Power retail package transaction optimization method for multi-element load demand

Abstract

The invention discloses an electric power retail package transaction optimization method for multi-element load demands, and the method comprises the steps: constructing an electric power retail package combination optimization model which comprises an upper-layer model and a lower-layer model, and constructing the upper-layer model with the maximum retailer income as a first target, constructing a lower-layer model by taking the minimum electricity purchase cost of the user as a second target; based on the daily load curve corresponding to the user type, the power retail package combination optimization model is solved, a package transaction optimization result is obtained, and the package transaction optimization result comprises the package price in each electricity price package mode, the optimal package selection result of the user, the power purchase cost of the user and the retailer income. The optimal package selection result of the user comprises the optimal electricity price package mode selected by the user in each time period and the proportion of the optimal electricity price package mode. According to the invention, personalized packages can be selected for the users according to the requirements of different types of users, so that the retailer income can be improved and the electricity purchasing cost of the users can be reduced.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to an electric power retail package transaction optimization method for multiple load demands. Background Art

[0002] The electricity retail market is the basic guarantee for building a unified national electricity market and promoting the operation of the new power system market. The new power system needs to be safe, efficient, clean, low-carbon, flexible, and intelligent, which requires the power market to be flexible and adaptable to support the integration and optimal allocation of diversified power resources. The multi-level unified system of the power market is the key to achieving the "dual carbon" goal. As a basic link, the electricity retail market can promote the consumption and market-based transactions of new energy and promote the green transformation of the power system. As part of the provincial power market, it should also play a basic role to ensure that power resources are optimally allocated and basically balanced.

[0003] In the context of diversified user needs and growing demand for energy retail packages, building green energy retail packages that coordinate the interests of retailers and users is a key issue. Flexible and diversified electricity retail packages can meet the personalized needs of users and improve the ability to ensure energy supply. With the rapid increase in the number of market-oriented electricity users and the risk of wholesale electricity price fluctuations, the design of electricity retail packages has brought huge challenges. Although a variety of retail package models have been proposed, the existing electricity retail packages are insufficient in meeting the diverse needs of users and increasing the profits of electricity sellers. Therefore, it is urgent to optimize the retail package combination and design green energy retail packages that meet the diverse needs of multiple user groups to promote the optimization of users' electricity energy structure.

[0004] Therefore, a power retail package transaction optimization method for multiple load demands is needed to solve the above technical problems. Summary of the invention

[0005] To this end, the present invention provides a method for optimizing electricity retail package transactions for multiple load demands to solve or at least alleviate the above problems.

[0006] According to one aspect of the present invention, there is provided a method for optimizing power retail package transactions for multiple load demands, wherein the package types of the power retail packages include green power packages and thermal power packages, and each package type has multiple electricity price package modes, wherein the multiple electricity price package modes include a fixed electricity price package mode, a time-of-use electricity price package mode, a step electricity price package mode, and a two-part electricity price package mode. The method comprises: determining a user type of a user and a daily load curve corresponding to the user type, wherein the user type is a peak-facing user, a peak-avoiding user, or a stable user; constructing a power retail package combination optimization model comprising an upper model and a lower model, wherein the upper model is constructed with the maximum retailer profit as a first goal, and the lower model is constructed with the minimum user power purchase cost as a second goal; based on the daily load curve corresponding to the user type, solving the power retail package combination optimization model to obtain a package transaction optimization result, wherein the package transaction optimization result includes a package price under each electricity price package mode, an optimal package selection result of the user, the user power purchase cost, and the retailer profit, wherein the optimal package selection result of the user includes the optimal electricity price package mode selected by the user in each time period and the proportion of the optimal electricity price package mode.

[0007] Optionally, in the electricity retail package transaction optimization method for multiple load demands according to the present invention, an upper-level model is constructed with maximizing the retailer's profit as the first goal, including: constructing a first objective function based on the proportion of each user selecting each electricity price package model and the net profit of each electricity price package model; and constructing an upper-level model based on the first objective function.

[0008] Optionally, in the electricity retail package transaction optimization method for multiple load demands according to the present invention, an upper-level model is constructed based on the first objective function, including: constructing an upper-level model based on the first objective function and a first constraint condition, wherein the first constraint condition includes an electricity quantity constraint condition and an electricity price constraint condition.

[0009] Optionally, in the electricity retail package transaction optimization method for multiple load demands according to the present invention, a lower-level model is constructed with the second objective of minimizing the user's electricity purchase cost, including: constructing a second objective function based on the package price under each electricity price package mode selected by the user, the electricity consumption under each electricity price package mode, and the proportion of each electricity price package mode; wherein, for thermal power packages, the package price under each electricity price package mode includes the package base electricity price under the electricity price package mode; for green electricity packages, the package price under each electricity price package mode includes the package base electricity price and the green electricity premium price under the electricity price package mode; and constructing the lower-level model based on the second objective function.

[0010] Optionally, in the electricity retail package transaction optimization method for multiple load demands according to the present invention, a lower-level model is constructed based on the second objective function, including: constructing a lower-level model based on the second objective function and a second constraint, wherein the second constraint includes a user demand response constraint, a user package composition constraint, a package selection constraint, and a peak-valley difference constraint.

[0011] Optionally, in the electricity retail package transaction optimization method for multiple load demands according to the present invention, the electricity retail package combination optimization model is solved based on the daily load curve corresponding to the user type to obtain a package transaction optimization result, including: obtaining model basic parameters, the model basic parameters include the electricity wholesale market transaction price, the user's initial load demand, the user's demand response elasticity coefficient, the green electricity consumption ratio, the load upper limit and the load lower limit, wherein the electricity wholesale market transaction price includes the electricity wholesale market thermal power transaction price and the electricity wholesale market green electricity transaction price, the user's initial load demand includes the user's initial demand for thermal power package electricity and green electricity package electricity, and the demand response elasticity coefficient includes a self-elasticity coefficient and a cross-elasticity coefficient; based on the model basic parameters and the daily load curve corresponding to the user type, the electricity retail package combination optimization model is solved to obtain a global optimal solution, and the package transaction optimization result is obtained based on the global optimal solution.

[0012] Optionally, in the power retail package transaction optimization method for multiple load demands according to the present invention, the upper model is constructed with maximizing the retailer's profit as the first goal, and further includes:

[0013] The net profit of the fixed electricity price package model is determined by the following formula:

[0014]

[0015] in, It is the basic electricity price of the fixed package under the fixed electricity price package model; is the net profit obtained from user i under the fixed electricity price package model; is the electricity consumption of user i in period t under the fixed electricity price package mode, is the thermal power transaction volume of user i in period t, is the green electricity trading volume of user i in period t; C i,α is the transaction price of electricity in the wholesale market; α=0 indicates thermal power package transaction, α=1 indicates green power package transaction;

[0016] The net profit of the time-of-use electricity price package model is determined by the following formula:

[0017]

[0018] Among them, P i,t P is the basic electricity price of the time-sharing package for user i in time period t under the time-sharing electricity price package mode; i,f , P i,p , P i,g They are the peak-period electricity price, normal-period electricity price, and valley-period electricity price under the time-of-use electricity price package model; is the net profit obtained from user i under the time-of-use electricity price package model; is the electricity consumption of user i in period t under the time-of-use electricity price package mode;

[0019] The net profit of the tiered electricity price package model is determined by the following formula:

[0020]

[0021] in, is the total electricity consumption of user i in the settlement period under the tiered electricity price package mode; is the electricity consumption of user i in period t under the tiered electricity price package mode; is the basic electricity price of the tiered electricity price package for user i in period t; p s1 、p s2 、p s3 They are the electricity prices corresponding to the three electricity consumption gradients under the tiered electricity price package mode; Used to indicate the classification criteria for user i between different electricity consumption gradients under the tiered electricity price package mode; is the net profit obtained from user i under the tiered electricity price package model;

[0022] The net profit of the two-part electricity price retail package model is determined by the following formula:

[0023]

[0024] Among them, d is the capacity electricity price classified by different voltage levels under the two-part electricity price package mode; M is the user's contract capacity under the two-part electricity price package mode; is the electricity consumption of user i in period t under the two-part electricity price package mode; a i,t P is the electricity price of user i in time period t classified by different voltage levels under the two-part electricity price package mode; i tp is the capacity electricity price of user i in the two-part electricity price package mode; is the green electricity premium price of user i; is the net profit obtained from user i under the two-part electricity price retail package model.

