Integrated Energy Security System Control Modules and Methods

Abstract

This invention relates to the field of security technology, specifically to a control module and method for a comprehensive energy security system. It includes an analysis and prediction unit, an evaluation and calculation unit, a variable consumption unit, and a control strategy unit. The demand prediction module of this invention receives user periodic electricity consumption, temperature deviation, and precipitation deviation from the analysis and analysis module. It calculates a baseline energy demand based on the user periodic electricity consumption, and then calculates the impact of temperature deviation on user energy demand under extreme weather conditions. By combining the baseline energy demand and the impact of extreme weather conditions, it predicts the user's energy demand under extreme weather conditions. This combination allows for more accurate prediction of energy demand under extreme weather conditions, enabling advance adjustments to power generation plans and power dispatch, avoiding energy waste caused by supply-demand imbalances or excessive microgrid generation, optimizing resource allocation, and improving resource utilization efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of security guarantee, in particular, to a comprehensive energy security guarantee system control module and method. BACKGROUND

[0002] The control module of comprehensive energy security guarantee gradually develops with the increase of energy system device complexity and multi-energy complementary demand, and its core is to realize the coordinated management and optimized scheduling of multiple energies (such as electricity, natural gas, heat 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 electricity supply is unstable. Therefore, the micro-grid device is combined with the comprehensive energy device to provide reliable electricity supply for remote areas. When some extreme weather occurs in remote areas, the electricity demand under the predicted extreme weather is inaccurate due to the influence of extreme weather factors, which leads to insufficient prediction accuracy, so that the power generation plan and power dispatch of the comprehensive energy device cannot be adjusted in advance, resulting in imbalance between supply and demand or electricity waste caused by excessive power generation of the micro-grid, reducing resource utilization efficiency. When the prediction accuracy is insufficient, the deviation of the predicted electricity demand under the extreme weather condition is found in time, which causes a large area power outage in remote areas, increases the loss and inconvenience caused by the large area power outage, and thus reduces the efficiency and reliability of electricity supply. Therefore, we provide a comprehensive energy security guarantee system control module and method. SUMMARY

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

[0004] To achieve the above purpose, one of the purposes of the present application is to provide a comprehensive energy security guarantee system control module, which comprises an analysis and prediction unit, an evaluation and calculation unit, a change consumption unit and a control strategy unit.

[0005] The analysis and prediction unit obtains meteorological data, running state and historical data of the micro-grid device in real time, analyzes the periodicity of user electricity behavior, calculates the benchmark electricity demand, and predicts the electricity demand of the user under extreme weather conditions.

[0006] The evaluation and calculation unit is used to receive the predicted electricity demand of the analysis and prediction unit and the obtained micro-grid device running state data to calculate the relative error of the electricity demand, evaluate the accuracy of the predicted electricity demand of the user under extreme weather conditions, and calculate the energy change of the micro-grid device under extreme weather conditions according to the obtained micro-grid device running state data.

[0007] The change consumption unit is configured to receive historical data in the analysis prediction unit and an amount of energy change generated by the micro-grid equipment in the evaluation calculation unit in the extreme weather duration, calculate an additional amount of energy consumption of the micro-grid equipment in the extreme weather, evaluate the accuracy of the additional amount of energy consumption, and determine whether there is a significant difference according to the evaluated accuracy of the additional amount of energy consumption, and when there is no significant difference, calculate a total demand amount of energy of the micro-grid equipment in the extreme weather.

[0008] The control strategy unit is configured to receive the total demand amount of energy of the micro-grid equipment in the extreme weather in the evaluation calculation unit to formulate a safety guarantee control strategy.

[0009] As a further improvement of the technical solution, the analysis prediction unit comprises an analysis rule module and a prediction demand module.

[0010] The analysis rule module is configured to acquire meteorological data of remote areas, operation state data and historical data of the micro-grid equipment in real time, analyze whether there is extreme weather in the remote areas according to the acquired meteorological data, record temperature, precipitation, temperature deviation, precipitation deviation and extreme weather duration when it is analyzed that there is extreme weather in the remote areas, collect time domain power consumption signals from the acquired operation state data of the micro-grid equipment, convert the time domain power consumption signals into power consumption frequency domain signals, analyze periodicity of user power consumption behavior according to the power consumption frequency domain signals, and record user periodic power consumption.

[0011] The prediction demand module is configured to receive the user periodic power consumption, the temperature deviation and the precipitation deviation in the analysis rule module, calculate a reference amount of energy demand according to the user periodic power consumption, calculate an influence amount of the user energy demand in the extreme weather according to the temperature deviation and the precipitation deviation, and predict an amount of energy demand of the user in the extreme weather by using the reference amount of energy demand and the influence amount of the user energy demand in the extreme weather.

[0012] As a further improvement of the technical solution, the evaluation calculation unit comprises an evaluation accuracy module and an efficiency judgment module.

