Virtual power plant scheduling method considering multi-class demand response and user comprehensive satisfaction

Abstract

The invention relates to the field of power systems, and particularly discloses a virtual power plant scheduling method considering multi-class demand response and user comprehensive satisfaction, which comprises the following steps: constructing a virtual power plant aggregated with a distributed unit, a renewable energy unit, an energy storage system and a load user; constructing a scheduling optimization model of the virtual power plant by taking the maximum operation income in the scheduling period of the virtual power plant as the target, and constructing a user comprehensive satisfaction model by taking the maximum average value of the comprehensive satisfaction of all load users in the scheduling period of the virtual power plant as the target; and by taking the scheduling optimization model as an upper layer model and the user comprehensive satisfaction model as a lower layer model, constructing a virtual power plant double-layer scheduling model considering the multi-class demand response and the user comprehensive satisfaction, and solving to obtain a scheduling decision of the virtual power plant. According to the method, the maximization of economic benefits and environmental benefits can be realized while the virtual power plant ensures the power supply and maintains the stability of a power grid, and the power utilization demand and satisfaction of a user can be ensured.

Description

Technical Field

[0001] The present invention relates to the field of electric power systems, and in particular to a virtual power plant dispatching method taking into account multiple types of demand responses and user comprehensive satisfaction. Background Art

[0002] my country 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 photovoltaics. The effective large-scale aggregation and efficient deployment of these resources are crucial to the construction of a new power system, which can not only optimize the allocation of power resources, but also enhance the system's supply and demand matching capabilities and operational flexibility, providing strong support for building a clean, low-carbon, safe and efficient modern energy system.

[0003] Among them, Virtual Power Plant (VPP), as a flexible distributed resource management method, can be used as a special power plant to participate in the power market and power grid operation power coordination management system. It can play an important role in fully tapping the flexibility of resource regulation capabilities, promoting the consumption of renewable energy, and ensuring the balance of supply and demand in the power grid.

[0004] However, although the widespread access to distributed energy has enhanced the flexibility of the power system, the randomness of renewable energy output, the diversity of user load demand, and the time-varying characteristics of electricity market prices have brought significant challenges to the operation of VPPs. Summary of the invention

[0005] To this end, the present invention provides a virtual power plant scheduling method that takes into account multiple types of demand responses and user comprehensive satisfaction, in an effort to solve or at least alleviate the above problems.

[0006] According to one aspect of the present invention, a virtual power plant scheduling method taking into account multiple types of demand responses and comprehensive user satisfaction is provided, comprising: constructing a virtual power plant that aggregates distributed units, renewable energy units, energy storage systems and load users; constructing a scheduling optimization model of the virtual power plant with the goal of maximizing the operating profit within the virtual power plant scheduling cycle, and constructing a user comprehensive satisfaction model with the goal of maximizing the mean value of the comprehensive satisfaction of all load users in the virtual power plant within the scheduling cycle, wherein the comprehensive satisfaction is composed of satisfaction with electricity usage mode and satisfaction with electricity cost; constructing a two-layer scheduling model of the virtual power plant taking into account multiple types of demand responses and comprehensive user satisfaction with the scheduling optimization model as the upper model and the user comprehensive satisfaction model as the lower model; solving the two-layer scheduling model of the virtual power plant 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 time period within the scheduling cycle, the charging power and discharging power of the energy storage system in each time period, and the load reduction and load transfer of the load users in each time period.

[0007] Optionally, in the virtual power plant scheduling method that takes into account multiple types of demand responses and comprehensive user satisfaction according to the present invention, the distributed units are gas units, and a scheduling optimization model of the virtual power plant is constructed with the goal of maximizing the operating profit within the virtual power plant scheduling cycle, including: determining the operating profit within the virtual power plant scheduling cycle based on the profit of the virtual power plant selling electricity to the power grid in each period within the scheduling cycle, the profit of the virtual power plant participating in demand response, the incentive cost of the virtual power plant to load users participating in demand response, the carbon trading cost of the virtual power plant, the power generation cost and start-up and shutdown cost of the gas units, and the power generation cost of the renewable energy units, and constructing a scheduling optimization model for the virtual power plant with the maximization of the operating profit as the first objective function.

[0008] Optionally, in the virtual power plant scheduling method according to the present invention that takes into account multiple types of demand response and user comprehensive satisfaction, a user comprehensive satisfaction model is constructed with the goal of maximizing the mean value of the comprehensive satisfaction of all load users in the virtual power plant within the scheduling period, including: for each load user, obtaining the product of its electricity usage mode satisfaction within the scheduling period and the weight coefficient of the electricity usage mode satisfaction, as well as the product of its electricity cost satisfaction within the scheduling period and the weight coefficient of the electricity cost satisfaction, and taking the sum of the two products as the comprehensive satisfaction of the load user within the scheduling period; based on the comprehensive satisfaction of each load user within the scheduling period, obtaining the mean value of the comprehensive satisfaction of all load users within the scheduling period, and taking the maximum mean value as the second objective function to construct a user comprehensive satisfaction model.

[0009] Optionally, in the virtual power plant scheduling method according to the present invention that takes into account multiple types of demand response and user comprehensive satisfaction, the satisfaction with the electricity usage mode of each load user within the scheduling cycle is obtained, including: based on the total load reduction and total load demand within the scheduling cycle of each load user, obtaining the load reduction degree value within the scheduling cycle of each load user; based on the load transfer and load demand in each time period within the scheduling cycle of each load user, obtaining the load transfer degree value within the scheduling cycle of each load user; based on the load reduction degree value and load transfer degree value within the scheduling cycle of each load user, obtaining the satisfaction with the electricity usage mode of each load user within the scheduling cycle.

[0010] Optionally, in the virtual power plant dispatching method taking into account multiple types of demand responses and comprehensive user satisfaction according to the present invention, based on the total load reduction amount and the total load demand amount in the dispatching period of each load user, the load reduction degree value in the dispatching period of each load user is obtained, including:

[0011]

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

[0013] Optionally, in the virtual power plant dispatching method taking into account multiple types of demand responses and comprehensive user satisfaction according to the present invention, based on the load transfer amount and load demand amount in each time period in the dispatching cycle of each load user, the load transfer degree value in the dispatching cycle of each load user is obtained, including:

[0014]

[0015] in, represents the load transfer degree value within the scheduling period of load user i, represents the load transfer amount of load user i in period t, x i,t It represents the load demand of load user i in the load baseline during period t, and T represents the total number of periods in the scheduling cycle.

