Dynamic aggregation multi-element novel energy storage frequency modulation auxiliary service bidding decision-making method

Abstract

The invention discloses a dynamically aggregated multi-element novel energy storage frequency modulation auxiliary service bidding decision-making method, and the method comprises the steps: building an upper-layer dispatching center clearing model by taking a dispatching center in a frequency modulation market as a first target, and taking the minimum power purchase total cost of each energy storage cluster, each independent energy storage power station and each thermal power generating unit as a first target, each energy storage power station is suitable for dynamically selecting to participate in an energy storage cluster or independently bidding as an independent energy storage power station; constructing a middle-level energy storage cluster bidding model by taking the maximum frequency modulation total income of each energy storage cluster in the frequency modulation market as a second target; taking the minimum frequency modulation cost of the energy storage cluster in the frequency modulation market as a third target, and constructing a lower-layer energy storage cluster income distribution model; therefore, a three-layer bidding decision model is obtained; and solving the three-layer bidding decision model to obtain a bidding decision result. According to the invention, the dynamic aggregation of the energy storage power stations can be realized, and the total energy storage income is improved while the total scheduling cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power, and in particular to a dynamic aggregation multi-element new energy storage frequency modulation auxiliary service bidding decision method. BACKGROUND

[0002] New energy storage has the ability to quickly adjust power in both directions, and is a high-quality frequency modulation resource. Its participation in the electricity market can increase the flexibility of the power system. The clear strategic direction of building a new power system dominated by new energy further highlights the importance of flexible regulation resources. China attaches great importance to the development of new energy storage and actively promotes its transition from the early stage of commercialization to large-scale development. Currently, independent energy storage, as an important form of new energy storage, is gradually developing from single energy storage power stations to clusterization. By participating in the electricity market bidding, the overall regulation capacity and market bargaining capacity are improved. However, independent energy storage resources are scattered, have large capacity differences, and have diverse operating characteristics. How to effectively aggregate independent energy storage and build a collaborative optimization and flexible response energy storage cluster has become a key problem that needs to be solved.

[0003] For new energy storage participating in the grid frequency modulation market, existing research mainly includes three aspects: energy storage frequency modulation form and collaborative control, multi-agent game and sharing mechanism, and frequency modulation auxiliary service market clearing theory. The research on the form of energy storage frequency modulation currently mainly includes thermal power + energy storage, new energy + energy storage, independent energy storage, and dispersed energy storage aggregation for frequency modulation. In the aspect of multi-agent decision-making, two methods of master-slave game and cooperative game are mainly used. The master-slave game in energy storage mainly involves three aspects: optimal scheduling of energy storage, capacity configuration, and energy storage pricing. In the aspect of frequency modulation auxiliary service market bidding and clearing theory, related research mainly includes joint clearing and collaborative optimization model, clearing model considering new subjects, and stochastic uncertainty clearing model based on robust optimization.

[0004] Existing research provides a theoretical research basis for energy storage participating in the frequency modulation market to play its flexible resource regulation capacity. However, current energy storage power stations mostly participate in the market in a fixed combination, and cannot dynamically choose whether to participate in the energy storage cluster. While taking advantage of cluster aggregation, the individual advantages of independent energy storage power stations are ignored, and the research on the income distribution method within the cluster is also not mature, ignoring the actual contribution of each subject.

[0005] Therefore, a dynamic aggregation multi-element new energy storage frequency modulation auxiliary service bidding decision method is needed to solve the problems in the above solutions. SUMMARY

[0006] To this end, the present application provides a dynamic aggregation multi-element new energy storage frequency modulation auxiliary service bidding decision method to solve or at least alleviate the above problems.

[0007] According to an aspect of the present application, a dynamic aggregated multi-element novel energy storage frequency modulation auxiliary service bidding decision method is provided, which is executed in a computing device and comprises: constructing a first objective function with a first target of minimizing total purchase cost of a dispatch center in a frequency modulation market from each energy storage cluster, each independent energy storage power station and each thermal power unit, constructing an upper-layer dispatch center clearing model based on the first objective function, wherein each energy storage power station is adapted to dynamically select participation in an energy storage cluster or independent bidding as an independent energy storage power station, the total purchase cost is related to capacity clearing price and mileage clearing price of each period of the frequency modulation market and capacity bid amount and mileage bid amount of each period of each energy storage cluster, each independent energy storage power station and each thermal power unit in the frequency modulation market; constructing a second objective function with a second target of maximizing total frequency modulation income of each energy storage cluster in the frequency modulation market, constructing a middle-layer energy storage cluster bidding model based on the second objective function, wherein the total frequency modulation income is related to income of each energy storage cluster in the frequency modulation market, frequency modulation cost of each energy storage cluster in the frequency modulation market and income obtained by an independent energy storage power station not participating in an energy storage cluster, the income of each energy storage cluster in the frequency modulation market is equal to the purchase cost of the dispatch center in the frequency modulation market from each energy storage cluster; constructing a third objective function with a third target of minimizing frequency modulation cost of an energy storage cluster in the frequency modulation market, constructing a lower-layer energy storage cluster income distribution model based on the third objective function, wherein the frequency modulation cost of the energy storage cluster in the frequency modulation market is related to mileage bid amount of each period of each energy storage power station in the energy storage cluster in the frequency modulation market and unit mileage price; obtaining a three-layer bidding decision model based on the upper-layer dispatch center clearing model, the middle-layer energy storage cluster bidding model and the lower-layer energy storage cluster income distribution model; solving the three-layer bidding decision model to obtain a bidding decision result, the bidding decision result is used to indicate capacity clearing price and mileage clearing price of each period of the frequency modulation market, capacity bid amount and mileage bid amount of each period of each energy storage cluster, each independent energy storage power station and each thermal power unit in the frequency modulation market, energy storage cluster aggregation result and energy storage power station output curve; wherein the energy storage cluster aggregation result contains decision result of whether each energy storage power station in each energy storage cluster participates in the energy storage cluster, and the energy storage power station output curve contains capacity output and mileage output of each period of the energy storage power station.

[0008] Optionally, in the dynamic aggregated multi-element novel energy storage frequency modulation auxiliary service bidding decision method according to the application, the income of the energy storage cluster in the frequency modulation market is determined according to the capacity clearing price and the mileage clearing price of each period of the frequency modulation market, the capacity bid amount and the mileage bid amount of each period of the energy storage cluster in the frequency modulation market; the frequency modulation cost of the energy storage cluster in the frequency modulation market is determined according to the mileage bid amount of each period of the energy storage cluster in the frequency modulation market and the unit frequency modulation cost of the energy storage cluster; the income of the independent energy storage power station not participating in the energy storage cluster is determined according to the income unit price allocated by the energy storage cluster and the mileage bid amount of each period of the independent energy storage power station in the frequency modulation market.

[0009] Optionally, in the dynamic aggregated multi-element novel energy storage frequency modulation auxiliary service bidding decision method according to the application, the upper dispatching center clearing model is constructed based on the first objective function, including: constructing the upper dispatching center clearing model based on the first objective function and upper constraint conditions, the upper constraint conditions including supply-demand balance constraint conditions, capacity and mileage constraint conditions of the energy storage cluster, capacity and mileage constraint conditions of the thermal power unit; wherein the supply-demand balance constraint conditions indicate that the total capacity bid amount of all energy storage clusters and thermal power units in each period is consistent with the total capacity demand amount of the dispatching center in each period, and the total mileage bid amount of all energy storage clusters and thermal power units in each period is consistent with the total mileage demand amount of the dispatching center in each period; the capacity and mileage constraint conditions of the energy storage cluster indicate that the capacity bid amount of each energy storage power station in the energy storage cluster does not exceed the capacity requirement range of each energy storage power station, and the mileage bid amount of each energy storage power station in the energy storage cluster does not exceed the mileage requirement range of each energy storage power station; the capacity and mileage constraint conditions of the thermal power unit indicate that the capacity bid amount of the thermal power unit does not exceed the capacity requirement range of the thermal power unit, and the mileage bid amount of the thermal power unit does not exceed the mileage requirement range of the thermal power unit.