[0013] The evaluation accuracy module is configured to receive the historical data in the analysis rule module and the predicted power energy demand in the prediction demand module, calculate the relative error of the power energy demand according to the historical data and the predicted power energy demand, evaluate the accuracy of the predicted power energy demand of the user in the extreme weather according to the relative error of the power energy demand by using the average absolute percentage error algorithm, and determine whether there is a significant difference by using the evaluated accuracy of the predicted power energy demand, when the evaluated accuracy of the predicted power energy demand is less than 10%, it is determined that there is no significant difference between the historical real power demand of the user in the extreme weather and the predicted power energy demand, and the evaluated accuracy of the predicted power energy demand is high.

[0014] The efficiency judgment module is configured to receive the command that the evaluated accuracy of the predicted power energy demand in the evaluation accuracy module is high, obtain the micro-grid equipment operation state data, temperature and precipitation from the analysis rule module, collect the micro-grid equipment output power from the obtained micro-grid equipment operation state data, calculate the micro-grid equipment operation efficiency according to the temperature and the precipitation, and determine whether the micro-grid equipment has a running efficiency decline by using the micro-grid equipment operation efficiency and the set micro-grid equipment operation efficiency threshold.

[0015] As a further improvement of the technical solution, the evaluation accuracy module uses the average absolute percentage error algorithm to evaluate the prediction accuracy, and the implementation steps are as follows:

[0016] Step 1, collect the relative error of the power energy demand and the number of users to calculate the sum of the relative error of the power energy demand;

[0017] Step 2, calculate the average relative error of the power energy demand according to the sum of the relative error of the power energy demand and the number of users;

[0018] Step 3, convert the average relative error of the power energy demand into a percentage form to obtain the evaluated accuracy of the predicted power energy demand.

[0019] As a further improvement of the technical solution, the evaluation calculation unit further comprises a data calculation module.

[0020] The data calculation module is configured to receive the command that the micro-grid equipment has a running efficiency decline in the efficiency judgment module, obtain the micro-grid equipment output power from the efficiency judgment module, obtain the historical data from the analysis rule module, and obtain the benchmark power energy demand from the prediction demand module, calculate the energy change amount generated by the micro-grid equipment in the extreme weather duration according to the micro-grid equipment output power, and calculate the total energy consumed by the protection measures in the extreme weather duration according to the historical data.

[0021] As a further improvement of the technical solution, the change consumption unit comprises a change amount module and an additional consumption module.

[0022] The change amount module is configured to receive historical data in the analysis rule module and benchmark power energy demand obtained in the predicted demand module, calculate total energy consumed by the monitoring device in the extreme weather duration according to the historical data, and calculate the change amount of the micro-grid device in the transmission or storage of energy according to the historical data and the benchmark power energy demand.

[0023] The additional consumption module is configured to receive the energy change amount of the micro-grid device in the extreme weather duration, the total energy consumed by the protection measure in the extreme weather duration, the total energy consumed by the monitoring device in the extreme weather duration, and the change amount of the micro-grid device in the transmission or storage of energy, to calculate the additional power energy consumption of the micro-grid device in the extreme weather.

[0024] As a further improvement of the technical solution, the evaluation accuracy module is configured to receive the additional power energy consumption of the micro-grid device in the extreme weather, the historical data in the analysis rule module, to evaluate the accuracy of the additional power energy consumption, and to determine whether there is a significant difference according to the evaluated accuracy of the additional power energy consumption, when it is determined that there is no significant difference, the evaluated accuracy of the additional power energy consumption is high, and the total demand of the power energy of the micro-grid device in the extreme weather is calculated according to the additional power energy consumption of the micro-grid device in the extreme weather and the predicted power energy demand.

[0025] As a further improvement of the technical solution, the control strategy unit is configured to receive the total demand of the power energy of the micro-grid device in the extreme weather in the evaluation accuracy module to develop a safety control strategy, and to flexibly adjust the total demand of the power energy of the micro-grid device in the extreme weather by the energy storage device according to the developed safety control strategy.

[0026] The second object of the present application is to provide a method for operating the comprehensive energy safety control module, comprising the following method steps:

[0027] S1, the analysis prediction unit obtains meteorological data of remote areas, operation state data and historical data of micro-grid devices in real time, records temperature and temperature deviation, analyzes the periodicity of user power consumption behavior according to the obtained operation state data of micro-grid devices, calculates the benchmark power energy demand, and predicts the power energy demand of users in extreme weather;

[0028] S2. Evaluate the relative error of the calculation unit in calculating the power demand, assess the accuracy of predicting the user's power demand under extreme weather conditions based on the relative error of the power demand, and then calculate the energy change generated by the microgrid equipment during the duration of extreme weather based on the obtained microgrid equipment operating status data.

[0029] S3. The variable consumption unit calculates the total energy consumed by the monitoring equipment during the duration of extreme weather based on historical data, then calculates the additional power consumption of the microgrid equipment under extreme weather and evaluates the accuracy of the calculated additional power consumption. Then, it uses the evaluated accuracy of the additional power consumption to determine whether there is a significant difference.

