Electric power spot market dynamic risk early warning system and method

Abstract

The invention relates to the technical field of electric power risk early warning, in particular to an electric power spot market dynamic risk early warning system and method, and the system comprises a data collection module, a data processing module, a model construction module, a risk analysis module, an instruction generation module and an alarm module. The method determines the corresponding relation between the actual electricity purchase amount and the estimated electricity purchase amount, analyzes the specific operation condition of the model construction module, further analyzes the risk condition of the electric power spot market, sends out corresponding alarm information, and sends out a corresponding adjustment instruction when the operation condition of the model construction module is determined to be abnormal, thereby improving the estimation precision of the electricity purchase amount, and improving the user experience. Therefore, the risk identification efficiency of the electric power spot market is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power risk early warning, and in particular to a power spot market dynamic risk early warning system and method. Background Art

[0002] To ensure optimal resource allocation, the production and supply of electricity needs to be planned and arranged in advance to avoid waste of resources or insufficient supply. For power suppliers, accurate knowledge of the amount of electricity purchased can better control production costs and avoid cost increases caused by overinvestment or insufficient production. At the same time, in order to ensure the balance and stability of the operation of the power grid and prevent problems such as grid failures and power outages caused by imbalances in supply and demand, the estimation of the amount of electricity purchased provides basic data support for early warning of dynamic risks in the power spot market, promoting the healthy and orderly development of the market.

[0003] By determining an accurate estimated amount of electricity to be purchased, the risk of price fluctuations, power shortages or surpluses, caused by a large deviation between the estimated amount of electricity purchased and the actual amount of electricity supplied, is reduced.

[0004] Changes in electricity purchase volume directly affect the price in the electricity market. Accurate estimates of electricity purchase volume help to more accurately predict price trends, identify the risks of large price fluctuations in advance, provide decision-making basis for market participants, and reduce economic losses caused by price fluctuations.

[0005] For market participants such as power generation companies, power sales companies and large users, accurate electricity purchase volume forecasts can help them formulate reasonable production, sales and procurement strategies, and avoid default risks and economic losses caused by misjudgment.

[0006] Accurately understanding the amount of electricity purchased will help power grid companies to rationally arrange power dispatch and equipment operation and maintenance, ensure the safe and stable operation of the power grid, and reduce the risk of impact on the power grid caused by an imbalance in power supply and demand.

[0007] In the dynamic risk warning system of the electricity spot market, the estimated electricity purchase amount is an important variable in constructing risk indicators.

[0008] Chinese Patent Publication No.: CN118521157A discloses a risk assessment method and device for an electric power market, the method comprising: obtaining electric power market simulation data of an electric power market to be assessed; simulating the electric power market to be assessed using the electric power market simulation data to obtain simulation results of the electric power market to be assessed; determining an electric power market operation risk index value of the electric power market to be assessed according to the simulation results; comparing the electric power market operation risk index value with a risk index value threshold to obtain a risk day for the electric power market to be assessed; generating a risk handling strategy based on the risk type that occurs in the electric power market to be assessed on the risk day; handling the risk corresponding to the risk type that occurs on the risk day according to the risk handling strategy; it can be seen that the above technical solution has the following problems: the inability to process the collected data in a targeted manner according to the specific circumstances of the estimated power purchase amount and the actual power purchase amount affects the estimated accuracy of the power purchase amount, thereby affecting the risk identification efficiency for the electric power spot market. Summary of the invention

[0009] To this end, the present invention provides a dynamic risk warning system and method for the electricity spot market, which is used to overcome the problem in the prior art that the collected data cannot be processed in a targeted manner according to the specific circumstances of the estimated power purchase volume and the actual power purchase volume, which affects the estimated accuracy of the power purchase volume and further affects the risk identification efficiency for the electricity spot market.

[0010] In one aspect, the present invention provides a dynamic risk early warning system for a power spot market, comprising:

[0011] A data collection module is used to collect power data and influencing factor data. The power data includes actual power purchase and user power consumption of each user. The influencing factor data includes weather information, date and population growth.

[0012] A data processing module, which is connected to the data acquisition module, is used to clean, screen, convert and integrate the data to ensure the consistency of the data and to smooth the data through moving average;

[0013] A model building module, which is connected to the data processing module and is used to transmit the data output by the data processing module to the power estimation model and obtain the estimated power purchase amount;

[0014] A risk analysis module, which is connected to the data acquisition module, the data processing module and the model building module respectively, and is used to determine the evaluation fluctuation parameter based on the actual power purchase amount at each time node within the preset monitoring time, and determine whether the operation status of the model building module is qualified in combination with the power ratio of the actual power purchase amount to the estimated power purchase amount, and issue a corresponding adjustment instruction when it is determined that the operation status of the model building module is abnormal;

[0015] An instruction generation module, which is respectively connected to the data acquisition module, the data processing module and the risk analysis module, and is used to issue corresponding adjustment instructions based on the determination of the risk analysis module, including controlling the data processing module to adjust the window size of the moving average of the historical actual power purchase amount to a corresponding value, and adjusting the amount of data obtained by the data acquisition module to a corresponding value;

[0016] An alarm module is connected to the risk analysis module and is used to issue corresponding alarm information according to the determination of the risk analysis module.

[0017] Furthermore, the risk analysis module is used to determine whether the operation status of the model construction module is qualified based on the ratio of the actual purchased electricity to the estimated purchased electricity, including:

[0018] If the power ratio is less than the first preset power ratio, it is determined that the operation status of the model building module is qualified, and the power spot market is determined to be operating stably.

