Master-Slave Game Optimization Method for Differentiated Shared Energy Storage Services Considering Flexible Resource Adjustment Capabilities

Abstract

This invention discloses a master-slave game optimization method for differentiated service combinations in shared energy storage, taking into account the flexible adjustment capabilities of resources. Shared energy storage clusters are suitable for providing capacity leasing services, electricity retail services, and frequency regulation ancillary services. The method includes: constructing an upper-level model with the goal of maximizing the total revenue of the shared energy storage cluster; constructing a lower-level model with the goal of minimizing the energy consumption costs of energy storage users; obtaining a two-layer master-slave game optimization model based on the upper and lower-level models; and solving the two-layer master-slave game optimization model to determine the optimal service price of the shared energy storage cluster and the optimal energy consumption strategy for energy storage users. This invention enables shared energy storage operators to design a scientific and reasonable pricing mechanism, improving the revenue of shared energy storage while reducing the energy consumption costs for users.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a master-slave game optimization method for a shared energy storage differentiated service portfolio that takes into account the flexible adjustment capability of resources. Background Technology

[0002] Shared energy storage is an important model for leveraging the flexibility of new energy storage systems and enhancing the flexibility and regulation capabilities of new power systems. However, current energy storage service models are simplistic and pricing mechanisms lack specificity, making it difficult to adapt to the diverse needs of various stakeholders and hindering efficient resource allocation and renewable energy consumption.

[0003] Driven by the national strategy of building a new power system and achieving "dual carbon" goals, new energy storage has become a crucial flexible resource for ensuring the safe and stable operation of the new power system. By the end of December 2025, my country's cumulative installed capacity of new energy storage had reached 144.7 gigawatts. Furthermore, with the abolition of mandatory energy storage requirements for new energy sources and the inclusion of energy storage-related costs and benefits in a market-based pricing system, the relationship between energy storage revenue and market revenue has become increasingly close. However, the large-scale promotion of energy storage facilities still faces the dual challenges of high investment costs and complex operating models, limiting its practical application. Among these challenges, resource mismatch is particularly prominent, with some energy storage projects exhibiting low utilization rates and significant capacity idleness, hindering the overall improvement of the industry's efficiency. Shared energy storage, as an innovative business model, maintains the flexibility of distributed deployment by centrally managing and optimizing the allocation of energy storage resources. It provides flexible and efficient energy storage services to power generators, electricity users, and the grid, effectively solving the problem of idle energy storage and achieving a win-win situation for all parties. However, there are still shortcomings in how to balance the interests of various demanders through a reasonable pricing mechanism and achieve the optimal allocation of energy storage resources in shared energy storage. Therefore, it is necessary and urgent to optimize the combination of shared energy storage business models.

[0004] In terms of shared energy storage service types, they are mainly divided into those targeting the generation side, the grid side, and the user side. Generation-side energy storage is mainly used in the development model of equipping renewable energy with energy storage, increasing the absorption capacity of renewable energy. Shared energy storage can design corresponding trading models for users with different energy storage needs. Users can choose appropriate strategies to meet their own needs based on their characteristics, which is conducive to the management of the energy storage market by relevant market institutions and the protection of consumer rights.

[0005] However, current research on shared energy storage pricing mechanisms focuses on pricing strategies of single stakeholders, lacking a comprehensive analysis of multi-demand game dynamics. Existing research has failed to fully reveal the inherent laws governing the complex game relationships among energy storage power plants, power grids, power generators, and electricity users.

[0006] In summary, while existing research has explored shared energy storage from multiple perspectives, including service type and pricing mechanism, it still lacks a pricing and operation framework that can synergistically optimize frequency regulation services and energy arbitrage revenue, and characterize the dynamic strategic interactions among multiple parties in a complex game environment involving multiple stakeholders and market objectives. Therefore, it is urgent to construct a master-slave game model that integrates the aforementioned composite revenues, and to design a market-based pricing mechanism that balances efficiency and fairness for shared energy storage operators, so as to promote their sustainable commercial development in new power systems.

[0007] Therefore, a master-slave game optimization method for the shared energy storage differentiated service portfolio that takes into account the flexible adjustment capability of resources is needed to solve the problems existing in the above schemes. Summary of the Invention

[0008] To address this, the present invention provides a master-slave game optimization method for the shared energy storage differentiated service combination that takes into account the flexible adjustment capability of resources, in order to solve or at least alleviate the problems mentioned above.

[0009] According to one aspect of the present invention, a master-slave game optimization method for a differentiated shared energy storage service portfolio, taking into account the flexible adjustment capability of resources, is provided and executed in a computing device. The method includes: constructing an upper-level objective function with maximizing the total revenue of the shared energy storage cluster as the upper-level objective; constructing an upper-level model based on the upper-level objective function, wherein the shared energy storage cluster is suitable for providing capacity leasing services to renewable energy generators, providing electricity retail services to industrial and commercial users, and participating in grid frequency regulation ancillary services; the total revenue of the shared energy storage cluster is suitable for determination based on the capacity leasing revenue, electricity retail revenue, frequency regulation ancillary service revenue, and charging and operation and maintenance costs of the shared energy storage cluster; constructing a lower-level objective function with minimizing the energy consumption costs of energy storage demand users as the lower-level objective; constructing a lower-level model based on the lower-level objective function; wherein the energy storage demand users include renewable energy generators and industrial and commercial users, wherein renewable energy generators... The energy costs of renewable energy generators include the cost of leasing capacity from the shared energy storage cluster and the output deviation penalty cost of the renewable energy generators. The energy costs of industrial and commercial users include the electricity purchase costs from the shared energy storage cluster and the electricity purchase costs from the grid. Based on the upper-level model and the lower-level model, a two-layer master-slave game optimization model is obtained. The two-layer master-slave game optimization model is solved to determine the optimal service price of the shared energy storage cluster and the optimal energy consumption strategy of energy storage demand users. The optimal service price is used to indicate the capacity leasing price of the shared energy storage cluster and the electricity retail price for each time period. The optimal energy consumption strategy is used to indicate the capacity leased by the renewable energy generators from the shared energy storage cluster, the charging and discharging power of the renewable energy generators to the shared energy storage cluster for each time period, and the electricity purchase power of industrial and commercial users from the shared energy storage cluster for each time period.

[0010] Optionally, in the master-slave game optimization method for the differentiated shared energy storage service portfolio that takes into account the flexible adjustment capability of resources according to the present invention, the capacity leasing revenue represents the revenue of the shared energy storage cluster from providing capacity leasing services to new energy power generators, and is related to the capacity leasing price of the shared energy storage cluster and the capacity leased by the new energy power generators from the shared energy storage cluster; the electricity retail revenue represents the revenue of the shared energy storage cluster from providing electricity retail services to industrial and commercial users, and is related to the electricity retail price of the shared energy storage cluster in each time period and the power purchased by industrial and commercial users from the shared energy storage cluster; the frequency regulation ancillary service revenue represents the revenue of the shared energy storage cluster from participating in grid frequency regulation ancillary services, and is related to the frequency regulation capacity price, frequency regulation mileage price, and the day-ahead declared power of the shared energy storage cluster participating in frequency regulation ancillary services in each time period.

[0011] Optionally, in the master-slave game optimization method for the differentiated service portfolio of shared energy storage that takes into account the flexible adjustment capability of resources according to the present invention, the cost for the new energy generator to lease capacity from the shared energy storage cluster is related to the capacity leasing price of the shared energy storage cluster and the capacity leased by the new energy generator from the shared energy storage cluster; the output deviation penalty cost of the new energy generator is suitable for determination based on the output deviation penalty coefficient of the new energy generator, the charging and discharging power of the new energy generator to the shared energy storage cluster, and the actual output and planned output of the new energy generator; the electricity purchase cost of the industrial and commercial users from the shared energy storage cluster is related to the electricity retail price of the shared energy storage cluster and the electricity purchased by the industrial and commercial users from the shared energy storage cluster, and the electricity purchase cost from the grid is related to the electricity price and electricity purchased by the industrial and commercial users from the grid.