[0016] Optionally, in the virtual power plant dispatching method taking into account multiple types of demand responses and comprehensive user satisfaction according to the present invention, the electricity cost satisfaction of each load user during the dispatching period includes:

[0017]

[0018] in, represents the electricity cost satisfaction of load user i during the scheduling period, represents the price at which the virtual power plant purchases electricity from the grid during period t, Δx i,t represents the load amount of load user i participating in demand response in period t, x i,t represents the load amount in period t of the load baseline of load user i, Represents the incentive subsidy price of the virtual power plant during period t.

[0019] Optionally, in the virtual power plant scheduling method according to the present invention that takes into account multiple types of demand responses and user comprehensive satisfaction, the scheduling optimization model includes a first constraint condition and the user comprehensive satisfaction model includes a second constraint condition, wherein: the first constraint condition includes: the load quantity constraint of each load user participating in the demand response, the power balance constraint of the virtual power plant, the actual load quantity constraint of each load user after participating in the demand response, the operation constraint of the gas unit, the output constraint of the renewable energy unit, and the charging and discharging constraint of the energy storage system; the second constraint condition includes: the deviation constraint of the comprehensive satisfaction of all load users; the reducible load quantity constraint, the minimum continuous reduction time constraint, the maximum continuous reduction time constraint, and the reduction number constraint of each load user; the transferable load quantity constraint and the minimum continuous transfer time constraint of each load user.

[0020] According to another aspect of the present invention, there is provided a computing device, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be suitable for execution by at least one processor, and the program instructions include instructions for executing a virtual power plant dispatching method according to the present invention that takes into account multiple types of demand response and user comprehensive satisfaction.

[0021] According to another aspect of the present invention, a readable storage medium storing program instructions is provided. When the program instructions are read and executed by a computing device, the computing device executes the virtual power plant scheduling method according to the present invention that takes into account multiple types of demand response and user comprehensive satisfaction.

[0022] According to the virtual power plant dispatching method taking into account multiple types of demand response and comprehensive user satisfaction of the present invention, a two-layer optimization dispatching model of a virtual power plant taking into account multiple demand response methods and comprehensive user satisfaction is established, which optimizes the operating benefits of the virtual power plant and fully considers the satisfaction of users after participating in the demand response. Based on this, while ensuring power supply and maintaining the stability of the power grid, the virtual power plant can not only maximize economic benefits and environmental benefits, but also ensure the power demand and satisfaction of users. Therefore, the present invention is conducive to the virtual power plant to further promote the development of new power systems and enhance the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] To achieve the above and related purposes, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings, which indicate various ways in which the principles disclosed herein can be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. The above and other purposes, features and advantages of the present disclosure will become more apparent by reading the following detailed description in conjunction with the accompanying drawings. Throughout the present disclosure, the same reference numerals generally refer to the same parts or elements.

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

[0025] Figure 2 A flowchart of a virtual power plant scheduling method 200 taking into account multiple types of demand responses and user comprehensive satisfaction is shown according to an embodiment of the present invention;

[0026] Figure 3 A schematic diagram showing a structural framework of a virtual power plant according to an embodiment of the present invention;

[0027] Figure 4 A schematic diagram showing a solution process of a virtual power plant double-layer scheduling model according to an embodiment of the present invention;

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

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

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

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

[0033] Fig.10 A schematic diagram showing the scheduling of various VPP entities according to an embodiment of the present invention is shown;

[0034] Fig.11 A schematic diagram showing a VPP power purchasing and power selling situation according to an embodiment of the present invention;

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

[0036] Fig.13 A schematic diagram showing a demand response situation of each user according to an embodiment of the present invention;

[0037] Fig.14 A schematic diagram showing the impact of the peak-to-valley price difference on VPP profits and user comprehensive satisfaction according to an embodiment of the present invention;

[0038] Fig.15 A schematic diagram showing the impact of peak incentive price on user comprehensive satisfaction according to an embodiment of the present invention;

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

[0040] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0041] Virtual power plants can tap the regulatory potential of flexible resources, realize the aggregation and coordinated optimization of distributed energy resources, and are a new model for promoting the safe and stable operation of new power systems. At the same time, as an important part of virtual power plants, users' response willingness is the key bottleneck restricting demand response. Based on this, the present invention provides a virtual power plant scheduling method that takes into account multiple types of demand response and user comprehensive satisfaction.

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

[0043] According to one aspect, operating system 105 is suitable for controlling the operation of computing device 100, for example. Furthermore, examples are practiced in conjunction with graphics libraries, other operating systems, or any other application programs, and are not limited to any particular application or system. Figure 1 This basic configuration is illustrated in FIG. 1 by those components within dashed line 108. According to one aspect, computing device 100 has additional features or functionality. For example, according to one aspect, computing device 100 includes additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or tapes. Such additional storage Figure 1 1 is illustrated by removable storage 109 and non-removable storage 110.

[0044] As stated above, according to one aspect, program modules are stored in the system memory 104. According to one aspect, the program modules may include one or more application programs, and the present invention does not limit the type of application programs, for example, the application programs may include: email and contact applications, word processing applications, spreadsheet applications, database applications, slide show applications, drawing or computer-aided applications, web browser applications, etc.

[0045] According to one aspect, the examples may be practiced on a circuit comprising discrete electronic components, a packaged or integrated electronic chip containing logic gates, a circuit utilizing a microprocessor, or a single chip containing electronic components or a microprocessor. Figure 1 Each or many components shown in can be integrated into a system on a chip (SOC) on a single integrated circuit to practice examples. According to one aspect, such a SOC device may include one or more processing units, a graphics unit, a communication unit, a system virtualization unit, and various application functions, all of which are integrated (or "burned") into a chip substrate as a single integrated circuit. When operated via SOC, the functions described in this article can be operated via a dedicated logic integrated with other components of the computing device 100 on a single integrated circuit (chip). Embodiments of the present invention can also be practiced using other technologies capable of performing logical operations (such as AND, OR, and NOT), including but not limited to mechanical, optical, fluid, and quantum technologies. In addition, embodiments of the present invention can be practiced in a general-purpose computer or in any other circuit or system.

[0046] According to one aspect, the computing device 100 may also have one or more input devices 112, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, etc. Output devices 114 may also be included, such as a display, a speaker, a printer, etc. The aforementioned devices are examples and other devices may also be used. The computing device 100 may include one or more communication connections 116 that allow communication with other computing devices 118. Examples of suitable communication connections 116 include, but are not limited to: RF transmitter, receiver and / or transceiver circuits; Universal Serial Bus (USB), parallel and / or serial ports.

[0047] The term computer-readable medium as used herein includes computer storage media. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in 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 all examples of computer storage media (i.e., memory storage). Computer storage media may include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, cassettes, tapes, disk storage or other magnetic storage devices, or any other products that can be used to store information and can be accessed by computer device 100. According to one aspect, any such computer storage medium can be a part of computing device 100. Computer storage media do not include carrier waves or other propagated data signals.