[0019] If the power ratio is less than or equal to the second preset power ratio and greater than or equal to the first preset power ratio, determining whether the operation status of the model building module is qualified based on the evaluation fluctuation parameter;

[0020] If the power ratio is greater than the second preset power ratio, it is determined that the operation status of the model building module is abnormal, and the processing method for the model building module is determined based on the ratio difference between the power ratio and the second preset power ratio.

[0021] Furthermore, the risk analysis module is used to determine the evaluation fluctuation parameters, including:

[0022] Based on the actual amount of electricity purchased at each time point within the preset monitoring time period, a time domain curve of the actual amount of electricity purchased is drawn;

[0023] It is used to obtain the ratio of the number of extreme points of the actual purchased electricity time domain curve to the preset monitoring time, and obtain the evaluation fluctuation parameter;

[0024] The risk analysis module is used to determine whether the operation status of the model construction module is qualified based on the evaluation fluctuation parameter, including:

[0025] If the evaluation fluctuation parameter is less than or equal to the preset evaluation fluctuation parameter, it is determined that the operation status of the model building module is abnormal, and it is determined that the window size of the moving average of the historical actual electricity purchase volume of the data processing module is adjusted to a corresponding value based on the evaluation fluctuation parameter;

[0026] If the evaluation fluctuation parameter is greater than the preset evaluation fluctuation parameter, the operation status of the model building module is determined to be qualified, and an alarm message is issued regarding the risk of power grid operation.

[0027] Furthermore, the instruction generation module is used to adjust the window size of the moving average of the historical actual electricity purchase amount of the data processing module to a corresponding value based on the evaluation fluctuation parameter, wherein:

[0028] The increase in the window size determined based on the evaluation fluctuation parameter is inversely proportional to the evaluation fluctuation parameter.

[0029] Furthermore, the risk analysis module is used to determine a processing method for the model construction module based on the ratio difference between the power ratio and the second preset power ratio, including:

[0030] If the ratio difference is less than or equal to the first preset power comparison threshold, it is determined that the amount of data acquired by the data acquisition module is adjusted to a corresponding value based on the deviation evaluation parameter;

[0031] If the ratio difference is less than or equal to the second preset power comparison threshold and greater than the first preset power comparison threshold, determining a processing method for the model building module based on the power consumption concentration;

[0032] If the ratio difference is greater than a second preset power comparison threshold, it is determined that the alarm module issues an abnormal power purchase alarm message.

[0033] Furthermore, the instruction generation module is used to obtain the deviation evaluation parameter, including:

[0034] It is used to solve the absolute value of the difference between the estimated power purchase amount and the actual power purchase amount to obtain the power deviation;

[0035] Based on the obtained power deviation at each time node within the preset monitoring time, a power deviation time domain curve is drawn;

[0036] To obtain the variance of the time domain curve of power deviation and obtain the deviation evaluation parameter;

[0037] The instruction generation module is used to adjust the amount of data acquired by the data acquisition module to a corresponding value based on the deviation evaluation parameter, wherein:

[0038] The increase in the amount of data acquired by the data acquisition module determined based on the deviation evaluation parameter is proportional to the deviation evaluation parameter.

[0039] Further, the risk analysis module determines whether the operation status of the model building module is qualified under the condition that the adjustment of the data volume acquired by the data acquisition module is completed. If it is still determined that the data volume acquired by the data acquisition module is adjusted to the corresponding value based on the deviation evaluation parameter, the current data volume is continuously used as the operation parameter of the data acquisition module, and it is determined that the detection threshold of the data processing module for outlier screening is adjusted to the corresponding value based on the absolute value of the difference between the power ratio and the re-acquired power ratio;

[0040] The instruction generation module is used to adjust the detection threshold of the data processing module for outlier screening to a corresponding value, wherein:

[0041] The absolute value of the difference between the power ratio and the re-acquired power ratio is determined as the corrected span parameter, and the reduction range of the detection threshold determined based on the corrected span parameter is inversely proportional to the corrected span parameter.

[0042] Furthermore, the risk analysis module is used to determine a processing method for the model building module based on the power consumption concentration, including:

[0043] The risk analysis module is used to determine the power consumption concentration, the power consumption of each user in the previous power consumption cycle, and determine the users whose power consumption is greater than the preset user power consumption as key users, and determine the number of key users as the power consumption concentration;

[0044] If the power consumption concentration is less than or equal to the preset power consumption concentration, it is determined that the alarm module issues an abnormal power purchase alarm message;

[0045] If the power consumption concentration is greater than the preset power consumption concentration, the processing method for the model building module is determined based on the oscillation tendency parameter.

[0046] Furthermore, the risk analysis module is used to determine the oscillation tendency parameter of the power consumption concentration and determine the processing method for the model building module, including:

[0047] It is used to obtain the oscillation tendency parameter of the power consumption concentration, obtain the sum of the power consumption of each key user, and draw the power and time domain curve based on historical data, obtain the variance of each peak value of the power and time domain curve, and obtain the oscillation tendency parameter;

[0048] If the oscillation tendency parameter is less than or equal to the first preset oscillation tendency parameter, it is determined to use the preset offset coefficient to adjust the estimated power purchase output by the model building module to a corresponding value;

[0049] If the oscillation tendency parameter is less than or equal to the second preset oscillation tendency parameter and greater than the first preset oscillation tendency parameter, then adjusting the window size of the moving average of the historical actual electricity purchase volume by the data processing module to a corresponding value based on the evaluation fluctuation parameter;

[0050] If the oscillation tendency parameter is greater than the second preset oscillation tendency parameter, the model building module is determined to continue to operate using the current operating parameters, and the alarm module is controlled to issue an early warning message for power dispatching.

