Virtual power plant optimization scheduling method and related equipment

Abstract

One or more embodiments of the present application provide a method for optimizing the scheduling of a load-based virtual power plant and related equipment. The method includes: determining the user responsiveness of multiple users based on the current market electricity price and the relationship between the market electricity price and the user responsiveness; determining the user adjustable potential of the multiple users based on the user responsiveness of the multiple users and the adjustable potential of the corresponding power equipment, wherein the multiple users include commercial building users and residential users; and optimizing the scheduling of the load-based virtual power plant based on the user adjustable potential of the multiple users. The scheduling scheme of the load-based virtual power plant can be optimized through the present application.

Description

Technical Field

[0001] One or more embodiments of the present application relate to the technical field of power system management, and in particular, to a load-type virtual power plant optimization scheduling method and related equipment. Background Art

[0002] In the process of low-carbon transformation of electricity, the development and utilization of distributed resources has become a highly concerned issue. Load-based virtual power plants have been widely studied as an effective means to aggregate distributed resources to achieve coordinated scheduling and promote the complementarity of multiple energy sources.

[0003] At present, the optimization dispatching methods of load-based virtual power plants mainly include two categories: methods that consider economic objectives and methods that consider technical objectives. Among them, the methods that consider economic objectives mainly pursue minimizing operating costs and maximizing operating benefits, while the methods that consider technical objectives include pursuing stability, reliability, power quality and safety.

[0004] However, the optimization scheduling method of load-type virtual power plants in related technologies does not take into account the responsiveness of different users to market factors during regulation, and cannot flexibly schedule according to the subjective wishes of multiple users. Summary of the invention

[0005] In view of this, the purpose of one or more embodiments of the present application is to propose a load-type virtual power plant optimization scheduling method and related equipment to solve the problems raised by the background technology.

[0006] Based on the above objectives, one or more embodiments of the present application provide a method for optimizing the scheduling of a load-based virtual power plant. The method comprises:

[0007] Determine the user responsiveness of multiple users based on the current market electricity price and the relationship between the market electricity price and the user responsiveness;

[0008] Determining user adjustable potentials of the plurality of users based on user responsiveness of the plurality of users and adjustable potentials of corresponding power equipment, the plurality of users including commercial building users and residential users;

[0009] The load-based virtual power plant is optimally dispatched based on the user adjustable potential of the multiple users.

[0010] Optionally, the power equipment corresponding to the commercial building users includes central air conditioners and electric vehicles;

[0011] The determining the user adjustable potential of the multiple users based on the user responsiveness of the multiple users and the adjustable potential of the corresponding power equipment includes:

[0012] Based on the user responsiveness of the commercial building user and the adjustable potential of the central air conditioner and the electric vehicle, the user adjustable potential of the commercial building user is determined by the following formula;

[0013] ;

[0014] in, is the annual average failure rate of the central air conditioner, is the annual average failure rate of the electric vehicle, For the adjustable potential of the central air conditioner, For the The adjustable potential of the electric vehicle described above, is the total number of electric vehicles included in the commercial building users, is the user responsiveness of the commercial building users.

[0015] Optionally, the adjustable potential of the central air conditioner is determined by the following formula;

[0016] ;

[0017] ;

[0018] ;

[0019] in, is the external environment temperature, is the pre-dispatch temperature of the central air conditioner, is the dispatched temperature of the central air conditioner, is the standby power of the central air conditioner, is the parameter energy efficiency coefficient of the central air conditioner, is the equivalent heat capacity, is the equivalent thermal resistance, To schedule time;

[0020] The adjustable potential of the electric vehicle is determined by the following formula;

[0021] ;

[0022] ;

[0023] in, is the adjustable potential of the electric vehicle, The potential for electric vehicles to be reduced, is the current maximum charging power of the electric vehicle, is the current minimum charging power of the electric vehicle, is the current charging power of the electric vehicle, is the battery capacity of the electric vehicle after responding to the dispatch, To respond to the dispatch time, is the current battery charge of the electric vehicle, is the battery capacity of the electric vehicle, Energy efficiency for charging the battery.

