Virtual power plant dispatching method considering multiple types of demand response and user comprehensive satisfaction

Abstract

The application relates to the field of power systems, and particularly discloses a virtual power plant scheduling method considering multiple types of demand response and user comprehensive satisfaction, which comprises the following steps: a virtual power plant is constructed by aggregating distributed units, renewable energy units, energy storage systems and load users; a scheduling optimization model of the virtual power plant is constructed by taking the maximum operation income in a scheduling period of the virtual power plant as a target; a user comprehensive satisfaction model is constructed by taking the maximum average value of the comprehensive satisfaction of all load users in the scheduling period of the virtual power plant as a target; a double-layer scheduling model of the virtual power plant considering multiple types of demand response and user comprehensive satisfaction is constructed by taking the scheduling optimization model as an upper-layer model and taking the user comprehensive satisfaction model as a lower-layer model, and the scheduling decision of the virtual power plant is obtained by solving the double-layer scheduling model. The application can make the virtual power plant ensure power supply and maintain grid stability, maximize economic benefits and environmental benefits, and ensure the power demand and satisfaction of users.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems, and particularly relates to a virtual power plant scheduling method considering multiple types of demand response and user comprehensive satisfaction. BACKGROUND

[0002] China has accelerated the pace of building a new power system and actively promoted the development and utilization of renewable energy such as wind power and photovoltaic power. Effective large-scale aggregation and efficient deployment of these resources are crucial in the construction of a new power system, which not only optimizes power resource allocation but also enhances the system's supply-demand matching capability and operational flexibility, providing strong support for building a clean, low-carbon, safe and efficient modern energy system.

[0003] Among them, the virtual power plant (VPP) as a flexible distributed resource management method can participate in the power market and the power grid operation as a special power plant, which can play an important role in fully tapping the flexibility of resource regulation, promoting renewable energy consumption, and ensuring power supply and demand balance.

[0004] However, the widespread access of distributed energy has enhanced the flexibility of the power system, but the randomness of renewable energy output, the diversity of user load demand, and the time-varying characteristics of the power market price have brought significant challenges to the operation of VPP. SUMMARY

[0005] Therefore, the present application provides a virtual power plant scheduling method considering multiple types of demand response and user comprehensive satisfaction to try to solve or at least alleviate the above problems.

[0006] According to one aspect of the present application, a virtual power plant scheduling method considering multiple types of demand response and user comprehensive satisfaction is provided, comprising: constructing a virtual power plant aggregating distributed units, renewable energy units, energy storage systems and load users; constructing a scheduling optimization model of the virtual power plant with the maximum operating income in the scheduling period as the target, and constructing a user comprehensive satisfaction model with the maximum mean value of the comprehensive satisfaction of all load users in the scheduling period as the target, the comprehensive satisfaction being composed of power usage satisfaction and power cost satisfaction; constructing a virtual power plant double-layer scheduling model considering multiple types of demand response and user comprehensive satisfaction with the scheduling optimization model as the upper model and the user comprehensive satisfaction model as the lower model; solving the virtual power plant double-layer scheduling model to obtain the scheduling decision of the virtual power plant, the scheduling decision including the output of the distributed units and renewable energy units in each period, the charging power and discharging power of the energy storage system in each period, and the load reduction and load transfer of the load users in each period.

[0007] Optionally, in the virtual power plant scheduling method considering multiple types of demand response and user comprehensive satisfaction according to the present application, the distributed unit is a gas unit, and a scheduling optimization model of the virtual power plant is constructed with the maximum operation income in the scheduling period of the virtual power plant as the target, including: determining the operation income of the virtual power plant in the scheduling period according to the income of the virtual power plant selling electricity to the power grid, the income of the virtual power plant participating in demand response, the incentive cost of the virtual power plant to the load users participating in demand response, the carbon trading cost of the virtual power plant, the generation cost of the gas unit and the start-stop cost, and the generation cost of the renewable energy unit, and constructing the scheduling optimization model of the virtual power plant with the maximum operation income as the first objective function.

[0008] Optionally, in the virtual power plant scheduling method considering multiple types of demand response and user comprehensive satisfaction according to the present application, a user comprehensive satisfaction model is constructed with the maximum mean value of the comprehensive satisfaction of all load users in the scheduling period of the virtual power plant as the target, including: for each load user, obtaining the product of the power consumption mode satisfaction and the weight coefficient of the power consumption mode satisfaction in the scheduling period, and the product of the power consumption cost satisfaction and the weight coefficient of the power consumption cost satisfaction in the scheduling period, and taking the sum of the two products as the comprehensive satisfaction of the load user in the scheduling period; based on the comprehensive satisfaction of each load user in the scheduling period, obtaining the mean value of the comprehensive satisfaction of all load users in the scheduling period, and constructing the user comprehensive satisfaction model with the maximum mean value as the second objective function.

[0009] Optionally, in the virtual power plant scheduling method considering multiple types of demand response and user comprehensive satisfaction according to the present application, the power consumption mode satisfaction of each load user in the scheduling period is obtained, including: based on the total load reduction amount and the total load demand amount of each load user in the scheduling period, obtaining the load reduction degree value of each load user in the scheduling period; based on the load transfer amount and the load demand amount of each load user in each period in the scheduling period, obtaining the load transfer degree value of each load user in the scheduling period; based on the load reduction degree value and the load transfer degree value of each load user in the scheduling period, obtaining the power consumption mode satisfaction of each load user in the scheduling period.

[0010] Optionally, in the virtual power plant scheduling method considering multiple types of demand response and user comprehensive satisfaction according to the present application, the load reduction degree value of each load user in the scheduling period is obtained based on the total load reduction amount and the total load demand amount of each load user in the scheduling period, including:

[0011]

[0012] wherein, represents the load reduction degree value of the load user i in the scheduling period, represents the load reduction amount of the load user i at the t period, x i,t represents the load demand amount of the load user i at the t period in the load baseline, and T represents the total number of periods in the dispatch cycle.

[0013] Optionally, in the virtual power plant dispatching method considering multiple types of demand response and user comprehensive satisfaction according to the application, the load transfer degree value of each load user in the dispatch cycle is obtained based on the load transfer amount and the load demand amount of each period in the dispatch cycle of each load user, and includes:

[0014]

[0015] wherein, represents the load transfer degree value of the load user i in the dispatch cycle, represents the load transfer amount of the load user i at the t period, x i,t represents the load demand amount of the load user i at the t period in the load baseline, and T represents the total number of periods in the dispatch cycle.

[0016] Optionally, in the virtual power plant dispatching method considering multiple types of demand response and user comprehensive satisfaction according to the application, the electricity cost satisfaction degree of each load user in the dispatch cycle includes:

[0017]

[0018] wherein, represents the electricity cost satisfaction degree of the load user i in the dispatch cycle, represents the price of the virtual power plant purchasing electricity from the power grid at the t period, Δx i,t represents the load amount of the load user i participating in the demand response at the t period, x i,t represents the load amount of the load user i at the t period in the load baseline, represents the incentive subsidy price of the virtual power plant at the t period.

[0019] Optionally, in the virtual power plant dispatching method considering multiple types of demand response and user comprehensive satisfaction according to the application, the dispatch optimization model includes a first constraint condition, and the user comprehensive satisfaction model includes a second constraint condition, wherein: the first constraint condition includes: load amount constraint of each load user participating in the demand response, power balance constraint of the virtual power plant, actual load amount constraint of each load user after participating in the demand response, operation constraint of the gas turbine unit, output constraint of the renewable energy unit, charge and discharge constraint of the energy storage system; the second constraint condition includes: deviation constraint of the comprehensive satisfaction of all load users; the reducible load amount constraint of each load user, the minimum continuous reduction time constraint, the maximum continuous reduction time constraint, the reduction times constraint; the transferable load amount constraint of each load user, the minimum continuous transfer time constraint.