[0030] S4. The control strategy unit formulates a safety assurance control strategy based on the total power demand of the microgrid equipment under extreme weather conditions. The formulated safety assurance control strategy is used to flexibly adjust the total power demand of the microgrid equipment under extreme weather conditions through energy storage devices.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0032] 1. In the control module and method of this integrated energy security system, the demand prediction module receives user periodic electricity consumption, temperature deviation, and precipitation deviation from the pattern analysis module. Based on the user periodic electricity consumption, it calculates the baseline electricity demand. Then, based on the temperature deviation and precipitation deviation, it calculates the impact of extreme weather on user electricity demand. By combining the baseline electricity demand and the impact of extreme weather, it predicts the user's electricity demand under extreme weather conditions. By combining the baseline electricity demand and the impact of extreme weather, it can more accurately predict the electricity demand under extreme weather conditions, thereby adjusting the power generation plan and power dispatch in advance, avoiding energy waste caused by supply and demand imbalance or excessive power generation by microgrids, optimizing resource allocation, and improving resource utilization efficiency.

[0033] 2、The comprehensive energy security guarantee system control module and method, the evaluation precision module calculates the relative error of the electricity energy demand according to the historical data and the predicted electricity energy demand, evaluates the precision of predicting the electricity energy demand of the user in the extreme weather according to the relative error of the electricity energy demand by using the average absolute percentage error algorithm, and then judges whether there is a significant difference by using the evaluated precision of predicting the electricity energy demand, when the evaluated precision of predicting the electricity energy demand is less than 10%, it is determined that there is no significant difference between the historical true electricity demand of the user in the extreme weather and the predicted electricity energy demand, then the evaluated precision of predicting the electricity energy demand is high, through the precision evaluation of the prediction, the deviation of the predicted electricity energy demand can be found in time, the large-area power failure accident can be avoided, the normal operation of the electricity life of the user in the remote area is guaranteed, the loss and inconvenience caused by power failure are reduced, and the efficiency and reliability of the electricity energy supply are improved. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 The unit block diagram of the present application is shown in the figure.

[0035] Figure 2 The module unit block diagram of the present application is shown in the figure.

[0036] Figure 3 The overall step diagram of the present application is shown in the figure.

[0037] The meanings of various labels in the figure are as follows:

[0038] 1, analysis and prediction unit; 11, analysis rule module; 12, prediction demand module;

[0039] 2, evaluation and calculation unit; 21, evaluation precision module; 22, efficiency judgment module; 23, data calculation module;

[0040] 3, change consumption unit; 31, change amount module; 32, additional consumption module; 4, control strategy unit. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. 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.

[0042] Embodiment 1

[0043] Please refer to Figure 1As shown, one of the purposes of the present embodiment is to provide a comprehensive energy security guarantee system control module, which comprises an analysis and prediction unit 1, an evaluation and calculation unit 2, a change and consumption unit 3 and a control strategy unit 4;

[0044] The analysis and prediction unit 1 obtains meteorological data, operation state data and historical data of the micro-grid equipment in remote areas in real time, records temperature, temperature deviation and extreme weather duration, analyzes the periodicity of user electricity consumption behavior according to the obtained micro-grid equipment operation state data, records user periodic electricity consumption, calculates the benchmark electricity energy demand according to the user periodic electricity consumption, and further predicts the electricity energy demand of the user in extreme weather; the evaluation and calculation unit 2 is used to receive the historical data, the predicted electricity energy demand, the temperature and the obtained micro-grid equipment operation state data in the analysis and prediction unit 1, calculate the relative error of the electricity energy demand, evaluate the accuracy of the predicted electricity energy demand of the user in extreme weather by using the average absolute percentage error algorithm according to the relative error of the electricity energy demand, and calculate the energy change amount generated by the micro-grid equipment in the extreme weather duration according to the obtained micro-grid equipment operation state data; the change and 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 micro-grid equipment in the extreme weather duration in the evaluation and calculation unit 2, calculate the total energy consumed by the monitoring equipment in the extreme weather duration according to the historical data, and further calculate the additional electricity energy consumption of the micro-grid equipment in the extreme weather; the evaluation and calculation unit 2 is used to receive the additional electricity energy consumption of the micro-grid equipment in the extreme weather in the change and consumption unit 3 and the historical data in the analysis and prediction unit 1 to evaluate and calculate the accuracy of the additional electricity energy consumption, and further judge whether there is a significant difference by using the evaluated accuracy of the additional electricity energy consumption; the control strategy unit 4 is used to receive the total demand of the electricity energy of the micro-grid equipment in the extreme weather in the evaluation and calculation unit 2 to develop a security guarantee control strategy, and further flexibly adjust the total demand of the electricity energy of the micro-grid equipment in the extreme weather by using the developed security guarantee control strategy through the energy storage equipment.