[0051] On the other hand, the present invention also provides a dynamic risk warning method for controlling the dynamic risk warning system of the power spot market, characterized in that it includes:

[0052] Obtain power data and influencing factor data, where the power data includes actual power purchase and user power consumption of each user, and the influencing factor data includes weather information, date, and population growth;

[0053] Clean, filter, convert and integrate data, and smooth data through moving average;

[0054] The data output by the data processing module is transmitted to the power estimation model, and the estimated power purchase amount is obtained;

[0055] Determine the evaluation fluctuation parameter based on the actual power purchase amount at each time node within the preset monitoring period, and determine whether the operation status of the model building module is qualified based on the power ratio of the actual power purchase amount to the estimated power purchase amount;

[0056] When it is determined that the operation status of the model building module is abnormal, corresponding adjustment instructions and alarm instructions are issued, including:

[0057] Adjust the window size of the moving average of the historical actual electricity purchase amount in the data processing module to a corresponding value;

[0058] Adjust the amount of data acquired by the data acquisition module to the corresponding value;

[0059] Send out abnormal electricity purchase alert information;

[0060] When it is determined that the operating status of the model building module is qualified, the model building module is controlled to continue to operate using the current operating parameters.

[0061] Compared with the prior art, the beneficial effects of the present invention lie in that the estimated power purchase amount obtained by the model construction module is monitored, the corresponding relationship between the actual power purchase amount and the estimated power purchase amount is determined, the specific operating status of the model construction module is analyzed, the risk situation of the electricity spot market is further analyzed, and corresponding alarm information is issued. When it is determined that the operating status of the model construction module is abnormal, corresponding adjustment instructions are issued, thereby improving the estimation accuracy of the power purchase amount, and further improving the risk identification efficiency for the electricity spot market.

[0062] Furthermore, based on the electricity ratio between the actual electricity purchase amount and the estimated electricity purchase amount, it is determined whether the operation status of the model construction module is qualified. When the electricity ratio is less than the first preset electricity ratio, in this case, the deviation between the estimated electricity purchase amount and the actual power supply is small, the supply and demand in the electricity spot market is balanced, and the electricity spot market is continuously monitored. When the power ratio is less than or equal to the second preset power ratio and greater than or equal to the first preset power ratio, the operating status of the model construction module is determined in combination with the evaluation fluctuation parameter. The evaluation fluctuation parameter characterizes the fluctuation of the actual power purchase amount. When the evaluation fluctuation parameter is less than or equal to the preset evaluation fluctuation parameter, the actual power purchase amount in the historical data fluctuates smoothly. In this case, due to the abnormality in the processing of the collected data, the input data of the model construction module has abnormal values, which affects the accuracy of the estimated power purchase amount. At this time, the moving average window size is adjusted to smooth out the noise and fluctuations in the short term, so that the data trend is more obvious and stable; when the evaluation fluctuation parameter is greater than the preset evaluation fluctuation parameter, the actual power purchase amount in the historical data has a large fluctuation intensity, resulting in a deviation in the estimated power purchase amount. In this case, an alarm message about the risk of power grid operation is issued to remind the power grid operator to pay attention to the equipment overload in the power grid operation due to the deviation in the estimated power purchase amount, so as to take timely prevention measures, thereby ensuring the stable supply of electricity and further improving the risk identification efficiency for the power spot market.

[0063] Furthermore, when the power ratio is greater than the second preset power ratio, there is a large deviation between the estimated power purchase amount and the actual power supply. At this time, the model building module is processed. When the ratio difference is less than or equal to the first preset power comparison threshold, the data acquisition module is processed to increase the data acquisition amount to ensure accurate estimation of the power purchase amount; when the ratio difference is greater than the second preset power comparison threshold, the alarm module is determined to issue an abnormal power purchase alarm message; when the ratio difference is less than or equal to the second preset power comparison threshold and greater than the first preset power comparison threshold, the power consumption concentration is obtained. The power consumption concentration characterizes the situation of users who can greatly affect the power purchase amount. When the power concentration is less than or equal to the preset power consumption concentration, there is When there are a small number of users who can greatly affect the amount of electricity purchased, in this case, it is determined that the alarm module issues an alarm message of abnormal electricity purchase; when the electricity consumption concentration is greater than the preset electricity consumption concentration, in this case, there are a large number of users who can greatly affect the amount of electricity purchased, and there is a situation where the amount of electricity purchased by each key user suddenly increases due to abnormal circumstances. At this time, the oscillation tendency parameter is obtained, and the oscillation tendency parameter characterizes the fluctuation of the influence of each key user on the actual amount of electricity purchased. When the oscillation tendency parameter is less than or equal to the first preset oscillation tendency parameter, the fluctuation of the influence of each key user on the actual amount of electricity purchased is small, and the estimated amount of electricity purchased is low. For each determined estimated amount of electricity purchased, a geometric increase is performed to reduce the deviation between the estimated amount of electricity purchased and the actual amount of electricity purchased. When the oscillation tendency parameter is less than or equal to the second preset oscillation tendency parameter and greater than the first preset oscillation tendency parameter, in this case, the sum of the user purchase amounts of each key user fluctuates and there is a fluctuation pattern. At this time, it is determined that the data processing module adjusts the window size of the moving average of the historical actual amount of electricity purchased to reduce the overreaction to short-term random fluctuations and capture the long-term pattern and law of the historical actual amount of electricity purchased. When the oscillation tendency parameter is greater than the second preset oscillation tendency parameter, the sum of the user power purchase amounts of each key user fluctuates and the fluctuation is irregular. In this case, it is determined to issue an early warning information for power dispatch to ensure stable power supply; while improving the estimation accuracy of the power purchase amount, the risk identification efficiency for the power spot market is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a module block diagram of a dynamic risk early warning system for the power spot market according to an embodiment of the present invention;

