Load demand response compensation optimization method and device and storage medium

Abstract

The embodiment of the invention provides a load demand response compensation optimization method and device and a storage medium, and the method comprises the steps: obtaining a historical response compensation price and a response load; based on a TSK fuzzy model, according to the response compensation price and the response load, relation data of the response compensation price and the response load are determined, and the relation data of the response compensation price and the response load represent different response loads corresponding to different compensations; obtaining user load response characteristic data, wherein the user load response characteristic data is a series of data representing user response behaviors; constructing a load demand response optimization model according to a preset constraint condition, the relational data and the user load response characteristic data; and the load demand response optimization model is used for calculating an optimal response compensation price. Therefore, power grid supply and demand are better balanced, power grid fluctuation is reduced, and power grid operation stability and efficiency are improved.

Description

Technical Field

[0001] This document relates to the field of power demand response technology, and in particular to a load demand response compensation optimization method, device and storage medium. Background Art

[0002] Demand response is to encourage electricity users to reduce electricity consumption when the power system demand is high and to increase electricity consumption when the demand is low through incentives, thereby adjusting the balance of electricity supply and demand, improving the flexibility of the power system, and ensuring the safe and stable operation of the system.

[0003] Existing technologies mainly refer to foreign load response experience or electricity price levels in some industries.

[0004] However, it is difficult to quickly and accurately balance the relationship between compensation price and user load response in the existing technology. Too low compensation price will lead to insufficient user response, resulting in power shortage and affecting the safe and stable operation of the power system. Summary of the invention

[0005] In view of the above scheme, the present application aims to propose a load demand response compensation optimization method, device and storage medium to solve at least one of the above technical problems.

[0006] In a first aspect, one or more embodiments of this specification provide a load demand response compensation optimization method, including:

[0007] Obtaining historical response compensation prices and response loads, wherein the response compensation price represents the unit compensation that a user can obtain by participating in load response and actively adjusting electricity consumption behavior, and the response load represents the electricity power that a user autonomously adjusts at a specific price;

[0008] Based on the TSK fuzzy model, according to the response compensation price and the response load, determining the relationship data between the response compensation price and the response load, wherein the relationship data between the response compensation price and the response load represents different response loads corresponding to different compensations;

[0009] Acquiring user load response characteristic data, wherein the user load response characteristic data is a series of data characterizing user response behavior; and

[0010] A load demand response optimization model is constructed according to preset constraints, the relationship data and the user load response characteristic data; the load demand response optimization model is used to calculate the optimal response compensation price.

[0011] Furthermore, the relationship data between the response compensation price and the response load is determined based on the TSK fuzzy model according to the response compensation price and the response load, including:

[0012] Based on the TSK fuzzy model, determining a fuzzy subset according to the response compensation price and the response load;

[0013] Determining a membership value according to the fuzzy subset; and

[0014] Based on the preset fuzzy rules and according to the membership value, the relationship data between the response compensation price and the response load is determined.

[0015] Furthermore, the TSK fuzzy model calculation formula is as follows:

[0016]

[0017] Among them, λ represents the linear relationship data between response compensation price and response load;

[0018] K represents the number of fuzzy rules;

[0019] k represents the kth rule;

[0020] represents the fuzzy subset corresponding to the kth rule;

[0021] represents the membership function corresponding to the fuzzy subset of the kth rule;

[0022] x1 represents the response compensation price;

[0023] x2 represents the response load;

[0024] f k (x) represents the linear function value corresponding to the kth fuzzy rule under a specific parameter x;

[0025] f k represents the linear function corresponding to the kth fuzzy rule;

[0026] The fuzzy rules are as follows:

[0027]

[0028] in, represents the fuzzy subset corresponding to the input variable compensation price of the kth rule;

[0029] represents the fuzzy subset corresponding to the response capacity of the input variable of the kth rule; and

[0030] represents the fixed parameters of the k-th rule.

[0031] Furthermore, the preset constraints include a user maximum response capacity constraint, a user response duration constraint, a user response capability and compensation price relationship constraint, and a unit compensation upper limit constraint;

[0032] The load demand response optimization model is constructed according to the preset constraint conditions, the relationship data and the user load response characteristic data. The load demand response optimization model is used to calculate the optimal response compensation price, including:

[0033] Based on the user maximum response capacity constraint, the user response duration constraint, the relationship constraint between the user response capability and the compensation price, and the unit compensation upper limit constraint, according to the relationship data and the user load response characteristic data, determine the response load, state variables, and response compensation data; and

[0034] Based on the load demand response optimization model and taking the optimal response compensation price as the target, the optimal response compensation data is obtained according to the relationship data, the response load, the state variable, the response compensation data and the user load response characteristic data.

