Integrated energy safety guarantee system control module and method

Abstract

The invention relates to the technical field of safety guarantee, in particular to a comprehensive energy safety guarantee system control module and method. The system comprises an analysis prediction unit, an evaluation calculation unit, a change consumption unit and a control strategy unit. The demand prediction module is used for receiving the user cycle electricity consumption, the temperature deviation and the precipitation deviation in the rule analysis module, calculating the reference electricity energy demand according to the user cycle electricity consumption, and then calculating the influence quantity of the user electricity energy demand in the extreme weather according to the temperature deviation. The electric quantity energy demand quantity of the user in the extreme weather is predicted by using the reference electric quantity energy demand and the influence quantity of the electric quantity energy demand of the user in the extreme weather, and the electric quantity energy demand in the extreme weather can be predicted more accurately by combining the reference electric quantity energy demand and the influence quantity of the extreme weather. Therefore, the power generation plan and power dispatching are adjusted in advance, energy waste caused by supply-demand imbalance or excessive power generation of the microgrid is avoided, resource configuration is optimized, and the resource utilization efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of security technology, and in particular to a control module and method for an integrated energy security system. Background Art

[0002] The control module of comprehensive energy security has gradually developed with the increase of complexity of energy system equipment and the demand for multi-energy complementarity. Its core lies in the coordinated management and optimized scheduling of multiple energy sources (such as electricity, natural gas, thermal energy, and renewable energy) through advanced control strategies and technical means. Since some remote areas are usually difficult to access the main power grid, the power energy supply is unstable. Therefore, reliable power energy supply is provided to remote areas through microgrid equipment combined with comprehensive energy equipment. When some extreme weather occurs in remote areas, due to the influence of extreme weather factors, the prediction of power consumption and energy demand under extreme weather is inaccurate, resulting in insufficient prediction accuracy, and thus the power generation plan and power scheduling of comprehensive energy equipment cannot be adjusted in advance, resulting in power waste caused by imbalance of supply and demand or excessive power generation of microgrids, and reducing resource utilization efficiency. When the prediction accuracy is insufficient, it is impossible to timely discover the deviation of the predicted power energy demand under extreme weather conditions, resulting in large-scale power outages in remote areas, increasing the losses and inconvenience caused by large-scale power outages, and thus reducing the efficiency and reliability of power energy supply. Therefore, we provide a control module and method for a comprehensive energy security system. Summary of the invention

[0003] The purpose of the present invention is to provide a comprehensive energy security system control module and method to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned object, one of the objects of the present invention is to provide a comprehensive energy security system control module, including an analysis and prediction unit, an evaluation and calculation unit, a change consumption unit and a control strategy unit; The analysis and prediction unit obtains the partial weather, the operating status of the microgrid equipment and historical data in real time to analyze the periodic law of the user's electricity consumption behavior, and then calculates the benchmark power energy demand, and predicts the user's power energy demand under extreme weather conditions; The evaluation and calculation unit is used to receive the power energy demand predicted in the analysis and prediction unit and the acquired microgrid equipment operating status data to calculate the relative error of the power energy demand, and evaluate the accuracy of predicting the user's power energy demand under extreme weather conditions, and then calculate the energy change generated by the microgrid equipment during the duration of the extreme weather according to the acquired microgrid equipment operating status data; The change consumption unit is used to receive the historical data in the analysis and prediction unit and the energy change amount generated by the microgrid equipment in the evaluation and calculation unit during the duration of extreme weather, calculate the additional power energy consumption of the microgrid equipment under extreme weather and evaluate the accuracy of the additional power energy consumption, and then use the evaluated accuracy of the additional power energy consumption to determine whether there is a significant difference, and when there is no significant difference, the total power energy demand of the microgrid equipment under extreme weather; The control strategy unit is used to receive the total demand for electricity and energy of the microgrid equipment in the evaluation calculation unit under extreme weather conditions to formulate a safety assurance control strategy.

[0005] As a further improvement of the technical solution, the analysis and prediction unit includes an analysis rule module and a demand prediction module; The analysis rule module acquires meteorological data, operating status data and historical data of the remote area in real time, analyzes whether there is extreme weather in the remote area based on the acquired meteorological data, and when it is analyzed that there is extreme weather in the remote area, records the temperature, precipitation, temperature deviation, precipitation deviation, and duration of extreme weather, collects time domain power consumption signals from the acquired microgrid equipment operating status data, converts the time domain power consumption signals into power frequency domain signals, and then analyzes the periodic law of the user's power consumption behavior based on the power frequency domain signals, and records the user's periodic power consumption; The demand prediction module is used to receive the user's periodic electricity consumption, temperature deviation, and precipitation deviation in the analysis rule module, calculate the baseline electricity energy demand based on the user's periodic electricity consumption, and then calculate the impact of the user's electricity energy demand under extreme weather conditions based on the temperature deviation and precipitation deviation, and use the baseline electricity energy demand and the impact of the user's electricity energy demand under extreme weather conditions to predict the user's electricity energy demand under extreme weather conditions.

