CN105116723B - A kind of energy consumption for requirement response cuts down assessment algorithm - Google Patents

Abstract

The invention relates to an energy consumption reduction evaluation algorithm for demand response. The method is divided into two tasks in the execution process, which are the energy consumption data collection and preprocessing tasks on weekdays, and the demand response processing on event days. affairs. The advantage of the present invention is that: by adopting the historical data analysis method, the historical operating rules such as climate conditions and operating conditions on the demand side are converted into baseline calculation factors, which are applied to the prediction of the current energy consumption situation, ensuring the accuracy of the energy consumption baseline prediction accuracy. Simultaneously measure and evaluate the instantaneous value and the total amount of energy consumption, firstly to avoid the limitation that the instantaneous value cannot reflect the overall energy consumption, and secondly to meet the needs of the power supply side for evaluating peak energy consumption.

Description

An Evaluation Algorithm for Energy Consumption Reduction for Demand Response

technical field

The invention relates to a method for evaluating energy consumption reduction in a demand response system.

Background technique

The power supply enterprise plans, executes and monitors the grid activities on the demand side by designing a series of operation plans. This kind of plan planning is called demand side management. Demand response is one of the solutions of demand side management. It refers to the mechanism that power supply companies guide users to adjust electricity consumption by modifying real-time electricity prices, selling energy consumption reduction indicators and other economic incentives, so as to achieve peak load reduction and valley filling, and ensure power balance.

In the demand response scheme, the power supply enterprise sends real-time electricity prices or energy consumption reduction requirements to users by establishing "demand response events", and the users execute energy consumption reduction actions and give feedback on the results. In this system, an evaluation system needs to be established to evaluate whether the user has completed the energy consumption reduction request proposed by the energy supplier, and how effective it is.

Because it involves the economic interests of both power supply and electricity consumption, a fair, objective and automatic energy consumption reduction evaluation algorithm is the core issue for realizing demand response.

Existing several related patents relate to this field at present, but there are still some deficiencies.

1. Patent 201410061409.4 "Automatic Demand Response Evaluation System and Method for Demand Side Management"

This patent discloses an automatic demand response evaluation system and method for demand side management, including a data collection module, a user demand response implementation effect evaluation module, a user simulation settlement module, a user assessment and filing module, and a regional power grid response capability evaluation module; The reasonable evaluation of demand response data is realized, which provides a basis for the improvement of demand response, is conducive to continuously improving the implementation effect of demand response, and fully exerts the important role of demand response in regional energy optimization.

The "User Demand Response Implementation Effect Evaluation Module" involved in this patent may have the following problems in actual implementation.

1) The instantaneous value of 5 minutes or 15 minutes is used as the energy consumption measurement value.

The energy consumption curves of industrial enterprises often have large fluctuations. In this case, only instantaneous value collection is used, which cannot reflect the actual energy consumption within a period of time, which is not fair and objective enough.

2) When calculating the baseline, the calculation of the meteorological adjustment factor is based on the collected values two hours before the occurrence of the demand response event as the calculation basis.

This calculation method has the possibility of user fraud. Taking cold storage as an example, users can lower the temperature and increase energy consumption two hours before a demand response event occurs, and the energy consumption baseline will increase on the original basis. During the demand response event period, the user can complete the event assessment index by maintaining a slightly lower temperature than usual. The actual energy consumption has not been reduced.

2. Patent 201310732406.4 "A Comprehensive Evaluation Method for Intelligent Power Demand Response Plan"

This patent discloses a macro-execution process of a demand response plan and requirements for evaluation indicators. Among them, the "obtaining user response rate index, user effective response rate index, and user power reduction ratio index" mentioned in claim 4 is mainly used to make statistics on the execution status of all user sides on the power supply side. The specific evaluation algorithm of each indicator does not give details.

Contents of the invention

The purpose of the present invention is to provide a more objective, fair and operable method for evaluating energy consumption reduction in the demand response system.