[0024] Optionally, the electrical equipment corresponding to the residential users includes household split air conditioners, electric water heaters and electric vehicles;

[0025] The determining the user adjustable potential of the multiple users based on the user responsiveness of the multiple users and the adjustable potential of the corresponding power equipment includes:

[0026] Based on the user responsiveness of the residential user and the adjustable potential of the household split air conditioner, electric water heater and electric vehicle, the user adjustable potential of the residential user is determined by the following formula;

[0027] ;

[0028] in, is the annual average failure rate of the household split air conditioner, is the annual average failure rate of the electric water heater, is the annual average failure rate of the electric vehicle, For the adjustable potential of the household split air conditioner, For the adjustable potential of the electric water heater, For the The adjustable potential of the electric vehicle described above, is the total number of electric vehicles included in the residential users, is the user responsiveness of the residential user.

[0029] Optionally, the adjustable potential of the household split air conditioner is determined by the following formula;

[0030] ;

[0031] in, is the proportion of adjustable potential, The average aggregate power when the household split air conditioner of the residential user cluster corresponding to the residential user is set to a preset temperature;

[0032] The adjustable potential of the electric vehicle is determined by the following formula;

[0033] ;

[0034] ;

[0035] in, is the adjustable potential of the electric vehicle, The potential for electric vehicles to be reduced, is the current maximum charging power of the electric vehicle, is the current minimum charging power of the electric vehicle, is the current charging power of the electric vehicle, is the battery capacity of the electric vehicle after responding to the dispatch, To respond to the dispatch time, is the current battery charge of the electric vehicle, is the battery capacity of the electric vehicle, Energy efficiency for charging the battery.

[0036] Optionally, the adjustable potential of the electric water heater is determined by performing the following operations:

[0037] In response to the current day being a working day, based on the load power curve of the electric water heater on historical working days and the current power data, predicted power data is obtained through a first prediction model and the predicted power data is used as the adjustable potential of the electric water heater;

[0038] In response to the current being a non-working day, based on the load power curve of the electric water heater on historical rest days and the current power data, the predicted power data is obtained by a second prediction model and the predicted power data is used as the adjustable potential of the electric water heater;

[0039] The parameters of the first prediction model and the second prediction model are adjusted based on the following formula:

[0040] ;

[0041] in, is the number of training samples, is the predicted water heater power data, It is the actual water heater power data.

[0042] Optionally, the optimizing scheduling of the load-based virtual power plant based on the user adjustable potential of the multiple users includes:

[0043] Based on the user adjustable potential of the multiple users, solving the optimal solution of the following objective function and performing optimal scheduling of the load-based virtual power plant;

[0044] ;

[0045] in, is the number of residential user clusters included in the load-based virtual power plant, For the The energy cost of the residential user cluster is: , , are respectively the proportion of the adjustable potential of household split air conditioners and electric vehicles in the residential user cluster to the total adjustable potential of the residential user cluster, is the number of commercial building user clusters included in the load-based virtual power plant, For the The energy cost of the commercial building user cluster is: , is the proportion of the adjustable potential of the central air conditioner in the commercial building user cluster to the total adjustable potential of the commercial building cluster, , and is the average energy cost coefficient of the household split air conditioner, , and is the average energy cost coefficient of the central air conditioner, is the average energy cost coefficient of the electric vehicle, is the average energy cost coefficient of the electric water heater, The load-based virtual power plant is allocated to the The regulation amount of each of the residential user clusters, The load-based virtual power plant is allocated to the first The regulation amount of a commercial building user cluster, and The value constraint of and , , Respectively represent the The adjustable potential of the residential user cluster and the The adjustable potential of a cluster of commercial buildings.