[0012] Optionally, in the master-slave game optimization method for the differentiated shared energy storage service portfolio that takes into account the flexible adjustment capability of resources according to the present invention, constructing an upper-level model based on the upper-level objective function includes: constructing an upper-level model based on the upper-level objective function and upper-level constraints; wherein, the upper-level constraints include shared energy storage cluster energy balance constraints, shared energy storage cluster power constraints, shared energy storage cluster capacity constraints, and shared energy storage cluster price constraints.

[0013] Optionally, in the master-slave game optimization method for the differentiated shared energy storage service portfolio considering the flexible adjustment capability of resources according to the present invention, the energy balance constraint condition of the shared energy storage cluster represents: the sum of the charging amount of each new energy generator to the shared energy storage cluster, the charging amount of the shared energy storage cluster to each industrial and commercial user, and the charging amount of the shared energy storage cluster for frequency regulation auxiliary services in each time period is equal to the sum of the discharging amount of each new energy generator to the shared energy storage cluster, the discharging amount of the shared energy storage cluster to each industrial and commercial user, and the discharging amount of the shared energy storage cluster for frequency regulation auxiliary services in each time period; the power constraint condition of the shared energy storage cluster is used to constrain the charging and discharging power of the new energy generator to the shared energy storage cluster, the discharging power of the shared energy storage cluster to industrial and commercial users, the daily declared power of the shared energy storage cluster participating in frequency regulation auxiliary services, and the upper and lower limits of the total charging and discharging power of the shared energy storage cluster in each time period; the capacity constraint condition of the shared energy storage cluster represents: the lower limit of .... The actual usage capacity of a shared energy storage cluster in a given time period is equal to the actual usage capacity of the shared energy storage cluster in the current time period plus the difference between the total charging and total discharging of the shared energy storage cluster in the current time period; and the state of charge (SOC) of the shared energy storage cluster in each time period is equal to the ratio of the actual usage capacity of the shared energy storage cluster in each time period to the total capacity, and the SOC of the shared energy storage cluster in each time period does not exceed the upper and lower limits of the SOC of the shared energy storage cluster; the price constraints of the shared energy storage cluster include capacity leasing price constraints and electricity retail price constraints, wherein the capacity leasing price constraint indicates that the capacity leasing price of the shared energy storage cluster is greater than or equal to the average daily unit capacity operation and maintenance cost of the shared energy storage cluster, and less than or equal to the average daily unit capacity cost of self-built energy storage; the electricity retail price constraint indicates that the electricity retail price of the shared energy storage cluster in each time period should fluctuate between the electricity purchase price of the shared energy storage cluster from the grid and the electricity sales price of the grid during peak hours.

[0014] Optionally, in the master-slave game optimization method for the differentiated shared energy storage service portfolio that takes into account the flexible adjustment capability of resources according to the present invention, constructing a lower-level model based on the lower-level objective function includes: constructing a lower-level model based on the lower-level objective function and lower-level constraints; wherein, the lower-level constraints include the output stability constraints of renewable energy generators and the demand response constraints of industrial and commercial users; the output stability constraints of renewable energy generators indicate that the capacity leased by renewable energy generators meets the requirement of compensating for the maximum deviation in electricity volume at a confidence level; the demand response constraints of industrial and commercial users indicate that industrial and commercial users adjust their load demand according to the initial and adjusted electricity retail prices for each time period.

[0015] Optionally, in the master-slave game optimization method for the differentiated service portfolio of shared energy storage considering the flexible adjustment capability of resources according to the present invention, solving the two-layer master-slave game optimization model to determine the optimal service price of the shared energy storage cluster and the optimal energy consumption strategy of the energy storage demand user includes: using a genetic algorithm and a particle swarm optimization algorithm to collaboratively solve the two-layer master-slave game optimization model to obtain a globally optimal solution; and determining the optimal service price of the shared energy storage cluster and the optimal energy consumption strategy of the energy storage demand user based on the globally optimal solution.

[0016] Optionally, in the master-slave game optimization method for the shared energy storage differentiated service portfolio considering the flexible adjustment capability of resources according to the present invention, a genetic algorithm and a particle swarm optimization algorithm are used to collaboratively solve the two-layer master-slave game optimization model to obtain the global optimal solution. This includes: initializing the genetic algorithm parameters and the particle swarm optimization algorithm parameters, wherein the genetic algorithm parameters include the population size and the maximum number of iterations of the genetic algorithm, and the particle swarm optimization algorithm parameters include the particle swarm size and the maximum number of iterations of the particle swarm optimization algorithm; randomly generating an initial population based on the genetic algorithm parameters, wherein the initial population contains multiple initial individuals, each representing an initial service price; and generating an initial particle swarm based on the particle swarm optimization algorithm parameters, wherein the initial particle swarm contains multiple initial particles, each representing an initial energy consumption strategy; calculating the first fitness of each initial individual in the initial population based on the upper-level objective function, and performing genetic operations based on the first fitness of each initial individual to generate... A new particle swarm is formed by merging the initial particle swarm with the offspring particle swarm. Based on the lower-level objective function and the merged particle swarm, the second fitness of each initial particle in the initial particle swarm is calculated, and the velocity and position of each initial particle are updated based on the second fitness to obtain a new particle swarm. The second fitness of each new particle in the new particle swarm is also calculated, and the velocity and position of each new particle are updated based on the second fitness, until the maximum number of iterations of the particle swarm algorithm is reached, yielding a Pareto front solution. A compromise decision method based on comprehensive satisfaction is used to select the optimal compromise solution from the Pareto front solutions. Based on the optimal compromise solution and the upper-level objective function, the first fitness of each individual in the merged particle swarm is calculated, and genetic operations are performed based on the first fitness of each individual in the merged particle swarm to generate a new offspring particle swarm, until the maximum number of iterations of the genetic algorithm is reached, yielding a globally optimal solution.

[0017] 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 executed by the at least one processor, the program instructions including instructions for executing the master-slave game optimization method for shared energy storage differentiated service portfolios that takes into account resource flexibility.

[0018] According to one aspect of the present invention, a computer program product is provided, comprising a computer program / instructions, wherein the computer program / instructions, when executed by a processor, implement the method as described above.

[0019] According to one aspect of the present invention, a readable storage medium storing program instructions is provided, which, when read and executed by a computing device, causes the computing device to perform the master-slave game optimization method for shared energy storage differentiated service portfolios that takes into account the resource flexibility adjustment capability as described above.

[0020] According to the technical solution of this invention, a master-slave game optimization method for differentiated service combinations in shared energy storage, taking into account the flexible adjustment capability of resources, is provided. Shared energy storage clusters can provide differentiated service combinations to meet the needs of multiple stakeholders, including capacity leasing services, electricity retail services, and frequency regulation ancillary services. Based on this, a two-layer master-slave game optimization model is constructed, with the maximization of the total revenue of the shared energy storage cluster as the upper-level objective and the minimization of energy costs for energy storage demand users (new energy power generators and industrial and commercial users) as the lower-level objective. Solving this model yields the optimal service price of the shared energy storage cluster and the optimal energy consumption strategy for energy storage demand users. Based on this, a scientific and reasonable pricing mechanism can be designed for shared energy storage operators, significantly improving the utilization efficiency and operating revenue of shared energy storage while reducing user energy costs, achieving synergistic development for both parties.

[0021] Furthermore, the capacity leasing service proposed in this invention can provide stable capacity reserves for renewable energy generators, enhancing system stability; the electricity retail service increases the options for purchasing electricity for industrial and commercial users, increasing the diversity of the electricity market; and the frequency regulation ancillary service can alleviate the pressure on the power grid during peak periods and ensure power supply. By flexibly combining multiple service models, the capacity and electricity needs of various entities can be met, thereby improving the overall benefits of the entire shared energy storage cluster.