In order to achieve the above object, the technical solution of the present invention is to provide an energy consumption reduction evaluation algorithm for demand response, which is characterized in that it includes collecting energy consumption data on the power consumption side on weekdays and receiving power supply at the power consumption side After the demand response event on the power consumption side, the demand response event transaction is executed, and the energy consumption data collection on the power consumption side on weekdays includes the following steps:

The user side calculates the demand value of each working day and time period in daily operation in real time according to the preset collection interval, and stores it in the database. Each working day is divided into multiple time periods according to the collection interval , the demand value of a certain period = (active power at the end of the current time period – active power at the beginning of the current time period) * (60/minute value of the collection interval), and store the data and the corresponding collection time in the database Inside;

After the consumer side receives the demand response event from the power supply side, executing the demand response event transaction includes the following steps:

Step 1. The power consumption side obtains demand response event requirements from the power supply side, including at least the following information: event time, demand response total value DRtotal and maximum power consumption DRins;

Step 2. Taking several working days before the event as the baseline calculation date, where the event date is the date when the demand response event occurs;

Step 3. Calculate the mean value of the demand in the same time period on each baseline calculation day, and use the mean value of the demand as the baseline value to obtain the baseline value corresponding to each time period, thereby forming the user demand baseline, where the i-th The baseline value of the time period is DM _i ;

Step 4. Calculating the energy consumption reduction index, the energy consumption reduction index includes at least the achievement rate of the total energy consumption reduction value and the achievement rate of the instantaneous power, wherein:

In the formula, n is the total number of time periods included in the event time, and DE _i is the demand value of the i-th time period calculated in real time;

Instantaneous power achievement rate = number of DE _i that do not exceed DRins/n;

Step 5. Send back the energy consumption reduction index.

Preferably, in said step 2, the selection method of the baseline calculation day includes the following steps:

Step 2-1. Select several working days before the event date as candidate baseline calculation days;

Step 2-2. Sum the demand values corresponding to all time periods of each candidate baseline calculation day, and then average them to obtain the average value of the demand value of each candidate baseline calculation day, and judge whether each average value meets the preset value of the power consumption side. Judgment criteria for normal working days, if there are unqualified candidate baseline calculation days, go to step 2-3, otherwise, go to step 2-4;

Step 2-3. Eliminate the non-compliant candidate baseline calculation days, postpone the previous working day as the new candidate baseline calculation day, and then return to step 2-2;

Steps 2-4, use the candidate baseline calculation date as the baseline calculation date.

Preferably, in said step 3, the baseline value is adjusted using the adjustment factor P, then the baseline value of the i-th time period DM _ij is the demand value of the i-th time period on the j-th baseline calculation day, and m is the total number of baseline calculation days.

Preferably, the adjustment factor P is:

The average value of the demand on the adjustment factor day with the highest degree of conformity with the adjustment factor of the event day/the average value of the demand on the adjustment factor day with the highest degree of conformity with the average value of the adjustment factor on the baseline calculation day, where:

If there are K adjustment factors, K≥1, and the event date and baseline calculation date are set as the base date, then there are K base adjustment factors, and each historical working day is calculated separately within the pre-set adjustment factor date limit The degree of conformity between the adjustment factor of the adjustment factor and the adjustment factor or the average value of the adjustment factor on the base day, the historical working day with the highest degree of conformity is taken as the adjustment factor day, and the lth historical working day is defined as the lth searched day, then the lth The degree of coincidence between the adjustment factor on the searched date and the adjustment factor or the average value of the adjustment factor on the base date is:

The advantages of the present invention are:

First, adopt the method of historical data analysis to convert the historical operating laws such as climate conditions and operating conditions on the demand side into baseline calculation factors, and apply them to the prediction of current energy consumption to ensure the accuracy of energy consumption baseline predictions.

Second, measure and evaluate the instantaneous value and the total amount of energy consumption at the same time, firstly to avoid the limitation that the instantaneous value cannot reflect the overall energy consumption, and secondly to meet the needs of the power supply side for evaluating peak energy consumption.

Description of drawings

Figure 1 is a flow chart of collecting energy consumption data on the power consumption side on weekdays;

Fig. 2 is a flow chart of executing a demand response event transaction after receiving a demand response event from a power supply side at the power consumer side.

detailed description

In order to make the present invention more comprehensible, preferred embodiments are described in detail below with accompanying drawings.

Some specific concepts used in the present invention are now set forth as follows:

1) Working day: the date when the electricity side conducts production and operation activities.

2) Working day period: When the production and operation situation changes greatly, it needs to be distinguished. Therefore, normal production days can be set by the user and belong to different periods. For example, if a new production line is added to the power consumption side, the production energy consumption will inevitably change greatly, and a working day period needs to be restarted.

3) Demand: Refers to the energy consumption generated by the electricity side during normal daily production and operation. This value is statistically calculated from the measured value "active power" of the electricity meter.

