Shared energy storage power retail package optimization method based on master-slave game

Abstract

The invention discloses a shared energy storage power retail package optimization method based on a master-slave game. The method comprises the following steps: constructing an upper layer shared energy storage main body decision model by taking the maximum total profit obtained by a shared energy storage main body through selling multiple packages of thermal power and green power as an upper layer target; constructing a lower-layer user decision model by taking the minimum total electricity purchasing cost of the user using the shared energy storage as a lower-layer target; obtaining a double-layer master-slave game model based on the upper-layer shared energy storage subject decision model and the lower-layer user decision model; solving the double-layer master-slave game model by using a genetic algorithm and a particle swarm algorithm in a cooperative manner to obtain an optimal package pricing strategy and an optimal package electricity purchasing strategy, the optimal package pricing strategy being used for indicating the electricity price of each package in each time period, and the optimal package electricity purchasing strategy being used for indicating the electricity price of each package in each time period. The optimal package electricity purchasing strategy is used for indicating the package selected by the user and the thermal electricity quantity and the green electricity quantity purchased under the selected package in each time period. According to the invention, the profit maximization of the shared energy storage main body can be realized, and the power utilization cost of the user is reduced.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a method for optimizing a shared energy storage power retail package based on a master-slave game. Background Art

[0002] As an important model for the operation of the new energy storage market, shared energy storage plays an important role in promoting the potential of the new energy storage market, improving the capacity for new energy consumption, and ensuring the safe and stable operation of the new power system through resource aggregation and optimized allocation. However, the current shared energy storage model is still in its early stages of development and lacks a collection of retail packages for multi-scenario services on the user side. As a result, its commercial application faces key problems such as unclear profit models and insufficient user participation, which are the bottlenecks of the current development of shared energy storage. Specifically, the current commercialization process of shared energy storage still faces three core challenges: First, the business model is not sustainable enough. The capacity leasing and electricity spot market revenue distribution mechanisms have not yet formed an effective synergy, resulting in the project investment payback period being extended to 8-10 years. Second, the demand-side response mechanism is missing. The user-side package design lacks dynamic pricing and personalized service capabilities, making it difficult to match the volatility characteristics of renewable energy power generation. Third, the multi-party interest coordination mechanism has not yet been established. The revenue distribution contradictions between operators, new energy sites and users are prominent, which restricts the large-scale application process.

[0003] With the development of electricity market reform, developing attractive retail packages has become a key research focus to increase profits for electricity retailers. Furthermore, as electricity market reform deepens, the types and number of participants in the power system continue to increase, leading to uncertainty and information asymmetry in power market transactions. Game theory, as a fair decision-making method that balances the interests of all parties, has been widely used in the power industry in recent years.

[0004] Based on this, a shared energy storage power retail package optimization method based on master-slave game is needed. Summary of the Invention

[0005] To this end, the present invention provides a shared energy storage power retail package optimization method based on master-slave game to solve or at least alleviate the above problems.

[0006] According to one aspect of the present invention, a method for optimizing a shared energy storage power retail package based on a master-slave game is provided, wherein the shared energy storage power retail package includes a variety of packages for selling thermal power and green power, and the various packages include a time-of-use electricity price retail package, a ladder electricity price retail package, a peak-valley incentive retail package, a valley electricity consumption compensation retail package, a two-part electricity price retail package, and a capacity leasing package. The method comprises: constructing an upper-level objective function with the maximum total profit obtained by the shared energy storage entity through the sale of various packages of thermal power and green power as the upper-level objective, and constructing an upper-level shared energy storage entity decision model based on the upper-level objective function; and constructing an upper-level shared energy storage entity decision model based on the user A lower-level objective function is constructed using the minimization of the total cost of electricity purchase using shared energy storage as the lower-level objective, and a lower-level user decision model is constructed based on the lower-level objective function; a two-layer master-slave game model is obtained based on the upper-layer shared energy storage entity decision model and the lower-layer user decision model; a genetic algorithm and a particle swarm algorithm are used to collaboratively solve the two-layer master-slave game model to obtain an optimal package pricing strategy and an optimal package electricity purchasing strategy, wherein the optimal package pricing strategy is used to indicate the electricity price of each package in each time period, and the optimal package electricity purchasing strategy is used to indicate the package selected by the user and the amount of thermal power and green power purchased under the selected package in each time period.

[0007] Optionally, in the shared energy storage power retail package optimization method based on master-slave game according to the present invention, the total profit is equal to: the sum of the profits obtained from selling various packages of thermal power, the profits obtained from selling various packages of green power, and the income obtained from capacity leasing through capacity leasing packages, minus the initial investment cost of the shared energy storage entity and the operating cost of the shared energy storage entity; wherein, the initial investment cost of the shared energy storage entity includes the investment cost of each shared energy storage equipment and the technical transformation cost of the power grid.

[0008] Optionally, in the shared energy storage power retail package optimization method based on master-slave game according to the present invention, the profit obtained from the various packages of selling thermal power is equal to: the sum of the revenue obtained from the various packages of selling thermal power through each shared energy storage device minus the total cost of selling thermal power during the operation of the shared energy storage entity; the profit obtained from the various packages of selling green electricity is equal to: the sum of the revenue obtained from the various packages of selling green electricity through each shared energy storage device minus the total cost of selling green electricity during the operation of the shared energy storage entity.

[0009] Optionally, in the shared energy storage power retail package optimization method based on principal-agent game according to the application, an upper shared energy storage principal decision model is constructed based on the upper target function, comprising: constructing an upper shared energy storage principal decision model based on the upper target function and a plurality of upper constraint conditions; wherein the plurality of upper constraint conditions comprise an electricity balance constraint condition, an electricity price constraint condition, a green right constraint condition, a shared energy storage device constraint condition, and a capacity leasing constraint condition; the electricity balance constraint condition is used to constrain that the total electricity quantity of thermal power purchased by the user is equal to the sum of the electricity quantities of the thermal power sold by a plurality of packages of thermal power, and the total electricity quantity of green power purchased by the user is equal to the sum of the electricity quantities of the green power sold by a plurality of packages of green power; the green right constraint condition is used to constrain that the price of green power is higher than the price of thermal power; the shared energy storage device constraint condition is used to constrain that the charging power of the shared energy storage device is balanced with the discharging power, and does not exceed the maximum safe discharging power of the shared energy storage device, and the state of charge of the shared energy storage device is directly proportional to the charging power of the shared energy storage device, and the state of charge of the shared energy storage device is between the minimum state of charge and the maximum state of charge at any time, and the change period of the state of charge remains unchanged; and the capacity leasing constraint condition is used to constrain that the capacity leased by the user does not exceed the available energy storage capacity.

[0010] Optionally, in the shared energy storage power retail package optimization method based on principal-agent game according to the application, a lower target function is constructed with the minimum total electricity purchase cost of the user using the shared energy storage as a lower target, and a lower user decision model is constructed based on the lower target function, comprising: constructing a first lower target function with the minimum total electricity purchase cost of the user using the shared energy storage as a first lower target; constructing a second lower target function with the minimum total carbon emission of the user purchasing electricity as a second lower target; and constructing a lower user decision model based on the first lower target function and the second lower target function.

[0011] Optionally, in the shared energy storage power retail package optimization method based on principal-agent game according to the application, the total electricity purchase cost of the user using the shared energy storage comprises a total thermal power purchase cost of the user, a total green power purchase cost of the user, a demand response cost of the user using the shared energy storage, and a capacity leasing cost of the user.

[0012] Optionally, in the shared energy storage electricity retail package optimization method based on master-slave game according to the present invention, the two-layer master-slave game model is solved in collaboration with the genetic algorithm and the particle swarm algorithm to obtain the optimal package pricing strategy and the optimal package electricity purchase strategy, including: using the genetic algorithm to generate an initial population according to the upper-layer shared energy storage subject decision model and the shared energy storage subject parameters; and using the particle swarm algorithm to generate an initial particle swarm according to the lower-layer user decision model and user parameters; using the particle swarm algorithm to calculate the fitness of the particles in the initial particle swarm based on the initial population, and The speed and position of each particle in the initial particle swarm are updated according to the particle fitness to obtain a Pareto front solution set, and the optimal individual is screened according to the Pareto front solution set using the TOPSIS method; the fitness of the initial population is calculated according to the optimal individual using a genetic algorithm, and the initial population is selected, crossover and mutation operations are performed on the initial population according to the fitness of the initial population to generate a new population; the above steps are executed cyclically until the termination condition is reached, and the global optimal solution of the two-layer master-slave game model is obtained, and the optimal package pricing strategy and the optimal package electricity purchasing strategy are determined based on the global optimal solution.