[0020] According to still another aspect of the present application, there is provided a computing device comprising at least one processor; and a memory storing program instructions configured to be executed by the at least one processor, the program instructions comprising instructions for performing the method for virtual power plant dispatching considering multi-type demand response and user comprehensive satisfaction according to the present application.

[0021] According to still another aspect of the present application, there is provided a readable storage medium storing program instructions, which, when read and executed by a computing device, cause the computing device to perform the method for virtual power plant dispatching considering multi-type demand response and user comprehensive satisfaction according to the present application.

[0022] The method for virtual power plant dispatching considering multi-type demand response and user comprehensive satisfaction according to the present application establishes a virtual power plant double-layer optimization dispatching model considering multi-type demand response and user comprehensive satisfaction, optimizes the operation revenue of the virtual power plant, and fully considers the satisfaction of users after participating in demand response. Based on this, the virtual power plant can maximize economic revenue and environmental benefits while ensuring power supply and maintaining grid stability, and can also ensure user electricity demand and satisfaction. Therefore, the present application is conducive to further promoting the development of new power systems and improving user experience. BRIEF DESCRIPTION OF DRAWINGS

[0023] To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings. These aspects are indicative of various ways in which the principles disclosed herein can be practiced and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The foregoing and other objects, features, and advantages of the disclosure will be apparent from the following description of one or more aspects and as illustrated in the accompanying drawings. The same reference numbers in different drawings identify the same components or elements.

[0024] Figure 1 A structural block diagram of a computing device 100 according to one embodiment of the present application is shown;

[0025] Figure 2 A flowchart of a method for virtual power plant dispatching considering multi-type demand response and user comprehensive satisfaction 200 according to one embodiment of the present application is shown;

[0026] Figure 3 A schematic diagram of a structural framework of a virtual power plant according to one embodiment of the present application is shown;

[0027] Figure 4 A schematic diagram of a solving process of a virtual power plant double-layer dispatching model according to one embodiment of the present application is shown;

[0028] Figure 5A schematic diagram showing a user load curve according to an embodiment of the present application is shown;

[0029] Figure 6 A schematic diagram showing a wind power output curve and a photovoltaic output curve according to an embodiment of the present application is shown;

[0030] Figure 7 A schematic diagram showing a power purchase price, a power sale price, and an incentive price of a virtual power plant according to an embodiment of the present application is shown;

[0031] Figure 8 A schematic diagram showing objective function values of three groups of comparison schemes according to an embodiment of the present application is shown;

[0032] Figure 9 A schematic diagram showing a VPP net load curve according to an embodiment of the present application is shown;

[0033] Figure 10 A schematic diagram showing dispatching situations of each subject of a VPP according to an embodiment of the present application is shown;

[0034] Figure 11 A schematic diagram showing power purchase and sale situations of a VPP according to an embodiment of the present application is shown;

[0035] Figure 12 A schematic diagram showing a VPP demand response situation according to an embodiment of the present application is shown;

[0036] Figure 13 A schematic diagram showing demand response situations of each user according to an embodiment of the present application is shown;

[0037] Figure 14 A schematic diagram showing influences of a peak-valley price difference on a VPP profit and a user comprehensive satisfaction according to an embodiment of the present application is shown;

[0038] Figure 15 A schematic diagram showing influences of a peak-shaving incentive price on a user comprehensive satisfaction according to an embodiment of the present application is shown;

[0039] Figure 16 A schematic diagram showing VPP profit and user comprehensive satisfaction situations under different weights according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0040] Exemplary embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings; however, they are not limited to the embodiments set forth herein but can be implemented in various forms. The present disclosure will be described herein with reference to individual embodiments, but combinations of these embodiments can also be used. The present disclosure should not be construed as being limited to the embodiments set forth herein; various changes in form and details can be made therein without departing from the spirit and scope of the present disclosure. Rather, the embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0041] The virtual power plant can tap the regulation potential of flexible resources, realize the aggregation and coordinated optimization of distributed energy resources, and is a new mode for promoting the safe and stable operation of a new type of power system. Meanwhile, as an important part of the virtual power plant, the response willingness of the user is a key bottleneck restricting demand response. Based on this, the application provides a virtual power plant scheduling method considering multiple types of demand response and user comprehensive satisfaction.

[0042] The virtual power plant scheduling method considering multiple types of demand response and user comprehensive satisfaction of the application can be executed in a computing device. Figure 1 A block diagram of the physical components (i.e., hardware) of computing device 100 is shown. In a basic configuration, computing device 100 includes at least one processing unit 102 and system memory 104. According to an aspect, depending on the configuration and type of computing device, processing unit 102 can be implemented as a processor. System memory 104 includes, but is not limited to, volatile (e.g., random access memory (RAM)), non-volatile (e.g., read-only memory (ROM)), flash memory, or any combination thereof. According to an aspect, system memory 104 includes operating system 105 and program module 106, which includes scheduling module 120 configured to execute the virtual power plant scheduling method 200 considering multiple types of demand response and user comprehensive satisfaction of the application.

[0043] According to an aspect, operating system 105 is suitable for controlling the operation of computing device 100, for example. Furthermore, examples are practiced in conjunction with a graphics library, other operating systems, or any other application program, and are not limited to any particular application or system. In Figure 1 This basic configuration is illustrated in FIG. 1 by those components within dashed line 108. According to an aspect, computing device 100 has additional features or functionality. For example, according to an aspect, computing device 100 includes additional data storage devices (removable and / or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 1 by removable storage 109 and non-removable storage 110. Figure 1 According to an aspect, removable storage 109 includes a computer-readable storage medium having stored thereon computer executable instructions (e.g., software) that, when executed by a processor of a computing device, cause the computing device to perform desired functions. According to an aspect, non-removable storage 110 includes a computer-readable storage medium having stored thereon computer executable instructions (e.g., software) that, when executed by a processor of a computing device, cause the computing device to perform desired functions.

[0044] As stated above, according to an aspect, program module is stored in system memory 104. According to an aspect, program module can include one or more applications. The application is not limited to the type of application, for example, the application can include: email and contact application, word processing application, spreadsheet application, database application, slide show application, drawing or computer-aided application, web browser application, etc.

[0045] According to an aspect, examples can be practiced with electronic circuitry integrated on a single integrated circuit chip, with separate electronic elements interconnected off the chip, with a microprocessor, or with any other physical configuration. For example, examples can be implemented via a general-purpose computer, a special-purpose computer, a microprocessor, or a state machine. Examples can be implemented using any of a wide variety of Figure 1 microprocessors of one or more processors of a multi-processor core, micro-controllers, digital signal processors, dedicated circuitry, or any other circuitry or processor. Examples can be implemented using a computer-readable medium having computer-executable instructions embodied thereon. Examples can also be implemented using computer-readable media for carrying or having computer-executable instructions embodied thereon. For example, examples can be implemented using a computer-readable medium that is one of: a floppy disk, a flexible disk, a hard disk, a solid state drive, a magnetic tape, a holographic media, a punch card, a paper tape, an optical data storage medium, an aspiration, a CD-ROM, a CD-R, a CD-RW, a DVD, a Blu-ray Disc, a flash memory, a phase change memory, and / or another non-transitory medium under Title 17, United States Code. Examples can also be embodied as a computer-readable medium used in the transmission real-time of computer-executable instructions. Accordingly, examples can also be embodied as a computer-readable transmission medium carrying computer-executable instructions embodied thereon. Examples can also be embodied as a computer program product having computer-executable instructions embodied thereon.

