Distributed flexibility resource aggregation optimization regulation and control method, device, equipment and medium

Abstract

The invention belongs to the field of power system demand side resource regulation and control, and discloses a distributed flexible resource aggregation optimization regulation and control method, device and equipment and a medium. The method comprises the following steps: acquiring distributed flexibility resources, and forming an optimization model for aggregation regulation and control of the distributed flexibility resources; quantizing the distributed flexibility resource based on the flexibility index Flexl to obtain the flexible capacity of the distributed flexibility resource; calculating the total flexibility quantity and the reference flexibility quantity of each distributed flexibility resource based on the flexible capacities of the distributed flexibility resources; constructing a distributed flexible resource profit allocation model; and jointly solving the optimization model of distributed flexible resource aggregation regulation and control and the distributed flexible resource profit allocation model to obtain a distributed flexible resource aggregation optimization regulation and control scheme. According to the method, the distributed flexible resources can be maximized to participate in scheduling of the novel power system, and the problem that the distributed flexible resources on the demand side are insufficient in monomer capacity and difficult to participate in the day-ahead energy market is well solved.

Description

Technical Field

[0001] The present invention belongs to the field of demand-side resource regulation of power systems, and in particular relates to a distributed flexibility resource aggregation optimization regulation method, device, equipment and medium. Background Art

[0002] The new power system is a new era power system with ensuring energy and power security as its basic premise, meeting the power demand for high-quality economic and social development as its primary goal, building a high-proportion new energy supply and consumption system as its main task, multi-directional coordination and flexible interaction between sources, grids, loads and storage as its strong support, a strong, intelligent and flexible power grid as its hub platform, and technological innovation as well as institutional and mechanism innovation as its basic guarantee. It has four basic characteristics: safety and efficiency, clean and low-carbon, flexibility and smart integration.

[0003] The construction of a new power system is of great significance for responding to global climate change, promoting energy transformation, and promoting sustainable economic and social development. It can not only improve energy efficiency and reduce carbon emissions, but also ensure the safety and stability of power supply and meet the power needs of economic and social development.

[0004] At present, with the demand for the construction of new power systems, distributed flexibility resources are becoming increasingly popular, but their small individual scale and scattered locations make them unable to directly participate in the electricity market. Summary of the invention

[0005] The purpose of the present invention is to provide a distributed flexibility resource aggregation optimization and control method, device, equipment and medium to solve the technical problem that the demand side distributed flexibility resource single capacity is insufficient and it is difficult to participate in the day-ahead energy market.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a distributed flexibility resource aggregation optimization control method, comprising:

[0008] Access to distributed flexibility resources;

[0009] Based on distributed flexibility resources, a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market is constructed to form an optimization model for the aggregation and regulation of distributed flexibility resources;

[0010] Quantify distributed flexibility resources based on the flexibility index Flexl to obtain the flexible capacity of distributed flexibility resources;

[0011] Based on the flexible capacity calculation of distributed flexibility resources, the total flexibility and benchmark flexibility of each distributed flexibility resource are obtained; a distributed flexibility resource profit distribution model based on the flexibility index Flexl is constructed; based on the total flexibility and benchmark flexibility, the optimization model of distributed flexibility resource aggregation regulation and the distributed flexibility resource profit distribution model are jointly solved to obtain the distributed flexibility resource aggregation optimization regulation plan.

[0012] A further improvement of the present invention is that the step of acquiring distributed flexibility resources specifically includes:

[0013] Get the user set S of distributed flexibility resources, the power provided by each user's photovoltaic power to the external market during period t, the amount of electricity purchased by each user from the external market and the system during period t, the fixed load and flexibility load demand of each user during period t, the amount of electricity purchased by each user's energy storage from the external market and the system during period t, and the amount of electricity sold by each user's energy storage to the external market and the system during period t.

[0014] A further improvement of the present invention is that: in the step of constructing a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market based on distributed flexibility resources and forming an optimization model for the aggregation and regulation of distributed flexibility resources, the objective function of the optimization model for the aggregation and regulation of distributed flexibility resources is:

[0015]

[0016] Where: is the photovoltaic power generation cost of user s; r m is the net income earned by the aggregator from the day-ahead market trading volume throughout the day; p The net income from transactions within the system;

[0017]

[0018] in, is the predicted market price for time period t; T represents the time set; is the power of the aggregator’s transactions with the external day-ahead market during period t;

[0019]

[0020]

[0021] Among them, NC is the network fee, represents the amount of electricity sold by user s’s energy storage to the system during period t, It represents the power provided by the photovoltaic power of user s to the system during period t, represents the amount of electricity purchased from the system by user s during period t, It represents the amount of electricity purchased by user s from the system during period t.