[0035] Furthermore, the objective function of the load demand response optimization model is as follows:

[0036]

[0037] Among them, f 成本 Represents the social cost function

[0038] t represents time;

[0039] It represents the power gap of the system at time t;

[0040] represents the response load of user i at time t;

[0041] u i,t A state variable indicating whether user i responds at time t, 0 for no response and 1 for response;

[0042] C 度电 Indicates the regional electricity output value;

[0043] c t 响应 represents the response compensation data at time t;

[0044] The constraints are as follows:

[0045] (1) User maximum response capacity constraint:

[0046]

[0047] in, is the maximum response capacity of user i;

[0048] (2) User response duration constraints:

[0049]

[0050] Among them, U i is the maximum response duration of user i;

[0051] u i,t-1 A state variable indicating whether user i responds at time t-1. The value is 0 if the user does not respond and 1 if the user responds.

[0052] τ represents a computational auxiliary variable;

[0053] (3) Constraints on the relationship between user response capacity and compensation price:

[0054]

[0055] Among them, λ i Data representing the linear relationship between response compensation price and response load;

[0056] b i represents a constant; when the incentive response based on compensation can cover the loss of the load reduction user but cannot cover the cost part, the user will not respond;

[0057]

[0058] (4) Unit compensation upper limit constraints:

[0059]

[0060] in, Indicates the upper limit of unit compensation price;

[0061] Indicates the upper limit of the electricity compensation allocation; and

[0062] D represents the power consumption of all power users during the period when load response occurs.

[0063] Furthermore, the load demand response optimization model is solved by a branch and bound algorithm.

[0064] In a second aspect, an embodiment of the present application provides a load demand response compensation optimization device, comprising:

[0065] The first acquisition module is used to acquire historical response compensation prices and response loads, wherein the response compensation price represents the unit compensation that a user can obtain by participating in load response and actively adjusting electricity consumption behavior, and the response load represents the electricity power that a user adjusts autonomously at a specific price;

[0066] A generating module, for determining, based on a TSK fuzzy model, relational data of a response compensation price and a response load according to the response compensation price and the response load, wherein the relational data of the response compensation price and the response load represents different response loads corresponding to different compensations;

[0067] A second acquisition module is used to acquire user load response characteristic data, wherein the user load response characteristic data is a series of data characterizing user response behavior; and

[0068] The optimization module is used to construct a load demand response optimization model according to preset constraints, the relationship data and the user load response characteristic data; the load demand response optimization model is used to calculate the optimal response compensation price.

[0069] Furthermore, the generation module is configured to:

[0070] Based on the TSK fuzzy model, determining a fuzzy subset according to the response compensation price and the response load;

[0071] Determining a membership value according to the fuzzy subset; and

[0072] Based on the preset fuzzy rules and according to the membership value, the relationship data between the response compensation price and the response load is determined.

[0073] Furthermore, the generating module is further configured to:

[0074] The TSK fuzzy model calculation formula is as follows:

[0075]

[0076] Among them, λ represents the relationship data between response compensation price and response load;

[0077] K represents the number of fuzzy rules;

[0078] k represents the kth rule;

[0079] represents the fuzzy subset corresponding to the kth rule;

[0080] represents the membership function corresponding to the fuzzy subset of the kth rule;

[0081] x1 represents the response compensation price;

[0082] x2 represents the response load;

[0083] f k (x) represents the linear function value corresponding to the kth fuzzy rule under a specific parameter x;

[0084] f k represents the linear function corresponding to the kth fuzzy rule;

[0085] The fuzzy rules are as follows:

[0086]

[0087] in, represents the fuzzy subset corresponding to the input variable compensation price of the kth rule;

[0088] represents the fuzzy subset corresponding to the response capacity of the input variable of the kth rule; and

[0089] represents the fixed parameters of the k-th rule.

[0090] In a third aspect, an embodiment of the present application provides a storage medium for storing computer-executable instructions, characterized in that when the computer-executable instructions are executed, the steps of the load demand response optimization method described in any one of the first aspects are implemented.