[0065] Figure 2 This is a flowchart of the steps of the method for early warning of dynamic risks in the electricity spot market according to an embodiment of the present invention;

[0066] Figure 3 A logical decision diagram for determining whether the operation status of the model building module is qualified based on the actual amount of electricity purchased by the risk analysis module in an embodiment of the present invention;

[0067] Figure 4This is a logical decision diagram for the risk analysis module of an embodiment of the present invention to determine whether the operating status of the model construction module is qualified based on the evaluation fluctuation parameter. DETAILED DESCRIPTION

[0068] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0069] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0070] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0071] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0072] See also Figure 1 As shown, it is a block diagram of a module of a power spot market dynamic risk early warning system according to an embodiment of the present invention. The power spot market dynamic risk early warning system according to the present invention comprises:

[0073] A data collection module is used to collect power data and influencing factor data. The power data includes the actual amount of power purchased and the amount of power consumed by each user. The influencing factor data includes weather information, date, and population growth.

[0074] A data processing module, which is connected to the data acquisition module, is used to clean, screen, convert and integrate the data to ensure the consistency of the data and to smooth the data through moving average;

[0075] A model building module, which is connected to the data processing module and is used to transmit the data output by the data processing module to the power estimation model and obtain the estimated power purchase amount;

[0076] A risk analysis module, which is connected to the data acquisition module, the data processing module and the model building module respectively, and is used to determine the evaluation fluctuation parameter based on the actual power purchase amount at each time node within the preset monitoring time, and determine whether the operation status of the model building module is qualified in combination with the power ratio of the actual power purchase amount to the estimated power purchase amount, and issue a corresponding adjustment instruction when it is determined that the operation status of the model building module is abnormal;

[0077] An instruction generation module, which is respectively connected to the data acquisition module, the data processing module and the risk analysis module, and is used to issue corresponding adjustment instructions based on the determination of the risk analysis module, including controlling the data processing module to adjust the window size of the moving average of the historical actual power purchase amount to a corresponding value, and adjusting the amount of data obtained by the data acquisition module to a corresponding value;

[0078] An alarm module is connected to the risk analysis module and is used to issue corresponding alarm information according to the determination of the risk analysis module.

[0079] Specifically, the data processing module processes data, including data smoothing, that is, smoothing the data transmitted to the model building module using the moving average method to reduce the impact of outliers and short-term fluctuations on the data.

[0080] Specifically, there is no limitation on the specific method for the data acquisition module to acquire data. It can be connected through interfaces with the trading system, monitoring system, meteorological database, etc. of the power market, and use data acquisition tools, sensors, data capture tools and data interfaces to acquire real-time data and historical data, and transmit them to the data processing module. It can be understood that it is possible to collect data related to the power spot market from various data sources, including power supply and demand, prices, user power consumption patterns, power grid operating status, weather information, dates and population growth, etc., which will not be elaborated here.

[0081] Specifically, there is no limitation on the specific way the data is processed by the data processing module. A data cleaning algorithm can be used to remove noise and outliers, perform data standardization or normalization, and integrate and associate data in different formats and sources. It can be understood that the collected data can be cleaned, screened, converted and integrated to ensure the quality and consistency of the data, and prepare for subsequent analysis and modeling. This will not be elaborated.

[0082] Specifically, there is no limitation on the specific model applied by the model building module. The model can be trained using the data output by the data processing module through a deep learning model, such as a convolutional neural network (CNN), a recurrent neural network (RNN), or a long short-term memory network (LSTM). The data output by the data processing module can be used as an input variable, and the estimated power purchase amount can be used as an output variable. This is a prior art and will not be elaborated on here.

[0083] Specifically, the estimated power purchase amount obtained by the model construction module is monitored, the corresponding relationship between the actual power purchase amount and the estimated power purchase amount is determined, the specific operating status of the model construction module is analyzed, the risk situation of the electricity spot market is further analyzed, and the corresponding alarm information is issued. When it is determined that the operating status of the model construction module is abnormal, the corresponding adjustment instructions are issued, which improves the estimation accuracy of the power purchase amount, and thereby improves the risk identification efficiency for the electricity spot market.

[0084] See also Figure 2 As shown, it is a flowchart of the steps of the power spot market dynamic risk early warning method according to an embodiment of the present invention. The power spot market dynamic risk early warning method according to the present invention comprises:

[0085] S1, obtaining power data and influencing factor data, the power data includes actual power purchase and user power consumption of each user, and the influencing factor data includes weather information, date and population growth;

[0086] S2, cleans, screens, converts and integrates the data, and smoothes the data by moving average;

[0087] S3, transmitting the data output by the data processing module to the power estimation model, and obtaining the estimated power purchase amount;

[0088] S4, determining the evaluation fluctuation parameter based on the actual power purchase amount at each time node within the preset monitoring time, and determining whether the operation status of the model building module is qualified based on the power ratio of the actual power purchase amount to the estimated power purchase amount;

[0089] When it is determined that the operation status of the model building module is abnormal, corresponding adjustment instructions and alarm instructions are issued, including:

[0090] Adjust the window size of the moving average of the historical actual electricity purchase amount in the data processing module to a corresponding value;

[0091] Adjust the amount of data acquired by the data acquisition module to the corresponding value;

[0092] Send out abnormal electricity purchase alert information;

[0093] When it is determined that the operating status of the model building module is qualified, the model building module is controlled to continue to operate using the current operating parameters.