[0022] A further improvement of the present invention is that: in the step of constructing a system framework for the aggregation of demand-side distributed flexibility resources to participate in the day-ahead market based on distributed flexibility resources and forming an optimization model for the aggregation and regulation of distributed flexibility resources, the constraints of the optimization model for the aggregation and regulation of distributed flexibility resources include:

[0023]

[0024] Where: It represents the power provided by the photovoltaic power of user s to the external market during period t; It represents the power provided by the photovoltaic power of user s to the system during period t; represents the total photovoltaic output of user s in period t;

[0025]

[0026] Where: represents the amount of electricity purchased by user s from the external market during period t; and They represent the fixed load and flexible load demands of user s in period t respectively;

[0027]

[0028] Where: soc t,s represents the capacity status of user s's energy storage in period t; represents the amount of electricity purchased by user s’s energy storage from the external market during period t; represents the amount of electricity sold by user s’s energy storage to the external market during period t; and They represent the charging and discharging efficiency of the energy storage of user s respectively;

[0029]

[0030] Where: and are the upper and lower limits of the capacity state of energy storage respectively; is the first binary parameter of the energy storage of user s in period t, is the maximum charging and discharging power of the energy storage of user s; soc 0,s and soc T,s They represent the capacity status of user s in time period 0 and time period T respectively;

[0031]

[0032] Maximum limit for user's flexibility needs; is the second binary parameter of the energy storage of user s in period t; The total flexibility demand expected for user s;

[0033]

[0034] A further improvement of the present invention is that the step of quantifying the distributed flexibility resources based on the flexibility index Flex1 to obtain the flexible capacity of the distributed flexibility resources specifically includes:

[0035] The flexible capacity of energy storage of user S in the distributed flexibility resource is:

[0036]

[0037] The flexible capacity of the load of user S in the distributed flexibility resource is:

[0038]

[0039] The flexible capacity of the photovoltaic power of user S in the distributed flexibility resource is:

[0040]

[0041] A further improvement of the present invention is that in the step of calculating the total flexibility and the reference flexibility of each distributed flexibility resource based on the flexible capacity of the distributed flexibility resources:

[0042]

[0043] In the formula, is the total flexibility provided by user s to the aggregator; b ,ω d and ω g are the weights of energy storage, load and photovoltaic respectively, ω b +ω d +ω g =1; Flexible amount as a baseline.

[0044] The present invention is further improved in that: in the step of constructing a distributed flexibility resource profit distribution model based on the flexibility index Flexl; and jointly solving the distributed flexibility resource aggregation control optimization model and the distributed flexibility resource profit distribution model based on the total flexibility and the benchmark flexibility to obtain a distributed flexibility resource aggregation optimization control scheme:

[0045]

[0046] In the formula, is the profit difference, Π A is the total profit obtained by all users through the aggregator, Π s The profit obtained by user s when trading with the market alone; f s is the normalized flexibility;

[0047] The distributed flexibility resource profit distribution model based on the flexibility index Flexl is:

[0048]

[0049] In the formula, is the profit allocated to user s.

[0050] In a second aspect, the present invention provides a distributed flexibility resource aggregation optimization control device, comprising:

[0051] An acquisition module, used to acquire distributed flexibility resources;

[0052] Modeling module, which is used to build a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market based on distributed flexibility resources, and form an optimization model for the aggregation and regulation of distributed flexibility resources;

[0053] A flexible module, used for quantifying distributed flexibility resources based on a flexibility index Flexl, and obtaining the flexible capacity of the distributed flexibility resources;

[0054] The optimization and control module is used to obtain the total flexibility and benchmark flexibility of each distributed flexibility resource based on the flexible capacity calculation of distributed flexibility resources; to build a distributed flexibility resource profit distribution model based on the flexibility index Flexl; based on the total flexibility and benchmark flexibility, to jointly solve the optimization model of distributed flexibility resource aggregation control and the distributed flexibility resource profit distribution model to obtain the distributed flexibility resource aggregation optimization control plan.

