Baseline load calculation and evaluation method for complex transmission systems participating in demand response

Abstract

The present application provides a method for calculating and evaluating the baseline load of a complex transportation system participating in demand response. When a demand response event occurs, historical load data of the complex transportation system without implementing demand response within the preset sampling time can be collected at a preset sampling period; and the historical load data is autocorrelated to obtain an autocorrelation curve, and the periodic characteristics of the historical load data are found according to the autocorrelation curve, and the load period of the complex transportation system is determined. Then, multiple similar days of the demand response day are selected by delaying an integer number of the load periods, and after obtaining the load value of each sampling period in each similar day, the load values ​​of multiple similar days and each sampling period in each similar day are used to calculate the baseline load of each sampling period in the demand response day. The algorithm of this process has low complexity and strong feasibility.

Description

Technical Field

[0001] The present application relates to the field of power control technology, and in particular to a method for calculating and evaluating a baseline load for a complex transmission system participating in demand response. Background Art

[0002] Demand response (DR) uses electricity price signals or financial subsidies to guide users to voluntarily change their electricity usage behavior, achieving two-way interaction between users and the power grid. DR is categorized into two types: price-based and incentive-based. Incentive-based DR encourages user participation through agreements and compensation payments. Baseline load calculation is a key component of incentive-based DR programs and serves as an important basis for compensation for participating users.

[0003] Currently, users participating in demand response programs fall into a variety of categories, including residential and large-scale commercial and industrial users. A typical example is a complex conveying system consisting of long-distance belt conveyors. Belt conveyors are essential equipment for transporting bulk materials over long distances in the industrial sector. Multiple belt conveyors, combined with buffer facilities such as silos and stockpiles, can form a complex conveying system. In existing complex conveying systems, the belt conveyor's on / off control mode results in complex characteristics in the total load of the conveying system, making it difficult to calculate the baseline load when the belt conveyor participates in load response. Summary of the Invention

[0004] The purpose of this application is to solve at least one of the above-mentioned technical defects, especially the technical defect that in the complex conveying system of the prior art, the switching control mode of the belt conveyor makes the total load of the conveying system present complex characteristics, which makes it difficult to calculate the baseline load when the belt conveyor participates in the load response.

[0005] The present application provides a method for calculating a baseline load for a complex transmission system participating in demand response, the method comprising:

[0006] When a demand response event occurs, historical load data of the complex transmission system without implementing demand response within the preset sampling time is collected at a preset sampling period;

[0007] performing autocorrelation processing on the historical load data to obtain an autocorrelation curve, and determining the load cycle of the complex transportation system according to the autocorrelation curve;

[0008] Selecting multiple similar days of the demand response day by delaying an integer number of the load cycles, and obtaining the load value of each sampling period in each similar day;

[0009] The baseline load of each sampling period in the demand response day is calculated by using multiple similar days of the demand response day and the load value of each sampling period in each similar day.

[0010] Optionally, collecting historical load data of the complex transportation system in a preset sampling period when no demand response is implemented within a preset sampling time includes:

[0011] Determine the preset sampling period, preset sampling time, and typical operation mode of complex conveying systems;

[0012] According to the typical operation mode of the complex transportation system, the historical load data of the complex transportation system without implementing demand response in each preset sampling period of the preset sampling time is simulated to obtain the historical load data of the complex transportation system in each preset sampling period.

[0013] Optionally, the performing autocorrelation processing on the historical load data to obtain an autocorrelation curve includes:

[0014] Determining a sequence width of an autocorrelation function according to the preset sampling period and the preset sampling time;

[0015] The autocorrelation function is used to perform autocorrelation processing on the historical load data, and an autocorrelation curve corresponding to the historical load data is determined according to the sequence width.

[0016] Optionally, determining the load cycle of the complex conveying system according to the autocorrelation curve includes:

[0017] According to the time interval of the periodic peak in the autocorrelation curve, the hysteresis value of the autocorrelation function is determined, and the hysteresis value is used as the load period of the complex conveying system.

[0018] Optionally, the calculation formula for calculating the baseline load of each sampling period on the demand response day by using multiple similar days of the demand response day and the load values ​​of each sampling period on each similar day is:

[0019]

[0020] in, is the baseline load of sampling period j on demand response day n, where j is the sampling period determined with a preset sampling period of 15 minutes. is the load value in the jth sampling period on the ith similar day of demand response day n, K is the total number of similar days, and τ is the lag value in the autocorrelation function, that is, the load period.

[0021] The present application also provides an evaluation method for a complex transmission system participating in a demand response baseline load calculation method based on any one of the above embodiments, the method comprising:

[0022] Multiple similar days of the demand response day are used as evaluation days, and multiple similar days corresponding to each evaluation day are selected by delaying an integer number of load cycles;

[0023] Obtain the load value of each sampling period in each similar day corresponding to each evaluation day;

[0024] Using the load values ​​of multiple similar days corresponding to each evaluation day and each sampling period on each similar day, calculate the average baseline load accuracy of each evaluation day and the average daily baseline load accuracy of the total number of evaluation days;

[0025] evaluating the average baseline load accuracy according to the evaluation index of the average baseline load accuracy, and evaluating the average daily average baseline load accuracy according to the evaluation index of the average daily average baseline load accuracy;

[0026] Based on the evaluation results, the baseline load calculation method for the complex transmission system participating in demand response is evaluated.