[0045] The following is a refinement of the above-mentioned units, please refer to Figure 2 ;

[0046] The analysis and prediction unit 1 comprises an analysis rule module 11 and a prediction demand module 12;

[0047] Because some remote areas are usually difficult to access the main power grid, the power supply is unstable, and the micro-grid equipment and the integrated energy are combined to provide reliable power supply for remote areas. The analysis rule module 11 obtains the meteorological data of the remote area, the running state data and the historical data of the micro-grid equipment in real time, analyzes whether there is extreme weather in the remote area according to the obtained meteorological data, records the temperature T, the precipitation P, the wind speed W, the humidity L, the temperature deviation ΔT (the difference between the current temperature and the historical average temperature), the precipitation deviation ΔP (the difference between the current precipitation and the historical average precipitation), the wind speed deviation ΔW (the difference between the current wind speed and the historical average wind speed), the extreme weather duration T jdtq , the time domain power signal f(t) is collected from the obtained micro-grid equipment running state data, that is, a function that changes with time (the data of the user's power consumption changes with time), and the power signal frequency ω and the time variable t are recorded, the time domain power signal f(t) is converted into the power frequency domain signal F(ω) according to the time domain power signal f(t), the power frequency domain signal F(ω), and the time variable t, the periodicity of the user's power consumption behavior is analyzed according to the power frequency domain signal F(ω), and the user's periodic power consumption E and the number of cycles K are recorded.

[0048] The historical data includes historical integrated energy data (including the user's historical real power demand E actual under extreme weather, protective measure power P protec (representing the power consumed by the protective measure when the micro-grid equipment is running), monitoring operation power P mon (monitoring operation includes sensors, communication equipment, data processing equipment, and is used to monitor the power consumed by the micro-grid equipment running state in real time), energy consumption coefficient ε (referring to the loss of energy in the process of transmission, conversion or storage), and the historical additional power consumption A of the micro-grid equipment under extreme weather.

[0049] The realization principle of converting the time domain power signal into the power frequency domain signal is as follows:

[0050] The time domain power signal f(t), the power signal frequency ω and the time variable t are collected, and the time domain power signal f(t) is converted into the power frequency domain signal F(ω). The specific algorithm formula is as follows:

[0051]

[0052] Where, ∞ represents the upper and lower limits of integration are infinite, i refers to the imaginary unit, e -iωtIt is a complex exponential function used to decompose a time-domain signal into sine and cosine components of different frequencies. dt represents the integral over the time variable t, and is the infinitesimal element in the integral. This formula is used to convert the time-domain electricity consumption signal into the frequency-domain electricity consumption signal. In the time domain, the change in electricity consumption may be affected by a variety of factors and exhibit complex fluctuations. However, after converting to the frequency domain, some hidden periodic patterns may be found, which can improve the accuracy of load forecasting.

[0053] The demand forecasting module 12 receives user cycle electricity consumption E, cycle number K, temperature deviation ΔT, precipitation deviation ΔP, and wind speed deviation ΔW from the pattern analysis module 11. It inputs the user cycle electricity consumption E into a linear regression model. The linear regression model learns from the input data and outputs corresponding weight coefficients w. Based on the user cycle electricity consumption E, cycle number K, and weight coefficients w, it calculates the baseline energy demand E. base (This refers to the typical electricity demand of users under normal conditions (i.e., without extreme weather or other special factors). Then, based on the temperature deviation ΔT, precipitation deviation ΔP, and wind speed deviation ΔW, the impact ΔE on the user's electricity demand under extreme weather conditions is calculated. weather Then, based on the baseline electricity energy demand E base The impact of extreme weather on user electricity demand ΔE weather Predict the user's electricity demand under extreme weather conditions to derive the predicted electricity demand E. pred =E base +ΔE weather Record the number of users N;

[0054] The principle behind calculating baseline electricity demand:

[0055] Collect user's periodic electricity consumption E, number of periods K, and weighting coefficient w to calculate the baseline electricity energy demand E. base Specific algorithm formula:

[0056]

[0057] Among them, E k This refers to the electricity consumption in the k-th period of the analyzed periodic pattern of user electricity consumption behavior, w k This refers to the k-th weighting coefficient corresponding to the electricity consumption in the k-th period, w. k 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 a baseline energy demand, which helps to better address changes in energy demand.

[0058] The steps for calculating the impact of extreme weather on user electricity demand in the demand forecasting module 12 are as follows:

[0059] Step 1: Input the temperature deviation ΔT, precipitation deviation ΔP, and wind speed deviation ΔW into the linear regression model. The linear regression model learns from the input data and outputs the corresponding weight coefficients α, β, and γ.

[0060] Step 2: Calculate the impact ΔE of user electricity demand under extreme weather conditions using temperature deviation ΔT, precipitation deviation ΔP, wind speed deviation ΔW, and weighting coefficients α, β, and γ. weather Specific algorithm formula;

[0061] ΔE weather =ΔT·α+ΔP·β+ΔW·γ;

[0062] This formula is used to calculate the impact of users' electricity demand under extreme weather conditions. By taking into account the deviation and combining the weighting coefficient to calculate the impact, it can more accurately predict users' actual electricity demand during extreme weather and improve the accuracy of electricity supply forecasting.