[0055] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the distributed flexibility resource aggregation optimization and control method.

[0056] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the distributed flexibility resource aggregation optimization and control method is implemented.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The present invention provides a distributed flexibility resource aggregation optimization control method, including: obtaining distributed flexibility resources; based on the distributed flexibility resources, constructing a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market, forming an optimization model for the aggregation and control of distributed flexibility resources; quantifying distributed flexibility resources based on the flexibility index Flexl to obtain the flexible capacity of distributed flexibility resources; calculating the total flexibility and benchmark flexibility of each distributed flexibility resource based on the flexible capacity of the distributed flexibility resources; constructing a distributed flexibility resource profit distribution model based on the flexibility index Flexl; based on the total flexibility and benchmark flexibility, jointly solving the optimization model for the aggregation and control of distributed flexibility resources and the distributed flexibility resource profit distribution model to obtain a distributed flexibility resource aggregation optimization control scheme. The distributed flexibility resource aggregation optimization control method proposed by the present invention can maximize the participation of distributed flexibility resources in the dispatching of new power systems, and better handle the problem that the demand side distributed flexibility resource monomer capacity is insufficient and difficult to participate in the day-ahead energy market. Considering heterogeneous distributed flexibility resources, aggregators are used to interact with the day-ahead market and internal resources are encouraged to share energy, which effectively reduces the management energy consumption and costs of both buyers and sellers. At the same time, the distributed flexibility resource profit distribution model based on the flexibility index Flexl can better solve the profit distribution problem after distributed flexibility resources participate in aggregation regulation, and has transparency and high computing advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0060] Figure 1 It is a flow chart of a distributed flexibility resource aggregation optimization and control method of the present invention;

[0061] Figure 2 It is a system framework diagram of distributed flexibility resources aggregating and participating in the day-ahead market;

[0062] Figure 3 It is a flow chart of another distributed flexibility resource aggregation optimization and control method of the present invention;

[0063] Figure 4 It is a structural schematic diagram of the distributed flexibility resource aggregation optimization and control device of the present invention;

[0064] Figure 5 It is a structural schematic diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0065] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0066] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0067] Example 1

[0068] See also Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a distributed flexibility resource aggregation optimization and control method, including the following steps:

[0069] Step 1: Construct a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market, and form an optimization model for the aggregation and regulation of distributed flexibility resources.

[0070] The system framework for the aggregation of demand-side distributed flexibility resources participating in the day-ahead market constructed by the present invention takes into account the different distributed flexibility resources of users, including photovoltaics, flexibility loads and energy storage. The framework assumes that users share the information required for transactions and allocations. The aggregator counts the availability, specifications and constraint information of the distributed flexibility resources for the next day and participates in the day-ahead energy market as a price receiver. The aggregator maximizes its profit by optimizing regulation and distributes the obtained profit to the distributed flexibility resources in the system.

[0071] Each distributed flexibility resource can be optionally traded in external markets and within the system.

[0072]

[0073] Where: S represents the user set; T represents the time set; It represents the power provided by the photovoltaic power of user s to the external market during period t; It represents the power provided by the photovoltaic power of user s to the system during period t; Represents the total photovoltaic output of user s in period t.

[0074]

[0075] Where: and They represent the amount of electricity purchased by user s from the external market and the system during period t; and They represent the fixed load and flexible load demands of user s in period t respectively.

[0076]

[0077] Where: soc t,s represents the capacity status of user s's energy storage in period t; and represents the amount of electricity purchased by user s’s energy storage from the external market and the system during period t; and It represents the amount of electricity sold by user s’s energy storage to the external market and the system during period t; and They respectively represent the charging and discharging efficiency of the energy storage of user s.

[0078] Each distributed flexibility resource has its own constraints, and its operating constraints must be considered when determining its transaction volume.

[0079]

[0080] Formula (4) constrains that the capacity state of the energy storage should not exceed its upper and lower limits ( and ). Equations (5) and (6) show that only when the binary parameter When available, the energy storage of user s can be traded with the external market and the system within the t period, and the tradable power cannot exceed the maximum charging and discharging power Constraint (7) ensures that the capacity state SoC of each energy storage is the same at the beginning and end of the day.