[0022] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

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

[0024] Figure 1 A schematic diagram of a shared energy storage trading system 100 according to an embodiment of the present invention is shown; Figure 2 A schematic diagram of a computing device 200 provided according to an embodiment of the present invention is shown; Figure 3 The diagram illustrates a master-slave game optimization method 300 for a shared energy storage differentiated service portfolio that takes into account the flexible adjustment capability of resources, according to an embodiment of the present invention. Detailed Implementation

[0025] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0026] To address the problems of existing energy storage service models being too simplistic, pricing mechanisms lacking specificity, and failing to adapt to the differentiated needs of diverse stakeholders, this invention proposes a master-slave game optimization method for differentiated shared energy storage service combinations that considers the flexible adjustment capabilities of resources. This method provides differentiated service combinations for the needs of multiple stakeholders. By analyzing the game behavior between shared energy storage clusters and energy storage demand users, and considering the shared energy storage clusters' participation in grid frequency regulation ancillary services and peak-valley arbitrage, a two-layer master-slave game optimization model is constructed. The upper-level objective is to maximize the total revenue of the shared energy storage cluster, while the lower-level objective is to minimize the energy costs for energy storage demand users. Based on this, a scientific and reasonable pricing mechanism can be designed for shared energy storage operators, improving the revenue of shared energy storage while reducing the energy costs for users.

[0027] The master-slave game optimization method for differentiated shared energy storage service combinations, which takes into account the flexible adjustment capability of resources, provided by embodiments of the present invention, can be used in shared energy storage trading systems. The following first introduces a shared energy storage trading system provided by an embodiment of the present invention.

[0028] Figure 1A schematic diagram of a shared energy storage trading system 100 provided according to an embodiment of the present invention is shown.

[0029] like Figure 1 As shown, the shared energy storage trading system 100 includes a shared energy storage cluster (shared energy storage), energy storage demand users, and the power grid. Among them, energy storage demand users include renewable energy generators and industrial and commercial users. In this embodiment of the invention, the analysis mainly focuses on the game theory between the shared energy storage cluster and the energy storage demand users.

[0030] It should be noted that a shared energy storage cluster comprises multiple energy storage devices (energy storage resources) capable of providing energy storage and retrieval services to users with energy storage needs. If a user has surplus energy and a need for energy storage, they can store the energy in the shared energy storage cluster. When there is a need to discharge, they can retrieve energy from the shared energy storage cluster to meet their needs. Furthermore, the shared energy storage cluster can not only allocate its capacity to leasing users but also utilize its capacity to participate in grid ancillary services and peak-valley arbitrage to generate revenue. Compared to traditional shared energy storage power stations, this type of large-scale shared energy storage, formed by the aggregation of multiple independently operated energy storage units through physical or virtual means, retains the flexibility of distributed deployment while achieving overall efficiency improvement through centralized management and control.

[0031] For renewable energy generators with energy storage requirements, this invention analyzes wind power generators and photovoltaic (PV) generators, whose output varies with wind speed and solar irradiance, respectively. The output characteristics of wind power generators are mainly characterized by intermittency, volatility, instability, seasonality, and regional variability. The output power of wind power generation directly depends on changes in wind speed, and fluctuations in wind speed can lead to significant variations in wind power output, especially at different times of the day. Due to the uncertainty of wind, wind power output may fluctuate considerably at different times of the day, posing a significant challenge to the stable operation of the power grid without energy storage. The output characteristics of PV generators are mainly characterized by significant diurnal variation and seasonality. The output power of PV power generation directly depends on solar radiation intensity; therefore, its output varies with the duration and intensity of sunshine, typically being higher during the day when there is sufficient sunshine and lower at night and on cloudy or rainy days. Therefore, these characteristics of PV power generation result in output fluctuations caused by sunrise and sunset, seasonal changes, and weather variations, as well as output instability caused by geographical and seasonal factors. These shortcomings pose challenges to the integration and application of photovoltaic power generation in the power grid. Energy storage and other regulation methods are needed to compensate for these limitations in output characteristics, ensuring the stable operation of the power system and the continuity of power supply. Therefore, renewable energy generators typically require energy storage to smooth out power generation fluctuations, thereby minimizing their own costs.

[0032] For electricity users (industrial and commercial users) with energy storage needs, this invention assumes that electricity users include two different user groups: industrial users and commercial users. Industrial users' load is concentrated during the day (9:00-17:00), with lower load at night, while commercial users' load is concentrated during morning and evening peak hours (8:00-11:00 and 17:00-21:00), with lower load at midday. Referring to domestic time-of-use pricing policies, electricity prices are lower during off-peak hours, and electricity users tend to purchase electricity for charging during these times to reduce costs; while electricity prices are higher during peak hours, and electricity users tend to sell electricity to obtain higher profits. Therefore, for industrial users, shared energy storage can smooth peak loads, reduce capacity charges, and simultaneously reduce energy costs by utilizing peak-valley price differences; for commercial users, shared energy storage can ensure power supply reliability during critical periods and optimize energy use efficiency.

[0033] According to embodiments of the present invention, during the transaction process, the shared energy storage cluster attracts renewable energy generators and industrial and commercial users by providing corresponding service models and prices, thereby increasing its own profits. Simultaneously, renewable energy generators and industrial and commercial users can minimize their own costs through shared energy storage. The present invention adopts different service strategies based on the different energy storage needs of renewable energy generators and industrial and commercial users (industrial users and commercial users).

[0034] For shared energy storage clusters, an operational model is adopted in which the shared energy storage operator manages the energy storage resources uniformly. The energy storage resources can be represented as: (1) In the formula, E represents the total capacity of the shared energy storage cluster. This represents the nth shared energy storage unit.

[0035] For renewable energy generators, the shared energy storage cluster charges them fees by providing capacity leasing services. The shared energy storage cluster sets a daily capacity leasing price, and renewable energy generators, based on their actual needs, choose to lease the right to use a certain capacity of energy storage equipment for a specific period to mitigate fluctuations in their power output. The formula for calculating the revenue of the shared energy storage cluster from providing capacity leasing services to renewable energy generators is as follows: (2) In the formula, The revenue generated by the shared energy storage cluster from providing capacity leasing services to renewable energy generators (capacity leasing revenue of the shared energy storage cluster), where α is the index of the renewable energy generator. This refers to the unit price of capacity leased by a new energy power generator from a shared energy storage cluster within one day (the capacity leasing price of the shared energy storage cluster). The capacity that a new energy power generator α leases from a shared energy storage cluster within a day.

[0036] For industrial and commercial users (both industrial and commercial), shared energy storage clusters achieve peak-valley arbitrage through time-of-use pricing strategies. The core of this strategy lies in integrating the differentiated electricity consumption characteristics of industrial and commercial users during off-peak hours. Shared energy storage clusters charge their storage devices at low prices during off-peak periods and then sell electricity to industrial and commercial users at more favorable prices during peak hours. This electricity retail service model, on the one hand, guides energy-intensive enterprises to proactively reduce unnecessary loads during peak hours, and on the other hand, incentivizes industrial and commercial users to utilize surplus electricity during off-peak periods to improve production efficiency. By precisely matching the spatiotemporal characteristics of supply and demand, it optimizes the grid load curve and creates a stable channel for electricity cost savings for industrial and commercial users. The formula for calculating the revenue of shared energy storage clusters providing electricity retail services to industrial and commercial users is as follows: (3) In the formula, The revenue generated by the shared energy storage cluster from providing electricity retail services to industrial and commercial users (electricity retail revenue of the shared energy storage cluster), where β is the index of industrial and commercial users. The retail electricity price for industrial and commercial users to purchase electricity from the shared energy storage cluster during time period t (i.e., the retail electricity price of the shared energy storage cluster during time period t). β represents the electricity purchased by industrial and commercial users from the shared energy storage cluster during time period t.

