Power load regulation and control quantity distribution optimization method and device and power load management system

Abstract

The invention relates to a power load regulation and control quantity distribution optimization method and device and a power load management system, and the method comprises the steps: determining the regulation and control response completion capability of each load user through a pre-constructed polynomial regression model; and distributing the power load regulation and control quantity to each load user according to the regulation and control response completion capability of each load user. The power utilization property, the load type and other information of the load users are used as one of variables of the polynomial regression model, and power load regulation and control quantity distribution can be carried out on the load users in various fields and various types, so that the power load regulation and control quantity of each load user can be comprehensively coordinated; and the problem of non-uniform distribution of the power load regulation and control quantity among load users in different fields or different types is avoided. In conclusion, the problem that the existing power load regulation and control quantity distribution method cannot comprehensively coordinate and distribute various types of power load regulation and control quantities is solved.

Description

Technical Field

[0001] The present application relates to the field of electric power regulation, and in particular to a method and device for optimizing the distribution of electric power load regulation, and an electric power load management system. Background Art

[0002] In the current power system, there are many types of power loads, covering multiple fields such as industry, commerce, and residents. Their electricity consumption characteristics and needs are different, which makes load regulation also present diversified characteristics. The existing power load control quantity allocation method has low versatility and cannot match different types of power loads at the same time. Different load control allocation calculation methods are required for different types of power loads, which makes the operation of the power load management system more complicated. At the same time, different load control allocation calculation methods are independent of each other, which may lead to uneven distribution of different types of power load control quantities, resulting in excessive concentration of power load control quantities in a certain field.

[0003] Therefore, there is a need for an optimization method for allocating power load control quantities that can comprehensively coordinate and allocate various types of power load control quantities. Summary of the invention

[0004] The present invention provides a method and device for optimizing the distribution of electric load control quantities and an electric load management system to solve the problem that the existing electric load control quantity distribution method cannot comprehensively coordinate and distribute various types of electric load control quantities.

[0005] In a first aspect, the present invention provides a method for optimizing the distribution of power load control quantity, comprising:

[0006] The control response completion capability of each load user is determined by a pre-built polynomial regression model, and the polynomial regression model is:

[0007] Y=β1X4+β2X5+β3X6+β4X1X2X3+β5X7X8X9X 10

[0008] Among them, Y represents the control response completion capability, β1, β2, β3, β4 and β5 represent the first coefficient, the second coefficient, the third coefficient, the fourth coefficient and the fifth coefficient respectively, X1, X2 and X3 represent the power consumption nature, load type and control method of the load user respectively, X4, X5 and X6 represent the maximum controllable load, control participation rate and control completion degree of the load user respectively, X7, X8, X9 and X10 represent the load user's maximum controllable load, control participation rate and control completion degree respectively, 10 They represent holiday status, weather, temperature and control time period respectively;

[0009] The power load regulation amount is allocated to each of the load users according to the regulation response completion capability of each of the load users.

[0010] In a second aspect, the present invention provides a device for optimizing the distribution of power load control quantity, comprising:

[0011] The control completion calculation module is used to determine the control response completion capability of each load user through a pre-built polynomial regression model, wherein the polynomial regression model is:

[0012] Y=β1X4+β2X5+β3X6+β4X1X2X3+β5X7X8X9X 10

[0013] Among them, Y represents the control response completion capability, β1, β2, β3, β4 and β5 represent the first coefficient, the second coefficient, the third coefficient, the fourth coefficient and the fifth coefficient respectively, X1, X2 and X3 represent the power consumption nature, load type and control method of the load user respectively, X4, X5 and X6 represent the historical maximum controllable load, historical control participation rate and historical control completion degree of the load user respectively, X7, X8, X9 and X10 represent the load user's historical maximum controllable load, historical control participation rate and historical control completion degree respectively, 10 They represent holiday status, weather, temperature and control time period respectively;

[0014] The load distribution module is used to distribute the power load regulation amount to each of the load users according to the regulation response completion capability of each of the load users.

[0015] In a third aspect, the present invention provides a load control platform, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the power load control quantity distribution optimization method described in the first aspect.

[0016] In a fourth aspect, the present invention provides a power load management system, comprising the load control platform described in the third aspect, wherein the load control platform is deployed at the master station side of the power load management system.

[0017] In a fifth aspect, the present invention provides a method for managing power load, comprising:

[0018] The power load control quantity allocation optimization method described in the first aspect is used to allocate power load control quantity to each load user.