[0063] The evaluation calculation unit 2 includes an evaluation accuracy module 21 and an efficiency judgment module 22;

[0064] The accuracy assessment module 21 is used to receive the historical real electricity demand E of users under extreme weather conditions from the pattern analysis module 11. actual And the predicted electricity energy demand E in the predicted demand module 12 pred The number of users, N, is based on the users' historical actual electricity demand, E, under extreme weather conditions. actual And the projected electricity energy demand E pred The absolute error in calculating electricity energy demand |E pred,j -E actual,j |, that is, the absolute deviation between the predicted value and the historical actual value, where E pred,j This refers to the predicted electricity demand of the j-th user, E. actual,j This refers to the historical actual electricity demand of the j-th user under extreme weather conditions. The formula represents the difference between the predicted and actual values, ignoring positive and negative directions, and is then calculated based on the absolute error of the electricity demand |E. pred,j -E actual,j | and the historical actual electricity demand E of the j-th user under extreme weather conditions actual,j The relative error in calculating electricity energy demand This formula is used to standardize the absolute error, facilitating comparisons between predicted and actual values ​​of different magnitudes. It utilizes the mean absolute percentage error algorithm based on the relative error of electricity demand. And the number of users N to evaluate the accuracy of predicting the user's electricity energy demand in extreme weather, and then use the evaluated accuracy of predicting the electricity energy demand MAPE to judge whether there is a significant difference, when the evaluated accuracy of predicting the electricity energy demand MAPE is less than 10% (referring to a widely recognized error threshold), it is determined that the user's historical true electricity demand E in extreme weather actual There is no significant difference between the predicted electricity energy demand, so the evaluated accuracy of predicting the electricity energy demand is high. Through the accurate evaluation of the prediction accuracy, the deviation of the predicted electricity energy demand can be found in time to avoid large-scale power outages, ensure the normal operation of the user's life in remote areas, reduce the loss and inconvenience caused by power outages, and improve the efficiency and reliability of electricity energy supply,

[0065] The implementation steps of evaluating the prediction accuracy in the evaluation accuracy module 21 using the mean absolute percentage error algorithm are as follows:

[0066] Step 1, collect the relative error of electricity energy demand And the number of users N to calculate the sum of the relative error of electricity energy demand This formula is used to calculate all the average relative error;

[0067] Step 2, according to the sum of the relative error of electricity energy demand And the number of users N to calculate the average relative error of electricity energy demand This formula calculates the average deviation of all user electricity energy demand;

[0068] Step 3, convert the average relative error of electricity energy demand Into a percentage form to obtain the evaluated accuracy of predicting the electricity energy demand MAPE, the specific algorithm formula is:

[0069]

[0070] The formula is used to evaluate the accuracy of predicting the user's electricity energy demand in extreme weather. Improving the prediction accuracy can reduce the risk of power outages caused by insufficient power supply, and provide more stable electricity energy supply services for users. In extreme weather, users can normally use electrical equipment, ensure the convenience and comfort of life, and reduce the loss and inconvenience caused by power outages.

[0071] The efficiency judgment module 22 is used to receive the command of the high accuracy of predicting the electricity energy demand evaluated in the evaluation accuracy module 21. The efficiency judgment module 22 obtains the micro-grid device operating state data, temperature T, precipitation P, wind speed W, and humidity L from the analysis rule module 11. From the obtained micro-grid device operating state data, the micro-grid device output power Pout Microgrid equipment input power P in (representing the power obtained by the device from external energy) and the rated power P of the microgrid device. rated Temperature T, precipitation P, wind speed W, and humidity L are input into a linear regression model. The linear regression model learns from the input data and outputs corresponding model coefficients χ1, χ2, χ3, and χ4. The microgrid equipment operating efficiency η is calculated based on temperature T, precipitation P, wind speed W, humidity L, and model coefficients χ1, χ2, χ3, and χ4. pred =χ1·T+χ2·P+χ3·W+χ4·L, utilizing the microgrid equipment operating efficiency η pred The system compares the microgrid equipment's operating efficiency with a predefined threshold to determine if there is a decline in the equipment's efficiency. When the microgrid equipment's operating efficiency η... pred When the operating efficiency of the microgrid equipment is less than the set threshold, it is determined that the operating efficiency of the microgrid equipment has decreased.

[0072] The evaluation calculation unit 2 also includes a data calculation module 23;

[0073] The data calculation module 23 receives a command from the efficiency judgment module 22 indicating that the microgrid equipment is experiencing a decline in operating efficiency. The data calculation module 23 then obtains the output power P of the microgrid equipment from the efficiency judgment module 22. out Microgrid equipment input power P in and the rated power P of microgrid equipment rated And the duration T of extreme weather in module 11 of the analysis pattern jdtq Protection measures power P protec Monitoring operating power P mon The energy consumption coefficient ε is used to obtain the baseline electricity energy demand E from the predicted demand module 12. base According to the output power P of the microgrid equipment out Microgrid equipment input power P in Rated power P of microgrid equipment rated and the duration of extreme weather T jdtq Calculate the energy changes generated by microgrid devices during the duration of extreme weather. Then, based on the protection measures power P protec and the duration of extreme weather T jdtq Calculate the total energy consumed by protective measures during the duration of extreme weather, ΔE2 = P protec ·T jdtq 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 the protective measures can operate normally during extreme weather, while avoiding the failure of protective measures due to insufficient energy reserves or the waste of energy due to excessive reserves.