[0013] Optionally, in the shared energy storage power retail package optimization method based on master-slave game according to the present invention, the TOPSIS method is used to screen the optimal individual according to the Pareto front solution set, including: performing data standardization processing on the Pareto front solution set to determine the positive ideal solution and the negative ideal solution; determining the closeness of each solution in the Pareto front solution set to the positive ideal solution and the negative ideal solution; and sorting each solution in the Pareto front solution set according to the closeness to determine the optimal compromise solution in the Pareto front solution set as the optimal individual.

[0014] According to one aspect of the present invention, a computing device is provided, 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 master-slave game-based shared energy storage power retail package optimization method as described above.

[0015] 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 described above when executed by a processor.

[0016] 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 above-mentioned shared energy storage power retail package optimization method based on master-slave game.

[0017] According to the technical solution of the present invention, a method for optimizing shared energy storage power retail packages based on master-slave game is provided. The upper-level shared energy storage entity decision model is constructed with the goal of maximizing the total profit obtained by the shared energy storage entity through selling multiple packages of thermal power and green power, and the lower-level user decision model is constructed with the goal of minimizing the total cost of electricity purchased by users using shared energy storage. Based on this, a two-layer master-slave game model between the shared energy storage entity and the user can be obtained. Then, the two-layer master-slave game model is solved collaboratively using genetic algorithm and particle swarm algorithm to obtain the optimal package pricing strategy of the shared energy storage entity and the optimal package electricity purchasing strategy of the user. Based on this, the interests of the shared energy storage entity and the user can be fully considered, and the shared energy storage entity's package pricing strategy and the user's package electricity purchasing strategy can be collaboratively optimized, thereby maximizing the profit of the shared energy storage entity while reducing the user's electricity cost.

[0018] 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 in accordance with 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 specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To achieve the above and related purposes, the present invention describes certain illustrative aspects in conjunction with the following description and 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. The above and other purposes, features and advantages of the present disclosure will become more apparent by reading the following detailed description in conjunction with the accompanying drawings. Throughout the present disclosure, the same reference numerals generally refer to the same parts or elements.

[0020] Figure 1 A schematic diagram of a shared energy storage operation framework 100 provided according to an embodiment of the present invention is shown;

[0021] Figure 2 A schematic diagram of a computing device 200 provided according to an embodiment of the present invention is shown;

[0022] Figure 3 A schematic flow chart of a method 300 for optimizing a shared energy storage power retail package based on a master-slave game according to an embodiment of the present invention is shown;

[0023] Figure 4 A schematic diagram showing the relationship between the benefits of electricity buyers and sellers according to an embodiment of the present invention is shown;

[0024] Figure 5 A schematic diagram showing the main factors constituting a power retail package according to an embodiment of the present invention is shown;

[0025] Figure 6 A schematic diagram showing the principle of a shared energy storage capacity leasing mode according to an embodiment of the present invention is shown;

[0026] Figure 7 A schematic diagram of a process for solving a two-layer master-slave game model using a genetic algorithm and a particle swarm algorithm in collaboration according to some embodiments of the present invention is shown;

[0027] Figure 8 A schematic diagram of load curves for peak-avoiding users, peak-approaching users, and stable users according to some embodiments of the present invention is shown;

[0028] Figure 9 shows a schematic diagram of an electricity market curve according to some embodiments of the present invention;

[0029] Figure 10 A schematic diagram of package pricing results according to some embodiments of the present invention is shown;

[0030] Figure 11 A schematic diagram of load curves before and after a peak-avoidance user invokes a shared energy storage strategy and selects a package according to some embodiments of the present invention is shown;

[0031] Figure 12 A schematic diagram of load curves before and after a peak-peak user invokes a shared energy storage strategy and selects a package according to some embodiments of the present invention is shown;

[0032] Figure 13 A schematic diagram of load curves before and after a stable user invokes a shared energy storage strategy and selects a package according to some embodiments of the present invention is shown;

[0033] Figures 14 to 16 Schematic diagrams of electricity purchasing strategies for peak-avoiding users, peak-onset users, and stable users according to some embodiments of the present invention are respectively shown;

[0034] Figure 17 A schematic diagram of output of three shared energy storage devices according to some embodiments of the present invention is shown. DETAILED DESCRIPTION

[0035] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although 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. Rather, 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.

[0036] In response to the problems existing in the current application of shared energy storage, an embodiment of the present invention proposes a shared energy storage electricity retail package optimization method based on master-slave game. By establishing a two-layer master-slave game model between the shared energy storage entity and the user, and using genetic algorithm and particle swarm algorithm to collaboratively solve the model, it is possible to achieve collaborative optimization of the shared energy storage entity's package pricing strategy and the user's package electricity purchasing strategy, thereby maximizing the profit of the shared energy storage entity while reducing the user's electricity cost.

[0037] The shared energy storage power retail package optimization method based on master-slave game provided in an embodiment of the present invention can be used in a shared energy storage operation framework. A shared energy storage operation framework provided in an embodiment of the present invention is first introduced below.

[0038] Figure 1 A schematic diagram of a shared energy storage operation framework 100 provided according to an embodiment of the present invention is shown.

[0039] like Figure 1 As shown, the shared energy storage operation framework 100 includes: energy storage resources, energy storage owners, shared energy storage operators (shared energy storage entities), and energy storage users (users). Energy storage owners provide equipment to energy storage resources. Shared energy storage operators gain control of energy storage resources by establishing lease agreements with energy storage owners. They can then establish service agreements with energy storage users and provide shared energy storage power services to them.

[0040] It should be noted that user-side shared energy storage refers to the concept of sharing economy, in which the energy storage owner sells the right to use the energy storage to users with energy storage needs (energy storage users) through leasing. In this way, the ownership and use rights of the energy storage can be separated, thereby providing a portion of income for the energy storage owner and improving the utilization efficiency of the energy storage. In an embodiment of the present invention, the lease agreement established between the shared energy storage operator and the energy storage owner is manifested in that the shared energy storage operator pays the relevant fees to the energy storage owner, and the energy storage owner provides the corresponding capacity to the shared energy storage operator. The service agreement established between the shared energy storage operator and the energy storage user is specifically manifested in that as a user of energy storage resources, the user needs to pay a service fee to the shared energy storage operator as the cost of using the energy storage. At the same time, the shared energy storage operator provides shared energy storage power services to the energy storage user.

[0041] Currently, for the application scenarios of shared energy storage, the specific uses, implementation methods and beneficial effects of different types of shared energy storage in different scenarios are mainly explained from three perspectives: the power supply side, the grid side and the user side.

[0042] The embodiments of the present invention are mainly aimed at the application of shared energy storage on the user side. By sharing energy storage capacity and power among users, it is possible to achieve complementary energy storage resources based on different user usage characteristics, thereby effectively improving the utilization efficiency of energy storage and reducing user energy costs.

[0043] In an embodiment of the present invention, the computing device may be configured to execute a master-slave game-based shared energy storage electricity retail package optimization method 300. The master-slave game-based shared energy storage electricity retail package optimization method 300 of the present invention will be described below.

[0044] The following introduces a computing device 200 provided by an embodiment of the present invention.

[0045] 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, computing device 200 includes at least one processing unit 202 and system memory 204. According to one aspect, depending on the configuration and type of computing device, processing unit 202 can be implemented as a processor. 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, system memory 204 includes an operating system 205.

[0046] According to one aspect, operating system 205 is suitable for controlling the operation of computing device 200, for example. Furthermore, examples may be 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 2 The 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 In the figure, removable storage device 209 and non-removable storage device 210 are shown.

[0047] 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 is not limited to the type of application program. For example, the application program may include an email and contact application program, a word processing application program, a spreadsheet application program, a database application program, a slide show application program, a drawing or computer-aided application program, a web browser application program, etc.

[0048] In an embodiment according to the present invention, the program module 203 includes a plurality of program instructions for executing the master-slave game-based shared energy storage power retail package optimization method 300 of the present invention.

[0049] According to one aspect, 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 an 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") onto a chip substrate as a single integrated circuit. When operated via SOC, the functions described in the present invention can be operated via 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, and the other technologies include but are 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.