[0019] Compared with the related art, the present invention mainly calculates the control response completion capability of load users through a polynomial regression model, and uses information such as the power consumption characteristics and load type of load users as one of the variables of the polynomial regression model. Thus, the power load control quantity allocation optimization method can jointly allocate power load control quantities to load users in various fields and various types, rather than only allocating power load control quantities to load users in a certain field or a certain type. In this way, the power load control quantities of various load users can be fully coordinated to avoid the problem of uneven distribution of power load control quantities between load users in different fields or of different types. In summary, the present invention solves the problem that the existing power load control quantity allocation method cannot fully coordinate and allocate various types of power load control quantities.

[0020] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of a method for optimizing the distribution of power load control quantity provided in some embodiments of the present invention;

[0022] Figure 2 It is a flow chart of a method for optimizing distribution of electric load control quantity provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0024] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the general meaning understood by people with ordinary skills in the technical field to which this application belongs. The words "one", "a", "a", "the", "these" and the like in this application do not represent quantitative restrictions, and they can be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusions; for example, a process, method and system, product or device comprising a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether directly or indirectly. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: A exists alone, A and B exist at the same time, and B exists alone. Usually, the character " / " indicates that the objects associated with each other are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0025] In an embodiment of the present invention, a method for optimizing the distribution of power load control quantity is provided. Figure 1 is a flow chart of a method for optimizing the distribution of power load control quantity provided in some embodiments of the present invention, such as Figure 1 As shown, the process includes the following steps:

[0026] Step S110, determining the control response completion capability of each load user through a pre-built polynomial regression model, the polynomial regression model is:

[0027] Y=β1X4+β2X5+β3X6+β4X1X2X3+β5X7X8X9X 10

[0028] Among them, Y represents the control response completion capability, β1, β2, β3, β4 and β5 represent the first coefficient, the second coefficient, the third coefficient, the fourth coefficient and the fifth coefficient respectively, X1, X2 and X3 represent the power consumption nature, load type and control method of the load user respectively, X4, X5 and X6 represent the historical maximum controllable load, historical control participation rate and historical control completion degree of the load user respectively, X7, X8, X9 and X10 represent the load user's historical maximum controllable load, historical control participation rate and historical control completion degree respectively, 10 They respectively represent holiday status, weather, temperature and control time period.

[0029] Before determining the load user's control response completion capability, it is also necessary to obtain the load user's basic information, historical load control record information, current time and environmental factor information, etc.

[0030] The basic information of load users includes the nature of electricity consumption, load type and control method. The nature of electricity consumption is divided into three types: industrial electricity consumption, commercial electricity consumption and residential electricity consumption; the load type is mainly divided according to the purpose of electricity, such as lighting, industrial production, electric vehicle charging, air conditioning, etc.; the control method is divided into hard control and soft control.

[0031] The historical load control record information of load users includes the number of demand control times, the number of participation control times, the total load control demand, the load control completion amount, etc. Through these data, the control participation rate and control completion degree can be calculated. The control participation rate is the ratio of the number of participation control times to the number of demand control times, and the control completion degree is the ratio of the load control completion amount to the total load control demand.

[0032] The current time and environmental factor information includes the holiday status, weather, temperature and control time period of the control day. The holiday status refers to whether it is a holiday, the weather can be sunny, cloudy and rainy, and the temperature refers to whether it is in a certain characteristic temperature range.

[0033] After obtaining various information of a load user, the control response completion capability of the load user can be determined through a polynomial regression model.

[0034] Among them, the first coefficient, the second coefficient, the third coefficient, the fourth coefficient and the fifth coefficient are obtained by fitting historical data by the least square method, that is, fitting by the historical control response completion capability of each user and the corresponding basic information and historical load control record information and the current time and environmental factor information. Since the above data is continuously updated, the first coefficient, the second coefficient, the third coefficient, the fourth coefficient and the fifth coefficient can be updated in real time in combination with the latest control response completion capability and corresponding information data of each user.

[0035] It should be noted that the above information is usually categorical features, and in order to facilitate calculation, it needs to be converted into numerical features. Therefore, before this step, it is necessary to preprocess the basic information of load users, historical load control record information, and current time and environmental factors. Data preprocessing mainly includes:

[0036] Use label encoding to convert categorical features into numerical features; missing values ​​can be handled by filling (such as mean, median, mode filling), deletion or interpolation (such as linear interpolation, multiple interpolation) and other methods; for numerical features with large dimensional differences, normalization (scaling the features to between 0 and 1) is performed.

[0037] Step S120: Allocate the power load regulation amount to each load user according to the regulation response completion capability of each load user.