4) Judgment standard of normal working day: the standard used to judge whether the demand value of a working day can reflect the normal production and operation situation is a percentage range set by the user.

5) Normal working days: select normal working days from several working days, and the demand of normal working days should be within the normal working day judgment standard range set by the user. The determination steps are as follows: Step 1, select several working days; Step 2, calculate the average demand of these working days; Step 3, divide the demand of each working day by the average value; Step 4, if a certain working day calculates If the income percentage is within the standard range of normal working days, then the day is a normal working day. For example, there are 5 working days, and the daily demand is 101, 102, 98, 30, 99 respectively, and the user sets the demand for normal working days to reach 50% of the average value, then the day when the demand is 30 cannot meet the conditions , not a normal working day. Load fluctuation is a normal performance in industrial enterprises. Therefore, when selecting normal working days, it is necessary to eliminate abnormal energy consumption and eliminate deviations caused by large production fluctuations in industrial enterprises.

6) User demand curve: refers to the curve formed according to the time of the demand on the electricity side within a day. The demand curve can fully express the energy consumption of the power consumption side on the day, which is the basis for the calculation of the demand baseline.

7) Event day: the date when a demand response event occurs, and the event day is not counted as a working day.

8) Baseline calculation day: the normal working day used to calculate the user demand baseline, usually select several days before the event day, and the number of days is set by the user according to his own situation. When selecting the baseline calculation day, non-normal working days need to be excluded, and if the number of days is not enough, it will be postponed forward. For example, if the event date is 2015/5/20 (Wednesday), you want to select 5 days as the baseline calculation date. First remove the rest days 5/16 and 5/17, and select the working days 5/13, 5/14, 5/15, 5/18, and 5/19. If 5/14 of the working day does not meet the requirements of normal working days, it will be postponed forward and choose 5/12. The final baseline calculation days are: 5/12, 5/13, 5/15, 5/18, 5/19.

9) User demand baseline: a benchmark curve that reflects the daily energy consumption of users on the power consumption side, calculated based on the average value of user demand on the baseline calculation day. The calculation of the baseline is to calculate the average value of the demand value at the same time point on the user demand curve on the original baseline calculation day to form a new curve. For example, if the demand collection is performed every 15 minutes on the baseline calculation day, 96 demand values should be generated in one day. There are baseline calculation days D1, D2, and D3. Then the demand values of the user demand curves of D1 are D1Wn (n=1~96), and so on, the demand values of D2 and D3 are D2Wn and D3Wn. Then the demand value of the user demand baseline is (D1Wn+D2Wn+D3Wn)/3 (n=1-96).

10) Baseline adjustment: Since there may be differences between the external environment of the event day and the baseline calculation day, it is necessary to introduce an adjustment mechanism to make up for this difference as much as possible to make the user demand baseline accurate.

11) Adjustment factors: In baseline adjustment, adjustment factors refer to various external conditions that will affect the baseline, such as temperature, air pressure, humidity, wind speed and other weather conditions, or other factors that may affect the baseline. The adjustment factor is specified by the user. For example, the user may specify air temperature and humidity as adjustment factors.

12) Adjustment factor date limit: It is used to specify the validity period of the data used in the adjustment factor, which is a period of time before the current date. For example, if the date is limited to 1 year, you can only use the data within 1 year before the current date to participate in the calculation of the compliance degree of the adjustment factor.

13) Adjustment factor compliance: an index used to evaluate whether the adjustment factor between two dates is close enough, wherein one date is a date within the date limit of the adjustment factor, defined as the searched date, and the other date is the current date , which is defined as the base date, assuming that both the searched date and the base date have K adjustment factors, the calculation method of the degree of compliance of the adjustment factors is as follows: For example, the adjustment factors are temperature and humidity. The temperature on the day of the event was 37.2°C and the humidity was 40%, and the temperature on the searched day was 37°C and the humidity was 41%. The highest humidity is 93%, and the lowest humidity is 11%. Taking the event date as the base date, the coincidence degree of adjustment factors is 1-(|(37-37.2)/(39.2-7.1)|+|(41-40)/93-11|)≈98.18%. The higher the value, the closer the adjustment factor between the two dates.