[0074] The variable consumption unit 3 includes a variable amount module 31 and an additional consumption module 32;

[0075] The change module 31 is used to receive the duration T of extreme weather from the analysis pattern module 11. jdtq Monitoring operating power P mon The energy consumption coefficient ε and the baseline energy demand E are obtained from the predicted demand module 12. base According to the duration T of extreme weather jdtq and monitoring operating power P mon Calculate the total energy consumed by the monitoring equipment during the duration of extreme weather, ΔE3 = P mon ·T jdtq Then, based on the energy consumption coefficient ε and the baseline electricity demand E base Calculate the change in energy transmission or storage in microgrid devices: ΔE₄ = ε·E base By calculating energy changes, the power supply capacity of a microgrid under different conditions can be predicted, and corresponding measures can be taken to optimize the flexible scheduling strategy of microgrid equipment, ensuring that the microgrid can operate stably under various conditions and improving the stability of predicted power supply.

[0076] The additional consumption module 32 receives the energy change ΔE1 generated by the microgrid equipment during the extreme weather duration from the data calculation module 23, the total energy consumed by the protection measures during the extreme weather duration ΔE2, and the total energy consumed by the monitoring equipment during the extreme weather duration ΔE3 and the energy change ΔE4 of the microgrid equipment during energy transmission or storage during the extreme weather duration from the change module 31. Based on the energy change ΔE1 generated by the microgrid equipment during the extreme weather duration, the total energy consumed by the protection measures during the extreme weather duration ΔE2, the total energy consumed by the monitoring equipment during the extreme weather duration ΔE3, and the energy change of the microgrid equipment during energy transmission or storage during the extreme weather duration, the additional power consumption ΔE of the microgrid equipment under extreme weather conditions is calculated as ΔE = ΔE1 + ΔE2 + ΔE3 + ΔE4. By accurately calculating the additional power consumption, it is possible to assess 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 scheduled in a timely manner. It is also possible to optimize the operation strategy of the equipment according to the energy consumption situation, and reduce energy consumption while ensuring safe monitoring.

[0077] The evaluation accuracy module 21 is configured to receive the additional power consumption ΔE of the micro-grid equipment in the additional consumption module 32 under extreme weather, analyze the historical additional power consumption A of the micro-grid equipment under extreme weather in the analysis rule module 11, calculate the accuracy of the additional power consumption according to the additional power consumption ΔE of the micro-grid equipment under extreme weather and the historical additional power consumption A of the micro-grid equipment under extreme weather by using the mean absolute percentage error algorithm, and determine whether there is a significant difference by using the evaluation accuracy MAPE of the additional power consumption. When the evaluation accuracy MAPE of the additional power consumption is less than 10% (which refers to a widely recognized error threshold), it is determined that there is no significant difference between the additional power consumption of the micro-grid equipment under extreme weather and the historical additional power consumption of the micro-grid equipment under extreme weather, and the evaluation accuracy of the additional power consumption is high. Then, the predicted power demand E of the micro-grid equipment under extreme weather is calculated according to the additional power consumption ΔE of the micro-grid equipment under extreme weather and the predicted power demand E of the micro-grid equipment under extreme weather pred The total power demand S of the micro-grid equipment under extreme weather is calculated as ΔE+E pred After calculating the total demand of the micro-grid equipment under extreme weather, the power of the standby generator or the capacity of the energy storage battery can be reasonably increased to avoid power interruption caused by insufficient equipment capacity, and to prevent resource waste caused by excessive configuration.

[0078] The implementation principle of the evaluation accuracy by using the mean absolute percentage error algorithm is as follows:

[0079] The evaluation accuracy of the additional power consumption is calculated by collecting the additional power consumption ΔE of the micro-grid equipment under extreme weather and the historical additional power consumption A of the micro-grid equipment under extreme weather, and the evaluation accuracy MAPE of the additional power consumption is obtained. The specific algorithm formula is as follows:

[0080]

[0081] Wherein, n refers to the number of data points, A m refers to the mth historical additional power consumption of the micro-grid equipment under extreme weather. This formula is used to evaluate the accuracy of the additional power consumption. By accurately evaluating the accuracy of the additional power consumption, the micro-grid manager can reasonably allocate energy resources according to the actual demand, improve the prediction accuracy, and arrange the power generation plan of the power generation equipment and the charging and discharging strategy of the energy storage equipment in advance according to the prediction result, so as to avoid energy waste or insufficient supply.

[0082] The control strategy unit 4 is used for receiving the total demand S of the power energy of the micro-grid equipment under the extreme weather in the evaluation accuracy module 21, formulating the safety guarantee control strategy according to the total demand S of the power energy of the micro-grid equipment under the extreme weather, flexibly adjusting the total demand of the power energy of the micro-grid equipment under the extreme weather by the energy storage equipment by using the formulated safety guarantee control strategy, avoiding frequent start and stop of the micro-grid equipment due to the change of the demand of the power energy, the frequent start and stop can cause mechanical wear and electrical impact to the equipment, shorten the service life of the equipment, increase the probability of fault occurrence, through the flexible adjustment, the equipment is operated in the relatively stable condition, the possibility of equipment damage is reduced, and the reliability of the whole micro-grid system is improved.

