Electric power spot-frequency modulation-reserve combined bidding clearing method oriented to participation of energy storage clusters

Abstract

The invention discloses an energy storage cluster participation-oriented electric power spot-frequency modulation-reserve combined bidding clearing method. The method comprises the following steps of: constructing an upper layer model by taking the minimum total cost of a dispatching center in a combined market and the maximum renewable energy consumption as double targets; constructing a lower-layer model by taking the maximum total profit of the energy storage cluster in the united market as a target; based on the upper-layer model and the lower-layer model, obtaining a joint clearing double-layer model; the combined clearing double-layer model is solved, a combined clearing result is obtained, and the combined clearing result is used for indicating the bid winning amount and the bid winning price of each subject in the spot market, the frequency modulation market and the standby market in each time period. According to the method, the utilization rate of the novel energy storage can be improved, channels and earnings for obtaining earnings of the novel energy storage are increased, social welfare maximization and renewable energy source maximization can be achieved, and meanwhile energy storage cluster profit maximization is achieved.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a method for joint bidding and clearing of electricity spot market, frequency regulation and reserve markets with the participation of energy storage clusters. Background Technology

[0002] In the context of new power systems, the large-scale integration of renewable energy has brought unavoidable new challenges to the electricity market. Simultaneously, due to the large-scale grid connection of renewable energy, the power system faces a series of problems such as wind and solar curtailment and difficulties in grid integration and absorption of renewable energy. Energy storage, with its ability to charge during periods of high renewable energy generation and discharge during off-peak periods, can mitigate the volatility of renewable energy, thus playing an increasingly important role in maintaining grid stability. However, current new energy storage systems distributed across different nodes suffer from high costs, low utilization rates, high barriers to entry for independent participation in the spot-frequency regulation-reserve market, and a single source of revenue.

[0003] Therefore, a joint bidding and clearing method for electricity spot market, frequency regulation, and reserve markets with the participation of energy storage clusters is needed to solve the problems existing in the above schemes. Summary of the Invention

[0004] To address this, the present invention provides a method for joint bidding and clearing of electricity spot market, frequency regulation, and reserve markets with the participation of energy storage clusters, in order to solve or at least alleviate the problems mentioned above.

[0005] According to one aspect of the present invention, a method for joint bidding and clearing of the electricity spot market, frequency regulation market, and reserve market with the participation of energy storage clusters is provided. This method is executed in a computing device and includes: constructing a higher-level objective function with the goal of minimizing the total cost of the dispatch center in the joint market and maximizing renewable energy consumption; and constructing a higher-level model based on the objective function. The joint market includes a spot market, a frequency regulation market, and a reserve market. The main entities in the joint market include multiple thermal power units, multiple green power units, and an energy storage cluster. The energy storage cluster includes multiple energy storage units distributed across multiple nodes. The total cost of the dispatch center in the joint market includes: the cost of electricity purchased by the dispatch center from each thermal power unit, each green power unit, and the energy storage cluster in the spot market; the cost of frequency regulation capacity and frequency regulation mileage purchased by the dispatch center from each thermal power unit and the energy storage cluster in the frequency regulation market; and the cost of electricity purchased by the dispatch center from each thermal power unit in the reserve market. The costs of standby capacity for energy storage clusters and the start-up and shutdown costs of each thermal power unit are considered. The renewable energy consumption is related to the winning bids and curtailment of each green power unit in the spot market. A lower-level objective function is constructed with the goal of maximizing the total profit of the energy storage cluster in the joint market. A lower-level model is then built based on this objective function, where the total profit of the energy storage cluster in the joint market represents the sum of the revenues obtained by the energy storage cluster in the spot market, frequency regulation market, and standby market, minus the variable costs and fixed costs of the energy storage cluster in these markets. Based on the upper-level and lower-level models, a joint clearing two-layer model is obtained. Solving the joint clearing two-layer model yields a joint clearing result, which indicates the winning bids and prices of each entity in the spot market, frequency regulation market, and standby market at each time period.

[0006] Optionally, in the electricity spot-frequency regulation-reserve joint bidding clearing method for energy storage clusters according to the present invention, the electricity purchase cost of the dispatch center from each thermal power unit and each green power unit in the spot market is suitable for being determined based on the declared volume and declared price of each thermal power unit and each green power unit in the spot market at each time period; the electricity purchase cost of the dispatch center from the energy storage cluster in the spot market is suitable for being determined based on the declared discharge volume, declared discharge price, declared charging volume, and declared charging price of the energy storage cluster in the spot market at each time period; the dispatch center purchases electricity from each thermal power unit in the frequency regulation market. The frequency regulation capacity cost and frequency regulation mileage cost of energy storage clusters are suitable for determination based on the frequency regulation capacity declaration volume, adjusted frequency regulation capacity declaration price, and adjusted frequency regulation mileage declaration price of each thermal power unit and energy storage cluster in the frequency regulation market at each time period; the cost of the dispatch center purchasing the standby capacity of each thermal power unit and energy storage cluster in the standby market is suitable for determination based on the standby capacity declaration volume and standby capacity declaration price of each thermal power unit and energy storage cluster in the standby market at each time period; the renewable energy consumption is suitable for determination based on the winning bid volume, wind and solar curtailment volume, capacity, and output coefficient of each green power unit in the spot market.

[0007] Optionally, in the electricity spot-frequency regulation-reserve joint bidding clearing method for energy storage clusters according to the present invention, the revenue obtained by the energy storage cluster in the spot market is suitable for determination based on the energy storage cluster's winning bid volume, winning bid price for discharge, winning bid volume for charging, and winning bid price for charging in the spot market for each time period; the revenue obtained by the energy storage cluster in the frequency regulation market is suitable for determination based on the energy storage cluster's declared frequency regulation capacity, winning bid price for frequency regulation capacity, and winning bid price for frequency regulation mileage in the frequency regulation market for each time period; the revenue obtained by the energy storage cluster in the reserve market is suitable for determination based on the energy storage cluster's winning bid volume and winning bid price for reserve capacity in the reserve market for each time period. The variable costs of the energy storage cluster in the spot market are suitable for determination based on the unit cost and total cost of each energy storage unit in the spot market at each time period; the variable costs of the energy storage cluster in the frequency regulation market are suitable for determination based on the unit cost of frequency regulation capacity, the total cost of frequency regulation capacity, the unit cost of frequency regulation mileage, and the total cost of frequency regulation mileage of each energy storage unit in the frequency regulation market at each time period; the variable costs of the energy storage cluster in the standby market are suitable for determination based on the unit cost of standby capacity and the total cost of standby capacity of each energy storage unit in the standby market at each time period; the fixed costs of the energy storage cluster include the initial construction investment cost, annual operation and maintenance costs, replacement costs, and recovery costs.

[0008] Optionally, in the electricity spot-frequency regulation-reserve joint bidding clearing method for energy storage clusters according to the present invention, a higher-level objective function is constructed with the goal of minimizing the total cost of the dispatch center in the joint market and maximizing the absorption of renewable energy. A higher-level model is then constructed based on the higher-level objective function, including: constructing a higher-level first objective function with the goal of minimizing the total cost of the dispatch center in the joint market; constructing a higher-level second objective function with the goal of maximizing the absorption of renewable energy; and constructing a higher-level model based on the higher-level first objective function and the higher-level second objective function.

[0009] Optionally, in the electricity spot-frequency regulation-reserve joint bidding clearing method for energy storage clusters according to the present invention, constructing an upper-level model based on the upper-level objective function includes: constructing an upper-level model based on the upper-level objective function and multiple upper-level constraints; wherein, the multiple upper-level constraints include: green power unit output constraints, thermal power unit output constraints, thermal power unit ramping capacity constraints, thermal power unit start-stop time constraints, thermal power unit frequency regulation capacity constraints, thermal power unit reserve capacity constraints, energy storage cluster charging and discharging constraints, energy storage cluster state of charge constraints, energy storage cluster charging and discharging power constraints, energy storage cluster market application constraints, energy storage cluster winning bid constraints, energy storage cluster SOH constraints, market application restriction constraints, frequency regulation price adjustment constraints, and power flow constraints.

[0010] Optionally, in the electricity spot-frequency regulation-reserve joint bidding clearing method for energy storage clusters according to the present invention, the output constraints of green power units include constraints on the winning bid amount of green power units, constraints on the curtailed wind and solar energy of green power units, and constraints on the composition of the winning bid amount of green power units; the output constraints of thermal power units include constraints on the power generation of thermal power units, constraints on the declared amount of thermal power units, constraints on the start-up and shutdown of thermal power units, and constraints on the winning bid amount of thermal power units; the start-up and shutdown time constraints of thermal power units include constraints on the start-up time of thermal power units, constraints on the shutdown time of thermal power units, constraints on the total start-up time of thermal power units, and constraints on the total shutdown time of thermal power units; the state of charge constraints of energy storage clusters include constraints on the state of charge of energy storage clusters at various time periods, constraints on the state of charge of energy storage clusters at the initial time period, and constraints on the composition of the winning bid amount of thermal power units. The constraints include: state of charge (SBC) conditions for the final period of the energy storage cluster; market application constraints for the energy storage cluster include those for the spot market, frequency regulation market, and reserve market; bidding constraints for the energy storage cluster include those for the spot market charge / discharge volume, frequency regulation market, and reserve market, as well as those for the spot market charge / discharge price, frequency regulation market price, and reserve market price; market application restrictions include those for the spot market, frequency regulation market, and reserve market; and power flow constraints include node line power flow constraints and upper / lower limits for power flow transmission.