[0037] With the continuous expansion of renewable energy integration and the rapid growth in the number of electrification devices, power grid transmission lines are prone to heavy loads and overloads, and substations face problems such as widening peak-valley differences in electricity demand and deteriorating power quality, leading to a decline in power grid reliability. Shared energy storage, with its rapid response characteristics and relatively efficient energy storage capacity, actively participates in grid frequency regulation ancillary services. When electricity demand is relatively low, it can store excess energy; when electricity demand is high, it can quickly respond to grid demand and release the stored energy, thereby helping the grid achieve frequency stability. The revenue calculation formula for shared energy storage clusters participating in grid frequency regulation ancillary services is as follows: (4) in, The revenue of shared energy storage clusters participating in grid frequency regulation ancillary services (revenue from frequency regulation ancillary services of shared energy storage clusters). The unit capacity price (frequency regulation capacity price) of the shared energy storage cluster during time period t. Let t represent the unit mileage price (frequency regulation mileage price) of the shared energy storage cluster during time period t, and k represent the comprehensive frequency regulation performance index. The declared power for shared energy storage clusters participating in frequency regulation ancillary services.

[0038] In embodiments of the present invention, a computing device may be configured to execute a master-slave game optimization method 300 for a shared energy storage differentiated service portfolio that takes into account resource flexibility. The master-slave game optimization method 300 for a shared energy storage differentiated service portfolio that takes into account resource flexibility will be described below.

[0039] The following describes a computing device 200 provided by an embodiment of the present invention.

[0040] Figure 2 A schematic diagram of a computing device 200 according to an embodiment of the present invention is shown. 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 the computing device, the processing unit 202 may be implemented as a processor. System memory 204 includes, but is not limited to, volatile memory (e.g., random access memory), non-volatile memory (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.

[0041] According to one aspect, operating system 205 is, for example, suitable for controlling the operation of computing device 200. Furthermore, examples are practiced in conjunction with graphics libraries, other operating systems, or any other applications, and are not limited to any particular application or system. Figure 2 The basic configuration is illustrated by the components within the dashed lines. According to one aspect, the computing device 200 has additional features or functions. For example, according to one aspect, the computing device 200 includes additional data storage devices (removable and / or non-removable), such as disks, optical discs, or magnetic tapes. This additional storage... Figure 2 The middle part is shown by removable storage device 209 and non-removable storage device 210.

[0042] As stated above, according to one aspect, program module 203 is stored in system memory 204. According to one aspect, program module 203 may include one or more applications. The present invention does not limit the type of application; for example, applications may include: email and contact applications, word processing applications, spreadsheet applications, database applications, slideshow applications, drawing or computer-aided applications, web browser applications, etc.

[0043] In an embodiment of the present invention, program module 203 includes multiple program instructions for executing the master-slave game optimization method 300 of the shared energy storage differentiated service combination that takes into account the resource flexibility adjustment capability of the present invention.

[0044] According to one aspect, examples can be practiced on circuits including discrete electronic components, packaged or integrated electronic chips containing logic gates, circuits utilizing microprocessors, or on a single chip containing electronic components or a microprocessor. For example, it can be practiced via wherein... Figure 2 Each or many of the components shown can be implemented as an example by integrating a System-on-a-Chip (SOC) on a single integrated circuit. According to one aspect, such an SOC device may include one or more processing units, graphics units, communication units, system virtualization units, and various application functions, all integrated (or “burned in”) as a single integrated circuit onto a chip substrate. When operated via the SOC, the functions described in this invention can be operated via dedicated logic integrated on a single integrated circuit (chip) with other components of the computing device 200. Embodiments of the invention can also be implemented using other techniques capable of performing logical operations (e.g., AND, OR, and NOT), including but not limited to mechanical, optical, fluid, and quantum technologies. Additionally, embodiments of the invention can be implemented within a general-purpose computer or in any other circuit or system.

[0045] According to one aspect, computing device 200 may also have one or more input devices 212, such as a keyboard, mouse, pen, voice input device, touch input device, etc. It may also include output devices 214, such as a display, speaker, printer, etc. The foregoing devices are examples and other devices may also be used. 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.

[0046] As used herein, the term computer-readable medium includes computer storage medium. Computer storage medium can include volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information (e.g., computer-readable instructions, data structures, or program modules). System memory 204, removable storage device 209, and non-removable storage device 210 are examples of computer storage media (i.e., memory storage). Computer storage medium can include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital universal disc (DVD) or other optical storage, magnetic tape, magnetic tape, disk storage or other magnetic storage devices, or any other article of manufacture that can be used to store information and is accessible by computing device 200. According to one aspect, any such computer storage medium can be part of computing device 200. Computer storage medium does not include carrier waves or other transmitted data signals.

[0047] According to one aspect, a communication medium is implemented by computer-readable instructions, data structures, program modules, or other data in a modulated data signal (e.g., a carrier wave or other transmission mechanism), and includes any information transmission medium. According to one aspect, the term "modulated data signal" describes a signal having one or more sets of characteristics or altered in a manner that encodes information in the signal. By way of example and not limitation, a communication medium includes wired media such as wired networks or direct wired connections, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0048] In an embodiment of the present invention, computing device 200 is configured to execute a master-slave game optimization method 300 for a shared energy storage differentiated service portfolio that takes into account resource flexibility. Computing device 200 includes one or more processors and one or more readable storage media storing program instructions that, when configured to be executed by one or more processors, cause the computing device to execute the master-slave game optimization method 300 for a shared energy storage differentiated service portfolio that takes into account resource flexibility in an embodiment of the present invention.

[0049] In some embodiments, the computing device 200 that performs the master-slave game optimization method 300 for the shared energy storage differentiated service portfolio taking into account the resource flexibility adjustment capability in the embodiments of the present invention may be a terminal or a server.

[0050] The following is a detailed description of the master-slave game optimization method 300 for the shared energy storage differentiated service combination that takes into account the flexible adjustment capability of resources in the embodiments of the present invention.

[0051] Figure 3A flowchart illustrating a master-slave game optimization method 300 for a shared energy storage differentiated service portfolio considering flexible resource adjustment capabilities, according to an embodiment of the present invention, is shown. Figure 3 As shown, the master-slave game optimization method 300 for the shared energy storage differentiated service portfolio that takes into account the flexible adjustment capability of resources includes the following steps 310-340.

[0052] Step 310: The computing device 200 can construct an upper-level objective function with the goal of maximizing the total revenue of the shared energy storage cluster, and then construct an upper-level model based on the upper-level objective function.

[0053] In this embodiment of the invention, the shared energy storage cluster can provide differentiated service combinations to meet the needs of multiple stakeholders, including capacity leasing services, electricity retail services, and frequency regulation ancillary services. Specifically, the shared energy storage cluster can provide capacity leasing services to renewable energy generators, electricity retail services to industrial and commercial users, and participate in grid frequency regulation ancillary services. Based on this, the total revenue of the shared energy storage cluster can be determined based on the capacity leasing revenue (revenue from providing capacity leasing services to renewable energy generators), electricity retail revenue (revenue from providing electricity retail services to industrial and commercial users), frequency regulation ancillary service revenue (revenue from participating in grid frequency regulation ancillary services), and charging and operation and maintenance costs.