[0006] As a further improvement of the technical solution, the evaluation calculation unit includes an evaluation accuracy module and an efficiency judgment module; The evaluation accuracy module is used to receive the historical data in the analysis rule module and the predicted electricity energy demand in the prediction demand module, calculate the relative error of the electricity energy demand according to the historical data and the predicted electricity energy demand, and use the mean absolute percentage error algorithm to evaluate the accuracy of predicting the user's electricity energy demand under extreme weather conditions according to the relative error of the electricity energy demand, and then use the evaluated accuracy of the predicted electricity energy demand to determine whether there is a significant difference. When the accuracy of the evaluated predicted electricity energy demand is less than 10%, it is determined that there is no significant difference between the user's historical actual electricity demand and the predicted electricity energy demand under extreme weather conditions, and the evaluated predicted electricity energy demand has a high accuracy. The efficiency judgment module is used to receive a command with high accuracy of the predicted power energy demand evaluated in the evaluation accuracy module. The efficiency judgment module obtains the microgrid equipment operating status data, temperature, and precipitation in the analysis rule module, collects the output power of the microgrid equipment from the acquired microgrid equipment operating status data, calculates the operating efficiency of the microgrid equipment according to the temperature and precipitation, and uses the microgrid equipment operating efficiency and the set microgrid equipment operating efficiency threshold to make a judgment to determine whether the microgrid equipment has a decline in operating efficiency.

[0007] As a further improvement of the technical solution, the implementation steps of evaluating the prediction accuracy using the mean absolute percentage error algorithm in the evaluation accuracy module are as follows: Step ①, collect the relative error of the power energy demand and the number of users to calculate the sum of the relative errors of the power energy demand; Step ②, calculate the average relative error of the power energy demand according to the sum of the relative errors of the power energy demand and the number of users; Step ③: Convert the average relative error of the power energy demand into a percentage to obtain the accuracy of the estimated power energy demand.

[0008] As a further improvement of the technical solution, the evaluation calculation unit also includes a data calculation module; The data calculation module is used to receive a command from the efficiency judgment module to determine that the operating efficiency of the microgrid equipment has decreased. The data calculation module obtains the output power of the microgrid equipment from the efficiency judgment module and the historical data from the law analysis module and the benchmark power energy demand from the demand prediction module. The energy change generated by the microgrid equipment during the duration of extreme weather is calculated based on the output power of the microgrid equipment, and the total energy consumed by the protection measures during the duration of extreme weather is calculated based on the historical data.

[0009] As a further improvement of the technical solution, the variable consumption unit includes a variable amount module and an additional consumption module; The variation module is used to receive the historical data in the analysis rule module and the benchmark power energy demand obtained in the forecast demand module, calculate the total energy consumed by the monitoring equipment during the duration of the extreme weather according to the historical data, and then calculate the variation of the microgrid equipment when transmitting or storing energy according to the historical data and the benchmark power energy demand; The additional consumption module is used to receive the energy change generated by the microgrid equipment during the duration of extreme weather in the data calculation module, the total energy consumed by the protection measures during the duration of extreme weather, and the total energy consumed by the monitoring equipment in the change module during the duration of extreme weather, and the change in the microgrid equipment when transmitting or storing energy to calculate the additional energy consumption of the microgrid equipment under extreme weather.

[0010] As a further improvement of the present technical solution, the evaluation accuracy module is used to receive the additional power and energy consumption of the microgrid equipment under extreme weather in the additional consumption module and the historical data in the analysis rule module to evaluate the accuracy of calculating the additional power and energy consumption, and then use the accuracy of the evaluated additional power and energy consumption to determine whether there is a significant difference. When it is determined that there is no significant difference, the accuracy of the evaluated additional power and energy consumption is high, and then the total power and energy demand of the microgrid equipment under extreme weather is calculated based on the additional power and energy consumption of the microgrid equipment under extreme weather and the predicted power and energy demand.

[0011] As a further improvement of the present technical solution, the control strategy unit is used to receive the total demand for electricity and energy of the microgrid equipment in extreme weather in the evaluation accuracy module to formulate a safety control strategy, and use the formulated safety control strategy to flexibly adjust the total demand for electricity and energy of the microgrid equipment in extreme weather through energy storage equipment.