[0025] Optionally, in the electricity retail package transaction optimization method for multiple load demands according to the present invention, the first objective function is as follows:

[0026]

[0027] Among them, α=0 indicates thermal power package transaction, and α=1 indicates green power package transaction; The proportion of user i who chooses fixed electricity price package mode, time-of-use electricity price package mode, tiered electricity price package mode, and two-part electricity price package mode; is the net profit obtained from user i under the fixed electricity price package model; is the net profit obtained from user i under the time-of-use electricity price package model; is the net profit obtained from user i under the tiered electricity price package model; is the net profit obtained from user i under the two-part electricity price retail package model.

[0028] Optionally, in the electricity retail package transaction optimization method for multiple load demands according to the present invention, the second objective function is as follows:

[0029]

[0030] Among them, α=0 indicates thermal power package transaction, and α=1 indicates green power package transaction; They are the electricity consumption of user i in period t under the fixed electricity price package mode, time-of-use electricity price package mode, tiered electricity price package mode, and two-part electricity price package mode; The proportion of user i who chooses fixed electricity price package mode, time-of-use electricity price package mode, tiered electricity price package mode, and two-part electricity price package mode; is the green electricity premium price of user i; is the fixed package basic electricity price under the fixed electricity price package mode, P i,t is the basic electricity price of the time-sharing package for user i in period t under the time-sharing electricity price package mode, is the basic electricity price of the tiered electricity price package for user i in period t, a i,t is the electricity price of user i in time period t classified by different voltage levels under the two-part electricity price package mode, P i tp is the capacity electricity price of user i under the two-part electricity price package mode.

[0031] Optionally, in the power retail package transaction optimization method for multiple load demands according to the present invention, the demand response constraint condition includes:

[0032]

[0033] in, is the load of user i in period t; ΔL i,tis the demand load of user i in period t after selecting the electricity price package mode; P i,0 is the initial package price of user i in period t; ε i,tt is the self-elasticity coefficient of type i load, which is used to indicate the user's willingness to increase or decrease electricity consumption; ε i,τt is the cross elasticity coefficient of type i load, which is used to indicate the user’s willingness to change the electricity consumption time; are the upper and lower load limits of user i in period t respectively;

[0034] The user package composition constraints are as follows:

[0035]

[0036] in, is the corrected load of user i in period t;

[0037] The package selection constraints are as follows:

[0038]

[0039] The peak-to-valley difference constraint conditions are as follows:

[0040]

[0041] Optionally, in the power retail package transaction optimization method for multiple load demands according to the present invention, the power constraint condition is as follows:

[0042]

[0043] in, They are respectively the lower and upper limits of the electricity consumption of the package under the contract signed between the retailer and the user. is the electricity consumption of user i in period t under the fixed electricity price package mode; is the electricity consumption of user i in period t under the time-of-use electricity price package mode; is the electricity consumption of user i in period t under the tiered electricity price package mode; is the thermal power transaction volume of user i in period t; is the green electricity trading volume of user i in period t;

[0044] The electricity price constraints include:

[0045]

[0046] Among them, p 1 、p 2 They are the electricity price ratio during peak-to-normal periods and the electricity price ratio during normal-to-valley periods respectively. is the green electricity premium price of user i; is the fixed package basic electricity price under the fixed electricity price package mode; P i, f、P i,p , P i,g They are the peak electricity price, normal electricity price and valley electricity price under the time-of-use electricity price package mode; p s1 、p s2 、p s3 They are the electricity prices corresponding to the three electricity consumption gradients under the tiered electricity price package mode; a is the electricity price of electricity quantity classified by different voltage levels under the two-part electricity price package mode, and d is the capacity electricity price classified by different voltage levels under the two-part electricity price package mode;

[0047] P avg ≤P reg

[0048] Among them, P avg is the average price of each package type; P reg an average electricity price cap set by the regulator;

[0049] P min ≤P i,t ≤P max

[0050] Among them, P min , P max are the minimum price and maximum price agreed upon by the seller and the user. i,t is the basic electricity price of the time-sharing package for user i in period t under the time-sharing electricity price package mode.

[0051] According to one 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 the at least one processor, and the program instructions include instructions for executing the electricity retail package transaction optimization method for multiple load demands as described above.

[0052] According to one aspect of the present invention, a computer program product is provided, comprising a computer program / instruction, wherein the computer program / instruction implements the method as described above when executed by a processor.

[0053] According to one 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 electricity retail package transaction optimization method for multiple load demands as described above.

[0054] According to the technical solution of the present invention, a method for optimizing power retail package transactions for multiple load demands is provided, which divides power users into peak-facing users, peak-avoiding users, and stable users, and takes the maximum profit of retailers as the first goal and the minimum cost of electricity purchase by users as the second goal to construct a power retail package combination optimization model for multiple load demands. Then, for each type of user, the package price under each electricity price package mode, the optimal package selection result of the user, the cost of electricity purchase by the user, and the profit of the retailer can be determined through the power retail package combination optimization model. Based on this, personalized packages can be selected for users according to the needs of different types of users, thereby increasing the profit of retailers and reducing the cost of electricity purchase by users.

[0055] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to achieve the above and related purposes, the present invention describes certain illustrative aspects in conjunction with the following description and the accompanying drawings, which indicate various ways in which the principles disclosed in the present invention can be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. By reading the following detailed description in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present disclosure will become more apparent. Throughout the present disclosure, the same reference numerals generally refer to the same parts or elements.

[0057] Figure 1 A schematic diagram of a multi-load retail package transaction system 100 provided according to an embodiment of the present invention is shown;

[0058] Figure 2 A schematic diagram of a computing device 200 provided according to an embodiment of the present invention is shown;

[0059] Figure 3 A schematic flow chart of a method 300 for optimizing electric power retail package transactions for multiple load demands provided according to an embodiment of the present invention is shown;

[0060] Figure 4 to Figure 7 Schematic diagrams of electricity price trends of a fixed electricity price package mode, a time-of-use electricity price package mode, a tiered electricity price package mode, and a two-part electricity price package mode according to some embodiments of the present invention are respectively shown;

[0061] Figure 8 A schematic diagram showing a retail package electricity price according to some embodiments of the present invention;

[0062] Fig. 9A schematic diagram of power distribution in each time period according to some embodiments of the present invention is shown;

[0063] Fig.10 A schematic diagram showing the power composition of user packages in different time periods according to some embodiments of the present invention is shown. DETAILED DESCRIPTION

[0064] 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.