[0081]

[0082] Formula (8) stipulates that the flexibility requirement of user s must always be less than the maximum limit allowed Binary parameters Ensure that flexible load is scheduled only in the time slots required by users. Equation (9) stipulates that the flexible demand scheduled throughout the day must be equal to the expected total flexible demand

[0083]

[0084] Formula (10) indicates that in each time period, the energy sharing between the distributed flexibility resources in the system must be balanced. Formula (11) calculates the power of the aggregator in the external day-ahead market transaction in time period t:

[0085]

[0086] The net revenue that the aggregator receives from the day-ahead market trading volume for the entire day m As shown in formula (12), is the predicted market price for time period t.

[0087] In order to encourage resource sharing within the system, an internal electricity price is introduced. The cost of internal transactions should be better than the cost of external market transactions because it reduces the net power exchange between assets and the market, thereby saving network costs NC. Since there is a network access fee, sellers in internal transactions can sell energy at a price higher than the external market price, and buyers can buy energy at a price lower than the external market price. The purchase and sale prices within the system are:

[0088]

[0089] The net income from transactions within the system is:

[0090]

[0091] The objective function of the constructed model is shown in Equation (16), which aims to maximize the profit of the aggregator while encouraging energy sharing within the system.

[0092]

[0093] Where: is the photovoltaic power generation cost of user s.

[0094] Based on the above-mentioned system framework of distributed flexibility resource aggregation participating in the day-ahead market, the present invention constructs an optimization model for resource aggregation and regulation according to the characteristics of different users.

[0095] Step 2: Define the flexibility index Flexl of heterogeneous distributed flexibility resources to quantify the degree of flexibility that distributed flexibility resources provide to aggregators.

[0096] The flexibility index Flexl of a user is calculated based on the total flexible capacity it provides to the aggregator. Defined, it can be regulated to maximize its profit. The present invention considers the flexible capacity of three distributed flexibility resources, namely, energy storage, transferable load and flexible capacity provided by photovoltaic power generation.

[0097] The flexibility of energy storage is considered as the total amount it provides to the aggregator for trading throughout the day, expressed as follows:

[0098]

[0099] Load flexibility is the total flexible load provided throughout the day and is expressed as follows:

[0100]

[0101] The flexibility of photovoltaics lies in its total power generation, expressed as follows:

[0102]

[0103] Based on the definition of the flexibility index Flexl of the above-mentioned distributed flexibility resources, the present invention targets heterogeneous distributed flexibility resources and quantifies the degree of flexibility provided by the distributed flexibility resources to aggregators, thereby helping the aggregators to complete the profit distribution of the distributed flexibility resources after aggregation optimization and regulation.

[0104] Step 3: Construct a distributed flexibility resource regulation profit distribution model based on the flexibility index Flexl to solve the ex post profit distribution problem of aggregators regulating distributed flexibility resources.

[0105] The present invention defines A is the total profit obtained by all users through the aggregator, Π s It is the profit obtained by users when trading with the market alone.

[0106]

[0107] Formula (20) represents the residual profit obtained by aggregation. This means that aggregation will generate additional value, and each user has an economic incentive to participate in the market through an aggregator; if Then the aggregation will not generate any economic value.

[0108] The profit distribution mechanism should ensure that each user obtains at least the profit that he or she would obtain if he or she participated alone, so as to ensure that no user suffers losses. The present invention proposes a profit distribution mechanism as follows:

[0109]

[0110] Formula (21) calculates the total flexibility provided by user s to the aggregator: ω b ,ω d and ω g is the weight of each part, which is a number between 0 and 1 and satisfies ω b +ω d +ω g = 1. Formula (22) calculates the baseline flexibility Used for normalization of flexible quantities in profit distribution mechanisms.

[0111]

[0112] is the profit allocated to user s.

[0113] The invention guarantees the profit of each user, can maximize the participation of distributed flexibility resources in the dispatch of the new power system, and promote the consumption of distributed flexibility resources. The second term in formula (24) is the surplus allocated to each user. From the definition of the flexibility index Flexl in the invention, it can be concluded that the greater the flexibility of the user, the higher the profit. Therefore, the allocation mechanism proposed in the invention encourages users to provide the maximum flexibility within their capabilities.

