Digital zero-carbon energy management method and system based on renewable energy coupling

Abstract

The application discloses a digital zero-carbon energy management method and system based on renewable energy coupling, relates to the technical field of energy management, and comprises the following steps: firstly, collecting voltage data in real time and preprocessing; then, calculating a voltage disorder factor; if the voltage is disordered, combining a power-off trend factor and predicting a power-off situation according to time series; if the voltage is not disordered, returning; if the voltage is disordered, starting a standby energy source, and optimizing subsequent prediction accuracy. The application constructs a double-layer analysis model, quantifies voltage fluctuation and predicts trends, determines risks based on threshold values, starts standby energy sources on demand, avoids early or late starting, balances power supply safety and zero-carbon targets, compares current and historical times, dynamically adjusts adjustment factors: if lagging, the adjustment factors are improved; if sensitive, the adjustment factors are reduced, the power grid scene is adapted, the response speed and prediction accuracy are balanced, and the system reliability is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of energy management, in particular to a digital zero-carbon energy management method and system based on renewable energy coupling. BACKGROUND

[0002] Traditional fossil energy supply results in high carbon emissions, and single renewable energy has intermittent defects. The digital zero-carbon energy management based on renewable energy coupling works as follows: multiple renewable energies such as solar energy, wind energy, geothermal energy and industrial waste heat are integrated, wind power generation, photovoltaic power generation, heat pumps and heat storage devices are used, real-time monitoring of energy supply and demand data is realized by means of a digital control system, the start-stop sequence and energy supply priority of each device are dynamically set, the coupling of multiple technologies is realized, and an intelligent and unattended zero-carbon energy supply system is formed.

[0003] For example, the zero-carbon rural comprehensive energy management method and system disclosed in the patent with the publication number CN117955143B includes the following steps: obtaining the electricity consumption data of the pilot village, the power generation data of the power generation source, the charging data of the power storage system and the SOC data. The difference between the electricity consumption data and the SOC data and the change synchronization are analyzed, and the change trend of the SOC data is obtained. The health coefficient of the power storage system is obtained as an index for adjusting the discharge strategy. The power generation stability characteristic value of each power generation source is calculated. According to the health coefficient of the power storage system and the power generation stability characteristic value of all power generation sources, the required dispatching power at the current time is adjusted to obtain the actual discharge power. The actual discharge power takes into account the health condition of the power storage system and the power generation stability of the power generation source, which can avoid deep discharge of the battery, improve the recycling ecology between the power supply system and the storage system, and prolong the service life of the battery.

[0004] However, the above-mentioned patent does not solve the problem that when the stability of the power grid faces uncertainty, the starting time of the standby energy source is uncertain. In order to avoid the risk of power failure, the standby energy source often needs to be started in advance before the actual energy gap occurs to avoid huge losses caused by power grid failure. Since the timing of the chaotic power grid failure cannot be grasped, starting the standby energy source too early will cause waste of part of the energy and increase pollution emissions, which is not in line with the zero-carbon concept. SUMMARY

[0005] Technical problems to be solved

[0006] In view of the deficiencies of the prior art, the present application provides a digital zero-carbon energy management method and system based on renewable energy coupling, which solves the problem that when the stability of the power grid faces uncertainty, the standby energy source needs to be started in advance to ensure safety due to the long starting time of the standby energy source, but starting too early will lead to energy waste and conflict with the zero-carbon goal.