[0046] According to an aspect, the computing device 100 can also have one or more input device(s) 112 such as a keyboard, a mouse, a pen, a voice input device, a touch input device, etc. Output device(s) 114 such as a display, speakers, a printer, etc. can also be included. The aforementioned devices are examples and others can also be used. The computing device 100 can include one or more communication connections 116 allowing communications with other computing devices 118. Examples of suitable communication connections 116 include, but are not limited to: an RF transmitter, receiver, and / or transceiver circuitry; universal serial bus (USB), parallel, and / or serial ports.

[0047] As used herein, the term computer-readable medium includes computer storage medium. Computer storage medium can include volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information (e.g., computer-readable instructions, data structures, or program modules). System memory 104, removable storage 109, and non-removable storage 110 are examples of computer storage media (i.e., memory storage). Computer storage media can include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical storage, magnetic tape, magnetic tape, disk storage or other magnetic storage devices, or any other article of manufacture that can be used to store information and is accessible by computer device 100. According to one aspect, any such computer storage medium can be part of computing device 100. Computer storage media does not include carrier waves or other transmitted data signals.

[0048] According to one aspect, a communication medium is implemented by computer-readable instructions, data structures, program modules, or other data in a modulated data signal (e.g., a carrier wave or other transmission mechanism), and includes any information transmission medium. According to one aspect, the term "modulated data signal" describes a signal having one or more sets of characteristics or altered in a manner that encodes information in the signal. By way of example and not limitation, a communication medium includes wired media such as wired networks or direct wired connections, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0049] Figure 2 A flowchart of a virtual power plant scheduling method 200 considering multiple types of demand response and overall user satisfaction according to an embodiment of the present invention is shown. Method 200 is adapted to be used on a computing device (e.g., Figure 1 The invention is executed in the computing device 100 shown. In some embodiments, a VPP (Virtual Platform Package) framework for aggregating multiple distributed resources can be constructed first. The invention will be described in detail below.

[0050] like Figure 2 As shown, the virtual power plant scheduling method 200 of the present invention, which takes into account multiple types of demand response and overall user satisfaction, begins at 210.

[0051] In 210, a virtual power plant (VPP) is constructed, which aggregates distributed generators, renewable energy source (RES) generators, energy storage systems (ESSs), and load users. The distributed generators can be gas generators, and the RES generators can include wind generators and photovoltaic generators. Of course, this is only an example, and the present application is not limited thereto.

[0052] For the framework structure diagram of the VPP constructed in the present embodiment, please refer to Figure 3 . In the present embodiment, the VPP aggregates distributed generators (DGs, mainly gas generators), wind power generation, photovoltaic power generation, energy storage, loads, and other adjustable resources including load sides and power source sides, and realizes the optimal configuration of resources in a wide area through information transmission and energy interaction. In the dispatching process, the VPP operator will also make power purchase or power sale transactions with the power grid according to price information, so as to coordinate the supply and demand balance of power in the VPP and obtain benefits.

[0053] In addition, the VPP actively responds to demand response invitations issued by the power grid, and adjusts the load demand in a specified period to obtain corresponding subsidies. On the basis of the original price-based demand response (PDR) price signal, the VPP adjusts the power demand of the load users who meet the conditions by issuing incentive-based demand response (IDR) subsidies, encourages users to respond to demand, and adjusts the power demand of the load users who meet the conditions by issuing incentive-based demand response (IDR) subsidies, encourages users to respond to demand. In order to ensure the stability and economy of the system, the VPP operator needs to comprehensively consider the output of the distributed generators, the renewable energy generation, the charging and discharging operation of the ESSs, and the load demand changes in each period. At the same time, the VPP considers the power consumption satisfaction of users, and adjusts the power generation and consumption scheme of the VPP from the aspects of power consumption comfort and economy in the dispatching process. Through the coordinated management of these resources, the VPP operator can maximize the economic benefits and the comprehensive satisfaction of users while ensuring power supply and maintaining the stability of the power grid.

[0054] The above is the structural framework of the VPP constructed by the present application. Next, in 220, a dispatching optimization model of the VPP is constructed, with the maximum running benefit in the dispatching period of the VPP as the target, and a user comprehensive satisfaction model is constructed, with the maximum average value of the comprehensive satisfaction of all load users in the dispatching period of the VPP as the target. The comprehensive satisfaction is composed of power consumption mode satisfaction and power consumption cost satisfaction.

[0055] Next, the construction of the dispatching optimization model of the VPP and the construction of the user comprehensive satisfaction model will be described.

[0056] 1. A scheduling optimization model of a virtual power plant

[0057] According to one embodiment of the present application, the operation revenue of the virtual power plant in the scheduling period can be determined according to the revenue of the virtual power plant from selling electricity to the power grid in each time period in the scheduling period, the revenue of the virtual power plant from participating in demand response, the incentive cost of the virtual power plant to the load users (for the convenience of description, the load users are referred to as users in the following part of the description) participating in demand response, the carbon trading cost of the virtual power plant, the generation cost and start-stop cost of the gas unit, and the generation cost of the renewable energy unit, and the scheduling optimization model of the virtual power plant is constructed with the maximum operation revenue in the scheduling period as the first objective function. In this embodiment, the operation cost of the energy storage system is usually very low, and in order to simplify the model and improve the calculation efficiency, the energy storage system is not included in the construction of the scheduling optimization model.

[0058] In some embodiments, the first objective function can be specifically as follows.

[0059]

[0060] In the formula, R represents the operation revenue of the virtual power plant in the scheduling period, represents the revenue of the virtual power plant from selling electricity to the power grid in the t time period, represents the revenue of the virtual power plant from participating in the demand response invitation in the t time period, represents the incentive cost paid by the virtual power plant to the responding users in the t time period, represents the generation cost of the gas unit in the t time period, represents the start-stop cost of the gas unit in the t time period, represents the carbon trading cost of the virtual power plant in the t time period, represents the generation cost of the renewable energy unit in the t time period, and T represents the total number of time periods.

[0061] Regarding the scheduling period and the scheduling time period (i.e. the t time period), it is explained as follows. In some embodiments, the scheduling period can be 24 h, the scheduling time period can be 1 h, the value of T is 24, and the operation revenue of the virtual power plant in the scheduling period is the daily operation revenue of the virtual power plant. Of course, this is only an example, and the present application is not limited thereto. In specific embodiments, the person skilled in the art can set it according to actual needs.

[0062] Next, the acquisition method of is described respectively.

[0063] 1) Demand response cost: when the load of the user participating in the demand response reaches the standard set by the virtual power plant, the virtual power plant will give an incentive subsidy to the user. In the t time period, the virtual power plant pays the incentive which can be specifically acquired by the following formula.

[0064]

[0065] wherein, represents the incentive price of the virtual power plant at time t, wherein, represents that the virtual power plant does not issue demand response at time t; Δx i,t represents the load amount of user i participating in demand response at time t, and in relation thereto, in some embodiments, the user load baseline and the actual load curve can be calculated based on, wherein Δx i,t = 0 represents that user i does not participate in demand response at time t; N represents the total number of users.

[0066] In addition, after demand response, the load demand D t of the virtual power plant at time t can be calculated by the following formula.

[0067]

[0068] wherein, x i,t represents the load demand amount of the load user i in the load baseline at time t.

[0069] 2) Gas turbine generator cost: the generation cost of the gas turbine generator at time t which can be calculated by the following formula.