[0094] See also Figure 3 As shown, it is a logical decision diagram for the risk analysis module of an embodiment of the present invention to determine whether the operating status of the model construction module is qualified based on the actual amount of purchased electricity. The risk analysis module of the present invention is used to determine whether the operating status of the model construction module is qualified based on the ratio of the actual amount of purchased electricity to the estimated amount of purchased electricity, including:

[0095] If the power ratio is less than the first preset power ratio, it is determined that the operation status of the model building module is qualified, and the power spot market is determined to be operating stably.

[0096] If the power ratio is less than or equal to the second preset power ratio and greater than or equal to the first preset power ratio, determining whether the operation status of the model building module is qualified based on the evaluation fluctuation parameter;

[0097] If the power ratio is greater than the second preset power ratio, it is determined that the operation status of the model building module is abnormal, and the processing method for the model building module is determined based on the ratio difference between the power ratio and the second preset power ratio.

[0098] Specifically, the first preset power ratio L1 is 1, and the second preset power ratio L2 is selected within the interval [1.7, 2.5].

[0099] See also Figure 3 As shown, it is a logical decision diagram for the risk analysis module of an embodiment of the present invention to determine whether the operation status of the model construction module is qualified based on the evaluation fluctuation parameter. The risk analysis module of the present invention is used to determine the evaluation fluctuation parameter, including:

[0100] Based on the actual amount of electricity purchased at each time point within the preset monitoring time period, a time domain curve of the actual amount of electricity purchased is drawn;

[0101] It is used to obtain the ratio of the number of extreme points of the actual purchased electricity time domain curve to the preset monitoring time, and obtain the evaluation fluctuation parameter;

[0102] The risk analysis module is used to determine whether the operation status of the model construction module is qualified based on the evaluation fluctuation parameter, including:

[0103] If the evaluation fluctuation parameter is less than or equal to the preset evaluation fluctuation parameter, it is determined that the operation status of the model building module is abnormal, and it is determined that the window size of the moving average of the historical actual electricity purchase volume of the data processing module is adjusted to a corresponding value based on the evaluation fluctuation parameter;

[0104] If the evaluation fluctuation parameter is greater than the preset evaluation fluctuation parameter, the operation status of the model building module is determined to be qualified, and an alarm message is issued regarding the risk of power grid operation.

[0105] Specifically, the preset evaluation fluctuation parameter G0 is selected within the interval [1.4B0, 2.3B0], and B0 is the average value of the evaluation fluctuation parameter within each preset monitoring period in the historical data.

[0106] Specifically, whether the operating status of the model construction module is qualified is determined based on the electricity ratio between the actual electricity purchase amount and the estimated electricity purchase amount. When the electricity ratio is less than the first preset electricity ratio, the deviation between the estimated electricity purchase amount and the actual power supply is small, the supply and demand in the electricity spot market is balanced, and the electricity spot market is continuously monitored. When the power ratio is less than or equal to the second preset power ratio and greater than or equal to the first preset power ratio, the operating status of the model construction module is determined in combination with the evaluation fluctuation parameter. The evaluation fluctuation parameter characterizes the fluctuation of the actual power purchase amount. When the evaluation fluctuation parameter is less than or equal to the preset evaluation fluctuation parameter, the actual power purchase amount in the historical data fluctuates smoothly. In this case, due to the abnormality in the processing of the collected data, the input data of the model construction module has abnormal values, which affects the accuracy of the estimated power purchase amount. At this time, the moving average window size is adjusted to smooth out the noise and fluctuations in the short term, so that the data trend is more obvious and stable; when the evaluation fluctuation parameter is greater than the preset evaluation fluctuation parameter, the actual power purchase amount in the historical data has a large fluctuation intensity, resulting in a deviation in the estimated power purchase amount. In this case, an alarm message about the risk of power grid operation is issued to remind the power grid operator to pay attention to the equipment overload in the power grid operation due to the deviation in the estimated power purchase amount, so as to take timely prevention measures, thereby ensuring the stable supply of electricity and further improving the risk identification efficiency for the power spot market.

[0107] Specifically, the instruction generation module is used to adjust the window size of the moving average of the historical actual power purchase amount of the data processing module to a corresponding value based on the evaluation fluctuation parameter, wherein:

[0108] The increase in the window size determined based on the evaluation fluctuation parameter is inversely proportional to the evaluation fluctuation parameter.

[0109] In this embodiment, optionally,

[0110] Comparing the evaluation fluctuation parameter with a first preset fluctuation comparison threshold and a second preset fluctuation comparison threshold;

[0111] If the evaluation fluctuation parameter is less than or equal to the first preset fluctuation comparison threshold, the window size is adjusted to 1.29 times the initial window size;

[0112] If the evaluation fluctuation parameter is less than or equal to the second preset fluctuation comparison threshold and greater than the first preset fluctuation comparison threshold, the window size is adjusted to 1.21 times the initial window size;

[0113] If the evaluation fluctuation parameter is greater than the second preset fluctuation comparison threshold, the window size is adjusted to 1.11 times the initial window size;

[0114] The first preset fluctuation comparison threshold is 0.62G0, and the second preset fluctuation comparison threshold is 0.85G0.

[0115] Specifically, the risk analysis module is used to determine a processing method for the model construction module based on the ratio difference between the power ratio and the second preset power ratio, including:

[0116] If the ratio difference is less than or equal to the first preset power comparison threshold, it is determined that the amount of data acquired by the data acquisition module is adjusted to a corresponding value based on the deviation evaluation parameter;

[0117] If the ratio difference is less than or equal to the second preset power comparison threshold and greater than the first preset power comparison threshold, determining a processing method for the model building module based on the power consumption concentration;

[0118] If the ratio difference is greater than a second preset power comparison threshold, it is determined that the alarm module issues an abnormal power purchase alarm message.