[0007] Technical scheme

[0008] To achieve the above object, the application is implemented by the following technical solutions: a digital zero-carbon energy management method and system based on renewable energy coupling, comprising the following specific steps and modules: Step 1: start collecting voltage data in real time, and execute Step 2; Step 2: preprocess the voltage data, and execute Step 3; Step 3: comprehensively calculate the voltage data to obtain a voltage disturbance factor, perform disturbance analysis according to the voltage disturbance factor, if normal is analyzed, end the detection, if disturbance is analyzed, comprehensively calculate the outage trend slope according to the time sequence and the voltage disturbance factor, sum the ratio of the outage trend slope to reflect the acceleration of the outage trend, obtain the outage trend factor, establish a coordinate system through the time sequence and the voltage disturbance factor, obtain a disturbance point, the abscissa of the disturbance point is the time sequence, and the ordinate is the voltage disturbance factor, calculate the slope of the next disturbance point and the previous disturbance point according to the time sequence, obtain the outage trend slope, set the ratio number and the adjustment factor, and perform standardized processing and comprehensive calculation according to the ratio number, the adjustment factor, the next outage trend slope and the previous outage trend slope to obtain the outage trend factor, ; wherein, represents the outage trend factor, represents the next outage trend slope, represents the previous outage trend slope, represents the ratio number, represents the adjustment factor; perform outage prediction according to the outage trend factor, if the prediction does not tend to outage, return to Step 1, if the prediction tends to outage, execute Step 4, and optimize the accuracy of subsequent outage prediction again; Step 4: start the standby energy source.

[0009] Further, the specific method for analyzing disturbance according to the voltage disturbance factor is: comparing the voltage disturbance factor with zero, if the voltage disturbance factor is initially greater than zero, it indicates that the voltage is disturbed, and if the voltage disturbance factor is always less than or equal to zero, it indicates that the voltage is normal.

[0010] Further, the specific acquisition method of the voltage disturbance factor is as follows: comprehensively calculate the voltage data to obtain a voltage fluctuation parameter, set a voltage fluctuation threshold, and perform difference calculation on the voltage fluctuation parameter and the voltage fluctuation threshold to obtain the voltage disturbance factor.

[0011] Further, the specific acquisition method of the voltage fluctuation parameter is as follows: perform mean value calculation on the voltage data according to the number of voltage values in the voltage data to obtain a voltage mean value, perform square calculation of the difference between the voltage data and the voltage mean value to obtain a deviation value, perform sum calculation on the deviation degree of each deviation value and the voltage mean value to obtain a total fluctuation degree, and perform mean value calculation on the total fluctuation degree to obtain the voltage fluctuation parameter.

[0012] Further, the specific method for predicting power failure according to the power failure trend factor is: setting a power failure trend threshold, comparing the power failure trend factor with the power failure trend threshold, if the power failure trend factor is greater than the power failure trend threshold, it indicates that the power failure trend is power failure, if the power failure trend factor is less than or equal to the power failure trend threshold, it indicates that the power failure trend is not power failure.

[0013] Further, the specific method for obtaining the power failure trend slope is: calculating the difference between the voltage disturbance factor of the next disturbance point and the voltage disturbance factor of the previous disturbance point to obtain a voltage disturbance factor deviation value, calculating the difference between the time sequence of the next disturbance point and the time sequence of the previous disturbance point to obtain a time sequence deviation value, and calculating the quotient of the voltage disturbance factor deviation value and the time sequence deviation value to obtain the power failure trend slope.

[0014] Further, the specific method for obtaining the power failure trend threshold is: summing the ratio times to obtain the power failure trend threshold.

[0015] Further, the specific method for optimizing the accuracy of the subsequent power failure prediction is: recording the time when the power failure trend factor is obtained as the starting time, starting the standby energy source at the starting time, recording the time when the voltage data disappears as the final time, recording the time period between the starting time and the final time as the current adjustment time, setting a sample time, comparing the current adjustment time with the sample time, if the current adjustment time is greater than the upper limit of the sample time, increasing the adjustment factor to reduce the current adjustment time to the range of the sample time, if the current adjustment time is less than the lower limit of the sample time, decreasing the adjustment factor to increase the current adjustment time to the range of the sample time.

[0016] Further, the voltage data acquisition module, the data preprocessing module, the central computing processing module, and the standby energy source operation module; the voltage data acquisition module: starts to collect voltage data in real time, and calls the data preprocessing module; the data preprocessing module: preprocesses the voltage data, and calls the central computing processing module; the central computing processing module: analyzes the voltage data, if the analysis result is normal, the detection is ended, if the analysis result is disturbed, the power failure prediction is continued, if the prediction result is not power failure, the voltage data acquisition module is returned, if the prediction result is power failure, the standby energy source operation module is called, and the accuracy of the subsequent power failure prediction is adjusted; the standby energy source operation module: starts the standby energy source.