[0091] Compared with the prior art, this application can at least achieve the following technical effects:

[0092] This application can accurately predict the user's load response under different response compensation prices through the TSK fuzzy model, so as to more accurately predict and optimize the user's electricity consumption behavior; and then optimize the load demand response strategy through the load demand response optimization model to better balance the supply and demand of the power grid, reduce power grid fluctuations, and improve the stability and efficiency of power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0094] Figure 1 A flow chart of a load demand response compensation optimization method provided for one or more embodiments of this specification;

[0095] Figure 2 A schematic diagram of the structure of a load demand response compensation optimization device provided in one or more embodiments of this specification. DETAILED DESCRIPTION

[0096] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0097] Power load management is to ensure the stable operation of the power system by effectively managing and regulating the load according to the needs of users and the supply of the power grid. However, the existing technology does not make sufficient use of the power data in the power system. For example, the power supply cannot be balanced with the power consumption, resulting in voltage overload or power line failure under power load, thus affecting the stable operation of the power system.

[0098] In order to solve the above technical problems, this application proposes a load demand response compensation optimization method, such as Figure 1 As shown, the specific steps are as follows:

[0099] Step S1, obtaining historical response compensation prices and response loads, wherein the response compensation price represents the unit compensation that a user can obtain by participating in load response and actively adjusting electricity consumption behavior, and the response load represents the electricity power that a user autonomously adjusts at a specific price.

[0100] In an embodiment of the present application, response compensation price data at different historical prices and electricity response load data adjusted by users under different price incentives are obtained through institutions such as power market trading platforms, power companies or energy service companies.

[0101] By obtaining historical response compensation prices and response loads on a professional platform, the accuracy and reliability of the data can be ensured.

[0102] Step S2, based on the TSK fuzzy model, according to the response compensation price and the response load, determine the relationship data between the response compensation price and the response load, the relationship data between the response compensation price and the response load represents different response loads corresponding to different compensations.

[0103] In an embodiment of the present application, first, fuzzy rules are defined based on historical response compensation prices and response loads. For response compensation prices, fuzzy subsets are cheap, suitable, and expensive; for response loads, fuzzy subsets are insufficient, medium, and sufficient, and membership functions (such as Gaussian functions, trigonometric functions, etc.) are specified for each fuzzy set to describe the degree to which the input feature belongs to a fuzzy set. Then, using historical data, the coefficients of the linear function in each fuzzy rule are learned by the least squares method or other optimization algorithms. Secondly, the historical data is divided into a training set and a validation set. The TSK fuzzy model is trained using the training set data, that is, the fuzzy rules, fuzzy sets, membership functions, and coefficients of the linear function are determined. The performance of the model is then verified using the validation set data to ensure that the model can accurately predict the response capacity of the user at a specific price. Finally, when a new response compensation price is given, the trained TSK fuzzy model can be used to predict the user's response load at that price.

[0104] Specifically, corresponding fuzzy subsets are defined for the two influencing factors of response willingness (response compensation price) and response capability (response load data). Three fuzzy subsets are defined based on the user's perception of compensation price, namely "cheap" (basically unable to cover costs), "appropriate" (able to cover costs and have profit margins), and "expensive" (large profit margins for participating in response); three fuzzy subsets are defined based on the level of response capability, namely "insufficient", "medium", and "sufficient".

[0105] For each set of determined input compensation price and response load, the membership function of each subset is used to determine the corresponding membership value, and the membership value is used as the input of the TSK fuzzy rule to obtain the dispatch willingness under a single rule. The fuzzy rules corresponding to the TSK fuzzy model are as follows:

[0106]

[0107] in, The fuzzy subset corresponding to the input variable compensation price of the kth rule is represented by A1 = {cheap, suitable, expensive};

[0108] The fuzzy subset corresponding to the response capacity of the input variable of the kth rule is represented by A2 = {insufficient, medium, sufficient};

[0109] represents the fixed parameters of the kth rule;

[0110] f k (x) represents the linear function value corresponding to the kth fuzzy rule under a specific parameter x;

[0111] f k represents the linear function corresponding to the kth fuzzy rule;

[0112] The final response willingness output of the TSK fuzzy model is the result of each rule f k The weighted average of (x) and the TSK fuzzy model calculation formula are as follows:

[0113]

[0114] Among them, λ represents the relationship data between response compensation price and response load;

[0115] K represents the number of fuzzy rules;

[0116] k represents the kth rule;

[0117] represents the fuzzy subset corresponding to the kth rule;

[0118] represents the membership function corresponding to the fuzzy subset of the kth rule;

[0119] x1 represents the response compensation price;

[0120] x2 represents the response load.

[0121] Step S3, obtaining user load response characteristic data, wherein the user load response characteristic data is a series of data characterizing user response behavior.