[0012] A second object of the present invention is to provide a method for operating a control module of an integrated energy security system including any one of the above, comprising the following method steps: S1. The analysis and prediction unit obtains meteorological data of remote areas, operating status data and historical data of microgrid equipment in real time, and records temperature and temperature deviation. It analyzes the periodic law of user electricity consumption behavior based on the acquired microgrid equipment operating status data, calculates the baseline power energy demand, and predicts the power energy demand of users under extreme weather conditions; S2. The evaluation calculation unit calculates the relative error of the power energy demand, evaluates the accuracy of predicting the user's power energy demand under extreme weather conditions based on the relative error of the power energy demand, and then calculates the energy change generated by the microgrid equipment during the duration of the extreme weather conditions based on the acquired microgrid equipment operating status data; S3. The change consumption unit calculates the total energy consumed by the monitoring equipment during the duration of the extreme weather according to the historical data, calculates the additional power energy consumption of the microgrid equipment under the extreme weather and evaluates the accuracy of the additional power energy consumption, and then uses the accuracy of the evaluated additional power energy consumption to determine whether there is a significant difference; S4. The control strategy unit formulates a safety control strategy based on the total demand for electricity and energy of the microgrid equipment under extreme weather conditions, and uses the formulated safety control strategy to flexibly adjust the total demand for electricity and energy of the microgrid equipment under extreme weather conditions through energy storage devices.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. In the control module and method of the comprehensive energy security system, the demand prediction module is used to receive the user's periodic electricity consumption, temperature deviation, and precipitation deviation in the analysis rule module, calculate the benchmark electricity energy demand based on the user's periodic electricity consumption, and then calculate the impact of the user's electricity energy demand under extreme weather conditions based on the temperature deviation and precipitation deviation. The benchmark electricity energy demand and the impact of the user's electricity energy demand under extreme weather conditions are used to predict the user's electricity energy demand under extreme weather conditions. By combining the benchmark electricity demand and the impact of extreme weather conditions, the electricity energy demand under extreme weather conditions can be more accurately predicted, thereby adjusting the power generation plan and power dispatching in advance, avoiding energy waste caused by supply and demand imbalance or excessive power generation of microgrids, optimizing resource allocation, and improving resource utilization efficiency.

[0014] 2. In the control module and method of the comprehensive energy security system, the evaluation accuracy module calculates the relative error of the electricity energy demand based on the historical data and the predicted electricity energy demand, and uses the mean absolute percentage error algorithm to evaluate the accuracy of predicting the user's electricity energy demand under extreme weather conditions based on the relative error of the electricity energy demand. The evaluated accuracy of the predicted electricity energy demand is then used to determine whether there is a significant difference. When the accuracy of the evaluated predicted electricity energy demand is less than 10%, it is determined that there is no significant difference between the user's historical actual electricity demand and the predicted electricity energy demand under extreme weather conditions, and the evaluated predicted electricity energy demand has a high accuracy. By accurately evaluating the accuracy of the prediction, the deviation of the predicted electricity energy demand can be discovered in a timely manner, large-scale power outages can be avoided, the normal operation of users' electricity life in remote areas can be guaranteed, the losses and inconveniences caused by power outages can be reduced, and the efficiency and reliability of electricity energy supply can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A unit block diagram of the present invention; Figure 2 It is a block diagram of a module unit of the present invention; Figure 3 It is an overall step diagram of the present invention.

[0016] The meaning of each number in the figure is: 1. Analysis and prediction unit; 11. Analysis rule module; 12. Demand prediction module; 2. Evaluation calculation unit; 21. Evaluation accuracy module; 22. Efficiency judgment module; 23. Data calculation module; 3. Variable consumption unit; 31. Variable quantity module; 32. Additional consumption module; 4. Control strategy unit. DETAILED DESCRIPTION

[0017] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example 1 See also Figure 1 As shown, one of the purposes of this embodiment is to provide a comprehensive energy security system control module, including an analysis and prediction unit 1, an evaluation and calculation unit 2, a change consumption unit 3 and a control strategy unit 4; The analysis and prediction unit 1 obtains the meteorological data of the remote area, the operating status data and historical data of the microgrid equipment in real time, and records the temperature, temperature deviation, and duration of extreme weather. According to the obtained microgrid equipment operating status data, the periodic law of the user's electricity consumption behavior is analyzed, and the user's periodic electricity consumption is recorded. According to the user's periodic electricity consumption, the benchmark electricity energy demand is calculated, and then the user's electricity energy demand under extreme weather conditions is predicted; the evaluation and calculation unit 2 is used to receive the historical data, the predicted electricity energy demand, the temperature and the obtained microgrid equipment operating status data in the analysis and prediction unit 1, calculate the relative error of the electricity energy demand, and use the mean absolute percentage error algorithm to evaluate the accuracy of predicting the user's electricity energy demand under extreme weather conditions according to the relative error of the electricity energy demand, and calculate the energy change generated by the microgrid equipment during the duration of the extreme weather according to the obtained microgrid equipment operating status data; the change consumption The consumption unit 3 is used to receive the historical data in the analysis and prediction unit 1 and the energy change generated by the microgrid equipment in the evaluation and calculation unit 2 during the duration of extreme weather, calculate the total energy consumed by the monitoring equipment during the duration of extreme weather according to the historical data, and then calculate the additional power energy consumption of the microgrid equipment under extreme weather; the evaluation and calculation unit 2 is used to receive the additional power energy consumption of the microgrid equipment under extreme weather in the change consumption unit 3 and the historical data in the analysis and prediction unit 1 to evaluate the accuracy of the calculation of the additional power energy consumption, and then use the evaluated accuracy of the additional power energy consumption to determine whether there is a significant difference; the control strategy unit 4 is used to receive the total power energy demand of the microgrid equipment under extreme weather in the evaluation and calculation unit 2 to formulate a safety control strategy, and use the formulated safety control strategy to flexibly adjust the total power energy demand of the microgrid equipment under extreme weather through the energy storage device.