[0050] 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, speakers, 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 transmitter, receiver, and / or transceiver circuitry; Universal Serial Bus (USB), parallel, and / or serial ports.

[0051] As used in the present invention, the term computer-readable medium includes computer storage media. Computer storage media can include volatile and non-volatile, removable and non-removable media implemented with 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 can 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, cassette tape, magnetic tape, magnetic disk storage or other magnetic storage device, or any other product that can be used to store information and can be accessed by computing device 200. According to one aspect, any such computer storage medium can be a part of computing device 200. Computer storage media does not include carrier waves or other propagated data signals.

[0052] 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 such a manner as to encode 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.

[0053] In an embodiment according to the present invention, a computing device 200 is configured to execute a method 300 for optimizing a shared energy storage power retail package based on a master-slave game. 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 the one or more processors, the computing device executes the method 300 for optimizing a shared energy storage power retail package based on a master-slave game according to an embodiment of the present invention.

[0054] In some embodiments, the computing device 200 that executes the shared energy storage power retail package optimization method 300 based on master-slave game in the embodiment of the present invention may be a terminal or a server.

[0055] The following is a detailed description of a method 300 for optimizing a shared energy storage power retail package based on a master-slave game in an embodiment of the present invention.

[0056] First of all, it should be noted that the shared energy storage power retail package in the embodiment of the present invention includes a variety of packages for selling thermal power and green electricity, including time-of-use electricity price retail packages, tiered electricity price retail packages, peak-valley incentive retail packages, valley electricity consumption compensation retail packages, two-part electricity price retail packages, and capacity leasing packages.

[0057] Figure 3 FIG. 3 is a flow chart showing a method 300 for optimizing a shared energy storage power retail package based on a master-slave game according to an embodiment of the present invention. Figure 3 As shown, the shared energy storage power retail package optimization method 300 based on master-slave game includes the following steps 310 to 340.

[0058] Step 310: construct an upper-level objective function with the maximum total profit obtained by the shared energy storage entity (i.e., the shared energy storage operator) from selling various packages of thermal power and green power through each shared energy storage device as the upper-level goal, and construct an upper-level shared energy storage entity decision model based on the upper-level objective function.

[0059] Step 320: construct a lower-level objective function with minimizing the total cost of electricity purchase (i.e., total energy consumption cost) of users using shared energy storage as the lower-level objective, and construct a lower-level user decision model based on the lower-level objective function.

[0060] Step 330: Based on the upper-layer shared energy storage entity decision model and the lower-layer user decision model, a two-layer master-slave game model is obtained.

[0061] It should be understood that in the two-layer master-slave game model established in the embodiment of the present invention, the shared energy storage entity is the leader.

[0062] Step 340: A genetic algorithm and a particle swarm algorithm are used to collaboratively solve the two-layer master-slave game model to obtain the optimal package pricing strategy for the shared energy storage entity and the optimal package electricity purchasing strategy for the user. The optimal package pricing strategy indicates the electricity price for each package at each time period. The optimal package electricity purchasing strategy indicates the package selected by the user and the amount of thermal power and green power purchased under the selected package at each time period (i.e., the electricity purchasing strategy for the selected package).

[0063] The pricing principle of the shared energy storage electricity retail package in an embodiment of the present invention is described below.

[0064] It can be understood that in the electricity retail market, electricity is traded as a commodity, and the market equilibrium is determined by the supply and demand of electricity.

[0065] Figure 4The figure shows the relationship between the benefits of electricity buyers and sellers in an embodiment of the present invention. While considering maximizing profits, the electricity retail market also needs to take into account social welfare, which mainly includes producer surplus and consumer surplus. Producer surplus is the revenue generated by the electricity seller from selling electricity minus the cost of obtaining electricity, and consumer welfare is the revenue generated by the electricity buyer from purchasing electricity minus the cost of purchasing electricity. The relationship between the two is as follows: Figure 4 shown. Figure 4 K0 is the equilibrium point, at which social welfare is maximized. At this point, all entities in the electricity retail market can achieve the optimal allocation strategy and obtain maximum economic benefits.

[0066] As individuals with independent decision-making capabilities, electricity retailers and users have certain conflicts in their pursuit of interests. Electric retailers pursue the maximization of overall profits, while users pursue the minimization of energy costs. Designing a reasonable pricing mechanism and cost allocation rules can, on the one hand, coordinate the conflicts of interest between users and electricity retailers and fairly distribute costs and benefits; on the other hand, it can encourage more users to participate in shared energy storage and reduce energy costs through economies of scale.

[0067] As the research on shared energy storage deepens and the number of projects being implemented increases, the pricing mechanisms can currently be divided into three categories: pricing mechanisms based on electricity prices, pricing mechanisms based on game theory, and pricing mechanisms based on experience and agreements.

[0068] Figure 5 Schematic diagram showing the main factors constituting the electricity retail package according to an embodiment of the present invention. Figure 5 The design and construction of electricity retail packages is that electricity retailers comprehensively consider the needs and characteristics of different types of users, specifically consider key factors such as pricing mechanism, basic fees, proportion of purchased electricity, contract duration, and optimize the combination of these key factors, thereby forming customized and diversified electricity retail packages and obtaining rich profits.

[0069] The key to profitability in shared energy storage lies in reducing users' energy costs through scale. Currently, this model is still in its infancy, with providers primarily profiting by offering users peak-shaving, capacity leasing, compensation for ancillary services (peak shaving, frequency regulation), capacity pricing, and priority power generation rights.

[0070] It is worth noting that the users of shared energy storage services are numerous and widely distributed, and their electricity demand is diverse, volatile, and complementary. There are many types of users according to different classification standards. However, no matter what type of user, the differences and fluctuations in electricity consumption can be achieved through shared energy storage to achieve energy complementarity and balance at different time nodes. Electricity price is a key factor affecting users' electricity consumption behavior. Based on the impact of peak-valley price differences on users' electricity consumption behavior, the present invention divides shared energy storage users into three categories: peak-avoiding users, peak-facing users, and stable users.

[0071] Peak-avoiding users are those who can flexibly adjust their electricity consumption during peak hours. By reducing or terminating some interruptible loads, they can effectively avoid peak periods of electricity demand, helping the grid alleviate pressure on power supply. Peak-facing users are those whose electricity load characteristics overlap significantly with peak periods of electricity demand. These users' loads peak with the onset of peak electricity demand, placing high demands on power supply stability. Stable users are those whose electricity loads fluctuate slightly, have no significant peak or trough periods, and have relatively stable electricity demand. These users are numerous, have relatively stable electricity demand, and are less affected by objective factors such as weather and time. Compared to peak-facing users, their load distribution is more regular and uniform, with relatively higher demands for electricity stability and lower price sensitivity. However, in special circumstances such as extreme weather and equipment failures, their electricity demand can also fluctuate significantly.

[0072] In the early stages of the electricity market, power retailers purchased electricity in a relatively simple manner, focusing primarily on medium- and long-term market transactions and spot market transactions. They rarely utilized tools like interruptible loads and energy storage to reduce the risk of deviation assessments and improve economic efficiency. Therefore, in this embodiment of the present invention, taking into account the current state of the electricity spot market, various electricity retail packages have been designed to enhance the market competitiveness and profitability of power retailers.

[0073] Considering the current limited development of the electricity market, this paper sets the following application conditions when designing electricity retail packages: the electricity seller is the shared energy storage entity; the shared energy storage entity can accurately understand user electricity usage behavior and levels; and the shared energy storage entity is connected to the upper-level electricity market and can purchase electricity from the electricity market at any time to meet user-side electricity needs. Under these conditions, a shared energy storage electricity retail package is designed.

[0074] In an embodiment of the present invention, the shared energy storage power retail package includes a variety of packages for selling thermal power and green power. The various packages include a time-of-use electricity price retail package, a tiered electricity price retail package, a peak-valley incentive retail package, a valley electricity consumption compensation retail package, a two-part electricity price retail package, and a capacity leasing package. The specific details are as follows:

[0075] Time-of-use electricity price retail packages are a relatively mature pricing strategy. They mainly consider the user's electricity consumption characteristics and are designed to encourage users to reduce electricity consumption during peak periods and increase electricity consumption during low periods. This can play a certain role in peak-to-valley shifting. This invention divides the electricity consumption period into off-peak hours, normal hours, and peak hours. The off-peak hours are 23:00-6:00, the normal hours are 6:00-8:00 and 12:00-18:00, and the peak hours are 8:00-12:00 and 18:00-23:00. The electricity prices for off-peak hours, normal hours, and peak hours are:

[0076]

[0077] Where, is the time-of-use electricity retail package price during period t; are the electricity prices of the TOU retail package during peak, normal and valley periods respectively; T P 、T n 、T v They are peak period, normal period and valley period respectively.