[0119] Specifically, the first preset power comparison threshold is 1.45L2, and the second preset power comparison threshold is 1.83L2.

[0120] Specifically, the instruction generation module is used to obtain deviation evaluation parameters, including:

[0121] It is used to solve the absolute value of the difference between the estimated power purchase amount and the actual power purchase amount to obtain the power deviation;

[0122] Based on the obtained power deviation at each time node within the preset monitoring time, a power deviation time domain curve is drawn;

[0123] To obtain the variance of the time domain curve of power deviation and obtain the deviation evaluation parameter;

[0124] The instruction generation module is used to adjust the amount of data acquired by the data acquisition module to a corresponding value based on the deviation evaluation parameter, wherein:

[0125] The increase in the amount of data acquired by the data acquisition module determined based on the deviation evaluation parameter is proportional to the deviation evaluation parameter.

[0126] In this embodiment, optionally,

[0127] comparing the deviation evaluation parameter with a first preset deviation evaluation parameter and a second preset deviation evaluation parameter;

[0128] If the deviation evaluation parameter is less than or equal to the first preset deviation evaluation parameter, the amount of data acquired by the data acquisition module is adjusted to 1.12 times the initial amount of data;

[0129] If the deviation evaluation parameter is less than or equal to the second preset deviation evaluation parameter and greater than the first preset deviation evaluation parameter, the amount of data acquired by the data acquisition module is adjusted to 1.18 times the initial data amount;

[0130] If the deviation evaluation parameter is greater than the second preset deviation evaluation parameter, the amount of data acquired by the data acquisition module is adjusted to 1.29 times the initial amount of data;

[0131] The first preset deviation evaluation parameter is 0.4P0, and the second preset deviation evaluation parameter is 6.4P0, where P0 is the average value of each power deviation in the historical data.

[0132] Specifically, optionally, the increase in the amount of data acquired by the data acquisition module can be achieved by shortening the time interval for acquiring data.

[0133] Specifically, the risk analysis module determines whether the operation status of the model building module is qualified under the condition that the adjustment of the data volume obtained by the data acquisition module is completed. If it is still determined that the data volume obtained by the data acquisition module is adjusted to the corresponding value based on the deviation evaluation parameter, the current data volume is continuously used as the operation parameter of the data acquisition module, and it is determined that the detection threshold of the data processing module for outlier screening is adjusted to the corresponding value based on the absolute value of the difference between the power ratio and the re-acquired power ratio;

[0134] The instruction generation module is used to adjust the detection threshold of the data processing module for outlier screening to a corresponding value, wherein:

[0135] The absolute value of the difference between the power ratio and the re-acquired power ratio is determined as the corrected span parameter, and the reduction range of the detection threshold determined based on the corrected span parameter is inversely proportional to the corrected span parameter.

[0136] In this embodiment, optionally,

[0137] comparing the corrected span parameter with a first preset corrected span parameter and a second preset corrected span parameter;

[0138] If the corrected span parameter is less than or equal to the first preset corrected span parameter, the detection threshold is adjusted to 0.73 times the initial detection threshold;

[0139] If the corrected span parameter is less than or equal to the second preset corrected span parameter and greater than the first preset corrected span parameter, the detection threshold is adjusted to 0.82 times the initial detection threshold;

[0140] If the corrected span parameter is greater than the second preset corrected span parameter, the detection threshold is adjusted to 0.91 times the initial detection threshold;

[0141] The first preset correction span parameter is 0.2, and the second preset correction span parameter is 0.4.

[0142] Specifically, the detection threshold is lowered by moving the two end values ​​toward the middle value.

[0143] Specifically, the risk analysis module is used to determine the processing method for the model building module based on the power consumption concentration, including:

[0144] The risk analysis module is used to determine the power consumption concentration, the power consumption of each user in the previous power consumption cycle, and determine the users whose power consumption is greater than the preset user power consumption as key users, and determine the number of key users as the power consumption concentration;

[0145] If the power consumption concentration is less than or equal to the preset power consumption concentration, it is determined that the alarm module issues an abnormal power purchase alarm message;

[0146] If the power consumption concentration is greater than the preset power consumption concentration, the processing method for the model building module is determined based on the oscillation tendency parameter.

[0147] The default power consumption concentration is 2.

[0148] Specifically, the preset user power consumption is selected within the interval [6A0, 8A0], and A0 is the average power consumption of each user.

[0149] Specifically, the risk analysis module is used to determine the oscillation tendency parameter of the power consumption concentration and determine the processing method for the model building module, including:

[0150] It is used to obtain the oscillation tendency parameter of the power consumption concentration, obtain the sum of the power consumption of each key user, and draw the power and time domain curve based on historical data, obtain the variance of each peak value of the power and time domain curve, and obtain the oscillation tendency parameter;

[0151] If the oscillation tendency parameter is less than or equal to the first preset oscillation tendency parameter, it is determined to use the preset offset coefficient to adjust the estimated power purchase output by the model building module to a corresponding value;

[0152] If the oscillation tendency parameter is less than or equal to the second preset oscillation tendency parameter and greater than the first preset oscillation tendency parameter, then adjusting the window size of the moving average of the historical actual electricity purchase volume by the data processing module to a corresponding value based on the evaluation fluctuation parameter;

[0153] If the oscillation tendency parameter is greater than the second preset oscillation tendency parameter, the model building module is determined to continue to operate using the current operating parameters, and the alarm module is controlled to issue an early warning message for power dispatching.