[0048] According to one aspect, communication media 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 transport mechanism), and includes any information delivery media. According to one aspect, the term "modulated data signal" describes a signal that has one or more characteristics set or changed in a manner that encodes information in the signal. By way of example and not limitation, communication media includes wired media such as a wired network or direct-wired connection, 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 taking into account multiple types of demand response and user comprehensive satisfaction according to an embodiment of the present invention is shown. The method 200 is suitable for a computing device (e.g. Figure 1 In some embodiments, a VPP structure framework for aggregating multiple distributed resources may be constructed first, and the present invention is described in detail below.

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

[0051] In 210, a virtual power plant is constructed that aggregates distributed units, renewable energy units (RES), energy storage systems (ESS), and load users. The distributed units may specifically be gas units, and the renewable energy units may specifically include wind turbines and photovoltaic generators. Of course, this is only an example, and the present invention is not limited to this.

[0052] For the framework diagram of the virtual power plant constructed in this embodiment, please refer to Figure 3 Among them, the VPP of this embodiment aggregates distributed generators (DG, mainly gas units), wind power generation, photovoltaic power generation, energy storage, loads and other adjustable resources covering the load side and the power side, and realizes the optimal allocation of resources in a wide area through information transmission and energy interaction. During the scheduling process, the VPP operator will also purchase or sell electricity with the power grid based on price information to coordinate the supply and demand balance of electricity within the VPP and obtain benefits.

[0053] In addition, VPP actively responds to the demand response invitation issued by the power grid and adjusts the load demand in the specified period to obtain corresponding subsidies. On the basis of the original time-of-use electricity price (price-based demand response, PDR) price signal, VPP guides and adjusts its electricity demand by issuing incentive subsidies (incentive-based demand response, IDR) to qualified load users to encourage users to respond to demand. In order to ensure the stability and economy of the system, VPP operators need to comprehensively consider the output of distributed units, renewable energy generation, charging and discharging operations of energy storage systems, and changes in load demand in various periods. At the same time, VPP considers the user's electricity satisfaction and adjusts the VPP's power generation and consumption plan from the two aspects of electricity comfort and economy during the scheduling process. By coordinating and managing these resources, VPP operators can maximize economic benefits and comprehensive user satisfaction while ensuring power supply and maintaining grid stability.

[0054] The above is the structural framework of the virtual power plant constructed by the present invention. Next, enter 220, with the goal of maximizing the operating profit within the virtual power plant dispatch cycle, construct a dispatch optimization model for the virtual power plant, and with the goal of maximizing the mean value of the comprehensive satisfaction of all load users in the virtual power plant within the dispatch cycle, construct a user comprehensive satisfaction model. Among them, the comprehensive satisfaction is composed of the satisfaction with the electricity consumption method and the satisfaction with the electricity cost.

[0055] Next, the construction of the virtual power plant dispatch optimization model and the construction of the user comprehensive satisfaction model are explained respectively.

[0056] 1. Dispatch optimization model of virtual power plant

[0057] According to one embodiment of the present invention, the operating income of the virtual power plant during the scheduling cycle can be determined based on the income of the virtual power plant from selling electricity to the power grid during each period of the scheduling cycle, 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 (for the convenience of description, the load users will be referred to as users in the following description), 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 scheduling optimization model of the virtual power plant is constructed with the maximum operating income as the first objective function. Among them, considering that the operating cost of the energy storage system is usually very low, in order to simplify the model and improve the calculation efficiency, this implementation does not include it when constructing the scheduling optimization model.

[0058] In some embodiments, the first objective function may specifically be expressed as the following formula.

[0059]

[0060] In the formula, R represents the operating income during the virtual power plant dispatch period, represents the revenue of VPP selling electricity to the grid during period t, represents the revenue of VPP participating in the demand response invitation during period t, represents the incentive cost paid by VPP to the responding user during period t, represents the power generation cost of the gas unit during period t, represents the start-up and shutdown cost of the gas generator set during period t, represents the carbon trading cost of VPP in period t, It represents the power generation cost of renewable energy units in period t, and T represents the total number of periods.

[0061] Regarding the scheduling cycle and the scheduling period (i.e., period t), it is explained here. In some embodiments, the scheduling cycle can be 24 hours, and the scheduling period can be 1 hour, so the value of T is 24, and the operating income within the virtual power plant scheduling cycle is the daily operating income of the virtual power plant. Of course, this is only an example, and the present invention is not limited to this. In a specific embodiment, those skilled in the art can set it according to actual needs.

[0062] Next, The methods of obtaining are described respectively.

[0063] 1) Demand response cost: When the load of users participating in demand response reaches the standard set by VPP, VPP will provide incentive subsidies. Among them, the incentive paid by VPP to all users in period t is The specific information can be obtained through the following formula.

[0064]

[0065] In the formula, represents the incentive subsidy price for period t set by the virtual power plant, where When t indicates that the virtual power plant does not issue demand response during period t; Δx i,t represents the load of user i participating in demand response in period t. In this regard, in some embodiments, it can be calculated based on the user load baseline and the actual load curve, where Δx i,t =0 means that user i does not participate in demand response in period t; N represents the total number of users.

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

[0067]

[0068] In the formula, x i,t It represents the load demand of load user i in the load baseline during period t.

[0069] 2) Gas-fired unit power generation cost: In period t, the power generation cost of the gas-fired unit is The specific calculation can be done through the following formula.

[0070]

[0071] In the formula, g t represents the power generation of the gas unit during period t, is a binary variable representing the state of the gas unit during period t, where if the gas unit is in operation during period t, then Otherwise (i.e. the gas unit is not in operation during period t), a g , b g , c both represent the cost parameters of the gas unit.

[0072] 3) Start-up and shutdown costs of gas-fired units: Start-up and shutdown costs of gas-fired units The startup cost c st and closing costsc sh The composition can be specifically expressed as the following formula.

[0073]

[0074] In the formula, is a binary variable representing the state of the gas generator set during the period (t-1), where if the gas generator set is in operation during the period (t-1), then otherwise,

[0075] 4) Carbon trading cost: This embodiment adopts the carbon quota method of free quota and the tiered pricing mechanism, and assumes that the market purchase of electricity comes from coal-fired units. Among them, the tiered pricing mechanism divides multiple purchase intervals. As the number of carbon emission quotas to be purchased increases, the purchase price of the corresponding interval increases. Specifically, in some embodiments, the tiered carbon trading cost of time period t is It can be expressed as the following formula.

[0076]

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

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

[0079]

[0080] In the formula, represents the actual carbon emissions of VPP during period t, Represents the carbon emission quota of VPP in period t.

[0081] Among them, the actual carbon emissions of VPP in period t It can be obtained by the following formula.

