Method and system for interactive regulation and control of load and power distribution network in multiple scenes

Abstract

The invention discloses a method and a system for interactive regulation and control of a load and a power distribution network under multiple scenes, relates to the field of electric power, and solves the problem of hysteresis of an existing power distribution network interactive regulation and control method. S2, obtaining a target time period temperature and humidity coefficient, a plurality of time period temperature and humidity coefficients and a plurality of load regulation and control indexes, obtaining power distribution regulation and control analysis data, obtaining a plurality of power utilization matching units matched for the target power utilization unit according to an analysis result, and obtaining power utilization scene matching data; and S3, carrying out linear fitting on the temperature and humidity coefficients of the plurality of time periods and the load regulation and control indexes of the plurality of time periods to obtain a load fitting prediction model, obtaining a load regulation and control index of the target power consumption unit in the power consumption regulation and control period according to the fitting prediction model, and carrying out power regulation and control on the target power consumption unit according to the target load regulation and control index. The regulation and control accuracy of the power distribution network interactive regulation and control method can be improved.

Description

Technical Field

[0001] The present invention belongs to the electric power field and relates to data fitting technology, specifically a method and system for interactively controlling loads and distribution networks in multiple scenarios. Background Art

[0002] The existing power load control method has the following specific defects when controlling power consumption units:

[0003] (1) Existing power load control methods usually rely on obtaining real-time power data to control power consumption units, which can easily lead to lags in the control process;

[0004] (2) When regulating power for power users, existing power load regulation methods usually use the historical power data of a single power user to predict power data for future time periods. When a power user adds new power equipment, the prediction results based on historical power data may lack accuracy, making it difficult to ensure the effectiveness of interactive regulation of the distribution network.

[0005] Therefore, a method and system for interactive control of load and distribution network in multiple scenarios are proposed. Summary of the invention

[0006] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method and system for interactive regulation of loads and distribution networks in multiple scenarios. The present invention aims to improve the accuracy of power regulation in the power load regulation method.

[0007] In order to achieve the above object, the present invention adopts the following technical solution: a method for interactive control of load and distribution network in multiple scenarios, specifically comprising the following steps:

[0008] Step S1: Perform a multi-scenario power consumption analysis on the target power consumption unit, obtain a multi-scenario power consumption coefficient according to the analysis result, match multiple power consumption matching units for the target power consumption unit according to the multi-scenario power consumption coefficient, and obtain power consumption scenario matching data;

[0009] Step S2: Obtain a power consumption control period, perform a time period temperature and humidity analysis on the target power consumption unit in the power consumption control period, obtain the temperature and humidity coefficient of the target time period, select multiple historical power consumption periods with the same power consumption scene as the power consumption control period in the historical power consumption period, obtain the time period temperature and humidity coefficient and load control index of each power consumption matching unit in the historical power consumption period according to the power consumption scene matching data, and obtain the distribution control analysis data;

[0010] Step S3: Perform linear fitting on the temperature and humidity coefficients of multiple time periods and the load control indexes of multiple time periods according to the distribution control analysis data to obtain a load fitting prediction model, obtain the load control index of the target power consumer in the power control period according to the fitting prediction model, obtain the target load control index, and perform power control on the target power consumer according to the target load control index.

[0011] Furthermore, the step S1 further specifically includes the following steps:

[0012] Step S11: selecting a target power consumption unit from a plurality of power consumption control units belonging to the power distribution network;

[0013] Step S12: In the power consumption period of the target power user, the statutory working days are marked as the first power consumption scenario period, the weekend period other than the statutory holidays is marked as the second power consumption scenario period, and the statutory holidays are marked as the third power consumption scenario period;

[0014] Step S13: Analyze the historical power consumption data of the target power user in the first power consumption scenario period to obtain the first scenario load matching interval;

[0015] Step S14: performing power load analysis on the target power consuming units in the second power consumption scenario period and the third power consumption scenario period respectively to obtain the second scenario load matching interval and the third scenario load matching interval.

[0016] Step S15: Select several sample power consumption units in the power distribution network, obtain the average power load, average base load and peak load of each sample power consumption unit in the first power consumption scenario period, and average the average power load, average base load and peak load of the period to obtain multiple first scenario load matching coefficients;

[0017] Step S16: Obtain the average power load, average base load and average peak load of each sample power user unit in the second power usage scenario period, and average the average power load, average base load and average peak load of the period to obtain multiple second scenario load matching coefficients;

[0018] Step S17: obtaining the average power load, average base load and average peak load of each sample power user in the third power usage scenario period, and averaging the average power load, average base load and average peak load of each sample power user to obtain multiple third scenario load matching coefficients;

[0019] Step S18: if the first scenario load matching coefficient corresponding to the sample power unit is in the first scenario load matching interval, the second scenario load matching coefficient is in the second scenario load matching interval, and the third scenario load matching coefficient is in the third scenario load matching interval, then the corresponding sample power unit is marked as a power matching unit to obtain multiple power matching units;

[0020] Step S19: Name the multiple power usage matching units obtained as P1 power usage matching unit to Py power usage matching unit to obtain power usage scenario matching data.

[0021] Furthermore, the step S13 further specifically includes the following steps:

[0022] Step S131: selecting a number of scene sample dates from the natural date to which the first power consumption scene period belongs, and marking the selected scene sample dates as C1 scene sample date to Ca scene sample date;

[0023] Step S132: Obtain the daily average load of the first scenario and the daily average load deviation of the first scenario, obtain the daily average base load of the first scenario and the daily average base load deviation of the first scenario, obtain the daily average peak load of the first scenario and the daily average peak load deviation of the first scenario;

[0024] Step S133: Calculate the first scenario daily average load, the first scenario daily average load deviation, the first scenario daily average base load, the first scenario daily average base load deviation, the first scenario daily average peak load and the first scenario daily average peak load deviation to obtain a first load interval reference value;

[0025] The first load interval reference value is calculated, and the specific formula is as follows:

[0026]

[0027] Among them, Fjz1 is the first load interval reference value, Fj1 is the first scenario daily average load, Fp1 is the first scenario daily average load deviation, Jj1 is the first scenario daily average base load, Jp1 is the first scenario daily average base load deviation, Zj1 is the first scenario daily average peak load, and Zp1 is the first scenario daily average peak load deviation;

[0028] Step S134: Calculate the first scenario daily average load, the first scenario daily average load deviation, the first scenario daily average base load, the first scenario daily average base load deviation, the first scenario daily average peak load and the first scenario daily average peak load deviation to obtain a second load interval reference value;

[0029] The second load interval reference value is calculated using the following formula:

[0030]

[0031] Among them, Fjz2 is the second load interval reference value, Fj1 is the daily average load of the first scenario, Fp1 is the daily average load deviation of the first scenario, Jj1 is the daily average base load of the first scenario, Jp1 is the daily average base load deviation of the first scenario, Zj1 is the daily average peak load of the first scenario, and Zp1 is the daily average peak load deviation of the first scenario;

[0032] Step S135: Taking the first load interval reference value as the left endpoint of the interval and the second load interval reference value as the right endpoint of the interval, a first scenario load matching interval is obtained.

[0033] Furthermore, the step S132 further specifically includes the following steps:

[0034] Obtain the historical electricity consumption records of the target electricity user, obtain the daily average electricity load corresponding to the sample date of the C1 scenario to the sample date of the Ca scenario according to the historical electricity consumption records, obtain the daily average electricity load of C1 to the daily average electricity load of Ca, calculate the average of the daily average electricity load of C1 to the daily average electricity load of Ca, and obtain the daily average load of the first scenario;

[0035] Calculate the difference between the daily average power load of C1 and the daily average power load of C2, and take the absolute value of the difference to obtain the first daily average power load deviation. Calculate the difference between the daily average power load of C2 and the daily average power load of C3, and take the absolute value of the difference to obtain the second daily average power load deviation. Similarly, calculate the difference between the daily average power load of Ca-1 and the daily average power load of Ca, and take the absolute value of the difference to obtain the a-1th daily average power load deviation. Calculate the average value of the first daily average power load deviation to the a-1th daily average power load deviation to obtain the first scenario daily average load deviation.