[0019] The following is a refinement of the above units, see Figure 2 ; The analysis and prediction unit 1 includes a rule analysis module 11 and a demand prediction module 12; Since some remote areas are usually difficult to access the main power grid, the power supply is unstable. Through the combination of microgrid equipment and comprehensive energy, reliable power supply is provided to remote areas. The analysis module 11 obtains the meteorological data of the remote areas, the operating status data and historical data of the microgrid equipment in real time, and analyzes whether there is extreme weather in the remote areas based on the acquired meteorological data. When it is analyzed that there is extreme weather in the remote areas, the temperature is recorded. , precipitation , wind speed ,humidity , Temperature deviation (the difference between the current temperature and the historical average temperature), precipitation deviation (the difference between current precipitation and historical average precipitation), wind speed deviation (the difference between current wind speed and historical average wind speed), duration of extreme weather , collect time domain power consumption signals from the acquired microgrid equipment operating status data , that is, a function that changes over time (data on the user's power consumption over time), and records the power consumption signal frequency and time variables , according to the time domain power consumption signal , power signal frequency and time variables Time domain power consumption signal Converted into power frequency domain signal , and then according to the power frequency domain signal Analyze the periodic patterns of users' electricity consumption behavior and record users' periodic electricity consumption and number of cycles ; Historical data includes historical comprehensive energy data (including historical actual electricity demand of users under extreme weather conditions) ), Protection measures power (indicates the power consumed by the protection measures when the microgrid equipment is running), monitors the operating power (Monitoring operations include sensors, communication equipment, and data processing equipment, which are used to monitor the power consumed by the operating status of microgrid equipment in real time), energy consumption coefficient (refers to the loss of energy during transmission, conversion or storage), the historical additional power energy consumption of microgrid equipment under extreme weather conditions ; The implementation principle of converting time domain power consumption signal into power consumption frequency domain signal: Collecting time domain power consumption signals , power signal frequency and time variables Time domain power consumption signal Converted into power frequency domain signal , the specific algorithm formula is: ; in, Indicates that the upper and lower limits of the integral are infinite, refers to the imaginary unit, is a complex exponential function used to decompose a time domain signal into sine and cosine components of different frequencies. Represents the time variable Integration is the differential element in integration. This formula is used to convert the time domain power consumption signal into the power consumption frequency domain signal. In the time domain, the change of power consumption may be affected by many factors and present complex fluctuations. After conversion to the frequency domain, some hidden periodic laws may be found, which improves the accuracy of load forecasting. The demand prediction module 12 is used to receive the user's periodic power consumption in the rule analysis module 11 , Number of cycles , Temperature deviation , precipitation deviation , Wind speed deviation , the user's cycle power consumption Input into the linear regression model, the linear regression model learns the input data and outputs the corresponding weight coefficient , based on the user's cycle power consumption , Number of cycles and weight coefficient Calculate Baseline Energy Requirements (refers to the typical electricity demand of users under normal conditions (i.e. without the influence of extreme weather or other special factors)), and then according to the temperature deviation , precipitation deviation , Wind speed deviation Calculate the impact of extreme weather on user electricity and energy demand , and then according to the benchmark power energy demand and the impact of extreme weather on user electricity and energy demand Predict the user's electricity and energy demand under extreme weather conditions and obtain the predicted electricity and energy demand , record the number of users ; Principle of calculating the benchmark power energy demand: Collect user cycle power consumption , Number of cycles and weight coefficient Calculate Baseline Energy Requirements , the specific algorithm formula is: ; in, It refers to the power consumption of the user in the kth cycle of the periodic law of the user's power consumption behavior under analysis. Refers to the kth weight coefficient corresponding to the power consumption in the kth cycle, It is usually a value between 0 and 1, and the sum of all weights is usually 1, that is, , this formula is used to calculate the baseline electricity energy demand. Calculating the baseline demand helps to better cope with changes in electricity energy demand.