[0027] Optionally, the method of calculating the average baseline load accuracy of each evaluation day and the average daily baseline load accuracy of the total number of evaluation days by using the load values ​​of multiple similar days corresponding to each evaluation day and each sampling period of each similar day includes:

[0028] Calculate the baseline load of each sampling period on each evaluation day by using the load values ​​of multiple similar days corresponding to each evaluation day and each sampling period on each similar day;

[0029] Based on the baseline load of each sampling period on each evaluation day, the average baseline load accuracy of each evaluation day and the average daily average baseline load accuracy of the total number of evaluation days are calculated.

[0030] The present application also provides a device for calculating a baseline load for a complex transmission system participating in demand response, comprising:

[0031] A load data acquisition module is used to collect historical load data of the complex transmission system when a demand response event occurs, using a preset sampling period, when no demand response is implemented within the preset sampling time;

[0032] a load cycle determination module, configured to perform autocorrelation processing on the historical load data to obtain an autocorrelation curve, and determine the load cycle of the complex transportation system according to the autocorrelation curve;

[0033] A similar day determination module is used to select multiple similar days of the demand response day by delaying an integer number of the load cycles, and obtain the load value of each sampling period in each similar day;

[0034] The baseline load calculation module is used to calculate the baseline load of each sampling period in the demand response day by using multiple similar days of the demand response day and the load values ​​of each sampling period in each similar day.

[0035] The present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the method for calculating the baseline load of a complex transportation system participating in demand response as described in any of the above embodiments, and / or the steps of the method for evaluating the method for calculating the baseline load of a complex transportation system participating in demand response as described in any of the above embodiments.

[0036] The present application also provides a computer device, comprising: one or more processors, and a memory;

[0037] The memory stores computer-readable instructions, which, when executed by the one or more processors, execute the steps of the method for calculating the baseline load of a complex transportation system participating in demand response as described in any of the above embodiments, and / or the method for evaluating the method for calculating the baseline load of a complex transportation system participating in demand response as described in any of the above embodiments.

[0038] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0039] The present application provides a method for calculating and evaluating the baseline load of a complex transportation system participating in demand response. When a demand response event occurs, historical load data of the complex transportation system without implementing demand response within the preset sampling time can be collected at a preset sampling period. Since the historical load data of the complex transportation system using switch control has step characteristics and periodic characteristics, the traditional method of selecting similar days for baseline load processing on a daily basis as working days or non-working days has a low accuracy in the calculation results. Therefore, after obtaining the historical load data of the complex transportation system, the present application performs autocorrelation processing on the historical load data to obtain an autocorrelation curve, finds the periodic characteristics of the historical load data based on the autocorrelation curve, and determines the load cycle of the complex transportation system, and then selects multiple similar days of the demand response day by delaying an integer number of the load cycles, thereby improving the accuracy of the selection of similar days; further, the present application can also obtain the load value of each sampling period in each similar day, and use the load values ​​of multiple similar days of the demand response day and each sampling period in each similar day to calculate the baseline load of each sampling period in the demand response day. This process uses the averaging method to calculate the baseline load of the demand response day based on the reasonable selection of similar days, so that the complexity of the algorithm is low and it has strong feasibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 A flowchart of a method for calculating a baseline load for a complex transmission system participating in demand response provided in an embodiment of the present application;

[0042] Figure 2 A system architecture diagram of a cement clinker transportation system provided in an embodiment of the present application;

[0043] Figure 3 The load curve diagram provided in the embodiment of the present application is drawn based on 10 days of historical load data as an example;

[0044] Figure 4 A schematic diagram of the structure of the autocorrelation curve provided in an embodiment of the present application;

[0045] Figure 5 A flowchart of an evaluation method for a complex transmission system participating in a demand response baseline load calculation method provided in an embodiment of the present application;

[0046] Figure 6 A schematic diagram of a 100-day load curve for a complex transportation system provided in an embodiment of the present application;

[0047] Figure 7 Evaluation date provided for the embodiment of this application n-1 Schematic diagram of baseline load deviation rate curve;

[0048] Figure 8 The structure and schematic diagram of a complex transmission system participating in demand response baseline load calculation device provided in an embodiment of the present application;

[0049] Figure 9 A schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0051] Currently, users participating in demand response programs fall into a variety of categories, including residential and large-scale commercial and industrial users. A typical example is a complex conveying system consisting of long-distance belt conveyors. Belt conveyors are essential equipment for transporting bulk materials over long distances in the industrial sector. Multiple belt conveyors, combined with buffer facilities such as silos and stockpiles, can form a complex conveying system. In existing complex conveying systems, the belt conveyor's on / off control mode results in complex characteristics in the total load of the conveying system, making it difficult to calculate the baseline load when the belt conveyor participates in load response.