[0036] According to the historical electricity consumption records, the daily minimum electricity load corresponding to the sample date of the C1 scenario to the sample date of the Ca scenario is obtained, and the daily electricity base load of the C1 scenario to the daily electricity base load of the Ca scenario is calculated, and the average value of the daily electricity base load of the C1 scenario to the daily electricity base load of the Ca scenario is calculated to obtain the daily average base load of the first scenario;

[0037] Calculate the difference between the daily electricity base load of the C1 scenario and the daily electricity base load of the C2 scenario, and take the absolute value of the obtained difference to obtain the base load deviation of the first scenario; calculate the difference between the daily electricity base load of the C2 scenario and the daily electricity base load of the C3 scenario, and take the absolute value of the obtained difference to obtain the base load deviation of the second scenario; and so on, calculate the difference between the daily electricity base load of the Ca-1 scenario and the daily electricity base load of the Ca scenario, and take the absolute value of the obtained difference to obtain the base load deviation of the a-1th scenario; calculate the average value of the base load deviation from the first scenario to the a-1th scenario, and obtain the daily average base load deviation of the first scenario;

[0038] According to the historical electricity consumption records, the daily maximum electricity load corresponding to the sample date of the C1 scenario to the sample date of the Ca scenario is obtained, and the daily peak electricity load of the C1 scenario to the daily peak electricity load of the Ca scenario is calculated, and the average value of the daily peak electricity load of the C1 scenario to the daily peak electricity load of the Ca scenario is calculated to obtain the daily average peak load of the first scenario;

[0039] Calculate the difference between the daily peak electricity load of the C1 scenario and the daily peak electricity load of the C2 scenario, and take the absolute value of the difference to obtain the peak load deviation of the first scenario; calculate the difference between the daily peak electricity load of the C2 scenario and the daily peak electricity load of the C3 scenario, and take the absolute value of the difference to obtain the peak load deviation of the second scenario; and so on, calculate the difference between the daily peak electricity load of the Ca-1 scenario and the daily peak electricity load of the Ca scenario, and take the absolute value of the difference to obtain the peak load deviation of the a-1th scenario; calculate the average value of the peak load deviation from the first scenario to the a-1th scenario, and obtain the daily average peak load deviation of the first scenario.

[0040] Furthermore, the step S2 further specifically includes the following steps:

[0041] Step S21: Acquire power usage scenario matching data, and acquire P1 power usage matching unit to Py power usage matching unit and target power usage unit respectively according to the power usage scenario matching data;

[0042] Step S22: selecting a power consumption regulation period from the future power consumption period corresponding to the target power consumption unit;

[0043] Step S23: Analyze the power consumption influencing index of the target power-consuming unit in the power consumption control period, and obtain the temperature and humidity coefficient of the target period according to the analysis result;

[0044] Step S24: selecting a number of historical power consumption periods with the same power consumption scenario as the power consumption control period from the historical power consumption period to obtain a plurality of historical power consumption periods;

[0045] Step S25: Obtain the time period temperature and humidity coefficients corresponding to each historical power consumption period from the P1 power consumption matching unit to the Py power consumption matching unit, and average the obtained multiple time period temperature and humidity coefficients to obtain the P1 time period temperature and humidity coefficient to the Py time period temperature and humidity coefficient;

[0046] Step S26: Perform power load analysis on the P1 power matching unit in the historical power consumption period to obtain the load regulation index of the P1 period;

[0047] Step S27: Perform power load analysis on the P2 power matching unit to the Py power matching unit in the historical power consumption period respectively, and obtain the load regulation index of the P2 period to the load regulation index of the Py period.

[0048] Furthermore, the step S23 further specifically includes the following steps:

[0049] Step S231: Divide the power consumption control period into a number of control sub-periods of equal duration, and name the marked control sub-periods as Z1 control sub-period to Zb control sub-period in chronological order;

[0050] Step S232: obtaining the average temperature value of the target power unit in the Z1 control sub-period, obtaining the temperature value of the Z1 period, obtaining the average humidity value of the target power unit in the Z1 control sub-period through the weather forecast, obtaining the humidity value of the Z1 period;

[0051] Step S233: Calculate the temperature value of the Z1 period and the humidity value of the Z1 period to obtain the temperature and humidity index of the Z1 period;

[0052] The temperature and humidity index in the Z1 period is calculated as follows:

[0053] THI1=0.8Tz1+Rz1×(Tz1-14.4)+46.4;

[0054] Among them, THI1 is the temperature and humidity index of the Z1 period, Tz1 is the temperature value of the Z1 period, and Rz1 is the humidity value of the Z1 period;

[0055] Step S234: respectively obtaining the time period temperature and humidity indexes corresponding to the Z2 control sub-period to the Zb control sub-period, and obtaining the Z2 time period temperature and humidity index to the Zb time period temperature and humidity index;

[0056] Step S235: Acquire the temperature and humidity index of the first area to the temperature and humidity index of the c-1th area;

[0057] Step S236: Calculate the temperature and humidity index of the first area to the temperature and humidity index of the c-1th area, and the number of the first temperature period to the number of the c-1th temperature period to obtain the time period temperature and humidity analysis coefficient;

[0058] Calculate the time period temperature and humidity analysis coefficient, the specific formula is as follows:

[0059]

[0060] Among them, Sdx is the temperature and humidity coefficient of the target period, Qwsi is the temperature and humidity index of the i-th area, Sdsi is the quantity value of the i-th temperature period, and b is the quantity value corresponding to the control sub-period.

[0061] Furthermore, the step S235 further specifically includes the following steps:

[0062] Step S2351: Use the control sub-period as the horizontal coordinate and the period temperature and humidity index as the vertical coordinate to create a plane rectangular coordinate system, mark the temperature and humidity index of the Z1 period to the temperature and humidity index of the Zb period in the plane rectangular coordinate system, and obtain the temperature and humidity coordinate point Z1 to the temperature and humidity coordinate point Zb, mark the first temperature and humidity characteristic point to the c-th temperature and humidity characteristic point in the coordinate Y axis, and draw straight lines from the first temperature and humidity characteristic point to the c-th temperature and humidity characteristic point parallel to the coordinate X axis to obtain the first temperature and humidity characteristic line to the c-th temperature and humidity characteristic line, mark the coordinate system area between the first temperature and humidity characteristic line and the second temperature and humidity characteristic line as the first characteristic temperature and humidity area, mark the coordinate system area between the second temperature and humidity characteristic line and the third temperature and humidity characteristic line as the second characteristic temperature and humidity area, and so on, mark the coordinate system area between the c-1th temperature and humidity characteristic line and the c-th temperature and humidity characteristic line as the c-1th characteristic temperature and humidity area, and obtain the temperature and humidity division coordinate system;

[0063] Step S2352: acquiring the number of temperature and humidity coordinate points from the first characteristic temperature and humidity region to the c-1th characteristic temperature and humidity region according to the temperature and humidity division coordinate system, and obtaining the number of temperature time periods from the first temperature time period to the c-1th temperature time period;

[0064] Step S2353: Obtain the median of the ordinates of the first characteristic temperature and humidity area to the c-1th characteristic temperature and humidity area in the temperature and humidity division coordinate system to obtain the temperature and humidity index of the first area to the c-1th area.

[0065] Furthermore, the step S26 further specifically includes the following steps:

[0066] Step S261: acquiring historical electricity consumption data corresponding to the P1 electricity consumption unit, and selecting a sample electricity consumption period from the acquired multiple historical electricity consumption periods;

[0067] Step S262: obtaining the average power load of the P1 power user unit in the sample power consumption period according to the historical power consumption data, and obtaining the duration of the sample power consumption period to obtain the sample power consumption duration;

[0068] Step S263: obtaining the peak power load corresponding to the P1 power unit in the sample power consumption period according to the historical power consumption data, and obtaining the power consumption duration corresponding to the peak power load to obtain the peak load duration;

[0069] The P1 load regulation index is obtained by calculating the average power load, sample power consumption duration, peak power load, and peak load duration during the period;

[0070] The P1 load regulation index is calculated, and the specific formula is as follows:

[0071]

[0072] Among them, Ptx1 is the P1 load regulation index, Ffh is the peak power load, Fsc is the peak load duration, Pfh is the average power load during the period, and Psc is the sample power duration;

[0073] Step S264: Obtain the load regulation index of the P1 power unit in each historical power consumption period to obtain multiple P1 load regulation indexes, and average the obtained multiple P1 load regulation indexes to obtain the P1 period load regulation index.

[0074] Furthermore, the step S3 further specifically includes the following steps:

[0075] Step S31: Obtain distribution control analysis data, and obtain the load control index of the P1 period to the load control index of the Py period, the temperature and humidity coefficient of the P1 period to the temperature and humidity coefficient of the Py period, and the temperature and humidity coefficient of the target period according to the distribution control analysis data;

[0076] Step S32: establishing a load fitting prediction model according to the temperature and humidity coefficient of the P1 period to the temperature and humidity coefficient of the Py period and the load regulation index of the P1 period to the load regulation index of the Py period;

[0077] Step S33: Substitute the temperature and humidity coefficient of the target period into the load fitting prediction model, and obtain the result value output by the load fitting prediction model to obtain the load control index of the target period;

[0078] Step S34: Obtain a reasonable range of the load control index. If the load control index in the target time period is within the reasonable range of the load control index, the distribution network does not need to perform power control on the target power user. If the load control index in the target time period is not within the reasonable range of the load control index, the distribution network needs to perform power control on the target power user.