[0020] The steps for calculating the impact of user power energy demand under extreme weather conditions in the demand forecasting module 12 are as follows: Step 1: Set the temperature deviation , precipitation deviation , Wind speed deviation Input the linear regression model, the linear regression model learns and outputs the corresponding weight coefficient according to the input data , and ; Step 2: Use temperature deviation , precipitation deviation , Wind speed deviation and weight coefficient , , Calculate the impact of extreme weather on user electricity and energy demand , specific algorithm formula; ; Among them, this formula is used to calculate the impact of users' electricity energy demand under extreme weather conditions. By considering the deviation and combining the weight coefficient to calculate the impact, it can more accurately predict the actual electricity demand of users during extreme weather conditions and improve the accuracy of predicting electricity supply; The evaluation calculation unit 2 includes an evaluation accuracy module 21 and an efficiency judgment module 22; The evaluation accuracy module 21 is used to receive the historical real electricity demand of the user under extreme weather conditions in the analysis rule module 11. and the power energy demand predicted in the prediction demand module 12 , Number of users , based on the actual historical electricity demand of users under extreme weather conditions and predicted electricity and energy demand Absolute error in calculating power energy demand , that is, the absolute deviation between the predicted value and the historical actual value, where Refers to the predicted The energy demand of each user, Refers to the The actual historical electricity demand of each user under extreme weather conditions. This formula represents the difference between the predicted value and the actual value, ignoring the positive and negative directions, and then based on the absolute error of the power energy demand and The actual historical electricity demand of each user under extreme weather conditions Relative error in calculating power and energy demand This formula is used to standardize the absolute error, which is convenient for comparing the predicted values ​​and actual values ​​of different magnitudes. The mean absolute percentage error algorithm is used to calculate the relative error of the power energy demand. and number of users Evaluate the accuracy of predicting user power and energy demand under extreme weather conditions, and then use the estimated accuracy of the predicted power and energy demand Determine whether there is a significant difference when evaluating the accuracy of the predicted power and energy demand When the error is less than 10% (a widely recognized error threshold), the actual historical electricity demand of users under extreme weather conditions is determined. If there is no significant difference with the predicted electricity and energy demand, the estimated predicted electricity and energy demand has high accuracy. By accurately evaluating the accuracy of the prediction, the deviation of the predicted electricity and energy demand can be discovered in time, large-scale power outages can be avoided, and the normal operation of electricity life of users in remote areas can be guaranteed, reducing the losses and inconveniences caused by power outages, and improving the efficiency and reliability of electricity and energy supply. The implementation steps of evaluating the prediction accuracy using the mean absolute percentage error algorithm in the evaluation accuracy module 21 are as follows: Step 1: Collect the relative error of power energy demand and number of users To calculate the relative error of the energy demand , which is used to calculate all the average relative errors; Step ②: Based on the sum of the relative errors of the power energy demand With the number of users To calculate the average relative error of the power energy demand ,This formula calculates the average deviation degree of all users’ electricity energy demand; Step 3: The average relative error of the power energy demand Convert to percentage to estimate the accuracy of the predicted power and energy demand , the specific algorithm formula is: ; Among them, this formula is used to evaluate the accuracy of predicting users' electricity and energy demand under extreme weather conditions. Improving the prediction accuracy can reduce the risk of power outages caused by insufficient power supply and provide users with more stable electricity and energy supply services. In extreme weather conditions, users can use electrical equipment normally, ensure the convenience and comfort of life, and reduce the losses and inconveniences caused by power outages.

[0021] The efficiency judgment module 22 is used to receive the command of the high accuracy of the predicted power energy demand evaluated by the evaluation accuracy module 21, and the efficiency judgment module 22 sends the microgrid equipment operating status data and temperature obtained by the analysis rule module 11 to the microgrid equipment operating status data and temperature , precipitation , wind speed ,humidity , collect the output power of microgrid equipment from the acquired microgrid equipment operating status data , Microgrid equipment input power (indicates the power obtained by the device from external energy) and the rated power of the microgrid equipment , the temperature , precipitation , wind speed ,humidity Input the linear regression model, the linear regression model learns according to the input data and outputs the corresponding model coefficients , , , , according to the temperature , precipitation , wind speed ,humidity and model coefficients , , , Calculate the operating efficiency of microgrid equipment , using the operating efficiency of microgrid equipment The microgrid equipment operating efficiency threshold is compared to the set threshold to determine whether the microgrid equipment has a decline in operating efficiency. When it is less than the set microgrid equipment operating efficiency threshold, it is determined that the microgrid equipment has a reduced operating efficiency; The evaluation calculation unit 2 also includes a data calculation module 23; The data calculation module 23 is used to receive the command from the efficiency judgment module 22 to determine that the microgrid equipment has a reduced operating efficiency. The data calculation module 23 obtains the output power of the microgrid equipment from the efficiency judgment module 22. , Microgrid equipment input power and the rated power of the microgrid equipment and the duration of extreme weather in Module 11 of the analysis rules , Protection measures power , monitor operating power , Energy consumption coefficient , obtain the benchmark power energy demand from the forecast demand module 12 , according to the output power of microgrid equipment , Microgrid equipment input power , Rated power of microgrid equipment and duration of extreme weather Calculate the change in energy generated by microgrid equipment over the duration of extreme weather events , and then according to the protection measures power and duration of extreme weather Calculate the total energy consumed by conservation measures over the duration of the extreme weather event By accurately calculating the total energy consumption of protective measures during extreme weather, sufficient energy can be planned and reserved in advance to ensure that protective measures can operate normally during extreme weather, while avoiding the failure of protective measures due to insufficient energy reserves, or energy waste due to excessive reserves.