[0054] It should be understood that the capacity leasing revenue of a shared energy storage cluster represents the revenue from providing capacity leasing services to renewable energy generators, and is related to the capacity leasing price of the shared energy storage cluster and the capacity leased by the renewable energy generators from the shared energy storage cluster. The electricity retail revenue of a shared energy storage cluster represents the revenue from providing electricity retail services to industrial and commercial users, and is related to the electricity retail price of the shared energy storage cluster at different time periods and the power purchased by industrial and commercial users from the shared energy storage cluster. The frequency regulation ancillary service revenue of a shared energy storage cluster represents the revenue from participating in grid frequency regulation ancillary services, and is related to the frequency regulation capacity price, frequency regulation mileage price, and the day-ahead declared power of the shared energy storage cluster participating in frequency regulation ancillary services at different time periods.

[0055] In this embodiment of the invention, the shared energy storage cluster is powered by the power grid and can deliver electricity to industrial and commercial users. The power sources for industrial and commercial users are the power grid and the shared energy storage cluster.

[0056] As the leader in the master-slave game, shared energy storage operators can prioritize setting capacity leasing prices, electricity retail prices, and the day-ahead declared power for participating in frequency regulation ancillary services, and reflect the corresponding prices to users with energy storage needs. The frequency regulation capacity price and frequency regulation mileage price for shared energy storage clusters participating in frequency regulation ancillary services are known.

[0057] Step 320: The computing device 200 can construct a lower-level objective function with the goal of minimizing the energy consumption cost for users with energy storage needs, and then construct a lower-level model based on this objective function. Here, users with energy storage needs include renewable energy generators and industrial and commercial users.

[0058] In this embodiment of the invention, energy storage demand users act as followers in a master-slave game, aiming to minimize their own electricity costs. They can adjust their strategies in a timely manner based on the leader's pricing strategy. The energy costs for renewable energy generators include the cost of leasing capacity from the shared energy storage cluster and the penalty cost for output deviation. Here, the cost of leasing capacity from the shared energy storage cluster, i.e., the revenue from providing capacity leasing services to the renewable energy generator by the shared energy storage cluster, is related to the capacity leasing price of the shared energy storage cluster and the capacity leased by the renewable energy generator from the shared energy storage cluster.

[0059] In this embodiment of the invention, the output deviation penalty cost for renewable energy generators can be determined based on the renewable energy generator's output deviation penalty coefficient, the charging and discharging power of the renewable energy generator to the shared energy storage cluster, and the actual and planned output of the renewable energy generator. The electricity cost for industrial and commercial users includes the cost of purchasing electricity from the shared energy storage cluster and the cost of purchasing electricity from the grid. The cost of purchasing electricity from the shared energy storage cluster is related to the retail electricity price of the shared energy storage cluster and the power purchased by the industrial and commercial users from the shared energy storage cluster. The cost of purchasing electricity from the grid is related to the electricity price and the power purchased by the industrial and commercial users from the grid.

[0060] Step 330: The computing device 200 can obtain a two-layer master-slave game optimization model based on the upper-layer model and the lower-layer model.

[0061] Step 340: The computing device 200 can solve the two-layer master-slave game optimization model to determine the optimal service price of the shared energy storage cluster and the optimal energy consumption strategy for users with energy storage needs. It should be noted that the optimal service price indicates the capacity leasing price of the shared energy storage cluster and the retail electricity price for each time period (each time period within a day). The optimal energy consumption strategy indicates the capacity leased by renewable energy generators from the shared energy storage cluster, the charging and discharging power of renewable energy generators on the shared energy storage cluster for each time period, and the electricity purchased by industrial and commercial users from the shared energy storage cluster for each time period.

[0062] In some embodiments, in step 310, the computing device 200 constructs an upper-level objective function with the goal of maximizing the total revenue of the shared energy storage cluster, as shown in the following equation: (5) (6) In the formula, To share the total revenue of the energy storage cluster, Revenue generated from providing capacity leasing services to renewable energy power generator α from shared energy storage clusters; Revenue from providing retail electricity services to industrial and commercial users (β) through shared energy storage clusters; For the revenue of shared energy storage clusters participating in grid frequency regulation ancillary services (frequency regulation ancillary service revenue). The charging and maintenance costs of shared energy storage clusters. , These represent the electricity purchase price and power capacity of the shared energy storage cluster from the grid during time period t. The lifespan loss cost per unit charge / discharge capacity of a shared energy storage cluster. , These represent the total charging power and total discharging power of the shared energy storage cluster during time period t, respectively.

[0063] In some embodiments, the upper-level model includes upper-level constraint functions and upper-level constraint conditions. In other words, in step 310, the computing device 200 can construct the upper-level model based on the upper-level objective function and upper-level constraint conditions.

[0064] The upper-level constraints include the energy balance constraints of the shared energy storage cluster, the power constraints of the shared energy storage cluster, the capacity constraints of the shared energy storage cluster, and the price constraints of the shared energy storage cluster.

[0065] The energy balance constraint of the shared energy storage cluster is expressed as follows: the sum of the charging amount of each renewable energy generator to the shared energy storage cluster, the charging amount of the shared energy storage cluster to each industrial and commercial user, and the charging amount of the shared energy storage cluster for frequency regulation auxiliary services in each time period is equal to the sum of the discharging amount of each renewable energy generator to the shared energy storage cluster, the discharging amount of the shared energy storage cluster to each industrial and commercial user, and the discharging amount of the shared energy storage cluster for frequency regulation auxiliary services in each time period.

[0066] It should be noted that for shared energy storage clusters connected to the grid, the total daily charging and discharging volume must be equal to provide the grid with more predictable and stable regulation capabilities. Grid dispatching departments can more accurately arrange power production and distribution based on the charging and discharging plans of the shared energy storage clusters, improving the stability and reliability of grid operation. Maintaining equal daily charging and discharging volumes also ensures the continuity of services provided by the shared energy storage clusters, enabling them to offer users more stable capacity leasing and electricity retail services. Furthermore, shared energy storage clusters cannot use electricity from renewable energy generators that is within their capacity leasing range. Considering that some losses occur during the charging and discharging activities of shared energy storage clusters in actual production processes, the energy balance constraints for shared energy storage clusters are as follows: (7) (8) In the formula, , These represent the charging and discharging amounts of the renewable energy generator α to the shared energy storage cluster during time period t. , These represent the charging and discharging amounts from the shared energy storage cluster to industrial and commercial users β during time period t, respectively. , These represent the charging and discharging amounts of the shared energy storage cluster used for frequency regulation auxiliary services during time period t.

[0067] The power constraint condition for shared energy storage clusters is used to constrain the upper and lower limits of the charging and discharging power of new energy generators to the shared energy storage clusters, the discharging power of the shared energy storage clusters to industrial and commercial users, the day-ahead declared power of the shared energy storage clusters participating in frequency regulation ancillary services, and the total charging and discharging power of the shared energy storage clusters in each time period. In other words, the power constraint condition for shared energy storage clusters means that the charging and discharging power of new energy generators to the shared energy storage clusters, the discharging power of the shared energy storage clusters to industrial and commercial users, the day-ahead declared power of the shared energy storage clusters participating in frequency regulation ancillary services, and the total charging and discharging power of the shared energy storage clusters must be within a certain range, as shown in the following formula: (9) (10) (11) (12) (13) (14) In the formula, , These represent the charging power and discharging power of the renewable energy generator to the shared energy storage cluster during time period t. , , , These are the lower limit of charging power, lower limit of charging power, lower limit of discharging power, and upper limit of discharging power for new energy power generators to the shared energy storage cluster during time period t. The discharge power of the shared energy storage cluster to industrial and commercial users during time period t. , These represent the lower and upper limits of the discharge power from the shared energy storage cluster to industrial and commercial users during time period t, respectively. The day-ahead declared power for shared energy storage clusters participating in frequency regulation ancillary services. , These are the lower and upper power limits for shared energy storage clusters to participate in frequency regulation ancillary services, respectively. The total charging power of the shared energy storage cluster during time period t. The maximum total charging power of the shared energy storage cluster during time period t (maximum total charging power). The total discharge power of the shared energy storage cluster during time period t. The maximum total discharge power of the shared energy storage cluster during time period t is the upper limit of the total discharge power (maximum total discharge power).