[0079] A system for interactive control of loads and distribution networks in multiple scenarios, comprising:

[0080] Power consumption scenario module: used to perform multi-scenario power consumption analysis on the target power consumption unit, obtain the multi-scenario power consumption coefficient according to the analysis results, match multiple power consumption matching units for the target power consumption unit according to the multi-scenario power consumption coefficient, and obtain power consumption scenario matching data;

[0081] Power distribution analysis module: used to obtain a power consumption control period, perform time period temperature and humidity analysis on the target power consumption unit in the power consumption control period, obtain the temperature and humidity coefficient of the target time period, select multiple historical power consumption periods with the same power consumption scenario as the power consumption control period in the historical power consumption period, obtain the time period temperature and humidity coefficient and load control index of each power consumption matching unit in the historical power consumption period according to the power consumption scenario matching data, and obtain the power distribution control analysis data;

[0082] Load forecasting module: used to perform linear fitting on temperature and humidity coefficients of multiple time periods and load control indexes of multiple time periods according to distribution control analysis data to obtain a load fitting forecasting model, obtain the load control index of the target power user in the power control period according to the fitted forecasting model, and obtain the target load control index;

[0083] Grid control module: used to control the power of target power users according to the load control index during the target period.

[0084] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0085] (1) The present invention performs a multi-scenario power consumption analysis on a target power consumption unit, obtains a multi-scenario power consumption coefficient based on the analysis results, matches a plurality of power consumption matching units to the target power consumption unit based on the multi-scenario power consumption coefficient, and performs power prediction on the target power consumption unit by obtaining the time period temperature and humidity coefficient and the load control index of each power consumption matching unit. Power control based on the prediction results can effectively avoid the lag of the control process;

[0086] (2) The present invention performs linear fitting on the temperature and humidity coefficients of multiple time periods and the load control indexes of multiple time periods based on the distribution control analysis data to obtain a load fitting prediction model. The load control index of the target power user in the power control period is obtained according to the fitting prediction model to obtain the target load control index. The target power user is controlled according to the load control index of the target period, which can effectively ensure the accuracy of the control results. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0088] Figure 1 It is a diagram of the implementation steps of the present invention;

[0089] Figure 2 is the overall system block diagram of the present invention;

[0090] Figure 3 It is the temperature and humidity division coordinate system of the present invention. DETAILED DESCRIPTION

[0091] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0092] Embodiment 1

[0093] See also Figure 1 The present invention provides a technical solution: a method for interactively controlling loads and distribution networks in multiple scenarios, specifically comprising the following steps:

[0094] Step S1: Perform a multi-scenario power consumption analysis on the target power consumption unit, obtain a multi-scenario power consumption coefficient according to the analysis result, match multiple power consumption matching units for the target power consumption unit according to the multi-scenario power consumption coefficient, and obtain power consumption scenario matching data;

[0095] The step S1 further specifically includes the following steps:

[0096] Step S11: selecting a target power consumption unit from a plurality of power consumption control units belonging to the power distribution network;

[0097] Step S12: In the power consumption period of the target power user, the statutory working days are marked as the first power consumption scenario period, the weekend period other than the statutory holidays is marked as the second power consumption scenario period, and the statutory holidays are marked as the third power consumption scenario period;

[0098] Step S13: Analyze the historical power consumption data of the target power user in the first power consumption scenario period to obtain the first scenario load matching interval;

[0099] The step S13 further specifically includes the following steps:

[0100] Step S131: selecting a number of scene sample dates from the natural date to which the first power consumption scene period belongs, and marking the selected scene sample dates as C1 scene sample date to Ca scene sample date;

[0101] Step S132: Obtain the daily average load of the first scenario and the daily average load deviation of the first scenario, obtain the daily average base load of the first scenario and the daily average base load deviation of the first scenario, obtain the daily average peak load of the first scenario and the daily average peak load deviation of the first scenario;

[0102] The step S132 further specifically includes the following steps:

[0103] Obtain the historical electricity consumption records of the target electricity user, obtain the daily average electricity load corresponding to the sample date of the C1 scenario to the sample date of the Ca scenario according to the historical electricity consumption records, obtain the daily average electricity load of C1 to the daily average electricity load of Ca, calculate the average of the daily average electricity load of C1 to the daily average electricity load of Ca, and obtain the daily average load of the first scenario;

[0104] Calculate the difference between the daily average power load of C1 and the daily average power load of C2, and take the absolute value of the difference to obtain the first daily average power load deviation. Calculate the difference between the daily average power load of C2 and the daily average power load of C3, and take the absolute value of the difference to obtain the second daily average power load deviation. Similarly, calculate the difference between the daily average power load of Ca-1 and the daily average power load of Ca, and take the absolute value of the difference to obtain the a-1th daily average power load deviation. Calculate the average value of the first daily average power load deviation to the a-1th daily average power load deviation to obtain the first scenario daily average load deviation.

[0105] According to the historical electricity consumption records, the daily minimum electricity load corresponding to the sample date of the C1 scenario to the sample date of the Ca scenario is obtained, and the daily electricity base load of the C1 scenario to the daily electricity base load of the Ca scenario is calculated, and the average value of the daily electricity base load of the C1 scenario to the daily electricity base load of the Ca scenario is calculated to obtain the daily average base load of the first scenario;

[0106] Calculate the difference between the daily electricity base load of the C1 scenario and the daily electricity base load of the C2 scenario, and take the absolute value of the obtained difference to obtain the base load deviation of the first scenario; calculate the difference between the daily electricity base load of the C2 scenario and the daily electricity base load of the C3 scenario, and take the absolute value of the obtained difference to obtain the base load deviation of the second scenario; and so on, calculate the difference between the daily electricity base load of the Ca-1 scenario and the daily electricity base load of the Ca scenario, and take the absolute value of the obtained difference to obtain the base load deviation of the a-1th scenario; calculate the average value of the base load deviation from the first scenario to the a-1th scenario, and obtain the daily average base load deviation of the first scenario;

[0107] According to the historical electricity consumption records, the daily maximum electricity load corresponding to the sample date of the C1 scenario to the sample date of the Ca scenario is obtained, and the daily peak electricity load of the C1 scenario to the daily peak electricity load of the Ca scenario is calculated, and the average value of the daily peak electricity load of the C1 scenario to the daily peak electricity load of the Ca scenario is calculated to obtain the daily average peak load of the first scenario;

[0108] Calculate the difference between the daily electricity peak load of the C1 scenario and the daily electricity peak load of the C2 scenario, and take the absolute value of the difference to obtain the peak load deviation of the first scenario; calculate the difference between the daily electricity peak load of the C2 scenario and the daily electricity peak load of the C3 scenario, and take the absolute value of the difference to obtain the peak load deviation of the second scenario; and so on, calculate the difference between the daily electricity peak load of the Ca-1 scenario and the daily electricity peak load of the Ca scenario, and take the absolute value of the difference to obtain the peak load deviation of the a-1th scenario; calculate the average value of the peak load deviation from the first scenario to the a-1th scenario, and obtain the daily average peak load deviation of the first scenario;

[0109] Step S133: Calculate the first scenario daily average load, the first scenario daily average load deviation, the first scenario daily average base load, the first scenario daily average base load deviation, the first scenario daily average peak load and the first scenario daily average peak load deviation to obtain a first load interval reference value;

[0110] The first load interval reference value is calculated, and the specific formula is as follows:

[0111]

[0112] Among them, Fjz1 is the first load interval reference value, Fj1 is the first scenario daily average load, Fp1 is the first scenario daily average load deviation, Jj1 is the first scenario daily average base load, Jp1 is the first scenario daily average base load deviation, Zj1 is the first scenario daily average peak load, and Zp1 is the first scenario daily average peak load deviation;

[0113] Step S134: Calculate the first scenario daily average load, the first scenario daily average load deviation, the first scenario daily average base load, the first scenario daily average base load deviation, the first scenario daily average peak load and the first scenario daily average peak load deviation to obtain a second load interval reference value;

[0114] The second load interval reference value is calculated using the following formula:

[0115]

[0116] Among them, Fjz2 is the second load interval reference value, Fj1 is the daily average load of the first scenario, Fp1 is the daily average load deviation of the first scenario, Jj1 is the daily average base load of the first scenario, Jp1 is the daily average base load deviation of the first scenario, Zj1 is the daily average peak load of the first scenario, and Zp1 is the daily average peak load deviation of the first scenario;

[0117] Step S135: taking the first load interval reference value as the interval left endpoint and the second load interval reference value as the interval right endpoint, to obtain the first scenario load matching interval;

[0118] Step S14: performing power load analysis on the target power consuming units in the second power consumption scenario period and the third power consumption scenario period respectively to obtain the second scenario load matching interval and the third scenario load matching interval.