[0070]

[0071] wherein, g t represents the generation power of the gas turbine generator at time t, is a binary variable representing the state of the gas turbine generator at time t, wherein, if the gas turbine generator at time t is in a running state, otherwise (i.e., the gas turbine generator at time t is not in a running state), a g , b g , c all represent cost parameters of the gas turbine generator.

[0072] 3) Gas turbine generator start-stop cost: the start-stop cost of the gas turbine generator which is composed of a start cost c st and a shutdown cost c sh , and can be represented by the following formula.

[0073]

[0074] wherein, is a binary variable representing the state of the gas turbine generator at time (t-1), wherein, if the gas turbine generator at time (t-1) is in a running state, otherwise,

[0075] 4) Carbon trading cost: The carbon quota method of free allocation of quotas and the step pricing mechanism are adopted in this embodiment, and it is considered that all market electricity purchases are from coal-fired generating units. Among them, the step pricing mechanism divides multiple purchase intervals, and the more carbon emission rights quotas need to be purchased, the higher the purchase price of the corresponding interval. Specifically, in some embodiments, the step carbon trading cost of the t period can be expressed as follows.

[0076]

[0077] In the formula, represents the carbon trading base price, l represents the carbon emission interval length, a represents the price growth rate, represents the carbon emission rights trading amount of the VPP in the t period.

[0078] The carbon emission rights trading amount of the VPP in the t period According to one embodiment of the present application, it can be calculated by the following formula.

[0079]

[0080] In the formula, represents the actual carbon emission amount of the VPP in the t period, represents the carbon emission rights quota of the VPP in the t period.

[0081] The actual carbon emission amount of the VPP in the t period can be obtained by the following formula.

[0082]

[0083] In the formula, respectively represent the actual carbon emission amounts of the VPP, the grid electricity purchase, and the gas-fired generating unit in the t period, represents the electricity purchase amount of the VPP in the market in the t period, a1, b1, c1 represent the carbon emission calculation parameters of the coal-fired generating unit, and a2, b2, c2 represent the carbon emission calculation parameters of the gas-fired generating unit.

[0084] The carbon emission rights quota of the VPP in the t period can be obtained by the following formula.

[0085]

[0086] In the formula, respectively represent the carbon emission rights quotas of the VPP, the grid electricity purchase, and the gas-fired generating unit in the t period, χ e , χ g respectively represent the carbon emission rights quotas per unit of power consumption of the coal-fired generating unit and the gas-fired generating unit.

[0087] 5) Renewable energy generation cost: The wind and solar generator owners supply power to the VPP at a fixed contract price, so the renewable energy generation cost is the price paid by the VPP to the wind and solar generator owners. Specifically, the renewable energy generation cost at time period t can be obtained by the following formula.

[0088]

[0089] wherein, represents the supply price of the wind and solar generators at time period t, wp t and sp t represent the supply power of the wind and solar generators at time period t, respectively.

[0090] 6) Grid trading revenue: The VPP trades power with the grid at time-of-use prices to balance the supply and demand of power. The revenue of the VPP from selling power to the grid at time period t can be obtained by the following formula.

[0091]

[0092] wherein, and pt represent the selling and buying price of the VPP to the grid at time period t, respectively, represent the selling and buying quantity of the VPP to the grid at time period t, respectively.

[0093] 7) Revenue from participating in demand response: The VPP can obtain revenue by responding to the demand response invitation issued by the grid. The revenue of the VPP from participating in the demand response invitation at time period t can be obtained by the following formula.

[0094]

[0095] wherein, represents the subsidy price agreed by the VPP and the grid for the response time period t.

[0096] Further, the dispatch optimization model of the virtual power plant further includes a first constraint condition corresponding to the first objective function. Specifically, in some embodiments, the first constraint condition includes a load quantity constraint of each load user participating in demand response, a power balance constraint of the virtual power plant, an actual load quantity constraint of each load user after participating in demand response, an operation constraint of the gas generator, an output constraint of the renewable energy generator, and a charge and discharge constraint of the energy storage system, which are described below respectively.

[0097] 1) The load amount constraint of each load user participating in demand response: the user obtains the incentive subsidy given by the VPP through demand response, and the load amount participating in response in each period should reach the standard set by the VPP. Based on this, in some embodiments, the load amount constraint of each load user participating in demand response in each period can be specifically expressed as the following formula.

[0098] Δx i,t ≥r IDR x i,t

[0099] In the formula, r IDR is the incentive ratio set by the VPP operator, which means that when the load participating in demand response of the user reaches a certain proportion of the load baseline of the user, the incentive subsidy from the VPP operator can be obtained, x i,t represents the load amount (or load demand amount) of user i in period t in the load baseline.

[0100] 2) The power balance constraint of the virtual power plant: each period of the VPP needs to maintain the balance of power supply and demand, which can be specifically expressed as the following formula.

[0101]

[0102] In the formula, g t represents the power generation of the gas turbine unit in period t, wp t , sp t respectively represent the power supply of the wind turbine unit and the photovoltaic turbine unit in period t, respectively represent the power supply and power purchase of the VPP to the power grid in period t, D t represents the load demand amount of the VPP in period t after demand response, respectively represent the charging power and discharging power of the ESS in period t.

[0103] 3) The actual load amount constraint of each load user participating in demand response: also known as the user basic load constraint, specifically, the load of the user after demand response should meet the rigid load, which can be expressed as the following formula.

[0104]

[0105] In the formula, x i,t represents the load amount of user i in period t in the load baseline, Δx i,t represents the response amount of user i in period t, represents the rigid load of user i in period t in the VPP, that is, the part of the load that cannot be adjusted in the residential load.

[0106] 4) The operation constraint of the gas turbine unit: the operation of the gas turbine unit needs to meet the output and climbing constraints, which can be specifically expressed as the following formula.

[0107]

[0108] In the formula, P min P max These represent the minimum and maximum output of the gas turbine unit, respectively, g t-1 r represents the power generation of the gas turbine unit during the time period (t-1). down r up These represent the upper limits of the downhill ramp rate and the uphill ramp rate of the gas turbine unit, respectively.

[0109] 5) Output constraints of renewable energy units: The output of non-adjustable units such as wind power and photovoltaic power is fluctuating, but should not exceed their output limit, which can be expressed as the following formula.

[0110]

[0111] In the formula, sp max ,wp max These represent the upper limits of output for photovoltaic generator sets and wind turbine generator sets, respectively.

[0112] 6) Charge and discharge constraints of the energy storage system: During each time period, the amount of energy charged and discharged by the ESS is limited by the maximum charge and discharge rate, which can be expressed as the following formula.

[0113]

[0114] In the formula, These represent the charging power and discharging power of the ESS during time period t, respectively. Let be a binary variable representing the charging state of the ESS during time period t, where if the ESS is in a charging state during time period t, then If the ESS is not charging during time period t, then Let be a binary variable representing the discharge state of the ESS during time period t, where if the ESS is in a discharge state during time period t, then If the ESS is not in a discharged state during time period t, then These represent the maximum charging rate and maximum discharging rate of the ESS, respectively.

[0115] Furthermore, this constraint also includes that the ESS will not charge and discharge simultaneously during the same period, which can be expressed as follows.

[0116]

[0117] In addition, in some embodiments, the first constraint also includes the energy storage capacity constraint of the energy storage system and the initial and final state constraint of the energy storage system.

[0118] The energy storage capacity constraint of the energy storage system, specifically, the energy storage capacity of the ESS is limited by the upper limit of the capacity of the ESS, which in some embodiments can be expressed as follows.