[0038] In the above technical scheme, the control response completion capability of the load user is mainly calculated through a polynomial regression model, and the information such as the power consumption nature and load type of the load user is used as one of the variables of the polynomial regression model. Then, the power load control quantity allocation optimization method can jointly allocate power load control quantities to various fields and various types of load users, rather than only allocating power load control quantities to a certain field or a certain type of load users. In this way, the power load control quantities of various load users can be fully coordinated to avoid the problem of uneven distribution of power load control quantities between different fields or different types of load users. In summary, the present invention solves the problem that the existing power load control quantity allocation method cannot fully coordinate and allocate various types of power load control quantities.

[0039] In some embodiments, step S120 includes:

[0040] Step S121, calculating the first power load regulation amount of each load user according to the regulation response completion capability of each load user, and the calculation formula is as follows:

[0041]

[0042] Among them, P Y represents the first power load control quantity, P Yk represents the first power load control quantity of the kth load user, P A represents the total demand for power load regulation, Y k and Y i They represent the control response completion capabilities of the kth and ith load users respectively, and n represents the total number of load users.

[0043] Step S122, calculate the actual adjustable load of each load user, the calculation formula is as follows:

[0044] P dk =P rk -P mink

[0045] Among them, P d , P r and P min They represent the actual controllable load, real-time load and minimum operating load respectively, P dk , P rk and P mink They respectively represent the actual controllable load, real-time load and minimum operating load of the kth load user.

[0046] Step S123, calculating the second power load control amount of each load user according to the actual controllable load amount of each load user, and the calculation formula is as follows:

[0047]

[0048] Among them, P w represents the second power load control quantity, P wk represents the second power load control quantity of the kth load user, P di Represents the actual controllable load of the i-th load user.

[0049] Step S124, determining the final power load regulation amount of each load user according to the first power load regulation amount and the second power load regulation amount of each load user, and the formula for determining the final power load regulation amount of each load user is as follows:

[0050]

[0051] Among them, P f Represents the final power load control amount, P fk Represents the final power load regulation amount of the kth load user.

[0052] 1. When the load user's first power load control amount is greater than the actual controllable load amount, that is, The final power load control amount of the load user is the actual controllable load amount. Ensure that the load control amount allocated to the user is within the user's controllable range and the rationality of task allocation.

[0053] 2. When the load user's first power load control amount is less than the actual controllable load amount and greater than half of the actual controllable load amount, that is, The final power load control amount of the load user is half of the sum of the first power load control amount and the second power load control amount.

[0054] 3. When the load user's first power load control amount is less than the actual controllable load amount, that is, The final power load control amount of the load user is the second power load control amount P w When the user's control response completion capability is low, the waste of user-controllable load resources is reduced.

[0055] As follows, the power load control quantity allocation optimization method in the present invention is illustrated by a specific embodiment.

[0056] Reference Figure 2 In a specific overall embodiment, the power load control quantity allocation optimization method includes:

[0057] S11, obtain load user information based on the electricity consumption information collection system, obtain time and environmental data based on any weather website, and obtain user historical load control record data based on the new power load management system.

[0058] The multi-dimensional data of the five participating users in this implementation case are shown in the following table:

[0059] Table 1 Multi-dimensional data table

[0060]

[0061]

[0062] S12, performing data preprocessing on the above user multi-dimensional data features.

[0063] (1) Based on the participation and influence of different data ranges in each single data item on load regulation, the categorical features are defined differently using label encoding, as shown in the following table:

[0064] Table 2 Feature encoding rules

[0065]

[0066]

[0067] (2) For numerical features with large dimensional differences, normalization (scaling the features to between 0 and 1) is performed.

[0068] Calculate user regulation participation rate and regulation completion rate.

[0069]

[0070] The user historical load regulation data in this embodiment is processed, and the processed data is as follows:

[0071] Table 3 Historical load control record processing results

[0072]

[0073] (3) Define the relationship between the variables of each eigenvalue of the function:

[0074] Table 4 Relationship between variable items and data items

[0075]

[0076]

[0077] First order: maximum controllable load X4 (the user's maximum controllable load directly affects the rationality of load distribution and the user's completion ability. It has a different effect from other data items and is not affected by other data items. Therefore, it is an independent first-order item in the polynomial), control participation rate X5 (the user's control participation rate directly affects the user's load control response and completion. It has a different effect from other data items and is not affected by other data items. Therefore, it is an independent first-order item in the polynomial), and control completion degree X6 (the user's historical control completion degree serves as a reference and direct judgment condition for the user's load control completion. It has a different effect from other data items and is not affected by other items. Therefore, it is an independent first-order item in the polynomial).

[0078] Third order: X1, X2 and X3 (the various data items in the user's basic information jointly affect the load regulation bearing capacity and response capacity basis, and therefore together constitute the third-order terms of the polynomial).