[0010] Optionally, in the dynamic aggregated multi-element novel energy storage frequency modulation auxiliary service bidding decision method according to the application, a middle-layer energy storage cluster bidding model is constructed based on the second objective function, including: constructing a middle-layer energy storage cluster bidding model based on the second objective function and middle-layer constraint conditions, the middle-layer constraint conditions including energy storage cluster price constraint conditions, energy storage cluster quantity constraint conditions, energy storage power station charging and discharging power constraint conditions, thermal power unit quantity constraint conditions, and thermal power unit frequency modulation cost constraint conditions; wherein the energy storage cluster price constraint conditions represent that the capacity price of the energy storage cluster in each period does not exceed the upper and lower limits of the energy storage capacity price, and the mileage price of the energy storage cluster in each period does not exceed the upper and lower limits of the energy storage mileage price; the energy storage cluster quantity constraint conditions represent that the capacity declared quantity of the energy storage cluster in each period is less than or equal to the maximum available capacity of the energy storage cluster, and the mileage declared quantity of the energy storage cluster in each period is less than or equal to the maximum available mileage of the energy storage cluster; the energy storage power station charging and discharging power constraint conditions represent that the charging and discharging power of the energy storage power station does not exceed the maximum charging and discharging power of the energy storage power station, and the energy storage power station cannot charge and discharge at the same time at the same time; the capacity declared quantity of the thermal power unit in each period is less than or equal to the maximum available capacity of the thermal power unit, and the mileage declared quantity of the thermal power unit in each period is less than or equal to the maximum available mileage of the thermal power unit; and the thermal power unit frequency modulation cost constraint conditions represent that the frequency modulation capacity and mileage cost of the thermal power unit need to exceed the marginal cost thereof.

[0011] Optionally, in the dynamic aggregated multi-element novel energy storage frequency modulation auxiliary service bidding decision method according to the application, a lower energy storage cluster benefit distribution model is constructed based on the third objective function, including: a lower energy storage cluster benefit distribution model is constructed based on the third objective function and lower constraint conditions, the lower constraint conditions including energy storage cluster supply and demand balance constraint conditions, energy storage power station output constraint conditions, energy storage power station charging and discharging power constraint conditions, energy storage power station state of charge constraint conditions, energy storage power station health state constraint conditions, and energy storage cluster benefit distribution constraint conditions; wherein the energy storage cluster supply and demand balance constraint conditions indicate that the sum of the capacity output and the mileage output of all energy storage power stations in the energy storage cluster in each time period is equal to the capacity bid amount and the mileage bid amount of the energy storage cluster in each time period respectively; the energy storage power station output constraint conditions indicate that the capacity output of the energy storage power station in each time period does not exceed the maximum frequency modulation capacity of the energy storage power station, and the mileage output of the energy storage power station in each time period does not exceed the maximum frequency modulation mileage of the energy storage power station; the energy storage power station charging and discharging power constraint conditions indicate that the charging and discharging power of the energy storage power station does not exceed the maximum charging and discharging power of the energy storage power station, and the energy storage power station cannot charge and discharge at the same time; the energy storage power station state of charge constraint conditions indicate that the available capacity of the energy storage power station in the next time period is determined according to the state of charge of the energy storage power station, and the state of charge of the energy storage power station in each time period does not exceed the upper and lower limits of the state of charge of the energy storage power station; and the energy storage power station health state constraint conditions indicate that the health state of the energy storage power station in each time period does not exceed the upper and lower limits of the health state of the energy storage power station.

[0012] Optionally, in the dynamic aggregated multi-element novel energy storage frequency modulation auxiliary service bidding decision method according to the application, the energy storage cluster benefit distribution constraint conditions include: based on the capacity output contribution degree and the mileage output contribution degree of each energy storage power station, the original cost distribution factor of each energy storage power station is improved to obtain an improved profit distribution factor of each energy storage power station; and the frequency modulation benefit of the energy storage cluster in the frequency modulation market is distributed based on the improved profit distribution factor of each energy storage power station by using the Shapley value method, so as to distribute the frequency modulation benefit of the energy storage cluster in the frequency modulation market to each energy storage power station participating in the energy storage cluster.

[0013] Optionally, in the dynamic aggregated multi-element novel energy storage frequency modulation auxiliary service bidding decision method according to the application, based on the capacity output contribution degree and the mileage output contribution degree of each energy storage power station, the original cost distribution factor of each energy storage power station is improved to obtain an improved profit distribution factor of each energy storage power station, including: based on the capacity output contribution degree and the mileage output contribution degree of each energy storage power station and the corresponding improvement weight coefficient, the original cost distribution factor of each energy storage power station is improved to obtain an improved profit distribution factor of each energy storage power station.

[0014] Optionally, in the dynamic aggregation multi-element novel energy storage frequency regulation auxiliary service bidding decision method according to the present invention, the three-layer bidding decision model is solved to obtain day-ahead forecast data and bidding information of each bidding entity, and input into the upper-level dispatch center clearing model to solve the upper-level dispatch center clearing model and obtain the upper-level clearing result. The day-ahead forecast data includes load forecast information, frequency regulation demand forecast information, and forecasted electricity price. The bidding information of each bidding entity includes the capacity bids of each energy storage cluster, each independent energy storage power station, and each thermal power unit in each time period. The mileage bidding process involves several steps. The upper-level clearing results include capacity and mileage clearing prices for each time period in the frequency regulation market, as well as the capacity and mileage bids won by each energy storage cluster, independent energy storage power station, and thermal power unit in the frequency regulation market for each time period. Based on these upper-level clearing results, the bidding model for the middle-level energy storage clusters is solved to obtain the middle-level bidding results, which include the aggregated results of the energy storage clusters. Finally, based on these middle-level bidding results, the revenue distribution model for the lower-level energy storage clusters is solved to obtain the lower-level solution results, which include the output curves of each energy storage power station.

[0015] Optionally, in the dynamic aggregation multi-dimensional novel energy storage frequency regulation auxiliary service bidding decision method according to the present invention, solving the three-layer bidding decision model includes: using the Gurobi toolbox in Python to solve the upper-layer dispatch center clearing model, the middle-layer energy storage cluster bidding model, and the lower-layer energy storage cluster revenue distribution model respectively.

[0016] 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 a novel multi-element energy storage frequency regulation ancillary service bidding decision method for dynamic aggregation as described above.

[0017] 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.

[0018] 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 novel dynamic aggregation multi-element energy storage frequency regulation ancillary service bidding decision method as described above.

[0019] According to the technical solution of this invention, a novel dynamic aggregation-based bidding decision method for multi-level energy storage frequency regulation ancillary services is provided. Each energy storage power station can dynamically choose to participate in energy storage clusters or bid independently as an independent energy storage power station. An upper-level dispatch center clearing model is constructed with the objective of minimizing the total electricity purchase cost of the dispatch center from each energy storage cluster, each independent energy storage power station, and each thermal power unit in the frequency regulation market. A middle-level energy storage cluster bidding model is constructed with the objective of maximizing the total frequency regulation revenue of each energy storage cluster in the frequency regulation market. A lower-level energy storage cluster revenue distribution model is constructed with the objective of minimizing the frequency regulation cost of the energy storage cluster in the frequency regulation market. This leads to a three-level bidding decision model, which is then solved to obtain the bidding decision results. Based on this, this invention allows each energy storage power station to autonomously choose whether to participate in a cluster, realizing on-demand aggregation and dynamic convergence of energy storage power stations. Furthermore, the three-level bidding decision model constructed according to this invention can increase the total energy storage revenue while reducing the total dispatch cost. Moreover, the three-layer bidding decision model can incorporate bidding clearing and revenue sharing into the same decision chain, providing a quantitative basis for whether energy storage power stations choose to participate in the cluster, thereby promoting the healthy and coordinated development of the frequency regulation market.

[0020] In addition, the introduction of a dual contribution correction factor of "capacity-mileage" can improve the cost allocation factor of the Shapley value method, thereby allocating the frequency regulation revenue of energy storage clusters in the frequency regulation market. This can achieve reasonable and accurate allocation of cluster revenue based on actual output, and improve the fairness of revenue distribution.

[0021] 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

[0022] 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.

[0023] Figure 1 This diagram illustrates the structure of a frequency modulation market dynamic aggregation trading framework 100 according to an embodiment of the present invention. Figure 2 A schematic diagram of a computing device 200 provided according to an embodiment of the present invention is shown; Figure 3A flowchart illustrating a novel dynamic aggregation multi-element energy storage frequency regulation ancillary service bidding decision method 300 provided according to an embodiment of the present invention is shown. Figure 4 The diagram illustrates the changes in the returns of each entity under two different model schemes. Figure 5 The diagram illustrates the changes in the revenue of each entity under three cluster combination schemes. Detailed Implementation

[0024] 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.