[0052] Based on this, this application proposes the following technical solutions, as shown below:

[0053] In one embodiment, Figure 1 As shown, Figure 1 This is a flow chart of a method for calculating a baseline load for a complex transportation system participating in demand response, provided in an embodiment of the present application. This application provides a method for calculating a baseline load for a complex transportation system participating in demand response, which may include:

[0054] S110: When a demand response event occurs, historical load data of the complex transportation system without implementing demand response within the preset sampling time is collected at a preset sampling period.

[0055] In this step, when calculating the baseline load of the complex transportation system participating in demand response, the demand response day when the demand response event occurs can be determined first, and then the historical load data of the complex transportation system when no demand response is implemented within the preset sampling time can be collected using a preset sampling period. In this way, the baseline load of the demand response day can be calculated based on the historical load data.

[0056] Among them, since the complex conveying system is a system constructed by multiple belt conveyors in conjunction with buffer facilities such as material warehouses and stockpiles, when collecting historical load data for this type of system, it is necessary to first determine the coverage of the baseline load calculation, as well as the preset sampling period and preset sampling time. In this way, the preset sampling period can be used to periodically collect historical load data of the complex conveying system when no demand response is implemented within the preset sampling time.

[0057] It is understood that the preset sampling period and preset sampling time of the present application can be determined based on the complexity of the complex conveying system and the coverage of the baseline load calculation. Of course, for more complex conveying systems, a longer preset sampling time can be set, such as one month, one quarter, etc. The preset sampling period can be 10 minutes, 20 minutes, half an hour, etc., without limitation.

[0058] S120: Performing autocorrelation processing on the historical load data to obtain an autocorrelation curve, and determining the load cycle of the complex transportation system according to the autocorrelation curve.

[0059] In this step, after obtaining the historical load data of the complex transportation system when no demand response is implemented within the preset sampling time through S110, since the historical load data of the complex transportation system using the switch control method has step characteristics and periodic characteristics, the traditional method of selecting similar days for baseline load processing on a daily basis as working days or non-working days results in a low accuracy of the calculation results.

[0060] Therefore, after obtaining the historical load data of the complex conveying system, the present application uses correlation analysis to process the load data in order to find the periodic characteristics of the historical load data of the complex conveying system. Specifically, the present application can perform autocorrelation processing on the historical load data to obtain an autocorrelation curve, and then use the autocorrelation curve to find the periodic characteristics of the historical load data, thereby determining the load cycle of the complex conveying system.

[0061] Among them, when the present application performs autocorrelation processing on historical load data, the autocorrelation function can be used to perform autocorrelation processing on the historical load data. Since autocorrelation (ACF) refers to a certain degree of correlation between a sequence and a sequence formed by itself after a certain order of lag. Therefore, when there is a periodic component in the historical load data obtained by the present application, the maximum value of the autocorrelation function can well reflect this periodicity. Based on this, the present application can also further determine the load cycle of the complex transmission system based on the autocorrelation curve, so that the similar day of the demand response day can be determined based on the load cycle.

[0062] S130: Selecting multiple similar days of the demand response day by delaying an integer number of load cycles, and obtaining the load value of each sampling period in each similar day.

[0063] In this step, after determining the load cycle of the complex transmission system according to the autocorrelation curve through S120, the present application can select multiple similar days of the demand response day by delaying an integer number of load cycles, and obtain the load value of each sampling period in each similar day.

[0064] Specifically, after the present application determines the load cycle of the complex transmission system, the present application can obtain five similar days before the demand response day from the preset sampling time by lagging an integer number of load cycles. For example, when the load cycle of the complex transmission system of the present application is one month, the present application can use the dates of one month, two months, three months, four months, and five months before the demand response day as the corresponding similar days, and obtain the load value of each sampling period on each similar day through the historical load data collected in the complex transmission history within the preset sampling time.

[0065] It is understood that the sampling period here is determined based on the preset sampling cycle. For example, when the preset sampling cycle of this application is 15 minutes, the sampling period of a demand response day or a similar day can be divided into 96 segments. This application can obtain the load value of each sampling period on similar days through historical load data, and calculate the baseline load of each sampling period on the demand response day based on the load value of each sampling period on each similar day.

[0066] S140: Calculate the baseline load of each sampling period in the demand response day by using multiple similar days of the demand response day and the load values ​​of each sampling period in each similar day.

[0067] In this step, after selecting multiple similar days of the demand response day through S130 and obtaining the load values ​​of each sampling period in each similar day, the present application can use the averaging method to calculate the baseline load of the demand response day so as to obtain the baseline load of each sampling period in the demand response day.