[0082]

[0083] In the formula, They represent the actual carbon emissions of VPP, power purchase from the power grid, and power generation from gas-fired units during period t, represents the amount of electricity purchased by VPP in the market during period t, a1, b1, c1 represent the carbon emission calculation parameters of coal-fired units, and a2, b2, c2 represent the carbon emission calculation parameters of gas-fired units.

[0084] Carbon emission quota of VPP in period t It can be obtained by the following formula.

[0085]

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

[0087] 5) Renewable energy generation cost: Wind and solar power generation unit owners supply electricity to VPPs at a fixed contract price, so the cost of renewable energy generation is is the price paid by VPP to it. Specifically, the cost of renewable energy generation in period t It can be obtained by the following formula.

[0088]

[0089] In the formula, represents the power supply price of wind and solar power generation units during period t, wp t 、sp t They represent the power supply of wind turbines and photovoltaic generators during period t respectively.

[0090] 6) Grid trading income: VPP purchases and sells electricity with the grid at a time-of-use electricity price to ensure the balance of power supply and demand. The income from VPP selling electricity to the grid during period t can be obtained by the following formula.

[0091]

[0092] In the formula, They represent the electricity selling price and electricity purchasing price to the power grid during period t, They represent the amount of electricity sold and purchased from the grid by VPP during period t respectively.

[0093] 7) Benefits of participating in demand response: VPPs can obtain benefits by responding to demand response invitations issued by the power grid. The benefits of VPPs participating in demand response invitations during period t can be obtained by the following formula.

[0094]

[0095] In the formula, It represents the subsidy price for response period t agreed between VPP and the grid.

[0096] Furthermore, the scheduling optimization model of the virtual power plant also 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 the demand response, a power balance constraint of the virtual power plant, an actual load quantity constraint after each load user participates in the demand response, an operation constraint of the gas unit, an output constraint of the renewable energy unit, and a charge and discharge constraint of the energy storage system, which are described below.

[0097] 1) Load constraints for each load user participating in demand response: Users obtain incentive subsidies from VPP through demand response, and the load volume participating in the response in each period should meet the standards set by VPP. Based on this, in some embodiments, the load constraints for 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 of the user participating in demand response reaches a certain proportion of the user's load baseline, the user can obtain incentive subsidies from the VPP operator. i,t It represents the load (or load demand) of user i in the load baseline during period t.

[0100] 2) Power balance constraint of virtual power plant: The power supply and demand must be balanced in each time period of VPP, which can be expressed as the following formula.

[0101]

[0102] In the formula, g t represents the power generation power of the gas unit during period t, wp t 、sp t They represent the power supply of wind turbine generator set and photovoltaic generator set during period t, They represent the amount of electricity sold and purchased from the grid by VPP during period t, and D t represents the VPP load demand in period t after demand response, They represent the charging power and discharging power of ESS in period t respectively.

[0103] 3) The actual load constraint of each load user after participating in demand response: It can also be called the user's basic load constraint. Specifically, the load of the user after demand response should satisfy the rigid load, which can be expressed as the following formula.

[0104]

[0105] In the formula, x i,t represents the load of user i in the load baseline during period t, Δx i,t represents the response amount of user i in period t, It represents the rigid load of user i in VPP during period t, that is, the non-adjustable load part of the residents' load.

[0106] 4) Operation constraints of gas-fired units: The operation of gas-fired units needs to meet output and ramp constraints, which can be specifically expressed as the following formula.

[0107]

[0108] Where P min , P max Respectively represent the minimum output and maximum output of the gas unit, g t-1 represents the power generation of the gas generator set during the period (t-1), r down 、r up They respectively represent the upper limits of the downward climbing rate and upward climbing rate of the gas unit.

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

[0110]

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

[0112] 6) Charging and discharging constraints of the energy storage system: In each time period, the amount of electricity charged and discharged by the ESS is limited by the maximum charging and discharging rate, which can be specifically expressed as the following formula.

[0113]

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

[0115] Furthermore, the constraint also includes that the ESS will not be charged and discharged at the same time in the same period, which can be specifically expressed as the following formula.

[0116]

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

[0118] The power storage capacity constraint of the energy storage system, specifically, the power storage capacity of the ESS is limited by its capacity upper limit. In some embodiments, it can be expressed as the following formula.

[0119]

[0120] In the formula, S t , S t-1 They represent the power storage status (power storage amount) of ESS in period t and period (t-1) respectively, They represent the charging power and discharging power of ESS in period t, respectively, ch , dis Respectively represent the charging coefficient and discharging coefficient of ESS, ess cap It represents the upper capacity limit of ESS, and Δt represents the duration of a time period.

[0121] The initial and final state constraints of the energy storage system, specifically, the battery energy storage state value at the end of each scheduling cycle of the energy storage should be equal to the energy storage state value at the initial time period. In some embodiments, it can be expressed as the following formula.

[0122] S1=S T

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

[0124] 2. Comprehensive User Satisfaction Model

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

[0126] Step 1), for each load user, obtain the product of the satisfaction of the electricity consumption mode and the weight coefficient of the satisfaction of the electricity consumption mode in the scheduling period, and the product of the satisfaction of the electricity cost and the weight coefficient of the satisfaction of the electricity cost in the 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 the following formula.

[0127]

[0128] In the formula, represents the comprehensive satisfaction of user i in the VPP scheduling cycle, Respectively represent the user i's electricity consumption mode satisfaction and user cost satisfaction in the scheduling period, ω1 and ω2 represent the weight coefficients of electricity consumption mode satisfaction and electricity cost satisfaction, respectively, reflecting the user's attention to electricity consumption mode and electricity cost expenditure. That is to say, in this embodiment, the comprehensive satisfaction The power comfort index and electricity economy indicators constitute.

[0129] Furthermore, in some embodiments, the satisfaction of each load user with respect to electricity usage during the scheduling period can be obtained in the following manner.

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

[0131]

[0132] In the formula, is the load reduction degree value of load user i within the dispatching period, which indicates the relationship between the load reduction amount and the power load before response, and reflects the impact of the total load reduction amount within the dispatching period (such as within a day) on the total load demand of the user. represents the load reduction of load user i in period t, x i,t It represents the load demand of load user i in the load baseline during period t, and T represents the total number of periods in the scheduling cycle.

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

[0134]

[0135] In the formula, is the load transfer degree value within the dispatching period of load user i, reflecting the average impact of load transfer amount on user load demand in each period (such as every hour). represents the load transfer amount of load user i in period t, x i,t It represents the load demand of load user i in the load baseline during period t, and T represents the total number of periods in the scheduling cycle.

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

[0137]

[0138] In the formula, υ1 and υ2 respectively represent weight coefficients of the load reduction degree value (or load reduction degree index) and the load transfer degree value (or load transfer degree index), and specific empirical values ​​can be used, which is not limited in the present invention.