[0079] Level 4: X7, X8, X9 and X 10 (The various data items in the time and environmental factors jointly affect the user's ability to control the execution of tasks each time, and therefore together constitute the fourth-order term of the polynomial).

[0080] The programming library scikit-learn uses the least squares method to automatically complete the fitting of the coefficients of each function order.

[0081] The polynomial regression model for outputting the user regulation response completion capability is:

[0082] Y=0.001836X4+0.0171X5+0.0203X6+0.00133X1X2X3+

[0083] 0.00128X7X8X9X 10

[0084] The control response completion capability of each user is calculated through the polynomial regression model, as shown in the following table:

[0085] Table 5 Control response completion capability of each user

[0086]

[0087] S21, the total demand for this regulation is 860kW. According to the control response completion capability of the participating users, the control load is initially allocated using the following formula.

[0088]

[0089] Table 6 User control response completion capacity load distribution of each user (first power load control amount)

[0090]

[0091] S22, calculate the actual adjustable load P of the participating users d .

[0092] P dk =P rk -P mink

[0093] Table 7 Load data of each user

[0094]

[0095]

[0096] S23, performing secondary distribution of the regulated load according to the regulated load amounts of the participating users.

[0097]

[0098] Table 8 Distribution of adjustable load for each user (second power load adjustment amount)

[0099]

[0100] S24, make the final load control amount allocation for the user according to the results of the two allocations. Compare the load of the user's control response to complete the capacity allocation with the user's controllable load:

[0101]

[0102] Table 9 Load of each user

[0103]

[0104] In summary, the load control and allocation optimization method under power demand management provided by the present invention accurately describes the relationship between the load and the characteristics of multiple factors by constructing a polynomial function. As the user's control task is completed, the nonlinear regression model is automatically iterated and optimized, and the user's control response completion capability analysis is made more accurate based on load user information, time and environmental factors, and historical load control record data. Based on the comparison of the relationship between the user's controllable load, the rationality of the control allocation is optimized, and the completion of the control task is continuously improved without affecting the user's basic electricity consumption.

[0105] In an embodiment of the present invention, a device for optimizing the distribution of power load control quantity is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. The terms "module", "unit", "subunit", etc. used below may implement a combination of software and / or hardware of predetermined functions. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0106] The power load control quantity distribution optimization device includes:

[0107] The control completion calculation module is used to determine the control response completion capability of each load user through a pre-built polynomial regression model. The polynomial regression model is:

[0108] Y=β1X4+β2X5+β3X6+β4X1X2X3+β5X7X8X9X 10

[0109] Among them, Y represents the control response completion capability, β1, β2, β3, β4 and β5 represent the first coefficient, the second coefficient, the third coefficient, the fourth coefficient and the fifth coefficient respectively, X1, X2 and X3 represent the power consumption nature, load type and control method of the load user respectively, X4, X5 and X6 represent the historical maximum controllable load, historical control participation rate and historical control completion degree of the load user respectively, X7, X8, X9 and X10 represent the load user's historical maximum controllable load, historical control participation rate and historical control completion degree respectively, 10 They represent holiday status, weather, temperature and control time period respectively;

[0110] The load distribution module is used to distribute the power load regulation amount to each load user according to the regulation response completion capability of each load user.

[0111] In the above technical scheme, the control response completion capability of the load user is mainly calculated through a polynomial regression model, and the information such as the power consumption nature and load type of the load user is used as one of the variables of the polynomial regression model. Then, the power load control quantity allocation optimization method can jointly allocate power load control quantities to various fields and various types of load users, rather than only allocating power load control quantities to a certain field or a certain type of load users. In this way, the power load control quantities of various load users can be fully coordinated to avoid the problem of uneven distribution of power load control quantities between different fields or different types of load users. In summary, the present invention solves the problem that the existing power load control quantity allocation method cannot fully coordinate and allocate various types of power load control quantities.

[0112] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0113] In an embodiment of the present invention, a load control platform is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the power load control quantity distribution optimization method provided by the present invention.

[0114] In an embodiment of the present invention, there is also provided an electric load management system, comprising the load control platform provided by the present invention, wherein the load control platform is deployed at the master station side of the electric load management system.

[0115] In an embodiment of the present invention, a method for managing power load is also provided, comprising:

[0116] The power load regulation quantity allocation optimization method provided by the present invention is used to allocate the power load regulation quantity to each load user.

[0117] It should be understood that the specific embodiments described herein are only used to explain the application, rather than to limit it. Based on the embodiments provided in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the protection scope of this application.