[0122] In an embodiment of the present application, load response characteristic data is obtained through industrial users, commercial users or residential users, and the load response characteristic data may include power gap, kilowatt-hour output value, maximum response capacity, maximum response duration, unit compensation price cap, kilowatt-hour compensation apportionment cap and all user power consumption data during the load response period. The power gap is a situation of imbalance between supply and demand in the power system, that is, the difference when the power supply cannot meet the power demand at a certain point in time. The kilowatt-hour output value refers to the amount of GDP contributed by 1 kilowatt-hour of electricity, which can reflect the degree of utilization of electric energy. The higher the kilowatt-hour output value, the higher the utilization efficiency of electric energy. The maximum response capacity refers to the maximum power load that a user can respond to. The maximum response duration refers to the longest time that a user can continuously reduce or adjust the load when responding to a grid demand response event. The unit compensation price cap refers to the maximum value set by the grid for the compensation price per kilowatt-hour of electricity or per kilowatt capacity provided by the grid to users participating in the demand response in scenarios such as power demand response. The kilowatt-hour compensation apportionment cap refers to the maximum apportionment limit set for the compensation fee per kilowatt-hour of electricity in markets such as power demand response. The electricity consumption of all users during the load response period refers to the sum of the electricity consumption of all users during the specific time period when the load response event occurs.

[0123] Step S4, constructing a load demand response optimization model according to preset constraints, the relationship data and the user load response characteristic data; the load demand response optimization model is used to calculate the optimal response compensation price.

[0124] In an embodiment of the present application, the preset constraints include a maximum response capacity constraint of a user, a duration constraint of a user response, a relationship constraint between a user response capability and a compensation price, and a unit compensation upper limit constraint; based on the user response capacity constraint, the user response duration constraint, the relationship constraint between a user response capability and a compensation price, and a unit compensation upper limit constraint, the response load, state variables, and response compensation data are determined according to the relationship data between the response compensation price and the response load and the user load response characteristic data; based on the load demand response optimization model, with the optimal response compensation price as the target, the optimal response compensation data is obtained according to the relationship data between the response compensation price and the response load, the response load, the state variables, the response compensation data, and the user load response characteristic data.

[0125] Specifically, in order to ensure the stable operation of the power system, with power shortage loss as the optimization target, this load demand response optimization model selects the kilowatt-hour output value as the core indicator to measure power shortage loss, and uses the branch and bound algorithm to solve the model. The objective function of the load demand response optimization model is as follows:

[0126]

[0127] Among them, f 成本 Represents the social cost function

[0128] t represents time;

[0129] It represents the power gap of the system at time t;

[0130] represents the response load of user i at time t;

[0131] u i,t A state variable indicating whether user i responds at time t, 0 for no response and 1 for response;

[0132] C 度电 Indicates the regional electricity output value;

[0133] c t 响应 represents the response compensation data at time t;

[0134] The constraints are as follows:

[0135] (1) User maximum response capacity constraint:

[0136]

[0137] in, is the maximum response capacity of user i;

[0138] (2) User response duration constraints:

[0139]

[0140] Among them, U i is the maximum response duration of user i;

[0141] u i,t-1 A state variable indicating whether user i responds at time t-1. The value is 0 if the user does not respond and 1 if the user responds.

[0142] τ represents a computational auxiliary variable;

[0143] (3) Constraints on the relationship between user response capacity and compensation price:

[0144]

[0145] Among them, λ i The linear relationship data between the response compensation price and response load of user i;

[0146] b i Represents a constant;

[0147] When incentive response is based on compensation that can cover the losses of load reduction users but cannot cover the cost part, users will not respond;

[0148]

[0149] (4) Unit compensation upper limit constraints:

[0150]

[0151]

[0152] in, Indicates the upper limit of unit compensation price;

[0153] Indicates the upper limit of the electricity compensation allocation; and

[0154] D represents the power consumption of all power users during the period when load response occurs.

[0155] The present application embodiment provides a load demand response compensation optimization device, such as Figure 2 As shown, including:

[0156] The first acquisition module 101 is used to acquire historical response compensation prices and response loads, wherein the response compensation price represents the unit compensation that a user can obtain by participating in load response and actively adjusting electricity consumption behavior, and the response load represents the electricity power that a user adjusts autonomously at a specific price;

[0157] A generating module 102 is used to determine the relationship data between the response compensation price and the response load based on the TSK fuzzy model according to the response compensation price and the response load, wherein the relationship data between the response compensation price and the response load represents different response loads corresponding to different compensations;

[0158] A second acquisition module 103 is used to acquire user load response characteristic data, wherein the user load response characteristic data is a series of data characterizing user response behavior; and

[0159] The optimization module 104 is used to construct a load demand response optimization model according to preset constraints, the relationship data and the user load response characteristic data; the load demand response optimization model is used to calculate the optimal response compensation price.