[0139] It can be seen that this embodiment divides the flexible load of users in VPP for demand response into curtailable load and transferable load, that is, Furthermore, in view of the differences in the user response modes of these two types of controllable loads, this embodiment establishes their respective power consumption comfort models.

[0140] In addition, regarding the electricity cost satisfaction of each load user during the scheduling period, according to an embodiment of the present invention, the economic efficiency can be determined by comparing the electricity expenditure of the user before and after participating in the demand response, and then the electricity cost satisfaction can be obtained, which can be specifically obtained by the following formula.

[0141]

[0142] In the formula, represents the electricity cost satisfaction of load user i during the scheduling period, represents the price at which the virtual power plant purchases electricity from the grid during period t, Δx i,t represents the load amount of load user i participating in demand response in period t, x i,t represents the load amount in period t of the load baseline of load user i, It represents the incentive subsidy price of the virtual power plant during period t. It should be noted that the electricity price of VPP users is the price at which VPP purchases electricity from the power grid.

[0143] At this point, the comprehensive satisfaction of each load user in the dispatching cycle is obtained. Next, enter step 2), based on the comprehensive satisfaction of each load user in the dispatching cycle, obtain the mean of the comprehensive satisfaction of all load users in the dispatching cycle, and use the maximum mean as the second objective function to build a user comprehensive satisfaction model.

[0144] In some embodiments, the second objective function may be specifically expressed as the following formula.

[0145]

[0146] In the formula, It represents the average value of the comprehensive satisfaction of all load users in the VPP during the dispatch period. Taking the above T value as 24 as an example, It represents the average of the comprehensive daily satisfaction of all load users in the VPP.

[0147] Furthermore, the user comprehensive satisfaction model also includes a second constraint condition corresponding to the second objective function. Specifically, in some embodiments, the second constraint condition includes: a deviation constraint of the comprehensive satisfaction of all load users; a load reduction constraint, a minimum continuous reduction time constraint, a maximum continuous reduction time constraint, and a reduction number constraint for each load user; and a load transfer constraint and a minimum continuous transfer time constraint for each load user, which are described below.

[0148] 1) Deviation constraint of the comprehensive satisfaction of all load users: It is used to ensure that the satisfaction of each user in the virtual power plant is within an appropriate range, which can be specifically expressed as the following formula.

[0149] M=s max -s min ,0≤M≤k

[0150] Where M represents the deviation of the comprehensive satisfaction of all load users in the VPP, s max 、s min They respectively represent the maximum and minimum values ​​of the comprehensive satisfaction of all users, and k represents the upper limit of the comprehensive satisfaction deviation. By constraining the range of M, it can be ensured that the satisfaction of virtual power plant users is within an appropriate range.

[0151] 2) Reducible load constraints: Reducible load is a load that can withstand certain interruptions or power reductions, and can be partially or completely reduced according to supply and demand conditions. For each load user, it is necessary to set constraints on the amount of load that can be reduced (or called power reduction), the minimum continuous reduction time, the maximum continuous reduction time, and the number of reductions, as shown below.

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

[0153]

[0154] In the formula, α i,t is a 0-1 variable representing the load reduction state of user i in period t, where if the load of user i is reduced in period t, then α i,t =1, if the load of user i does not decrease during period t, then α i,t =0, They represent the minimum and maximum values ​​of load power that user i can reduce in period t.

[0155] The minimum continuous reduction time constraint can be specifically expressed as the following formula.

[0156]

[0157] In the formula, represents the minimum continuous reduction time, α i,t-1 is a 0-1 variable representing the load reduction state of user i in period (t-1), where if the load of user i is reduced in period (t-1), then α i,t-1 =1, if the load of user i does not decrease during the period (t-1), then α i,t-1 =0.

[0158] The maximum continuous reduction time constraint can be specifically expressed as the following formula.

[0159]

[0160] In the formula, Indicates the maximum continuous reduction time.

[0161] The reduction number constraint can be specifically expressed as the following formula.

[0162]

[0163] Where N max Indicates the maximum number of reductions.

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

[0165] The transferable load constraint can be specifically expressed as the following formula.

[0166]

[0167] In the formula, β i,t is a 0-1 variable representing the load transfer state of user i in period t, where if the load of user i is transferred in period t, then β i,t =1, if the load of user i does not shift during period t, then β i,t =0; They represent the minimum and maximum values ​​of the load power that can be transferred by user i in period t, respectively. When it is positive, it means the load is transferred out, otherwise it is transferred in.

[0168] The minimum continuous transfer time constraint can be specifically expressed as the following formula.

[0169]

[0170] In the formula, represents the minimum continuous transfer time, β i,t-1 is a 0-1 variable representing the load transfer state of user i in period (t-1), where if the load of user i is transferred in period (t-1), then β i,t-1=1, if the load of user i does not transfer during the period (t-1), then β i,t-1 =0.

[0171] In addition, although the transferable load can be flexibly adjusted in each time period, the total load of the entire period after the transfer must remain unchanged from that before the transfer. Therefore, in some embodiments, the transferable load constraint also includes a total load transfer constraint for each load user in the entire scheduling period, which can be specifically expressed as the following formula.

[0172]

[0173] At this point, the construction of the dispatch optimization model and the user comprehensive satisfaction model has been completed. Next, enter 230, and use the dispatch optimization model as the upper model and the user comprehensive satisfaction model as the lower model to construct a virtual power plant two-layer dispatch model that takes into account multiple types of demand responses and user comprehensive satisfaction. That is, the virtual power plant two-layer dispatch model of this embodiment is composed of an upper-layer dispatch optimization model and a lower-layer user comprehensive satisfaction model. The upper-layer dispatch optimization model includes a first objective function and a first constraint condition corresponding to the first objective function, and the lower-layer user comprehensive satisfaction model includes a second objective function and a second constraint condition corresponding to the second objective function.

[0174] After completing the construction of the two-layer scheduling model of the virtual power plant that takes into account multi-type demand response and comprehensive user satisfaction, enter 240 to solve the two-layer scheduling model of the virtual power plant and obtain the scheduling decision of the virtual power plant. Among them, according to one embodiment of the present invention, the scheduling decision of the virtual power plant specifically includes the output of distributed units and renewable energy units in each period of the scheduling cycle, the charging power and discharging power of the energy storage system in each period of the scheduling cycle, and the load reduction and load transfer of load users in each period of the scheduling cycle. To further explain, it means the output of gas units in each period of the scheduling cycle, the output of wind power generators in each period of the scheduling cycle, the output of photovoltaic generators in each period of the scheduling cycle, the charging power and discharging power of the energy storage system in each period of the scheduling cycle, and the load reduction and load transfer of load users in each period of the scheduling cycle.