[0065] In view of the problem that existing electricity retail packages are insufficient in meeting the diverse needs of users and improving the profits of electricity sellers, an embodiment of the present invention proposes an electricity retail package transaction optimization method for multiple load demands, which divides electricity users into peak-facing users, peak-avoiding users and stable users, and constructs an electricity retail package combination optimization model for multiple load demands. It can select personalized packages for users according to the needs of different types of users, thereby improving the profits of retailers and reducing users' electricity purchasing costs.

[0066] The power retail package transaction optimization method for multiple load demands provided in an embodiment of the present invention can be used in a multiple load retail package transaction system.

[0067] Figure 1 A schematic diagram of a multi-load retail package transaction system 100 provided according to an embodiment of the present invention is shown.

[0068] like Figure 1 As shown, the multi-load retail package trading system 100 includes a supply side, an operator, and a demand side. The supply side includes power generators, power trading centers, and other power sellers. Operators include power sellers (hereinafter referred to as "retailers"), who can purchase electricity (thermal power and green power) from the supply side through the power wholesale market, and then provide power retail packages (for example, including package 1, package 2, package 3... package N) to various end users on the demand side through the power retail market, wherein the package types of power retail packages include thermal power packages and green power packages.

[0069] It should be noted that retailers participate in multi-level markets in the process of participating in the electricity market competition, purchase electricity through bilateral transactions, centralized bidding transactions, power transfer transactions, etc., and can design thermal power packages and green power packages suitable for different types of users. After purchasing electricity from the upper-level electricity wholesale market, retailers can design diversified electricity retail packages according to the load characteristics of electricity users to meet the electricity needs of various users with different electricity consumption behaviors. In the process of retailers participating in the purchase and sale of electricity, there is a leadership and follower cooperation relationship between retailers and multiple end users on the demand side. Based on this, the present invention establishes a multi-load retail package trading system 100 with retailers as the leader and multiple loads as subordinates and interacting.

[0070] In the electricity retail market transaction process, retailers need to purchase thermal power and green power from the upper-level electricity wholesale market, and then formulate price parameters for multiple electricity retail packages to attract more users, thereby improving market competitiveness and maximizing their own profits. At the same time, each user on the demand side can choose a suitable thermal power package or green power package according to their own electricity consumption characteristics, and can adjust their own electricity consumption methods and electricity demand according to the electricity price structure and electricity terms reflected in the selected package to minimize electricity expenses.

[0071] The main goal of the power grid is to provide high-quality and stable electricity to all types of electricity users. Electricity load refers to the total amount of electricity consumed by the power plant to the user end. The electricity load characteristics of different types of users in the power market are somewhat different. The coupling between electricity prices and loads makes the formulation of electricity price strategies closely related to the changes in load characteristics. Clarifying the user's electricity consumption behavior and electricity load characteristics is a prerequisite for retailers to formulate the optimal electricity price strategy. By effectively analyzing electricity consumption data, typical electricity consumption patterns of electricity users can be obtained.

[0072] The number of power grid users is huge. When analyzing the power consumption characteristics of users, the number of power users can usually be reduced, and the power users can be classified and processed. Representative typical users in each user group can be selected for power consumption characteristics analysis. Common user power consumption characteristics mainly include absolute quantities (such as maximum load, minimum load, average load, peak-to-valley difference in power consumption) and relative quantities (such as average load rate, minimum load rate, peak-to-valley difference rate, etc.). These characteristics can not only reflect the overall power consumption of users, but also describe the time characteristics of the user's power load. The present invention uses the daily load curve as a load characteristic indicator to describe the change of power load over time within a day.

[0073] The daily load curve can graphically display the changes in power load within a day and clearly show the fluctuations of load over time, so that power system dispatchers and operators can quickly grasp the dynamic changes of load. In addition, the daily load curve can provide important decision-making basis for power system operation. By analyzing the shape, peak and valley values ​​of the daily load curve, operators can reasonably arrange power production and dispatch to ensure the balance of power supply and demand.

[0074] In an embodiment of the present invention, power users can be divided into multiple user types, including peak-facing users, peak-avoiding users, and stable users, wherein each user type corresponds to a daily load curve. The daily load curve is used to describe the change of power load over time within a day. Specifically, the peak-facing users, the peak-avoiding users, and the stable users correspond to the peak-facing user daily load curve, the peak-avoiding user daily load curve, and the stable user daily load curve, respectively.

[0075] In an embodiment of the present invention, the computing device may be configured to execute the power retail package transaction optimization method 300 for multiple load demands. By executing the power retail package transaction optimization method 300 for multiple load demands, it is possible to select personalized packages for users according to the needs of different types of users, thereby increasing the revenue of retailers and reducing the cost of electricity purchase for users. The power retail package transaction optimization method 300 for multiple load demands of the present invention will be described below.

[0076] A computing device 200 provided by an embodiment of the present invention is introduced below.

[0077] Figure 2 FIG. 2 shows a schematic diagram of a computing device 200 provided according to an embodiment of the present invention. Figure 2 As shown, in a basic configuration, the computing device 200 includes at least one processing unit 202 and a system memory 204. According to one aspect, depending on the configuration and type of the computing device, the processing unit 202 can be implemented as a processor. The system memory 204 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 204 includes an operating system 205.

[0078] According to one aspect, operating system 205 is suitable for controlling the operation of computing device 200, 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 2The basic configuration is shown in FIG. 2 by those components within the dashed lines. According to one aspect, the computing device 200 has additional features or functionality. For example, according to one aspect, the computing device 200 includes additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or tapes. Such additional storage Figure 2 2 is illustrated by a removable storage device 209 and a non-removable storage device 210.

[0079] As stated above, according to one aspect, a program module 203 is stored in the system memory 204. According to one aspect, the program module 203 may include one or more application programs, and the present invention does not limit the type of application program, for example, the application program may include: email and contact application programs, word processing application programs, spreadsheet application programs, database application programs, slide show application programs, drawing or computer-aided application programs, web browser application programs, etc.

[0080] In an embodiment according to the present invention, the program module 203 includes a plurality of program instructions for executing the electricity retail package transaction optimization method 300 for multiple load demands of the present invention.

[0081] 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 2 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 the present invention can be operated via a dedicated logic integrated with other components of the computing device 200 on a single integrated circuit (chip). Other technologies capable of performing logical operations (such as AND, OR, and NOT) can also be used to practice embodiments of the present invention, 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.

[0082] According to one aspect, the computing device 200 may also have one or more input devices 212, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, etc. Output devices 214 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 200 may include one or more communication connections 216 that allow communication with other computing devices 218. Examples of suitable communication connections 216 include, but are not limited to: RF transmitters, receivers and / or transceiver circuits; Universal Serial Bus (USB), parallel and / or serial ports.

[0083] The term computer-readable medium as used in the present invention 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 204, removable storage device 209, and non-removable storage device 210 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, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other products that can be used to store information and can be accessed by computing device 200. According to one aspect, any such computer storage medium may be part of computing device 200. Computer storage media do not include carrier waves or other propagated data signals.

[0084] 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.

[0085] In an embodiment according to the present invention, the computing device 200 is configured to execute the power retail package transaction optimization method 300 for multiple load demands. The computing device 200 includes one or more processors and one or more readable storage media storing program instructions. When the program instructions are configured to be executed by one or more processors, the computing device executes the power retail package transaction optimization method 300 for multiple load demands in the embodiment of the present invention.