[0078] The benefits of using time-of-use electricity price retail packages for shared energy storage entities are:

[0079]

[0080] Where R k1 The income that the shared energy storage entity can obtain through the time-of-use electricity price retail package; R k1,i The revenue that the shared energy storage entity obtains from user i through the time-of-use electricity price retail package; is the electricity consumption (i.e., the amount of electricity purchased) of the time-of-use electricity price retail package of user i in period t; I is the total number of users; and T represents the time period.

[0081] The tiered electricity price retail package is a common package for industrial and commercial users, and is now gradually expanding to residential users. The package sets different gradients based on the user's electricity consumption level. The electricity price increases with the increase in electricity consumption, and different electricity consumption corresponds to different gradient electricity prices. The tiered electricity price retail package electricity price is:

[0082]

[0083] Where Q i The total electricity consumption of user i in the tiered electricity price retail package during the settlement period; is the retail package electricity price for the tiered electricity price during period t; There are three different gradient electricity prices for the tiered electricity price retail package; Used to represent the electricity consumption classification standards for the three different levels of tiered electricity price retail packages.

[0084] The benefits of adopting the tiered electricity price retail package for shared energy storage entities are:

[0085]

[0086] Where R k2 The income that the shared energy storage entity can obtain through the tiered electricity price retail package; R k2,i It is the revenue that the shared energy storage entity obtains from user i through the tiered electricity price retail package.

[0087] The peak-valley incentive retail package is primarily used to encourage users to use more electricity during off-peak hours and less during peak hours. The design of this package draws on the two-part electricity price system. The incentive or penalty is determined by measuring the difference between the user's peak and valley consumption. The peak-valley difference is determined based on both electricity consumption and power consumption, as follows:

[0088]

[0089] Where, δ i is the incentive coefficient of user i in the settlement period; ε1 and ε2 are the energy and power vector conversion coefficients respectively; is the total electricity consumption of user i during peak hours; is the total electricity consumption of user i during the off-peak period; Set quotas for electricity consumption during peak hours; The electricity consumption quota during off-peak period; P i p,max is the maximum power consumption of user i during peak hours; P i v,min The minimum power consumption of user i during off-peak hours; Power quota for peak hours; The power quota for off-peak hours.

[0090] When users choose the peak-valley incentive retail package, they need to pay the basic electricity fee and determine the penalty fee or incentive reward they need to pay based on the user incentive coefficient. The basic electricity fee can be paid according to the agreed basic electricity price.

[0091] When the incentive coefficient is greater than 0 and greater than the coefficient limit, it means that the user used more electricity or had a higher load during peak hours, but less electricity and a lower load during off-peak hours. In this case, the user will be required to pay a penalty fee according to the penalty electricity price, and the larger the incentive coefficient, the greater the penalty. When the incentive coefficient is less than 0 and less than the negative coefficient limit, it means that the user used more electricity or had a higher load during off-peak hours, but less electricity and a lower load during peak hours. In this case, the user will receive an incentive reward according to the incentive electricity price, and the smaller the incentive coefficient, the greater the reward. When the incentive coefficient is between the coefficient limits, there is no reward or penalty, and only the basic electricity fee is paid.

[0092] The benefits that the peak-valley incentive retail package brings to shared energy storage entities are:

[0093]

[0094]

[0095] Where R k3 The income that the shared energy storage entity can obtain through the peak-valley incentive retail package; R k3,i The revenue that the shared energy storage entity obtains from user i through the peak-valley incentive retail package; The base electricity price for the peak-valley incentive retail package is the same as the electricity price for the time-of-use electricity price retail package during normal hours; Reward and penalty electricity prices for peak-valley incentive retail packages; Δδ i is the incentive coefficient of user i; δ st It is the incentive coefficient standard for peak-valley incentive retail packages.

[0096] The off-peak electricity consumption compensation retail package is designed to encourage users to use more electricity during the off-peak period. Based on the user's off-peak electricity consumption, a certain amount of electricity consumption in other time periods can be deducted. When the off-peak electricity consumption exceeds the quota standard, it can be directly deducted from the electricity consumption in the remaining time periods. The more the excess electricity, the more electricity can be deducted. This encourages users to use more electricity during the off-peak period and achieve the effect of peak shaving and valley filling. The price of the off-peak electricity consumption compensation retail package is:

[0097]

[0098] Where, The retail package electricity price is used to compensate for the off-peak electricity consumption during period t; Compensate the retail package for electricity consumption at other times; Compensate the retail package for electricity prices during off-peak hours for electricity consumption; T o For other time periods.

[0099] The benefits that the off-peak electricity consumption compensation retail package brings to shared energy storage entities are:

[0100]

[0101] Where R k4 It represents the income that the shared energy storage entity can obtain by compensating the retail package through off-peak electricity consumption; R k4,i The revenue that the shared energy storage entity obtains from user i by compensating the retail package with off-peak electricity consumption; μ is the conversion coefficient of off-peak electricity consumption to offset other periods; The off-peak electricity consumption is deducted from the normal electricity consumption value; The off-peak electricity consumption is deducted from the rated value.

[0102] The two-part electricity pricing model divides the on-grid electricity price into two parts: a capacity price and a volume price. The capacity price primarily reflects fixed costs, primarily investment costs, and is implemented according to the approved electricity price. The capacity price essentially pays for the generator's ability to generate electricity for the grid. Therefore, the settlement of capacity charges is closely related to the generator's availability. The volume price primarily reflects variable costs, primarily fuel costs, and is implemented according to the price determined by spot market competition.

[0103] The two-part electricity price is an advanced electricity price system commonly adopted by countries around the world today. It can give full play to the role of economic leverage, increase the load rate, and enable power generation companies to obtain more stable capital investment returns due to their power generation capacity. It can also reserve more spare capacity for the power grid, so that the security of the power grid has a reliable capacity guarantee, guarantee the profits of power companies, and make the burden on electricity users more reasonable.

[0104] The benefits of adopting a two-part electricity price retail package for shared energy storage entities are:

[0105]

[0106] Where R K5 The revenue that the shared energy storage entity can obtain through the two-part electricity price retail package; P K5 The electricity price for the two-part electricity price retail package; P is the total electricity consumption of user i in the two-part electricity price retail package during period t; C is the capacity electricity price of the two-part electricity price retail package; C represents the shared energy storage capacity.

[0107] Figure 6 The schematic diagram of the principle of the shared energy storage capacity leasing model in an embodiment of the present invention is shown. Shared energy storage capacity leasing is a kind of "commodity" that is leased after a third-party investment institution or energy storage equipment manufacturer is responsible for the construction of an energy storage power station as an investment entity. The lessor provides the power and capacity of the energy storage system as a leaseable "commodity". During the validity period specified in the leasing service, the user has the right to charge and discharge the energy storage system to meet its own energy supply needs. At the same time, the user must pay rent to the lessor regularly in strict accordance with the time and method agreed in the contract. The shared energy storage capacity lessor (shared energy storage entity) is responsible for the daily operation and maintenance of the energy storage power station. It is also necessary to provide the agreed energy storage capacity services to the new energy station on time and in sufficient quantity in accordance with the terms of the contract, and collect rent from the new energy station on a regular basis.

[0108] The benefits of using the capacity leasing package are:

[0109]

[0110] wherein R K6 is the income that the shared energy storage subject can obtain through the capacity leasing package; is the capacity that user i leases under the capacity leasing package at time period t; is the price of the capacity that user i leases under the capacity leasing package at time period t.

[0111] It should be noted that the above shared energy storage power retail packages (six packages) are package types common to thermal power retail and green power retail, that is, the shared energy storage subject can sell thermal power and green power using the above package types. For each package, the package prices of thermal power and green power can be respectively formulated to attract users to participate in transactions. Among them, the time-of-use electricity price retail package and the tiered electricity price retail package are the current mainstream packages, while the peak-valley incentive retail package, the valley-time electricity consumption compensation retail package, and the two-part electricity price retail package provide more choices for users and can also promote users to do demand side management and respond to new energy output characteristics.