[0068] The capacity constraint of a shared energy storage cluster states that the actual usage capacity of the shared energy storage cluster in the next time period (time period t+1) is equal to the actual usage capacity of the shared energy storage cluster in the current time period (time period t) plus the difference between the total charging and total discharging of the shared energy storage cluster in the current time period. Specifically, it is shown in the following formula: (15) In the formula, This indicates the actual usage capacity of the shared energy storage cluster during time period t+1; This represents the actual usage capacity of the shared energy storage cluster during time period t; , These represent the total charging and total discharging of the shared energy storage cluster during time period t, respectively.

[0069] Furthermore, the capacity constraints for shared energy storage clusters also include the formula for calculating the state of charge (SOC) of the shared energy storage cluster and the upper and lower limits of the SOC. More specifically, the capacity constraints for shared energy storage clusters also indicate that: the SOC of the shared energy storage cluster in each time period is equal to the ratio of the actual used capacity of the shared energy storage cluster in each time period to the total capacity, and the SOC of the shared energy storage cluster in each time period does not exceed the upper and lower limits of the SOC. Specifically, the formulas are as follows: (16) (17) In the formula, The state of charge of the shared energy storage cluster is represented by t, and E represents the total capacity of the shared energy storage cluster. , These represent the lower limit and upper limit of the state of charge of the shared energy storage cluster, respectively.

[0070] The price constraints for shared energy storage clusters include capacity leasing price constraints and electricity retail price constraints.

[0071] It's important to note that the pricing of shared energy storage clusters is influenced by both external market conditions and internal operational goals. While excessively low prices may attract more users, they may fail to cover costs and squeeze out peak-valley arbitrage opportunities. Conversely, excessively high prices will suppress user demand, leading to idle capacity. Therefore, the capacity leasing price of shared energy storage clusters needs to strike a balance between recovering capacity investment and encouraging renewable energy consumption, while ensuring that its overall cost does not exceed that of self-built energy storage or alternatives that incur high deviation penalties. The retail electricity price of shared energy storage clusters, in turn, needs to fall between the real-time grid price and the user's self-generation or electricity purchase cost to create an effective price guidance signal, ensuring both peak-valley arbitrage opportunities for shared energy storage and attractiveness to users.

[0072] To ensure that the capacity leasing service of shared energy storage clusters is attractive to renewable energy power generators, the capacity leasing price of shared energy storage clusters should be lower than the average daily cost per unit capacity of self-built energy storage, while being higher than the average daily operation and maintenance cost per unit capacity of shared energy storage clusters. In other words, the capacity leasing price constraint states that the capacity leasing price of shared energy storage clusters is greater than or equal to the average daily operation and maintenance cost per unit capacity of shared energy storage clusters, and less than or equal to the average daily cost per unit capacity of self-built energy storage. The specific formula is as follows: (18) (19) In the formula, This indicates the capacity leasing price (unit price) of the shared energy storage cluster. This represents the average daily unit capacity operation and maintenance cost of a shared energy storage cluster; This represents the average daily cost per unit capacity (or opportunity cost per unit capacity) of self-built energy storage. E represents the operation and maintenance cost of the shared energy storage cluster, T represents the total capacity of the shared energy storage cluster, and T represents the number of days.

[0073] The electricity retail price constraint states that the electricity retail price of the shared energy storage cluster should fluctuate between the price at which the shared energy storage cluster purchases electricity from the grid and the peak-hour electricity price sold by the grid, in order to simultaneously ensure the arbitrage space for the shared energy storage operator and its attractiveness to industrial and commercial users. The specific formula is as follows: (20) In the formula, The retail electricity price of the shared energy storage cluster during time period t; The electricity purchase price from the grid for the shared energy storage cluster during time period t. The minimum markup is set to account for losses and management fees; is the electricity price during peak hours on the power grid, and k is the discount coefficient.

[0074] In some embodiments, in step 320, the computing device 200 constructs a lower-level objective function with the goal of minimizing the energy cost for users with energy storage needs, as shown in the following equation: (twenty one) (twenty two) (twenty three) In the formula, The energy cost for new energy power generator α; The cost for renewable energy generator α to lease capacity from a shared energy storage cluster, of which, This indicates the capacity leasing price of a shared energy storage cluster. The capacity that a new energy power generator α can lease from a shared energy storage cluster within one day; The penalty cost for the output deviation of the new energy generator α. The output deviation penalty coefficient for the new energy power generator α. The actual output of the renewable energy generator α during period t. , These represent the charging power and discharging power of the renewable energy generator α to the shared energy storage cluster during time period t. To contribute to the plan of new energy power generator α during period t. For time intervals. For industrial and commercial users, β represents the energy cost. The retail electricity price of the shared energy storage cluster during time period t. For industrial and commercial users during time period t, β represents the electricity purchased from the shared energy storage cluster. The electricity purchase price from the grid for industrial and commercial users during time period t. Let β be the amount of electricity purchased from the power grid by industrial and commercial users during time period t.

[0075] In some embodiments, the lower-level model includes a lower-level objective function and lower-level constraints. In other words, in step 320, the computing device 200 can construct the lower-level model based on the lower-level objective function and lower-level constraints. The lower-level constraints include constraints on the output stability of new energy power generators and constraints on the demand response of industrial and commercial users.

[0076] It should be noted that, given the significant intermittency and volatility of power output from renewable energy generators, it is necessary to constrain the capacity leased by generators by setting a minimum capacity leasing threshold. This will improve the stability of system operation, thereby ensuring that generators maintain a minimum capacity reserve in the electricity market. When renewable energy output is insufficient, they can still supplement the power supply through leased capacity, reducing the risk of power supply gaps caused by power output fluctuations.

[0077] Assuming the actual output of new energy power generators Follows a normal distribution At confidence level α, the output range of renewable energy generators is as follows: (twenty four) (25) The output stability constraint condition for renewable energy generators states that the capacity leased by renewable energy generators must compensate for the maximum deviation in power output at confidence level α, as shown in the following formula: (26) In the formula, To contribute to the plans of new energy power generators This indicates the maximum negative deviation between the lower limit of the output of new energy power generators and their planned output; This indicates the maximum positive deviation between the upper limit of the output of new energy power generators and their planned output.

[0078] Demand response constraints for industrial and commercial users: Industrial and commercial users adjust their load demand in a timely manner based on the electricity retail price for each time period of the day. After receiving the price signal from the shared energy storage cluster, they can adjust their electricity consumption habits according to their expected electricity price for each time period to ultimately reduce costs. The load demand that industrial and commercial users feed back to the shared energy storage cluster based on the adjusted electricity retail price for each time period after receiving the price signal can be expressed as: (27) In the formula, The load of industrial and commercial users β after adjusting the retail electricity price for the shared energy storage cluster in time period t (load after demand response). β represents the historical load of industrial and commercial users during time period t; The change in load β for industrial and commercial users after adjusting the retail electricity price for the shared energy storage cluster during time period t; Let t be the initial retail electricity price of the shared energy storage cluster during time period t. The adjusted retail electricity price for the shared energy storage cluster during time period t; is the sub-elasticity coefficient for the i-th type of load, representing the degree of willingness of industrial and commercial users to adjust their electricity consumption.

[0079] In other words, the demand response constraints for industrial and commercial users indicate that industrial and commercial users can adjust their load demand based on the initial and adjusted electricity retail prices for each time period.

[0080] Considering that in reality, customers such as residents may have unadjustable loads, the following formula can be used to correct the load after demand response to obtain the corrected load: (28) In the formula, and These are the upper and lower load limits for industrial and commercial users β during time period t, respectively.