[0083] Please refer to Figure 3 The second object of the present application is to provide a method for operating the comprehensive energy safety guarantee system control module, which comprises the following method steps:

[0084] S1, the analysis prediction unit 1 obtains the meteorological data of remote areas, the running state data and historical data of the micro-grid equipment in real time, records the temperature and temperature deviation, analyzes the periodicity of the user's power consumption behavior according to the obtained micro-grid equipment running state data, calculates the benchmark power energy demand, and predicts the power energy demand of the user under the extreme weather;

[0085] S2, the evaluation calculation unit 2 calculates the relative error of the power energy demand, evaluates the accuracy of predicting the power energy demand of the user under the extreme weather according to the relative error of the power energy demand, and calculates the energy change amount generated by the micro-grid equipment in the extreme weather duration according to the obtained micro-grid equipment running state data;

[0086] S3, the change consumption unit 3 calculates the total energy consumed by the monitoring equipment in the extreme weather duration according to the historical data, calculates the additional power energy consumption of the micro-grid equipment under the extreme weather, evaluates the accuracy of the additional power energy consumption, and judges whether there is a significant difference by using the evaluated accuracy of the additional power energy consumption;

[0087] S4, the control strategy unit 4 formulates the safety guarantee control strategy according to the total demand of the power energy of the micro-grid equipment under the extreme weather, flexibly adjusts the total demand of the power energy of the micro-grid equipment under the extreme weather by using the formulated safety guarantee control strategy.

[0088] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Various changes and improvements can be made to the present application without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A control module for an integrated energy security assurance system, characterized by: It comprises 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 meteorological data, operation state data of micro-grid equipment and historical data in real time, analyzes periodicity of user electricity consumption behavior, calculates benchmark electricity energy demand, and predicts electricity energy demand of the user in extreme weather; The analysis and prediction unit (1) comprises an analysis rule module (11) and a prediction demand module (12); The analysis rule module (11) obtains meteorological data, operation state data of micro-grid equipment and historical data in real time, analyzes whether there is extreme weather in remote areas according to the obtained meteorological data, records temperature, precipitation, temperature deviation, precipitation deviation and extreme weather duration when it is analyzed that there is extreme weather in remote areas, collects time domain electricity consumption signals from the obtained operation state data of micro-grid equipment, converts the time domain electricity consumption signals into electricity consumption frequency domain signals, analyzes periodicity of user electricity consumption behavior according to the electricity consumption frequency domain signals, and records user periodic electricity consumption; The prediction demand module (12) is used for receiving user periodic electricity consumption, temperature deviation and precipitation deviation in the analysis rule module (11), calculating benchmark electricity energy demand according to the user periodic electricity consumption, calculating influence of electricity energy demand of the user in extreme weather according to the temperature deviation and the precipitation deviation, and predicting electricity energy demand of the user in extreme weather by using the benchmark electricity energy demand and the influence of electricity energy demand of the user in extreme weather; The evaluation and calculation unit (2) is used for receiving the predicted electricity energy demand in the analysis and prediction unit (1) and the obtained operation state data of micro-grid equipment to calculate relative error of the electricity energy demand, evaluating accuracy of predicting electricity energy demand of the user in extreme weather, and calculating energy change amount of the micro-grid equipment in extreme weather duration according to the obtained operation state data of micro-grid equipment; The evaluation and calculation unit (2) comprises an evaluation accuracy module (21); The evaluation accuracy module (21) is used for receiving historical data in the analysis rule module (11) and predicted electricity energy demand in the prediction demand module (12), calculating relative error of the electricity energy demand according to the historical data and the predicted electricity energy demand, evaluating accuracy of predicting electricity energy demand of the user in extreme weather by using the average absolute percentage error algorithm according to the relative error of the electricity energy demand, and judging whether there is significant difference by using the evaluated accuracy of predicting electricity energy demand, when the evaluated accuracy of predicting electricity energy demand is less than 10%, it is determined that there is no significant difference between historical true electricity consumption demand of the user in extreme weather and the predicted electricity energy demand, and the evaluated accuracy of predicting electricity energy demand is high. The change consumption unit (3) is configured to receive the historical data in the analysis prediction unit (1) and the energy change amount generated by the micro-grid equipment in the extreme weather duration in the evaluation calculation unit (2), calculate the additional energy consumption amount of the micro-grid equipment in the extreme weather, evaluate the accuracy of the additional energy consumption amount, and determine whether there is a significant difference according to the evaluated accuracy of the additional energy consumption amount, and when there is no significant difference, calculate the total demand amount of the energy of the micro-grid equipment in the extreme weather. The control strategy unit (4) is configured to receive the total demand amount of the energy of the micro-grid equipment in the extreme weather in the evaluation calculation unit (2) to formulate a safety guarantee control strategy.