[0046] Optionally, the user responsiveness is determined by performing the following operations:

[0047] Obtaining the current market electricity price and subsidized electricity price;

[0048] Based on the current market electricity price and the subsidized electricity price, the user responsiveness is determined by the following responsiveness model, wherein the responsiveness model is used to represent the relationship between the market electricity price, the subsidized electricity price and the user responsiveness, and the parameters of the responsiveness model are obtained by fitting based on historical market electricity prices and historical user responsiveness data;

[0049] ;

[0050] in, , , , , , , , is the responsivity coefficient of the responsivity model, is the current market electricity price, To subsidize electricity prices, is the highest order of the current market electricity price appearing in the responsiveness model, is the highest order at which the subsidized electricity price appears in the responsiveness model.

[0051] Based on the same inventive concept, one or more embodiments of the present application further provide a load-type virtual power plant optimization scheduling device, including:

[0052] A first calculation module is configured to determine user responsiveness of multiple users based on the current market electricity price and the relationship between the market electricity price and the user responsiveness;

[0053] A second calculation module is configured to determine the user adjustable potential of the plurality of users based on the user responsiveness of the plurality of users and the adjustable potential of the corresponding power equipment, wherein the plurality of users include commercial building users and residential users;

[0054] The optimization scheduling module is configured to optimize the scheduling of the load-type virtual power plant based on the user adjustable potential of the multiple users.

[0055] Based on the same inventive concept, one or more embodiments of the present application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the load-type virtual power plant optimization scheduling method as described in any one of the above items.

[0056] Based on the same inventive concept, one or more embodiments of the present application also provide a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute any of the above-mentioned load-type virtual power plant optimization scheduling methods.

[0057] From the above, it can be seen that the load-type virtual power plant optimization scheduling method provided by one or more embodiments of the present application determines the user responsiveness of multiple users based on the current market electricity price and the relationship between the market electricity price and the user responsiveness; determines the user adjustable potential of the multiple users based on the user responsiveness of the multiple users and the adjustable potential of the corresponding power equipment, and the multiple users include commercial building users and residential users; and optimizes the scheduling of the load-type virtual power plant based on the user adjustable potential of the multiple users.

[0058] The technical solution of the present application can calculate the responsiveness of multiple users based on market information, and can effectively quantify the potential of various adjustable resources contained in multiple users, thereby obtaining an optimized scheduling method that takes into account the responsiveness of multiple users and optimizing the scheduling plan of load-type virtual power plants.

[0059] The load-type virtual power plant optimization scheduling method and related equipment provided in this application can implement the steps of the above-mentioned load-type virtual power plant optimization scheduling method, and therefore also have the beneficial effects of the above-mentioned load-type virtual power plant optimization scheduling method. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate one or more embodiments of the present application 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 one or more embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 A schematic diagram of a flow chart of a method for optimizing scheduling of a load-based virtual power plant according to one or more embodiments of the present application;

[0062] Figure 2 A schematic diagram of the structure of a load-based virtual power plant optimization scheduling device according to one or more embodiments of the present application;

[0063] Figure 3 A schematic diagram of the hardware structure of an electronic device according to one or more embodiments of the present application. DETAILED DESCRIPTION

[0064] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0065] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present application should be understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in one or more embodiments of the present application are not for any order, quantity or importance, but are only used to distinguish different components. "Including" or "including" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used for relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0066] As described in the background technology section, the scheduling objectives of load-based virtual power plants in related technologies do not take into account the subjective responsiveness of multiple users.

[0067] In the process of implementing the present disclosure, the applicant discovered that multiple users are an important type of resource of load-type virtual power plants and have the particularity of user life, development needs and subjective feelings. In the future, considering the particularity of multiple users as a subject and incorporating this into the goals will inevitably become a trend for the widespread practice of load-type virtual power plants. Therefore, there is still room for improvement in the optimization and scheduling of load-type virtual power plants.