[0114] Example 2

[0115] See also Figure 3 As shown, the present invention provides a distributed flexibility resource aggregation optimization and control method, comprising:

[0116] S1. Obtain distributed flexibility resources;

[0117] In a specific implementation manner, the step of obtaining distributed flexibility resources specifically includes: obtaining a user set S of distributed flexibility resources, the power provided by each user's photovoltaic power to the external market during period t, the amount of electricity purchased by each user from the external market and the system during period t, the fixed load and flexibility load requirements of each user during period t, the amount of electricity purchased by each user's energy storage from the external market and the system during period t, and the amount of electricity sold by each user's energy storage to the external market and the system during period t.

[0118] S2. Based on distributed flexibility resources, a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market is constructed to form an optimization model for the aggregation and regulation of distributed flexibility resources;

[0119] In a specific implementation, the objective function of the optimization model for distributed flexibility resource aggregation control is:

[0120]

[0121] Where: is the photovoltaic power generation cost of user s; r m is the net income earned by the aggregator from the day-ahead market trading volume throughout the day; p The net income from transactions within the system;

[0122]

[0123] in, is the predicted market price for time period t; T represents the time set; is the power of the aggregator’s transactions with the external day-ahead market during period t;

[0124]

[0125] Among them, NC is the network fee, represents the amount of electricity sold by user s’s energy storage to the system during period t, It represents the power provided by the photovoltaic power of user s to the system during period t, represents the amount of electricity purchased from the system by user s during period t, It represents the amount of electricity purchased by user s from the system during period t.

[0126] In a specific implementation, the constraints of the optimization model for distributed flexibility resource aggregation and control include:

[0127]

[0128] Where: It represents the power provided by the photovoltaic power of user s to the external market during period t; It represents the power provided by the photovoltaic power of user s to the system during period t; represents the total photovoltaic output of user s in period t;

[0129]

[0130] Where: represents the amount of electricity purchased by user s from the external market during period t; and They represent the fixed load and flexible load demands of user s in period t respectively;

[0131]

[0132] Where: soc t,s represents the capacity status of user s's energy storage in period t; represents the amount of electricity purchased by user s’s energy storage from the external market during period t; represents the amount of electricity sold by user s’s energy storage to the external market during period t; and They represent the charging and discharging efficiency of the energy storage of user s respectively;

[0133]

[0134] Where: and are the upper and lower limits of the capacity state of energy storage respectively; is the first binary parameter of the energy storage of user s in period t, is the maximum charging and discharging power of the energy storage of user s; soc 0,s and soc T,s They represent the capacity status of user s in time period 0 and time period T respectively;

[0135]

[0136] Maximum limit for user's flexibility needs; is the second binary parameter of the energy storage of user s in period t; The total flexibility demand expected for user s;

[0137]

[0138] S3, quantifying distributed flexibility resources based on the flexibility index Flexl to obtain the flexible capacity of the distributed flexibility resources;

[0139] In a specific implementation, the flexible capacity of energy storage of user S in the distributed flexibility resource is:

[0140]

[0141] The flexible capacity of the load of user S in the distributed flexibility resource is:

[0142]

[0143] The flexible capacity of the photovoltaic power of user S in the distributed flexibility resource is:

[0144]

[0145] S4. Calculate the total flexibility and benchmark flexibility of each distributed flexibility resource based on the flexible capacity of distributed flexibility resources; construct a distributed flexibility resource profit distribution model based on the flexibility index Flexl; based on the total flexibility and benchmark flexibility, jointly solve the optimization model of distributed flexibility resource aggregation control and the distributed flexibility resource profit distribution model to obtain the distributed flexibility resource aggregation optimization control plan.

[0146] In a specific embodiment,

[0147]

[0148] In the formula, is the total flexibility provided by user s to the aggregator; b ,ω d and ω g are the weights of energy storage, load and photovoltaic respectively, ω b +ω d +ω g =1; The base flexible amount;

[0149]

[0150] In the formula, is the profit difference, Π A is the total profit obtained by all users through the aggregator, Π s The profit obtained by user s when trading with the market alone; f s is the normalized flexibility;

[0151] The distributed flexibility resource profit distribution model based on the flexibility index Flexl is:

[0152]

[0153] In the formula, is the profit allocated to user s.