[0154] Specifically, when the power ratio is greater than the second preset power ratio, there is a large deviation between the estimated power purchase amount and the actual power supply. At this time, the model building module is processed. When the ratio difference is less than or equal to the first preset power comparison threshold, the data acquisition module is processed to increase the data acquisition amount to ensure accurate estimation of the power purchase amount. When the ratio difference is greater than the second preset power comparison threshold, the alarm module is determined to issue an abnormal power purchase alarm message. When the ratio difference is less than or equal to the second preset power comparison threshold and greater than the first preset power comparison threshold, the power consumption concentration is obtained. The power consumption concentration characterizes the situation of users who can greatly affect the power purchase amount. When the power concentration is less than or equal to the preset power consumption concentration, there is When there are a small number of users who can greatly affect the amount of electricity purchased, in this case, it is determined that the alarm module issues an alarm message of abnormal electricity purchase; when the electricity consumption concentration is greater than the preset electricity consumption concentration, in this case, there are a large number of users who can greatly affect the amount of electricity purchased, and there is a situation where the amount of electricity purchased by each key user suddenly increases due to abnormal circumstances. At this time, the oscillation tendency parameter is obtained, and the oscillation tendency parameter characterizes the fluctuation of the influence of each key user on the actual amount of electricity purchased. When the oscillation tendency parameter is less than or equal to the first preset oscillation tendency parameter, the fluctuation of the influence of each key user on the actual amount of electricity purchased is small, and the estimated amount of electricity purchased is low. For each determined estimated amount of electricity purchased, a geometric increase is performed to reduce the deviation between the estimated amount of electricity purchased and the actual amount of electricity purchased. When the oscillation tendency parameter is less than or equal to the second preset oscillation tendency parameter and greater than the first preset oscillation tendency parameter, in this case, the sum of the user purchase amounts of each key user fluctuates and there is a fluctuation pattern. At this time, it is determined that the data processing module adjusts the window size of the moving average of the historical actual amount of electricity purchased to reduce the overreaction to short-term random fluctuations and capture the long-term pattern and law of the historical actual amount of electricity purchased. When the oscillation tendency parameter is greater than the second preset oscillation tendency parameter, the sum of the user power purchase amounts of each key user fluctuates and the fluctuation is irregular. In this case, it is determined to issue an early warning information for power dispatch to ensure stable power supply; while improving the estimation accuracy of the power purchase amount, the risk identification efficiency for the power spot market is further improved.

[0155] Specifically, the first preset oscillation tendency parameter is 0.9Y0, and the second preset oscillation tendency parameter is 4Y0, where Y0 is the average value of the electrical quantity and the time domain curve.

[0156] Specifically, in this embodiment, optionally, a preset offset coefficient is used to adjust the estimated power purchase amount output by the model construction module to a corresponding value. The preset offset coefficient is 1.13. The adjusted preset offset coefficient is the product of the preset offset coefficient and the initial preset offset coefficient, which will not be repeated here.

[0157] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0158] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dynamic risk early warning system and method for the electricity spot market, characterized in that: include: A data collection module is used to collect power data and influencing factor data. The power data includes actual power purchase and user power consumption of each user. The influencing factor data includes weather information, date and population growth. A data processing module, which is connected to the data acquisition module, is used to clean, screen, convert and integrate the data to ensure the consistency of the data and to smooth the data through moving average; A model building module, which is connected to the data processing module and is used to transmit the data output by the data processing module to the power estimation model and obtain the estimated power purchase amount; A risk analysis module, which is connected to the data acquisition module, the data processing module and the model building module respectively, and is used to determine the evaluation fluctuation parameter based on the actual power purchase amount at each time node within the preset monitoring time, and determine whether the operation status of the model building module is qualified in combination with the power ratio of the actual power purchase amount to the estimated power purchase amount, and issue a corresponding adjustment instruction when it is determined that the operation status of the model building module is abnormal; An instruction generation module, which is respectively connected to the data acquisition module, the data processing module and the risk analysis module, and is used to issue corresponding adjustment instructions based on the determination of the risk analysis module, including controlling the data processing module to adjust the window size of the moving average of the historical actual power purchase amount to a corresponding value, and adjusting the amount of data obtained by the data acquisition module to a corresponding value; An alarm module is connected to the risk analysis module and is used to issue corresponding alarm information according to the determination of the risk analysis module.

2. The power spot market dynamic risk early warning system according to claim 1 is characterized in that: The risk analysis module is used to determine whether the operation status of the model construction module is qualified based on the ratio of the actual amount of purchased electricity to the estimated amount of purchased electricity, including: If the power ratio is less than the first preset power ratio, it is determined that the operation status of the model building module is qualified, and the power spot market is determined to be operating stably. If the power ratio is less than or equal to the second preset power ratio and greater than or equal to the first preset power ratio, determining whether the operation status of the model building module is qualified based on the evaluation fluctuation parameter; If the power ratio is greater than the second preset power ratio, it is determined that the operation status of the model building module is abnormal, and the processing method for the model building module is determined based on the ratio difference between the power ratio and the second preset power ratio.

3. The power spot market dynamic risk early warning system according to claim 2 is characterized in that: The risk analysis module is used to determine the evaluation fluctuation parameters, including: Based on the actual amount of electricity purchased at each time point within the preset monitoring time period, a time domain curve of the actual amount of electricity purchased is drawn; It is used to obtain the ratio of the number of extreme points of the actual purchased electricity time domain curve to the preset monitoring time, and obtain the evaluation fluctuation parameter; The risk analysis module is used to determine whether the operation status of the model construction module is qualified based on the evaluation fluctuation parameter, including: If the evaluation fluctuation parameter is less than or equal to the preset evaluation fluctuation parameter, it is determined that the operation status of the model building module is abnormal, and it is determined that the window size of the moving average of the historical actual electricity purchase volume of the data processing module is adjusted to a corresponding value based on the evaluation fluctuation parameter; If the evaluation fluctuation parameter is greater than the preset evaluation fluctuation parameter, the operation status of the model building module is determined to be qualified, and an alarm message is issued regarding the risk of power grid operation.