[0017] Advantages

[0018] Compared with the prior art, the embodiments of the present application have at least the following advantages or beneficial effects:

[0019] 1. Through the construction of voltage disturbance factor-power-off trend factor double-layer analysis model, real-time quantification of voltage fluctuation anomaly and trend prediction, based on threshold comparison to determine power-off risk, realizing on-demand start of standby energy, which not only avoids premature start leading to energy waste and carbon emission increase, but also prevents start lag from causing power-off, balancing power supply safety and zero carbon target.

[0020] 2. By comparing the current adjustment time with the historical sample time, the adjustment factor is dynamically adjusted to optimize the prediction model: the factor is increased to accelerate the start in response to lag, and the factor is reduced to reduce false triggering in excessive sensitivity. This mechanism can adapt to different power grid fluctuation scenarios, dynamically balance the response speed and prediction accuracy, and iteratively improve system reliability and long-term operation efficiency.

[0021] Of course, implementing any product of the present application does not necessarily require all the advantages described above to be achieved simultaneously. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 For the present application: a flowchart of a digital zero-carbon energy management method based on renewable energy coupling.

[0023] Figure 2 For the present application: a structural diagram of a digital zero-carbon energy management system based on renewable energy coupling. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0025] It should be noted that in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0026] Embodiment one:

[0027] As shown in Figure 1 The embodiments of the present application provide a digital zero-carbon energy management method based on renewable energy coupling: including the following specific steps:

[0028] Step one: through the integration and optimization of technology, the energy of various renewable energy sources such as solar energy and wind energy is complementary to obtain clean energy. The voltage data of clean energy is collected in real time through the voltage sensor. The voltage data reflects the stability of clean energy, and step two is executed.

[0029] Step two: the voltage data is processed by data cleaning to remove redundant values of the voltage data, and the data quality of the voltage data is improved, and step three is executed.

[0030] Step three: the voltage data is standardized by variance method and then calculated, which helps to eliminate the dimension of different values during calculation and convert to a unified numerical range to obtain the voltage fluctuation parameter. The voltage fluctuation threshold is a set composed of normal voltage fluctuations. The voltage fluctuation parameter and the voltage fluctuation threshold are calculated by difference to obtain the voltage disturbance factor. According to the voltage disturbance factor and zero, if the voltage disturbance factor is greater than zero initially, it means that voltage disturbance, i.e. the imbalance of clean energy transmission, will destroy the power balance, cause generator power angle instability and surge in load reactive power demand, lead to voltage shock due to electromagnetic energy fluctuation, and continue to predict power failure. If the voltage disturbance factor is always less than or equal to zero, it means that the voltage is normal, and the detection is ended.

[0031] The specific acquisition method of the voltage fluctuation parameter is as follows:

[0032] The voltage data is calculated by mean value according to the number of voltage values in the voltage data to obtain the voltage mean value. The square of the difference between the voltage data and the voltage mean value is calculated to obtain the deviation value. The deviation degree of each deviation value and the voltage mean value is calculated, and then the sum is calculated to obtain the total fluctuation degree. The voltage fluctuation parameter is calculated by mean value according to the total fluctuation degree, which reflects the average fluctuation degree of the voltage data.

[0033] ;

[0034] Among them, represents the average fluctuation degree of the voltage data, represents the number of voltage values, and is not zero, represents the th voltage value.