[0118] Obviously, the drawings are only some examples or embodiments of the present application. For ordinary technicians in the field, the present application can also be applied to other similar situations based on these drawings without creative work. In addition, it is understandable that although the work done in this development process may be complicated and lengthy, for ordinary technicians in the field, certain changes in design, manufacturing or production based on the technical content disclosed in this application are only conventional technical means and should not be regarded as insufficient content disclosed in this application.

Claims

1. A method for optimizing the distribution of power load control quantity, characterized in that: include: The control response completion capability of each load user is determined by a pre-built polynomial regression model, and the polynomial regression model is: Y=β1X4+β2X5+β3X6+β4X1X2X3+β5X7X8X9X 10 Among them, Y represents the control response completion capability, β1, β2, β3, β4 and β5 represent the first coefficient, the second coefficient, the third coefficient, the fourth coefficient and the fifth coefficient respectively, X1, X2 and X3 represent the power consumption nature, load type and control method of the load user respectively, X4, X5 and X6 represent the maximum controllable load, control participation rate and control completion degree of the load user respectively, X7, X8, X9 and X10 represent the load user's maximum controllable load, control participation rate and control completion degree respectively, 10 They represent holiday status, weather, temperature and control time period respectively; The power load regulation amount is allocated to each of the load users according to the regulation response completion capability of each of the load users.

2. The method for optimizing the distribution of power load control according to claim 1, characterized in that: The allocating the power load regulation amount to each load user according to the regulation response completion capability of each load user comprises: The first power load regulation amount of each load user is calculated according to the regulation response completion capability of each load user, and the calculation formula is as follows: Among them, P Yk represents the first power load control quantity of the kth load user, P A represents the total demand for power load regulation, Y k and Y i They represent the control response completion capabilities of the kth and ith load users respectively, and n represents the total number of load users; The actual adjustable load of each load user is calculated using the following formula: P dk =P rk -P mink Among them, P dk , P rk and P mink They represent the actual controllable load, real-time load and minimum operating load of the kth load user respectively; The second power load control amount of each load user is calculated according to the actual controllable load amount of each load user, and the calculation formula is as follows: Among them, P wk represents the second power load control quantity of the kth load user, P di represents the actual adjustable load of the i-th load user; The final power load control amount of each of the load users is determined according to the first power load control amount and the second power load control amount of each of the load users.

3. The method for optimizing the distribution of power load control according to claim 2, characterized in that: The final power load control amount of each load user is determined by the following formula: Among them, P fk Represents the final power load regulation amount of the kth load user.

4. The method for optimizing the distribution of power load control according to claim 1, characterized in that: The first coefficient, the second coefficient, the third coefficient, the fourth coefficient and the fifth coefficient are obtained by fitting historical data by the least square method.

5. The method for optimizing the distribution of power load control quantity according to claim 4, characterized in that: Also includes: The first coefficient, the second coefficient, the third coefficient, the fourth coefficient and the fifth coefficient are updated in real time.

6. The method for optimizing the distribution of power load control quantity according to claim 1, characterized in that: The nature of electricity consumption is divided into industrial, commercial and residential; The regulation participation rate is the ratio of the number of participation regulation to the number of demand regulation; The degree of control completion is the ratio of the load control completion amount to the total load control demand.

7. A device for optimizing the distribution of power load control, characterized in that: include: The control completion calculation module is used to determine the control response completion capability of each load user through a pre-built polynomial regression model, wherein the polynomial regression model is: Y=β1X4+β2X5+β3X6+β4X1X2X3+β5X7X8X9X 10 Among them, Y represents the control response completion capability, β1, β2, β3, β4 and β5 represent the first coefficient, the second coefficient, the third coefficient, the fourth coefficient and the fifth coefficient respectively, X1, X2 and X3 represent the power consumption nature, load type and control method of the load user respectively, X4, X5 and X6 represent the historical maximum controllable load, historical control participation rate and historical control completion degree of the load user respectively, X7, X8, X9 and X10 represent the load user's historical maximum controllable load, historical control participation rate and historical control completion degree respectively, 10 They represent holiday status, weather, temperature and control time period respectively; The load distribution module is used to distribute the power load regulation amount to each of the load users according to the regulation response completion capability of each of the load users.

8. A load control platform, comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to execute the power load control quantity allocation optimization method according to any one of claims 1 to 6.

9. A power load management system, characterized in that: It includes the load control platform as described in claim 8, and the load control platform is deployed on the master station side of the power load management system.

10. A method for managing power load, characterized in that: include: The power load control quantity allocation is allocated to each load user through the power load control quantity allocation optimization method described in any one of claims 1-6.

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