[0025] To address the problem that most existing energy storage power stations participate in the market in a fixed combination and cannot dynamically choose whether to participate in energy storage clusters, this invention proposes a novel dynamic aggregation multi-element energy storage frequency regulation auxiliary service bidding decision method. This method allows each energy storage power station to choose whether to participate in the cluster, realizing on-demand aggregation and dynamic clustering of each energy storage power station. At the same time, by constructing a three-layer bidding decision model, it can improve the total energy storage revenue while reducing the total scheduling cost.

[0026] The dynamic aggregation-based multi-element energy storage frequency regulation ancillary service bidding decision method provided by the embodiments of the present invention can be used in the dynamic aggregation trading framework of the frequency regulation market. The following first introduces a dynamic aggregation trading framework for the frequency regulation market provided by the embodiments of the present invention.

[0027] Figure 1 A schematic diagram of a frequency regulation market dynamic aggregation trading framework 100 provided according to an embodiment of the present invention is shown. The frequency regulation market dynamic aggregation trading framework 100 according to an embodiment of the present invention combines hydrogen refueling stations with renewable energy power generation for unified scheduling.

[0028] like Figure 1 As shown, the frequency regulation market dynamic aggregation trading framework 100 includes a dispatch center (i.e., the ancillary service demander), one or more energy storage clusters (cluster 1…cluster n), and one or more thermal power units. Each energy storage cluster can contain multiple energy storage power stations (energy storage 1, energy storage 2, energy storage 3…energy storage n). Each energy storage power station can participate in the energy storage cluster bidding or bid independently as an independent energy storage power station.

[0029] In this embodiment of the invention, the multiple energy storage power stations in the energy storage cluster can cover multiple types of novel energy storage, such as compressed air energy storage, flywheel energy storage, liquid sulfur energy storage, and electrochemical energy storage.

[0030] It should be noted that this invention establishes a full-process framework for energy storage participation in the frequency regulation market, covering declaration, clearing, settlement, and revenue distribution. First, loads declare their electricity consumption to the grid operator. Then, the grid operator and energy storage operator respectively declare costs, electricity consumption, frequency regulation capacity, and frequency regulation mileage to the dispatch center (i.e., the power trading center, ancillary service demanders). Next, the dispatch center makes trading decisions, issuing the frequency regulation market transaction price and the thermal power unit output plan to the grid operator and energy storage operator. Finally, the grid operator and energy storage operator adjust the declared costs, declared electricity consumption, frequency regulation capacity, and frequency regulation mileage until the market reaches equilibrium, meaning that the declared volume, transaction price, and thermal power unit output plan no longer change.

[0031] In this embodiment of the invention, energy storage power stations are taken as the research object. Combined with the external frequency regulation market trading framework, and supplemented with the internal revenue distribution framework of the aggregated energy storage cluster, a three-layer bidding decision model coupling internal and external factors is constructed for energy storage participation in the frequency regulation market. When energy storage participates in the frequency regulation market bidding and clearing, the dispatch center predicts and analyzes the day-ahead frequency regulation demand based on the expected power generation and load demand of each power generation entity, and publishes information such as frequency regulation demand and price range in the frequency regulation market. Each power generation entity declares its frequency regulation capacity, frequency regulation mileage, capacity price, and mileage price in the frequency regulation market based on its available frequency regulation capacity and the technical characteristics of its units. The dispatch center sorts the mileage prices declared by each unit from low to high, clearing them sequentially until the cleared amount meets the day-ahead frequency regulation demand. The mileage price of the last cleared unit is used as the unified clearing price for each unit's day-ahead pre-clearing. After clearing, the system operator evaluates the actual frequency regulation performance of each unit, calculating the comprehensive frequency regulation performance of each unit from three aspects: regulation rate, regulation accuracy, and response time. Based on this calculation result, the cleared amount of each unit is settled.

[0032] like Figure 1 As shown, within an energy storage cluster, the cluster operator can utilize advanced intelligent system control technology to optimize the scheduling of each energy storage power station, achieving coordinated control of multiple types of new energy storage (including compressed air energy storage, flywheel energy storage, liquid sulfur energy storage, and various electrochemical energy storage). The cluster operator obtains information in advance regarding the idle capacity available for frequency regulation at each energy storage power station, as well as the cost and parameters of each power station. This information is then aggregated and analyzed to calculate the cluster's bid capacity and bid price in the day-ahead frequency regulation market. After the day-ahead bidding for the energy storage cluster concludes and the winning bid is determined, the cluster operator optimizes the scheduling of each energy storage power station within the cluster based on the winning bid in the day-ahead frequency regulation market. The revenue earned by the cluster operator in the frequency regulation market is then fairly distributed based on the actual output of each energy storage power station.

[0033] In embodiments of the present invention, a computing device may be configured to execute a dynamic aggregation-based novel multi-element energy storage frequency regulation ancillary service bidding decision method 300. The dynamic aggregation-based novel multi-element energy storage frequency regulation ancillary service bidding decision method 300 of the present invention will be described below.

[0034] The following describes a computing device 200 provided by an embodiment of the present invention.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] In an embodiment of the present invention, program module 203 includes multiple program instructions for executing the dynamic aggregation multi-element novel energy storage frequency regulation auxiliary service bidding decision method 300 of the present invention.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] In an embodiment of the present invention, a computing device 200 is configured to execute a dynamically aggregated multi-source novel energy storage frequency regulation ancillary service bidding decision method 300. The 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 the one or more processors, cause the computing device to execute the dynamically aggregated multi-source novel energy storage frequency regulation ancillary service bidding decision method 300 of the present invention.

[0044] In some embodiments, the computing device 200 that executes the dynamic aggregation multi-dimensional novel energy storage frequency regulation auxiliary service bidding decision method 300 in the embodiments of the present invention may be a terminal or a server.

[0045] The following is a detailed description of the dynamic aggregation multi-element novel energy storage frequency regulation auxiliary service bidding decision method 300 in the embodiments of the present invention.

[0046] Figure 3A flowchart illustrating a novel dynamic aggregation-based multi-element energy storage frequency regulation ancillary service bidding decision method 300 provided according to an embodiment of the present invention is shown. Figure 3 As shown, the dynamic aggregation multi-element novel energy storage frequency regulation ancillary service bidding decision method 300 includes the following steps 310-350.

[0047] Step 310: The computing device 200 constructs a first objective function with the first objective being to minimize the total electricity purchase cost of the dispatch center (system operator) in the frequency regulation market (i.e., the day-ahead frequency regulation market) from each energy storage cluster, each independent energy storage power station and each thermal power unit. Then, a clearing model of the upper-level dispatch center can be constructed based on the first objective function.

[0048] It should be understood that the total cost of electricity purchased by the dispatch center from each energy storage cluster and each thermal power unit in the frequency regulation market is the sum of the dispatch center's electricity purchase cost from each energy storage cluster in the frequency regulation market (equal to the revenue of the energy storage cluster in the frequency regulation market), the electricity purchase cost from each independent energy storage power station, and the electricity purchase cost from each thermal power unit (equal to the revenue of the thermal power unit in the frequency regulation market).

[0049] It should be noted that multiple energy storage power stations in an energy storage cluster can cover various types of new energy storage, including compressed air energy storage, flywheel energy storage, liquid sulfur energy storage, and electrochemical energy storage.

[0050] According to embodiments of the present invention, each energy storage power station can dynamically choose whether to participate in an energy storage cluster (i.e., it can choose to participate in an energy storage cluster or bid independently as an independent energy storage cluster), so as to realize the on-demand aggregation and dispersal of each energy storage power station in the parameter frequency regulation market.

[0051] The total cost of electricity purchased by the dispatch center from each energy storage cluster and each thermal power unit in the frequency regulation market is related to the capacity clearing price and mileage clearing price of each time period in the frequency regulation market, the capacity and mileage winning bids of each energy storage cluster in the frequency regulation market in each time period, the capacity and mileage winning bids of each independent energy storage power station in the frequency regulation market in each time period, and the capacity and mileage winning bids of each thermal power unit in the frequency regulation market in each time period.