[0011] Optionally, in the electricity spot-frequency regulation-reserve joint bidding clearing method for energy storage clusters according to the present invention, solving the joint clearing two-layer model to obtain the joint clearing result includes: using a hierarchical solving algorithm to solve the upper-layer model based on multiple upper-layer constraints to obtain the unit output plan; and solving the lower-layer model based on the unit output plan to obtain the joint clearing result.

[0012] Optionally, the electricity spot-frequency regulation-reserve joint bidding clearing method for energy storage clusters according to the present invention further includes: determining the total profit of the energy storage cluster in the joint market based on the joint clearing result; determining the original allocation factor of each energy storage unit based on the total profit of the energy storage cluster in the joint market using the Shapley value method; weighting and summing the original allocation factor, frequency regulation capacity contribution factor, frequency regulation mileage contribution factor, and reserve capacity contribution factor of each energy storage unit to obtain the improved allocation factor for each energy storage unit; and allocating the total profit of the energy storage cluster in the joint market to each energy storage unit based on the improved allocation factor of each energy storage unit.

[0013] According to one aspect of the present invention, a computing device is provided, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions including instructions for executing the electricity spot-frequency regulation-reserve joint bidding clearing method for energy storage cluster participation as described above.

[0014] 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.

[0015] 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 electricity spot-frequency regulation-reserve joint bidding clearing method as described above for energy storage cluster participation.

[0016] According to the technical solution of the present invention, a joint bidding clearing method for electricity spot-frequency regulation-reserve markets with the participation of energy storage clusters is provided. First, idle new energy storage distributed on multiple nodes is combined to form an energy storage cluster, which participates as an independent entity in the spot-frequency regulation-reserve joint market. This can improve the utilization rate of new energy storage, increase the channels for new energy storage to obtain revenue, and increase the revenue of new energy storage. Second, an upper-level model is constructed with the dual objectives of minimizing the total cost of the dispatch center in the joint market and maximizing renewable energy consumption, and a lower-level model is constructed with the objective of maximizing the total profit of the energy storage cluster in the joint market. Thus, a joint clearing two-layer model can be constructed. By solving the joint clearing two-layer model, the joint clearing result can be obtained, which can maximize social welfare and renewable energy, and simultaneously maximize the profit of the energy storage cluster.

[0017] Furthermore, the improved Shapley value method of this invention for allocating the total profit of the energy storage cluster can comprehensively consider the contributions of each energy storage unit in various aspects, ensuring that the allocation result is both fair and incentivizing, and further achieving a dual improvement in the economic efficiency of the power system and the utilization efficiency of energy storage resources.

[0018] 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

[0019] 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.

[0020] Figure 1 A schematic diagram of a spot-frequency regulation-standby joint market operation framework 100 provided according to an embodiment of the present invention is shown;

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

[0022] Figure 3 A flowchart illustrating a joint bidding and clearing method 300 for electricity spot-frequency regulation-reserve participation oriented towards energy storage clusters, provided by an embodiment of the present invention, is shown.

[0023] Figure 4 A schematic diagram of the node configuration of a node system according to some embodiments of the present invention is shown;

[0024] Figure 5 A schematic diagram of green electricity output curves according to some embodiments of the present invention is shown;

[0025] Figure 6 A schematic diagram of system load curves according to some embodiments of the present invention is shown;

[0026] Figure 7 A schematic diagram of frequency modulation capacity demand, frequency modulation mileage demand, and reserve capacity demand curves according to some embodiments of the present invention is shown.

[0027] Figure 8 This diagram illustrates the quantity of thermal power units awarded in the spot market according to some embodiments of the present invention;

[0028] Figure 9 This diagram illustrates the quantity of green generator sets awarded in the spot market according to some embodiments of the present invention.

[0029] Figure 10 This diagram illustrates the quantity of energy storage clusters in the spot market according to some embodiments of the present invention;

[0030] Figure 11 This diagram illustrates the bidding process for frequency modulation market capacity according to some embodiments of the present invention;

[0031] Figure 12A schematic diagram illustrating the bidding situation for FM market mileage according to some embodiments of the present invention is shown;

[0032] Figure 13 A schematic diagram of the standby market bid volume is shown according to some embodiments of the present invention. Detailed Implementation

[0033] 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.

[0034] To address the issues of low utilization rates and limited revenue streams in existing energy storage technologies, this invention proposes a joint bidding and clearing method for the electricity spot market, frequency regulation market, and reserve market, oriented towards the participation of energy storage clusters. First, idle new energy storage devices distributed across multiple nodes are grouped together to form an energy storage cluster, which participates as an independent entity in the spot market, frequency regulation market, and reserve market. This improves the utilization rate of new energy storage, increases revenue streams, and enhances its profitability. Second, by constructing a two-layer joint clearing model, social welfare and renewable energy can be maximized, while simultaneously maximizing the profits of the energy storage cluster.

[0035] The electricity spot-frequency regulation-reserve joint bidding clearing method for energy storage cluster participation provided by the embodiments of the present invention can be used in the operating framework of the spot-frequency regulation-reserve joint market. The following first introduces an operating framework for the spot-frequency regulation-reserve joint market provided by the embodiments of the present invention.

[0036] Figure 1 A schematic diagram of a spot-frequency regulation-reserve joint market operation framework 100 provided according to an embodiment of the present invention is shown. In this embodiment, the joint market includes a spot market, a frequency regulation market, and a reserve market.

[0037] like Figure 1As shown, load demand entities in the joint market (including high-energy-consuming users, commercial users, etc.) provide electricity demand information to the dispatch center. Electricity supply entities in the joint market include thermal power units, green power units, and energy storage clusters (as independent entities), which provide quantity and price quotes to the dispatch center. The dispatch center can clear the market based on the electricity demand information and the quantity and price quotes from each electricity supply entity, obtaining a joint clearing result to jointly maintain grid stability. Simultaneously, the energy storage cluster, as an independent entity integrating supply and demand, can both purchase electricity from and sell electricity to the grid. In this embodiment of the invention, the energy storage cluster includes multiple energy storage units distributed across multiple nodes. The energy storage cluster can contain various types of energy storage units, such as electrochemical energy storage units and mechanical energy storage units.

[0038] According to the spot-frequency regulation-reserve joint market operation framework 100 in this embodiment of the invention, the spot market, frequency regulation market, and reserve market in the electricity market are integrated together. This breaks the limitation of the spot market, frequency regulation market, and reserve market operating independently in the electricity market, enabling market participants such as thermal power units, green power units, and energy storage clusters to participate in multiple markets simultaneously. Based on their own characteristics and cost advantages, they can flexibly allocate resources to participate in different transactions. For example, energy storage clusters can generate profits by discharging electricity when spot market prices are high, provide frequency regulation services when load changes, and serve as reserve resources when power supply is tight, thereby improving overall resource utilization efficiency and reducing the operating costs of the power system.

[0039] Meanwhile, in this embodiment of the invention, considering the maximization of renewable energy absorption, the energy storage cluster can charge during periods of high renewable energy generation and discharge during peak load periods or when renewable energy output is insufficient, thus smoothing power fluctuations. Simultaneously, frequency regulation and reserve resources can cope with random changes in renewable energy, enhancing the grid's capacity to accommodate renewable energy.

[0040] In this embodiment of the invention, the energy storage cluster can coordinate and control different types of energy storage units (such as electrochemical energy storage units, mechanical energy storage units, etc.) within the cluster. The energy storage cluster can pre-collect information on each energy storage unit and its current status for the current time period, and then statistically analyze this information to determine the most suitable bid quantity and bid price for participating in the joint market. After the energy storage cluster's bidding process concludes and the winning bid quantity and price are determined, the energy storage cluster optimizes the scheduling of its various types of energy storage. This not only lowers the barrier to entry for small energy storage units distributed across different nodes to participate in the joint market but also further expands the revenue sources of the energy storage cluster.