[0022] The variation consumption unit 3 includes a variation amount module 31 and an additional consumption module 32; The variation module 31 is used to receive the extreme weather duration in the analysis rule module 11. , monitor operating power , Energy consumption coefficient , obtain the benchmark power energy demand in the forecast demand module 12 , depending on the duration of extreme weather and monitor operating power Calculate the total energy consumed by monitoring equipment during the duration of extreme weather , and then according to the energy consumption coefficient and baseline power energy demand Calculate the amount of change in microgrid equipment when transmitting or storing energy By calculating the energy change, the power and energy supply capacity of the microgrid under different circumstances can be predicted, and corresponding measures can be taken to optimize the flexible scheduling strategy of the microgrid equipment to ensure that the microgrid can operate stably under various circumstances, thus improving the stability of the predicted power and energy supply; The additional consumption module 32 is used to receive the energy change amount generated by the microgrid equipment in the data calculation module 23 during the duration of the extreme weather. , the total energy consumed by protective measures during the duration of extreme weather The total energy consumed by the monitoring equipment in the change module 31 during the duration of the extreme weather , the amount of change in microgrid equipment when transmitting or storing energy , based on the change in energy generated by microgrid equipment during the duration of extreme weather , the total energy consumed by protective measures during the duration of extreme weather , monitor the total energy consumed by equipment during the duration of extreme weather , the change in the amount of energy transmitted or stored by the microgrid equipment, and the calculation of the additional energy consumption of the microgrid equipment under extreme weather conditions By accurately calculating the additional power and energy consumption, it is possible to evaluate whether the configuration of the existing microgrid equipment can meet the needs under extreme weather conditions. When the calculation results show that the existing microgrid equipment cannot meet the needs, the microgrid equipment can be flexibly dispatched in a timely manner. The operation strategy of the equipment can also be optimized according to the energy consumption situation to reduce energy consumption under the premise of ensuring safety monitoring.

[0023] The evaluation accuracy module 21 is used to receive the additional power energy consumption of the microgrid equipment in the additional consumption module 32 under extreme weather conditions. , Analysis of the historical additional power consumption of microgrid equipment in module 11 under extreme weather conditions , using the mean absolute percentage error algorithm to calculate the additional power consumption of microgrid equipment under extreme weather conditions The historical additional power consumption of microgrid equipment in extreme weather conditions Evaluate the accuracy of calculating the additional power and energy consumption, and then use the estimated accuracy of the additional power and energy consumption Determine whether there is a significant difference when evaluating the accuracy of the additional energy consumption When the error is less than 10% (referring to a widely recognized error threshold), it is determined that there is no significant difference between the additional power and energy consumption of microgrid equipment under extreme weather conditions and the historical additional power and energy consumption of microgrid equipment under extreme weather conditions. The estimated additional power and energy consumption is highly accurate. and predicted electricity and energy demand Calculate the total energy demand of microgrid equipment in extreme weather conditions , by calculating the total demand for microgrid equipment under extreme weather conditions, the power of backup generators or the capacity of energy storage batteries can be reasonably increased to avoid power outages due to insufficient equipment capacity, while also preventing waste of resources due to over-configuration; The principle of using the mean absolute percentage error algorithm to evaluate accuracy is as follows: Collect additional power and energy consumption of microgrid equipment during extreme weather conditions The historical additional power consumption of microgrid equipment in extreme weather conditions Evaluate the accuracy of calculating the additional energy consumption and obtain the accuracy of the estimated additional energy consumption , the specific algorithm formula is: ; in, refers to the number of data points, Refers to the microgrid equipment under extreme weather conditions. The formula is used to evaluate the accuracy of calculating the additional energy consumption. By accurately evaluating the accuracy of the additional energy consumption, it helps microgrid managers to reasonably allocate energy resources according to actual needs. Improve the accuracy of prediction, and arrange the power generation plan of power generation equipment and the charging and discharging strategy of energy storage equipment in advance according to the prediction results to avoid energy waste or insufficient supply; The control strategy unit 4 is used to receive the total demand for power and energy of the microgrid equipment in the evaluation accuracy module 21 under extreme weather conditions. , based on the total energy demand of microgrid equipment in extreme weather conditions Formulate a safety control strategy, and use the formulated safety control strategy to flexibly adjust the total demand for electricity and energy of microgrid equipment in extreme weather through energy storage devices, so as to avoid frequent start and stop of microgrid equipment due to changes in electricity and energy demand. Frequent start and stop will cause mechanical wear and electrical shock to the equipment, shorten the service life of the equipment, and increase the probability of failure. Through flexible adjustment, the equipment can operate in a relatively stable state, reduce the possibility of equipment damage, and improve the reliability of the entire microgrid system.