[0052] Step 320: The computing device 200 constructs a second objective function with the second objective being to maximize the total frequency regulation revenue of each energy storage cluster in the frequency regulation market (i.e., the sum of the frequency regulation revenue of each energy storage cluster in the frequency regulation market). Then, a bidding model for mid-level energy storage clusters can be constructed based on the second objective function.

[0053] It should be noted that the total frequency regulation revenue of each energy storage cluster in the frequency regulation market is related to the revenue of each energy storage cluster in the frequency regulation market, the frequency regulation cost of each energy storage cluster in the frequency regulation market, and the revenue obtained by independent energy storage power stations that do not participate in the energy storage cluster. It should be understood that the revenue of each energy storage cluster in the frequency regulation market is equal to the electricity purchase cost of the dispatch center from each energy storage cluster in the frequency regulation market.

[0054] In this embodiment of the invention, the revenue of the energy storage cluster in the frequency regulation market can be determined based on the capacity clearing price and mileage clearing price of the frequency regulation market for each time period, as well as the capacity and mileage bid volume of the energy storage cluster in the frequency regulation market for each time period. The frequency regulation cost of the energy storage cluster in the frequency regulation market can be determined based on the mileage bid volume of the energy storage cluster in the frequency regulation market for each time period and the unit frequency regulation cost of the energy storage cluster.

[0055] The revenue earned by independent energy storage power stations that do not participate in energy storage clusters can be determined based on the unit price of revenue allocated by participating in energy storage clusters and the mileage won by independent energy storage power stations in the frequency regulation market at different times.

[0056] Step 330: The computing device 200 constructs a third objective function with the third objective being to minimize the frequency regulation cost of the energy storage cluster in the frequency regulation market. Then, a revenue distribution model for the lower-level energy storage cluster can be constructed based on the third objective function.

[0057] It should be noted that the frequency regulation cost of an energy storage cluster in the frequency regulation market is related to the mileage and unit mileage price of each energy storage power station in the cluster at each time period in the frequency regulation market.

[0058] Step 340: The computing device 200 can obtain a three-layer bidding decision model (Stackelberg game coupling model) based on the upper-layer scheduling center clearing model, the middle-layer energy storage cluster bidding model and the lower-layer energy storage cluster revenue distribution model constructed in the previous steps.

[0059] Step 350: The computing device 200 can solve the three-layer bidding decision model to obtain the bidding decision results. The bidding decision results indicate: the capacity clearing price and mileage clearing price in the frequency regulation market for each time period; the capacity and mileage winning bids for each energy storage cluster, each independent energy storage power station, and each thermal power unit in the frequency regulation market for each time period; the energy storage cluster aggregation results; and the energy storage power station output curves. Specifically, the energy storage cluster aggregation results include the decision results regarding whether each energy storage power station in each energy storage cluster participates in the energy storage cluster. The energy storage power station output curves include the capacity output and mileage output of the energy storage power stations for each time period.

[0060] It should be noted that the dispatch center can conduct a clearing process based on the collected bidding information and frequency regulation demand, aiming to minimize the total cost of purchasing frequency regulation capacity and mileage from the frequency regulation market. This will yield the winning bids for capacity and mileage for each thermal power unit and each energy storage cluster in the frequency regulation market for each time period, as well as the clearing prices (capacity clearing price and mileage clearing price). The total cost of electricity purchase by the dispatch center in the frequency regulation market includes the cost of purchasing frequency regulation capacity and the cost of purchasing frequency regulation mileage.

[0061] According to some embodiments of the present invention, in step 310, with the first objective being to minimize the total cost of electricity purchases by the dispatch center from each energy storage cluster, each independent energy storage power station and each thermal power unit in the frequency regulation market, the first objective function is constructed as shown in the following equation (1).

[0062] (1) (2) (3) In the formula, These represent the electricity purchase costs of the dispatch center from each energy storage cluster and each independent energy storage power station in the frequency regulation market, respectively, and the electricity purchase costs of the dispatch center from each thermal power unit in the frequency regulation market. and These are the capacity clearing price and mileage clearing price (RMB / MW) for each bidding entity (including each energy storage cluster, independent energy storage power station, and thermal power unit) in the frequency regulation market during time period t. and These represent the capacity and mileage (MWh) of the energy storage cluster m in the frequency regulation market during period t. and These represent the capacity and mileage (MWh) of an independent energy storage power station in the energy storage cluster m that bid independently in the frequency regulation market during time period t. and These represent the capacity and mileage (MW) of the thermal power unit n in the frequency regulation market during time period t, respectively. M indicates that there are M energy storage clusters participating in the frequency regulation market; N indicates that there are N thermal power units participating in the frequency regulation market. T indicates that the day is divided into T time periods.

[0063] (4) In the formula, The variable represents whether energy storage power station i participates in the energy storage cluster. 1 indicates participation in the energy storage cluster, and 0 indicates independent bidding as an independent energy storage power station (not participating in the energy storage cluster).

[0064] In some embodiments, after constructing the first objective function in step 310, an upper-level dispatch center clearing model can be constructed based on the first objective function and upper-level constraints. The upper-level constraints mainly include supply and demand balance constraints between generating units and the dispatch center, and output constraints for each generating unit. Specifically, the upper-level constraints may include supply and demand balance constraints, capacity and mileage constraints of energy storage clusters, and capacity and mileage constraints of thermal power units.

[0065] The supply and demand balance constraint means that the total capacity bid amount of all energy storage clusters and thermal power units in each time period is consistent with the total capacity demand of the dispatch center in each time period, and the total mileage bid amount of all energy storage clusters and thermal power units in each time period is consistent with the total mileage demand of the dispatch center in each time period. Specifically, it is shown in equations (5) and (6) below.

[0066] (5) (6) In the formula, The total capacity demand of the dispatch center during time period t (MWh); This represents the total mileage demand (MW) of the dispatch center during time period t.

[0067] The capacity and mileage constraints of the energy storage cluster are as follows: the bid amount for the capacity of each energy storage power station in the energy storage cluster shall not exceed the capacity requirement range of each energy storage power station, and the bid amount for the mileage of each energy storage power station in the energy storage cluster shall not exceed the mileage requirement range of each energy storage power station. Specifically, it is shown in the following formulas (7)-(9).

[0068] (7) (8) (9) In the formula, The maximum capacity output (MWh) of the energy storage cluster m. For the historical frequency regulation mileage call coefficient (frequency regulation mileage multiplier) of energy storage cluster m; This represents the maximum frequency regulation mileage (MW) that can be mobilized by the energy storage cluster m.

[0069] The capacity and mileage constraints for thermal power units are as follows: the contracted capacity of the thermal power unit shall not exceed the capacity requirement range, and the contracted mileage of the thermal power unit shall not exceed the mileage requirement range. Specifically, they are shown in equations (10) and (11) below.

[0070] (10) (11) In the formula, It is the frequency regulation mileage multiplier for thermal power units.

[0071] According to some embodiments of the present invention, in step 320, the second objective function is constructed as follows: the second objective function is to maximize the total frequency regulation revenue of each energy storage cluster in the frequency regulation market.

[0072] It should be noted that the total frequency regulation revenue of each energy storage cluster in the frequency regulation market (the sum of the frequency regulation revenue of all energy storage clusters in the frequency regulation market) is calculated based on the revenue of each energy storage cluster in the frequency regulation market. And the frequency regulation costs (cost of providing frequency regulation services) of each energy storage cluster in the frequency regulation market. Composition. In other words, the total frequency regulation revenue of each energy storage cluster in the frequency regulation market is related to the revenue of each energy storage cluster in the frequency regulation market and the frequency regulation cost of each energy storage cluster in the frequency regulation market. Frequency regulation revenue includes capacity revenue and mileage revenue. Specifically, the formula for calculating the total frequency regulation revenue of each energy storage cluster in the frequency regulation market is shown in Equation (12) below, and the formulas for calculating the revenue of the energy storage cluster in the frequency regulation market and the frequency regulation cost of the energy storage cluster in the frequency regulation market are shown in Equations (13) and (14) below, respectively.