[0119] Step S15: Select several sample power consumption units in the power distribution network, obtain the average power load, average base load and peak load of each sample power consumption unit in the first power consumption scenario period, and average the average power load, average base load and peak load of the period to obtain multiple first scenario load matching coefficients;

[0120] Step S16: Obtain the average power load, average base load and average peak load of each sample power user unit in the second power usage scenario period, and average the average power load, average base load and average peak load of the period to obtain multiple second scenario load matching coefficients;

[0121] Step S17: obtaining the average power load, average base load and average peak load of each sample power user in the third power usage scenario period, and averaging the average power load, average base load and average peak load of each sample power user to obtain multiple third scenario load matching coefficients;

[0122] Step S18: if the first scenario load matching coefficient corresponding to the sample power unit is in the first scenario load matching interval, the second scenario load matching coefficient is in the second scenario load matching interval, and the third scenario load matching coefficient is in the third scenario load matching interval, then the corresponding sample power unit is marked as a power matching unit to obtain multiple power matching units;

[0123] Step S19: naming the acquired multiple power usage matching units as P1 power usage matching unit to Py power usage matching unit, and defining the P1 power usage matching unit to Py power usage matching unit and the target power usage unit as power usage scenario matching data.

[0124] Step S2: Obtain a power consumption control period, perform a time period temperature and humidity analysis on the target power consumption unit in the power consumption control period, obtain the temperature and humidity coefficient of the target time period, select multiple historical power consumption periods with the same power consumption scene as the power consumption control period in the historical power consumption period, obtain the time period temperature and humidity coefficient and load control index of each power consumption matching unit in the historical power consumption period according to the power consumption scene matching data, and obtain the distribution control analysis data;

[0125] The step S2 further specifically includes the following steps:

[0126] Step S21: Acquire power usage scenario matching data, and acquire P1 power usage matching unit to Py power usage matching unit and target power usage unit respectively according to the power usage scenario matching data;

[0127] Step S22: selecting a power consumption regulation period from the future power consumption period corresponding to the target power consumption unit;

[0128] Step S23: Analyze the power consumption influencing index of the target power-consuming unit in the power consumption control period, and obtain the temperature and humidity coefficient of the target period according to the analysis result;

[0129] The step S23 further specifically includes the following steps:

[0130] Step S231: Divide the power consumption control period into a number of control sub-periods of equal duration, and name the marked control sub-periods as Z1 control sub-period to Zb control sub-period in chronological order;

[0131] Step S232: obtaining the average temperature value of the target power unit in the Z1 control sub-period, obtaining the temperature value of the Z1 period, obtaining the average humidity value of the target power unit in the Z1 control sub-period through the weather forecast, obtaining the humidity value of the Z1 period;

[0132] Step S233: Calculate the temperature value of the Z1 period and the humidity value of the Z1 period to obtain the temperature and humidity index of the Z1 period;

[0133] The temperature and humidity index in the Z1 period is calculated as follows:

[0134] THI1=0.8Tz1+Rz1×(Tz1-14.4)+46.4;

[0135] Among them, THI1 is the temperature and humidity index of the Z1 period, Tz1 is the temperature value of the Z1 period, and Rz1 is the humidity value of the Z1 period;

[0136] Step S234: respectively obtaining the time period temperature and humidity indexes corresponding to the Z2 control sub-period to the Zb control sub-period, and obtaining the Z2 time period temperature and humidity index to the Zb time period temperature and humidity index;

[0137] Step S235: Acquire the temperature and humidity index of the first area to the temperature and humidity index of the c-1th area;

[0138] The step S235 further specifically includes the following steps:

[0139] Step S2351: Use the control sub-period as the horizontal coordinate and the period temperature and humidity index as the vertical coordinate to create a plane rectangular coordinate system, mark the temperature and humidity index of the Z1 period to the temperature and humidity index of the Zb period in the plane rectangular coordinate system, and obtain the temperature and humidity coordinate point Z1 to the temperature and humidity coordinate point Zb, mark the first temperature and humidity characteristic point to the c-th temperature and humidity characteristic point in the coordinate Y axis, and draw straight lines from the first temperature and humidity characteristic point to the c-th temperature and humidity characteristic point parallel to the coordinate X axis to obtain the first temperature and humidity characteristic line to the c-th temperature and humidity characteristic line, mark the coordinate system area between the first temperature and humidity characteristic line and the second temperature and humidity characteristic line as the first characteristic temperature and humidity area, mark the coordinate system area between the second temperature and humidity characteristic line and the third temperature and humidity characteristic line as the second characteristic temperature and humidity area, and so on, mark the coordinate system area between the c-1th temperature and humidity characteristic line and the c-th temperature and humidity characteristic line as the c-1th characteristic temperature and humidity area, and obtain the temperature and humidity division coordinate system;

[0140] Step S2352: acquiring the number of temperature and humidity coordinate points from the first characteristic temperature and humidity region to the c-1th characteristic temperature and humidity region according to the temperature and humidity division coordinate system, and obtaining the number of temperature time periods from the first temperature time period to the c-1th temperature time period;

[0141] Step S2353: Obtain the median of the ordinates of the first characteristic temperature and humidity region to the c-1th characteristic temperature and humidity region in the temperature and humidity division coordinate system to obtain the temperature and humidity index of the first region to the temperature and humidity index of the c-1th region;

[0142] Step S236: Calculate the temperature and humidity index of the first area to the temperature and humidity index of the c-1th area, and the number of the first temperature period to the number of the c-1th temperature period to obtain the time period temperature and humidity analysis coefficient;

[0143] Calculate the time period temperature and humidity analysis coefficient, the specific formula is as follows:

[0144]

[0145] Among them, Sdx is the temperature and humidity coefficient of the target period, Qwsi is the temperature and humidity index of the i-th region, Sdsi is the quantity value of the i-th temperature period, and b is the quantity value corresponding to the control sub-period;

[0146] Step S24: selecting a number of historical power consumption periods with the same power consumption scenario as the power consumption control period from the historical power consumption period to obtain a plurality of historical power consumption periods;

[0147] Step S25: Obtain the time period temperature and humidity coefficients corresponding to each historical power consumption period from the P1 power consumption matching unit to the Py power consumption matching unit, and average the obtained multiple time period temperature and humidity coefficients to obtain the P1 time period temperature and humidity coefficient to the Py time period temperature and humidity coefficient;

[0148] Step S26: Perform power load analysis on the P1 power matching unit in the historical power consumption period to obtain the load regulation index of the P1 period;

[0149] The step S26 further specifically includes the following steps:

[0150] Step S261: acquiring historical electricity consumption data corresponding to the P1 electricity consumption unit, and selecting a sample electricity consumption period from the acquired multiple historical electricity consumption periods;

[0151] Step S262: obtaining the average power load of the P1 power user unit in the sample power consumption period according to the historical power consumption data, and obtaining the duration of the sample power consumption period to obtain the sample power consumption duration;

[0152] Step S263: obtaining the peak power load corresponding to the P1 power unit in the sample power consumption period according to the historical power consumption data, and obtaining the power consumption duration corresponding to the peak power load to obtain the peak load duration;

[0153] The P1 load regulation index is obtained by calculating the average power load, sample power consumption duration, peak power load, and peak load duration during the period;

[0154] The P1 load regulation index is calculated, and the specific formula is as follows:

[0155]

[0156] Among them, Ptx1 is the P1 load regulation index, Ffh is the peak power load, Fsc is the peak load duration, Pfh is the average power load during the period, and Psc is the sample power duration;

[0157] Step S264: acquiring the load regulation index of the P1 power unit in each historical power consumption period to obtain multiple P1 load regulation indexes, and averaging the obtained multiple P1 load regulation indexes to obtain the P1 period load regulation index;

[0158] Step S27: Perform power load analysis on the P2 power matching unit to the Py power matching unit in the historical power consumption period, and obtain the load regulation index of the P2 period to the load regulation index of the Py period;

[0159] Step S3: linearly fitting the temperature and humidity coefficients of multiple time periods and the load control indexes of multiple time periods according to the power distribution control analysis data to obtain a load fitting prediction model, and obtaining the load control index of the target power user in the power control period according to the fitting prediction model to obtain the target load control index;

[0160] The step S3 further specifically includes the following steps:

[0161] Step S31: Obtain distribution control analysis data, and obtain the load control index of the P1 period to the load control index of the Py period, the temperature and humidity coefficient of the P1 period to the temperature and humidity coefficient of the Py period, and the temperature and humidity coefficient of the target period according to the distribution control analysis data;

[0162] Step S32: establishing a load fitting prediction model according to the temperature and humidity coefficient of the P1 period to the temperature and humidity coefficient of the Py period and the load regulation index of the P1 period to the load regulation index of the Py period;

[0163] The details are as follows:

[0164] Establish a polynomial fitting function by converting the temperature and humidity coefficient of the P1 period to the temperature and humidity coefficient of the Py period and the load regulation index of the P1 period to the load regulation index of the Py period;