[0086] In some embodiments, the computing device 200 executing the electricity retail package transaction optimization method 300 for multiple load demands in the embodiment of the present invention may be a terminal or a server.

[0087] The following is a detailed description of the power retail package transaction optimization method 300 for multiple load demands in an embodiment of the present invention.

[0088] Figure 3 FIG. 3 is a flow chart showing a method 300 for optimizing a power retail package transaction for multiple load demands according to an embodiment of the present invention. Figure 3 As shown, the electricity retail package transaction optimization method 300 for multiple load demands includes the following steps 310-340.

[0089] Step 310: The computing device 200 may determine the user type of the user and the daily load curve corresponding to the user type. Here, the user type of the user may be a peak-facing user, a peak-avoiding user, or a stable user.

[0090] In an embodiment of the present invention, power users may be divided into multiple user types in advance, including peak users, peak avoidance users, and stable users. Each user type corresponds to a daily load curve. The daily load curve is used to describe the change of the user's power load over time in a day (i.e., the user's power consumption behavior characteristics).

[0091] In an embodiment of the present invention, retailers can purchase thermal power and green electricity in combination from the upper-level electricity wholesale market. Furthermore, retailers can provide electricity retail packages (e.g., Package 1, Package 2, Package 3...Package N) to various users on the demand side through the electricity retail market.

[0092] Among them, the package types of electricity retail packages can include thermal power packages and green power packages. Each package type has multiple electricity price package modes, including fixed electricity price package mode, time-of-use electricity price package mode, tiered electricity price package mode, and two-part electricity price package mode.

[0093] Step 320: The computing device 200 may construct an electricity retail package combination optimization model for multiple load demands, and the electricity retail package combination optimization model includes an upper model and a lower model. Specifically, the computing device 200 may construct an upper model with the first goal of maximizing the retailer's profit, and construct a lower model with the second goal of minimizing the user's electricity purchase cost.

[0094] Step 330: The computing device 200 can obtain the package transaction optimization result by solving the power retail package combination optimization model. Specifically, the computing device 200 can solve the power retail package combination optimization model based on the daily load curve corresponding to the user type of the user to obtain the package transaction optimization result. The package transaction optimization result includes the package price under each electricity price package mode, the user's optimal package selection result, the user's electricity purchase cost and the retailer's profit, wherein the user's optimal package selection result includes the optimal electricity price package mode selected by the user in each time period and the proportion of the optimal electricity price package mode.

[0095] In the embodiment of the present invention, the peak-facing users, the peak-avoiding users, and the stable users correspond to the peak-facing users' daily load curve, the peak-avoiding users' daily load curve, and the stable users' daily load curve, respectively. It should be noted that the peak-facing users' electricity load characteristics have a high degree of overlap with the peak period of electricity demand, and have high requirements for the stability of power supply. Since the production activities of the peak-facing users are concentrated, the equipment operation load is large, and the peak period electricity demand is strong, therefore, in the distribution of the peak-facing users' daily load curve, the load curve shows the characteristics of rapid climbing during the peak period of a day, and its electricity consumption behavior is mainly affected by demand and working cycle, and the load fluctuation is relatively small. In addition, due to the different equipment operation cycles, special production needs, etc., the peak-facing users are prone to multiple peak periods in a day. The life pattern of the peak-facing users has produced their special electricity consumption pattern, which puts forward high requirements for the quality and quantity of electricity supply. The power supplier can balance the supply and demand of electricity during peak periods through reasonable grid scheduling, demand-side management, energy storage facilities and other means, and encourage users to use electricity in staggered peaks and use distributed energy to reduce the pressure on the power supply of the grid.

[0096] Peak-avoiding users mainly refer to users who can flexibly adjust their own electricity consumption behavior during peak electricity consumption, effectively avoid peak periods of electricity demand by reducing or terminating some interruptible loads, help the power grid relieve power supply pressure, reduce power grid operation risks, and ensure safe and stable operation of the power system. Peak-avoiding users actively reduce their electricity load during peak periods, and can reduce their electricity load by reducing the use and duration of electrical equipment; during off-peak periods when electricity supply is stable and relatively sufficient, they increase the use of electrical equipment and increase the electricity load to meet their own electricity needs at a lower electricity cost. Since peak-avoiding users can actively adjust their electricity consumption strategies based on electricity prices, the daily load curve of peak-avoiding users tends to show greater volatility.

[0097] Stable users mainly refer to user groups with small changes in electricity load, no significant peak and valley periods, and relatively stable electricity demand. Stable users have relatively regular electricity consumption behaviors, and their daily electricity consumption is less affected by objective factors such as weather and time, and the load impact on the power grid is relatively small. In special circumstances such as extreme weather and equipment failure, electricity demand will fluctuate greatly. Stable users usually include the following categories: some service institutions that provide all-weather services; some industrial users that need to operate continuously; public infrastructure such as traffic lights; telecommunication facilities such as base stations and exchange centers that operate 24 hours a day, and some experimental equipment that needs to operate continuously in scientific research experiments. These activities require electricity support, but because their time distribution is relatively uniform, the electricity demand will also show a certain degree of stability. For this type of stable user, power suppliers can allocate power resources more flexibly and improve the operating efficiency of the power grid.

[0098] In some embodiments, retailers can formulate thermal power packages and green power packages by comprehensively considering multiple factors such as the daily load curve characteristics of the above-mentioned types of users, the ability to continuously adjust strategies, and the target weight of renewable energy power consumption responsibility, and can determine the package price of each package according to the following formula (1). It should be noted that all market-based electricity within the contract period of the power retail package can be settled according to the selected price and tariff standards. Among them, the standardized green power package needs to add a green power premium price on the basis of the existing package basic electricity price formation mechanism.

[0099]

[0100] Where P is the package price of user i; P i is the basic electricity price of user i’s package; is the green electricity premium price of user i, which needs to be determined based on the results of green electricity transactions and the matchmaking between the power sales company and the user; α is the green electricity transaction indicator variable.

[0101] It should be noted that when users select a package, they can select a package type and the proportion of the package type in each electricity price package mode in each time period. When users select a package, the amount of green electricity consumed needs to meet a certain green electricity consumption weight. According to the green electricity tracking and certification theory, the proportion of green electricity purchased by retailers from the upper power grid should be consistent with the proportion of green electricity in the power grid during the period. When retailers supply electricity to lower-level end users, they can directly apply the green electricity proportion to the electricity retail packages they provide, so as to sell users a corresponding proportion of green electricity packages for them to fulfill their green electricity consumption responsibilities. It should be understood that the proportion of green electricity packages in all electricity retail packages is consistent with the proportion of green electricity when retailers purchase electricity. Therefore, the user's green electricity trading volume can be determined based on the green electricity proportion curve of the power grid in each period, as shown in the following formulas (2) and (3).

[0102]

[0103] In the formula, β is the proportion of green electricity consumption; Q i,t is the total transaction power of user i in period t; is the thermal power transaction volume of user i in period t; is the green electricity trading volume of user i in period t.

[0104] In the embodiment of the present invention, each package type of electricity retail package (green power package, thermal power package) has multiple electricity price package modes. Specifically, the multiple electricity price package modes include a fixed electricity price package mode, a time-of-use electricity price package mode, a tiered electricity price package mode, and a two-part electricity price package mode. Among them, each electricity price package mode corresponds to an electricity price trend.