[0073] (12) (13) (14) (15) In the formula, This indicates that independent energy storage power stations do not participate in the revenue generated by energy storage clusters; This indicates the unit price of revenue allocated to participating in the energy storage cluster. This refers to the comprehensive frequency regulation performance indicators of energy storage clusters; The capacity clearing price for the frequency regulation market during time period t (RMB / MWh); The mileage clearing price for the frequency regulation market during period t (RMB / MW); The capacity of the energy storage cluster in the frequency regulation market during time period t (MWh); The mileage (MW) of the energy storage cluster in the frequency regulation market during time period t; The unit frequency regulation cost of the energy storage cluster (RMB / MW).

[0074] As can be seen from equations (12)-(15) above, the total frequency regulation revenue of each energy storage cluster in the frequency regulation market is related to the revenue of each energy storage cluster in the frequency regulation market, the frequency regulation cost of each energy storage cluster in the frequency regulation market, and the revenue obtained by independent energy storage power stations that do not participate in the energy storage cluster. It should be understood that the revenue of each energy storage cluster in the frequency regulation market is equal to the electricity purchase cost of the dispatch center from each energy storage cluster in the frequency regulation market.

[0075] The revenue of an energy storage cluster in the frequency regulation market can be determined based on the capacity clearing price and mileage clearing price in the frequency regulation market for each period, as well as the amount of capacity and mileage the energy storage cluster wins in the frequency regulation market for each period. The frequency regulation cost of an energy storage cluster in the frequency regulation market can be determined based on the amount of mileage the energy storage cluster wins in the frequency regulation market for each period and the unit frequency regulation cost of the energy storage cluster.

[0076] The revenue obtained by an independent energy storage power station that does not participate in an energy storage cluster can be determined based on the revenue per unit allocated by participating in the energy storage cluster and the mileage of the independent energy storage power station in the frequency regulation market at different times.

[0077] In some embodiments, after constructing the second objective function in step 320, a mid-level energy storage cluster bidding model can be constructed based on the second objective function and mid-level constraints. Specifically, the mid-level constraints include energy storage cluster bidding constraints, energy storage cluster quantity constraints, and energy storage power station charging and discharging power constraints.

[0078] Among them, the energy storage cluster pricing constraint is used to ensure that the capacity pricing and mileage pricing of the energy storage cluster strictly follow the pricing range requirements issued by the dispatch center. Specifically, the energy storage cluster pricing constraint means that the capacity pricing of the energy storage cluster in each time period does not exceed the upper and lower limits of the energy storage capacity pricing, and the mileage pricing of the energy storage cluster in each time period does not exceed the upper and lower limits of the energy storage mileage pricing. See equations (16) and (17) below for details.

[0079] (16) (17) In the formula, , These are the upper and lower limits of the energy storage capacity price (RMB / MWh); The capacity quotation for the energy storage cluster during time period t (RMB / MWh); , The upper and lower limits of the energy storage range price (RMB / MW); Price per mileage (RMB / MW) for the energy storage cluster during time period t.

[0080] The energy storage cluster reporting constraints are used to ensure that the frequency regulation capacity (capacity reporting amount) and frequency regulation mileage (mileage reporting amount) reported by the energy storage cluster in each time period must be within its actual available capacity and mileage range. That is, the energy storage cluster reporting constraints mean that the capacity reporting amount of the energy storage cluster in each time period is less than or equal to the maximum available capacity of the energy storage cluster, and the mileage reporting amount of the energy storage cluster in each time period is less than or equal to the maximum available mileage of the energy storage cluster. Specifically, it is shown in the following formulas (18) and (19).

[0081] (18) (19) In the formula, The capacity application amount (MWh) for the energy storage cluster during time period t. The mileage (MW) reported for the energy storage cluster during time period t. The maximum available capacity (MWh) for frequency regulation of the energy storage cluster. For the frequency regulation mileage multiplier of energy storage clusters; This represents the maximum frequency regulation mileage (maximum available mileage) that the energy storage cluster can utilize (MW).

[0082] The power constraints for charging and discharging of energy storage power stations are as follows: the charging and discharging power of an energy storage power station participating in the frequency regulation market cannot exceed the maximum charging and discharging power of the energy storage power station, and the energy storage power station cannot charge and discharge simultaneously at the same time. See equations (20) and (21) below for details.

[0083] (20) (twenty one) In the formula, Let be the 0 / 1 variables representing the charge / discharge state of the energy storage power station, where A value of 1 indicates that the energy storage station is in a charging state. =0 indicates that the energy storage power station is in a discharging state; , These are the maximum charging power and maximum discharging power (MW) of the energy storage power station, respectively.

[0084] The frequency regulation reporting constraint for thermal power units means that the frequency regulation capacity and mileage reported by thermal power units in each time period must be within the range of their actual available capacity and mileage.

[0085] (twenty two) (twenty three) In the formula, This represents the upper limit of the unit's frequency regulation capacity. The rated power of the unit (MW); It is the mileage multiplier for frequency regulation of thermal power plants.

[0086] The frequency regulation cost constraint for thermal power units means that the frequency regulation capacity and mileage cost of thermal power units must exceed their marginal cost.

[0087] (twenty four) (25) In the formula, , The prices are the frequency regulation capacity and mileage quotes for thermal power unit n during time period t. , These are the marginal cost of frequency regulation capacity (RMB / MW) and the marginal cost of mileage (RMB / MWh) of the generating unit, respectively. According to some embodiments of the present invention, in step 330, the third objective function is constructed as shown in the following equation (22), with the goal of minimizing the frequency regulation cost of the energy storage cluster in the frequency regulation market.

[0088] It should be noted that, considering the economics of frequency regulation for energy storage clusters, a third objective function was constructed with the goal of minimizing the frequency regulation cost of energy storage clusters in the frequency regulation market. The specific calculation method is shown in equation (22) below.

[0089] (twenty two) In the formula, The frequency regulation cost (in yuan) of the energy storage cluster in the frequency regulation market; T represents dividing a day into T frequency regulation time points; i represents that there are i energy storage power stations in the energy storage cluster; The mileage (MW) of energy storage power station i in the frequency regulation market during time period t; Price per unit mileage (RMB / MW) for i-frequency regulation of energy storage power stations.

[0090] As can be seen from the above formula, the frequency regulation cost of energy storage clusters in the frequency regulation market is related to the mileage bid volume and unit mileage price of each energy storage power station in the energy storage cluster in the frequency regulation market at each time period.

[0091] In some embodiments, after constructing the third objective function in step 330, a lower-level energy storage cluster revenue allocation model can be constructed based on the third objective function and the lower-level constraints. It should be noted that when energy storage clusters participate in frequency regulation, in addition to considering the capacity and mileage output range constraints of the energy storage power stations, the real-time state of charge and battery health status of different energy storage power stations must also be considered.

[0092] Specifically, the lower-level constraints include: energy storage cluster supply and demand balance constraints, energy storage power station output constraints, energy storage power station charging and discharging power constraints, energy storage power station state of charge constraints, energy storage power station health status constraints, and energy storage cluster revenue distribution constraints.

[0093] The energy storage cluster supply and demand balance constraint means that the sum of the capacity output and the sum of the mileage output of all energy storage power stations in the energy storage cluster at each time period are equal to the bid amount of the capacity and the bid amount of the mileage of the energy storage cluster at each time period, respectively. Specifically, it is shown in equations (23) and (24) below.

[0094] (twenty three) (twenty four) In the formula, The capacity output (MWh) of energy storage power station i during time period t; The mileage output (MW) of energy storage power station i during time period t; , These represent the capacity and mileage of the energy storage clusters in the frequency regulation market during time period t, respectively.

[0095] The output constraints of the energy storage power station are as follows: the capacity output of the energy storage power station in each time period shall not exceed the maximum frequency regulation capacity of the energy storage power station, and the mileage output of the energy storage power station in each time period shall not exceed the maximum frequency regulation mileage of the energy storage power station. Specifically, it is shown in equations (25) and (26) below.

[0096] (25) (26) In the formula, The maximum frequency regulation capacity of energy storage power station i is (MWh). For the frequency regulation mileage multiplier of energy storage power station i; The maximum frequency regulation range (MW) of energy storage power station i.

[0097] The power constraint condition for charging and discharging of an energy storage power station means that the charging and discharging power of the energy storage power station shall not exceed the maximum allowable charging and discharging power of the energy storage power station, and the energy storage power station can only charge or discharge at the same time (it cannot charge and discharge simultaneously). Specifically, the power constraint condition for charging and discharging of an energy storage power station can be expressed as the following formula (27).

