship of each electricity data over time. The trend relationship of each electricity data is used to predict each electricity data in month x. Using the historical values of various power data and the results of centralized processing, we can obtain the seasonal items of various power data for month x. The seasonal items of various electricity data are used to revise the preliminary forecast results; The evaluation module is used to obtain the adjustable power supply in month x and use all the supply and demand data of month x to evaluate the adequacy of month x.

9. A computer device, characterized in that, The method comprises a processor and a memory; wherein, when the processor executes the computer program stored in the memory, the steps of the method for evaluating the primary energy abundance according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: For storing a computer program; when the computer program is executed by a processor, it implements the steps of the method for evaluating the adequacy of primary energy according to any one of claims 1-7.

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