[0081] In some embodiments, to solve the two-layer master-slave game optimization model constructed in this invention, and to fully consider the coupling between the upper and lower layer decisions and the engineering practicality of the equilibrium solution, this invention designs an upper and lower layer solution architecture, employing a typical nested optimization process. The upper-layer model optimizes the service pricing of the shared energy storage cluster with the objective of maximizing the total revenue of the cluster; the lower-layer model optimizes the energy consumption strategies (electricity consumption and leasing strategies) of various energy storage users at a given price. Both iterate continuously through feedback between setting prices and calculating revenue based on strategies. A compromise decision-making method based on comprehensive satisfaction is introduced to select the most stable Stackelberg equilibrium solution from the set of approximate equilibrium solutions after algorithm convergence, as the globally optimal solution.

[0082] In some embodiments, in step 340, a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm (GA nested with PSO) can be used to collaboratively solve the two-layer master-slave game optimization model to obtain a globally optimal solution. Furthermore, based on the globally optimal solution, the optimal service price of the shared energy storage cluster and the optimal energy consumption strategy for users with energy storage needs can be determined.

[0083] Specifically, the process of using the genetic algorithm (GA) and particle swarm optimization (PSO) to collaboratively solve the two-level master-slave game optimization model is as follows: First, the parameters for the genetic algorithm and the particle swarm optimization (PSO) algorithm can be initialized. The genetic algorithm parameters include population size, maximum number of iterations, and genetic operation probabilities. The PSO algorithm parameters include particle swarm size, maximum number of iterations, and motion parameters. Furthermore, the parameters for the shared energy storage cluster and the energy storage demand parameters of individual users can be obtained.

[0084] Subsequently, a genetic algorithm can be used to randomly generate an initial population based on the genetic algorithm parameters. The initial population contains multiple initial individuals, each representing an initial service price. Then, a particle swarm algorithm can be used to generate an initial particle swarm based on the particle swarm algorithm parameters. The initial particle swarm contains multiple initial particles, each representing an initial energy consumption strategy.

[0085] Next, based on the upper-level objective function, the first fitness of each initial individual in the initial population (the value of the upper-level objective function) can be calculated, and genetic operations (including selection, crossover, and mutation) can be performed based on the first fitness of each initial individual to generate a progeny population (at this point, the number of iterations and the number of generations of the population are 2). Then, the initial population and the progeny population can be merged to obtain a merged population, and it can be determined whether the number of individuals in the merged population has reached a predetermined number. If it has not reached the predetermined number, the first fitness of each individual in the merged population can be calculated based on the upper-level objective function, and genetic operations (including selection, crossover, and mutation) can be performed based on the first fitness of each individual in the merged population to generate a new progeny population (at this point, the number of iterations and the number of generations of the population are 3). The initial population and the new progeny population can then be merged to obtain a new merged population, and it can be determined whether the number of individuals in the new merged population has reached a predetermined number.

[0086] If the number of individuals in the merged population (or the new merged population) reaches a predetermined number, the merged population can be passed to the Particle Swarm Optimization (PSO) algorithm. Using the PSO algorithm, based on the lower-level objective function and the merged population, the second fitness (lower-level objective function value) of each initial particle in the initial particle swarm is calculated. The velocity and position of each initial particle are then updated based on its second fitness to obtain a new particle swarm (the iteration count is 2 at this point). Then, the above steps are repeated: based on the lower-level objective function and the merged population, the second fitness of each new particle in the new particle swarm is calculated, and the velocity and position of each new particle are updated based on its second fitness, until the maximum number of iterations of the PSO algorithm is reached, at which point the Pareto front solution is obtained.

[0087] Furthermore, a compromise decision-making method based on comprehensive satisfaction can be used to select the optimal compromise solution from the Pareto front solutions, and then feed this optimal compromise solution back into the genetic algorithm.

[0088] Then, a genetic algorithm can be used to calculate the first fitness of each individual in the merged population based on the compromise optimal solution and the upper objective function, and genetic operations (including selection, crossover, and mutation operations) can be performed based on the first fitness of each individual in the merged population to generate a new offspring population.

[0089] By repeatedly interacting with the genetic algorithm and the particle swarm optimization algorithm until the termination condition is met (the maximum number of iterations of the genetic algorithm is reached), the global optimal solution of the two-layer collaborative optimization can be obtained.

[0090] Therefore, through GA-PSO nested iteration, joint optimization of parameters on both sides of the shared energy storage cluster and energy storage demand users can be achieved.

[0091] In summary, according to the master-slave game optimization method 300 of the shared energy storage differentiated service combination considering the flexible adjustment capability of resources, the shared energy storage cluster can provide differentiated service combinations to meet the needs of multiple entities, including capacity leasing services, electricity retail services, and frequency regulation ancillary services. Based on this, a two-layer master-slave game optimization model is constructed, with the maximization of the total revenue of the shared energy storage cluster as the upper-level objective and the minimization of the energy consumption costs of energy storage demand users (new energy power generators and industrial and commercial users) as the lower-level objective. Solving this model yields the optimal service price of the shared energy storage cluster and the optimal energy consumption strategy for energy storage demand users. Based on this, a scientific and reasonable pricing mechanism can be designed for shared energy storage operators, significantly improving the utilization efficiency and operating revenue of shared energy storage while reducing user energy costs, achieving synergistic development for both parties.

[0092] Furthermore, the capacity leasing service proposed in this invention can provide stable capacity reserves for renewable energy generators, enhancing system stability; the electricity retail service increases the options for purchasing electricity for industrial and commercial users, increasing the diversity of the electricity market; and the frequency regulation ancillary service can alleviate the pressure on the power grid during peak periods and ensure power supply. By flexibly combining multiple service models, the capacity and electricity needs of various entities can be met, thereby improving the overall benefits of the entire shared energy storage cluster.

[0093] By way of example, and not limitation, readable media include readable storage media and communication media. Readable storage media stores information such as computer-readable instructions, data structures, program modules, or other data. Communication media generally embodies computer-readable instructions, data structures, program modules, or other data in the form of modulated data signals such as carrier waves or other transmission mechanisms, and includes any information delivery medium. Any combination of the above is also included within the scope of readable media.

[0094] In the specification provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used with the examples of this invention. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0095] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0096] Similarly, it should be understood that, in order to streamline this disclosure and aid in understanding one or more of the various aspects of the invention, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof.

[0097] Those skilled in the art will understand that modules, units, or components of the device in the examples disclosed in this invention can be arranged in the device as described in this embodiment, or alternatively, can be located in one or more devices different from the device in this example. The modules in the foregoing examples can be combined into a single module or further divided into multiple sub-modules.

[0098] Unless otherwise specified, the use of ordinal numbers such as “first,” “second,” “third,” etc., to describe ordinary objects merely indicates different instances of similar objects and is not intended to imply that the objects being described must have a given order in time, space, ordering, or any other manner.

Claims

1. A master-slave game optimization method for shared energy storage differentiated service portfolios that takes into account the flexible adjustment capability of resources, executed in a computing device, comprising: A higher-level objective function is constructed with the goal of maximizing the total revenue of the shared energy storage cluster. A higher-level model is then constructed based on the objective function. The shared energy storage cluster is suitable for providing capacity leasing services to renewable energy generators, providing electricity retail services to industrial and commercial users, and participating in grid frequency regulation ancillary services. The total revenue of the shared energy storage cluster is suitable for determination based on the capacity leasing revenue, electricity retail revenue, frequency regulation ancillary service revenue, and charging and operation and maintenance costs of the shared energy storage cluster. A lower-level objective function is constructed with the goal of minimizing the energy consumption cost of energy storage demand users. A lower-level model is then constructed based on the lower-level objective function. The energy storage demand users include renewable energy generators and industrial and commercial users. The energy consumption cost of renewable energy generators includes the cost of leasing capacity from the shared energy storage cluster and the output deviation penalty cost of the renewable energy generators. The energy consumption cost of industrial and commercial users includes the cost of purchasing electricity from the shared energy storage cluster and the cost of purchasing electricity from the grid. Based on the upper-level model and the lower-level model, a two-layer master-slave game optimization model is obtained; The two-layer master-slave game optimization model is solved to determine the optimal service price of the shared energy storage cluster and the optimal energy consumption strategy of energy storage demand users. The optimal service price is used to indicate the capacity leasing price of the shared energy storage cluster and the electricity retail price in each time period. The optimal energy consumption strategy is used to indicate the capacity leased by the renewable energy generator from the shared energy storage cluster, the charging and discharging power of the renewable energy generator to the shared energy storage cluster in each time period, and the electricity purchased by industrial and commercial users from the shared energy storage cluster in each time period.