[0112] In the embodiments of the present application, the income of the shared energy storage subject mainly comes from the multiple packages of selling thermal power and green power to users and the income from capacity leasing, and the cost of the shared energy storage subject mainly comes from the front-end investment cost of the shared energy storage subject, the grid technical improvement cost, and the cost of electricity purchased from the upper power market during the operation period.

[0113] Specifically, the total profit obtained by the shared energy storage subject through the multiple packages of selling thermal power and green power is equal to the sum of the profit obtained by the shared energy storage subject through the multiple packages of selling thermal power, the profit obtained by the shared energy storage subject through the multiple packages of selling green power, and the income obtained by the shared energy storage subject through capacity leasing (the income obtained by capacity leasing, that is, the profit obtained by capacity rent), minus the front-end investment cost of the shared energy storage subject and the operation cost (discharge cost) of the shared energy storage subject. The front-end investment cost of the shared energy storage subject includes the investment cost of each shared energy storage device and the grid technical improvement cost.

[0114] The profit obtained by the multiple packages of selling thermal power is equal to the sum of the income obtained by the multiple packages of selling thermal power through each shared energy storage device, minus the total cost of selling thermal power during the operation period of the shared energy storage subject (that is, the total cost of purchasing thermal power from the upper power market).

[0115] The profit obtained by the multiple packages of selling green power is equal to the sum of the income obtained by the multiple packages of selling green power through each shared energy storage device, minus the total cost of selling green power during the operation period of the shared energy storage subject (that is, the total cost of purchasing green power from the upper power market).

[0116] Based on this, in step 310, the upper-level objective function constructed with the maximum total profit obtained by the shared energy storage entity through selling multiple packages of thermal power and green power as the upper-level goal is as follows:

[0117] max M=M F +M G +M L -C fix -C op (14)

[0118]

[0119]

[0120] Where M F Profits earned from selling various thermal power packages to shared energy storage entities. are the revenues obtained from selling thermal power packages for the j-th shared energy storage device.

[0121] C fix is the initial investment cost of the shared energy storage entity, including the investment cost of each energy storage device and the cost of grid technical transformation (the cost of each cycle is calculated given the total investment); ct invest,j ,ct jg,j are the unit investment cost and grid technical transformation cost of the jth shared energy storage device respectively; λ is the benchmark rate of return; TD is the total operating cycle of the shared energy storage entity (in years). F It is the total cost of thermal power sold during the operation of the shared energy storage entity; The price of thermal power in the upper power market; The shared energy storage entity purchases thermal electricity from the upstream market during period t.

[0122] M G Profits earned from selling various green electricity packages to shared energy storage entities. The revenues obtained from selling green electricity packages for the j-th shared energy storage device.

[0123] C G The total cost of selling green electricity during the operation of the shared energy storage entity; Green electricity prices for the upper power market; The shared energy storage entity purchases green electricity from the upstream market during period t.

[0124] C op It is the operating cost (discharge cost) of the shared energy storage entity. is the discharge power of distributed energy storage n in period t. dis The discharge efficiency when used by user m; is the rated capacity of distributed energy storage; c is the marginal cost of the battery; χ, φ are constants related to the energy storage discharge efficiency and battery capacity.

[0125] M L The income from capacity leasing through the capacity leasing package; The capacity rented by the user during the time period. The price at which users rent capacity during a time period.

[0126] In some embodiments, the upper-level shared energy storage entity decision model also includes multiple upper-level constraints. In other words, in step 310, after constructing an upper-level objective function based on maximizing the total profit earned by the shared energy storage entity from selling multiple packages of thermal power and green power as the upper-level goal, the upper-level shared energy storage entity decision model can be constructed based on the upper-level objective function and the multiple upper-level constraints.

[0127] The multiple upper-level constraints may specifically include electricity balance constraints, electricity price constraints, green equity constraints, shared energy storage equipment constraints, and capacity leasing constraints.

[0128] Among them, the electricity balance constraint is used to constrain: the total amount of thermal power purchased by users is equal to the sum of the thermal power sold by various thermal power packages (that is, the total amount of thermal power purchased by each user in each time period is equal to the sum of the thermal power purchased by each user under various thermal power packages in each time period), and the total amount of green electricity purchased by users is equal to the sum of the green electricity sold by various green electricity packages (that is, the total amount of green electricity purchased by each user in each time period is equal to the sum of the green electricity purchased by each user under various green electricity packages in each time period).

[0129] Specifically, the total amount of thermal power purchased by the user is equal to the sum of the thermal power sold under various thermal power packages, as shown in the following formula:

[0130]

[0131] Where, is the total amount of thermal power purchased by user i in period t (the total amount of thermal power purchased under various packages); are the thermal power quantities purchased by user i under each package during period t.

[0132] The total amount of green electricity purchased by the user is equal to the sum of the green electricity sold by various green electricity packages, as shown in the following formula:

[0133]

[0134] Where, is the total amount of green electricity purchased by user i during period t; are the green electricity quantities purchased by user i under each package during period t.

[0135] Regarding the electricity price constraints, it should be noted that the time-of-use electricity price retail package sets a tiered price by dividing the time periods into peak, normal and valley hours, where the normal electricity price is lower than the peak electricity price and higher than the valley electricity price, in order to guide users to shave peaks and fill valleys. The tiered electricity price retail package is priced according to the electricity consumption, and adopts an incremental pricing method, where the third tier electricity price is higher than the second tier electricity price, which is higher than the first tier electricity price. In the peak-valley incentive retail package, the peak electricity price is higher than the valley electricity price in the basic peak-valley electricity price package, and a dynamic reward and punishment mechanism is introduced. The greater the peak-valley difference (electricity or power) of the user, the higher the punitive price increase, and vice versa, a discount can be obtained. The valley electricity consumption compensation retail package reduces the actual electricity cost of users by setting valley low prices and excess electricity deduction mechanisms. Based on this, the thermal power price constraints are set as follows:

[0136]

[0137] The constraints on green electricity prices are as follows:

[0138]

[0139] Since green electricity has green attribute rights, green rights constraints are set to constrain the price of green electricity to be higher than the price of thermal power, as follows:

[0140] p F <p G (27)

[0141] The shared energy storage device constraint is used to ensure that: the charging power and discharging power of the shared energy storage device are balanced and do not exceed the maximum safe discharge power of the shared energy storage device; the state of charge of the shared energy storage device is proportional to the charging power of the shared energy storage device; the state of charge of the shared energy storage device is always between the minimum state of charge and the maximum state of charge, and the change cycle of the state of charge remains unchanged. Specifically, the shared energy storage device constraint is shown in the following formula:

[0142]

[0143] SOC min,n ≤SOC n,t ≤SOC max,n (31)

[0144] SOC n,1 =SOC n,T (32)

[0145] Where, The green electricity amount purchased by the shared energy storage entity from the upper power market during period t; is the discharge power of the shared energy storage device n during period t; η dis The discharge efficiency of user m when using shared energy storage equipment; Indicates the rated capacity of the shared energy storage device; The period t represents the charging power of the shared energy storage device n; is the rated power of the shared energy storage device; SOC n,t is the state of charge of the shared energy storage device n at time t; η ch The charging efficiency of user m when using shared energy storage equipment.

[0146] The capacity leasing constraint is used to constrain the user's leased capacity to not exceed the available energy storage capacity, as shown in the following formula:

[0147]

[0148] Where, is the capacity rented by user i in period t; C available,t is the energy storage capacity available during period t; C total is the total available capacity of the shared energy storage equipment.

[0149] It should be noted that the user decision model in the embodiment of the present invention aims to optimize the user's choice of multiple packages provided by the shared energy storage entity and the amount of electricity purchased in each period, as well as the application of the shared energy storage charging and discharging strategy, while ensuring the user's own electricity demand. The amount of thermal power purchased by the user under each package in period t is

[0150] In some embodiments of the present invention, considering that the primary concern for electricity users is cost, minimizing the user's total energy cost (i.e., the total cost of electricity purchased) can be used as the first lower-level objective. Furthermore, to increase the use of clean energy, minimizing the user's total carbon emissions from electricity purchases can be used as the second lower-level objective.

[0151] Based on this, in some embodiments, in step 320, a first lower-level objective function (F1) can be constructed with minimizing the total cost of electricity purchased by users using shared energy storage as the first lower-level objective, and a second lower-level objective function (F2) can be constructed with minimizing the total carbon emissions from electricity purchased by users as the second lower-level objective. Furthermore, a lower-level user decision model is constructed based on the first and second lower-level objective functions.