[0041] It should be noted that the spot market encompasses the day-ahead market, intraday market, and real-time balancing market. In the day-ahead market, market participants need to forecast their own power generation capacity or electricity demand, and then submit their bids and quantity information for each trading session of the power operation day to the market platform. This forms a feasible trading plan that fits the system operating conditions of the day. The core function of this market is to plan reasonable dispatch schemes for the power operation day, thereby reducing the risks brought about by real-time electricity price fluctuations. Market organizers conduct centralized optimization calculations on this information based on specific optimization objectives, ultimately determining the start-up and shutdown plans of generating units for the operation day, and implementing corresponding allocation and dispatch work. The intraday market is located in the period from the end of the day-ahead market to one hour before real-time transmission. It is a trading platform that allows market participants to adjust their day-ahead trading plans. Compared to the day-ahead market, the intraday market has a relatively smaller trading volume, serving as a continuation and supplement to the day-ahead market. Its main function is to provide protection for market participants, shielding them from various forecast deviations and unforeseen circumstances during the day, while also building a support mechanism for renewable energy sources with less stability to participate in market competition. The power market dispatching agency continuously updates and adjusts the clearing plans for electricity and ancillary services within hours and promptly publishes relevant information. The real-time balancing market is a real-time trading platform based on real-time system load forecasts, generator output changes, and day-ahead and intraday market transactions and dispatching plans. It features short trading times and complex scenarios, enabling it to more accurately reflect the actual operating status of the power grid and effectively assisting dispatching departments in revising and improving real-time dispatching plans. The key function of the real-time market is to ensure the real-time balance of the power system, timely and accurately displaying the short-term resource shortage and congestion levels of the system, and providing control tools for the trading platform and dispatching agency through economic signals.

[0042] Considering the characteristics of energy storage, this embodiment of the invention focuses on the day-ahead market. Based on the electricity demand forecast of the day-ahead market, energy storage can charge during off-peak hours and discharge during peak hours, transferring surplus electricity from off-peak hours to peak hours, thereby improving the overall utilization efficiency of the power system. Furthermore, the day-ahead market provides control mechanisms for trading platforms and dispatching agencies using economic signals. Energy storage participates in this process, flexibly adjusting its charging and discharging strategies based on these economic signals, thus satisfying its own profit needs while providing effective regulation services to the market.

[0043] Regarding the frequency regulation market, frequency regulation (ancillary services) refers to services that, when the power system frequency deviates, adjust generation output or load consumption to quickly restore and maintain the system frequency within a specified range. In a power system, electrical load is constantly changing, and a balance must be maintained between generation power and load; otherwise, frequency fluctuations will occur. Once the frequency deviates from the normal range, it will not only affect the normal operation of power equipment but may even lead to system collapse in severe cases. The main goal of frequency regulation ancillary services is to address these frequency changes. The generation side is a crucial force in providing frequency regulation ancillary services. Generating units participate in frequency regulation by adjusting their own active power output. When the system frequency drops, generating units need to increase active power output; conversely, when the frequency rises, they need to reduce active power output. This requires generating units to have rapid response and flexible adjustment capabilities. For example, traditional thermal power plants change generation power by adjusting the steam flow into the turbine, while hydropower plants can adjust power output by adjusting the turbine guide vane opening.

[0044] The mechanism by which energy storage systems participate in grid frequency regulation is as follows: when the power system frequency deviates from the predetermined target frequency, in order to ensure that the power system frequency remains stable, the grid-connected entity adjusts its active power output to reduce system frequency fluctuations. Adjustment methods include speed control systems and automatic power control, encompassing both primary and secondary frequency regulation. This invention embodiment only considers the scenario of secondary frequency regulation.

[0045] The standby market is a crucial link in the electricity market system, primarily used to ensure that the power system can quickly allocate additional generating capacity or interruptible loads in the face of various uncertainties, maintaining power supply and demand balance and ensuring reliable system operation. Standby mainly refers to spinning reserve, which is the reserve capacity provided by generating units that are in operation and can increase their generating output within a short period (generally within 10 minutes). These units are always on standby, and can quickly increase generating power to make up for the power shortage once the system experiences a power deficit. For example, during peak electricity demand periods, even at full capacity, conventional generating units may not be able to meet the sudden increase in load demand. In this case, spinning reserve units can respond immediately, preventing a significant drop in system frequency. Another type is non-spinning reserve, which covers the reserve capacity provided by generating equipment that, although not in operation, can be started and put into operation within a specified time (usually around 30 minutes). For example, some gas turbine generator units are normally shut down, but can be quickly started when the system needs them, providing additional power support. Meanwhile, energy storage systems have extremely fast response speeds, capable of completing power adjustments within milliseconds to seconds. Compared to traditional power generation equipment, they can react more quickly to power shortages or surpluses in the power system, effectively improving the dynamic stability of the system.

[0046] It's important to note that the State of Charge (SOC) of energy storage is a key parameter measuring the current remaining usable capacity of an energy storage system, usually expressed as a percentage—the proportion of current charge to its maximum usable capacity. It is one of the core indicators for energy storage system operation management, performance optimization, and safety control. The SOC reflects the real-time available capacity of the energy storage device. Setting the SOC within a reasonable range can extend the lifespan of the energy storage system to some extent and reduce damage caused by overcharging and over-discharging. Similarly, excessively high lower and upper SOC limits will lead to excessive charging and discharging in the market, affecting energy storage profitability. Therefore, setting appropriate upper and lower limits for energy storage is beneficial for maintaining a healthy lifespan of the energy storage system, increasing its profitability, and promoting its further development. The calculation formula is shown below.

[0047]

[0048] In the formula, SOC e,t The state of charge of energy storage unit e at time t. The remaining capacity of energy storage unit e at time t-1. The remaining capacity of energy storage unit e at time t, η Cha η Dis The charging and discharging efficiency of energy storage unit e, Rated capacity of energy storage unit e.

[0049] State of Health (SOH) is a key indicator for measuring the performance and aging of energy storage, usually expressed as a percentage. It reflects the ratio of the current actual available capacity or energy of the energy storage system to its rated capacity or energy, directly reflecting its health status and remaining lifespan. The accuracy of the SOH directly affects the economy, safety, and availability of energy storage. Calculating the SOH using appropriate methods can maximize the utilization value of energy storage.

[0050] Methods for calculating the State of Harmony (SOH) of energy storage include direct capacity testing, internal resistance detection, open-circuit voltage method, ampere-hour integration method, and machine learning method. Among these, the ampere-hour integration method estimates capacity decay by accumulating charge and discharge volumes. Its advantage is its simplicity and ease of calculation, but its disadvantage is that errors accumulate over time, requiring periodic calibration. The formula for calculating the SOH of energy storage using the ampere-hour integration method is shown below.

[0051]

[0052] In the formula, SOH e,t This represents the SOH of energy storage unit e at time t. This represents the cumulative capacity decay of energy storage unit e at time t. The rated capacity of energy storage unit e at time t.

[0053] In some embodiments, the spot-frequency regulation-standby joint market operation framework 100 needs to consider joint market operation constraints, which include spot market reporting constraints, frequency regulation market reporting constraints, and standby market reporting constraints. Specific constraints will be described below.

[0054] In embodiments of the present invention, the computing device may be configured to execute a joint bidding and clearing method 300 for electricity spot-frequency regulation-reserve participation by energy storage clusters. The joint bidding and clearing method 300 for electricity spot-frequency regulation-reserve participation by energy storage clusters of the present invention will be described below.

[0055] The following describes a computing device 200 provided by an embodiment of the present invention.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] In an embodiment of the present invention, program module 203 includes multiple program instructions for executing the present invention’s electricity spot-frequency regulation-reserve joint bidding clearing method 300 for energy storage cluster participation.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] In an embodiment of the invention, computing device 200 is configured to execute a joint bidding and clearing method 300 for electricity spot-frequency regulation-reserve participation involving energy storage clusters. 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 joint bidding and clearing method 300 for electricity spot-frequency regulation-reserve participation involving energy storage clusters as described in this embodiment of the invention.

[0065] In some embodiments, the computing device 200 that executes the electricity spot-frequency regulation-reserve joint bidding clearing method 300 for energy storage clusters in this embodiment of the invention may be a terminal or a server.

[0066] The following is a detailed description of the electricity spot-frequency regulation-reserve joint bidding clearing method 300 for energy storage clusters in this embodiment of the invention.

[0067] First, it should be noted that the joint market in this embodiment of the invention includes a spot market, a frequency regulation market, and a reserve market. The main entities (power supply entities) in the joint market include multiple thermal power units, multiple green power units, and energy storage clusters. Energy storage clusters include multiple energy storage units distributed across multiple nodes. Each thermal power unit, each green power unit, and each energy storage cluster can participate as an independent entity in its respective market within the joint market (spot market, frequency regulation market, and reserve market).

[0068] Figure 3 A schematic flowchart of a joint bidding and clearing method 300 for electricity spot-frequency regulation-reserve participation, based on an embodiment of the present invention, is shown. Figure 3 As shown, the electricity spot-frequency regulation-reserve joint bidding clearing method 300 for energy storage clusters includes the following steps 310 to 340.