[0068] In the above embodiment, when a demand response event occurs, historical load data of the complex transportation system without implementing demand response within the preset sampling time can be collected at a preset sampling period; since the historical load data of the complex transportation system using switch control has step characteristics and periodic characteristics, the traditional method of using days as units and selecting similar days for baseline load processing as working days or non-working days results in a lower accuracy of the calculation results. Therefore, after obtaining the historical load data of the complex transportation system, the present application performs autocorrelation processing on the historical load data to obtain an autocorrelation curve, finds the periodic characteristics of the historical load data based on the autocorrelation curve, and determines the load cycle of the complex transportation system, and then selects multiple similar days of the demand response day by delaying an integer number of the load cycles, thereby improving the accuracy of the selection of similar days; further, the present application can also obtain the load value of each sampling period in each similar day, and use the load values ​​of multiple similar days of the demand response day and each sampling period in each similar day to calculate the baseline load of each sampling period in the demand response day. This process uses the averaging method to calculate the baseline load of the demand response day based on the reasonable selection of similar days, so that the complexity of the algorithm is low and it has strong feasibility.

[0069] In one embodiment, collecting historical load data of the complex transportation system without implementing demand response within the preset sampling time at a preset sampling period in S110 may include:

[0070] S111: Determine a preset sampling period, a preset sampling time, and a typical operation mode of the complex conveying system.

[0071] S112: Based on the typical operation mode of the complex transportation system, the historical load data of the complex transportation system without implementing demand response in each preset sampling period of the preset sampling time is simulated to obtain the historical load data of the complex transportation system in each preset sampling period.

[0072] In this embodiment, when collecting historical load data of a complex transportation system, the present application can first determine a preset sampling period, a preset sampling time, and a typical operating mode of the complex transportation system, and then simulate the historical load data of the complex transportation system in each preset sampling period of the preset sampling time without implementing demand response based on the typical operating mode of the complex transportation system. In this way, the historical load data of the complex transportation system in each preset sampling period can be obtained.

[0073] In a specific embodiment, Figure 2 As shown, Figure 2The system architecture diagram of the cement clinker transportation system provided in the embodiment of the present application; the present application can simulate the load data with the cement clinker transportation system as the object. Before simulation, the typical operation mode of the cement clinker transportation system can be determined first: the current cement clinker transportation system adopts a conventional switch control scheme, and the switch control starts and stops the transportation line conveyor completely according to the inventory. For example, if the clinker inventory in silo 1 is greater than or equal to its upper limit, transportation line 1 is started; on the contrary, when the inventory of silo 1 is less than or equal to its lower limit, line 1 is stopped; the transportation line 2 conveyor starts and stops according to the inventory of silo 2, and when the inventory of silo 2 is greater than or equal to its upper limit, transportation line 2 is started; when the inventory of silo 2 is less than or equal to its lower limit, transportation line 2 is stopped; the transportation line 3 conveyor starts and stops according to the inventory of silo 3, and when the inventory of silo 3 is greater than or equal to its upper limit, transportation line 3 is started; when the inventory of silo 3 is less than or equal to its lower limit, transportation line 3 is stopped.

[0074] Therefore, when calculating the baseline load for the above-mentioned cement clinker transportation system, it is sufficient to obtain long-term load data through the acquisition system or management system. This application temporarily obtains load data in a simulation manner for the following analysis:

[0075] This application can follow the typical operation mode of the cement clinker transportation system mentioned above, take the daily clinker output of cement plants 1 to 3 as a fixed value, set the amount of clinker transported from other places as a fixed value, take the preset sampling period as 15 minutes, and set the preset sampling time as 100 days, so that a total of 9600 historical load data can be obtained through simulation calculation. Figure 3 As shown, Figure 3 The load curve diagram provided in the embodiment of the present application is drawn based on 10 days of historical load data as an example; Figure 3 It can be seen that since the three transport lines adopt the switch control mode, when a transport line is started and its power is constant, the total load curve presents a step-like feature, and from Figure 3 It can also be seen from the load curve diagram that the load curve of the complex transmission system has periodic characteristics, but its period is not 24 hours. This also means that when selecting similar days for baseline load calculation, it cannot be selected according to working days or non-working days, but rather a similar day to the demand response day must be selected according to the periodic characteristics of the load curve.

[0076] In one embodiment, performing autocorrelation processing on the historical load data in S120 to obtain an autocorrelation curve may include:

[0077] S121: Determine a sequence width of an autocorrelation function according to the preset sampling period and the preset sampling time.

[0078] S122: Performing autocorrelation processing on the historical load data using the autocorrelation function, and determining an autocorrelation curve corresponding to the historical load data according to the sequence width.

[0079] In this embodiment, when performing autocorrelation processing on historical load data, the sequence width of the autocorrelation function can be determined according to a preset sampling period and a preset sampling time, and then the autocorrelation function is used to perform autocorrelation processing on the historical load data, and the autocorrelation curve corresponding to the historical load data is determined according to the sequence width.