[0152] Among them, the total electricity purchase cost for users using shared energy storage includes: the total cost of users purchasing thermal power, the total cost of users purchasing green electricity, the demand response cost of users using shared energy storage, and the capacity leasing cost of users.

[0153] Specifically, the first lower layer objective function is as follows:

[0154]

[0155] Where C e The total cost of purchasing electricity for users using shared energy storage; The total cost of thermal power purchased by users (the total cost of thermal power purchased by users under various packages); The total cost of green electricity purchased by users (the total cost of green electricity purchased by users under various packages); the cost of demand response using shared energy storage for customers; The capacity leasing cost for users (the cost of leasing capacity under the capacity leasing package); P t EES,j is the output power of the jth shared energy storage device; μ EES,j is the operation and maintenance cost coefficient of the jth shared energy storage device; α1 and α2 are the user load transfer discomfort cost coefficients respectively.

[0156] The second lower layer objective function is as follows:

[0157]

[0158] Where CO2 is the total carbon emissions from electricity purchases by users; γ is the carbon emission coefficient of thermal power generation.

[0159] In some embodiments, the lower-level user decision model also includes multiple lower-level constraints. Specifically, the lower-level user decision model must first meet its own load requirements and secondly ensure shared energy storage. Based on this, the lower-level constraints are set as follows:

[0160]

[0161] In addition, to protect the legitimate rights and interests of operators, when users select a package, they can only choose one package per period. The following lower-level constraints are set:

[0162]

[0163] It should be noted that according to the two-layer master-slave game model established in the embodiment of the present invention, the upper-layer shared energy storage entity decision-making model aims to maximize the total profit of the shared energy storage entity to optimize the pricing strategy of each package. The model has fewer variables, so a genetic algorithm (GA) can be used to efficiently solve the upper-layer shared energy storage entity decision-making model. The lower-layer user decision-making model aims to minimize the total cost of user electricity purchases. It is necessary to simultaneously optimize package selection, electricity purchase, and energy storage call strategies. The variables are complex and it is a multi-objective problem. Therefore, a particle swarm algorithm (PSO) can be used to perform Pareto frontier search and screen the optimal compromise solution based on the TOPSIS method. The particle swarm algorithm is specifically a multi-objective particle swarm algorithm (MOPSO).

[0164] Based on this, in step 340, a genetic algorithm (GA) can be used to solve the upper-level shared energy storage entity decision model in order to optimize the package pricing strategy of the shared energy storage entity, and a particle swarm algorithm and TOPSIS method can be coupled to solve the lower-level user decision model in order to optimize the user package electricity purchasing strategy.

[0165] Figure 7 A schematic diagram of a process for solving a two-layer master-slave game model by collaboratively utilizing a genetic algorithm and a particle swarm algorithm according to some embodiments of the present invention is shown.

[0166] The following combination Figure 7 Let’s explain the solution process of the two-layer master-slave game model.

[0167] First, the genetic algorithm parameters and particle swarm algorithm parameters can be initialized. The genetic algorithm parameters include the population size, the maximum number of genetic algorithm iterations, and the genetic operation probability. The particle swarm algorithm parameters include the particle swarm size, the maximum number of particle swarm algorithm iterations, and the motion parameters.

[0168] Subsequently, a genetic algorithm is used to randomly generate an initial population based on the upper-level shared energy storage entity decision model and shared energy storage entity parameters (each individual in the initial population represents a package pricing strategy). Furthermore, a particle swarm algorithm is used to initialize the particle swarm based on the lower-level user decision model and user parameters to generate an initial particle swarm (each particle in the initial particle swarm represents a package electricity purchase strategy).

[0169] It should be noted that after generating the initial population, the genetic algorithm can pass the initial population to the particle swarm algorithm. In some embodiments, after generating the initial population, the genetic algorithm can generate a subpopulation based on the initial population. At this time, the population generation number (i.e., the number of genetic algorithm iterations) P = 2. Then, the initial population (parent generation) and the subpopulation (offspring) can be merged to obtain a merged initial population, and the merged initial population can be passed to the particle swarm algorithm. The particle swarm algorithm can then calculate the particle fitness based on the merged initial population. In addition, it can be determined whether the merged initial population has reached the population size. If it is determined that the population size has been reached, the subsequent steps (calculating the population fitness) can be continued.

[0170] Then, using the particle swarm algorithm, based on the initial population from the genetic algorithm (the merged initial population), the fitness of the particles in the initial particle swarm is calculated, and the speed and position of each particle in the initial particle swarm are updated according to the particle fitness (the local optimal position and the global optimal position of the particle are updated), and then the Pareto front solution set is obtained according to the speed and position of each particle in the updated initial particle swarm and the Pareto front solution set is output, and the TOPSIS method is used to screen the optimal individual (optimal compromise solution) according to the Pareto front solution set, and the optimal individual is fed back to the genetic algorithm. In some embodiments, after updating the speed and position of each particle in the initial particle swarm, it can be determined whether the maximum number of iterations of the particle swarm algorithm is reached at this time. If the maximum number of iterations of the particle swarm algorithm is reached, the Pareto front solution set is obtained according to the updated speed and position of each particle and the Pareto front solution set is output, and the TOPSIS method is used to screen the optimal individual according to the Pareto front solution set; if the maximum number of iterations of the particle swarm algorithm is reached, the step of calculating the particle fitness is returned to, and the particle speed and particle position are updated again according to the particle fitness.

[0171] Next, a genetic algorithm is used to calculate the fitness of the initial population (the merged initial population that has reached the population size) based on the optimal individual from the particle swarm algorithm, and the initial population (the merged initial population) is subjected to selection, crossover, and mutation operations based on the fitness of the initial population (the merged initial population) to generate a new population. In some embodiments, before calculating the fitness of the population, the genetic algorithm may first determine whether the initial population (the merged initial population) has reached the population size. If the population size has been reached, the fitness of the initial population (the merged initial population) is calculated based on the optimal individual from the particle swarm algorithm, and the initial population (the merged initial population) is subjected to selection, crossover, and mutation operations based on the fitness of the initial population (the merged initial population) to generate a new population. If the population size has not been reached, the initial population is generated again and subsequent steps are performed.

[0172] The above steps are then repeated for the new population: the new population is passed to the particle swarm algorithm. The particle swarm algorithm calculates the fitness of the particles in the initial particle swarm based on the new population from the genetic algorithm, and updates the speed and position of each particle in the initial particle swarm based on the particle fitness (updating the local optimal position and global optimal position of the particle). Furthermore, the Pareto front solution set can be output based on the updated speed and position of each particle. The TOPSIS method is used to select the optimal individual from the Pareto front solution set and feed the optimal individual back to the genetic algorithm. The genetic algorithm calculates the fitness of the new population based on the optimal individual from the particle swarm algorithm, and selection, crossover, and mutation operations are performed on the new population based on the fitness of the new population to generate the latest population.

[0173] By looping through the above steps until the termination condition (maximum number of iterations of the genetic algorithm) is met, the global optimal solution of the two-layer master-slave game model can be obtained. Based on the global optimal solution, the optimal package pricing strategy and the optimal package electricity purchase strategy can be determined.

[0174] In some embodiments, after using a genetic algorithm to perform selection, crossover, and mutation operations on the initial population based on the fitness of the initial population to generate a new population, it can be determined whether the number of genetic algorithm iterations has reached the maximum number of genetic algorithm iterations. If the maximum number of iterations has been reached, it means that the termination condition is met, and the global optimal solution of the two-layer master-slave game model can be obtained. If the maximum number of iterations is not reached, indicating that the termination condition is not met, the number of genetic algorithm iterations (i.e., the number of population generations) is increased by 1, and the process returns to the step of merging the parent and child generations. That is, the new population is merged with the initial population (including the initial population and the child population) to obtain a merged new population, and the subsequent steps are performed: the merged new population is passed to the particle swarm algorithm. The particle swarm algorithm calculates the fitness of the particles in the initial particle swarm based on the merged new population from the genetic algorithm, and updates the speed and position of each particle in the initial particle swarm based on the particle fitness (updating the local optimal position and global optimal position of the particle). Then, based on the updated particle speeds and particles, a Pareto front solution set is output. The TOPSIS method is used to select the optimal individual from the Pareto front solution set and feed the optimal individual back to the genetic algorithm. The genetic algorithm calculates the fitness of the merged new population based on the optimal individual from the particle swarm algorithm, and performs selection, crossover, and mutation operations on the merged new population based on the fitness of the merged new population to generate the latest population.