[0069] Step 310: The computing device 200 constructs an upper-level objective function with the goal of minimizing the total cost of the dispatch center in the joint market and maximizing the consumption of renewable energy. Based on the upper-level objective function, an upper-level model is constructed.

[0070] In this embodiment of the invention, the dispatch center can perform dispatch based on the reported quantity and price information from various power supply entities and the power demand information from load demand entities, with the goal of minimizing the total cost of purchasing electricity, frequency regulation capacity, frequency regulation mileage, and reserve capacity in the joint market and maximizing the absorption of renewable energy.

[0071] In this embodiment of the invention, each thermal power unit, each green power unit, and each energy storage cluster can participate in the spot market as an independent entity. Furthermore, each thermal power unit and each energy storage cluster can participate in the frequency regulation market and the reserve market as an independent entity. It should be noted that each green power unit only participates in the spot market and not in the frequency regulation market or the reserve market.

[0072] Based on this, in step 310, a higher-level objective function can be constructed with the goals of minimizing the total cost of the dispatch center in the joint market (corresponding to maximizing social welfare) and maximizing renewable energy consumption. Here, maximizing renewable energy consumption includes maximizing the amount of renewable energy won in the bidding (i.e., the amount of each green power unit won in the spot market) and minimizing the amount of wind and solar curtailment (the amount of wind and solar curtailment of each green power unit in the spot market). The total cost of the dispatch center in the joint market includes: the cost of purchasing thermal power, green power, and energy storage in the spot market; the cost of frequency regulation capacity and frequency regulation mileage for purchasing thermal power and energy storage in the frequency regulation market; and the cost of reserve capacity for purchasing thermal power and energy storage in the reserve market. In other words, the total cost of the dispatch center in the joint market includes: the cost of purchasing electricity from each thermal power unit, each green power unit, and energy storage cluster in the spot market; the cost of purchasing frequency regulation capacity and frequency regulation mileage from each thermal power unit and energy storage cluster in the frequency regulation market; and the cost of purchasing reserve capacity from each thermal power unit and energy storage cluster in the reserve market. Furthermore, the total cost of the dispatch center in the joint market also includes the start-up and shutdown costs of each thermal power unit.

[0073] Step 320: The computing device 200 constructs a lower-level objective function with the goal of maximizing the total profit of the energy storage cluster (as an independent entity) in the joint market, and then constructs a lower-level model based on this objective function. The total profit of the energy storage cluster in the joint market represents the sum of the revenues obtained by the energy storage cluster in the spot market, frequency regulation market, and standby market, minus the variable costs and fixed costs of the energy storage cluster in the spot market, frequency regulation market, and standby market.

[0074] Step 330: The computing device 200 can obtain a joint clearing two-layer model based on the upper-layer model and the lower-layer model.

[0075] Step 340: The computing device 200 can solve the joint clearing two-layer model to obtain the joint clearing results. The joint clearing results are used to indicate the winning bid volume and winning bid price of each entity (each thermal power unit, each green power unit, and energy storage cluster) in the spot market, frequency regulation market, and standby market at each time period.

[0076] In some embodiments, the upper-level objective function includes an upper-level first objective function and an upper-level second objective function. In step 310, the upper-level first objective function is constructed with the goal of minimizing the total cost of the dispatch center in the joint market, and the upper-level second objective function is constructed with the goal of maximizing renewable energy consumption. Then, the upper-level model is constructed based on the upper-level first objective function and the upper-level second objective function.

[0077] The first objective function of the upper layer is shown in the following equation:

[0078]

[0079] In the formula, Q j,t P represents the amount of thermal power unit J declared in the spot market during period t. j,t This represents the spot market price quoted by thermal power unit j during time period t. From this, the cost of purchasing electricity from thermal power unit j during time period t in the spot market can be calculated.

[0080] This indicates the declared frequency regulation capacity of thermal power unit j in the frequency regulation market during time period t. This indicates the declared price of the frequency regulation capacity of thermal power unit j after the time period t. This represents the declared price for the frequency regulation mileage of thermal power unit J after adjustment during time period t. From this, the cost of purchasing the frequency regulation capacity and mileage of thermal power unit J during time period t in the frequency regulation market can be calculated.

[0081] R j,t This indicates the amount of standby capacity declared by thermal power unit j in the standby market during time period t. This represents the declared price for the standby capacity of thermal power unit j during time period t. From this, the cost of purchasing the standby capacity of thermal power unit j during time period t in the standby market can be calculated.

[0082] This represents the start-up and shutdown cost of thermal power unit j during time period t.

[0083] P represents the amount of energy storage clusters that have submitted discharge declarations in the spot market during period t. t Dis This indicates the price at which the energy storage cluster discharges in the spot market during time period t; P represents the number of charging applications submitted by the energy storage cluster in the spot market during period t. t Cha This represents the bid price for charging the energy storage cluster in the spot market during time period t. From this, the cost of purchasing electricity from the energy storage cluster in the spot market during time period t can be calculated.

[0084] P represents the declared frequency regulation capacity of the energy storage cluster in the frequency regulation market during time period t. t a,cap P represents the adjusted frequency regulation capacity bid price for the energy storage cluster during time period t. t a,mil This represents the adjusted frequency regulation mileage declaration price for an energy storage cluster during time period t. From this, the cost of purchasing the frequency regulation capacity and mileage of an energy storage cluster during time period t in the frequency regulation market can be calculated.

[0085] R t P represents the amount of spare capacity declared by the energy storage cluster in the spare market during time period t. t RThis represents the bid price for the spare capacity of the energy storage cluster during time period t. From this, the cost of purchasing the spare capacity of the energy storage cluster during time period t in the spare market can be calculated.

[0086] This represents the number of green generator sets (n) declared in the spot market during period t. Let represent the price quoted by n green generator units in the spot market during time period t. From this, the cost of purchasing electricity from n green generator units in the spot market during time period t can be calculated.

[0087] The second objective function of the upper layer is shown in the following equation:

[0088]

[0089] In the formula, This represents the number of green generator sets (n) that were successfully bid for in the spot market during period t. This represents the amount of wind and solar power curtailment generated by n green power units during time period t. This represents the capacity of n green generator sets. This represents the output coefficient of the n-type green generator set.

[0090] Therefore, the cost of electricity purchased by the dispatch center from each thermal power unit and each green power unit in the spot market can be determined based on the declared volume and declared price of each thermal power unit and each green power unit in the spot market at each time period. The cost of electricity purchased by the dispatch center from the energy storage cluster in the spot market can be determined based on the declared discharge volume, declared discharge price, declared charging volume, and declared charging price of the energy storage cluster in the spot market at each time period. The cost of frequency regulation purchased by the dispatch center from each thermal power unit and energy storage cluster in the frequency regulation market can be determined based on the declared frequency regulation capacity volume, the adjusted declared frequency regulation capacity price, and the adjusted declared frequency regulation mileage price of each thermal power unit and energy storage cluster in the frequency regulation market at each time period. The cost of reserve capacity purchased by the dispatch center from each thermal power unit and energy storage cluster in the reserve market can be determined based on the declared reserve capacity volume and declared reserve capacity price of each thermal power unit and energy storage cluster in the reserve market at each time period.

[0091] The consumption of renewable energy is related to the amount of green power units that win bids in the spot market and the amount of wind and solar curtailment. Specifically, the consumption of renewable energy can be determined based on the amount of green power units that win bids in the spot market, the amount of wind and solar curtailment, their capacity, and their power output factor.

[0092] In some embodiments, after constructing the upper-level objective functions (upper-level first objective function and upper-level second objective function), the upper-level model can be constructed based on the upper-level objective functions and multiple upper-level constraints.

[0093] Multiple upper-level constraints include: green power unit output constraints, thermal power unit output constraints, thermal power unit ramping capability constraints, thermal power unit start-up and shutdown time constraints, thermal power unit frequency regulation capacity constraints, thermal power unit reserve capacity constraints, energy storage cluster charging and discharging constraints, energy storage cluster state of charge constraints, energy storage cluster charging and discharging power constraints, energy storage cluster market application constraints, energy storage cluster bidding constraints, energy storage cluster SOH constraints, market application restriction constraints, frequency regulation price adjustment constraints, and power flow constraints.

[0094] Among them, the output constraints of green power units include the green power unit bid quantity constraints, the green power unit wind and solar curtailment energy constraints, and the green power unit bid quantity composition constraints.

[0095] The green generator unit bidding volume constraint means that the winning bid volume for green generator units in each time period should be less than or equal to the actual output of the green generator units, and the winning bid volume for green generator units (in the spot market) in each time period should be less than or equal to the declared volume of green generator units (in the spot market). Specifically, the green generator unit bidding volume constraint is shown in the following formula:

[0096]

[0097] In the formula, This represents the amount of green generator units selected during time period t. This represents the capacity of n green generator sets. This represents the output coefficient of the n-type green generator set.