[0080] It can be understood that the autocorrelation function (ACF) is an average measure of the characteristics of a signal in the time domain. It is used to describe the dependence of the value of a signal at one moment on the value at another moment and is defined as:

[0081]

[0082] Where N is the sequence width, τ is the lag, x(τ) is the value at time τ, and x(n+τ) is the value at time n+τ. Figure 3 It can be seen that the total load curve of the complex transportation system of this application has a certain periodicity. Therefore, after the historical load data is autocorrelated, it will reach a peak at the period. This application can use the autocorr function in Matlab to complete the autocorrelation processing of the historical load data, and set 'NumLags' to the preset sampling time corresponding to the historical load data, such as 1000, to obtain the autocorrelation curve, such as Figure 4 As shown, Figure 4 A schematic diagram of the structure of the autocorrelation curve provided in the embodiment of the present application; Figure 4 It can be seen that the ACF value has periodic peaks. Further calculation shows that the peaks appear at sampling points multiple of 198, so the lag τ can be set to 198.

[0083] In one embodiment, determining the load cycle of the complex conveying system according to the autocorrelation curve in S120 may include:

[0084] S123: Determine a hysteresis value of the autocorrelation function according to a time interval at which periodic peaks appear in the autocorrelation curve, and use the hysteresis value as a load period of the complex transportation system.

[0085] In this embodiment, when determining the load cycle of a complex conveying system based on the autocorrelation curve, the present application can first determine the lag value of the autocorrelation function based on the time interval of the periodic peak in the autocorrelation curve, so that the lag value can be used as the load cycle of the complex conveying system.

[0086] In a specific embodiment, Figure 4 As shown, Figure 4 The ACF value of the autocorrelation curve in the graph shows a periodic peak. Further calculation shows that the peak appears at 198 times the sampling point, so the lag τ can be set to 198. Figure 3 It can also be seen that in a complex transmission system controlled by switching mode, the total load curve presents a step-like characteristic, so the load amplitude is not suitable as a feature for selecting similar days, and it is more reasonable to select similar days based on the hysteresis τ. In addition, the calculation is performed with the preset period of 15 minutes. Figure 4 The load cycle of the medium-complex transportation system is 198×15min=49.5 days. Therefore, similar days cannot be selected in units of 1 day to calculate the baseline load. Instead, similar days of demand response days should be selected based on the load cycle. This way, the similar days obtained are more accurate.

[0087] In one embodiment, the calculation formula for calculating the baseline load of each sampling period on the demand response day by using multiple similar days of the demand response day and the load values ​​of each sampling period on each similar day is:

[0088]

[0089] in, is the baseline load of sampling period j on demand response day n, where j is the sampling period determined with a preset sampling period of 15 minutes. is the load value in the jth sampling period on the ith similar day of demand response day n, K is the total number of similar days, and τ is the lag value in the autocorrelation function, that is, the load period.

[0090] It can be understood that the sampling period in the above calculation formula is the sampling period determined when 15 minutes is the preset sampling period. This application can also set other preset sampling periods according to actual conditions and obtain different sampling periods, which is not limited here.

[0091] In one embodiment, Figure 5 As shown, Figure 5 A flowchart of a method for evaluating a complex transportation system participating in a demand response baseline load calculation method provided in an embodiment of the present application is provided. The present application also provides a method for evaluating a complex transportation system participating in a demand response baseline load calculation method based on any of the above embodiments, which method may include:

[0092] S201: multiple similar days to the demand response day are used as evaluation days, and multiple similar days corresponding to each evaluation day are selected by delaying an integer number of load cycles.

[0093] S202: Obtain the load value of each sampling period in each similar day corresponding to each evaluation day.

[0094] S203: Calculate the average baseline load accuracy of each evaluation day and the average daily baseline load accuracy of the total number of evaluation days using the load values ​​of the multiple similar days corresponding to each evaluation day and the sampling periods of each similar day.

[0095] S204: Evaluate the average baseline load accuracy according to the evaluation index of the average baseline load accuracy, and evaluate the average daily average baseline load accuracy according to the evaluation index of the average daily average baseline load accuracy.

[0096] S205: Evaluate the baseline load calculation method for complex transmission systems participating in demand response based on the evaluation results.

[0097] In this embodiment, when evaluating the baseline load calculation method of the present application, multiple similar days of the demand response day can be used as evaluation days, and multiple similar days corresponding to each evaluation day can be selected by delaying an integer number of load cycles. Then, the load value of each sampling period in each similar day is obtained, and the multiple similar days corresponding to each evaluation day and the load value of each sampling period in each similar day are used to calculate the average baseline load accuracy of each evaluation day and the average daily average baseline load accuracy under the total number of evaluation days. In this way, the average baseline load accuracy can be evaluated according to the evaluation index of the average baseline load accuracy, and the average daily average baseline load accuracy can be evaluated according to the evaluation index of the average daily average baseline load accuracy, and then the complex transmission system participating in the demand response baseline load calculation method can be evaluated according to the evaluation results.