[0175] Based on this, the nested iteration of genetic algorithm and particle swarm algorithm can achieve the coordinated optimization of the package pricing strategy of shared energy storage entities and the user package electricity purchase strategy.

[0176] In some embodiments, as Figure 7 As shown in Figure 1, the specific process of using the TOPSIS method to select the optimal individual from the Pareto front solution set is as follows: First, the Pareto front solution set is obtained (i.e., the Pareto front solution set is input into the TOPSIS method). Then, the Pareto front solution set is subjected to data normalization to determine the positive ideal solution (the assumed optimal solution) and negative ideal solution (the assumed worst solution) in the Pareto front solution set. Subsequently, by calculating the distance between each solution in the Pareto front solution set and the positive ideal solution and the negative ideal solution, the closeness of each solution in the Pareto front solution set to the positive ideal solution and the negative ideal solution is determined. Furthermore, the solutions in the Pareto front solution set can be ranked according to their closeness to the positive ideal solution and the negative ideal solution to determine the optimal compromise solution in the Pareto front solution set, and the optimal compromise solution is selected as the optimal individual.

[0177] In order to verify the effectiveness of the master-slave game model of shared energy storage and users proposed in the present invention, in some embodiments, the following three typical user types can be set: peak-avoiding users, peak-facing users, and stable users. Based on the three user types, two users with different loads can be set under each user type, for a total of six users of the three user types. At the same time, three shared energy storage devices can be set for simulation analysis. Among them, Figure 8 A schematic diagram of load curves for peak-avoiding users, peak-approaching users, and stable users is shown according to some embodiments of the present invention. Figure 9 The figure shows a schematic diagram of the electricity market price curve according to some embodiments of the present invention. The prices of the thermal power day-ahead market and the green power day-ahead market on the shared energy storage are as follows: Figure 9 As shown, the carbon emission coefficient of thermal power is 0.997 kg / kWh. The overall simulation operation cycle is one month. For the convenience of calculation, it is assumed that the load curves of typical users are all curves under typical scenarios. The scheduling cycle (time period) for user electricity purchase and equipment operation is 1 hour.

[0178] The following is an analysis of user package selection.

[0179] Peak-to-valley incentive retail packages are suitable for users with low loads who avoid peak hours. Because their load is concentrated in the off-peak period, although the electricity consumption is low, they can obtain rewards for excess electricity consumption in the off-peak period through the incentive package, further reducing the total cost; while high-load users who avoid peak hours choose the off-peak electricity compensation package. Because their electricity consumption in the off-peak period is large, the compensation mechanism can be used to offset the excess electricity consumption in the off-peak period from the electricity charges of other periods, significantly reducing the electricity bill expenditure caused by high load.

[0180] Peak-load low-load users are suitable for tiered electricity price retail packages because their electricity consumption is relatively low, and the lower prices in the first two tiers of the tiered electricity price can cover their needs and avoid the impact of high electricity prices during peak hours; while peak-load high-load users choose a two-part electricity price package because their unit cost is lower after the fixed capacity electricity price is allocated, and high electricity demand can be optimized through variable electricity prices, which is more economical than time-of-use or tiered electricity prices.

[0181] Stable low-load users are suitable for time-of-use electricity price retail packages. Because their electricity consumption is small and evenly distributed, the stable price of time-of-use electricity prices during normal periods can provide a predictable cost structure. Stable high-load users are more suitable for capacity leasing packages. Because their long-term stable high-load demand can reduce unit electricity costs by leasing fixed-capacity energy storage resources, while avoiding the impact of volatile pricing of retail packages.

[0182] In addition, the electricity costs of the three types of users participating in the electricity market are compared with those of purchasing packages, and the results are shown in Table 1. According to the data in Table 1, it can be seen that the costs of all types of users who choose package transactions are significantly lower than those who directly participate in electricity market transactions, thus verifying the effectiveness of the two-tier master-slave game model.

[0183] Table 6 Electricity purchase costs for all packages for each user

[0184]

[0185]

[0186] Figure 10 The diagram shows the package pricing results in some embodiments of the present invention. When the global optimal state is achieved, the optimal package pricing result can be obtained. The thermal power package price and the green power package price are as follows: Figure 9 and Figure 10 shown.

[0187] The following is an analysis of users' electricity purchasing strategies.

[0188] Figure 11 A schematic diagram of load curves before and after a peak-avoidance user invokes a shared energy storage strategy and selects a package according to some embodiments of the present invention is shown.

[0189] from Figure 11 As can be seen from the figure, peak-avoiding users calling shared energy storage mainly increases the maximum load during valley hours and reduces the minimum load during peak hours. This is because the user has selected the peak-valley incentive retail package. Therefore, in order to respond to the package's incentive policy, energy storage is used to shift the electricity demand during peak hours to the valley hours, increasing the maximum load and total electricity consumption during the valley hours and reducing the minimum load and total electricity consumption during the peak hours, thereby increasing the excess coefficient of electricity and power and obtaining more incentive benefits.

[0190] Figure 12 A schematic diagram of load curves before and after a peak-peak user calls a shared energy storage strategy and selects a package according to some embodiments of the present invention is shown.

[0191] from Figure 12 It can be seen from the figure that peak-facing users mainly call on energy storage to transfer the load during the off-peak period to the peak period. This is because the user ultimately chooses a tiered electricity price retail package. If other packages are selected, even if part of the peak load is transferred to the off-peak period through energy storage, the overall load curve cannot be avoided from remaining peak-facing. Therefore, through reverse adjustment, the energy storage scheduling strategies of other users are complemented, thereby optimizing the overall scheduling strategy of shared energy storage and saving shared energy storage capacity.

[0192] Figure 13 A schematic diagram of load curves before and after a stable user calls a shared energy storage strategy and selects a package according to some embodiments of the present invention is shown.

[0193] from Figure 13As can be seen from the figure, stable users call on energy storage mainly to transfer the load during peak hours to off-peak hours. This is because the user has selected the electricity compensation package. Therefore, in order to respond to the package's incentive policy, the electricity consumption during peak hours is transferred to off-peak hours through shared energy storage. This can not only increase the total electricity consumption during off-peak hours and increase the free electricity quota in other periods, but also reduce the total electricity consumption in other high-price periods, thereby minimizing the total electricity cost.

[0194] Based on the above analysis, various types of users have called on shared energy storage to transfer load in response to the price of the retail package. The load curve after the transfer is the user's final electricity demand, and they meet their own load demand by purchasing thermal power packages and green electricity packages from the shared energy storage entity.

[0195] Figures 14 to 16 Schematic diagrams of electricity purchasing strategies for peak-avoiding users, peak-onset users, and stable users according to some embodiments of the present invention are respectively shown.

[0196] from Figure 14 It can be seen that peak-avoiding users purchase green electricity and thermal power to meet their electricity needs in different time periods. The price of purchasing thermal power is slightly lower than that of green electricity, but it will cause indirect carbon emissions. Taking into account the two goals of cost and carbon emissions, the optimal compromise solution of the Pareto solution set corresponds to the green electricity purchase strategy and thermal power purchase strategy (taking user A as an example).

[0197] from Figure 15 It can be seen that peak-facing users purchase green electricity and thermal power to meet their electricity needs in different time periods. The price of purchasing thermal power is slightly lower than that of green electricity, but it will cause indirect carbon emissions. Taking into account the two goals of cost and carbon emissions, the optimal compromise solution of the Pareto solution set corresponds to the green electricity purchase strategy and the thermal power purchase strategy.

[0198] from Figure 16 It can be seen that stable users purchase green electricity and thermal power to meet their electricity needs in different time periods. The price of purchasing thermal power is slightly lower than that of green electricity, but it will cause indirect carbon emissions. Taking into account the two goals of cost and carbon emissions, the optimal compromise solution of the Pareto solution set corresponds to the green electricity purchase strategy and the thermal power purchase strategy.