[0098]

[0099] In the formula, This represents the winning bid amount for the k-th segment of the g-green generator unit during time period t. This represents the electricity declared by the g-green generator unit during time period t in segment k.

[0100] The energy constraint condition for curtailment of wind and solar power units indicates that the amount of wind and solar power curtailed by green power units in each time period must be less than or equal to the actual output of the green power units. The specific formula is as follows:

[0101]

[0102] In the formula, This represents the amount of wind and solar power curtailment generated by n green power units during time period t. This represents the capacity of n green generator sets. This represents the output coefficient of the n-type green generator set.

[0103] The constraint condition for the winning bids of green generator units indicates that the total winning bids for green generator units in any given time period are the sum of the winning bids for green generator units in segment k during that time period. The specific formula is as follows:

[0104]

[0105] In the formula, This represents the winning bid amount for the g-type green generator unit during time period t. This represents the bid amount for the k-th segment of the g-green generator unit during time period t.

[0106] The output constraints of thermal power units include the power generation constraints, the application constraints, the start-up and shutdown constraints, and the winning bid constraints.

[0107] The power generation constraints for thermal power units indicate that the lower limit of power generation is the sum of the minimum generating capacity, the declared frequency regulation capacity, and the declared reserve capacity, while the upper limit is the difference between the maximum generating capacity and the declared frequency regulation capacity and reserve capacity. The specific formula is as follows:

[0108]

[0109] In the formula, u j,t Represents 0-1 variables, This represents the minimum generating capacity of thermal power unit j during time period t. R represents the declared frequency regulation capacity of thermal power unit j during time period t. j,t This indicates the declared reserve capacity of thermal power unit j during time period t.

[0110] The constraint on the application volume of thermal power units indicates that the total application volume of thermal power units in any given time period is the sum of the application volumes of thermal power units in segment k during that time period. The specific formula is as follows:

[0111]

[0112] In the formula, Q j,t P represents the number of thermal power units reported during time period t. j,t Q represents the declared price of thermal power unit j during time period t. j,k,t P represents the number of applications submitted by thermal power unit j in segment k during time period t. j,k,t This represents the declared price for thermal power unit j in segment k during time period t.

[0113] The start-stop constraints of a thermal power unit represent the costs incurred when the unit changes its start-up or shutdown state. The specific formula is as follows:

[0114]

[0115] In the formula, U represents the start-up and shutdown cost of thermal power unit j. j,t Representing 0-1 variables, C j This represents the startup cost of thermal power unit j.

[0116] The constraint condition for the winning bid quantity of thermal power units indicates that the winning bid quantity for each segment of a thermal power unit is less than or equal to the declared quantity for that segment, and the sum of the winning bid quantities for each time period is the sum of the winning bid quantities for each time period in segment k. The specific formula is as follows:

[0117]

[0118] In the formula, Q represents the winning bid amount for thermal power unit j in segment k during time period t. j,k,t This represents the number of applications submitted by thermal power unit j in segment k during time period t.

[0119]

[0120] In the formula, This represents the winning bid amount for thermal power unit j during time period t. This represents the bid amount won by thermal power unit j in segment k during time period t.

[0121] The ramp-up capability constraint for thermal power units indicates that the difference between the bid-winning amount in adjacent time periods (time period t-1 and time period t) must be less than or equal to the ramp-up capability of the thermal power unit per unit time period. The specific formula is as follows:

[0122]

[0123] In the formula, Q represents the winning bid amount for thermal power unit j during time period t. i,t-1 This indicates the amount of the winning bid for thermal power unit j during time period t-1. This represents the climbing ability of thermal power unit j per unit time period.

[0124] The start-up and shutdown time constraints of thermal power units include the start-up time constraints, the shutdown time constraints, the total start-up time constraints, and the total shutdown time constraints.

[0125] The start-up time constraint for thermal power units indicates that the minimum start-up time must be greater than the minimum start-up time parameter for the thermal power unit when it transitions from a shutdown to a start-up state at any given time. Specifically, it is shown in the following formula:

[0126]

[0127] In the formula, Indicates the start-up time of thermal power unit j during period t, u j,t-1 u j,t For 0-1 variables, This refers to the minimum start-up time parameter for thermal power unit j.

[0128] The shutdown time constraint for thermal power units indicates that when a thermal power unit transitions from an on-state to a shutdown state at any given time, the minimum shutdown time must be greater than the minimum shutdown time parameter of the thermal power unit. The specific formula is as follows:

[0129]

[0130] In the formula, Indicates the shutdown time of thermal power unit j during period t, u j,t-1 u j,t For 0-1 variables, This refers to the minimum shutdown time parameter for thermal power unit j during time period t.

[0131] The constraint on the total start-up time of thermal power units represents the summation of the time interval from the difference between time interval t and the minimum start-up time of the thermal power unit to time interval t-1, to calculate the start-up time of time interval t. The specific formula is as follows:

[0132]

[0133] In the formula, This represents the start-up time of thermal power unit j during time period t, u j,t Represents 0-1 variables, This indicates the minimum startup time of thermal power unit j.

[0134] The constraint on the total shutdown time of thermal power units represents the summation from time period t (the difference between time period t and the minimum shutdown time of the thermal power unit) to time period t-1, to calculate the shutdown time of time period t. The specific formula is as follows:

[0135]

[0136] In the formula, This represents the shutdown time of thermal power unit j during time period t. U represents the minimum shutdown time of thermal power unit j. j,t It represents a 0-1 variable.

[0137] The constraint condition for the frequency regulation capacity of thermal power units indicates that the awarded frequency regulation capacity of thermal power units is less than or equal to the declared frequency regulation capacity. The specific formula is as follows:

[0138]

[0139] In the formula, This indicates the FM capacity specified in the standard. This indicates the amount of frequency modulation capacity applied for.

[0140] The constraint condition for reserve capacity of thermal power units indicates that the awarded reserve capacity of thermal power units is less than or equal to the declared reserve capacity. The specific formula is as follows:

[0141]

[0142] In the formula, R represents the reserve capacity won in the k-th segment of time period t for thermal power unit j. j,k,t This represents the declared reserve capacity for thermal power unit j during time period t, segment k.

[0143] The energy storage cluster charging and discharging constraints include the energy storage cluster discharging constraints and the energy storage cluster charging constraints.

[0144] The discharge constraint condition for energy storage clusters indicates that the discharge amount of the energy storage cluster in each time period is less than or equal to the difference between the maximum discharge amount of the energy storage cluster and the declared frequency regulation capacity and reserve capacity of the energy storage cluster. The specific formula is as follows:

[0145]

[0146] In the formula, This represents the discharge amount of the energy storage cluster during time period t. This represents the maximum discharge of the energy storage cluster during time period t. R represents the declared frequency regulation capacity of the energy storage cluster during time period t. t This represents the amount of standby capacity declared for the energy storage cluster during time period t.

[0147] The charging constraint for energy storage clusters states that the charging amount of the energy storage cluster in any time period is less than or equal to the difference between the maximum charging amount of the energy storage cluster and the declared frequency regulation capacity and reserve capacity of the energy storage cluster. The specific formula is as follows:

[0148]

[0149] In the formula, This represents the charging amount of the energy storage cluster during time period t. This represents the maximum charging amount of the energy storage cluster during time period t. R represents the declared frequency regulation capacity of the energy storage cluster during time period t. t This represents the amount of standby capacity declared for the energy storage cluster during time period t.

[0150] The state of charge (SOC) constraints of the energy storage cluster include SOC constraints for each time period, SOC constraints for the initial time period, and SOC constraints for the final time period.

[0151] The state of charge (SOC) constraint for the energy storage cluster at each time period indicates that the SOC of the energy storage cluster at time period t is the sum of the difference between the SOC of the energy storage cluster at time period t-1 and the charge / discharge amount of the energy storage cluster at time period t. The specific formula is as follows:

[0152]

[0153] In the formula, S tS represents the state of charge of the energy storage cluster during time period t. t-1 This indicates the state of charge of the energy storage cluster during time period t-1. η represents the charging amount of the energy storage cluster during time period t. Cha This indicates the charging efficiency of the energy storage cluster.

[0154] The initial state of charge (SBC) constraint for an energy storage cluster indicates that the SBC of the energy storage cluster is a set value (initial state setpoint) during the initial period. The specific formula is as follows:

[0155]

[0156] In the formula, S e,0 This indicates the state of charge of the energy storage cluster at the initial stage. This indicates the initial state settings of the energy storage cluster.

[0157] The state of charge (SOC) constraint for the final period of an energy storage cluster indicates that the SOC of the cluster in the final period is a set value (final state set value). The specific formula is as follows:

[0158]

[0159] In the formula, This indicates the state of charge of the energy storage cluster at the end of the period. This indicates the final state setting value of the energy storage cluster.