[0154] In a specific implementation, an optimization model for distributed flexibility resource aggregation and control is first obtained, and then a distributed flexibility resource profit distribution model is solved based on the optimization result to distribute profits.

[0155] In a specific implementation, the power system dispatches distributed flexibility resources based on a distributed flexibility resource aggregation optimization control scheme.

[0156] Example 3

[0157] See also Figure 4 As shown, an embodiment of the present invention provides a distributed flexibility resource aggregation optimization and control device, including:

[0158] An acquisition module, used to acquire distributed flexibility resources;

[0159] Modeling module, which is used to build a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market based on distributed flexibility resources, and form an optimization model for the aggregation and regulation of distributed flexibility resources;

[0160] A flexible module, used for quantifying distributed flexibility resources based on a flexibility index Flexl, and obtaining the flexible capacity of the distributed flexibility resources;

[0161] The optimization and control module is used to obtain the total flexibility and benchmark flexibility of each distributed flexibility resource based on the flexible capacity calculation of distributed flexibility resources; to build a distributed flexibility resource profit distribution model based on the flexibility index Flexl; based on the total flexibility and benchmark flexibility, to jointly solve the optimization model of distributed flexibility resource aggregation control and the distributed flexibility resource profit distribution model to obtain the distributed flexibility resource aggregation optimization control plan.

[0162] Example 4

[0163] See also Figure 4As shown, an embodiment of the present invention provides an electronic device 100 for implementing a distributed flexibility resource aggregation optimization and control method; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0164] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the distributed flexibility resource aggregation optimization and control method described in Example 1 or 2 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0165] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0166] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a distributed flexibility resource aggregation optimization control method, and the processor 102 can execute the plurality of instructions to implement:

[0167] Access to distributed flexibility resources;

[0168] Based on distributed flexibility resources, a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market is constructed to form an optimization model for the aggregation and regulation of distributed flexibility resources;

[0169] Quantify distributed flexibility resources based on the flexibility index Flexl to obtain the flexible capacity of distributed flexibility resources;

[0170] Based on the flexible capacity calculation of distributed flexibility resources, the total flexibility and benchmark flexibility of each distributed flexibility resource are obtained; a distributed flexibility resource profit distribution model based on the flexibility index Flexl is constructed; based on the total flexibility and benchmark flexibility, the optimization model of distributed flexibility resource aggregation regulation and the distributed flexibility resource profit distribution model are jointly solved to obtain the distributed flexibility resource aggregation optimization regulation plan.

[0171] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0172] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0173] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0174] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0175] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0176] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distributed flexibility resource aggregation optimization and control method, characterized in that: include: Access to distributed flexibility resources; Based on distributed flexibility resources, a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market is constructed to form an optimization model for the aggregation and regulation of distributed flexibility resources; Quantify distributed flexibility resources based on the flexibility index Flexl to obtain the flexible capacity of distributed flexibility resources; Based on the flexible capacity calculation of distributed flexibility resources, the total flexibility and benchmark flexibility of each distributed flexibility resource are obtained; a distributed flexibility resource profit distribution model based on the flexibility index Flexl is constructed; based on the total flexibility and benchmark flexibility, the optimization model of distributed flexibility resource aggregation regulation and the distributed flexibility resource profit distribution model are jointly solved to obtain the distributed flexibility resource aggregation optimization regulation plan.

2. The distributed flexibility resource aggregation optimization and control method according to claim 1 is characterized in that: The step of obtaining distributed flexibility resources specifically includes: Get the user set S of distributed flexibility resources, the power provided by each user's photovoltaic power to the external market during period t, the amount of electricity purchased by each user from the external market and the system during period t, the fixed load and flexibility load demand of each user during period t, the amount of electricity purchased by each user's energy storage from the external market and the system during period t, and the amount of electricity sold by each user's energy storage to the external market and the system during period t.

3. The distributed flexibility resource aggregation optimization and control method according to claim 1 is characterized in that: In the step of constructing a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market based on distributed flexibility resources and forming an optimization model for the aggregation and regulation of distributed flexibility resources, the objective function of the optimization model for the aggregation and regulation of distributed flexibility resources is: Where: is the photovoltaic power generation cost of user s; r m is the net income earned by the aggregator from the day-ahead market trading volume throughout the day; p The net income from transactions within the system; in, is the predicted market price for time period t; T represents the time set; is the power of the aggregator’s transactions with the external day-ahead market during period t; Among them, NC is the network fee, represents the amount of electricity sold by user s’s energy storage to the system during period t, It represents the power provided by the photovoltaic power of user s to the system during period t, represents the amount of electricity purchased from the system by user s during period t, It represents the amount of electricity purchased by user s from the system during period t.