4. The power spot market dynamic risk early warning system according to claim 3 is characterized in that: The instruction generation module is used to adjust the window size of the moving average of the historical actual power purchase amount of the data processing module to a corresponding value based on the evaluation fluctuation parameter, wherein: The increase in the window size determined based on the evaluation fluctuation parameter is inversely proportional to the evaluation fluctuation parameter.

5. The power spot market dynamic risk early warning system according to claim 4 is characterized in that: The risk analysis module is used to determine a processing method for the model construction module based on a ratio difference between the power ratio and the second preset power ratio, including: If the ratio difference is less than or equal to the first preset power comparison threshold, it is determined that the amount of data acquired by the data acquisition module is adjusted to a corresponding value based on the deviation evaluation parameter; If the ratio difference is less than or equal to the second preset power comparison threshold and greater than the first preset power comparison threshold, determining a processing method for the model building module based on the power consumption concentration; If the ratio difference is greater than a second preset power comparison threshold, it is determined that the alarm module issues an abnormal power purchase alarm message.

6. The power spot market dynamic risk early warning system according to claim 5 is characterized in that: The instruction generation module is used to obtain deviation evaluation parameters, including: It is used to solve the absolute value of the difference between the estimated power purchase amount and the actual power purchase amount to obtain the power deviation; Based on the obtained power deviation at each time node within the preset monitoring time, a power deviation time domain curve is drawn; To obtain the variance of the time domain curve of power deviation and obtain the deviation evaluation parameter; The instruction generation module is used to adjust the amount of data acquired by the data acquisition module to a corresponding value based on the deviation evaluation parameter, wherein: The increase in the amount of data acquired by the data acquisition module determined based on the deviation evaluation parameter is proportional to the deviation evaluation parameter.

7. The power spot market dynamic risk early warning system according to claim 6 is characterized in that: The risk analysis module determines whether the operation status of the model building module is qualified under the condition that the adjustment of the data volume acquired by the data acquisition module is completed. If it is still determined that the data volume acquired by the data acquisition module is adjusted to the corresponding value based on the deviation evaluation parameter, the current data volume is continuously used as the operation parameter of the data acquisition module, and the detection threshold of the data processing module for outlier screening is adjusted to the corresponding value based on the absolute value of the difference between the power ratio and the re-acquired power ratio. The instruction generation module is used to adjust the detection threshold of the data processing module for outlier screening to a corresponding value, wherein: The absolute value of the difference between the power ratio and the re-acquired power ratio is determined as the corrected span parameter, and the reduction range of the detection threshold determined based on the corrected span parameter is inversely proportional to the corrected span parameter.

8. The power spot market dynamic risk early warning system according to claim 7 is characterized in that: The risk analysis module is used to determine a processing method for the model building module based on the power consumption concentration, including: The risk analysis module is used to determine the power consumption concentration, the power consumption of each user in the previous power consumption cycle, and determine the users whose power consumption is greater than the preset user power consumption as key users, and determine the number of key users as the power consumption concentration; If the power consumption concentration is less than or equal to the preset power consumption concentration, it is determined that the alarm module issues an abnormal power purchase alarm message; If the power consumption concentration is greater than the preset power consumption concentration, the processing method for the model building module is determined based on the oscillation tendency parameter.

9. The power spot market dynamic risk early warning system according to claim 8, characterized in that: The risk analysis module is used to determine the oscillation tendency parameter of the power consumption concentration and determine the processing method for the model building module, including: It is used to obtain the oscillation tendency parameter of the power consumption concentration, obtain the sum of the power consumption of each key user, and draw the power and time domain curve based on historical data, obtain the variance of each peak value of the power and time domain curve, and obtain the oscillation tendency parameter; If the oscillation tendency parameter is less than or equal to the first preset oscillation tendency parameter, it is determined to use the preset offset coefficient to adjust the estimated power purchase output by the model building module to a corresponding value; If the oscillation tendency parameter is less than or equal to the second preset oscillation tendency parameter and greater than the first preset oscillation tendency parameter, then adjusting the window size of the moving average of the historical actual electricity purchase volume by the data processing module to a corresponding value based on the evaluation fluctuation parameter; If the oscillation tendency parameter is greater than the second preset oscillation tendency parameter, the model building module is determined to continue to operate using the current operating parameters, and the alarm module is controlled to issue an early warning message for power dispatching.

10. A dynamic risk warning method using the power spot market dynamic risk warning system according to any one of claims 1 to 9, characterized in that: include, Obtain power data and influencing factor data, where the power data includes actual power purchase and user power consumption of each user, and the influencing factor data includes weather information, date, and population growth; Clean, filter, convert and integrate data, and smooth data through moving average; The data output by the data processing module is transmitted to the power estimation model, and the estimated power purchase amount is obtained; Determine the evaluation fluctuation parameter based on the actual power purchase amount at each time node within the preset monitoring period, and determine whether the operation status of the model building module is qualified based on the power ratio of the actual power purchase amount to the estimated power purchase amount; When it is determined that the operation status of the model building module is abnormal, corresponding adjustment instructions and alarm instructions are issued, including: Adjust the window size of the moving average of the historical actual electricity purchase amount in the data processing module to a corresponding value; Adjust the amount of data acquired by the data acquisition module to the corresponding value; Send out abnormal electricity purchase alert information; When it is determined that the operating status of the model building module is qualified, the model building module is controlled to continue to operate using the current operating parameters.

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