2. The integrated energy security assurance system control module of claim 1, wherein: The evaluation calculation unit (2) comprises an efficiency judgment module (22). The efficiency judgment module (22) is configured to receive a command of the high evaluation accuracy of the predicted energy demand amount in the evaluation accuracy module (21), acquire the micro-grid equipment operating state data, temperature and precipitation from the analysis rule module (11), collect the micro-grid equipment output power from the acquired micro-grid equipment operating state data, calculate the micro-grid equipment operating efficiency according to the temperature and the precipitation, and judge whether the micro-grid equipment has a running efficiency decline condition by using the micro-grid equipment operating efficiency and the set micro-grid equipment operating efficiency threshold.

3. The integrated energy security assurance system control module of claim 2, wherein: The implementation steps of evaluating the prediction accuracy by using the average absolute percentage error algorithm in the evaluation accuracy module (21) are as follows: Step 1, collect the relative errors of the energy demand amount and the number of users to calculate the sum of the relative errors of the energy demand amount; Step 2, calculate the average relative error of the energy demand amount according to the sum of the relative errors of the energy demand amount and the number of users; Step 3, convert the average relative error of the energy demand amount into a percentage form to obtain the evaluation accuracy of the predicted energy demand amount.

4. The integrated energy security assurance system control module of claim 3, wherein: The evaluation calculation unit (2) further comprises a data calculation module (23). The data calculation module (23) is configured to receive a command of the micro-grid equipment having a running efficiency decline condition in the efficiency judgment module (22), acquire the micro-grid equipment output power from the efficiency judgment module (22), acquire the historical data from the analysis rule module (11), and acquire the benchmark energy demand from the prediction demand module (12), calculate the energy change amount generated by the micro-grid equipment in the extreme weather duration according to the micro-grid equipment output power, and calculate the total energy consumed by the protection measures in the extreme weather duration according to the historical data.

5. The integrated energy security assurance system control module of claim 1, wherein: The change consumption unit (3) comprises a change amount module (31) and an additional consumption module (32). The change amount module (31) is configured to receive the historical data in the analysis rule module (11) and the benchmark energy demand in the prediction demand module (12), calculate the total energy consumed by the monitoring equipment in the extreme weather duration according to the historical data, and calculate the change amount of the micro-grid equipment when transmitting or storing energy according to the historical data and the benchmark energy demand. The additional consumption module (32) is used to receive the energy change amount of the micro-grid equipment in the extreme weather duration, the total energy consumed by the protection measures in the extreme weather duration, and the total energy consumed by the monitoring equipment in the extreme weather duration in the change amount module (31), and to calculate the additional energy consumption amount of the micro-grid equipment in the extreme weather.

6. The integrated energy security assurance system control module of claim 1, wherein: The evaluation accuracy module (21) is used to receive the additional energy consumption amount of the micro-grid equipment in the extreme weather in the additional consumption module (32), to analyze the historical data in the analysis rule module (11) to evaluate the accuracy of the additional energy consumption amount, and to determine whether there is a significant difference by using the evaluated accuracy of the additional energy consumption amount. When it is determined that there is no significant difference, the evaluated accuracy of the additional energy consumption amount is high, and the total demand amount of the energy of the micro-grid equipment in the extreme weather is calculated according to the additional energy consumption amount of the micro-grid equipment in the extreme weather and the predicted energy demand amount.

7. The integrated energy security assurance system control module of claim 6, wherein: The control strategy unit (4) is used to receive the total demand amount of the energy of the micro-grid equipment in the extreme weather in the evaluation accuracy module (21) to develop a safety protection control strategy, and to flexibly adjust the total demand amount of the energy of the micro-grid equipment in the extreme weather by the energy storage equipment by using the developed safety protection control strategy.

8. A method for operating an integrated energy security assurance system control module comprising any of the claims 1-7, characterized by: The method comprises the following steps: S1, the prediction unit (1) analyzes the meteorological data of the remote area, the running state data and the historical data of the micro-grid equipment in real time, records the temperature and the temperature deviation, analyzes the periodicity of the user's electricity consumption behavior according to the obtained micro-grid equipment running state data, calculates the benchmark energy demand, and predicts the energy demand amount of the user in the extreme weather; S2, the evaluation calculation unit (2) calculates the relative error of the energy demand amount, evaluates the accuracy of predicting the energy demand amount of the user in the extreme weather according to the relative error of the energy demand amount, and calculates the energy change amount of the micro-grid equipment in the extreme weather duration according to the obtained micro-grid equipment running state data; S3, the change consumption unit (3) calculates the total energy consumed by the monitoring equipment in the extreme weather duration according to the historical data, calculates the additional energy consumption amount of the micro-grid equipment in the extreme weather, and evaluates the accuracy of the additional energy consumption amount. Then, whether there is a significant difference is determined by using the evaluated accuracy of the additional energy consumption amount; S4, the control strategy unit (4) develops a safety protection control strategy according to the total demand amount of the energy of the micro-grid equipment in the extreme weather, and flexibly adjusts the total demand amount of the energy of the micro-grid equipment in the extreme weather by the energy storage equipment by using the developed safety protection control strategy.

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