[0068] refer to Figure 1 The load-based virtual power plant optimization scheduling method of one or more embodiments of the present application includes the following steps:

[0069] Step S101: Determine the user responsiveness of multiple users based on the current market electricity price and subsidized electricity price and the relationship between the market electricity price, subsidized electricity price and user responsiveness.

[0070] The above responsiveness is the ratio of the amount of user participation in adjustment to the maximum amount of adjustment by multiple users.

[0071] In the process of implementing this application, the applicant discovered that data mining technology can be applied to the generation of load-type virtual power plant scheduling plans, and the relationship between market electricity prices, subsidy policies and user responsiveness can be analyzed and predicted using nonlinear multivariate regression algorithms.

[0072] Therefore, this application establishes a mathematical relationship between the user's adjustable potential, the user's covered resource adjustable potential and the user's responsiveness based on the calculation of the adjustment potential of each type of adjustable resources of multiple users, and proposes an optimization scheduling method for load-type virtual power plants.

[0073] Optionally, the relationship between the above-mentioned market electricity price and user responsiveness can be determined based on historical market electricity prices, historical subsidized electricity prices and historical user responsiveness to regulation by using a multivariate nonlinear regression algorithm.

[0074] Specifically, the relationship between the above market electricity price and user responsiveness can be determined by performing the following operations:

[0075] Obtain historical market electricity prices, historical subsidy electricity prices, and historical user participation in regulation responsiveness, and construct training samples .

[0076]

[0077] in, The first column in the matrix is ​​the historical user participation regulation responsiveness sequence, and the second and third columns are the historical market electricity prices and subsidized electricity prices, respectively.

[0078] Pair Matrix The elements in are subjected to Min-Max homogenization to obtain the homogenized matrix .

[0079]

[0080]

[0081] Calculate the normalized matrix using multivariate nonlinear regression The second column To the third column With the first column The correlation coefficient between them gives the responsiveness model:

[0082] .

[0083] in, , , , , , , , is the responsiveness coefficient of the above responsiveness model, is the current market electricity price mentioned above, To subsidize electricity prices, is the highest order of the current market electricity price in the above responsiveness model, is the highest order of the above subsidized electricity price appearing in the above responsiveness model.

[0084] Because the responsiveness of users participating in the market is determined by the current market electricity price and the current subsidized electricity price, substituting the current market electricity price and the current subsidized electricity price into the above responsiveness model can obtain the responsiveness of multiple users in the current market.

[0085] Step S102: determining the user adjustable potential of the plurality of users based on the user responsiveness of the plurality of users and the adjustable potential of the corresponding power equipment, wherein the plurality of users include commercial building users and residential users.

[0086] In this application, a load-based virtual power plant is used to manage the optimal scheduling of multiple communities and multiple commercial buildings.

[0087] The above-mentioned power equipment is the power equipment that can be covered by the user. The adjustable potential is the proportion of the user's participation in the adjustment amount to the maximum adjustment amount of multiple users. Different power equipment has different algorithms for calculating the adjustable potential.

[0088] The load-based virtual power plant optimization scheduling method of the present application is aimed at scheduling multiple users including commercial building users and residential users. Among them, for commercial building users, the power equipment includes central air conditioners and electric vehicles. For residential users, the power equipment includes household split air conditioners, electric water heaters and electric vehicles.

[0089] Specifically, the calculation formula for the adjustable potential of central air conditioning is:

[0090] ;

[0091] ;

[0092] ;

[0093] in, is the external environment temperature, is the pre-dispatch temperature of the above central air conditioner, is the dispatched temperature of the above central air conditioner, is the standby power of the above central air conditioner, is the parameter energy efficiency coefficient of the above central air conditioner, is the equivalent heat capacity, is the equivalent thermal resistance, To schedule time. and It is an intermediate process parameter and has no actual physical meaning.

[0094] The formula for calculating the adjustable potential of electric vehicles is:

[0095] ;

[0096] ;

[0097] in, For the above electric vehicles, the potential for upward adjustment is For the above electric vehicles, the potential for downward adjustment is is the current maximum charging power of the above electric vehicle, is the current minimum charging power of the above electric vehicle, is the current charging power of the above electric vehicle, is the current battery charge of the electric car mentioned above, is the battery capacity of the electric vehicle mentioned above, Charging energy efficiency for the above batteries.