[0105] Figure 4 to Figure 7 Schematic diagrams of electricity price trends of a fixed electricity price package mode, a time-of-use electricity price package mode, a tiered electricity price package mode, and a two-part electricity price package mode according to some embodiments of the present invention are respectively shown.

[0106] It should be noted that if Figure 4 As shown, the fixed electricity price package model has a fixed electricity price at any time period (a fixed electricity price is agreed upon for 24 time periods), and the agreed electricity price does not change with the transaction price of the retailer in the upper-level wholesale electricity market.

[0107] In a specific embodiment, the net profit of the fixed electricity price package model can be determined by the following formula:

[0108]

[0109] Where P is the package price of user i (under the fixed electricity price package mode); It is the basic electricity price of the fixed package under the fixed electricity price package model; is the net profit obtained from user i under the fixed electricity price package model; is the electricity consumption of user i in period t under the fixed electricity price package mode, is the thermal power transaction volume of user i in period t, is the green electricity trading volume of user i in period t; C i,α is the transaction price per kilowatt-hour in the wholesale electricity market; α=0 indicates thermal power package transaction, and α=1 indicates green power package transaction.

[0110] like Figure 5As shown in the figure, the time-of-use electricity price package model is a relatively mature pricing strategy at present. It mainly considers the user's electricity consumption characteristics, attracting users to reduce electricity consumption during peak periods and increase electricity consumption during valley periods. In order to more effectively manage and dispatch power resources, under the time-of-use electricity price package model, the user's electricity consumption period can be divided into three different periods: valley period, peak period, and normal period.

[0111] In a specific embodiment, the net profit of the time-of-use electricity price package model can be determined by the following formula:

[0112]

[0113] Where P i,t P is the basic electricity price of the time-sharing package for user i in time period t under the time-sharing electricity price package mode; i,f , P i,p , P i,g They are the peak-period electricity price, normal-period electricity price and valley-period electricity price under the time-of-use electricity price package model.

[0114] is the net profit obtained by the retailer from user i under the time-of-use electricity price package model; is the electricity consumption of user i in period t under the time-of-use electricity price package mode.

[0115] like Figure 6 As shown, the tiered electricity price package model is a common package for industrial and commercial users. Multiple electricity consumption gradients are set according to the user's electricity consumption level. The electricity price increases with the increase in electricity consumption. Each electricity consumption gradient corresponds to an electricity price, and different electricity consumption gradients correspond to different electricity prices.

[0116] In a specific embodiment, the net profit of the tiered electricity price package model can be determined by the following formula:

[0117]

[0118]

[0119] In the formula, is the total electricity consumption of user i in the settlement period under the tiered electricity price package mode; is the electricity consumption of user i in period t under the tiered electricity price package mode; is the basic electricity price of the tiered electricity price package for user i in period t; p s1 、p s2 、p s3 They are the electricity prices corresponding to the three electricity consumption gradients under the tiered electricity price package mode; Used to represent the classification criteria for user i between different electricity consumption gradients under the tiered electricity price package model.

[0120] is the net profit obtained by (retailer) from user i under the tiered electricity price package model.

[0121] like Figure 7 As shown in the figure, the two-part electricity price package model is a system that combines the capacity electricity price corresponding to the capacity and the electricity price corresponding to the electricity consumption to determine the electricity price. The electricity price cost of the two-part electricity price package model includes the following three cost elements: a fixed fee proportional to the capacity, a variable fee proportional to the electricity consumption, and a user fee proportional to the number of users. Therefore, the method of jointly determining the electricity price by using a fixed capacity electricity price proportional to the capacity and a monthly variable electricity price proportional to the electricity consumption is the most reasonable electricity price system that can truly reflect the cost structure of the two-part electricity price package model.

[0122] The two-part electricity price package model divides the electricity price into two parts: capacity price and quantity price. Among them, the capacity price can be determined by the retailer and the user in accordance with the transformer capacity or maximum demand of the industrial enterprise. The limit is determined by the contract signed between the retailer and the user, and is charged monthly and is not subject to the actual amount of electricity consumption. The quantity price is determined according to the actual electricity consumption of the user.

[0123] In a specific embodiment, the net profit of the two-part electricity price retail package model can be determined by the following formula:

[0124]

[0125] Where, d is the capacity electricity price classified by different voltage levels under the two-part electricity price package mode (yuan / kVA·month); M is the user contract capacity under the two-part electricity price package mode (kVA); is the electricity consumption of user i in period t under the two-part electricity price package mode; a i,t is the electricity price (yuan / kwh) of user i in time period t according to different voltage levels under the two-part electricity price package mode; i tp is the capacity electricity price of user i under the two-part electricity price package mode (yuan / day); is the green electricity premium price of user i.

[0126] R i tp is the net profit obtained by (the retailer) from user i under the two-part electricity price retail package model.

[0127] It should be noted that in actual situations, it is difficult for retailers and users to grasp all the information about each other, and it is also difficult for users to respond quickly to the information released by retailers and make the best decision. The process of users choosing packages is an evolutionary game problem. After the retailer releases the initial package, various users can freely choose packages from the initial multiple packages and formulate electricity consumption optimization plans based on the selected packages. Furthermore, the utility calculation after electricity consumption optimization can be performed based on the packages selected by various users to modify and adjust the user's package selection strategy, obtain the optimal package selection results for various users, and return the package selection and optimized electricity consumption to the retailer. The retailer can evaluate the benefits of each package based on the user's package selection and response results, and optimize the price of each package until the retailer's benefits reach the maximum.

[0128] Based on this, in an embodiment of the present invention, the computing device can construct an electricity retail package combination optimization model including an upper-level model and a lower-level model. Specifically, the upper-level model can be constructed with the maximization of the retailer's profit as the first goal, and the lower-level model can be constructed with the minimization of the user's electricity purchase cost as the second goal.

[0129] It should be understood that the retailer's profit is the difference between the electricity energy profit obtained based on the electricity retail package selected by each user and the cost of purchasing electricity from the upper-level wholesale electricity market.

[0130] In an embodiment of the present invention, the retailer's profit is related to the package type selected by each user, the proportion of the selected package type in each electricity price package mode, and the net profit of each electricity price package mode.

[0131] Based on this, when constructing the upper model with the maximum profit of the retailer as the first goal, the first objective function (i.e., the objective function of maximizing the retailer's profit) can be constructed based on the proportion of each user's choice of each electricity price package mode (i.e., the proportion of the fixed electricity price package mode, the time-of-use electricity price package mode, the ladder electricity price package mode, and the two-part electricity price package mode), and the net profit of each electricity price package mode (the net profit of the fixed electricity price package mode, the net profit of the time-of-use electricity price package mode, the net profit of the ladder electricity price package mode, and the net profit of the two-part electricity price package mode). The upper model is constructed based on the first objective function. In other words, the upper model includes the first objective function. Among them, the net profit of the fixed electricity price package mode, the net profit of the time-of-use electricity price package mode, the net profit of the ladder electricity price package mode, and the net profit of the two-part electricity price package mode can be determined by the above formulas (1)-(13), and the details can be referred to the above embodiments.

[0132] The first objective function (the objective function for maximizing retailer revenue) is as follows:

[0133]

[0134] In the formula, α=0 represents thermal power package transaction, and α=1 represents green power package transaction.

[0135] The proportions of user i who chooses fixed electricity price package mode, time-of-use electricity price package mode, tiered electricity price package mode, and two-part electricity price package mode are shown respectively. is the net profit obtained by the retailer from user i under the fixed electricity price package model; is the net profit obtained by the retailer from user i under the time-of-use electricity price package model; is the net profit obtained by the retailer from user i under the tiered electricity price package model; is the net profit obtained by (the retailer) from user i under the two-part electricity price retail package model.