[0119]

[0120] In the formula, S t , S t-1 respectively represent the energy storage conditions (energy storage capacity) of the ESS at time period t and time period (t-1), respectively represent the charging power and discharging power of the ESS at time period t, λ ch , λ dis respectively represent the charging coefficient and discharging coefficient of the ESS, ess cap represents the upper limit of the capacity of the ESS, and Δt represents the length of a time period.

[0121] The start-end state constraint of the energy storage system, specifically, the energy storage state value of the battery at the end time period of each scheduling period of the energy storage should be equal to the energy storage state value at the initial time period, which in some embodiments can be expressed as follows.

[0122] S1=S T

[0123] S1, S T respectively represent the energy storage capacity at the initial time period (i.e., time period 1) and the end time period.

[0124] 2. User comprehensive satisfaction model

[0125] Regarding the user comprehensive satisfaction model, according to an embodiment of the present application, it can be constructed in the following manner.

[0126] Step 1), for each load user, obtain the product of the electricity usage method satisfaction and the weight coefficient of the electricity usage method satisfaction in its scheduling period, and the product of the electricity cost satisfaction and the weight coefficient of the electricity cost satisfaction in its scheduling period, and take the sum of the two products as the comprehensive satisfaction of the load user in the scheduling period. In some embodiments, it can be specifically expressed as follows.

[0127]

[0128] In the formula, V represents the comprehensive satisfaction of user i in the scheduling period of the VPP, respectively represent the electricity usage method satisfaction and the user cost satisfaction of user i in the scheduling period, ω1 and ω2 respectively represent the weight coefficients of the electricity usage method satisfaction and the electricity cost satisfaction, reflecting the importance of the user to the electricity usage method and the electricity cost, that is, in this embodiment, the comprehensive satisfaction is determined by the electricity comfort index and electricity economy index constitutions.

[0129] Further, as for the electricity usage mode satisfaction degree of each load user in the scheduling period, in some embodiments, it can be obtained in the following manner.

[0130] Firstly, based on the total load reduction amount and the total load demand amount of each load user in the scheduling period, the load reduction degree value of each load user in the scheduling period is obtained, which can be specifically expressed as the following formula.

[0131]

[0132] In the formula, is the load reduction degree value of the load user i in the scheduling period, which represents the relationship between the load reduction amount and the electricity load before response, and reflects the influence of the total load reduction amount on the total load demand of the user in the scheduling period (such as one day), represents the load reduction amount of the load user i at the t period, x i,t represents the load demand amount of the load user i at the t period in the load baseline, and T represents the total number of periods in the scheduling period.

[0133] Then, based on the load transfer amount and the load demand amount of each period of each load user in the scheduling period, the load transfer degree value of each load user in the scheduling period is obtained, which can be specifically expressed as the following formula.

[0134]

[0135] In the formula, is the load transfer degree value of the load user i in the scheduling period, which reflects the average influence of the load transfer amount of each period (such as each hour) on the load demand of the user, represents the load transfer amount of the load user i at the t period, x i,t represents the load demand amount of the load user i at the t period in the load baseline, and T represents the total number of periods in the scheduling period.

[0136] Finally, based on the load reduction degree value and the load transfer degree value of each load user in the scheduling period, the electricity usage mode satisfaction degree of each load user in the scheduling period is obtained, which can be specifically expressed as the following formula.

[0137]

[0138] In the formula, u1 and u2 respectively represent the weight coefficients of the load reduction degree value (or called load reduction degree index) and the load transfer degree value (or called load transfer degree index), which can be specifically an empirical value, and the present application is not limited thereto.

[0139] It can be seen that the embodiment divides the flexible load of the user in the VPP into the reducible load and the transferable load, i.e. In addition, for the difference between the two types of controllable load user response modes, the embodiment establishes the power consumption comfort degree model of each type of controllable load user.

[0140] In addition, according to one embodiment of the present application, the power consumption cost satisfaction degree of each load user in the dispatching period can be determined by comparing the electricity cost before and after the user participates in the demand response, and then the power consumption cost satisfaction degree is obtained, and specifically, the power consumption cost satisfaction degree can be obtained by the following formula.

[0141]

[0142] In the formula, represents the power consumption cost satisfaction degree of the load user i in the dispatching period, represents the price of the virtual power plant purchasing power from the power grid at the t period, Δx i,t represents the load amount of the load user i participating in the demand response at the t period, x i,t represents the load amount of the load user i in the load baseline at the t period, represents the incentive subsidy price of the virtual power plant at the t period. It is explained here that the power consumption price of the VPP user is the price of the virtual power plant purchasing power from the power grid

[0143] At this point, the comprehensive satisfaction degree of each load user in the dispatching period is obtained. Next, step 2) is entered, and based on the comprehensive satisfaction degree of each load user in the dispatching period, the average of the comprehensive satisfaction degree of all load users in the dispatching period is obtained, and the average is taken as the second objective function, and a user comprehensive satisfaction degree model is constructed.

[0144] In some embodiments, the second objective function can be the following formula.

[0145]

[0146] In the formula, represents the average of the comprehensive satisfaction degree of all load users in the dispatching period of the VPP, and the value of T is 24, represents the average of the daily comprehensive satisfaction degree of all load users in the VPP.

[0147] Furthermore, the overall user satisfaction model also includes a second constraint corresponding to the second objective function. Specifically, in some embodiments, the second constraint includes: a deviation constraint on the overall satisfaction of all load users; constraints on the load amount that can be reduced, the minimum continuous reduction time, the maximum continuous reduction time, and the number of reductions for each load user; and constraints on the load amount that can be transferred and the minimum continuous transfer time for each load user, which will be described below.

[0148] 1) Deviation constraint on the overall satisfaction of all load users: This is used to ensure that the satisfaction of each user in the virtual power plant is within a suitable range, and can be expressed as the following formula.

[0149] M = s max -s min ,0≤M≤k

[0150] In the formula, M represents the deviation of the overall satisfaction of all load users within the VPP, and s max s min M represents the maximum and minimum values ​​of overall satisfaction for all users, respectively, and k represents the upper limit of the deviation of overall satisfaction. By constraining the range of M, we can ensure that the satisfaction of virtual power plant users is within a suitable range.

[0151] 2) Reduceable load constraints: Reduceable loads are loads that can withstand certain interruptions or power reductions and reduced operating time. They can be partially or completely reduced based on supply and demand. For each load user, constraints need to be imposed on their reduceable load amount (or power reduction), minimum continuous reduction time, maximum continuous reduction time, and number of reductions, as detailed below.

[0152] The load constraint can be reduced, which can be expressed as the following formula.

[0153]

[0154] In the formula, α i,t Let α be a 0-1 variable representing the load reduction status of user i during time period t, where α represents the load reduction status of user i during time period t. i,t =1, if the load of user i is not reduced during time period t, then α i,t =0, These represent the minimum and maximum load power that user i can reduce during time period t, respectively.

[0155] The minimum continuous reduction time constraint can be expressed as follows.

[0156]

[0157] In the formula, denotes the minimum continuous shedding time, a i,t-1 is a 0-1 variable representing the shedding state of user i at period (t-1), where a i,t-1 = 1 if the load of user i is shed at period (t-1), and a i,t-1 = 0 if the load of user i is not shed at period (t-1).

[0158] The maximum continuous shedding time constraint can be specifically represented as follows.

[0159]

[0160] wherein, denotes the maximum continuous shedding time.

[0161] The shedding frequency constraint can be specifically represented as follows.

[0162]

[0163] wherein, N max denotes the maximum shedding frequency.

[0164] 3) Transferable load constraint: specifically including the transferable load amount constraint of each load user and the minimum continuous transfer time constraint, which are specifically as follows.

[0165] The transferable load amount constraint can be specifically represented as follows.