[0098] Based on the above, the formula for calculating the adjustable potential of commercial building users can be:

[0099] ;

[0100] in, is the annual average failure rate of the above central air conditioner, is the annual average failure rate of the above electric vehicles, For the adjustable potential of the above central air conditioner, For the The adjustable potential of the above electric vehicles, is the total number of electric vehicles mentioned above, It is the user responsiveness of the users in the above commercial buildings.

[0101] The calculation formula for the adjustable potential of household split air conditioners can be:

[0102] .

[0103] in, is the proportion of adjustable potential, The average aggregate power when the household split air conditioner of the residential user cluster corresponding to the residential user is set to a preset temperature. In some embodiments, the preset temperature can be set to 23 degrees Celsius.

[0104] Considering that the electricity consumption characteristics of electric water heaters on working days and non-working days are different, preferably, adjustable potential models can be constructed for the electricity consumption of electric water heaters on working days and non-working days respectively.

[0105] The adjustable potential of the electric water heater is determined by performing the following operations:

[0106] In response to the current day being a working day, based on the load power curve of the electric water heater on historical working days and the current power data, the predicted power data is obtained through a first prediction model and the predicted power data is used as the adjustable potential of the electric water heater;

[0107] In response to the current being a holiday, based on the load power curve of the electric water heater on historical holidays and the current power data, the predicted power data is obtained through a second prediction model and the predicted power data is used as the adjustable potential of the electric water heater;

[0108] The parameters of the first prediction model and the second prediction model are adjusted based on the following formula:

[0109] ;

[0110] in, is the number of training samples, For the above predicted water heater power data, It is the actual water heater power data.

[0111] The above-mentioned first prediction model and second prediction model can be implemented based on the Long Short Term Memory Network (LSTM) model training. The LSTM network model consists of an input layer, several hidden layers (the number of hidden layers in the present invention is 2) and an output layer. In some embodiments, the number of neurons in the input layer is determined according to the predicted working day / non-working day, the input data dimension of the first prediction model corresponding to the working day is 5, the input data dimension of the second prediction model corresponding to the non-working day is 4, and the output layer dimension is 1. For example, the data of the water heater power from Monday to Friday of the previous week is used as the working day data, and the water heater power data of the previous week and the Saturday and Sunday of the week before is used as the non-working day data.

[0112] Based on the above user responsiveness and adjustable potential of power equipment, the adjustable potential of residential users is determined by the following formula;

[0113] ;

[0114] in, is the annual average failure rate of the above household split air conditioner, is the annual average failure rate of the above electric water heaters, is the annual average failure rate of the above electric vehicles, For the adjustable potential of the above household split air conditioner, For the adjustable potential of the above electric water heater, For the The adjustable potential of the above electric vehicles, is the total number of electric vehicles mentioned above, It is the user responsiveness of the above residential users.

[0115] Step S103: Optimize the scheduling of the load-based virtual power plant based on the user adjustable potential of the above-mentioned multiple users.

[0116] In the technical solution of this application, the goal of optimizing scheduling is to minimize the total energy cost of the load-based virtual power plant. That is, the objective function is set as:

[0117] ;

[0118] in, For a diverse user group, including Residential user clusters and A commercial building user cluster.

[0119] in, is the number of residential user clusters included in the above load-type virtual power plant, For the Energy consumption cost of the above residential user cluster.

[0120]

[0121] , and is the average energy cost coefficient of the household split air conditioner mentioned above, , are the proportions of the adjustable potentials of household split air conditioners and electric vehicles in the above residential user cluster to the total adjustable potential of the residential user cluster, is the average energy cost coefficient of the above water heater, The load-based virtual power plant is allocated to the The regulation amount of the above residential user clusters is The value constraint of , Indicates the above The regulation potential of a residential user cluster, that is, the regulation amount allocated by the load-type virtual power plant to each residential user does not exceed the adjustable potential of the corresponding user cluster.