[0136] In addition, since retailers are located between the upper-level electricity wholesale market and users, in order to provide users with multiple electricity retail packages, the retailers' electricity supply must be able to meet the users' load demands. At the same time, in order to attract users to trade with retailers, the prices of their electricity retail packages will also be subject to restrictions by the upper-level electricity wholesale market and between packages.

[0137] Based on this, in some embodiments, the upper model also includes a first constraint condition. In other words, the upper model can be constructed based on the first objective function and the first constraint condition. The first constraint condition includes an electricity quantity constraint condition and an electricity price constraint condition.

[0138] It should be noted that when retailers set prices for each package, in order to ensure that the expected value of risk-return is positive, they can set a "package usage line" for each type of electricity retail package, that is, the user's actual electricity consumption must be within a reasonable range. The power consumption constraints based on this setting are as follows:

[0139]

[0140] in, They are respectively the lower and upper limits of the electricity consumption of the package under the contract signed between the retailer and the user. is the electricity consumption of user i in period t under the fixed electricity price package mode; is the electricity consumption of user i in period t under the time-of-use electricity price package mode; is the electricity consumption of user i in period t under the tiered electricity price package mode; is the thermal power transaction volume of user i in period t; is the green electricity trading volume of user i in period t.

[0141] Since different package prices reflect different price guidance signals, have different market competitiveness, and reflect the diversity of user needs, the package price must be set to meet the package's own characteristics. Among them, the peak-time electricity price, normal-time electricity price, and valley-time electricity price under the optimized time-of-use electricity price package mode should be ladder-shaped, and the peak-valley electricity price value should meet a certain ratio. Based on this, the electricity price constraints include:

[0142]

[0143] Among them, p 1 、p 2 They are the electricity price ratio during peak-to-normal periods and the electricity price ratio during normal-to-valley periods respectively. is the green electricity premium price of user i; is the fixed package basic electricity price under the fixed electricity price package mode; P i,f , P i,p , P i,g They are the peak electricity price, normal electricity price and valley electricity price under the time-of-use electricity price package mode; p s1 、p s2 、p s3 They are the electricity prices corresponding to the three electricity consumption gradients under the tiered electricity price package mode; a is the electricity price of electricity quantity classified by different voltage levels under the two-part electricity price package mode (yuan / kwh), and d is the capacity electricity price classified by different voltage levels under the two-part electricity price package mode (yuan / kVA·month).

[0144] The pricing of each package by the electricity seller will be subject to the price limit constraints of the government regulatory department. The average electricity price of the thermal power package and the green power package must be lower than the prescribed average electricity price limit to protect the rights and interests of consumers. Based on this, the electricity price constraints also include:

[0145] P avg ≤P reg (17)

[0146] Among them, P avg is the average price of each package type (under various electricity price package modes); P reg The average electricity price cap is set by the regulatory authorities.

[0147] In addition, when determining the price of each package, the minimum price and the maximum price should be agreed upon with the user in order to provide the user with a relatively rational psychological expectation. When the price is lower than or higher than the price limit, it will be handled in accordance with the price limit. Based on this, the electricity price constraints also include:

[0148] P min ≤P i,t ≤P max (18)

[0149] Among them, P min , P max are the minimum price and maximum price agreed upon by the seller and the user. i,t is the basic electricity price of the time-sharing package for user i in period t under the time-sharing electricity price package mode.

[0150] In an embodiment of the present invention, the user's electricity purchase cost is related to the package price (involving the package basic electricity price and the green electricity premium price) under each electricity price package mode selected by the user, the electricity consumption under each electricity price package mode selected, and the proportion of each electricity price package mode selected. Among them, for thermal power packages, the package price under each electricity price package mode includes the package basic electricity price under the electricity price package mode; for green electricity packages, the package price under each electricity price package mode includes the package basic electricity price and the green electricity premium price under the electricity price package mode.

[0151] Based on this, when constructing the lower-level model with the second goal of minimizing the user's electricity purchase cost, the second objective function can be constructed based on the package price under each electricity price package mode selected by the user, the electricity consumption under each electricity price package mode selected, and the proportion of each electricity price package mode, and then the lower-level model can be constructed based on the second objective function.

[0152] The second objective function is as follows:

[0153]

[0154] Among them, α=0 indicates thermal power package transaction, and α=1 indicates green power package transaction; They are the electricity consumption of user i in period t under the fixed electricity price package mode, time-of-use electricity price package mode, tiered electricity price package mode, and two-part electricity price package mode; The proportion of user i who chooses fixed electricity price package mode, time-of-use electricity price package mode, tiered electricity price package mode, and two-part electricity price package mode; is the green electricity premium price of user i; is the fixed package basic electricity price under the fixed electricity price package mode, P i,t is the basic electricity price of the time-sharing package for user i in period t under the time-sharing electricity price package mode, is the basic electricity price of the tiered electricity price package for user i in period t, a i,t is the electricity price of user i in time period t classified by different voltage levels under the two-part electricity price package mode, P i tp is the capacity electricity price of user i under the two-part electricity price package mode.

[0155] In some embodiments, the lower model also includes a second constraint. In other words, the lower model can be constructed based on the second objective function and the second constraint. Among them, the second constraint includes a user demand response constraint, a user package composition constraint, a package selection constraint, and a peak-to-valley difference constraint. These constraints are described below.

[0156] 1) Demand response constraints (including equations 22 and 23 below)

[0157] After selecting the electricity price package mode, the user can adjust the electricity usage habits according to the expected electricity price value of the electricity price package mode in each period to achieve the purpose of reducing electricity expenses. The present invention can construct a user demand response model based on the price elasticity coefficient. The load demand of the i-th type of user after selecting the electricity price package mode can be expressed as:

[0158]

[0159] In the formula, is the load of user i in period t; ΔL i,t is the load demand of user i in period t after selecting the electricity price package mode; P i,0 is the initial package price of user i in period t (i.e., the grid company's catalog price); ε i,tt is the self-elasticity coefficient of type i load, which is used to indicate the user's willingness to increase or decrease electricity consumption; ε i,τt is the cross elasticity coefficient of type i load, which is used to indicate the user's willingness to change the electricity consumption time.

[0160] Considering that in actual situations, user loads should be subject to upper and lower limits, such as the non-adjustable loads of residential users, the following formula can be used to correct the load after demand response:

[0161]

[0162] In the formula, are the upper and lower load limits of user i in period t respectively.

[0163] 2) Constraints on user package composition

[0164] The total electricity demand of each package is accumulated to form the total electricity demand of the user. To ensure that the load demand of the user after selecting the electricity price package mode is met, the user's load demand is composed of the thermal power package electricity and green power package electricity required by the user. The user package composition constraint condition is shown in the following formula (24).

[0165]

[0166] In the formula, is the corrected load of user i in period t.

[0167] 3) Package selection constraints

[0168] When users choose a package, they need to make a single electricity price package mode selection in each cycle, which means that in a given period (t period), users can only choose one electricity price package mode, whether it is a thermal power package transaction or a green power package transaction. In this way, it can ensure that users can plan and control their electricity consumption according to the specific terms of the package, which helps retailers to more accurately predict and manage electricity demand, thereby optimizing the allocation and operation efficiency of electricity resources. The package selection constraints are shown in the following formula (25).