[0098] (27) In the formula, , These represent the discharge power and charging power (MW) of energy storage power station i during time period t, respectively. and For energy storage charging and discharging, there are 0-1 variables, representing the charging and discharging state of energy storage station i during time period t; , These represent the maximum allowable discharge power and maximum charging power (MW) of energy storage station i, respectively.

[0099] The State of Charge (SOC) constraint for energy storage power stations states that when an energy storage cluster participates in frequency regulation, the available capacity of the energy storage power station in the next time period is determined based on its SOC. Furthermore, the SOC of the energy storage power station in each time period must not exceed its upper and lower SOC limits. It should be understood that if the SOC of an energy storage power station exceeds its upper and lower SOC limits, the energy storage power station can no longer be used.

[0100] Specifically, the formula for calculating the state of charge of an energy storage power station is shown in Equation (28), and the constraints on the state of charge of an energy storage power station are shown in Equation (29).

[0101] (28) (29) In the formula, Let represent the remaining capacity (MW) of energy storage station i at time (t-1). , The charging and discharging efficiency (%) of energy storage station i; The state of charge (%) of energy storage power station i during time period t; , These are the upper and lower limits of the SOC (State of Charge) of energy storage power station i.

[0102] The State of Health (SOH) constraint for an energy storage power station states that the state of health of the energy storage power station at any given time does not exceed the upper and lower limits of its state of health. When the state of health of the energy storage power station exceeds the upper and lower limits, the energy storage power station can no longer be activated. Specifically, the state of health constraint for the energy storage power station is shown in equation (30) below.

[0103] (30) In the formula, The SOH (health status) of energy storage power station i during time period t. , These represent the lower and upper limits of the State of Health (SOH) for energy storage power station i.

[0104] Constraints on the distribution of revenue from energy storage clusters: It should be noted that for an alliance consisting of n members S is a subset representing a combination method. Assume the cost allocated to member i in the alliance is... , The cost of combination S, Let i be the individual cost when member i does not cooperate. A cooperative alliance needs to meet three conditions: (1) individual rationality condition; (2) overall superadditivity condition; (3) overall constraint condition. The three conditions respectively satisfy the requirements of individual cost reduction, overall cost reduction, and total cost unchanged before and after allocation, as shown in equations (31)-(33) below.

[0105] (31) (32) (33) According to Shapley's calculation method, a cooperative game alliance containing each energy storage power station (main entity) is first constructed. According to Shapely's cooperative game theory, this alliance There are (2n-1) possible combinations. Describe the characteristic function, and Let the unions other than the empty set be denoted as . ,but It's the alliance The total cost after the participants collaborate on the operation.

[0106] The Shapley value calculation method is shown in equation (34).

[0107] (34) In the formula, This represents the cost allocation value for energy storage power station i. The number of energy storage power stations within Alliance S; The number of participants in the Grand Alliance; The total cost of Alliance S; The cost of Alliance S after deducting the energy storage power station i; The probability of alliance S occurring is also called the weighting factor.

[0108] It is worth noting that the traditional Shapley value method only considers the marginal contribution of each entity (energy storage power station) under different combination states, simplifying and ignoring other factors that need to be considered in profit distribution.

[0109] Therefore, in this embodiment of the invention, the Shapley value method is used to allocate the cost of each energy storage power station, and the actual capacity output contribution and mileage output contribution of each energy storage power station are further comprehensively considered. The specific constraints on the distribution of revenue for the energy storage cluster are as follows: Based on the actual capacity output contribution and mileage output contribution of each energy storage power station in the energy storage cluster, the original cost allocation factor of each energy storage power station is improved to obtain the improved profit allocation factor of each energy storage power station. Then, using the Shapley value method, the frequency regulation revenue of the energy storage cluster in the frequency regulation market is allocated based on the improved profit allocation factor of each energy storage power station, so as to reasonably distribute the frequency regulation revenue of the energy storage cluster in the frequency regulation market to each energy storage power station participating in the energy storage cluster. Assume that the capacity output contribution of each energy storage power station in the energy storage cluster is... Mileage output contribution rate is The allocation factor for each energy storage power station is calculated as shown in equation (35).

[0110] (35) In the formula, , , To improve the weighting coefficients, ; This serves as the original cost allocation factor for each energy storage power station; This represents the improved profit distribution factor for each energy storage power station.

[0111] As shown in Equation (35), in some embodiments, the original cost allocation factor of each energy storage power station can be improved based on the capacity output contribution and mileage output contribution of each energy storage power station in the energy storage cluster and the corresponding improved weight coefficient, so as to obtain the improved profit allocation factor of each energy storage power station.

[0112] It should be understood that the three-layer bidding decision model constructed according to the above embodiments includes an upper-layer dispatch center clearing model (first objective function and upper-layer constraints), a middle-layer energy storage cluster bidding model (second objective function and middle-layer constraints), and a lower-layer energy storage cluster revenue distribution model (third objective function and lower-layer constraints).

[0113] In some embodiments, when solving the three-layer bidding decision model in step 350, the Gurobi toolbox in Python can be used to solve the upper-layer dispatch center clearing model, the middle-layer energy storage cluster bidding model, and the lower-layer energy storage cluster revenue distribution model respectively. The "layered decoupling-dual aggregation" method can be used for solving.

[0114] Specifically, firstly, day-ahead forecast data and bidding information from each bidding entity can be obtained. This data is then input into the upper-level dispatch center clearing model to solve it. Here, day-ahead forecast data includes load forecast information, frequency regulation demand forecast information, and forecasted electricity prices. Bidding information from each bidding entity includes capacity and mileage bids from each energy storage cluster, each independent energy storage power station, and each thermal power unit for each time period. After solving the upper-level dispatch center clearing model based on the day-ahead forecast data and bidding information, the upper-level clearing results can be obtained. These results include the capacity and mileage clearing prices for each time period in the frequency regulation market, as well as the capacity and mileage bids won by each energy storage cluster, each independent energy storage power station, and each thermal power unit in the frequency regulation market for each time period.

[0115] Next, the upper-level clearing results can be input into the middle-level energy storage cluster bidding model. Based on the upper-level clearing results, the middle-level energy storage cluster bidding model can be solved to obtain the middle-level bidding results. The middle-level bidding results include the energy storage cluster aggregation results (the decision results of whether each energy storage power station in each energy storage cluster participates in the energy storage cluster), and can also include the capacity and mileage declarations of each energy storage cluster, each independent energy storage power station, and each thermal power unit in the frequency regulation market at each time period.

[0116] Furthermore, the mid-level bidding results can be input into the lower-level energy storage cluster revenue distribution model. Based on the mid-level bidding results, the lower-level energy storage cluster revenue distribution model can be solved to obtain the lower-level solution results, which include the output curves of each energy storage power station.

[0117] Ultimately, the bidding decision can be obtained based on the upper-level clearing results, the middle-level bidding results, and the lower-level solution results.

[0118] It should be noted that the mid-level energy storage entities can generate "independent / cluster" dual-track bids in parallel. The energy storage cluster side can call the lower-level improved Shapley sub-model to adjust costs using a two-dimensional "capacity-mileage" marginal contribution, forming a bidding decision. After benefit comparison, a 0-1 dynamic aggregation decision is locked, and updated bidding information is sent back. To eliminate the difference in objective direction between the upper and lower levels, a Lagrangian function can be established for the benefit maximization subproblems of the two mid-level entities. This can be transformed into a minimization form using duality theory, and constraints can be embedded into the upper level using KKT complementary relaxation conditions, forming a single-level solvable mixed integer programming model. A unified solution yields the final clearing result, achieving synergistic optimization of dynamic energy storage selection and fair benefit allocation.

[0119] The three-layer bidding decision model constructed according to embodiments of the present invention includes an upper-layer dispatch center clearing model corresponding to the dispatch center, a middle-layer energy storage cluster bidding model corresponding to energy storage clusters and independent energy storage power stations, and a lower-layer energy storage cluster revenue distribution model corresponding to energy storage power stations within the energy storage cluster. Based on this, the game relationship between the dispatch center, energy storage cluster, and independent energy storage power stations can be represented more realistically. Specifically, when energy storage power stations have excess revenue under two options, they will choose the option with higher revenue, rather than being fixed to participating in the energy storage cluster or independent bidding. To verify the effectiveness of the three-layer bidding decision model, two comparison schemes are set in the following embodiments.