[0175] Regarding the solution of the virtual power plant two-layer scheduling model, according to one embodiment of the present invention, the KKT condition and Big-M method can be used to convert it into a single-layer linear model for solution. Further, in some embodiments, the solution can be performed by calling the solver Gurobi and the YALMIP toolbox in Matlab. Figure 4 , which shows the solution process of the virtual power plant two-layer scheduling model according to an embodiment of the present invention.

[0176] The first step is to construct the Lagrangian function based on the model of the underlying problem.

[0177] The second step is to solve the KKT complementary relaxation conditions of the lower model and convert the lower model into the constraints of the upper model, so that the two-layer model is converted into a single-layer nonlinear model.

[0178] In the third step, the nonlinear terms in the transformed single-layer nonlinear model are linearized using the Big-M method to form a single-layer mixed integer linear programming problem for solution.

[0179] The solution to the two-tier dispatching model of virtual power plants will not be elaborated here. For details, please refer to the relevant descriptions of KKT conditions and Big-M method.

[0180] The above is the virtual power plant scheduling method of the present invention that takes into account multiple types of demand response and user comprehensive satisfaction. In order to verify the effectiveness and applicability of this method, the present invention also gives an example. Specifically, five representative load users in a certain area are selected, and they are aggregated with distributed resources such as thermal power, wind power, photovoltaics, and energy storage in the area 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] Assume that a dispatch cycle is 24 hours and a unit dispatch period is 1 hour. Select typical load demand data of a certain day in summer in the region, typical output data of wind power and photovoltaic power, power purchase and sales price data of VPP in the power grid, demand response incentive subsidy price formulated by virtual power plant and other data as input parameters, such as Figure 5-Figure 7 shown.

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

[0183] Table 1

[0184]

[0185] Carbon trading related parameters are shown in Table 2 below.

[0186] Table 2

[0187]

[0188] The contract price for VPP to purchase electricity from renewable energy units is 0.3 yuan / kWh, and the power grid issues demand response invitations during the daily peak hours of 12:00-13:00 and 19:00-21:00, with a subsidy price of 3 yuan / kWh. The range constraint of user comprehensive satisfaction is k=0.1, and the weight coefficients are ω1=0.68, ω2=0.32, ν1=0.6, and ν2=0.4.

[0189] 1. Validity Verification

[0190] This example sets up three groups of plans for comparison, respectively studying the benefits of VPP and the overall user satisfaction under different emphasis objectives: 1) Plan 1, only considering the maximum VPP operating profit; 2) Plan 2, only considering the maximum overall user satisfaction; 3) Plan 3, comprehensively considering the VPP operating profit and the overall user satisfaction.

[0191] The objective function values ​​of the three comparison schemes are as follows: Figure 8 As shown. It can be seen that when only considering the goal of maximizing VPP profit, VPP's profit is 20066.53 yuan, and the calculated user comprehensive satisfaction is 0.6965; when only considering the goal of maximizing user comprehensive satisfaction, VPP's profit is 9790.22 yuan, and the user comprehensive satisfaction is 0.7617. Obviously, the profit obtained by VPP in Scheme 1 is better than that in Scheme 2, but the user satisfaction of Scheme 2 is better than that of Scheme 1.

[0192] When considering both objective functions, VPP's profit is 18,176.96 yuan and its satisfaction is 0.7122. Compared with Scheme 1, user satisfaction has been significantly improved; compared with Scheme 2, VPP's profit has been significantly improved. Therefore, when considering both economic and satisfaction goals, Scheme 3 is the best choice.

[0193] 2. Results Analysis

[0194] Under Solution 3, which takes into account both the maximum VPP profit and the highest overall user satisfaction, Fig. 9 The power supply and demand of VPP in different periods are shown. When the power is positive, it means that the power supply of VPP exceeds the demand, and when the power is negative, it is the opposite. It can be seen from the figure that the net load reaches the peak in some periods (such as 10h-15h), and in other periods (such as 5h-10h and after 20h), the net load decreases or even becomes negative.

[0195] Depend on Fig.10The output information of each entity within the VPP can be observed. The energy storage device is charged during the period of low electricity price and discharged during the period of high electricity price. This operation strategy helps the VPP store energy during the period of low electricity price and release energy during the peak period, thereby reducing the cost of purchasing electricity and increasing the revenue of selling electricity. The gas unit reduces the output considering the power generation cost, and considers the start-up and shutdown costs at the same time to avoid frequent start-up and shutdown; and increases the output during the period of high electricity price to meet the user load and the demand of VPP to sell electricity to the market. VPP makes full use of renewable energy generators during the period of sufficient sunlight and good wind, which usually coincides with the peak period of electricity price, so that VPP can reduce the purchase of electricity from the power grid and improve self-sufficiency. In addition, the comparison of the load after demand response and the original load shows the ability of VPP to adjust the load through demand response management. VPP reduces or transfers user load during peak hours through two demand response methods, time-of-use electricity price and incentive price, taking into account both meeting user electricity demand and reducing VPP costs, and also obtaining high demand response subsidies.

[0196] Fig.11 The power purchase and sale situation of VPP is shown. The operation strategy of VPP is mainly to sell electricity during the period of high electricity price and purchase electricity during the period of low electricity price. This strategy helps VPP to maximize profits in the electricity market, and can also meet the load demand of users well through the assistance of energy storage unit charging and discharging.

[0197] In summary, when considering both economic and satisfaction goals, VPP will flexibly adjust the charging and discharging strategies of energy storage units, the output strategies of gas units, the power purchase and sales strategies in the market, and the electricity consumption behaviors of users according to changes in electricity prices and user needs, and find the best balance between reducing power generation costs, using price differences to arbitrage in the market, and meeting user needs, so as to achieve the dual goals.

[0198] To explore the impact of solution three on user demand response behavior, this example draws Fig.12 This reflects the demand response of VPP load users under Scheme 3. When the power is positive, it is reflected as a peak-cutting demand response, and the load is reduced; when the power is positive, it is reflected as a peak-cutting demand response, and the load is increased.

[0199] like Fig.12As shown in the figure, the load for demand response in various periods of the day is not simply expressed as a positive or negative value, but presents a more flexible and dynamic adjustment according to the electricity price signal of the power market and the comprehensive operation objectives of the VPP. Under Scheme 3, VPP pursues a balance between profit maximization and comprehensive user satisfaction. This means that during periods with low electricity prices, VPP allows users to increase the load moderately according to the needs of electricity comfort and economy to enjoy more economical electricity prices; while during periods with high electricity prices, demand response measures will be used to reduce the load to reduce the cost of purchasing electricity and obtain subsidies for external demand response. This strategy is manifested as a positive demand response load during peak electricity price periods, that is, the extent of user load reduction increases, and during periods of low electricity prices, it is manifested as a negative value or a smaller positive value, that is, a moderate increase or maintenance of user load. Through the strategy of Scheme 3, VPP achieves optimal management of user loads at different times, which not only ensures users' electricity demand and satisfaction, but also maximizes its own economic benefits.