[0166]

[0167] wherein, β i,t is a 0-1 variable representing the transfer state of user i at period t, where β i,t = 1 if the load of user i is transferred at period t, and β i,t = 0 if the load of user i is not transferred at period t. respectively denote the minimum value and the maximum value of the transferable load power of user i at period t, where, is positive when the load is transferred out, and vice versa.

[0168] The minimum continuous transfer time constraint can be specifically represented as follows.

[0169]

[0170] wherein, denotes the minimum continuous transfer time, β i,t-1 is a 0-1 variable representing the transfer state of user i at period (t-1), where β i,t-1= 1, if the load of user i does not change at the (t-1) period, then β i,t-1 = 0.

[0171] In addition, the transferable load can be flexibly adjusted at each time period, but the total amount of the load after the transfer should remain unchanged compared with that before the transfer. Therefore, in some embodiments, the transferable load constraint further includes a total amount of load transfer constraint of each load user within the entire scheduling period, which can be specifically expressed as the following formula.

[0172]

[0173] So far, the scheduling optimization model and the user comprehensive satisfaction model have been constructed. Next, at 230, a virtual power plant bi-level scheduling model considering multiple types of demand response and user comprehensive satisfaction is constructed, taking the scheduling optimization model as the upper model and the user comprehensive satisfaction model as the lower model. That is, the virtual power plant bi-level scheduling model of the present embodiment is composed of the upper scheduling optimization model and the lower user comprehensive satisfaction model, the upper scheduling optimization model includes the first objective function and the first constraint condition corresponding to the first objective function, and the lower user comprehensive satisfaction model includes the second objective function and the second constraint condition corresponding to the second objective function.

[0174] After the construction of the virtual power plant bi-level scheduling model considering multiple types of demand response and user comprehensive satisfaction, at 240, the virtual power plant bi-level scheduling model is solved to obtain the scheduling decision of the virtual power plant. According to an embodiment of the present application, the scheduling decision of the virtual power plant specifically includes the output of the distributed generator and the renewable energy generator at each time period, the charging power and the discharging power of the energy storage system at each time period, and the load reduction and the load transfer of the load user at each time period. In other words, it is the output of the gas generator at each time period, the output of the wind generator at each time period, the output of the photovoltaic generator at each time period, the charging power and the discharging power of the energy storage system at each time period, and the load reduction and the load transfer of the load user at each time period.

[0175] Regarding the solution of the virtual power plant bi-level scheduling model, according to an embodiment of the present application, the KKT condition and the Big-M method can be used to convert it into a single-level linear model for solving. Further, in some embodiments, the solver Gurobi and the YALMIP toolbox can be called in Matlab for solving. For example, Figure 4 which shows the solution process of the virtual power plant bi-level scheduling model according to an embodiment of the present application.

[0176] First, the Lagrange function is constructed according to the model of the lower problem.

[0177] Secondly, the KKT complementary relaxation condition of the lower model is solved, and the lower model is converted into the constraint condition of the upper model, so that the double-layer model is converted into a single-layer nonlinear model.

[0178] Thirdly, the nonlinear term in the converted single-layer nonlinear model is linearized by using the Big-M method to form a single-layer mixed integer linear programming problem for solving.

[0179] As for the solution of the double-layer scheduling model of the virtual power plant, it will not be described here again, and the specific description can be referred to the related description of the KKT condition and the Big-M method.

[0180] The above is the virtual power plant scheduling method considering multiple types of demand response and user comprehensive satisfaction. In order to verify the effectiveness and applicability of the method, an example is given. Specifically, five representative load users in a certain region are selected, and distributed resources such as thermal power, wind power, photovoltaic and energy storage in the region are aggregated to form a virtual power plant. Among them, user 1 and user 2 are two residential user clusters, user 3 and user 4 are two commercial user clusters, and user 5 is an industrial user cluster.

[0181] It is assumed that a scheduling period is 24 hours, and the unit scheduling period is 1 hour. The typical load demand data of a certain day in summer in the region, the typical output data of wind power and photovoltaic output, the purchase and sale electricity price data of the virtual power plant in the power grid, and the demand response incentive subsidy price data formulated by the virtual power plant are selected as input parameters, as shown in Figures 5-7 .

[0182] The unit parameters of the gas turbine and energy storage in the virtual power plant are shown in Table 1.

[0183] Table 1

[0184]

[0185] The carbon trading related parameters are shown in Table 2.

[0186] Table 2

[0187]

[0188] The contract price of the virtual power plant to the renewable energy unit is 0.3 yuan / kWh, the power grid issues a demand response invitation in the daily peak period of 12:00-13:00 and 19:00-21:00, and the subsidy price is 3 yuan / kWh. The range constraint of the user comprehensive satisfaction is k=0.1, the weight coefficients are ω1=0.68 and ω2=0.32, and the ν1=0.6 and ν2=0.4.

[0189] 1. Effectiveness verification

[0190] Three groups of schemes are set up for comparison, and the revenue of VPP and the comprehensive satisfaction of users are studied under different emphasis targets: 1) Scheme 1, only considering the maximum profit of VPP operation; 2) Scheme 2, only considering the maximum comprehensive satisfaction of users; 3) Scheme 3, considering the profit of VPP operation and the comprehensive satisfaction of users.

[0191] The objective function values of the three groups of comparison schemes are shown in Table 1. Figure 8 As can be seen, when only considering the target of maximum VPP profit, the profit of VPP is 20066.53 yuan, and the calculated comprehensive satisfaction of users is 0.6965; when only considering the target of maximum comprehensive satisfaction of users, the profit of VPP is 9790.22 yuan, and the comprehensive satisfaction of users is 0.7617. Obviously, the profit of VPP obtained in scheme one is better than that in scheme two, but the satisfaction of users in scheme two is better than that in scheme one.

[0192] When considering the two objective functions at the same time, the profit of VPP is 18176.96 yuan, and the satisfaction is 0.7122. Compared with scheme one, the satisfaction of users is significantly improved; compared with scheme two, the profit of VPP is significantly improved. Therefore, when considering the economic target and the satisfaction target at the same time, scheme three is the optimal choice.

[0193] 2、Results analysis

[0194] Under scheme three of considering the maximum profit of VPP and the maximum comprehensive satisfaction of users at the same time, Figure 9 The power supply and demand status of VPP in different time periods is shown. When the power is positive, it means that the power supply of VPP is greater than the demand, and when the power is negative, it is the opposite. From the figure, it can be seen that in some time periods (such as 10h-15h), the net load reaches the peak, and in other time periods (such as 5h-10h and after 20h), the net load decreases, even becomes negative.

[0195] From Table 2, Figure 10The output information of each subject inside the VPP can be observed. The energy storage device charges at a low electricity price period and discharges at a high electricity price period. This operation strategy helps the VPP store energy at the electricity price trough period and release energy at the peak period, thereby reducing the electricity purchase cost and increasing the electricity sales revenue. The gas unit considers the generation cost to reduce the output, while considering the start-stop cost to avoid frequent start-stop; and increases the output at the high electricity price period to meet the user load and the demand of the VPP selling electricity to the market. The VPP fully utilizes the renewable energy generator set at the period with sufficient light and good wind power, which is usually consistent with the electricity price peak period, so that the VPP can reduce the electricity purchase from the grid and improve the self-sufficiency rate. In addition, the comparison of the load after demand response and the original load shows the ability of the VPP to adjust the load through demand response management. The VPP reduces or shifts the user load at the peak period through the two demand response methods of time-of-use price and incentive price, takes into account the satisfaction of user electricity demand and the reduction of VPP cost, and also obtains high demand response subsidies.