[0169]

[0170] 4) Peak-to-valley difference constraints

[0171] The electricity price packages provided by retailers must not only ensure their own revenue, but also meet the target requirements of peak load reduction and valley filling to maintain grid stability. The following formula is the peak-valley difference constraint condition, which means that the peak-valley difference of electricity consumption after users choose the package must be less than the peak-valley difference before choosing the package.

[0172]

[0173] In some embodiments, in step 330, the specific method of solving the power retail package combination optimization model based on the daily load curve corresponding to the user type of the user is as follows: First, the basic parameters of the model can be obtained, and the basic parameters of the model include the transaction price of the wholesale power market (including the thermal power transaction price of the wholesale power market and the green power transaction price of the wholesale power market), the initial load demand of the user (including the thermal power package quantity and the green power package quantity initially selected by the user), the user's demand response elasticity coefficient (including the self-elasticity coefficient and the cross-elasticity coefficient), the green power consumption ratio, the upper load limit, the lower load limit, etc. Then, the power retail package combination optimization model can be solved based on the basic parameters of the model and the daily load curve corresponding to the user type of the user to obtain the global optimal solution, and the package transaction optimization result can be obtained based on the global optimal solution.

[0174] Furthermore, the process of solving the power retail package combination optimization model based on the basic parameters of the model and the daily load curve corresponding to the user type is as follows: First, the user's initial package price can be determined based on the basic parameters of the model and the daily load curve corresponding to the user type. Subsequently, the lower-level model can be called based on the user's initial package price to select a package (electricity price package mode and the proportion of the electricity price package mode) to obtain a package selection result. Furthermore, the upper-level model can be called based on the package selection result to optimize the initial package price to obtain the package price after the package is selected, and the user's electricity consumption behavior can be adjusted based on the selected package type, and the user's load demand can be updated to obtain the user's load demand after the package is selected. Afterwards, the above steps can be continued to be executed cyclically based on the package price after the package is selected and the load demand after the package is selected until the global optimal solution of the power retail package combination optimization model is obtained, and the package transaction optimization result is obtained based on the global optimal solution.

[0175] It should be noted that when the global optimal state is achieved, retailers can formulate the optimal retail package electricity price results (package prices of thermal power packages and green power packages under each electricity price package model).

[0176] Figure 8 Schematic diagram of retail package electricity prices according to some embodiments of the present invention is shown. Figure 8 As shown in the figure, under the scenario of maximizing the retailer's profit and minimizing the user's electricity purchase cost, the retailer optimized the retail package prices of thermal power and green power. Overall, the changes of the two are basically the same, and the price of the green power package is higher than that of the thermal power package, reflecting the environmental value of green power. The package prices of green power packages and thermal power packages under the two-part electricity price package model are similar in each period, which shows that when setting prices, retailers settle more green power premiums in the capacity cost part, and due to the volatility of time-of-use electricity prices and the progressiveness of tiered electricity prices, more premiums are allocated to the dispatch cycle. Under the premise of stipulating the weight of the user's green power consumption ratio, retailers can appropriately reduce the capacity electricity price and the electricity price, and encourage users to choose the two-part electricity price package model that reflects the electricity cost structure, which is helpful to promote the consumption of green energy and optimize the allocation of power resources. At the same time, it provides users with a more economical package selection strategy based on their respective electricity usage habits and needs.

[0177] Fig. 9 FIG. 1 shows a schematic diagram of power distribution in each time period according to some embodiments of the present invention. Fig. 9As shown in the figure, from the perspective of retailers, in order to meet the load demand of each user, the total amount of electricity purchased by retailers from the upper power grid needs to be reduced first and then increased within a cycle, and then reduced again and then increased again, reaching a peak at 19:00. This is because the load of peak-facing users is high during this period, while the load of peak-avoiding users is gradually rising from the bottom. The amount of electricity purchased by retailers is mainly affected by peak-facing users, but in the situation where multiple loads participate in the retail market together, it is still necessary to pay close attention to the electricity consumption dynamics of other users. From the perspective of the choice of electricity packages, the proportion of choosing the two-part electricity price package model is the highest in each period, which requires retailers to properly adjust the capacity price and electricity price of the two-part electricity price package model when market prices fluctuate, and ensure their own profits while maintaining the attractiveness of the package. The fixed electricity price package model and the tiered electricity price package model are mainly used to meet users' basic thermal power needs, while the green electricity consumption part is presented in the green electricity two-part electricity price package model. The chart data shows that the higher total amount of green electricity packages in the off-peak period coincides with the peak period of green electricity. This shows that the current pricing strategy of the two-part electricity price package model of retailers can effectively promote green electricity consumption, which fully proves the effectiveness of the two-part electricity price package model.

[0178] Fig.10A schematic diagram of the composition of the package electricity consumption of users in each period according to some embodiments of the present invention is shown. Under the current package price strategy, all types of users increase their green electricity consumption during their own peak electricity consumption periods. In the process of users fulfilling their green electricity consumption responsibilities, they also bring profits to electricity sellers. For thermal power package transactions, peak-facing users choose the time-of-use electricity price package mode from the four electricity price package modes, because the time-of-use electricity price package mode can reflect their electricity consumption behavior and help them better manage electricity costs. Although both the fixed electricity price package mode and the ladder electricity price package mode provide stable electricity prices, they cannot fully reflect the electricity consumption behavior of peak-facing users, and during non-peak hours, users need to pay a higher electricity price that is disproportionate to the electricity consumption, which may lead to increased costs. Peak-avoiding users choose the time-of-use electricity price package mode. This type of user has a peak electricity consumption at night. Thanks to the lower electricity price at night, the electricity bill expenditure is significantly reduced, and the overall electricity cost is effectively controlled. Similar to peak-facing users, although the ladder electricity price package mode provides a large amount of electricity and a relatively low electricity price, it will result in the electricity in the package cannot be fully utilized. The electricity load of stable users is relatively stable during the day, without significant peak or valley periods. If the time-of-use electricity price package model is adopted, a large amount of electricity purchase costs will be generated during peak and flat periods. The fixed electricity price package model will have higher costs under high load scenarios and lack preferential incentive policies. Therefore, they finally chose the tiered electricity price package model that can provide stable electricity prices, meet basic electricity needs and bring certain flexibility. For green electricity package transactions, the two-part electricity price package model provides price stability. Users can predict part of their electricity bill expenditure (capacity electricity price), while the other part (electricity price) is calculated based on actual electricity consumption, which helps users to plan their budgets. Given that the current wholesale green electricity costs of retailers are generally high, the two-part electricity price package model under the green electricity package is more effective in motivating users to optimize their electricity usage habits, while weakening the impact of electricity price differences in different periods on users' electricity bills, and maintaining the continuity of green electricity sales and consumption. Therefore, the three types of users all chose the two-part electricity price package model under the green electricity package based on the demand for green electricity supply reliability, cost-effectiveness pursuit, price stability and budget predictability pursuit.

[0179] According to the power retail package transaction optimization method 300 for multiple load demands of the present invention, power users are divided into peak users, peak avoidance users and stable users, and a power retail package combination optimization model for multiple load demands is constructed with the maximum profit of retailers as the first goal and the minimum power purchase cost of users as the second goal. Then, for each type of user, the package price under each electricity price package mode, the optimal package selection result of the user, the power purchase cost of the user and the profit of the retailer can be determined by the power retail package combination optimization model. Based on this, personalized packages can be selected for users according to the needs of different types of users, thereby increasing the profit of retailers and reducing the power purchase cost of users.