14) Adjustment factor days: There are two types of adjustment factor days, which are the days within the date limit of the adjustment factor that best meet the adjustment factors of the event day, and the days that best match the adjustment factors of the baseline calculation day within the limit of the date of the adjustment factor . These two dates need to meet within the same working day period, and the system will find them from historical data. The number of days for each date is set by the user. When there are multiple working day periods, it is necessary to find multiple groups of adjustment factor days from different periods, and use the group with a higher total coincidence degree as the adjustment factor day.

15) Adjustment factor: The adjustment factor is calculated from the demand value on the adjustment factor day. The specific way is:

The demand on the adjustment factor day that best matches the event day/the demand on the adjustment factor day that best meets the baseline calculation day.

16) Adjusted user demand baseline: the curve obtained by user demand baseline * adjustment factor.

17) Demand response event: A request for energy consumption reduction initiated by the power supply side, including event time, total reduction amount, maximum power consumption, and achievement rate index, is sent to the demand side.

18) Demand response event time: the time period when energy consumption needs to be reduced, such as 14:00-16:00.

An energy consumption reduction evaluation algorithm for demand response provided by the present invention is divided into two tasks in the execution process, which are the energy consumption data collection and preprocessing tasks on weekdays, and the demand response processing tasks on event days.

As shown in Figure 1, the steps of daily energy consumption data collection and preprocessing are as follows:

Step 1. Set the operating parameters

For daily energy consumption data collection and preprocessing tasks, users on the power consumption side need to set various system operating parameters according to external factors such as system environment and production and operation arrangements. The steps are as follows:

Step 1-1 sets the collection port parameters, including the following: port type, port specific collection parameters (according to different types of ports, such as Ethernet, serial port, have different configuration content), collection interval.

Step 1-2 sets the parameters of the collection device, including the following content: the device address of the device to be collected, and the data points to be collected in the device to be collected.

Steps 1-3 set the collection of demand response data points, including the following content: set the data points used for demand value calculation of demand response, and set the data points belonging to the adjustment factors.

Steps 1-4 set the demand baseline calculation parameters, including the following content: the number of days of baseline calculation days, the standard range of normal working days, and working days.

Steps 1-5 set the calculation parameters of the adjustment factor, including the following content: the calculation period of the adjustment factor.

Steps 1-6 set the server parameters of the demand response power supply side, including the following content: communication address, communication method, and access interval.

Step 2. Collect the daily operation data of the enterprise

Once the user configuration is complete, data collection in daily operations begins. When collecting, the steps are as follows:

Step 2-1 records the value and collection time of the data.

Step 2-2 stores the records into the database in the system for subsequent algorithms.

Step 3. Calculate the demand value of each time period

According to the collection interval set by the user, each working day is divided into different time periods. The demand calculation formula in a certain time period is as follows:

Demand = (active power at the end of the current time period – active power at the beginning of the current time period)*(60/minute value of collection interval). The demand of each time period is stored in the system for subsequent algorithms.

Step 4. The power consumer periodically accesses the power supply side to obtain demand response events

According to the access interval, the user side periodically asks the power supply side whether there is an unnecessary response event. If so, execute the demand response processing transaction for the event day. This step can be performed sequentially with steps 2 and 3, or it can be performed independently, and it is recommended to perform it independently.

Step 5. Set the period of the working day

When the production and operation situation changes greatly, the user should divide the working day into periods to distinguish it from the previous working days. This setting will be used later in the calculation of the climate adjustment factor. This step is not a fixed step and is performed by the user when needed.

Combined with Figure 2, the demand response processing transaction on the event day includes the following steps:

Step 1. Obtain demand response events

When a demand response event occurs, the power consumption side obtains the event request from the power supply side, including the following information: event time, demand response total value DRtotal, maximum power consumption DRins, and achievement rate index.

Step 2. Select the baseline calculation date

When the specific time of the demand response event is determined. Baseline calculation date must be selected first. Follow these steps:

Step 2-1. Select several working days before the event date as candidate baseline calculation days;

Step 2-2. Sum the demand values corresponding to all time periods of each candidate baseline calculation day, and then average them to obtain the average value of the demand value of each candidate baseline calculation day, and judge whether each average value meets the preset value of the power consumption side. Judgment criteria for normal working days, if there are unqualified candidate baseline calculation days, go to step 2-3, otherwise, go to step 2-4;

Step 2-3. Delay the non-compliant candidate baseline calculation day to the previous working day as the new candidate baseline calculation day, and then return to step 2-2;

Steps 2-4, use the candidate baseline calculation date as the baseline calculation date.