[0200] Fig.13 The specific demand response of each user under Scheme 3 is shown. When the load transfer value is positive, it is reflected as transfer-out, and vice versa, it is transfer-in.

[0201] The demand response of residential users is related to the daily electricity consumption pattern. Users 1 and 2 increase electricity consumption when the electricity price is low, such as using a washing machine or water heater at night, which is manifested as a negative load transfer. In peak or normal periods, electricity consumption is naturally reduced due to going out to work, or electricity is consciously saved, such as turning off unnecessary appliances, which is manifested as a positive load reduction. The demand of commercial users is relatively fixed, but it can be observed that users 3 and 4 consciously reduce load during peak electricity price periods, such as adjusting business hours or reducing unnecessary lighting and air conditioning use. During periods of low electricity prices, commercial users may increase some non-peak electricity consumption activities, such as preparing food in advance or using night lighting. Industrial users usually have a large electricity load and show more significant demand response behavior under Scheme 3. During peak periods, user 5 may reduce load by adjusting production plans or using energy storage equipment. During periods of low electricity prices, production may be increased or equipment maintenance may be performed, which is manifested as a negative load transfer.

[0202] 3. Sensitivity analysis

[0203] 1) Peak-to-valley price difference sensitivity analysis

[0204] As a collection of diversified distributed energy sources, VPP achieves profitability by integrating the unique advantages and complementarity of various energy sources. The fluctuation of electricity prices during peak and valley periods, namely the peak-valley price difference, has a direct impact on the output of each unit within the VPP, the power purchase and sales strategy, and the dispatch strategy of user load. This impact is further transmitted to the overall revenue of the VPP, because the change of the peak-valley price difference will change the economic feasibility of the VPP's power purchase and sales in different time periods, as well as the user's response pattern to electricity consumption. Based on the original peak-valley price difference of 0.4 yuan, this study set up three different price difference scenarios to conduct a sensitivity analysis of the model, in order to study the impact of the peak-valley price difference of VPP in the market electricity price on the VPP's revenue and the comprehensive satisfaction of users under the dual objectives. Among them, the peak-valley price difference scheme is set as shown in Table 3 below.

[0205] Table 3

[0206]

[0207] from Fig.14 It can be observed that the profit of VPP and the overall satisfaction of users increase with the increase of the peak-valley price difference. Specifically, with the widening of the peak-valley price difference, facing the lower electricity purchase cost during the off-peak period, VPP actively increases the purchase of electricity and gas-fired units, uses energy storage units to store electricity, prepares for the electricity demand during the peak period, and sells electricity at a higher price during the peak period. This strategy not only meets the load demand of users, but also significantly increases the profit space of VPP. At the same time, VPP provides demand response subsidies during peak hours through a flexible demand response mechanism, encourages users to reduce or transfer loads to low-price periods, reduces users' electricity costs, and further improves user satisfaction. In summary, the increase in the peak-valley price difference not only provides VPP with an opportunity to optimize its electricity purchase and sales strategy and achieve economic benefits, but also improves the economy of users' electricity use through active guidance on the demand side, and promotes the improvement of users' overall satisfaction.

[0208] 2) VPP incentive price sensitivity analysis

[0209] Incentive-based demand response is a behavior that uses incentive policies to mobilize users to participate in the adjustment of the power system. The demand response incentive compensation implemented by VPP to users is intended to increase the enthusiasm of users to participate in demand-side management, and to encourage users to reduce electricity consumption or shift electricity consumption to low-peak periods by providing economic incentives during peak electricity price periods. This type of compensation can not only reduce the load pressure during peak hours of the power grid and promote the optimal allocation of power resources, but also help reduce users' electricity costs, enable them to enjoy more flexible electricity services, and improve their satisfaction. In order to explore the impact of VPP incentive prices on user comprehensive satisfaction, this study set up 5 other different peak incentive price scenarios based on the original peak incentive price of 0.35 yuan to conduct a sensitivity analysis of the model. Among them, the peak incentive price scheme settings are shown in Table 4 below.

[0210] Table 4

[0211]

[0212]

[0213] Fig.15 The paper shows the changes in users' comprehensive satisfaction when VPP gives different peak incentive prices to users participating in demand response. When the peak incentive price increases, user satisfaction also increases. The increase in peak prices provides users with stronger economic incentives, allowing them to reduce electricity consumption during peak hours or shift electricity consumption to other periods to obtain more compensation; at the same time, as the peak price increases, users' electricity consumption behaviors and strategies will change accordingly, which may lead to a decrease in satisfaction in terms of electricity comfort, while the incentives obtained make users' satisfaction more significantly improved in terms of electricity economy, thus showing a positive correlation between users' comprehensive satisfaction and peak incentive prices overall.

[0214] 3) Sensitivity analysis of satisfaction weight coefficient

[0215] In the satisfaction model proposed in this invention, the comprehensive user satisfaction is composed of the electricity economy index and the electricity comfort index, and the weight coefficient of each index directly determines the value of the comprehensive user satisfaction. In order to explore the impact of these two indicators on VPP profits and comprehensive user satisfaction, different weights are assigned to the two, and the sum of the two coefficients is 1. Seven scenarios are set for sensitivity analysis. Among them, the weight coefficient scheme is set as shown in Table 5 below.

[0216] Table 5

[0217]

[0218] Draw the corresponding VPP profit and user comprehensive satisfaction results under different weight coefficients Fig.16As can be observed from the figure, the profit of VPP reaches its maximum value when the weight of the power comfort index is 0.6 and the weight of the power economy index is 0.4. After that, with the further change of the weight ratio, the profit of VPP reaches a stable state and no longer increases. As the weight of the power comfort index decreases (the weight of the power economy index increases), the user satisfaction gradually decreases.

[0219] At the same time, as the weight of the electricity comfort index decreases, VPP can shift the focus of the strategy from taking into account the user's load consumption experience to improving VPP profits, which will lead to an increase in VPP profits; and when VPP profits rise to a certain extent, it has reached a critical point of marginal effect, and continuing to reduce the weight of the electricity comfort index will not further increase VPP profits. Relatively speaking, the increase in the weight of the electricity economy index shows that users pay more attention to the electricity bills and incentives they receive when using electricity, which is obviously contrary to the goal of maximizing the profits of the upper-level VPP. Even if the model has fully considered the user's satisfaction, VPP still occupies a dominant position, so the user's satisfaction decreases.