[0122] For the Energy consumption cost of the above commercial building user cluster.

[0123]

[0124] is the proportion of the adjustable potential of central air conditioners in the above commercial building user cluster to the total adjustable potential of the commercial building cluster, , and is the average energy cost coefficient of the above central air conditioner, is the average energy cost coefficient of the above electric vehicles, The above load-based virtual power plant is allocated to the above The regulation amount of a commercial building user cluster, The value constraint of , Indicates The adjustable potential of each commercial building cluster, that is, the adjustment amount allocated by the load-based virtual power plant to each commercial building user does not exceed the adjustable potential of the corresponding user cluster. The solution of the above objective function can be achieved through the particle swarm algorithm, and the optimal solution is the allocation of the adjustment amount for each multi-user cluster. multi-user clusters, then each feasible solution allocation scheme is a dimensional vector.

[0125] According to the above, this application proposes a method for optimizing the scheduling of load-type virtual power plants, which uses a multivariate regression algorithm to extract correlation rules from market electricity prices, subsidy policies and user response data to effectively predict the responsiveness of multiple users; uses an LSTM model to separately predict the electricity consumption data of residential users' water heaters; on this basis, based on the calculation of the adjustment potential of each type of adjustable resources within multiple users, a mathematical relationship between the overall adjustable potential of multiple users and the adjustable potential of covered resources and user responsiveness is established; and a method for optimizing the scheduling of load-type virtual power plants that considers user responsiveness is proposed. This application can optimize the scheduling of load-type virtual power plants by comprehensively considering the scheduling cost and the user's willingness to respond in the current market, and optimizes the scheduling plan of load-type virtual power plants.

[0126] It can be understood that the method can be executed by any device, equipment, platform, or device cluster with computing and processing capabilities.

[0127] It should be noted that the method of one or more embodiments of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of one or more embodiments of the present application, and the multiple devices will interact with each other to complete the described method.

[0128] It should be noted that the above describes specific embodiments of the present application. 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 accompanying 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.

[0129] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a load-type virtual power plant optimization scheduling device.

[0130] refer to Figure 2 , the above-mentioned device comprises:

[0131] The first calculation module 11 is configured to determine the user responsiveness of the multiple users based on the current market electricity price and the relationship between the market electricity price and the user responsiveness;

[0132] The second calculation module 12 is configured to determine the user adjustable potential of the plurality of users based on the user responsiveness of the plurality of users and the adjustable potential of the corresponding power equipment, wherein the plurality of users include commercial building users and residential users;

[0133] The optimization scheduling module 13 is configured to perform optimization scheduling of the load-type virtual power plant based on the user adjustable potential of the above-mentioned multiple users.

[0134] For the convenience of description, the above devices are described in terms of functions and modules. Of course, when implementing one or more embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0135] The apparatus of the above-mentioned embodiment is used to implement the corresponding method in the above-mentioned embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0136] Figure 3 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0137] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0138] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solution provided in the embodiment of the present application is implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0139] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0140] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0141] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0142] It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiment of the present application, and does not necessarily include all the components shown in the figure.

[0143] The electronic device of the above embodiment is used to implement the corresponding method in the above embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0144] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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, read-only 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.

[0145] A person of ordinary skill in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0146] In addition, to simplify the description and discussion, and in order not to make one or more embodiments of the present application difficult to understand, the well-known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided figures. In addition, the device may be shown in the form of a block diagram to avoid making one or more embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which one or more embodiments of the present application will be implemented (that is, these details should be fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it is obvious to those skilled in the art that one or more embodiments of the present application can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0147] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the discussed embodiments.

[0148] One or more embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present application should be included in the scope of protection of this disclosure.