[0165] The polynomial fitting function is as follows:

[0166] y=e 0 +e 1 x+e 2 x 2 +e 3 x 3 +……+en x n ;

[0167] Among them, y is the load control index of the time period, x is the temperature and humidity coefficient of the time period, and e 0 To e n The coefficients of the polynomial fitting function, n is the order of the polynomial fitting function;

[0168] Substitute the temperature and humidity coefficient of the P1 period to the temperature and humidity coefficient of the Py period and the load control index of the P1 period to the load control index of the Py period into the polynomial fitting function respectively, and calculate the residual sum of squares function RSS of the load control index of the period;

[0169] The coefficients e of the polynomial fitting function in the residual sum of squares function RSS are 0 To e n Calculate partial derivatives and get n unknown numbers e 0 To e n Function expression, and put n containing unknown number e 0 To e n The function expressions of are combined to obtain n groups containing e 0 To e n The system of equations, and solve the system of equations to get e 0 To e n Specific value of

[0170] E 0 To e n Substitute the specific value of into the polynomial fitting function to obtain the load fitting prediction model;

[0171] Step S33: Substitute the temperature and humidity coefficient of the target period into the load fitting prediction model, and obtain the result value output by the load fitting prediction model to obtain the load control index of the target period;

[0172] Step S34: Obtain a reasonable range of the load control index. If the load control index in the target time period is within the reasonable range of the load control index, the distribution network does not need to perform power control on the target power user. If the load control index in the target time period is not within the reasonable range of the load control index, the distribution network needs to perform power control on the target power user.

[0173] In the present invention, if corresponding calculation formulas appear, the above calculation formulas are all dimensionless and take their numerical calculations. The weight coefficients, proportional coefficients and other coefficients in the formulas are set to a result value obtained by quantifying each parameter. The size of the weight coefficients and proportional coefficients can be determined as long as they do not affect the proportional relationship between the parameters and the result values.

[0174] Embodiment 2

[0175] See also Figure 2 , based on another concept of the same invention, a system for interactive control of loads and distribution networks under multiple scenarios is now proposed, the interactive control system includes a power usage scenario module, a power distribution analysis module, a load prediction module, a power grid control module and a server, the power usage scenario module, the power distribution analysis module, the load prediction module and the power grid control module are respectively connected to the server, and the server controls the power usage scenario module, the power distribution analysis module, the load prediction module and the power grid control module respectively;

[0176] The power consumption scenario module performs multi-scenario power consumption analysis on the target power consumption unit, obtains the multi-scenario power consumption coefficient according to the analysis results, matches multiple power consumption matching units for the target power consumption unit according to the multi-scenario power consumption coefficient, and obtains power consumption scenario matching data;

[0177] The details are as follows:

[0178] Selecting a target power consumption unit from a plurality of power consumption control units belonging to the power distribution network;

[0179] In the electricity consumption period of the target electricity user, the statutory working days are marked as the first electricity consumption scenario period, the weekend period other than the statutory holidays is marked as the second electricity consumption scenario period, and the statutory holidays are marked as the third electricity consumption scenario period;

[0180] Analyze the historical power consumption data of the target power user in the first power consumption scenario period to obtain the first scenario load matching interval;

[0181] The details are as follows:

[0182] Selecting a number of scene sample dates from the natural date to which the first electricity consumption scene period belongs, and marking the selected scene sample dates as C1 scene sample date to Ca scene sample date;

[0183] It should be noted here that:

[0184] In the present invention, C referred to herein is an identifier corresponding to the scene sample date, a is a quantity value corresponding to the scene sample date, and a is an integer greater than 0;

[0185] In the present invention, the C1 scene sample date to the Ca scene sample date involved herein are all historical dates.

[0186] Obtain the historical electricity consumption records of the target electricity user, obtain the daily average electricity load corresponding to the sample date of the C1 scenario to the sample date of the Ca scenario according to the historical electricity consumption records, obtain the daily average electricity load of C1 to the daily average electricity load of Ca, calculate the average of the daily average electricity load of C1 to the daily average electricity load of Ca, and obtain the daily average load of the first scenario;

[0187] Calculate the difference between the daily average power load of C1 and the daily average power load of C2, and take the absolute value of the difference to obtain the first daily average power load deviation. Calculate the difference between the daily average power load of C2 and the daily average power load of C3, and take the absolute value of the difference to obtain the second daily average power load deviation. Similarly, calculate the difference between the daily average power load of Ca-1 and the daily average power load of Ca, and take the absolute value of the difference to obtain the a-1th daily average power load deviation. Calculate the average value of the first daily average power load deviation to the a-1th daily average power load deviation to obtain the first scenario daily average load deviation.

[0188] According to the historical electricity consumption records, the daily minimum electricity load corresponding to the sample date of the C1 scenario to the sample date of the Ca scenario is obtained, and the daily electricity base load of the C1 scenario to the daily electricity base load of the Ca scenario is calculated, and the average value of the daily electricity base load of the C1 scenario to the daily electricity base load of the Ca scenario is calculated to obtain the daily average base load of the first scenario;

[0189] Calculate the difference between the daily electricity base load of the C1 scenario and the daily electricity base load of the C2 scenario, and take the absolute value of the obtained difference to obtain the base load deviation of the first scenario; calculate the difference between the daily electricity base load of the C2 scenario and the daily electricity base load of the C3 scenario, and take the absolute value of the obtained difference to obtain the base load deviation of the second scenario; and so on, calculate the difference between the daily electricity base load of the Ca-1 scenario and the daily electricity base load of the Ca scenario, and take the absolute value of the obtained difference to obtain the base load deviation of the a-1th scenario; calculate the average value of the base load deviation from the first scenario to the a-1th scenario, and obtain the daily average base load deviation of the first scenario;

[0190] According to the historical electricity consumption records, the daily maximum electricity load corresponding to the sample date of the C1 scenario to the sample date of the Ca scenario is obtained, and the daily peak electricity load of the C1 scenario to the daily peak electricity load of the Ca scenario is calculated, and the average value of the daily peak electricity load of the C1 scenario to the daily peak electricity load of the Ca scenario is calculated to obtain the daily average peak load of the first scenario;

[0191] Calculate the difference between the daily electricity peak load of the C1 scenario and the daily electricity peak load of the C2 scenario, and take the absolute value of the difference to obtain the peak load deviation of the first scenario; calculate the difference between the daily electricity peak load of the C2 scenario and the daily electricity peak load of the C3 scenario, and take the absolute value of the difference to obtain the peak load deviation of the second scenario; and so on, calculate the difference between the daily electricity peak load of the Ca-1 scenario and the daily electricity peak load of the Ca scenario, and take the absolute value of the difference to obtain the peak load deviation of the a-1th scenario; calculate the average value of the peak load deviation from the first scenario to the a-1th scenario, and obtain the daily average peak load deviation of the first scenario;

[0192] The first load interval reference value is obtained by calculating the first scenario daily average load, the first scenario daily average load deviation, the first scenario daily average base load, the first scenario daily average base load deviation, the first scenario daily average peak load and the first scenario daily average peak load deviation;

[0193] The first load interval reference value is calculated, and the specific formula is as follows:

[0194]

[0195] Among them, Fjz1 is the first load interval reference value, Fj1 is the first scenario daily average load, Fp1 is the first scenario daily average load deviation, Jj1 is the first scenario daily average base load, Jp1 is the first scenario daily average base load deviation, Zj1 is the first scenario daily average peak load, and Zp1 is the first scenario daily average peak load deviation;

[0196] The second load interval reference value is obtained by calculating the first scenario daily average load, the first scenario daily average load deviation, the first scenario daily average base load, the first scenario daily average base load deviation, the first scenario daily average peak load and the first scenario daily average peak load deviation;

[0197] The second load interval reference value is calculated using the following formula:

[0198]

[0199] Among them, Fjz2 is the second load interval reference value, Fj1 is the daily average load of the first scenario, Fp1 is the daily average load deviation of the first scenario, Jj1 is the daily average base load of the first scenario, Jp1 is the daily average base load deviation of the first scenario, Zj1 is the daily average peak load of the first scenario, and Zp1 is the daily average peak load deviation of the first scenario;

[0200] It should be noted here that:

[0201] In a specific implementation, if the target power user has a first scenario daily average load of 150kW, a first scenario daily average load deviation of 22.5, a first scenario daily average base load of 100kW, a first scenario daily average base load deviation of 10.5kW, a first scenario daily average peak load of 203kW, and a first scenario daily average peak load deviation of 40kW, then the first load interval reference value can be calculated to be 379.4, and the second load interval reference value is 526;

[0202] The first load interval reference value is used as the interval left endpoint, and the second load interval reference value is used as the interval right endpoint to obtain the first scenario load matching interval;

[0203] Repeat the process of obtaining the load matching interval of the first scenario, and perform power load analysis on the target power users in the second power usage scenario period and the third power usage scenario period respectively to obtain the second scenario load matching interval and the third scenario load matching interval.