Step 3. Calculate and adjust the user demand baseline

Step 3-1, the user demand baseline is calculated by the average value of the demand curve value on the baseline calculation day;

Step 3-2. Select the days with the highest matching degree of the adjustment factors as the adjustment factor days;

Step 3-3, calculating the adjustment factor P;

Step 3-4, adjust the user demand baseline, the baseline value of the i-th time period DM _ij is the demand value of the i-th time period on the j-th baseline calculation day, and m is the total number of baseline calculation days.

Step 4. Calculate energy consumption reduction index

Execute demand reduction, and calculate the real-time data of demand value collected during the time period of the demand response event to obtain the reduction result.

In the formula, n is the total number of time periods included in the event time, and DE _i is the demand value of the i-th time period calculated in real time;

Instantaneous power attainment rate = number of DE _i not exceeding DRins/n.

Step 6. Feedback on energy consumption reduction results

The energy consumption reduction achievement rate calculated in the previous step is sent back to the power supply side.

The present invention will be further described below in conjunction with specific data. Assume that a company implemented a demand response event on May 20, 2015, and the process is as follows.

7.1 Transaction 1 - demand data collection and preprocessing transaction

7.1.1 Setting operating parameters

Among them, the collection interval is 15 minutes. Other collection port parameters, collection device parameters, and demand response power supply side server parameters have little relationship with the method itself, and the process is omitted.

The data point collection settings are as follows: active power, active energy, temperature. Among them, the active power is the data point for calculating the demand value, the temperature is the data point for the adjustment factor, and the active electric energy is used for calculating the instantaneous value of energy consumption.

The baseline calculation parameters are set as follows: the baseline calculation days are 5 days, the standard range of normal working days is 50% to 120%, and working days are arranged according to national holidays.

The calculation parameters of the adjustment factor are set as follows: the calculation period of the adjustment factor is one year, that is, from May 20, 2014 to May 19, 2015.

The working day period is set according to the company's situation as follows: May 20, 2014 to July 17, 2014 is one period, and July 18, 2014 to May 19, 2015 is another period.

7.1.2 Acquisition, calculation, event

After the running parameters are set, start running.

The running steps are divided into two processes as described above. One is the collection process, which is responsible for collecting daily operation data, calculating and storing it; the other is responsible for periodically accessing the server on the power supply side to obtain demand response events.

When the demand response event is obtained, transaction 2-demand response event transaction is started.

7.2 Transaction 2 - Demand Response Event Transaction

7.2.1 Get Demand Response Events

The user obtains a demand response event from the power supply side. The event date is May 20, 2015, the time period is 12:00-14:00, the total reduction is 10KW, the maximum demand is 2KW, and the achievement rate of the total demand reduction The index is 90%, and the maximum demand fulfillment rate index is 95%. The temperature at the time of the incident was 37°C.

7.2.2 Select baseline calculation date

Assume that the daily demand for the 6 working days before the event (5/12, 5/13, 5/14, 5/15, 5/18, 5/19) is 100KW, 101KW, 102KW, 98KW, 30KW, 99KW respectively. If the daily demand on 5/18 fails to meet the criteria for normal working days by 50% to 120%, then 5/18 will be excluded and 5/12 will be added. Then 5 days are up to standard. Then the baseline calculation date is determined.

7.2.3 Calculate user demand baseline

Firstly, the adjustment factor is calculated based on the energy consumption of the demand-side users in the previous year.

Let the average temperature of the baseline calculation day be 35°C. Then choose the adjustment factor day according to 35 ℃ and 37 ℃. Since there are two working day periods, search them separately in the two working day periods, and finally obtain the two days with the highest total matching degree as the adjustment factor days. For example, 34.5 degrees on June 11, 2014 and 36.3 degrees on June 20, 2014 are a group, and 35.2 degrees on August 13, 2014 and 36.9 degrees on August 17, 2014 are a group. It is obvious that the latter group is closer, and the latter group is taken as the adjustment factor day. Let the adjustment factor be P, then

P = daily demand on August 17, 2014/daily demand on August 13, 2014.

According to the user settings, the calculation is performed in a period of 15 minutes. Each period calculates the baseline value of the period named DMi.

Wherein, i represents a time period within a day, and i=1 starts to increase sequentially from 0:00. j represents the baseline day, and the number range of j is set to 5 days by the user.