[0160] The power constraints for charging and discharging of energy storage clusters include the power constraints for charging and discharging of energy storage clusters.

[0161] The charging power constraint for an energy storage cluster indicates that the charging power of the cluster is less than or equal to the set maximum charging power. Specifically, it is shown in the following formula:

[0162]

[0163] In the formula, r t ch This indicates the charging power of the energy storage cluster, u t R represents a 0-1 variable. ch,max This indicates the maximum charging power of the energy storage cluster.

[0164] The discharge power constraint condition for an energy storage cluster indicates that the discharge power of the energy storage cluster is less than or equal to the set maximum discharge power. Specifically, it is shown in the following formula:

[0165] 0≤r t dis ≤(1-u t )R dis,max ,t∈β (27)

[0166] In the formula, r t dis u represents the discharge power of the energy storage cluster. t R represents a 0-1 variable. dis,max This indicates the maximum discharge power of the energy storage cluster.

[0167] The application constraints for energy storage cluster markets include those for the spot market, frequency regulation market, and standby market.

[0168] It should be noted that an energy storage cluster includes multiple energy storage units distributed across multiple nodes.

[0169] The reporting constraints for the energy storage cluster spot market are as follows: the sum of the discharge reporting amounts of each energy storage unit in segment k across all time periods represents the total discharge reporting amount for the energy storage cluster across all time periods; the sum of the charging reporting amounts of each energy storage unit in segment k across all time periods represents the total charging reporting amount for the energy storage cluster across all time periods. The specific formula is as follows:

[0170]

[0171] In the formula, This indicates the discharge declaration amount of the e-energy storage unit during time period t. This represents the amount of discharge reported by the energy storage cluster during time period t.

[0172]

[0173] In the formula, This indicates the charging application volume of the e-energy storage unit during time period t. This represents the number of charging applications submitted by the energy storage cluster during time period t.

[0174] The constraint condition for submitting frequency regulation market applications for energy storage clusters is as follows: the sum of the frequency regulation capacity applications of each energy storage unit in segment k for each time period is the total frequency regulation capacity application of the energy storage cluster for each time period. The specific formula is as follows:

[0175]

[0176] In the formula, This indicates the declared frequency regulation capacity of the e-energy storage unit during time period t. This represents the declared frequency regulation capacity of the energy storage cluster during time period t.

[0177] The constraint condition for reserve capacity application in the energy storage cluster market is as follows: the sum of the reserve capacity applications of each energy storage unit in time period k is the reserve capacity application of the energy storage cluster in time period t. The specific formula is as follows:

[0178]

[0179] In the formula, R e,k,tR represents the declared standby capacity of the e-energy storage unit during time period t. t This represents the amount of standby capacity declared for the energy storage cluster during time period t.

[0180] The constraints for winning bids in the energy storage cluster bidding process include: the volume constraints for winning bids in the spot market for charging and discharging of energy storage clusters, the volume constraints for winning bids in the frequency regulation market for energy storage clusters, the volume constraints for winning bids in the standby market for energy storage clusters, the price constraints for winning bids in the spot market for charging and discharging of energy storage clusters, the price constraints for winning bids in the frequency regulation market for energy storage clusters, and the price constraints for winning bids in the standby market for energy storage clusters.

[0181] The constraints on the winning bids for charging and discharging in the spot market for energy storage clusters are as follows: the winning bid for charging in an energy storage cluster is less than or equal to the declared charging volume, and the winning bid for discharging in an energy storage cluster is less than or equal to the declared discharging volume. The specific formula is as follows:

[0182]

[0183] In the formula, This represents the amount of electricity won in the charging of the energy storage cluster during time period t. This represents the number of charging applications submitted by the energy storage cluster during time period t.

[0184]

[0185] In the formula, This represents the amount of discharge won by the energy storage cluster during time period t. This represents the amount of discharge reported by the energy storage cluster during time period t.

[0186] The constraint condition for the winning bid volume in the energy storage cluster frequency regulation market indicates that the winning bid volume for frequency regulation capacity in the energy storage cluster frequency regulation market is less than or equal to the declared frequency regulation capacity in the energy storage cluster frequency regulation market. The specific formula is as follows:

[0187]

[0188] In the formula, This represents the winning bid amount for the frequency regulation capacity of the energy storage cluster during time period t. This represents the declared frequency regulation capacity of the energy storage cluster during time period t.

[0189] The constraint condition for the winning bid volume in the energy storage cluster standby market indicates that the winning bid volume for standby capacity in the energy storage cluster standby market is less than or equal to the declared standby capacity volume in the energy storage cluster standby market. The specific formula is as follows:

[0190]

[0191] In the formula, R represents the amount of standby capacity won in the energy storage cluster during time period t. t This refers to the declared standby capacity of the energy storage cluster during time period t.

[0192] The price constraints for charging and discharging in the spot market for energy storage clusters state that the winning bid price for charging must be less than or equal to the declared price for charging, and the winning bid price for discharging must be less than or equal to the declared price for discharging. The specific formula is as follows:

[0193] 0 <= P t bid,Dis <=P t Dis (36)

[0194] In the formula, P t bid,Dis P represents the winning bid price for the discharge of the energy storage cluster during time period t. t Dis This indicates the price declared for discharge of the energy storage cluster during time period t.

[0195] 0 <= P t bid,Cha <=P t Cha (37)

[0196] In the formula, P t bid,Dis P represents the winning bid price for charging the energy storage cluster during time period t. t Dis This indicates the charging price declared for the energy storage cluster during time period t.

[0197] The price constraints for winning bids in the energy storage cluster frequency regulation market are as follows: the winning bid price for frequency regulation mileage in the energy storage cluster frequency regulation market must be less than or equal to the declared price for frequency regulation mileage in the energy storage cluster frequency regulation market; and the winning bid price for frequency regulation capacity in the energy storage cluster frequency regulation market must be less than or equal to the declared price for frequency regulation capacity in the energy storage cluster frequency regulation market. The specific formula is as follows:

[0198]

[0200] In the formula, P t a,bidmil P represents the winning bid price for the frequency regulation mileage of the energy storage cluster during time period t. t a,mil The price to be declared for the frequency regulation mileage of the energy storage cluster during time period t.

[0201]

[0202] In the formula, P t a,bidcap P represents the winning bid price for the frequency regulation capacity of the energy storage cluster during time period t. t a,cap The price to be declared for the frequency regulation capacity of the energy storage cluster during time period t.

[0203] The constraint condition for the winning bid price in the energy storage cluster standby market is that the winning bid price for standby capacity in the energy storage cluster standby market must be less than or equal to the declared price for standby capacity in the energy storage cluster standby market. The specific formula is as follows:

[0204] 0 <= P t bid,R <=P t R (40)

[0205] In the formula, P t bid,R P represents the winning bid price for the standby capacity of the energy storage cluster during time period t. t R This indicates the bid price for the standby capacity of the energy storage cluster during time period t.

[0206] It should be noted that the State of Health (SOH) of energy storage is the ratio of the battery's capacity from a fully charged state to its rated capacity (actual initial capacity) under standard conditions, through a certain rate of discharge. This ratio reflects the battery's health status. When the health status of an energy storage unit exceeds or falls below the system's limits, that energy storage unit can no longer be used. The SOH constraint condition for an energy storage cluster is also the health status constraint condition for the energy storage cluster, as shown in the following formula:

[0207]

[0208] In the formula, SOH min SOH max These represent the minimum and maximum set energy storage health states, respectively. e,t This indicates the health status of the e-energy storage unit during time period t.

[0209] Market reporting restrictions include spot market reporting restrictions, frequency regulation market reporting restrictions, and standby market reporting restrictions.

[0210] The spot market declaration constraints represent the upper and lower limits of the quantity and price declarations made by entities (including thermal power units, green power units, and energy storage clusters) in the spot market. The specific formula for the spot market declaration constraints is as follows:

[0211]

[0212] In the formula, Q l,t This indicates the spot market declaration volume during the t-period of the main trading session. This indicates the minimum quoted volume in the spot market. This represents the maximum reported volume in the spot market.

[0213]

[0214] In the formula, P l,tThe main spot market price during the t-period is the price quoted by the market. This is the minimum price quoted in the spot market. This represents the maximum price quoted in the spot market.

[0215] The FM market bidding constraints represent the upper and lower limits of the bid prices submitted by entities in the FM market. The specific FM market bidding constraints are shown in the following formula:

[0216]

[0217] In the formula, The declared frequency regulation capacity for the main t-period. This is the minimum value for frequency modulation capacity reporting. This represents the maximum value of the frequency modulation capacity report.

[0218]

[0219] In the formula, The declared price for frequency modulation mileage during the main time period (t) is... The minimum price for FM mileage. This represents the maximum price quoted for frequency modulation mileage.