[0204] Selecting a number of sample power users in the power distribution network, obtaining the average power load, average base load and average peak load of each sample power user in the first power usage scenario period, and averaging the average power load, average base load and average peak load to obtain multiple first scenario load matching coefficients;

[0205] Obtain the average power load, average base load and average peak load of each sample power user unit in the second power usage scenario period, and average the average power load, average base load and average peak load of each sample power user unit to obtain multiple second scenario load matching coefficients;

[0206] Obtain the average power load, average base load and average peak load of each sample power user unit in the third power usage scenario period, and average the average power load, average base load and average peak load of each sample power user unit to obtain multiple third scenario load matching coefficients;

[0207] If the first scenario load matching coefficient corresponding to the sample power unit is in the first scenario load matching interval, the second scenario load matching coefficient is in the second scenario load matching interval, and the third scenario load matching coefficient is in the third scenario load matching interval, the corresponding sample power unit is marked as a power matching unit to obtain multiple power matching units;

[0208] It should be noted here that:

[0209] In the present invention, the first scenario load matching coefficient, the second scenario load matching coefficient and the third scenario load matching coefficient corresponding to the power matching unit all include interval boundaries of the corresponding scenario load matching intervals.

[0210] The obtained multiple power usage matching units are named as P1 power usage matching unit to Py power usage matching unit, and the P1 power usage matching unit to Py power usage matching unit and the target power usage unit are defined as power usage scenario matching data.

[0211] It should be noted here that:

[0212] In the present invention, P referred to herein is an identifier corresponding to the power matching unit, y is a quantity value corresponding to the power matching unit, and y is an integer greater than 0.

[0213] The power distribution analysis module obtains a power consumption control period, performs a time period temperature and humidity analysis on the target power consumption unit in the power consumption control period, obtains the temperature and humidity coefficient of the target time period, selects multiple historical power consumption periods with the same power consumption scenario as the power consumption control period in the historical power consumption period, obtains the time period temperature and humidity coefficient and load control index of each power consumption matching unit in the historical power consumption period according to the power consumption scenario matching data, and obtains the power distribution control analysis data;

[0214] The details are as follows:

[0215] Obtain power usage scenario matching data, and obtain P1 power usage matching units to Py power usage matching units and target power usage units according to the power usage scenario matching data;

[0216] Selecting a power consumption regulation period from the future power consumption period corresponding to the target power consumption unit;

[0217] Conduct power consumption influencing index analysis on target power users in the power consumption control period, and obtain the temperature and humidity coefficients of the target period based on the analysis results;

[0218] The details are as follows:

[0219] The power consumption control period is divided into a number of control sub-periods of equal duration, and the marked control sub-periods are named Z1 control sub-period to Zb control sub-period in chronological order;

[0220] It should be noted here that:

[0221] In the present invention, Z referred to herein is an identifier corresponding to the regulation sub-period, and b is a quantity value corresponding to the regulation sub-period, and b is an integer greater than 0;

[0222] The average temperature value of the target power user in the Z1 control sub-period is obtained through the weather forecast, and the temperature value of the Z1 period is obtained; the average humidity value of the target power user in the Z1 control sub-period is obtained through the weather forecast, and the humidity value of the Z1 period is obtained;

[0223] The temperature and humidity values ​​of the Z1 period are calculated to obtain the temperature and humidity index of the Z1 period;

[0224] The temperature and humidity index in the Z1 period is calculated as follows:

[0225] THI1=0.8Tz1+Rz1×(Tz1-14.4)+46.4;

[0226] Among them, THI1 is the temperature and humidity index of the Z1 period, Tz1 is the temperature value of the Z1 period, and Rz1 is the humidity value of the Z1 period;

[0227] It should be noted here that:

[0228] In the present invention, the time period temperature and humidity index involved here is an indicator used to characterize the environmental comfort of the target electricity consumption unit, and the air conditioning load and the time period temperature and humidity index show an obvious positive proportional relationship, that is, when the time period temperature and humidity index increases by 1 unit, the electricity consumption of the commercial building may increase by 3% to 5%;

[0229] Repeat the process of obtaining the temperature and humidity index of the Z1 period, and obtain the temperature and humidity index of the period corresponding to the Z2 control sub-period to the Zb control sub-period, and obtain the temperature and humidity index of the Z2 period to the Zb period;

[0230] See also Figure 3 , take the control sub-period as the horizontal coordinate, the period temperature and humidity index as the vertical coordinate, create a plane rectangular coordinate system, mark the temperature and humidity index of the Z1 period to the temperature and humidity index of the Zb period in the plane rectangular coordinate system, and obtain the temperature and humidity coordinate point Z1 to the temperature and humidity coordinate point Zb, mark the first temperature and humidity characteristic point to the c-th temperature and humidity characteristic point in the coordinate Y axis, and draw straight lines from the first temperature and humidity characteristic point to the c-th temperature and humidity characteristic point parallel to the coordinate X axis to obtain the first temperature and humidity characteristic line to the c-th temperature and humidity characteristic line, mark the coordinate system area between the first temperature and humidity characteristic line and the second temperature and humidity characteristic line as the first characteristic temperature and humidity area, mark the coordinate system area between the second temperature and humidity characteristic line and the third temperature and humidity characteristic line as the second characteristic temperature and humidity area, and so on, mark the coordinate system area between the c-1th temperature and humidity characteristic line and the c-th temperature and humidity characteristic line as the c-1th characteristic temperature and humidity area, and obtain the temperature and humidity division coordinate system;

[0231] It should be noted here that:

[0232] In the present invention, each characteristic temperature and humidity region includes an area covered by a temperature and humidity characteristic line at the bottom of the characteristic temperature and humidity region.

[0233] According to the temperature and humidity division coordinate system, the number of temperature and humidity coordinate points from the first characteristic temperature and humidity area to the c-1th characteristic temperature and humidity area are respectively obtained to obtain the number of values ​​from the first temperature period to the c-1th temperature period;

[0234] Obtain the median of the ordinates of the first characteristic temperature and humidity region to the c-1th characteristic temperature and humidity region in the temperature and humidity division coordinate system to obtain the temperature and humidity index of the first region to the temperature and humidity index of the c-1th region;

[0235] The temperature and humidity index of the first area to the temperature and humidity index of the c-1th area, and the number of the first temperature period to the number of the c-1th temperature period are calculated to obtain the time period temperature and humidity analysis coefficient;

[0236] Calculate the time period temperature and humidity analysis coefficient, the specific formula is as follows:

[0237]

[0238] Among them, Sdx is the temperature and humidity coefficient of the target period, Qwsi is the temperature and humidity index of the i-th region, Sdsi is the quantity value of the i-th temperature period, and b is the quantity value corresponding to the control sub-period;

[0239] Selecting several historical periods with the same electricity consumption scenario as the electricity consumption control period from the historical electricity consumption period to obtain multiple historical electricity consumption periods;

[0240] It should be noted here that:

[0241] If the electricity consumption scenario corresponding to the electricity consumption control period is the first electricity consumption scenario period, then the selected historical period is also the first electricity consumption scenario period. If the electricity consumption scenario corresponding to the electricity consumption control period is the second electricity consumption scenario period, then the selected historical period is also the second electricity consumption scenario period. If the electricity consumption scenario corresponding to the electricity consumption control period is the third electricity consumption scenario period, then the selected historical period is also the third electricity consumption scenario period.

[0242] Obtain the time period temperature and humidity coefficients corresponding to the P1 power matching unit to the Pa power matching unit in each historical power consumption period, and average the obtained multiple time period temperature and humidity coefficients to obtain the P1 time period temperature and humidity coefficient to the Pa time period temperature and humidity coefficient;

[0243] Conduct power load analysis on the P1 power matching unit in the historical power consumption period to obtain the load regulation index of the P1 period;

[0244] The details are as follows:

[0245] Obtain historical electricity consumption data corresponding to the P1 electricity consumption unit, and select a sample electricity consumption period from the multiple historical electricity consumption periods obtained;

[0246] According to the historical electricity consumption data, the average electricity load of the P1 electricity user unit in the sample electricity consumption period is obtained, and the duration of the sample electricity consumption period is obtained to obtain the sample electricity consumption duration;

[0247] According to the historical electricity consumption data, the peak power load corresponding to the P1 power consumption unit in the sample power consumption period is obtained, and the power consumption duration corresponding to the peak power load is obtained to obtain the peak load duration;

[0248] It should be noted here that:

[0249] The peak load duration involved here is not limited to the duration of power consumption sustained by a single load value, the peak power load, but the duration of power consumption sustained by the load value corresponding to the peak load interval formed with the peak power load as the midpoint of the interval. The specific value range corresponding to the peak load interval is set in the present invention to be between plus or minus 5% of the peak power load.