[0180] By way of example and not limitation, readable media include readable storage media and communication media. Readable storage media stores information such as computer readable instructions, data structures, program modules or other data. Communication media generally embody computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and include any information transfer medium. Any combination of the above is also included within the scope of readable media.

[0181] In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the examples of the present invention. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to implement the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the best mode of the present invention.

[0182] 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.

[0183] Similarly, 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.

[0184] Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in the devices described in the embodiments, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or may be divided into a plurality of submodules.

[0185] 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, order or in any other manner.

Claims

1. A method for optimizing power retail package transactions for multiple load demands, wherein the power retail package types include green power packages and thermal power packages, each package type has multiple power price package modes, and the multiple power price package modes include a fixed power price package mode, a time-of-use power price package mode, a tiered power price package mode, and a two-part power price package mode, wherein the method comprises: Determine a user type of the user and a daily load curve corresponding to the user type, wherein the user type is a peak-facing user, a peak-avoiding user, or a stable user; Constructing an electricity retail package combination optimization model including an upper-level model and a lower-level model, including: constructing an upper-level model with the maximum profit of retailers as the first goal, and constructing a lower-level model with the minimum cost of electricity purchase for users as the second goal; Based on the daily load curve corresponding to the user type, the electricity retail package combination optimization model is solved to obtain a package transaction optimization result, which includes the package price under each electricity price package mode, the user's optimal package selection result, the user's electricity purchase cost and the retailer's profit, wherein the user's optimal package selection result includes the optimal electricity price package mode selected by the user in each time period and the proportion of the optimal electricity price package mode.

2. The method of claim 1, wherein: The upper-level model is constructed with the primary goal of maximizing retailer profits, including: A first objective function is constructed based on the proportion of each user selecting each electricity price package mode and the net profit of each electricity price package mode; An upper-layer model is constructed based on the first objective function.

3. The method of claim 2, wherein: Building an upper model based on the first objective function includes: An upper-level model is constructed based on the first objective function and the first constraint condition, wherein the first constraint condition includes a power quantity constraint condition and an electricity price constraint condition.

4. The method according to any one of claims 1 to 3, wherein: The lower-level model is constructed with the second goal of minimizing the user's electricity purchase cost, including: The second objective function is constructed based on the package price under each electricity price package mode selected by the user, the electricity consumption under each electricity price package mode, and the proportion of each electricity price package mode; wherein, for the thermal power package, the package price under each electricity price package mode includes the package basic electricity price under the electricity price package mode; for the green power package, the package price under each electricity price package mode includes the package basic electricity price under the electricity price package mode and the green power premium price; A lower layer model is constructed based on the second objective function.

5. The method of claim 4, wherein: Constructing a lower-layer model based on the second objective function includes: A lower-layer model is constructed based on the second objective function and the second constraint condition, wherein the second constraint condition includes a user demand response constraint condition, a user package composition constraint condition, a package selection constraint condition, and a peak-valley difference constraint condition.

6. The method according to any one of claims 1 to 5, wherein: Based on the daily load curve corresponding to the user type, the power retail package combination optimization model is solved to obtain the package transaction optimization result, including: Obtaining basic model parameters, the basic model parameters include wholesale power market transaction price, user's initial load demand, user's demand response elasticity coefficient, green power consumption ratio, load upper limit and load lower limit, wherein the wholesale power market transaction price includes wholesale power market thermal power transaction price and wholesale power market green power transaction price, the user's initial load demand includes the user's initial demand for thermal power package electricity and green power package electricity, the demand response elasticity coefficient includes a self-elasticity coefficient and a cross-elasticity coefficient; Based on the basic parameters of the model and the daily load curve corresponding to the user type, the electricity retail package combination optimization model is solved to obtain a global optimal solution, and the package transaction optimization result is obtained based on the global optimal solution.

7. The method according to claim 2 or 3, wherein: The upper-level model is constructed with the primary goal of maximizing retailer profits, and also includes: The net profit of the fixed electricity price package model is determined by the following formula: in, It is the basic electricity price of the fixed package under the fixed electricity price package model; is the net profit obtained from user i under the fixed electricity price package model; is the electricity consumption of user i in period t under the fixed electricity price package mode, is the thermal power transaction volume of user i in period t, is the green electricity trading volume of user i in period t; C i,α is the transaction price of electricity in the wholesale market; α=0 indicates thermal power package transaction, α=1 indicates green power package transaction; The net profit of the time-of-use electricity price package model is determined by the following formula: Among them, P i,t P is the basic electricity price of the time-sharing package for user i in time period t under the time-sharing electricity price package mode; i,f , P i,p , P i,g They are the peak-period electricity price, normal-period electricity price, and valley-period electricity price under the time-of-use electricity price package model; is the net profit obtained from user i under the time-of-use electricity price package model; is the electricity consumption of user i in period t under the time-of-use electricity price package mode; The net profit of the tiered electricity price package model is determined by the following formula: in, is the total electricity consumption of user i in the settlement period under the tiered electricity price package mode; is the electricity consumption of user i in period t under the tiered electricity price package mode; is the basic electricity price of the tiered electricity price package for user i in period t; p s1 、p s2 、p s3 They are the electricity prices corresponding to the three electricity consumption gradients under the tiered electricity price package mode; Used to indicate the classification criteria for user i between different electricity consumption gradients under the tiered electricity price package mode; is the net profit obtained from user i under the tiered electricity price package model; The net profit of the two-part electricity price retail package model is determined by the following formula: Among them, d is the capacity electricity price classified by different voltage levels under the two-part electricity price package mode; M is the user's contract capacity under the two-part electricity price package mode; is the electricity consumption of user i in period t under the two-part electricity price package mode; a i,t is the electricity price of user i classified by different voltage levels in time period t under the two-part electricity price package mode; is the capacity electricity price of user i in the two-part electricity price package mode; is the green electricity premium price of user i; is the net profit obtained from user i under the two-part electricity price retail package model.

8. The method according to claim 2 or 3, wherein: The first objective function is as follows: Among them, α=0 indicates thermal power package transaction, and α=1 indicates green power package transaction; The proportion of user i who chooses fixed electricity price package mode, time-of-use electricity price package mode, tiered electricity price package mode, and two-part electricity price package mode; is the net profit obtained from user i under the fixed electricity price package model; is the net profit obtained from user i under the time-of-use electricity price package model; is the net profit obtained from user i under the tiered electricity price package model; is the net profit obtained from user i under the two-part electricity price retail package model.

9. The method according to claim 4 or 5, wherein: The second objective function is as follows: Among them, α=0 indicates thermal power package transaction, and α=1 indicates green power package transaction; They are the electricity consumption of user i in period t under the fixed electricity price package mode, time-of-use electricity price package mode, tiered electricity price package mode, and two-part electricity price package mode; The proportion of user i who chooses fixed electricity price package mode, time-of-use electricity price package mode, tiered electricity price package mode, and two-part electricity price package mode; is the green electricity premium price of user i; is the fixed package basic electricity price under the fixed electricity price package mode, P i,t is the basic electricity price of the time-sharing package for user i in period t under the time-sharing electricity price package mode, is the basic electricity price of the tiered electricity price package for user i in period t, a i,t is the electricity price of user i in time period t classified by different voltage levels under the two-part electricity price package mode, is the capacity electricity price of user i under the two-part electricity price package mode.

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

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