2. The method as described in claim 1, wherein, The capacity leasing revenue refers to the revenue that the shared energy storage cluster provides to new energy power generators by providing capacity leasing services, and is related to the capacity leasing price of the shared energy storage cluster and the capacity leased by the new energy power generators from the shared energy storage cluster. The electricity retail revenue refers to the revenue that the shared energy storage cluster provides to industrial and commercial users for electricity retail services, and is related to the electricity retail price of the shared energy storage cluster at different times and the power purchased by industrial and commercial users from the shared energy storage cluster. The revenue from frequency regulation ancillary services refers to the revenue that the shared energy storage cluster receives from participating in grid frequency regulation ancillary services, which is related to the frequency regulation capacity price, frequency regulation mileage price, and the day-ahead declared power of the shared energy storage cluster participating in frequency regulation ancillary services for each time period.

3. The method as described in claim 1 or 2, wherein, The cost for the new energy power generator to lease capacity from the shared energy storage cluster is related to the capacity leasing price of the shared energy storage cluster and the capacity leased by the new energy power generator from the shared energy storage cluster. The output deviation penalty cost of the new energy generator is suitable for determination based on the output deviation penalty coefficient of the new energy generator, the charging and discharging power of the new energy generator to the shared energy storage cluster, and the actual output and planned output of the new energy generator. The cost of electricity purchased by industrial and commercial users from the shared energy storage cluster is related to the retail price of electricity from the shared energy storage cluster and the power purchased by industrial and commercial users from the shared energy storage cluster. The cost of electricity purchased from the grid is related to the electricity price and power purchased by industrial and commercial users from the grid.

4. The method according to any one of claims 1-3, wherein, Constructing an upper-level model based on the aforementioned upper-level objective function includes: Based on the aforementioned upper-level objective function and upper-level constraints, an upper-level model is constructed. The upper-level constraints include energy balance constraints, power constraints, capacity constraints, and price constraints for the shared energy storage cluster.

5. The method of claim 4, wherein, The energy balance constraint of the shared energy storage cluster means that the sum of the charging amount of each new energy generator to the shared energy storage cluster, the charging amount of the shared energy storage cluster to each industrial and commercial user, and the charging amount of the shared energy storage cluster for frequency regulation auxiliary services in each time period is equal to the sum of the discharging amount of each new energy generator to the shared energy storage cluster, the discharging amount of the shared energy storage cluster to each industrial and commercial user, and the discharging amount of the shared energy storage cluster for frequency regulation auxiliary services in each time period. The power constraint conditions of the shared energy storage cluster are used to constrain the charging and discharging power of new energy generators to the shared energy storage cluster, the discharging power of the shared energy storage cluster to industrial and commercial users, the daily declared power of the shared energy storage cluster participating in frequency regulation auxiliary services, and the upper and lower limits of the total charging and discharging power of the shared energy storage cluster in each time period. The capacity constraint of the shared energy storage cluster means that the actual usage capacity of the shared energy storage cluster in the next time period is equal to the actual usage capacity of the shared energy storage cluster in the current time period plus the difference between the total charging and total discharging of the shared energy storage cluster in the current time period. Furthermore, the state of charge (SOC) of the shared energy storage cluster in each time period is equal to the ratio of the actual usage capacity of the shared energy storage cluster in each time period to the total capacity, and the SOC of the shared energy storage cluster in each time period does not exceed the upper and lower limits of the SOC of the shared energy storage cluster. The price constraints for the shared energy storage cluster include capacity leasing price constraints and electricity retail price constraints. The capacity leasing price constraint means that the capacity leasing price of the shared energy storage cluster is greater than or equal to the average daily unit capacity operation and maintenance cost of the shared energy storage cluster, and less than or equal to the average daily unit capacity cost of self-built energy storage. The electricity retail price constraint means that the electricity retail price of the shared energy storage cluster should fluctuate between the electricity purchase price of the shared energy storage cluster from the grid and the electricity sales price of the grid during peak hours.

6. The method according to any one of claims 1-5, wherein, Constructing a lower-level model based on the lower-level objective function includes: The lower-level model is constructed based on the lower-level objective function and lower-level constraints. The lower-level constraints include the output stability constraints of renewable energy generators and the demand response constraints of industrial and commercial users. The output stability constraints of renewable energy generators indicate that the capacity leased by renewable energy generators can compensate for the maximum deviation in electricity volume at a confidence level. The demand response constraints of industrial and commercial users indicate that industrial and commercial users adjust their load demand based on the initial and adjusted electricity retail prices for each time period.

7. The method according to any one of claims 1-6, wherein, Solving the aforementioned two-layer master-slave game optimization model to determine the optimal service price of the shared energy storage cluster and the optimal energy consumption strategy for users with energy storage needs includes: Genetic algorithm and particle swarm optimization algorithm are used to solve the two-level master-slave game optimization model in a collaborative manner to obtain the global optimal solution; Based on the global optimal solution, the optimal service price of the shared energy storage cluster and the optimal energy consumption strategy for users with energy storage needs are determined.

8. The method of claim 7, wherein, A genetic algorithm and a particle swarm optimization algorithm are used to collaboratively solve a two-level master-slave game optimization model to obtain the global optimal solution, including: Initialize the genetic algorithm parameters and the particle swarm optimization (PSO) algorithm parameters. The genetic algorithm parameters include the population size and the maximum number of iterations of the genetic algorithm. The PSO algorithm parameters include the particle swarm size and the maximum number of iterations of the PSO algorithm. An initial population is randomly generated based on the genetic algorithm parameters, the initial population containing multiple initial individuals, each representing an initial service price; and an initial particle swarm is generated based on the particle swarm algorithm parameters, the initial particle swarm containing multiple initial particles, each representing an initial energy consumption strategy. Based on the upper objective function, the first fitness of each initial individual in the initial population is calculated, and genetic operations are performed based on the first fitness of each initial individual to generate a progeny population. The initial population and the progeny population are then merged to obtain a merged population. Based on the lower-level objective function and the merged population, the second fitness of each initial particle in the initial particle swarm is calculated, and the velocity and position of each initial particle are updated based on the second fitness of each initial particle to obtain a new particle swarm. The second fitness of each new particle in the new particle swarm is calculated, and the velocity and position of each new particle are updated based on the second fitness of each new particle, until the maximum number of iterations of the particle swarm algorithm is reached, and the Pareto front solution is obtained. The optimal compromise solution is selected from the Pareto front solutions using a compromise decision-making method based on comprehensive satisfaction. Based on the compromise optimal solution and the upper-level objective function, the first fitness of each individual in the merged population is calculated, and genetic operations are performed based on the first fitness of each individual in the merged population to generate a new offspring population until the maximum number of iterations of the genetic algorithm is reached, at which point the global optimal solution is obtained.

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, the program instructions including instructions for processing the method as claimed in any one of claims 1-8.

10. A readable storage medium storing program instructions that, when read and processed by a computing device, cause the computing device to perform the method as described in any one of claims 1-8.

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