[0160] Furthermore, the generation module is configured as follows:

[0161] Based on the TSK fuzzy model, determining a fuzzy subset according to the response compensation price and the response load;

[0162] Determining a membership value according to the fuzzy subset; and

[0163] Based on the preset fuzzy rules and according to the membership value, the relationship data between the response compensation price and the response load is determined. Further, the generation module is also configured to:

[0164] The TSK fuzzy model calculation formula is as follows:

[0165]

[0166] Among them, λ represents the relationship data between response compensation price and response load;

[0167] K represents the number of fuzzy rules;

[0168] k represents the kth rule;

[0169] represents the fuzzy subset corresponding to the kth rule;

[0170] represents the membership function corresponding to the fuzzy subset of the kth rule;

[0171] x1 represents the response compensation price;

[0172] x2 represents the response load;

[0173] f k (x) represents the linear function value corresponding to the kth fuzzy rule under a specific parameter x;

[0174] f k represents the linear function corresponding to the kth fuzzy rule;

[0175] The fuzzy rules are as follows:

[0176]

[0177] in, represents the fuzzy subset corresponding to the input variable compensation price of the kth rule;

[0178] represents the fuzzy subset corresponding to the response capacity of the input variable of the kth rule; and

[0179] represents the fixed parameters of the k-th rule.

[0180] An embodiment of the present application provides a storage medium for storing computer-executable instructions, wherein the computer-executable instructions, when executed, implement the steps of the load demand response optimization method described in any one of the above embodiments.

[0181] It should be noted that the embodiments of the storage medium in this specification and the embodiments of the blockchain-based service provision method in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can refer to the aforementioned corresponding implementation of the blockchain-based service provision method, and the repeated parts will not be repeated.

[0182] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0183] In the 1930s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to circuit structures such as diodes, transistors, switches, etc.) or software improvements (improvements to method flows). However, with the development of technology, many improvements to method flows today can be regarded as direct improvements to hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0184] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0185] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0186] For the convenience of description, the above devices are described in terms of functions and are divided into various units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0187] It should be understood by those skilled in the art that one or more embodiments of this specification may be provided as a method, system or computer program product. Therefore, one or more embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0188] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0189] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0190] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0191] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0192] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0193] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0194] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0195] One or more embodiments of the present specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. One or more embodiments of the present specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0196] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0197] The above description is only an embodiment of this document and is not intended to limit this document. For those skilled in the art, this document may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this document should be included in the scope of the claims of this document.

Claims

1. A load demand response compensation optimization method, characterized in that include: Obtaining historical response compensation prices and response loads, wherein the response compensation price represents the unit compensation that a user can obtain by participating in load response and actively adjusting electricity consumption behavior, and the response load represents the electricity power that a user autonomously adjusts at a specific price; Based on the TSK fuzzy model, according to the response compensation price and the response load, determining the relationship data between the response compensation price and the response load, wherein the relationship data between the response compensation price and the response load represents different response loads corresponding to different compensations; Acquire user load response characteristic data, wherein the user load response characteristic data is a series of data characterizing user response behavior; as well as A load demand response optimization model is constructed according to preset constraints, the relationship data and the user load response characteristic data, and the load demand response optimization model is used to calculate the optimal response compensation price.

2. The method according to claim 1, characterized in that The determining of the relationship data between the response compensation price and the response load based on the TSK fuzzy model according to the response compensation price and the response load includes: Based on the TSK fuzzy model, determining a fuzzy subset according to the response compensation price and the response load; Determining a membership value according to the fuzzy subset; and Based on the preset fuzzy rules and according to the membership value, the relationship data between the response compensation price and the response load is determined.

3. The method according to claim 2, characterized in that The TSK fuzzy model calculation formula is as follows: Among them, λ represents the linear relationship data between response compensation price and response load; K represents the number of fuzzy rules; k represents the kth rule; represents the fuzzy subset corresponding to the kth rule; represents the membership function corresponding to the fuzzy subset of the kth rule; x1 represents the response compensation price; x2 represents the response load; f k (x) represents the linear function value corresponding to the kth fuzzy rule under a specific parameter x; f k represents the linear function corresponding to the kth fuzzy rule; The fuzzy rules are as follows: in, represents the fuzzy subset corresponding to the input variable compensation price of the kth rule; represents the fuzzy subset corresponding to the response capacity of the input variable of the kth rule; and represents the fixed parameters of the k-th rule.