[0120] Option 1 is the three-layer bidding decision-making model constructed in this invention. Option 2 is a two-stage model, in which the first stage model constructs upper and lower layers through Stackelberg master-slave game theory, with the upper layer being the system operator and the lower layer being the fixed energy storage cluster; the second stage constructs a revenue distribution model through Shapley cooperative game theory.

[0121] The table below verifies the effectiveness of the three-layer bidding decision model constructed in this invention by comparing revenue and scheduling costs.

[0122] Table 1 Model Comparison Scheme Settings Table 2 shows the changes in the returns of each entity under the two model comparison schemes.

[0123] Table 2 Comparison of Revenues for Each Entity under Different Model Schemes (Unit: Yuan) To more intuitively illustrate the cost changes of the two model schemes, the results in Table 2 are presented using... Figure 4 express.

[0124] Figure 4 The diagram illustrates the changes in the returns of each entity under two different model schemes.

[0125] Depend on Figure 4 It can be seen that the total scheduling cost of the three-layer bidding decision-making model constructed in this invention is 284,249 yuan, and the total scheduling cost of the two-stage model is 295,182 yuan, a reduction of 10,933 yuan, proving the effectiveness of the three-layer bidding decision-making model. The total revenue of energy storage increased by 3,597 yuan, while the total revenue of thermal power decreased by 38,032 yuan. The revenue of the energy storage cluster consisting of lithium-ion battery energy storage (E1), liquid sulfur battery energy storage (E3), and flywheel energy storage (E4) decreased by 7,346 yuan, while the revenue of the two types of compressed air energy storage (E2 and E5) increased by 6,785 yuan and 4,158 yuan respectively, while the revenue of thermal power units (G1, G2, G3) was negatively affected.

[0126] Depend on Figure 4 It is evident that the revenue of the energy storage cluster (E1+E3+E4) is negatively impacted. This is because individual E1, E3, and E4 energy storage units, due to capacity limitations, have limited winning bids for cluster capacity. Aggregating them into a cluster reduces scheduling costs. However, E2 and E5 energy storage units, by choosing independent bidding as the main entities for their bids, can leverage their performance advantages, leading to increased revenue. In contrast, E2 and E5's independent bidding fully utilizes their frequency regulation performance advantages, resulting in significantly improved revenue (E2's revenue increased by 38.8%), validating the effectiveness of the differentiated operation strategy. Simultaneously, the independent operation of energy storage units squeezes the frequency regulation space of thermal power (G1-G3), causing a decrease in their revenue, further demonstrating the rationality of the market model.

[0127] The following section verifies the effectiveness of the aggregation method.

[0128] Because the three-layer bidding decision model constructed in this invention uses 0-1 variables to represent the decision result of whether an energy storage power station participates in the cluster, it avoids the irrationality of the fixed combination of energy storage power stations in traditional models. To verify the effectiveness of the model and the superiority of dynamically changing cluster combination methods, the effectiveness is verified by comparing the revenue of each entity and the total scheduling cost under schemes one, two, and three.

[0129] Table 3. Cluster Combination Scheme Comparison Settings The changes in the revenue of each entity under the three cluster combination schemes are shown in Table 4.

[0130] Table 4 Comparison of revenues for each entity under different cluster combination schemes (unit: yuan) To more intuitively illustrate the cost changes of the three cluster combination schemes, the results in Table 4 are presented using... Figure 5 express.

[0131] Figure 5 The diagram illustrates the changes in the revenue of each entity under three cluster combination schemes. Figure 5 It can be seen that, under the condition that the cluster combination is not fixed in this scheme, the three types of energy storage, lithium-ion battery energy storage (E1), liquid sulfur battery energy storage (E3) and flywheel energy storage (E4), are aggregated into a cluster to participate in the bidding, while the two types of compressed air energy storage (E2 and E5) participate in the bidding as independent entities, and participate in the frequency regulation market together with thermal power energy storage power stations (G1, G2 and G3).

[0132] As shown in Table 4, under the non-fixed cluster setup, the total revenue of all energy storage power stations is 244,747 yuan; under the mandatory full combination of energy storage power stations, the total revenue of all entities is 255,848 yuan; and under the mandatory independent setup of all energy storage power stations, the total revenue of all entities is 260,880 yuan. The scheduling costs were reduced by 11,101 yuan and 16,133 yuan respectively, demonstrating the effectiveness of the stochastic energy storage power station combination. Figure 5 It can be seen that the profitability of energy storage power stations E2 and E5 is as follows: Option 2 > Option 1 > Option 3. This is because E2 and E5 compressed air energy storage, when participating in the market independently, suffer from low market bidding volume due to comprehensive costs and frequency regulation performance, resulting in correspondingly lower profitability. However, with mandatory participation of all energy storage in the cluster, E2 and E5 increase the cluster's declared capacity. During the distribution of revenue within the cluster, their capacity advantage leads to increased profitability. Thermal power energy storage power stations G1, G2, and G3, constrained by their own capacity, have higher costs for participating in frequency regulation compared to energy storage, resulting in smaller declared capacity and lower winning bids. Their profitability will suffer as more energy storage participates in the frequency regulation market, leading to decreased profitability.

[0133] In summary, according to the novel dynamic aggregation multi-element energy storage frequency regulation auxiliary service bidding decision method 300 of the present invention, each energy storage power station can dynamically choose to participate in energy storage clusters or bid independently as an independent energy storage power station. An upper-level dispatch center clearing model is constructed with the objective of minimizing the total electricity purchase cost of the dispatch center from each energy storage cluster, each independent energy storage power station, and each thermal power unit in the frequency regulation market; a middle-level energy storage cluster bidding model is constructed with the objective of maximizing the total frequency regulation revenue of each energy storage cluster in the frequency regulation market; and a lower-level energy storage cluster revenue distribution model is constructed with the objective of minimizing the frequency regulation cost of the energy storage cluster in the frequency regulation market. This leads to a three-layer bidding decision model, which is then solved to obtain the bidding decision results. Based on this, the present invention allows each energy storage power station to autonomously choose whether to participate in a cluster, realizing on-demand aggregation and dynamic convergence of each energy storage power station. Furthermore, the three-layer bidding decision model constructed according to the present invention can increase the total energy storage revenue while reducing the total dispatch cost. Moreover, the three-layer bidding decision model can incorporate bidding clearing and revenue sharing into the same decision chain, providing a quantitative basis for whether energy storage power stations choose to participate in the cluster, thereby promoting the healthy and coordinated development of the frequency regulation market.

[0134] In addition, the introduction of a dual contribution correction factor of "capacity-mileage" can improve the cost allocation factor of the Shapley value method, thereby allocating the frequency regulation revenue of energy storage clusters in the frequency regulation market. This can achieve reasonable and accurate allocation of cluster revenue based on actual output, and improve the fairness of revenue distribution.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 novel dynamic aggregation multi-element energy storage frequency regulation ancillary service bidding decision method, executed in a computing device, comprising: With the primary objective of minimizing the total cost of electricity purchases by the dispatch center from various energy storage clusters, independent energy storage power stations, and thermal power units in the frequency regulation market, a first objective function is constructed. Based on the first objective function, an upper-level dispatch center clearing model is built. Each energy storage power station is suitable for dynamically selecting to participate in energy storage clusters or independent bidding as an independent energy storage power station. The total cost of electricity purchase is related to the capacity clearing price and mileage clearing price in the frequency regulation market for each time period, as well as the capacity and mileage bid winning amounts of each energy storage cluster, independent energy storage power station, and thermal power unit in the frequency regulation market for each time period. With the goal of maximizing the total frequency regulation revenue of each energy storage cluster in the frequency regulation market, a second objective function is constructed. Based on the second objective function, a bidding model for mid-level energy storage clusters is constructed. The total frequency regulation revenue is related to the revenue of each energy storage cluster in the frequency regulation market, the frequency regulation cost of each energy storage cluster in the frequency regulation market, and the revenue obtained by independent energy storage power stations that do not participate in the energy storage clusters. With the goal of minimizing the frequency regulation cost of the energy storage cluster in the frequency regulation market, a third objective function is constructed. Based on the third objective function, a revenue distribution model for the lower-level energy storage cluster is constructed. The frequency regulation cost of the energy storage cluster in the frequency regulation market is related to the mileage bid volume and unit mileage price of each energy storage power station in the energy storage cluster in the frequency regulation market at each time period. Based on the upper-layer dispatch center clearing model, the middle-layer energy storage cluster bidding model, and the lower-layer energy storage cluster revenue distribution model, a three-layer bidding decision model is obtained. The three-layer bidding decision model is solved to obtain the bidding decision results, which are used to indicate: the capacity clearing price and mileage clearing price in the frequency regulation market for each time period, the capacity and mileage winning bids of each energy storage cluster, each independent energy storage power station, and each thermal power unit in the frequency regulation market for each time period, the energy storage cluster aggregation results, and the energy storage power station output curves; wherein, the energy storage cluster aggregation results include the decision results of whether each energy storage power station in each energy storage cluster participates in the energy storage cluster, and the energy storage power station output curves include the capacity output and mileage output of the energy storage power station for each time period.