[0035] The power-off trend factor is obtained by standardizing and comprehensively calculating the voltage disturbance factor through the power-off time-space prediction algorithm, which helps to eliminate the dimensional differences of the comprehensive calculation of the voltage disturbance factor, and convert the different orders of magnitude values after the comprehensive calculation of the voltage disturbance factor into a unified numerical range. The power-off trend threshold is set, and the power-off trend threshold is also standardized, so that the power-off trend factor and the power-off trend threshold are in the same numerical range. By comparing the power-off trend factor with the power-off trend threshold, if the power-off trend factor is greater than the power-off trend threshold, it indicates that the power-off trend is approaching. When the power-off trend is detected, the standby energy source is started, which reduces the premature start of the standby energy source and avoids the waste of energy that leads to increased carbon emissions, and also avoids the risk of power failure caused by the late start of the standby energy source. Therefore, step four is executed, and the power-off trend factor is adjusted through the prediction adjustment algorithm, that is, the voltage disturbance factor is re-computed through the adjustment factor to improve the accuracy of the subsequent power-off trend factor. If the power-off trend factor is less than or equal to the power-off trend threshold, it indicates that the power-off trend is not approaching, and returns to step one to re-detect.

[0036] The specific acquisition method of the power-off trend factor is as follows:

[0037] A coordinate system is established by the time series and the voltage disturbance factor to obtain the disturbance point. The horizontal coordinate of the disturbance point is the time series, and the vertical coordinate is the voltage disturbance factor. According to the time series, the slope of the next disturbance point and the previous disturbance point is calculated to obtain the power-off trend slope. The ratio number and the adjustment factor are set, and the power-off trend factor is obtained by comprehensively calculating the ratio number, the adjustment factor, the next power-off trend slope and the previous power-off trend slope;

[0038] ;

[0039] wherein, represents the power-off trend factor, represents the next power-off trend slope, represents the previous power-off trend slope, represents the ratio number, and the sum of the ratio of the next power-off trend slope to the previous power-off trend slope is neither increased in calculation complexity nor affects the accuracy of the power-off trend factor through experiments, represents an adjustment factor, used to accurately adjust the power-off trend factor, does not affect the positive and negative of the previous power-off trend slope, is in the same numerical range as other values, and avoids the case where the previous power-off trend slope is zero, causing the power-off trend factor to be meaningless. Due to the voltage fluctuation caused by the violent fluctuation of electromagnetic energy, the initial voltage fluctuation rises, the voltage disturbance factor is greater than zero, but the overall voltage is decreasing when there is a power-off trend, so the voltage disturbance factor decreases to less than zero and continues to gradually decrease, so the power-off trend slope is less than zero and continues to gradually decrease, resulting in the ratio of the next power-off trend slope to the previous power-off trend slope being greater than one.

[0040] The specific way to obtain the power-off trend slope is as follows:

[0041]

[0042] wherein, represents the power-off trend slope, represents the voltage disturbance factor of the next disturbance point, represents the voltage disturbance factor of the previous disturbance point, represents the time sequence of the next disturbance point, represents the time sequence of the previous disturbance point, each time of the time sequence is unique, so the difference between the time sequence of the next disturbance point and the time sequence of the previous disturbance point is not zero, and the time sequence of the next disturbance point and the time sequence of the previous disturbance point are both positive, while the voltage disturbance factor of the next disturbance point is less than the voltage disturbance factor of the previous disturbance point, making the power-off trend slope less than zero and continuously decreasing.

[0043] The specific way to obtain the power-off trend threshold is as follows:

[0044] According to the sum calculation of the ratio to one, the power-off trend threshold is obtained.

[0045] Step four: start the backup power source.

[0046] Example two, the difference between example one and example two is:

[0047] The specific steps of adjusting the power-off trend factor by the prediction adjustment algorithm are as follows:

[0048] ​The moment when the power-off trend factor is obtained is recorded as the starting moment, the standby energy is started at the starting moment, the moment when the voltage data disappears is recorded as the final moment, the time period of the starting moment and the final moment is recorded as the current adjustment time, the sample time is set, that is, the set interval after the adjustment time is statistically processed through historical experiments, and the current adjustment time is compared with the sample time. If the current adjustment time is greater than the upper limit of the sample time, that is, the moment when the power-off trend factor is obtained is too fast, which reflects that the power-off trend factor is too large, therefore, the adjustment factor is improved, so that the current adjustment time is reduced to the range of the sample time, further avoiding the inaccuracy of the power-off trend factor, which makes the standby energy start for a long time, increases energy waste and carbon emissions, and if the current adjustment time is less than the lower limit of the sample time, that is, the moment when the power-off trend factor is obtained is too slow, which reflects that the power-off trend factor is too small, therefore, the adjustment factor is reduced, so that the current adjustment time is increased to the range of the sample time, further avoiding the inaccuracy of the power-off trend factor, which causes the standby energy to miss the start and prevents the increase of power-off risk.