4. The method according to claim 1, characterized in that: The preset constraints include user maximum response capacity constraint, user response duration constraint, user response capability and compensation price relationship constraint and unit compensation upper limit constraint; The load demand response optimization model is constructed according to the preset constraint conditions, the relationship data and the user load response characteristic data. The load demand response optimization model is used to calculate the optimal response compensation price, including: Based on the user maximum response capacity constraint, the user response duration constraint, the relationship constraint between the user response capability and the compensation price, and the unit compensation upper limit constraint, according to the relationship data and the user load response characteristic data, determine the response load, state variables, and response compensation data; and Based on the load demand response optimization model and taking the optimal response compensation price as the target, the optimal response compensation data is obtained according to the relationship data, the response load, the state variable, the response compensation data and the user load response characteristic data.

5. The method according to claim 4, characterized in that The objective function of the load demand response optimization model is as follows: Among them, f 成本 Represents the social cost function t represents time; It represents the power gap of the system at time t; represents the response load of user i at time t; u i,t A state variable indicating whether user i responds at time t, 0 for no response and 1 for response; C 度电 Indicates the regional electricity output value; represents the response compensation data at time t; The constraints are as follows: (1) User maximum response capacity constraint: in, is the maximum response capacity of user i; (2) User response duration constraints: Among them, U i is the maximum response duration of user i; u i,t-1 A state variable indicating whether user i responds at time t-1. The value is 0 if the user does not respond and 1 if the user responds. τ represents a computational auxiliary variable; (3) Constraints on the relationship between user response capacity and compensation price: Among them, λ i Data representing the linear relationship between response compensation price and response load; b i represents a constant; when the incentive response based on compensation can cover the loss of the load reduction user but cannot cover the cost part, the user will not respond; (4) Unit compensation upper limit constraint: in, Indicates the upper limit of unit compensation price; Indicates the upper limit of the electricity compensation allocation; and D represents the power consumption of all power users during the period when load response occurs.

6. The method according to claim 1, characterized in that The load demand response optimization model is solved by a branch and bound algorithm.

7. A load demand response compensation optimization device, characterized in that include: The first acquisition module is used to acquire historical response compensation prices and response loads, wherein the response compensation price represents the unit compensation that a user can obtain by participating in load response and actively adjusting electricity consumption behavior, and the response load represents the electricity power that a user adjusts autonomously at a specific price; A generating module, for determining, based on a TSK fuzzy model, relational data of a response compensation price and a response load according to the response compensation price and the response load, wherein the relational data of the response compensation price and the response load represents different response loads corresponding to different compensations; A second acquisition module is used to acquire user load response characteristic data, wherein the user load response characteristic data is a series of data characterizing user response behavior; as well as The optimization module is used to construct a load demand response optimization model according to preset constraints, the relationship data and the user load response characteristic data, and the load demand response optimization model is used to calculate the optimal response compensation price.

8. The device according to claim 7, characterized in that The generation module is configured as follows: Based on the TSK fuzzy model, determining a fuzzy subset according to the response compensation price and the response load; Determining a membership value according to the fuzzy subset; as well as Based on the preset fuzzy rules and according to the membership value, the relationship data between the response compensation price and the response load is determined.

9. The device according to claim 8, characterized in that The generation module is also configured to: The TSK fuzzy model calculation formula is as follows: Among them, λ represents the relationship data between response compensation price and response load; K represents the number of fuzzy rules; k represents the kth rule; represents the fuzzy subset corresponding to the kth rule; represents the membership function corresponding to the fuzzy subset of the kth rule; x1 represents the response compensation price; x2 represents the response load; f k (x) represents the linear function value corresponding to the kth fuzzy rule under a specific parameter x; f k represents the linear function corresponding to the kth fuzzy rule; The fuzzy rules are as follows: in, represents the fuzzy subset corresponding to the input variable compensation price of the kth rule; represents the fuzzy subset corresponding to the response capacity of the input variable of the kth rule; and represents the fixed parameters of the k-th rule.

10. A storage medium for storing computer executable instructions, characterized in that: When the computer executable instructions are executed, the steps of the load demand response optimization method according to any one of claims 1 to 6 are implemented.

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