4. The distributed flexibility resource aggregation optimization and control method according to claim 3 is characterized in that: In the step of constructing a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market based on distributed flexibility resources and forming an optimization model for the aggregation and regulation of distributed flexibility resources, the constraints of the optimization model for the aggregation and regulation of distributed flexibility resources include: Where: It represents the power provided by the photovoltaic power of user s to the external market during period t; It represents the power provided by the photovoltaic power of user s to the system during period t; represents the total photovoltaic output of user s in period t; Where: represents the amount of electricity purchased by user s from the external market during period t; and They represent the fixed load and flexible load demands of user s in period t respectively; Where: soc t,s represents the capacity status of user s's energy storage in period t; represents the amount of electricity purchased by user s’s energy storage from the external market during period t; represents the amount of electricity sold by user s’s energy storage to the external market during period t; and They represent the charging and discharging efficiency of the energy storage of user s respectively; Where: and are the upper and lower limits of the capacity state of energy storage respectively; is the first binary parameter of the energy storage of user s in period t, is the maximum charging and discharging power of the energy storage of user s; soc 0,s and soc T,s They represent the capacity status of user s in time period 0 and time period T respectively; Maximum limit for user's flexibility needs; is the second binary parameter of the energy storage of user s in period t; The total flexibility demand expected for user s; 5. The distributed flexibility resource aggregation optimization and control method according to claim 4 is characterized in that: The step of quantifying the distributed flexibility resources based on the flexibility index Flex1 to obtain the flexible capacity of the distributed flexibility resources specifically includes: The flexible capacity of energy storage of user S in the distributed flexibility resource is: The flexible capacity of the load of user S in the distributed flexibility resource is: The flexible capacity of the photovoltaic power of user S in the distributed flexibility resource is:

6. The distributed flexibility resource aggregation optimization and control method according to claim 5 is characterized in that: In the step of calculating the total flexibility and the benchmark flexibility of each distributed flexibility resource based on the flexible capacity of the distributed flexibility resources: In the formula, is the total flexibility provided by user s to the aggregator; b ,ω d and ω g are the weights of energy storage, load and photovoltaic respectively, ω b +ω d +ω g =1; Flexible amount as a baseline.

7. The distributed flexibility resource aggregation optimization and control method according to claim 6 is characterized in that: In the step of constructing a distributed flexibility resource profit distribution model based on the flexibility index Flexl; and jointly solving the distributed flexibility resource aggregation control optimization model and the distributed flexibility resource profit distribution model based on the total flexibility and the benchmark flexibility to obtain a distributed flexibility resource aggregation optimization control scheme: In the formula, is the profit difference, Π A is the total profit obtained by all users through the aggregator, Π s The profit obtained by user s when trading with the market alone; f s is the normalized flexibility; The distributed flexibility resource profit distribution model based on the flexibility index Flexl is: In the formula, is the profit allocated to user s.

8. Distributed flexibility resource aggregation optimization and control device, characterized in that: include: An acquisition module, used to acquire distributed flexibility resources; Modeling module, which is used to build a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market based on distributed flexibility resources, and form an optimization model for the aggregation and regulation of distributed flexibility resources; A flexible module, used for quantifying distributed flexibility resources based on a flexibility index Flexl, and obtaining the flexible capacity of the distributed flexibility resources; An optimization control module is used to calculate the total flexibility and the benchmark flexibility of each distributed flexibility resource based on the flexible capacity of the distributed flexibility resources; Construct a distributed flexibility resource profit distribution model based on the flexibility index Flexl; based on the total flexibility and the benchmark flexibility, jointly solve the optimization model of distributed flexibility resource aggregation regulation and the distributed flexibility resource profit distribution model to obtain the distributed flexibility resource aggregation optimization regulation plan.