[0196] Figure 11 The electricity purchase and sales of the VPP are shown. The operation strategy of the VPP is mainly to sell electricity at a high electricity price period and to purchase electricity at a low electricity price period, which helps the VPP to maximize the profit in the electricity market, and the charge and discharge of the energy storage unit can also well meet the load demand of the user.

[0197] In summary, when considering both economic efficiency and satisfaction, the VPP will flexibly adjust the charge and discharge strategy of the energy storage unit, the output strategy of the gas unit, the electricity purchase and sales strategy in the market, and the user electricity behavior according to the changes of electricity price and user demand, to find the best balance point between reducing generation cost, arbitraging in the market and meeting user demand, so as to achieve the dual goals.

[0198] To explore the influence of scheme three on the demand response behavior of users, this example draws Figure 12 to reflect the demand response of the VPP load users under scheme three. The positive power value represents the peak shaving demand response, and the load decreases at this time; the positive power value represents the peak shaving demand response, and the load increases at this time.

[0199] As Figure 12As shown, the demand response load throughout the day is not simply represented by positive or negative values, but rather exhibits a more flexible and dynamic adjustment based on electricity market price signals and the VPP's overall operational objectives. Under Scheme 3, the VPP pursues a balance between maximizing profits and overall user satisfaction. This means that during periods of lower electricity prices, the VPP allows users to moderately increase their load based on their needs for electricity comfort and economy, in order to enjoy more economical electricity prices; while during periods of higher electricity prices, it will reduce load through demand response measures to reduce electricity purchase costs and obtain external demand response subsidies. This strategy manifests as a positive demand response load during peak electricity price periods, indicating a greater reduction in user load, while during off-peak electricity price periods, it manifests as a negative or slightly positive value, indicating a moderate increase or maintenance of user load. Through the strategy of Scheme 3, the VPP achieves optimized management of user load at different times, ensuring both user electricity demand and satisfaction while maximizing its own economic benefits.

[0200] Figure 13 This demonstrates the specific demand response status for each user under Option 3. A positive load transfer value indicates a transfer out, while a negative value indicates a transfer in.

[0201] Residential users' demand response is related to their daily electricity consumption patterns. Users 1 and 2 increase their electricity consumption when electricity prices are low, such as using washing machines or water heaters at night, resulting in a negative load shift. During peak or off-peak periods, they naturally reduce electricity consumption due to being away at work or consciously conserving electricity, such as turning off unnecessary appliances, resulting in a positive load reduction. Commercial users' demand is relatively stable, but users 3 and 4 are observed to consciously reduce their load during peak electricity price periods, such as adjusting business hours or reducing the use of unnecessary lighting and air conditioning. During periods of lower electricity prices, commercial users may increase some off-peak electricity consumption activities, such as preparing food in advance or using nighttime lighting. Industrial users typically have larger electricity loads and exhibit more significant demand response behavior under Scenario 3. During peak periods, user 5 may reduce load by adjusting production plans or using energy storage devices. During periods of lower electricity prices, they may increase production or perform equipment maintenance, resulting in a negative load shift.

[0202] 3. Sensitivity Analysis

[0203] 1) Sensitivity analysis of peak-valley price difference

[0204] VPP as a collection of diversified distributed energy, through the integration of the unique advantages and complementarity of each energy to achieve profitability. The fluctuation of electricity price in peak and off-peak periods, namely the peak-valley price difference, has a direct impact on the output of each unit within VPP, the strategy of buying and selling electricity, and the scheduling strategy of user load. This impact is further transmitted to the overall revenue of VPP, because the change of peak-valley price difference will change the economy of VPP in different time periods of buying and selling electricity, and the response mode of users to power consumption. In this study, on the basis of the original peak-valley price difference of 0.4 yuan, three different price difference scenarios are set to conduct sensitivity analysis on the model to study the impact of the change of peak-valley price difference of market electricity price on the revenue of VPP and the comprehensive satisfaction of users under the dual target. Among them, the peak-valley price difference scheme is set as shown in Table 3.

[0205] Table 3

[0206]

[0207] From Figure 14 it can be observed that the profit of VPP and the comprehensive satisfaction of users increase with the increase of peak-valley price difference. Specifically, with the expansion of peak-valley electricity price difference, VPP actively increases the purchase of electricity and the generation of gas units to store electric energy in energy storage units to prepare for the power demand in peak period and sell electricity at a higher price in peak period. This strategy not only meets the load demand of users, but also significantly improves the profit space of VPP. At the same time, VPP provides demand response subsidies in peak period through flexible demand response mechanism, encourages users to reduce or shift load to low-price period, reduces the electricity cost of users, and further improves the satisfaction of users. In summary, the increase of peak-valley price difference not only provides VPP with the opportunity to optimize the strategy of buying and selling electricity, realizes the optimization of economic benefit, but also improves the economy of user electricity consumption through the active guidance of demand side, and promotes the improvement of user comprehensive satisfaction.

[0208] 2) Sensitivity analysis of VPP incentive price

[0209] Incentive-based demand response is a kind of behavior that mobilizes users to participate in power system adjustment through incentive policy. The demand response incentive compensation implemented by VPP to users aims to improve the enthusiasm of users participating in demand side management, and encourages users to reduce electricity consumption or shift electricity consumption time to off-peak period by providing economic incentives during peak electricity price period. Such compensation not only can alleviate the load pressure of power grid during peak period, promote the optimal allocation of power resources, but also helps to reduce the electricity cost of users, so that they can enjoy more flexible electricity service and improve their satisfaction. In order to explore the influence of VPP incentive price on user comprehensive satisfaction, on the basis of the original peak incentive price of 0.35 yuan, five different peak incentive price scenarios are set for sensitivity analysis of the model. Among them, the peak incentive price scheme is set as shown in Table 4.

[0210] Table 4

[0211]

[0212]

[0213] Figure 15 The change of user comprehensive satisfaction is shown when VPP gives different peak incentive prices to users participating in demand response. When the peak incentive price increases, the user's satisfaction also improves. The increase of peak price provides stronger economic incentives for users, so that they reduce electricity consumption or shift electricity consumption time to other periods to obtain more compensation during peak period; at the same time, with the increase of peak price, the user's electricity behavior and strategy will change, which may lead to a decrease in satisfaction in electricity comfort, while the incentive obtained makes the user's satisfaction in electricity economy more significantly improved, so that the user's comprehensive satisfaction and the peak incentive price show a positive correlation.

[0214] 3) Sensitivity analysis of satisfaction weight coefficient

[0215] In the satisfaction model proposed in the present application, the user comprehensive satisfaction is composed of electricity economy index and electricity comfort index, and the weight coefficient of each index directly determines the value of user comprehensive satisfaction. In order to explore the influence of the two indexes on VPP profit and user comprehensive satisfaction, different weights are given to the two indexes respectively, and the sum of the two coefficients is 1, and 7 scenarios are set for sensitivity analysis. Among them, the weight coefficient scheme is set as shown in Table 5.

[0216] Table 5

[0217]

[0218] According to the corresponding VPP profit and user comprehensive satisfaction results under different weight coefficients, the curves of VPP profit and user comprehensive satisfaction are drawn as shown in Figs. 4 and 5 respectively. Figure 16It can be observed from the figure that the profit of the VPP reaches the maximum value when the weight of the electricity comfort index is 0.6 and the weight of the electricity economy index is 0.4, and then the profit of the VPP reaches a stable state and no longer increases with the further change of the weight ratio. With the decrease of the weight of the electricity comfort index (the increase of the weight of the electricity economy index), the satisfaction of the user gradually decreases.