[0250] The P1 load regulation index is obtained by calculating the average power load, sample power consumption duration, peak power load, and peak load duration during the period;

[0251] The P1 load regulation index is calculated, and the specific formula is as follows:

[0252]

[0253] Among them, Ptx1 is the P1 load regulation index, Ffh is the peak power load, Fsc is the peak load duration, Pfh is the average power load during the period, and Psc is the sample power duration;

[0254] It should be noted here that:

[0255] In the present invention, the load regulation index involved here reflects the intensity and duration of the peak load relative to the average load. If the load regulation index is high, it may mean that the system is under greater pressure at the peak and the duration is long, so more regulation measures are needed. On the contrary, if the load regulation index is low, the load is relatively stable and the regulation demand is small.

[0256] In actual operation, there are measured data: the average power load of the period is 800Kw, the sample power consumption duration is 12h, the peak power load is 1200Kw, and the peak load duration is 4h. The load regulation index can be calculated to be 0.5;

[0257] The load regulation index of the P1 power user in each historical power consumption period is obtained to obtain a plurality of P1 load regulation indexes, and the average of the obtained plurality of P1 load regulation indexes is calculated to obtain the P1 period load regulation index;

[0258] Conduct power load analysis on the P2 power matching unit to the Pa power matching unit in the historical power consumption period, and obtain the load regulation index of the P2 period to the load regulation index of the Pa period;

[0259] The load control index of the P1 period to the load control index of the Pa period, the temperature and humidity coefficient of the P1 period to the temperature and humidity coefficient of the Pa period, and the temperature and humidity coefficient of the target period are defined as distribution control analysis data;

[0260] The load forecasting module performs linear fitting on the temperature and humidity coefficients of multiple time periods and the load control indexes of multiple time periods according to the distribution control analysis data to obtain a load fitting forecasting model, and obtains the load control index of the target power user in the power control period according to the fitted forecasting model to obtain the target load control index;

[0261] Obtain distribution control analysis data, and obtain the load control index of the P1 period to the load control index of the Pa period, the temperature and humidity coefficient of the P1 period to the temperature and humidity coefficient of the Pa period, and the temperature and humidity coefficient of the target period according to the distribution control analysis data;

[0262] A load fitting prediction model is established based on the temperature and humidity coefficient of the P1 period to the temperature and humidity coefficient of the Pa period and the load regulation index of the P1 period to the load regulation index of the Pa period;

[0263] The details are as follows:

[0264] A polynomial fitting function is established by converting the temperature and humidity coefficient of the P1 period to the temperature and humidity coefficient of the Pa period and the load control index of the P1 period to the load control index of the Pa period;

[0265] The polynomial fitting function is as follows:

[0266] y=e 0 +e 1 x+e 2 x 2 +e 3 x 3 +……+e n x n ;

[0267] Among them, y is the load control index of the time period, x is the temperature and humidity coefficient of the time period, and e 0 To e n The coefficients of the polynomial fitting function, n is the order of the polynomial fitting function;

[0268] It should be noted here that:

[0269] In the present invention, the time period load control index is the dependent variable, and the time period temperature and humidity coefficient is the independent variable;

[0270] Substitute the temperature and humidity coefficient of period P1 to the temperature and humidity coefficient of period Pa and the load control index of period P1 to the load control index of period Pa into the polynomial fitting function respectively, and calculate the residual sum of squares function RSS of the period load control index;

[0271] The coefficients e of the polynomial fitting function in the residual sum of squares function RSS are 0 To e n Calculate partial derivatives and get n unknown numbers e 0 To e nFunction expression, and put n containing unknown number e 0 To e n The function expressions of are combined to obtain n groups containing e 0 To e n The system of equations, and solve the system of equations to get e 0 To e n Specific value of

[0272] E 0 To e n Substitute the specific value of into the polynomial fitting function to obtain the load fitting prediction model;

[0273] Assumptions: The temperature and humidity coefficients from the first period to the third period are 1, 2, and 3 respectively; the load control index from the first period to the third period are 2, 4, and 6 respectively;

[0274] Then there are the following three sets of data points: when x=1, y=2, when x=2, y=3, when x=3, y=6;

[0275] The residual sum of squares (RSS) function is:

[0276] in, is the predicted value of the model at xi, and

[0277] Substituting the three sets of data points, we get:

[0278] RSS=(2-(a 0 +a 1 ×1)) 2 +(4-(a 0 +a 1 ×2)) 2 +(6-(a 0 +a 1 ×3)) 2 ;

[0279] For RSS, calculate the 0 and e 1 The partial derivative of and set it to 0, we get e 0 =0,e 1 =2;

[0280] Substitute the temperature and humidity coefficient of the target period into the load fitting prediction model, and obtain the result value output by the load fitting prediction model to obtain the load control index of the target period;

[0281] The power grid control module controls the power of the target power user according to the load control index during the target period;

[0282] The details are as follows:

[0283] Obtain the load regulation index during the target period;

[0284] Obtain a reasonable range of load regulation index. If the load regulation index in the target period is within the reasonable range, the distribution network does not need to regulate the power of the target power user. If the load regulation index in the target period is not within the reasonable range, the distribution network needs to regulate the power of the target power user.

[0285] It should be noted here that:

[0286] The power control measures involved here include but are not limited to strengthening power monitoring, increasing power supply and implementing time-of-use electricity prices;

[0287] In the present invention, if the load control index of the target period is measured to be 2.1, and the reasonable range of the load control index is set to [0,1.1], it can be seen that the load control index of the target period is not in the reasonable range of the load control index, then the distribution network needs to perform power control on the target power user.

[0288] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for interactive control of loads and distribution networks in multiple scenarios, characterized in that: include: Step S1: Perform a multi-scenario electricity consumption analysis on the target electricity user, match multiple electricity consumption matching units to the target electricity user according to the analysis results, and obtain electricity consumption scenario matching data; Step S2: Obtain a power consumption control period, perform a time period temperature and humidity analysis on the target power consumption unit in the power consumption control period, obtain the target time period temperature and humidity coefficient, select multiple historical power consumption periods, and obtain the time period temperature and humidity coefficient and load control index of each power consumption matching unit in the historical power consumption period according to the power consumption scenario matching data to obtain the distribution control analysis data; Step S3: Perform linear fitting on the temperature and humidity coefficients of multiple time periods and the load control indexes of multiple time periods according to the distribution control analysis data to obtain a load fitting prediction model, obtain the target load control index according to the fitting prediction model, and perform power control on the target power unit according to the target load control index.

2. The method for interactively controlling loads and distribution networks in multiple scenarios according to claim 1 is characterized in that: The step S1 further specifically includes the following steps: Step S11: Select a target electricity user; Step S12: marking the power consumption time periods of the target power consumption unit as the first power consumption scenario time period, the second power consumption scenario time period and the third power consumption scenario time period; Step S13: Analyze the historical power consumption data of the target power user in the first power consumption scenario period to obtain the first scenario load matching interval; Step S14: performing power load analysis on the target power users in the second power usage scenario period and the third power usage scenario period to obtain a second scenario load matching interval and a third scenario load matching interval; Step S15: Select several sample power consumption units in the power distribution network, obtain the average power load, average base load and average peak load of each sample power consumption unit in the first power consumption scenario period, and calculate multiple first scenario load matching coefficients; Step S16: Obtain the average power load, average base load and average peak load of the second power usage scenario in the time period, and calculate a plurality of second scenario load matching coefficients; Step S17: Obtain the average power load, average base load and average peak load of the third power usage scenario in the time period, and calculate a plurality of load matching coefficients for the third scenario; Step S18: if the first scenario load matching coefficient, the second scenario load matching coefficient and the third scenario load matching coefficient are respectively in the first scenario load matching interval, the second scenario load matching interval and the third scenario load matching interval, then the corresponding sample power units are marked as power matching units to obtain multiple power matching units; Step S19: Name the acquired power usage matching units as P1 power usage matching units to Py power usage matching units, and obtain power usage scenario matching data.

3. The method for interactively controlling loads and distribution networks in multiple scenarios according to claim 2 is characterized in that: The step S13 further specifically includes the following steps: Step S131: Selecting a C1 scenario sample date to a Ca scenario sample date from the natural date to which the first power consumption scenario period belongs; Step S132: Obtain the first scenario daily average load Fj1, the first scenario daily average load deviation Fp1, the first scenario daily average base load Jj1, the first scenario daily average base load deviation Jp1, the first scenario daily average peak load Zj1 and the first scenario daily average peak load deviation Zp1; Step S133: Calculate and obtain the first load interval reference value Fjz1, the specific formula is as follows: Step S134: Calculate and obtain the second load interval reference value Fjz2, the specific formula is as follows: Step S135: Taking the first load interval reference value as the left endpoint of the interval and the second load interval reference value as the right endpoint of the interval, a first scenario load matching interval is obtained.