9. The distributed flexibility resource aggregation optimization and control device according to claim 8 is characterized in that: The step of obtaining distributed flexibility resources specifically includes: Get the user set S of distributed flexibility resources, the power provided by each user's photovoltaic power to the external market during period t, the amount of electricity purchased by each user from the external market and the system during period t, the fixed load and flexibility load demand of each user during period t, the amount of electricity purchased by each user's energy storage from the external market and the system during period t, and the amount of electricity sold by each user's energy storage to the external market and the system during period t.

10. The distributed flexibility resource aggregation optimization and control device according to claim 8 is characterized in that: In the step of constructing a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market based on distributed flexibility resources and forming an optimization model for the aggregation and regulation of distributed flexibility resources, the objective function of the optimization model for the aggregation and regulation of distributed flexibility resources is: Where: is the photovoltaic power generation cost of user s; r m is the net income earned by the aggregator from the day-ahead market trading volume throughout the day; p The net income from transactions within the system; in, is the predicted market price for time period t; T represents the time set; is the power of the aggregator’s transactions with the external day-ahead market during period t; Among them, NC is the network fee, represents the amount of electricity sold by user s’s energy storage to the system during period t, It represents the power provided by the photovoltaic power of user s to the system during period t, represents the amount of electricity purchased from the system by user s during period t, It represents the amount of electricity purchased by user s from the system during period t.

11. The distributed flexibility resource aggregation optimization and control device according to claim 10 is characterized in that: In the step of constructing a system framework for the aggregation of distributed flexibility resources on the demand side to participate in the day-ahead market based on distributed flexibility resources and forming an optimization model for the aggregation and regulation of distributed flexibility resources, the constraints of the optimization model for the aggregation and regulation of distributed flexibility resources include: Where: It represents the power provided by the photovoltaic power of user s to the external market during period t; It represents the power provided by the photovoltaic power of user s to the system during period t; represents the total photovoltaic output of user s in period t; Where: represents the amount of electricity purchased by user s from the external market during period t; and They represent the fixed load and flexible load demands of user s in period t respectively; Where: soc t,s represents the capacity status of user s's energy storage in period t; represents the amount of electricity purchased by user s’s energy storage from the external market during period t; represents the amount of electricity sold by user s’s energy storage to the external market during period t; and They represent the charging and discharging efficiency of the energy storage of user s respectively; Where: and are the upper and lower limits of the capacity state of energy storage respectively; is the first binary parameter of the energy storage of user s in period t, is the maximum charging and discharging power of the energy storage of user s; soc 0,s and soc T,s They represent the capacity status of user s in time period 0 and time period T respectively; Maximum limit for user's flexibility needs; is the second binary parameter of the energy storage of user s in period t; The total flexibility demand expected for user s; 12. The distributed flexibility resource aggregation optimization and control device according to claim 11 is characterized in that: The step of quantifying the distributed flexibility resources based on the flexibility index Flex1 to obtain the flexible capacity of the distributed flexibility resources specifically includes: The flexible capacity of energy storage of user S in the distributed flexibility resource is: The flexible capacity of the load of user S in the distributed flexibility resource is: The flexible capacity of the photovoltaic power of user S in the distributed flexibility resource is:

13. The distributed flexibility resource aggregation optimization and control device according to claim 12 is characterized in that: In the step of calculating the total flexibility and the benchmark flexibility of each distributed flexibility resource based on the flexible capacity of the distributed flexibility resources: In the formula, is the total flexibility provided by user s to the aggregator; b ,ω d and ω g are the weights of energy storage, load and photovoltaic respectively, ω b +ω d +ω g =1; Flexible amount as a baseline.

14. The distributed flexibility resource aggregation optimization and control device according to claim 13 is characterized in that: In the step of constructing a distributed flexibility resource profit distribution model based on the flexibility index Flexl; and jointly solving the distributed flexibility resource aggregation control optimization model and the distributed flexibility resource profit distribution model based on the total flexibility and the benchmark flexibility to obtain a distributed flexibility resource aggregation optimization control scheme: In the formula, is the profit difference, Π A is the total profit obtained by all users through the aggregator, Π s The profit obtained by user s when trading with the market alone; f s is the normalized flexibility; The distributed flexibility resource profit distribution model based on the flexibility index Flexl is: In the formula, is the profit allocated to user s.

15. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the distributed flexibility resource aggregation optimization and control method as described in any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, it implements the distributed flexibility resource aggregation optimization and control method as described in any one of claims 1 to 7.

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