[0049] As shown in Figure 2 : a digital zero-carbon energy management system based on renewable energy coupling, comprising:

[0050] a voltage data acquisition module: starting to collect voltage data in real time, and calling a data preprocessing module;

[0051] a data preprocessing module: preprocessing the voltage data, and calling a central computing and processing module;

[0052] a central computing and processing module: analyzing the voltage data for disorder, if normal, ending the detection, if disorder, continuing to predict power-off, if not tending to power-off, returning to the voltage data acquisition module, if tending to power-off, calling a standby energy operation module, and adjusting the accuracy of subsequent power-off prediction;

[0053] a standby energy operation module: starting the standby energy.

[0054] Application simulation:

[0055] The power plant obtains a power-off trend factor by comprehensively processing the voltage data, and conducts three experiments according to the actual change trend of the power-off trend factor to verify the speed of the current adjustment time, and further verify whether to avoid starting the standby energy too early.

[0056] Table 1: Influence of a power-off trend factor on the size of the current adjustment time

[0057] Group De-energizing trend factor Current adjustment time (min) 1 2.83 54 2 3.56 113 3 6.67 192

[0058] As shown in Table 1, in Group 1, when the power-off trend factor is 2.83, the current adjustment time is 54 minutes, i.e. the standby energy source has been started for 54 minutes, in Group 2, when the power-off trend factor is 3.56, the current adjustment time is 113 minutes, i.e. the standby energy source has been started for 113 minutes, in Group 3, when the power-off trend factor is 6.67, the current adjustment time is 192 minutes, i.e. the standby energy source has been started for 192 minutes, therefore, with the gradual increase of the power-off trend factor, the current adjustment time is gradually larger, and the more the carbon emission of the standby energy source.

[0059] The preferred embodiments of the application disclosed above are only used to help explain the application. The preferred embodiments do not describe all the details and limit the application to the specific embodiments described. Obviously, according to the content of the specification, many modifications and changes can be made. The specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the application, so that those skilled in the art can well understand and utilize the application. The application is limited by the claims and their entire scope and equivalents.

Claims

1. A digital zero-carbon energy management method based on renewable energy coupling, characterized by: The specific steps include: Step 1: Start collecting voltage data in real time and proceed to step 2; Step 2: Preprocess the voltage data and execute step 3; Step 3: Perform comprehensive calculations on the voltage data to obtain a voltage disorder factor. Perform disorder analysis based on the voltage disorder factor and compare the voltage disorder factor with zero. If the voltage disorder factor is initially greater than zero, it indicates voltage disorder. If the voltage disorder factor is always less than or equal to zero, it indicates voltage normal. If the analysis shows normal, the detection is terminated. If the analysis shows disorder, a comprehensive calculation is performed based on the time series and the voltage disorder factor to obtain the power outage trend slope. The power outage trend factor is obtained by summing the ratio of the power outage trend slopes to reflect the acceleration of the power outage trend. A coordinate system is established through the time series and the voltage disorder factor to obtain the disorder point. The horizontal coordinate of the disorder point is the time series, and the vertical coordinate is the voltage disorder factor. The slope of the next disorder point and the previous disorder point is calculated based on the time series to obtain the power outage trend slope. The ratio number and the adjustment factor are set. The power outage trend factor is obtained by standardizing and comprehensively calculating the ratio number, the adjustment factor, the next power outage trend slope and the previous power outage trend slope. ;in, represents the power outage trend factor, Indicates the slope of the next power outage trend, Indicates the slope of the previous power outage trend, represents the ratio of times, Represents an adjustment factor; power outage prediction is performed based on the power outage trend factor, a power outage trend threshold is set, and the power outage trend factor is compared with the power outage trend threshold. If the power outage trend factor is greater than the power outage trend threshold, it indicates that there is a trend toward power outage; if the power outage trend factor is less than or equal to the power outage trend threshold, it indicates that there is no trend toward power outage. If it is predicted that there is no trend toward power outage, the process returns to step 1. If it is predicted that there is a trend toward power outage, the process executes step 4 and optimizes the accuracy of subsequent power outage predictions. Step 4: Start the backup energy source.