[0220] In summary, the present invention establishes a two-layer optimization scheduling model for virtual power plants that takes into account multiple demand response methods and comprehensive user satisfaction, which optimizes the operating benefits of virtual power plants and fully considers the satisfaction of users after participating in demand response. Based on the scheduling decisions obtained in this way, virtual power plants can maximize economic and environmental benefits while ensuring power supply and maintaining grid stability, while also ensuring user electricity demand and satisfaction. Therefore, the present invention is beneficial for virtual power plants to further promote the development of new power systems and enhance user experience.

[0221] The various techniques described herein may be implemented in combination with hardware or software, or a combination thereof. Thus, the method and apparatus of the present invention, or some aspects or parts of the method and apparatus of the present invention may be in the form of program codes (i.e., instructions) embedded in a tangible medium, such as a removable hard disk, a USB flash drive, a floppy disk, a CD-ROM, or any other machine-readable storage medium, wherein when the program is loaded into a machine such as a computer and executed by the machine, the machine becomes a device for practicing the present invention.

[0222] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0223] It should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of disclosure should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0224] In addition, unless otherwise specified, the use of ordinal numbers "first", "second", "third", etc. to describe common objects merely indicates that different instances of similar objects are involved, and is not intended to imply that the objects so described must have a given order in time, space, ranking or in any other manner.

[0225] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is selected primarily for readability and teaching purposes, rather than for explaining or defining the subject matter of the present invention. Therefore, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. The disclosure of the present invention is illustrative, not restrictive, with respect to the scope of the present invention, which is defined by the appended claims.

Claims

1. A virtual power plant dispatching method taking into account multiple types of demand response and user comprehensive satisfaction, comprising: Build a virtual power plant that aggregates distributed units, renewable energy units, energy storage systems, and load users; A scheduling optimization model of a virtual power plant is constructed with the goal of maximizing the operating profit within the scheduling cycle of the virtual power plant, and a user comprehensive satisfaction model is constructed with the goal of maximizing the mean value of the comprehensive satisfaction of all load users in the virtual power plant within the scheduling cycle, wherein the comprehensive satisfaction is composed of satisfaction with electricity usage mode and satisfaction with electricity cost; Taking the dispatch optimization model as the upper model and the user comprehensive satisfaction model as the lower model, a virtual power plant two-layer dispatch model taking into account multiple types of demand response and user comprehensive satisfaction is constructed; The two-layer scheduling model of the virtual power plant is solved to obtain the scheduling decision of the virtual power plant, which includes the output of distributed units and renewable energy units in each time period within the scheduling cycle, the charging power and discharging power of the energy storage system in each time period, and the load reduction and load transfer of load users in each time period.

2. The method of claim 1, wherein: The distributed units are gas units, and the scheduling optimization model of the virtual power plant is constructed with the goal of maximizing the operating profit within the scheduling period of the virtual power plant, including: Based on the revenue of the virtual power plant selling electricity to the power grid in each period within the scheduling cycle, the revenue of the virtual power plant participating in demand response, the incentive cost of the virtual power plant to load users participating in demand response, the carbon trading cost of the virtual power plant, the power generation cost and start-up and shutdown cost of the gas units, and the power generation cost of the renewable energy units, the operating revenue of the virtual power plant within the scheduling cycle is determined, and a scheduling optimization model of the virtual power plant is constructed with the maximization of the operating revenue as the first objective function.

3. The method according to claim 1 or 2, wherein: The method of constructing a user comprehensive satisfaction model with the goal of maximizing the mean value of the comprehensive satisfaction of all load users in the virtual power plant within the dispatch period includes: For each load user, obtain the product of the satisfaction with the electricity usage mode and the weight coefficient of the satisfaction with the electricity usage mode during the dispatching period, as well as the product of the satisfaction with the electricity cost and the weight coefficient of the satisfaction with the electricity cost during the dispatching period, and take the sum of the two products as the comprehensive satisfaction of the load user during the dispatching period; Based on the comprehensive satisfaction of each load user during the scheduling period, the mean of the comprehensive satisfaction of all load users during the scheduling period is obtained, and the maximum mean is used as the second objective function to construct a user comprehensive satisfaction model.

4. The method of claim 3, wherein: Obtaining the satisfaction of each load user's electricity consumption mode during the dispatching period, including: Based on the total load reduction amount and the total load demand amount in the scheduling period of each load user, the load reduction degree value in the scheduling period of each load user is obtained; Based on the load transfer amount and load demand amount in each time period in the scheduling cycle of each load user, the load transfer degree value in the scheduling cycle of each load user is obtained; Based on the load reduction degree value and load transfer degree value within the scheduling period of each load user, the electricity consumption mode satisfaction of each load user within the scheduling period is obtained.

5. The method of claim 4, wherein: The step of obtaining the load reduction degree value within the scheduling period of each load user based on the total load reduction amount and the total load demand amount within the scheduling period of each load user includes: in, represents the load reduction degree value of load user i during the scheduling period, represents the load reduction of load user i in period t, x i,t It represents the load demand of load user i in the load baseline during period t, and T represents the total number of periods in the scheduling cycle.

6. The method of claim 4, wherein: The step of obtaining the load transfer degree value within each load user scheduling cycle based on the load transfer amount and the load demand amount in each time period within each load user scheduling cycle includes: in, represents the load transfer degree value within the scheduling period of load user i, represents the load transfer amount of load user i in period t, x i,t It represents the load demand of load user i in the load baseline during period t, and T represents the total number of periods in the scheduling cycle.

7. The method according to any one of claims 3 to 6, wherein: The electricity cost satisfaction of each load user during the dispatch cycle includes: in, represents the electricity cost satisfaction of load user i during the scheduling period, represents the price at which the virtual power plant purchases electricity from the grid during period t, Δx i,t represents the load amount of load user i participating in demand response in period t, x i,t represents the load amount in period t of the load baseline of load user i, Represents the incentive subsidy price of the virtual power plant during period t.

8. The method according to any one of claims 1 to 7, wherein: The scheduling optimization model includes a first constraint condition and the user comprehensive satisfaction model includes a second constraint condition, wherein: The first constraint conditions include: load quantity constraints of each load user participating in demand response, power balance constraints of virtual power plants, actual load quantity constraints of each load user after participating in demand response, operation constraints of gas units, output constraints of renewable energy units, and charging and discharging constraints of energy storage systems; The second constraint conditions include: deviation constraint of the comprehensive satisfaction of all load users; load reduction constraint, minimum continuous reduction time constraint, maximum continuous reduction time constraint, and reduction number constraint of each load user; load transfer constraint and minimum continuous transfer time constraint of each load user.

9. A computing device comprising: at least one processor; as well as A memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, and the program instructions include instructions for executing the method according to any one of claims 1 to 8.

10. A readable storage medium storing program instructions, when the program instructions are read and executed by a computing device, the computing device executes the method according to any one of claims 1 to 8.

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