[0199] Figure 17 The figure shows the output power diagram of three shared energy storage devices in some embodiments of the present invention. It should be noted that in order to ensure that the shared energy storage entity can meet the electricity demand of users at all times, in some embodiments, a shared energy storage cluster is designed to include three shared energy storage devices, which are charged and discharged at the same time to meet user needs. Taking the demand curve of peak-avoiding users as an example, the output power of the three shared energy storage devices and the load demand curve of peak-avoiding users are as follows: Figure 17 shown. Figure 17The positive part of the vertical coordinate is the discharge of the shared energy storage device during this period, and the negative part is the charging of the shared energy storage device during this period. Figure 17 As can be seen from the figure, the simultaneous output of the three shared energy storage devices can meet the full-time load demand of this type of user. This result shows that users can meet their electricity needs by purchasing electricity packages designed with shared energy storage, and also verifies the effectiveness of the two-layer master-slave game model.

[0200] In summary, according to the master-slave game-based shared energy storage electricity retail package optimization method 300 of the present invention, an upper-level shared energy storage entity decision model is constructed with the goal of maximizing the total profit earned by the shared energy storage entity through the sale of multiple packages of thermal power and green power, and a lower-level user decision model is constructed with the goal of minimizing the total cost of electricity purchased by users using shared energy storage. Based on this, a two-layer master-slave game model can be obtained between the shared energy storage entity and the user. Furthermore, the two-layer master-slave game model is collaboratively solved using a genetic algorithm and a particle swarm algorithm to obtain the optimal package pricing strategy for the shared energy storage entity and the optimal package electricity purchasing strategy for the user. Based on this, the interests of both the shared energy storage entity and the user are comprehensively considered, achieving collaborative optimization of the shared energy storage entity's package pricing strategy and the user's package electricity purchasing strategy. This can maximize the shared energy storage entity's profits while reducing user electricity costs by 15%-20%.

[0201] By way of example and not limitation, readable media include readable storage media and communication media. Readable storage media store information such as computer-readable instructions, data structures, program modules, or other data. Communication media typically embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and include any information delivery medium. Combinations of any of the above are also included within the scope of readable media.

[0202] In the description provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems may also be used in conjunction with the examples of the present invention. Based on the above description, it is apparent that the structure required for constructing such systems is well understood. In addition, the present invention is not directed to any specific programming language. It should be understood that various programming languages ​​may be utilized to implement the present invention described herein, and the description of specific languages ​​above is provided for the purpose of disclosing the preferred embodiment of the present invention.

[0203] 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.

[0204] Similarly, it should be understood that in order to streamline the disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0205] Those skilled in the art will appreciate that the modules, units, or components of the devices in the examples disclosed herein may be arranged in the device described in the embodiment, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the aforementioned examples may be combined into one module or further divided into multiple submodules.

[0206] Unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely indicates that different instances of similar objects are involved and is not intended to imply that the objects so described must have a given order in time, space, ranking, or in any other manner.

Claims

1. A method for optimizing a shared energy storage power retail package based on a master-slave game, wherein the shared energy storage power retail package includes multiple packages for selling thermal power and green power, the multiple packages including a time-of-use electricity price retail package, a tiered electricity price retail package, a peak-valley incentive retail package, a valley electricity consumption compensation retail package, a two-part electricity price retail package, and a capacity leasing package. The method comprises: An upper-level objective function is constructed with the maximum total profit obtained by the shared energy storage entity through selling various packages of thermal power and green power as the upper-level goal, and an upper-level shared energy storage entity decision model is constructed based on the upper-level objective function; A lower-level objective function is constructed with minimizing the total cost of electricity purchased by users using shared energy storage as the lower-level objective, and a lower-level user decision model is constructed based on the lower-level objective function; Based on the upper-layer shared energy storage entity decision model and the lower-layer user decision model, a two-layer master-slave game model is obtained; The two-layer master-slave game model is solved collaboratively using a genetic algorithm and a particle swarm algorithm to obtain an optimal package pricing strategy and an optimal package electricity purchasing strategy. The optimal package pricing strategy is used to indicate the electricity price of each package in each time period, and the optimal package electricity purchasing strategy is used to indicate the package selected by the user and the amount of thermal power and green power purchased under the selected package in each time period.

2. The method according to claim 1, wherein The total profit is equal to the sum of the profits from selling various thermal power packages, the profits from selling various green power packages, and the revenue from leasing capacity through capacity leasing packages, minus the initial investment costs and operating costs of the shared energy storage entity. Among them, the initial investment cost of the shared energy storage entity includes the investment cost of each shared energy storage device and the cost of power grid technical transformation.

3. The method according to claim 1 or 2, wherein The profit from selling the various packages of thermal power is equal to: the sum of the revenue from selling the various packages of thermal power through each shared energy storage device minus the total cost of selling thermal power during the operation of the shared energy storage entity; The profit obtained from selling various green electricity packages is equal to: the sum of the revenue obtained from selling various green electricity packages through each shared energy storage device minus the total cost of selling green electricity during the operation of the shared energy storage entity.

4. The method according to any one of claims 1 to 3, wherein An upper-level shared energy storage subject decision model is constructed based on the upper-level objective function, including: Based on the upper-level objective function and multiple upper-level constraints, construct an upper-level shared energy storage subject decision model; The plurality of upper-level constraints include power balance constraints, electricity price constraints, green equity constraints, shared energy storage equipment constraints, and capacity leasing constraints. The power balance constraint is used to constrain: the total amount of thermal power purchased by the user is equal to the sum of the thermal power sold by various thermal power packages, and the total amount of green power purchased by the user is equal to the sum of the green power sold by various green power packages; The green equity constraint condition is used to constrain the price of green electricity to be higher than the price of thermal electricity; The shared energy storage device constraint condition is used to constrain: the charging power and discharging power of the shared energy storage device are balanced and do not exceed the maximum safe discharge power of the shared energy storage device, and the state of charge of the shared energy storage device is proportional to the charging power of the shared energy storage device, and the state of charge of the shared energy storage device is between the minimum state of charge and the maximum state of charge at any time, while the change cycle of the state of charge remains unchanged; The capacity leasing constraint condition is used to constrain the capacity leased by the user to not exceed the available energy storage capacity.

5. The method according to any one of claims 1 to 4, wherein A lower-level objective function is constructed with minimizing the total cost of electricity purchased by users using shared energy storage as the lower-level goal, and a lower-level user decision model is constructed based on the lower-level objective function, including: The first lower-level objective function is constructed with the minimum total cost of electricity purchased by users using shared energy storage as the first lower-level objective; The second lower-level objective function is constructed with the lowest total carbon emissions from electricity purchases by users as the second lower-level objective; A lower-layer user decision model is constructed based on the first lower-layer objective function and the second lower-layer objective function.

6. The method according to claim 5, wherein: The total electricity purchase cost for users using shared energy storage includes: the total cost of users purchasing thermal power, the total cost of users purchasing green electricity, the demand response cost of users using shared energy storage, and the capacity leasing cost of users.

7. The method according to any one of claims 1 to 6, wherein The two-layer master-slave game model is solved collaboratively using a genetic algorithm and a particle swarm algorithm to obtain the optimal package pricing strategy and the optimal package electricity purchase strategy, including: Generate an initial population based on the upper-layer shared energy storage subject decision model and shared energy storage subject parameters using a genetic algorithm; and generate an initial particle swarm based on the lower-layer user decision model and user parameters using a particle swarm algorithm; Using a particle swarm algorithm, based on the initial population, calculating the fitness of particles in the initial particle swarm, and updating the velocity and position of each particle in the initial particle swarm according to the particle fitness to obtain a Pareto front solution set, and using a TOPSIS method to screen the optimal individual based on the Pareto front solution set; Utilizing a genetic algorithm, calculating the fitness of the initial population according to the optimal individuals, and performing selection, crossover, and mutation operations on the initial population according to the fitness of the initial population to generate a new population; The above steps are executed in a loop until the termination condition is reached, thereby obtaining the global optimal solution of the two-layer master-slave game model, and determining the optimal package pricing strategy and the optimal package electricity purchasing strategy based on the global optimal solution.

8. The method of claim 7, wherein: The TOPSIS method is used to screen the optimal individuals according to the Pareto front solution set, including: performing data normalization processing on the Pareto front solution set to determine a positive ideal solution and a negative ideal solution; Determining the closeness of each solution in the Pareto front solution set to the positive ideal solution and the negative ideal solution; The solutions in the Pareto front solution set are sorted according to the closeness, so as to determine the optimal compromise solution in the Pareto front solution set as the optimal individual.

9. 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 8.

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

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