[0098] For example, this application can define the demand response day as d n , and define the five consecutive typical days before the demand response day as d n-1 d n-2 d n-3 d n-4 d n-5 Since the baseline load of the evaluation day needs to obtain K similar days when calculating, the application can obtain similar days of the evaluation day by lagging an integer number of load cycles. n-1 The K similar days of a day are defined as The sampling period is 15 minutes, d n-1 The daily load sampling is expressed as Similar days The load is expressed as Similarly, d n-2 The K similar days of a day can be expressed as The daily load sampling is expressed as Similar days The load is expressed as Other evaluation date n-3 d n-4 and d n-5 Process in the same way.

[0099] Then, the present application can use the load values ​​of the multiple similar days corresponding to each evaluation day and the sampling periods of each similar day to calculate the average baseline load accuracy of each evaluation day and the average daily average baseline load accuracy of the total number of evaluation days. In this way, the average baseline load accuracy can be evaluated according to the evaluation index of the average baseline load accuracy, and the average daily average baseline load accuracy can be evaluated according to the evaluation index of the average daily average baseline load accuracy. For example, when the present application takes d n-1 d n-2 d n-3 d n-4 d n-5 For the evaluation day, the accuracy of the average daily baseline load for 5 days must be greater than or equal to 80% or d n-1 The accuracy of the daily average baseline load is greater than or equal to 85%, which means that the baseline load calculation method of the present application is relatively effective.

[0100] In one embodiment, in S203, the average baseline load accuracy of each evaluation day and the average daily baseline load accuracy of the total number of evaluation days are calculated using the load values ​​of the multiple similar days corresponding to each evaluation day and the sampling periods of each similar day. The calculation may include:

[0101] S2031: Calculate the baseline load of each sampling period in each evaluation day by using the load values ​​of the multiple similar days corresponding to each evaluation day and each sampling period in each similar day.

[0102] S2032: Calculate the average baseline load accuracy of each evaluation day and the average daily baseline load accuracy of the total number of evaluation days based on the baseline load of each sampling period in each evaluation day.

[0103] In this embodiment, when calculating the average baseline load accuracy of each evaluation day, the load values ​​of multiple similar days corresponding to each evaluation day and each sampling period in each similar day can be used to calculate the baseline load of each sampling period in each evaluation day, and then the average baseline load accuracy of each evaluation day is calculated based on the baseline load of each sampling period in each evaluation day, and then the average daily average baseline load accuracy under the total number of evaluation days is calculated.

[0104] Among them, d n-1 For example, this application calculates dn-1 When calculating the average baseline load accuracy of the day, you can first calculate d n-1 Daily baseline load:

[0105]

[0106] in, Evaluation date n-1 The baseline load of the j sampling period, j is the sampling period determined when the preset sampling period is 15 minutes, Evaluation date n-1 The load value of the j sampling period in the ith similar day of , K is the total number of similar days, τ is the lag value in the autocorrelation function, that is, the load cycle. The baseline load of the other four evaluation days is calculated using the same method, and the results are expressed as and

[0107] After calculating the baseline load of each sampling period on each evaluation day, the baseline load deviation rate of the evaluation day can be calculated, expressed as d n-1 For example, d n-1 The calculation formula of the baseline load deviation rate is as follows:

[0108]

[0109] In this way, n-1 The average baseline load accuracy is:

[0110] A n-1 =(1-E (n-1)-RMS )×100%

[0111] in, A n-1 d n-1 The average baseline load accuracy, E (n-1)-RMS d n-1 The baseline load deviation rates of the other four evaluation days were calculated in the same way and the results were expressed as and The average baseline load accuracy is also calculated according to the above formula, and the result is expressed as A n-2 、A n-3 、A n-4 and A n-5 .

[0112] After calculating the average baseline load accuracy for each evaluation day, this application can calculate the average daily average baseline load accuracy for the total number of evaluation days. The specific formula is as follows:

[0113]

[0114] In a specific embodiment, the present application can obtain 100 days of historical load data of a complex transportation system through simulation, such as Figure 6 As shown, Figure 6 Schematic diagram of the load curve of the complex transportation system for 100 days provided in the embodiment of this application; this application takes the 100th day as the demand response day n , the 99th is the evaluation day n-1 , the 98th day is the evaluation day n-2 , other evaluation days will be postponed accordingly. Figure 3 The period of the obtained load curve is 198 samples, so the hysteresis τ is set to 198.