Then the baseline value of time period i:

7.2.4 Calculation of energy consumption reduction indicators

According to the user setting, data collection is carried out in a period of 15 minutes.

In the formula, n is the total number of time periods included in the event time, and DE _i is the demand value of the i-th time period calculated in real time;

Instantaneous power achievement rate = number of _i DEs that do not exceed DRins/n, where i represents the time period within the event, and n represents the total number of time periods.

7.2.5 Feedback on energy consumption reduction results

The energy consumption reduction result calculated in the previous step is sent back to the power supply side.

Claims (4)

1. a kind of energy consumption for requirement response cuts down assessment algorithm, it is characterised in that is included in and carries out energy to electricity consumption side on ordinary days After consumption data acquisition and electricity consumption side receive the requirement response events of supply side, requirement response events affairs are performed, wherein, on ordinary days Energy consumption data acquisition is carried out to electricity consumption side to comprise the following steps：

User side is according to every workday each period that acquisition interval set in advance is calculated in daily operation in real time Requirement value, and be stored in database, wherein, the every workday is divided into multiple periods according to acquisition interval, some when Requirement value=(active power-current slot begins with work(power at the end of current slot) * (60/ acquisition intervals of section Minute value), and data and acquisition time corresponding with the data are stored in database；

After electricity consumption side receives the requirement response events of supply side, perform requirement response events affairs and comprise the following steps：

Step 1, electricity consumption side obtain the requirement of requirement response events from supply side, including at least following information：Event time, requirement are rung Aggregate values DRtotal and maximum power dissipation DRins should be required；

Step 2, using event several working days a few days ago as baseline day is calculated, wherein, event day responds thing for requirement occurs The date of part；

Step 3, the average that each baseline calculates the requirement of day same time period is calculated respectively, using the average of the requirement as baseline Value, obtains baseline value corresponding with each period, so as to form user's requirement baseline, wherein, the baseline value of i-th of period is DM_i；

Step 4, energy consumption reduction index is calculated, the energy consumption cuts down index and comprises at least energy consumption reduction aggregate values delivery rate and moment work( Rate delivery rate, wherein：

In formula, the total number for the period that n is included by event time, DE_iFor the need for i-th of period being calculated in real time Value；

The DE of instantaneous power delivery rate=not less than DRins_iNumber/n；

Step 5, energy consumption is cut down to index passback.

2. a kind of energy consumption for requirement response as claimed in claim 1 cuts down assessment algorithm, it is characterised in that in the step In rapid 2, the system of selection that baseline calculates day comprises the following steps：

Step 2-1, several working days of event a few days ago are selected as candidate's baseline and calculate day；

Each candidate is averagely obtained again step 2-2, each candidate's baseline to be calculated to requirement value summation corresponding to all periods of day after Baseline calculates the average of the requirement value of day, judges whether each average meets the judgement mark of electricity consumption side regular working day set in advance Standard, if incongruent candidate's baseline be present calculates day, into step 2-3, otherwise, into step 2-4；

Step 2-3, incongruent candidate's baseline is calculated and rejected day, the working day before postponing calculates as new candidate's baseline Day, it is then back to step 2-2；

Step 2-4, candidate's baseline is calculated and day calculates day as baseline.

3. a kind of energy consumption for requirement response as claimed in claim 1 cuts down assessment algorithm, it is characterised in that in the step In rapid 3, baseline value is adjusted using Dynamic gene P, then the baseline value of i-th of period DM_ijThe requirement value of i-th of period of day is calculated for j-th of baseline, m is the total number that baseline calculates day.

4. a kind of energy consumption for requirement response as claimed in claim 3 cuts down assessment algorithm, it is characterised in that the adjustment Factor P is：

With the adjustment factor degree of conformity highest of event day adjust the average of the requirement of factor day/with baseline calculate the adjustment of day because The average of the requirement of plain average degree of conformity highest adjustment factor day, wherein：

Provided with K adjustment factor, K >=1, and day on the basis of event day and baseline calculating day is set, then has K benchmark adjustment factor, Then the workaday adjustment factor of each history and Base day are calculated respectively in the range of adjustment factor date set in advance limitation Adjustment factor or adjust factor average degree of conformity, it is adjustment factor day to take degree of conformity highest history working day, by l-th History working day is defined as l-th and is searched day, then l-th of the adjustment factor and the adjustment factor or tune of Base day for being searched day The degree of conformity of whole factor average is：