Claims

1. A load-based virtual power plant optimization scheduling method, characterized in that: include: Based on the current market electricity price and subsidized electricity price and the relationship between the market electricity price and subsidized electricity price and user responsiveness, the user responsiveness of multiple users is determined by using nonlinear multiple regression algorithm analysis and prediction; the user responsiveness represents the proportion of the user's participation in regulation to the maximum regulation of multiple users; Determining user adjustable potentials of the plurality of users based on user responsiveness of the plurality of users and adjustable potentials of corresponding power equipment, the plurality of users including commercial building users and residential users; Based on the adjustable user potential of the plurality of users, optimal scheduling of the load-type virtual power plant is performed; the objective function of the optimal scheduling is to minimize the total energy cost of the virtual power plant; The optimizing scheduling of the load-based virtual power plant based on the adjustable user potential of the multiple users includes: Based on the user adjustable potential of the multiple users, solving the optimal solution of the following objective function and performing optimal scheduling of the load-based virtual power plant; ； in, is the number of residential user clusters included in the load-based virtual power plant, For the The energy cost of the residential user cluster is: , , are respectively the proportion of the adjustable potential of household split air conditioners and electric vehicles in the residential user cluster to the total adjustable potential of the residential user cluster, is the number of commercial building user clusters included in the load-based virtual power plant, For the The energy cost of the commercial building user cluster is: , is the proportion of the adjustable potential of the central air conditioner in the commercial building user cluster to the total adjustable potential of the commercial building cluster, , and is the average energy cost coefficient of the household split air conditioner, , and is the average energy cost coefficient of the central air conditioner, is the average energy cost coefficient of the electric vehicle, is the average energy cost coefficient of the electric water heater, The load-based virtual power plant is allocated to the The regulation amount of each of the residential user clusters, The load-based virtual power plant is allocated to the first The regulation amount of a commercial building user cluster, and The value constraint of and , , Respectively represent the The adjustable potential of the residential user cluster and the The adjustable potential of a cluster of commercial buildings.

2. The method according to claim 1, characterized in that The power equipment corresponding to the commercial building users includes central air conditioners and electric vehicles; The determining the user adjustable potential of the multiple users based on the user responsiveness of the multiple users and the adjustable potential of the corresponding power equipment includes: Based on the user responsiveness of the commercial building user and the adjustable potential of the central air conditioner and the electric vehicle, the user adjustable potential of the commercial building user is determined by the following formula; ； in, is the annual average failure rate of the central air conditioner, is the annual average failure rate of the electric vehicle, For the adjustable potential of the central air conditioner, For the The adjustable potential of the electric vehicle described above, The total number of electric vehicles included in the commercial building users, is the user responsiveness of the commercial building users.

3. The method according to claim 2, characterized in that The adjustable potential of the central air conditioner is determined by the following formula: ； ； ； in, is the external environment temperature, is the pre-dispatch temperature of the central air conditioner, is the dispatched temperature of the central air conditioner, is the standby power of the central air conditioner, is the parameter energy efficiency coefficient of the central air conditioner, is the equivalent heat capacity, is the equivalent thermal resistance, To schedule time; The adjustable potential of the electric vehicle is determined by the following formula; ； ； in, is the adjustable potential of the electric vehicle, The potential for electric vehicles to be reduced, is the current maximum charging power of the electric vehicle, is the current minimum charging power of the electric vehicle, is the current charging power of the electric vehicle, is the battery capacity of the electric vehicle after responding to the dispatch, To respond to the dispatch time, is the current battery charge of the electric vehicle, is the battery capacity of the electric vehicle, Energy efficiency for charging the battery.