[0219] Meanwhile, with the decrease of the weight of the electricity comfort index, the VPP can shift the focus of the strategy from the load consumption experience of the user to the increase of the profit of the VPP, thereby causing the increase of the profit of the VPP. When the profit of the VPP increases to a certain degree, a critical point of marginal effect has been reached, and the further decrease of the weight of the electricity comfort index will not further increase the profit of the VPP. Relatively, the increase of the weight of the electricity economy index indicates that the user pays more attention to the electricity fee and the incentive received during the electricity consumption, which obviously contradicts the goal of the maximum profit of the upper VPP. Even if the model has fully considered the satisfaction of the user, the VPP still occupies the dominant position, and thus the satisfaction of the user decreases.

[0220] In summary, the present application establishes a double-layer optimization scheduling model of a virtual power plant considering multiple demand response modes and the comprehensive satisfaction of users, optimizes the operation income of the virtual power plant, and fully considers the satisfaction of the users after participating in the demand response. Based on the scheduling decision obtained, the virtual power plant can not only realize the maximization of economic income and environmental benefit, but also guarantee the electricity demand and the satisfaction of the users while guaranteeing the power supply and maintaining the stability of the power grid. Therefore, the present application is not only conducive to the further development of the virtual power plant in promoting the development of the new power system, but also improves the experience of the users.

[0221] The various techniques described herein can be implemented in connection with hardware or software or, where appropriate, with a combination of hardware / software. Thus, the methods and apparatus of the present application, or certain aspects or portions thereof, can take the form of program code (i.e., instructions) embodied in tangible media, such as removable hard disks, USB flash drives, floppy diskettes, CD-ROMs, or any other machine-readable storage medium wherein, when the program code is loaded into an internal memory of the machine such as a computer, the machine becomes an apparatus for practicing the present application.

[0222] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application can be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

[0223] It should be understood that, for brevity and clarity sake, in the foregoing description of the exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description of related features, FIGS., or sub-paragraphs. However, this method of disclosure should not be interpreted as reflecting an intention that the application requires more features than are explicitly recited in each claim. Moreover, each

[0224] Further, unless otherwise specified, use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.

[0225] While the application has been described in terms of several embodiments, those skilled in the art will recognize that the application can be practiced with modifications and alterations limited only by the spirit and scope of the claims. Additionally, although the present application has been described above in terms of specific embodiments, it is anticipated that alternatives, modifications and equivalents will be apparent to those skilled in the art. Accordingly, the application is not limited to the embodiments described above, which are merely given as examples. Rather, this application is limited only by the claims appended hereto.

Claims

1. A virtual power plant scheduling method considering multiple types of demand response and user comprehensive satisfaction, comprising: constructing a virtual power plant aggregating distributed generators, renewable energy generators, energy storage systems, and load users; constructing a scheduling optimization model of the virtual power plant with the maximum operation benefit in a virtual power plant scheduling period as a target, and constructing a user comprehensive satisfaction model with the maximum mean of comprehensive satisfaction of all load users in the virtual power plant in the scheduling period as a target, the comprehensive satisfaction being composed of power usage mode satisfaction and power usage cost satisfaction; constructing a virtual power plant double-layer scheduling model considering multiple types of demand response and user comprehensive satisfaction with the scheduling optimization model as an upper-layer model and the user comprehensive satisfaction model as a lower-layer model; solving the virtual power plant double-layer scheduling model to obtain scheduling decisions of the virtual power plant, the scheduling decisions including output of the distributed generators and the renewable energy generators in each time period, charging power and discharging power of the energy storage systems in each time period, and load reduction and load transfer of the load users in each time period; wherein the user comprehensive satisfaction model is constructed with the maximum mean of comprehensive satisfaction of all load users in the virtual power plant in the scheduling period as a target, including: for each load user, obtaining the product of power usage mode satisfaction and the weight coefficient of power usage mode satisfaction in the scheduling period, and the product of power usage cost satisfaction and the weight coefficient of power usage cost satisfaction in the scheduling period, and taking the sum of the two products as the comprehensive satisfaction of the load user in the scheduling period; and based on the comprehensive satisfaction of each load user in the scheduling period, obtaining the mean of comprehensive satisfaction of all load users in the scheduling period, and constructing the user comprehensive satisfaction model with the maximum mean as a second objective function; the scheduling optimization model includes first constraint conditions and the user comprehensive satisfaction model includes second constraint conditions, wherein: the first constraint conditions include: load amount constraint of each load user participating in demand response, power balance constraint of the virtual power plant, actual load amount constraint of each load user after participating in demand response, operation constraint of gas generators, output constraint of renewable energy generators, and charging and discharging constraint of energy storage systems; the second constraint conditions include: deviation constraint of comprehensive satisfaction of all load users; reducible load amount constraint, minimum continuous reduction time constraint, maximum continuous reduction time constraint, and reduction times constraint of each load user; transferable load amount constraint, minimum continuous transfer time constraint of each load user.

2. The method of claim 1, wherein, the distributed generators are gas generators, and the scheduling optimization model of the virtual power plant is constructed with the maximum operation benefit in a virtual power plant scheduling period as a target, including: According to the income of the virtual power plant selling electricity to the power grid in each period of the scheduling period, the income of the virtual power plant participating in demand response, the incentive cost of the virtual power plant to the load user participating in demand response, the carbon trading cost of the virtual power plant, the power generation cost and start-stop cost of the gas unit, and the power generation cost of the renewable energy unit, the operation income of the virtual power plant in the scheduling period is determined, and the maximum operation income is taken as a first objective function to construct a scheduling optimization model of the virtual power plant.

3. The method of claim 1, wherein, The satisfaction degree of the power consumption mode of each load user in the scheduling period is obtained, and includes: Based on the total load reduction amount and the total load demand amount of each load user in the scheduling period, the load reduction degree value of each load user in the scheduling period is obtained. Based on the load transfer amount and the load demand amount of each load user in each period of the scheduling period, the load transfer degree value of each load user in the scheduling period is obtained. Based on the load reduction degree value and the load transfer degree value of each load user in the scheduling period, the power consumption mode satisfaction degree of each load user in the scheduling period is obtained.

4. The method of claim 3, wherein, The load reduction degree value of each load user in the scheduling period is obtained based on the total load reduction amount and the total load demand amount of each load user in the scheduling period, and includes: wherein, represents the load reduction degree value of the load user i in the dispatch period, represents the load reduction amount of the load user i in the t period, x i,t represents the load demand amount of the load user i in the t period in the load baseline, and T represents the total number of periods in the dispatch period.

5. The method of claim 3, wherein, The load transfer degree value of each load user in the scheduling period is obtained based on the load transfer amount and the load demand amount of each load user in each period of the scheduling period, and includes: wherein, represents the load shifting degree value of the load user i in the dispatching period, represents the load shifting amount of the load user i in the t period, x i,t represents the load demand amount of the load user i in the t period in the load baseline, and T represents the total number of periods in the dispatching period.

6. The method of any one of claims 1-5, wherein, The power consumption cost satisfaction degree of each load user in the scheduling period includes: wherein, represents the electricity cost satisfaction degree of the load user i in the dispatching period, represents the price of electricity purchased by the virtual power plant from the power grid at time period t, Δx i,t represents the load amount of the load user i participating in demand response at time period t, x i,t represents the load amount of the load user i at time period t in the load baseline, represents the incentive subsidy price of the virtual power plant at time period t.

7. A computing device comprising: at least one processor; and a memory storing program instructions configured to be executed by the at least one processor, the program instructions comprising instructions for performing the method of any one of claims 1-6.

8. A readable storage medium storing program instructions, which, when read and executed by a computing device, cause the computing device to perform the method of any one of claims 1-6.

Images