[0024] See also Figure 3 As shown, the second object of the present invention is to provide a method for operating the above-mentioned integrated energy security system control module, comprising the following method steps: S1, the analysis and prediction unit 1 obtains the meteorological data of the remote area, the operating status data and historical data of the microgrid equipment in real time, and records the temperature and temperature deviation, analyzes the periodic law of the user's electricity consumption behavior according to the obtained microgrid equipment operating status data, calculates the benchmark power energy demand, and predicts the user's power energy demand under extreme weather conditions; S2, the evaluation calculation unit 2 calculates the relative error of the power energy demand, evaluates the accuracy of predicting the user's power energy demand under extreme weather conditions based on the relative error of the power energy demand, and then calculates the energy change generated by the microgrid equipment during the duration of the extreme weather conditions based on the acquired microgrid equipment operating status data; S3, the change consumption unit 3 calculates the total energy consumed by the monitoring equipment during the duration of the extreme weather according to the historical data, then calculates the additional power energy consumption of the microgrid equipment under the extreme weather and evaluates the accuracy of the additional power energy consumption, and then uses the accuracy of the evaluated additional power energy consumption to determine whether there is a significant difference; S4, the control strategy unit 4 formulates a safety control strategy according to the total demand for electricity and energy of the microgrid equipment under extreme weather conditions, and uses the formulated safety control strategy to flexibly adjust the total demand for electricity and energy of the microgrid equipment under extreme weather conditions through energy storage devices.

[0025] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. Comprehensive energy security system control module, characterized by: It includes an analysis and prediction unit (1), an evaluation and calculation unit (2), a change consumption unit (3) and a control strategy unit (4); The analysis and prediction unit (1) acquires the partial weather, the operating status of the microgrid equipment and historical data in real time to analyze the periodic law of the user's electricity consumption behavior, and then calculates the baseline power energy demand to predict the user's power energy demand under extreme weather conditions; The evaluation and calculation unit (2) is used to receive the power energy demand predicted by the analysis and prediction unit (1) and the acquired microgrid device operating status data to calculate the relative error of the power energy demand, and evaluate the accuracy of predicting the user's power energy demand under extreme weather conditions, and then calculate the energy change generated by the microgrid device during the duration of the extreme weather according to the acquired microgrid device operating status data; The change consumption unit (3) is used to receive the historical data in the analysis and prediction unit (1) and the energy change amount generated by the microgrid equipment in the evaluation and calculation unit (2) during the duration of extreme weather, calculate the additional power energy consumption of the microgrid equipment under extreme weather and evaluate the accuracy of the additional power energy consumption, and then use the evaluated accuracy of the additional power energy consumption to determine whether there is a significant difference. When there is no significant difference, the total power energy demand of the microgrid equipment under extreme weather is calculated; The control strategy unit (4) is used to receive the total demand for electricity and energy of the microgrid equipment in the evaluation calculation unit (2) under extreme weather conditions to formulate a safety assurance control strategy.

2. The integrated energy security system control module according to claim 1, characterized in that: The analysis and prediction unit (1) comprises an analysis rule module (11) and a demand prediction module (12); The analysis rule module (11) acquires meteorological data, operating status data and historical data of the remote area in real time, analyzes whether there is extreme weather in the remote area based on the acquired meteorological data, and when it is analyzed that there is extreme weather in the remote area, records the temperature, precipitation, temperature deviation, precipitation deviation, and duration of extreme weather, collects time domain power consumption signals from the acquired microgrid equipment operating status data, converts the time domain power consumption signals into power consumption frequency domain signals, and then analyzes the periodicity of the user's power consumption behavior based on the power consumption frequency domain signals, and records the user's periodic power consumption; The demand prediction module (12) is used to receive the user's periodic electricity consumption, temperature deviation, and precipitation deviation in the analysis rule module (11), calculate the baseline electricity energy demand based on the user's periodic electricity consumption, and then calculate the impact of the user's electricity energy demand under extreme weather conditions based on the temperature deviation and precipitation deviation, and use the baseline electricity energy demand and the impact of the user's electricity energy demand under extreme weather conditions to predict the user's electricity energy demand under extreme weather conditions.

3. The integrated energy security system control module according to claim 2, characterized in that: The evaluation calculation unit (2) comprises an evaluation accuracy module (21) and an efficiency judgment module (22); The evaluation accuracy module (21) is used to receive the historical data in the analysis rule module (11) and the power energy demand predicted in the prediction demand module (12), calculate the relative error of the power energy demand based on the historical data and the predicted power energy demand, use the mean absolute percentage error algorithm to evaluate the accuracy of predicting the user's power energy demand under extreme weather conditions based on the relative error of the power energy demand, and then use the evaluated predicted power energy demand accuracy to determine whether there is a significant difference. When the evaluated predicted power energy demand accuracy is less than 10%, it is determined that there is no significant difference between the user's historical actual power demand under extreme weather conditions and the predicted power energy demand, and the evaluated predicted power energy demand accuracy is high; The efficiency judgment module (22) is used to receive a command with high accuracy of the predicted power energy demand evaluated in the evaluation accuracy module (21). The efficiency judgment module (22) obtains the microgrid device operating status data, temperature, and precipitation in the analysis rule module (11), collects the output power of the microgrid device from the obtained microgrid device operating status data, calculates the microgrid device operating efficiency based on the temperature and precipitation, and uses the microgrid device operating efficiency and the set microgrid device operating efficiency threshold to judge whether the microgrid device has a reduced operating efficiency.