2. The digital zero-carbon energy management method based on renewable energy coupling according to claim 1 is characterized by: The specific method of obtaining the voltage disorder factor is as follows: The voltage data is comprehensively calculated to obtain the voltage fluctuation parameter, a voltage fluctuation threshold is set, and the voltage fluctuation parameter and the voltage fluctuation threshold are calculated to obtain the voltage disorder factor.

3. The digital zero-carbon energy management method based on renewable energy coupling according to claim 2 is characterized by: The specific method of obtaining the voltage fluctuation parameter is as follows: The voltage data is averaged according to the number of voltage values ​​in the voltage data to obtain the voltage mean. The deviation value is obtained by squaring the difference between the voltage data and the voltage mean. The degree of deviation of each deviation value from the voltage mean is summed up to obtain the total fluctuation degree. The voltage fluctuation parameter is obtained by performing mean calculation based on the total fluctuation degree.

4. The digital zero-carbon energy management method based on renewable energy coupling according to claim 1 is characterized by: The specific method for obtaining the power outage trend slope is as follows: The voltage disorder factor of the latter disorder point is calculated by difference with the voltage disorder factor of the former disorder point to obtain the voltage disorder factor deviation value, the time series of the latter disorder point is calculated by difference with the time series of the former disorder point to obtain the time series deviation value, and the voltage disorder factor deviation value and the time series deviation value are calculated by quotient to obtain the power outage trend slope.

5. The digital zero-carbon energy management method based on renewable energy coupling according to claim 1 is characterized by: The specific method for obtaining the power outage trend threshold is as follows: The power outage trend threshold is obtained by summing the ratio times to one.

6. The digital zero-carbon energy management method based on renewable energy coupling according to claim 1 is characterized by: The specific method for optimizing the accuracy of the subsequent power outage prediction is as follows: The moment when the power outage trend factor is obtained is recorded as the starting moment, the backup energy is started at the starting moment, the moment when the voltage data disappears is recorded as the final moment, the time period between the starting moment and the final moment is recorded as the current adjustment time, the sample time is set, and the current adjustment time is compared with the sample time. If the current adjustment time is greater than the upper limit of the sample time, the adjustment factor is increased to reduce the current adjustment time to within the range of the sample time. If the current adjustment time is less than the lower limit of the sample time, the adjustment factor is reduced to increase the current adjustment time to within the range of the sample time.

7. A digital zero-carbon energy management system based on renewable energy coupling, used to implement the digital zero-carbon energy management method based on renewable energy coupling according to any one of claims 1 to 6, characterized in that: The system includes: a voltage data acquisition module, a data pre-processing module, a central calculation and processing module, and a backup energy operation module; The voltage data acquisition module starts to collect voltage data in real time and calls the data preprocessing module; The data preprocessing module preprocesses the voltage data and calls the central calculation processing module; The central computing and processing module performs a disorder analysis on the voltage data. If the analysis shows that the voltage data is normal, the detection ends. If the analysis shows disorder, the power outage prediction continues. If the prediction shows that the power outage is not likely to occur, the module returns to the voltage data acquisition module. If the prediction shows that the power outage is likely to occur, the module calls the backup energy operation module and adjusts the accuracy of the subsequent power outage prediction. The backup energy operation module starts the backup energy.

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