[0220]

[0221] In the formula, The price for frequency regulation capacity during the main time period is t. This is the minimum price quoted for frequency modulation capacity. This represents the maximum quoted price for frequency modulation capacity.

[0222] The standby market submission constraints represent the upper and lower limits of the bid volume and price submitted by entities in the standby market. The specific standby market submission constraints are shown in the following formula:

[0223]

[0224] In the formula, R l,k,t The amount of standby capacity declared for the main body during the t-period. This is the minimum reported value for reserve capacity. This is the maximum reported value for standby capacity.

[0225]

[0226] In the formula, The price for reserve capacity during the main t-period is to be declared. To submit the minimum bid for standby capacity, This is the maximum price quoted for spare capacity.

[0227] Market reporting restrictions include spot market reporting restrictions, frequency regulation market reporting restrictions, and standby market reporting restrictions.

[0228] The spot market equilibrium constraint condition states that, in the spot market, the winning bids for green power units, thermal power units, and energy storage cluster charging and discharging volumes in each time period are the same as the spot market's electricity demand. The specific formula is as follows:

[0229]

[0230] In the formula, This represents the amount of green generator units selected during the specified time period. This represents the winning bid amount for thermal power unit j during time period t. This represents the amount of energy storage cluster discharged during time period t. This represents the amount of energy storage cluster charging won in time period t. This represents the electricity demand in the spot market during period t.

[0231] The FM market equilibrium constraint states that, in the FM market, the amount of FM capacity and FM mileage won by each participant in each time period is the same as the FM capacity demand and FM mileage demand in the FM market, respectively. The specific formula is as follows:

[0232]

[0233] In the formula, This indicates the FM capacity specified in the standard. This indicates the required frequency modulation capacity.

[0234]

[0235] In the formula, Indicates the winning bid capacity for frequency modulation. Indicates capacity ratio, This indicates the required FM mileage.

[0236] The equilibrium constraint of the reserve market means that, in the reserve market, the amount of reserve capacity won by each participant in each time period is the same as the reserve capacity demand of the reserve market. The specific formula is as follows:

[0237]

[0238] In the formula, R l,t R represents the amount of reserve capacity won by market participant l in time period t. pre This represents the market plan reserve ratio coefficient. This indicates the required reserve capacity.

[0239] The frequency modulation (FM) price adjustment constraint indicates that the price of FM capacity and FM mileage are adjusted based on the FM performance of the participants in the FM market. The specific FM price adjustment constraint is shown in the following formula:

[0240]

[0241] In the formula, This indicates the adjusted frequency modulation price. f represents the frequency modulation price before adjustment. l This represents the performance coefficient.

[0242]

[0243] In the formula, This indicates the adjusted mileage price. This indicates the mileage price before the adjustment. f represents the capacity ratio. l This represents the performance coefficient.

[0244] Power flow constraints include node-line power flow constraints and upper and lower limits constraints for power flow transmission.

[0245] The power flow constraints for the nodes are as follows:

[0246]

[0247] In the formula, H ab,t θ represents the line power flow between nodes a and b. a,t θ represents the phase angle at node a. b,t γ represents the phase angle at node b. ab Indicates reactance.

[0248] The upper and lower limits of power flow transmission constraints are as follows:

[0249]

[0250] In the formula, H represents the power transmission limit of ba. ab,t Indicates ab power flow transmission. This represents the limit of power flow transmission in the ab direction.

[0251] In this embodiment of the invention, the total profit of the energy storage cluster in the joint market represents the sum of the revenues obtained by the energy storage cluster in the spot market, frequency regulation market, and standby market, minus the energy storage cluster's spot cost (representing the variable cost of the energy storage cluster in the spot market), frequency regulation cost (representing the variable cost of the energy storage cluster in the frequency regulation market), standby cost (representing the variable cost of the energy storage cluster in the standby market), and fixed cost. In other words, the total profit of the energy storage cluster in the joint market represents the sum of the revenues obtained by the energy storage cluster in the spot market, frequency regulation market, and standby market, minus the variable costs of the energy storage cluster in the spot market, frequency regulation market, and standby market, as well as the fixed costs of the energy storage cluster.

[0252] Based on this, in step 320, the lower-level objective function, constructed with the goal of maximizing the total profit of the energy storage cluster in the joint market, is shown in the following equation:

[0253] F prof =I e +I m +I r -C e -C m -C r -C n (57)

[0254] In the formula, I e I represents the revenue that energy storage clusters obtain in the spot market. m I represents the revenue that energy storage clusters gain in the frequency regulation market. r C represents the revenue that energy storage clusters gain in the standby market. e C represents the spot cost of the energy storage cluster (the variable cost of the energy storage cluster in the spot market). m C represents the frequency regulation cost of an energy storage cluster (the variable cost of an energy storage cluster in the frequency regulation market). r C represents the standby cost of an energy storage cluster (the variable cost of an energy storage cluster in the standby market). n This represents the fixed cost of the energy storage cluster.

[0255] The revenue obtained by energy storage clusters in the spot market can be determined based on the winning bids for discharge volume, discharge price, charging volume, and charging price at different times in the spot market. Specifically, the revenue obtained by energy storage clusters in the spot market can be calculated using the following formula:

[0256]

[0257] In the formula, P represents the amount of discharge achieved by the energy storage cluster during time period t. t Dis This indicates the winning bid price for the energy storage cluster's discharge during time period t. P represents the amount of electricity won in charging the energy storage cluster during time period t. t Cha This indicates the winning bid price for charging the energy storage cluster during time period t.

[0258] The revenue that energy storage clusters obtain in the frequency regulation market can be determined based on the frequency regulation capacity application volume, winning bid price for frequency regulation capacity, and winning bid price for frequency regulation mileage of the energy storage cluster in different time periods. Specifically, the revenue obtained by the energy storage cluster in the frequency regulation market can be calculated using the following formula:

[0259]

[0260] In the formula, P represents the winning bid amount of frequency regulation capacity of the energy storage cluster during time period t. t a,bidcap P represents the winning bid price for the frequency regulation capacity of the energy storage cluster during time period t. t a,bidmil This indicates the winning bid price for the frequency regulation mileage of the energy storage cluster during time period t.

[0261] The revenue that energy storage clusters obtain in the standby market can be determined based on the amount of standby capacity won by the cluster in each time period and the winning bid price for that standby capacity. Specifically, the revenue obtained by the energy storage cluster in the standby market can be calculated using the following formula:

[0262]

[0263] In the formula, P represents the amount of standby capacity won in the energy storage cluster during time period t. t bid This represents the winning bid price for the standby capacity of the energy storage cluster during time period t.

[0264] The variable costs of energy storage clusters in the spot market can be determined based on the unit cost and total cost of each energy storage unit in the spot market at different times. Specifically, the variable costs of energy storage clusters in the spot market can be calculated using the following formula:

[0265]

[0266] In the formula, This represents the unit cost parameter of energy storage unit e in the spot market during time period t. This represents the total cost of the e-energy storage unit in the spot market during period t.

[0267] The variable costs of energy storage clusters in the frequency regulation market can be determined based on the unit cost of frequency regulation capacity, the total cost of frequency regulation capacity, the unit cost of frequency regulation mileage, and the total cost of frequency regulation mileage for each energy storage unit in the frequency regulation market at different times. Specifically, the variable costs of energy storage clusters in the frequency regulation market can be calculated using the following formula:

[0268]

[0269] In the formula, This represents the unit cost parameter of the frequency regulation capacity of the e-energy storage unit during time period t. This represents the total cost of frequency regulation capacity of energy storage unit e during time period t. This represents the unit cost parameter of the frequency regulation mileage of the e-energy storage unit during time period t. This represents the total cost of frequency regulation mileage for the e-energy storage unit during time period t.

[0270] The variable costs of energy storage clusters in the standby market can be determined based on the unit cost of standby capacity and the total standby capacity cost of each energy storage unit in the standby market at different times. Specifically, the variable costs of energy storage clusters in the standby market can be calculated using the following formula:

[0271]

[0272] In the formula, This represents the unit cost parameter of the standby capacity of energy storage unit e during time period t. This represents the total cost of the standby capacity of the e-energy storage unit during time period t.

[0273] The fixed costs of an energy storage cluster include the initial construction investment cost during project construction, the annual operation and maintenance costs and replacement costs during actual operation, and the recovery cost of the energy storage cluster. Specifically, the fixed costs of an energy storage cluster can be calculated using the following formula:

[0274]

[0275] In the formula, C init C represents the initial construction investment cost of the energy storage cluster. yun For the annual operation and maintenance costs of energy storage clusters, C th For the annual replacement cost of energy storage clusters, C re Let r be the recovery cost of the energy storage cluster, and r be the interest rate.

[0276] Therefore, the lower-level model constructed based on the lower-level objective function includes the above equations (57)-(64).