[0115] Furthermore, the present application can calculate the baseline load deviation rate based on the load data obtained in the above manner and draw a curve, such as Figure 7 As shown, Figure 7 Evaluation date provided for the embodiment of this application n-1 Schematic diagram of the baseline load deviation rate curve; Then, the above calculation formula of this application can be used to calculate d n-1 The average baseline load accuracy of the day is A n-1 =93.78%.d n-2 d n-3 d n-4 d n-5 The daily baseline load deviation rate and average baseline load accuracy are also processed in the same way, and the results are statistically shown in Table 1:

[0116]

[0117] Table 1 Statistics of example results

[0118] As can be seen from Table 1, in this embodiment, the evaluation date d n-1 d n-2 d n-3 d n-4 d n-5 The accuracy of the average daily baseline load was 86.60%, and d n-1 The average baseline load accuracy rate reached 93.78%, which further verified the effectiveness of the calculation algorithm proposed in this application.

[0119] The following describes the complex transportation system participation demand response baseline load calculation device provided in an embodiment of the present application. The complex transportation system participation demand response baseline load calculation device described below and the complex transportation system participation demand response baseline load calculation method described above can be referenced to each other.

[0120] In one embodiment, Figure 8 As shown, Figure 8The present invention provides a structure and schematic diagram of a complex transmission system participating in demand response baseline load calculation device. The present invention also provides a complex transmission system participating in demand response baseline load calculation device, which can include a load data acquisition module 210, a load cycle determination module 220, a similar day determination module 230, and a baseline load calculation module 240, specifically including the following:

[0121] The load data acquisition module 210 is used to collect historical load data of the complex transportation system without implementing demand response within a preset sampling time at a preset sampling period when a demand response event occurs.

[0122] The load cycle determination module 220 is configured to perform autocorrelation processing on the historical load data to obtain an autocorrelation curve, and determine the load cycle of the complex transportation system according to the autocorrelation curve.

[0123] The similar day determination module 230 is configured to select multiple similar days of the demand response day by delaying an integer number of the load cycles, and obtain the load value of each sampling period in each similar day.

[0124] The baseline load calculation module 240 is configured to calculate the baseline load for each sampling period on the demand response day by using multiple similar days to the demand response day and the load values ​​for each sampling period on each similar day.

[0125] In the above embodiment, when a demand response event occurs, historical load data of the complex transportation system without implementing demand response within the preset sampling time can be collected at a preset sampling period; since the historical load data of the complex transportation system using switch control has step characteristics and periodic characteristics, the traditional method of using days as units and selecting similar days for baseline load processing as working days or non-working days results in a lower accuracy of the calculation results. Therefore, after obtaining the historical load data of the complex transportation system, the present application performs autocorrelation processing on the historical load data to obtain an autocorrelation curve, finds the periodic characteristics of the historical load data based on the autocorrelation curve, and determines the load cycle of the complex transportation system, and then selects multiple similar days of the demand response day by delaying an integer number of the load cycles, thereby improving the accuracy of the selection of similar days; further, the present application can also obtain the load value of each sampling period in each similar day, and use the load values ​​of multiple similar days of the demand response day and each sampling period in each similar day to calculate the baseline load of each sampling period in the demand response day. This process uses the averaging method to calculate the baseline load of the demand response day based on the reasonable selection of similar days, so that the complexity of the algorithm is low and it has strong feasibility.

[0126] In one embodiment, the present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the method for calculating the baseline load of a complex transportation system participating in demand response as described in any of the above embodiments, and / or the steps of the method for evaluating the method for calculating the baseline load of a complex transportation system participating in demand response as described in any of the above embodiments.

[0127] In one embodiment, the present application further provides a computer device, including: one or more processors, and a memory.

[0128] The memory stores computer-readable instructions, which, when executed by the one or more processors, execute the steps of the method for calculating the baseline load of a complex transportation system participating in demand response as described in any of the above embodiments, and / or the method for evaluating the method for calculating the baseline load of a complex transportation system participating in demand response as described in any of the above embodiments.

[0129] Schematically, as Figure 9 As shown, Figure 9 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 can be provided as a server. Figure 9 Computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions executable by processing component 302, such as application programs. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. In addition, processing component 302 is configured to execute instructions to perform the method for calculating a baseline load for a complex transportation system participating in demand response according to any of the above-mentioned embodiments, and / or the method for evaluating the method for calculating a baseline load for a complex transportation system participating in demand response.

[0130] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or the like.

[0131] Those skilled in the art will understand that Figure 9The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0132] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0133] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0134] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for calculating baseline load for a complex transmission system participating in demand response, characterized in that: The method comprises: When a demand response event occurs, historical load data of the complex transmission system without implementing demand response within the preset sampling time is collected at a preset sampling period; performing autocorrelation processing on the historical load data to obtain an autocorrelation curve, and determining the load cycle of the complex transportation system according to the autocorrelation curve; Selecting multiple similar days of the demand response day by delaying an integer number of the load cycles, and obtaining the load value of each sampling period in each similar day; Calculating the baseline load for each sampling period on the demand response day by using a plurality of similar days to the demand response day and the load values ​​for each sampling period on each similar day; The autocorrelation curve obtained after performing autocorrelation processing on the historical load data includes: Determining a sequence width of an autocorrelation function according to the preset sampling period and the preset sampling time; performing autocorrelation processing on the historical load data using the autocorrelation function, and determining an autocorrelation curve corresponding to the historical load data according to the sequence width; The calculation formula for calculating the baseline load of each sampling period on the demand response day by using multiple similar days of the demand response day and the load values ​​of each sampling period on each similar day is: ； in, Demand Response Day middle Baseline load during the sampling period, The sampling period is determined when 15 minutes is the preset sampling period. Demand Response Day No. Similar days The load value during the sampling period, is the total number of similar days, is the lag value in the autocorrelation function.