4. The integrated energy security system control module according to claim 3 is characterized in that: The steps for implementing the method of evaluating the prediction accuracy using the mean absolute percentage error algorithm in the evaluation accuracy module (21) are as follows: Step ①, collect the relative error of the power energy demand and the number of users to calculate the sum of the relative errors of the power energy demand; Step ②, calculate the average relative error of the power energy demand according to the sum of the relative errors of the power energy demand and the number of users; Step ③: Convert the average relative error of the power energy demand into a percentage to obtain the accuracy of the estimated power energy demand.

5. The integrated energy security system control module according to claim 4, characterized in that: The evaluation calculation unit (2) further comprises a data calculation module (23); The data calculation module (23) is used to receive a command from the efficiency judgment module (22) to determine that the microgrid device has a reduced operating efficiency. The data calculation module (23) obtains the output power of the microgrid device from the efficiency judgment module (22) and the historical data from the analysis rule module (11), and obtains the benchmark power energy demand from the demand prediction module (12), calculates the energy change generated by the microgrid device during the duration of extreme weather based on the output power of the microgrid device, and then calculates the total energy consumed by the protection measures during the duration of extreme weather based on the historical data.

6. The integrated energy security system control module according to claim 2, characterized in that: The variable consumption unit (3) comprises a variable amount module (31) and an additional consumption module (32); The variation module (31) is used to receive the historical data in the analysis rule module (11) and the benchmark power energy demand obtained in the prediction demand module (12), calculate the total energy consumed by the monitoring equipment during the duration of the extreme weather according to the historical data, and then calculate the variation of the microgrid equipment when transmitting or storing energy according to the historical data and the benchmark power energy demand; The additional consumption module (32) is used to receive the energy change generated by the microgrid equipment during the duration of extreme weather in the data calculation module (23), the total energy consumed by the protection measures during the duration of extreme weather, and the total energy consumed by the monitoring equipment during the duration of extreme weather in the change module (31), and the change in the microgrid equipment when transmitting or storing energy to calculate the additional energy consumption of the microgrid equipment under extreme weather.

7. The integrated energy security system control module according to claim 3 is characterized in that: The evaluation accuracy module (21) is used to receive the additional power and energy consumption of the microgrid equipment under extreme weather conditions in the additional consumption module (32) and the historical data in the analysis rule module (11) to evaluate the accuracy of calculating the additional power and energy consumption, and then use the evaluated additional power and energy consumption accuracy to determine whether there is a significant difference. When it is determined that there is no significant difference, the evaluated additional power and energy consumption has a high accuracy, and then calculate the total power and energy demand of the microgrid equipment under extreme weather conditions based on the additional power and energy consumption of the microgrid equipment under extreme weather conditions and the predicted power and energy demand.

8. The integrated energy security system control module according to claim 7, characterized in that: The control strategy unit (4) is used to receive the total demand for electricity and energy of the microgrid equipment under extreme weather conditions in the evaluation accuracy module (21) to formulate a safety assurance control strategy, and to flexibly adjust the total demand for electricity and energy of the microgrid equipment under extreme weather conditions through the energy storage device using the formulated safety assurance control strategy.

9. A method for operating a control module of an integrated energy security system comprising any one of claims 1 to 8, characterized in that: The method comprises the following steps: S1, analysis and prediction unit (1) obtains meteorological data of remote areas, operating status data and historical data of microgrid equipment in real time, and records temperature and temperature deviation, analyzes the periodic law of user electricity consumption behavior based on the obtained microgrid equipment operating status data, calculates the baseline power energy demand, and predicts the power energy demand of users under extreme weather conditions; S2, the evaluation calculation unit (2) calculates the relative error of the power energy demand, evaluates the accuracy of predicting the user's power energy demand under extreme weather conditions based on the relative error of the power energy demand, and then calculates the energy change generated by the microgrid device during the duration of the extreme weather conditions based on the acquired microgrid device operating status data; S3, the change consumption unit (3) calculates the total energy consumed by the monitoring equipment during the duration of the extreme weather according to the historical data, calculates the additional power energy consumption of the microgrid equipment under the extreme weather and evaluates the accuracy of the additional power energy consumption, and then uses the evaluated accuracy of the additional power energy consumption to determine whether there is a significant difference; S4. The control strategy unit (4) formulates a safety control strategy according to the total demand for electricity and energy of the microgrid equipment under extreme weather conditions, and uses the formulated safety control strategy to flexibly adjust the total demand for electricity and energy of the microgrid equipment under extreme weather conditions through the energy storage device.

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