4. The method according to claim 1, characterized in that: The electrical equipment corresponding to the residential users includes household split air conditioners, electric water heaters and electric vehicles; The determining the user adjustable potential of the multiple users based on the user responsiveness of the multiple users and the adjustable potential of the corresponding power equipment includes: Based on the user responsiveness of the residential user and the adjustable potential of the household split air conditioner, electric water heater and electric vehicle, the user adjustable potential of the residential user is determined by the following formula; ； in, is the annual average failure rate of the household split air conditioner, is the annual average failure rate of the electric water heater, is the annual average failure rate of the electric vehicle, For the adjustable potential of the household split air conditioner, For the adjustable potential of the electric water heater, For the The adjustable potential of the electric vehicle described above, is the total number of electric vehicles included in the residential users, is the user responsiveness of the residential user.

5. The method according to claim 4, characterized in that The adjustable potential of the household split air conditioner is determined by the following formula: ； in, is the proportion of adjustable potential, The average aggregate power when the household split air conditioner of the residential user cluster corresponding to the residential user is set to a preset temperature; The adjustable potential of the electric vehicle is determined by the following formula; ； ； in, is the adjustable potential of the electric vehicle, The potential for electric vehicles to be reduced, is the current maximum charging power of the electric vehicle, is the current minimum charging power of the electric vehicle, is the current charging power of the electric vehicle, is the battery capacity of the electric vehicle after responding to the dispatch, To respond to the dispatch time, is the current battery charge of the electric vehicle, is the battery capacity of the electric vehicle, Energy efficiency for charging the battery.

6. The method according to claim 4, characterized in that The adjustable potential of the electric water heater is determined by performing the following operations: In response to the current day being a working day, based on the load power curve of the electric water heater on historical working days and the current power data, predicted power data is obtained through a first prediction model and the predicted power data is used as the adjustable potential of the electric water heater; In response to the current being a non-working day, based on the load power curve of the electric water heater on historical rest days and the current power data, the predicted power data is obtained by a second prediction model and the predicted power data is used as the adjustable potential of the electric water heater; The parameters of the first prediction model and the second prediction model are adjusted based on the following formula: ； in, is the number of training samples, is the predicted water heater power data, It is the actual water heater power data.

7. A load-type virtual power plant optimization dispatching device, characterized in that: include: The first calculation module is configured to determine the user responsiveness of multiple users by using a nonlinear multiple regression algorithm for analysis and prediction based on the current market electricity price and the relationship between the market electricity price and the user responsiveness; the user responsiveness represents the proportion of the user's participation adjustment amount to the maximum adjustment amount of the multiple users; A second calculation module is configured to determine the user adjustable potential of the plurality of users based on the user responsiveness of the plurality of users and the adjustable potential of the corresponding power equipment, wherein the plurality of users include commercial building users and residential users; An optimization scheduling module is configured to optimize the scheduling of the load-based virtual power plant based on the user adjustable potential of the plurality of users; the objective function of the optimization scheduling is to minimize the total energy cost of the virtual power plant; The optimizing scheduling of the load-based virtual power plant based on the adjustable user potential of the multiple users includes: Based on the user adjustable potential of the multiple users, solving the optimal solution of the following objective function and performing optimal scheduling of the load-based virtual power plant; ； in, is the number of residential user clusters included in the load-based virtual power plant, For the The energy cost of the residential user cluster is: , , are respectively the proportion of the adjustable potential of household split air conditioners and electric vehicles in the residential user cluster to the total adjustable potential of the residential user cluster, is the number of commercial building user clusters included in the load-based virtual power plant, For the The energy cost of the commercial building user cluster is: , is the proportion of the adjustable potential of the central air conditioner in the commercial building user cluster to the total adjustable potential of the commercial building cluster, , and is the average energy cost coefficient of the household split air conditioner, , and is the average energy cost coefficient of the central air conditioner, is the average energy cost coefficient of the electric vehicle, is the average energy cost coefficient of the electric water heater, The load-based virtual power plant is allocated to the The regulation amount of each of the residential user clusters, The load-based virtual power plant is allocated to the first The regulation amount of a commercial building user cluster, and The value constraint of and , , Respectively represent the The adjustable potential of the residential user cluster and the The adjustable potential of a cluster of commercial buildings.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 6 is implemented.

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