2. The method for calculating baseline load of a complex transportation system participating in demand response according to claim 1, characterized in that: The collecting of historical load data of the complex transportation system without implementing demand response within the preset sampling time at a preset sampling period includes: Determine the preset sampling period, preset sampling time, and typical operation mode of complex conveying systems; According to the typical operation mode of the complex transportation system, the historical load data of the complex transportation system without implementing demand response in each preset sampling period of the preset sampling time is simulated to obtain the historical load data of the complex transportation system in each preset sampling period.

3. The method for calculating baseline load of a complex transportation system participating in demand response according to claim 1, characterized in that: Determining the load cycle of the complex conveying system according to the autocorrelation curve includes: According to the time interval of the periodic peak in the autocorrelation curve, the hysteresis value of the autocorrelation function is determined, and the hysteresis value is used as the load period of the complex conveying system.

4. An evaluation method for a complex transmission system participating in a demand response baseline load calculation method based on any one of claims 1 to 3, characterized in that: The method comprises: Multiple similar days of the demand response day are used as evaluation days, and multiple similar days corresponding to each evaluation day are selected by delaying an integer number of load cycles; Obtain the load value of each sampling period in each similar day corresponding to each evaluation day; Using the load values ​​of multiple similar days corresponding to each evaluation day and each sampling period on each similar day, calculate the average baseline load accuracy of each evaluation day and the average daily baseline load accuracy of the total number of evaluation days; evaluating the average baseline load accuracy according to the evaluation index of the average baseline load accuracy, and evaluating the average daily average baseline load accuracy according to the evaluation index of the average daily average baseline load accuracy; Based on the evaluation results, the baseline load calculation method for the complex transmission system participating in demand response is evaluated.

5. The method for calculating baseline load of a complex transmission system participating in demand response according to claim 4, characterized in that: The method of calculating the average baseline load accuracy of each evaluation day and the average daily baseline load accuracy of the total number of evaluation days by using the load values ​​of the multiple similar days corresponding to each evaluation day and the sampling period of each similar day includes: Calculate the baseline load of each sampling period on each evaluation day by using the load values ​​of multiple similar days corresponding to each evaluation day and each sampling period on each similar day; Based on the baseline load of each sampling period on each evaluation day, the average baseline load accuracy of each evaluation day and the average daily average baseline load accuracy of the total number of evaluation days are calculated.

6. A complex transmission system participating in demand response baseline load calculation device, characterized in that: include: A load data acquisition module is used to collect historical load data of the complex transmission system when a demand response event occurs, using a preset sampling period, when no demand response is implemented within the preset sampling time; a load cycle determination module, configured to perform autocorrelation processing on the historical load data to obtain an autocorrelation curve, and determine the load cycle of the complex transportation system according to the autocorrelation curve; A similar day determination module is used to select multiple similar days of the demand response day by delaying an integer number of the load cycles, and obtain the load value of each sampling period in each similar day; A baseline load calculation module, configured to calculate the baseline load for each sampling period on the demand response day by using a plurality of similar days to the demand response day and the load values ​​for each sampling period on each similar day; The load cycle determination module performs autocorrelation processing on the historical load data to obtain an autocorrelation curve, including: Determining a sequence width of an autocorrelation function according to the preset sampling period and the preset sampling time; performing autocorrelation processing on the historical load data using the autocorrelation function, and determining an autocorrelation curve corresponding to the historical load data according to the sequence width; The baseline load calculation module uses multiple similar days of the demand response day and the load values ​​of each sampling period on each similar day to calculate the baseline load of each sampling period on the demand response day. The calculation formula is: ； in, Demand Response Day middle Baseline load during the sampling period, The sampling period is determined when 15 minutes is the preset sampling period. Demand Response Day No. Similar days The load value during the sampling period, is the total number of similar days, is the lag value in the autocorrelation function.

7. A storage medium, characterized in that: The storage medium stores computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to execute the method for calculating the baseline load of a complex transportation system participating in demand response as described in any one of claims 1 to 3, and / or the steps of the evaluation method for calculating the baseline load of a complex transportation system participating in demand response as described in any one of claims 4-5.

8. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, execute the steps of the method for calculating the baseline load of a complex transportation system participating in demand response as described in any one of claims 1 to 3, and / or the steps of the method for evaluating the method for calculating the baseline load of a complex transportation system participating in demand response as described in any one of claims 4-5.

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