4. The method for interactively controlling loads and distribution networks in multiple scenarios according to claim 3 is characterized in that: The step S132 further specifically includes the following steps: Obtain the daily average power load corresponding to the sample date of scenario C1 to the sample date of scenario Ca respectively, and calculate the daily average load of the first scenario; Calculate the difference between the average power loads of two consecutive days, take the absolute value of the difference, obtain the average power load deviation of the first day to the average power load deviation of the a-1 day, and calculate the average to obtain the average load deviation of the first scenario; Obtain the daily minimum power load corresponding to the sample date of scenario C1 to the sample date of scenario Ca respectively, and calculate the daily average base load of the first scenario; Calculate the difference between the average base loads of two consecutive days, take the absolute value of the difference, obtain the average base load deviation of the first day to the average base load deviation of the a-1 day, and calculate the average to obtain the daily average base load deviation of the first scenario; According to the historical electricity consumption records, the daily maximum electricity load corresponding to the sample date of the C1 scenario to the sample date of the Ca scenario is obtained, and the daily average peak load of the first scenario is calculated; Calculate the difference between the average peak loads of electricity consumption on each of two consecutive days, take the absolute value of the difference, and obtain the average peak load deviation of the first day to the average peak load deviation of electricity consumption on the a-1th day, and calculate the average to obtain the average peak load deviation of the first scenario.

5. The method for interactively controlling loads and distribution networks in multiple scenarios according to claim 1 is characterized in that: The step S2 further specifically includes the following steps: Step S21: Acquire power usage scenario matching data, and acquire P1 power usage matching unit to Py power usage matching unit and target power usage unit respectively according to the power usage scenario matching data; Step S22: selecting a power consumption regulation period from the future power consumption period corresponding to the target power consumption unit; Step S23: Analyze the power consumption influencing index of the target power-consuming unit in the power consumption control period, and obtain the temperature and humidity coefficient of the target period according to the analysis result; Step S24: selecting a number of historical power consumption periods with the same power consumption scenario as the power consumption control period from the historical power consumption period to obtain a plurality of historical power consumption periods; Step S25: Obtain the time period temperature and humidity coefficients corresponding to each historical power consumption period from the P1 power consumption matching unit to the Py power consumption matching unit, and calculate the average to obtain the P1 time period temperature and humidity coefficients to the Py time period temperature and humidity coefficients; Step S26: Perform power load analysis on the P1 power matching unit in the historical power consumption period to obtain the load regulation index of the P1 period; Step S27: Perform power load analysis on the P2 power matching unit to the Py power matching unit in the historical power consumption period to obtain the load regulation index of the P2 period to the load regulation index of the Py period.

6. The method for interactively controlling loads and distribution networks in multiple scenarios according to claim 5 is characterized in that: The step S23 further specifically includes the following steps: Step S231: Divide the power consumption control period into Z1 control sub-period to Zb control sub-period; Step S232: obtaining the average temperature value of the target power unit in the Z1 control sub-period, obtaining the temperature value of the Z1 period, obtaining the average humidity value of the target power unit in the Z1 control sub-period, obtaining the humidity value of the Z1 period; Step S233: The temperature value Tz1 of the Z1 period and the humidity value Rz1 of the Z1 period are calculated to obtain the temperature and humidity index THI1 of the Z1 period. The specific formula is as follows: THI1=0.8Tz1+Rz1×(Tz1-14.4)+46.4; Step S234: respectively obtaining the time period temperature and humidity indexes corresponding to the Z2 control sub-period to the Zb control sub-period, and obtaining the Z2 time period temperature and humidity index to the Zb time period temperature and humidity index; Step S235: Acquire the temperature and humidity index of the first area to the temperature and humidity index of the c-1th area; Step S236: Calculate the temperature and humidity index of the first area to the temperature and humidity index of the c-1th area, and the number of the first temperature period to the number of the c-1th temperature period to obtain the time period temperature and humidity analysis coefficient; Calculate the time period temperature and humidity analysis coefficient Sdx, the specific formula is as follows: Among them, Qwsi is the temperature and humidity index of the i-th area, Sdsi is the quantity value of the i-th temperature period, and b is the quantity value corresponding to the control sub-period.

7. The method for interactively controlling loads and distribution networks in multiple scenarios according to claim 6 is characterized in that: The step S235 further specifically includes the following steps: Step S2351: Use the control sub-period as the horizontal coordinate and the period temperature and humidity index as the vertical coordinate to create a plane rectangular coordinate system, mark the temperature and humidity index of the Z1 to Zb period in the plane rectangular coordinate system, and obtain the temperature and humidity coordinate points Z1 to Zb, mark the first temperature and humidity characteristic point to the c-th temperature and humidity characteristic point in the coordinate Y axis, and draw straight lines parallel to the coordinate X axis respectively to obtain the first temperature and humidity characteristic line to the c-th temperature and humidity characteristic line, mark the coordinate system area between the first temperature and humidity characteristic line and the second temperature and humidity characteristic line as the first characteristic temperature and humidity area, and so on, mark the coordinate system area between the c-1th temperature and humidity characteristic line and the c-th temperature and humidity characteristic line as the c-1th characteristic temperature and humidity area, and obtain the temperature and humidity division coordinate system; Step S2352: acquiring the number of temperature and humidity coordinate points from the first characteristic temperature and humidity region to the c-1th characteristic temperature and humidity region according to the temperature and humidity division coordinate system, and obtaining the number of temperature time periods from the first temperature time period to the c-1th temperature time period; Step S2353: Obtain the median of the ordinates of the first characteristic temperature and humidity area to the c-1th characteristic temperature and humidity area in the temperature and humidity division coordinate system to obtain the temperature and humidity index of the first area to the c-1th area.

8. The method for interactively controlling loads and distribution networks in multiple scenarios according to claim 5 is characterized in that: The step S26 further specifically includes the following steps: Step S261: acquiring historical electricity consumption data corresponding to the P1 electricity consumption unit, and selecting a sample electricity consumption period from the acquired multiple historical electricity consumption periods; Step S262: obtaining the average power load of the P1 power user unit in the sample power consumption period, and obtaining the duration of the sample power consumption period to obtain the sample power consumption duration; Step S263: obtaining the peak power load corresponding to the P1 power consumption unit in the sample power consumption period, and obtaining the power consumption duration corresponding to the peak power load to obtain the peak load duration; The P1 load regulation index Ptx1 is obtained by calculating the average power load Pfh, sample power consumption duration Psc, peak power load Ffh, and peak load duration Fsc. The specific formula is as follows: Step S264: Obtain the load regulation index of the P1 power unit in each historical power consumption period to obtain multiple P1 load regulation indexes, and calculate the average to obtain the P1 period load regulation index.

9. The method for interactively controlling loads and distribution networks in multiple scenarios according to claim 1, characterized in that: The step S3 further specifically includes the following steps: Step S31: Obtain distribution control analysis data, and obtain the load control index of the P1 period to the load control index of the Py period, the temperature and humidity coefficient of the P1 period to the temperature and humidity coefficient of the Py period, and the temperature and humidity coefficient of the target period according to the distribution control analysis data; Step S32: establishing a load fitting prediction model according to the temperature and humidity coefficient of the P1 period to the temperature and humidity coefficient of the Py period and the load regulation index of the P1 period to the load regulation index of the Py period; Step S33: Substitute the temperature and humidity coefficient of the target period into the load fitting prediction model, and obtain the result value output by the load fitting prediction model to obtain the load control index of the target period; Step S34: Obtain a reasonable range of the load control index. If the load control index in the target time period is within the reasonable range of the load control index, the distribution network does not need to perform power control on the target power user. If the load control index in the target time period is not within the reasonable range of the load control index, the distribution network needs to perform power control on the target power user.

10. A system for interactively controlling loads and distribution networks in multiple scenarios, applicable to a method for interactively controlling loads and distribution networks in multiple scenarios as claimed in any one of claims 1 to 9, characterized in that: The system comprises: Power usage scenario module: conducts multi-scenario power usage analysis on the target power user, matches multiple power usage matching units to the target power user based on the analysis results, and obtains power usage scenario matching data; Power distribution analysis module: obtain a power consumption control period, perform time period temperature and humidity analysis on the target power consumption unit in the power consumption control period, obtain the target time period temperature and humidity coefficient, select multiple historical power consumption periods, obtain the time period temperature and humidity coefficient and load control index of each power consumption matching unit in the historical power consumption period according to the power consumption scenario matching data, and obtain the power distribution control analysis data; Load forecasting module: used to perform linear fitting on temperature and humidity coefficients of multiple time periods and load control indexes of multiple time periods according to distribution control analysis data, obtain a load fitting forecasting model, and obtain a target load control index according to the fitting forecasting model; Grid control module: used to control the power of target power users according to the load control index during the target period.

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