2. The method as described in claim 1, wherein, The revenue of the energy storage cluster in the frequency regulation market is suitable for determination based on the capacity clearing price and mileage clearing price in the frequency regulation market for each time period, and the capacity and mileage bid-winning amount of the energy storage cluster in the frequency regulation market for each time period. The frequency regulation cost of the energy storage cluster in the frequency regulation market is suitable for being determined based on the mileage bid volume of the energy storage cluster in the frequency regulation market at each time period and the unit frequency regulation cost of the energy storage cluster. The revenue obtained by the independent energy storage power station without participating in the energy storage cluster is suitable for determination based on the revenue unit price allocated by participating in the energy storage cluster and the mileage bid volume of the independent energy storage power station in the frequency regulation market at each time period.

3. The method as described in claim 1 or 2, wherein, Based on the first objective function, a higher-level scheduling center clearing model is constructed, including: An upper-level dispatch center clearing model is constructed based on the first objective function and upper-level constraints. The upper-level constraints include supply and demand balance constraints, capacity and mileage constraints of energy storage clusters, and capacity and mileage constraints of thermal power units. The supply and demand balance constraint means that the total capacity bid amount of all energy storage clusters and thermal power units in each time period is consistent with the total capacity demand of the dispatch center in each time period, and that the total mileage bid amount of all energy storage clusters and thermal power units in each time period is consistent with the total mileage demand of the dispatch center in each time period. The capacity and mileage constraints of the energy storage cluster are as follows: the bid amount for the capacity of each energy storage power station in the energy storage cluster shall not exceed the capacity requirement range of each energy storage power station, and the bid amount for the mileage of each energy storage power station in the energy storage cluster shall not exceed the mileage requirement range of each energy storage power station. The capacity and mileage constraints of the thermal power units mean that the bid amount for the capacity of the thermal power units does not exceed the capacity requirement range, and the bid amount for the mileage of the thermal power units does not exceed the mileage requirement range.

4. The method according to any one of claims 1-3, wherein, Based on the second objective function, a bidding model for mid-level energy storage clusters is constructed, including: A mid-level energy storage cluster bidding model is constructed based on the second objective function and mid-level constraints. The mid-level constraints include energy storage cluster bidding constraints, energy storage cluster quantity reporting constraints, energy storage power station charging and discharging power constraints, thermal power unit quantity reporting constraints, and thermal power unit frequency regulation cost constraints. The energy storage cluster pricing constraints mean that the capacity pricing of the energy storage cluster in each time period does not exceed the upper and lower limits of the energy storage capacity pricing, and the mileage pricing of the energy storage cluster in each time period does not exceed the upper and lower limits of the energy storage mileage pricing. The energy storage cluster reporting constraints mean that the capacity reporting volume of the energy storage cluster in each time period is less than or equal to the maximum available capacity of the energy storage cluster, and the mileage reporting volume of the energy storage cluster in each time period is less than or equal to the maximum available mileage of the energy storage cluster. The charging and discharging power constraint of the energy storage power station means that the charging and discharging power of the energy storage power station shall not exceed the maximum charging and discharging power of the energy storage power station, and the energy storage power station shall not charge and discharge at the same time. The reported capacity constraints for thermal power units are as follows: the reported capacity of thermal power units in each time period is less than or equal to the maximum available capacity of the thermal power units, and the reported mileage of thermal power units in each time period is less than or equal to the maximum available mileage of the thermal power units. The frequency regulation cost constraint of the thermal power unit means that the frequency regulation capacity and mileage cost of the thermal power unit must exceed its marginal cost.

5. The method according to any one of claims 1-4, wherein, Based on the third objective function, a lower-level energy storage cluster revenue distribution model is constructed, including: Based on the third objective function and the lower-level constraints, a lower-level energy storage cluster revenue distribution model is constructed. The lower-level constraints include energy storage cluster supply and demand balance constraints, energy storage power station output constraints, energy storage power station charging and discharging power constraints, energy storage power station state of charge constraints, energy storage power station health state constraints, and energy storage cluster revenue distribution constraints. The supply and demand balance constraint of the energy storage cluster means that the sum of the capacity output and the sum of the mileage output of all energy storage power stations in the energy storage cluster at each time period are equal to the bid amount of the capacity and the bid amount of the mileage of the energy storage cluster at each time period, respectively. The output constraints of the energy storage power station are as follows: the capacity output of the energy storage power station in each time period shall not exceed the maximum frequency regulation capacity of the energy storage power station, and the mileage output of the energy storage power station in each time period shall not exceed the maximum frequency regulation mileage of the energy storage power station. The charging and discharging power constraint of the energy storage power station means that the charging and discharging power of the energy storage power station shall not exceed the maximum charging and discharging power of the energy storage power station, and the energy storage power station shall not charge and discharge at the same time. The state of charge (SBC) constraint of the energy storage power station means: the available capacity of the energy storage power station in the next time period is determined based on the SBC of the energy storage power station, and the SBC of the energy storage power station in each time period does not exceed the upper and lower limits of the SBC of the energy storage power station. The health status constraint of the energy storage power station means that the health status of the energy storage power station at any time does not exceed the upper and lower limits of the health status of the energy storage power station.

6. The method of claim 5, wherein, The constraints on the distribution of revenue from the energy storage cluster include: Based on the capacity output contribution and mileage output contribution of each energy storage power station, the original cost allocation factor of each energy storage power station is improved to obtain the improved profit allocation factor of each energy storage power station. Using the Shapley value method, based on the improved profit distribution factor of each energy storage power station, the frequency regulation revenue of the energy storage cluster in the frequency regulation market is allocated, so as to distribute the frequency regulation revenue of the energy storage cluster in the frequency regulation market to each energy storage power station participating in the energy storage cluster.

7. The method of claim 6, wherein, Based on the capacity output contribution and mileage output contribution of each energy storage power station, the original cost allocation factors of each energy storage power station are improved to obtain the improved profit allocation factors of each energy storage power station, including: Based on the capacity output contribution and mileage output contribution of each energy storage power station, as well as the corresponding improvement weight coefficient, the original cost allocation factor of each energy storage power station is improved to obtain the improved profit allocation factor of each energy storage power station.

8. The method according to any one of claims 1-7, wherein, The three-layer bidding decision model is solved. The system acquires day-ahead forecast data and bidding information from each bidding entity, and inputs this data into the upper-level dispatch center clearing model to solve the model and obtain the upper-level clearing result. The day-ahead forecast data includes load forecast information, frequency regulation demand forecast information, and forecasted electricity price. The bidding information from each bidding entity includes the capacity and mileage bids of each energy storage cluster, each independent energy storage power station, and each thermal power unit in each time period. The upper-level clearing result includes the capacity clearing price and mileage clearing price in the frequency regulation market in each time period, as well as the capacity and mileage winning bids of each energy storage cluster, each independent energy storage power station, and each thermal power unit in the frequency regulation market in each time period. Based on the upper-layer clearing results, the bidding model of the middle-layer energy storage cluster is solved to obtain the middle-layer bidding results, which include the energy storage cluster aggregation results. Based on the mid-level bidding results, the revenue distribution model of the lower-level energy storage cluster is solved to obtain the lower-level solution results, which include the output curves of each energy storage power station.

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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