[0277] Based on the upper-level and lower-level models constructed in the above embodiments, a joint clearing two-level model can be obtained. Furthermore, the joint clearing two-level model can be solved.

[0278] In some embodiments, the CPLEX toolbox in Python can be used to solve the joint clearing two-level model.

[0279] In some embodiments, the specific process of solving the joint clearing two-level model is as follows:

[0280] First, the parameters of multiple upper-level constraints (including the upper and lower limits of each upper-level constraint) are used as input parameters and input into the joint clearing two-level model.

[0281] Subsequently, considering that the upper-level model is a linear bi-objective optimization scheduling problem, a hierarchical solution algorithm (based on multiple upper-level constraints) can be used to solve the upper-level model to obtain the unit output plan (i.e., the unit start-up and shutdown plan).

[0282] Furthermore, the unit output plan can be imported into the lower-level model, and the lower-level model can be solved based on the unit output plan to obtain the joint clearing result.

[0283] In some embodiments, after solving the joint clearing two-layer model to obtain the joint clearing result, the total profit of the energy storage cluster in the joint market can be determined based on the joint clearing result and the lower-layer model.

[0284] Furthermore, an improved Shapley value method can be used to allocate the total profit of the energy storage cluster in the joint market to the individual energy storage units within the cluster. Specifically, firstly, the Shapley value method can be used to determine the original allocation factor for each energy storage unit based on the total profit of the energy storage cluster in the joint market. Subsequently, the original allocation factor, frequency regulation capacity contribution factor, frequency regulation mileage contribution factor, and reserve capacity contribution factor of each energy storage unit are weighted and summed to obtain the improved allocation factor (corrected allocation factor) for each energy storage unit. Finally, the total profit of the energy storage cluster in the joint market can be allocated to each energy storage unit based on the improved allocation factor.

[0285] It should be noted that the Shapley value method must satisfy the following individual rationality conditions, global superadditivity conditions, and global constraint conditions.

[0286] Individual rationality conditions focus on changes in individual member costs, requiring that the costs incurred by members after joining the alliance be lower than the costs of acting independently. This ensures that each member can obtain the actual benefit of cost reduction from cooperation, as shown in the formula below. This implies that members' participation in the alliance is in their own best interest; otherwise, they might choose to withdraw from the cooperation.

[0287] R i ≤R({i}) (65)

[0288] The overall superadditivity condition focuses on the change in the overall cost of the alliance, that is, the cost of the alliance when cooperating as a whole should be lower than the sum of the costs of each member acting individually. This can be expressed by the following formula. This reflects the scale effect or synergy effect brought about by cooperation. Only when cooperation can reduce the overall cost is the alliance cooperation model more economically rational and attractive.

[0289]

[0290] The overall constraint emphasizes that the total cost of the alliance remains unchanged before and after cost allocation, as specified in the following formula. This ensures that the cost allocation among members is reasonable and that there will be no increase or decrease in total cost due to improper allocation methods, thus guaranteeing the fairness and stability of the alliance's cost allocation.

[0291]

[0292] In the Shapley value method, each participant's contribution is measured by comparing the difference in total cost of cooperation between when that participant participates and when they do not. Specifically, for each possible combination of participants, the total cost of cooperation under that combination is calculated, and each participant's marginal contribution to that combination's cost is determined. Then, based on the probability of each participant appearing in all possible combinations and their corresponding marginal contribution, each participant's Shapley value is calculated, which represents the cost they should be allocated.

[0293] According to Shapley's calculation method, a cooperative game alliance N containing each energy storage unit is first constructed. Based on Shapley's cooperative game theory, (2n-1) combinations can be randomly formed based on the cooperative game alliance. v represents the characteristic function, and v(Φ) = 0. Let S be the other alliances besides the empty set. Then v(S) is the total cost of cooperative operation by the participants in alliance S. The Shapley value method is calculated as follows.

[0294]

[0295] In the formula, Let |S| represent the cost allocation value for the i-th entity. i | represents the number of entities within alliance S, n represents the number of entities participating in the larger alliance, V(S) represents the total cost of alliance S, and V(S\{i}) represents the cost of alliance S after removing i. The probability of alliance S occurring is also called the weighting factor.

[0296] It is worth noting that the traditional Shapley value method only considers the marginal contribution of each entity under different combinations, simplifying and ignoring other factors that need to be considered in profit distribution.

[0297] Based on this, in some embodiments of the present invention, firstly, the Shapley value method can be used to determine the original allocation factor for each energy storage unit based on the total profit of the energy storage cluster in the joint market. Then, by comprehensively considering the contribution of each factor of each energy storage unit (including the original allocation factor, frequency regulation capacity contribution factor, frequency regulation mileage contribution factor, and reserve capacity contribution factor), the original allocation factor is improved to obtain an improved allocation factor. That is, the original allocation factor, frequency regulation capacity contribution factor, frequency regulation mileage contribution factor, and reserve capacity contribution factor of each energy storage unit can be weighted and summed based on their respective weight coefficients to obtain the improved allocation factor for each energy storage unit. Finally, the total profit of the energy storage cluster in the joint market can be allocated according to the improved allocation factor of each energy storage unit, so as to reasonably distribute the total profit of the energy storage cluster in the joint market to each energy storage unit.

[0298] Specifically, assuming that the factor 1 contribution of each energy storage unit in the energy storage cluster is... The contribution of factor 2 is {τ1, τ2, τ3, ..., τ} n Then, the allocation factor for each energy storage unit can be calculated using the following formula:

[0299]

[0300] Where ω1, ω2, and ω3 represent the improved weighting coefficients, ω1 + ω2 + ω3 = 1, and R n R' represents the original allocation factor. n This represents the improved allocation factor.

[0301] The improved Shapley value method of this invention, used to allocate the total profit of energy storage clusters, comprehensively considers the contributions of each energy storage unit in various aspects, ensuring that the allocation result is both fair and incentivizing. Ultimately, it can achieve a dual improvement in the economic efficiency of the power system and the utilization efficiency of energy storage resources.

[0302] In some embodiments, to verify the effectiveness and rationality of the joint clearing two-layer model constructed in the embodiments of the present invention, a calculation example can be set up in conjunction with the IEEE-39 node system, based on the Inner Mongolia West Electricity Market Rules.

[0303] Figure 4 A schematic diagram of the node configuration of a node system according to some embodiments of the present invention is shown.

[0304] like Figure 4As shown, in some embodiments, eight sets of thermal power units of different specifications and five sets of green power units of different specifications can be used, with energy storage units of different specifications set at nodes 8, 10, 13, 19, and 22. Simultaneously, the frequency regulation mileage price range for the frequency regulation units is set at 6-15 yuan / MW, and the frequency regulation capacity compensation price is 60 yuan / MW. The standby market price is 20-40 yuan / MW. The relevant parameters of the thermal power units are shown in Table 1.

[0305] Table 1 Relevant parameters of thermal power units

[0306]

[0307] Figure 5 A schematic diagram of green electricity output curves according to some embodiments of the present invention is shown. Figure 5 Units 1, 2, and 3 are wind turbines, while units 4 and 5 are photovoltaic units.

[0308] The relevant parameters of the energy storage unit are shown in Table 2.

[0309] Table 2 Relevant parameters of energy storage units

[0310]

[0311] Figure 6 A schematic diagram of system load curves according to some embodiments of the present invention is shown.

[0312] Figure 7 A schematic diagram of frequency modulation capacity demand, frequency modulation mileage demand, and reserve capacity demand curves according to some embodiments of the present invention is shown.

[0313] By setting up 8 sets of thermal power units of different specifications and 5 sets of green power units of different specifications, and setting up energy storage entities of different specifications at nodes 10, 13, 19, and 22, the joint clearing two-layer model can be solved using the CPLEX toolbox in Python to obtain the winning bid volume, winning bid price, and revenue of each entity in the joint market. Below, we compare and analyze the winning bid results and overall revenue of thermal power, green power, and energy storage in the spot-frequency regulation-reserve market, respectively.

[0314] Figure 8 A schematic diagram of the quantity of thermal power units in the spot market according to some embodiments of the present invention is shown.

[0315] like Figure 8As shown, the overall trend of winning bids for thermal power in the spot market converges with electricity demand. However, the winning bids for individual generating units vary significantly across different time periods. During some periods, the overall winning bid volume for generating units is high, such as between periods 55-70, when the total winning bid volume for all units reaches a high level, coinciding with a high level of electricity demand. Conversely, during periods 40-55, the winning bid volume for thermal power is lower, indicating that both electricity demand and renewable energy demand are high. This suggests that, under the goal of prioritizing the consumption of renewable energy, green electricity is being prioritized as a power supply. Thermal power, as a traditional power supply method, still occupies an important position in the power system, and its relatively stable output characteristics can, to a certain extent, meet the basic load demand of the power system.

[0316] Figure 9 A schematic diagram of the quantity of green generator sets in the spot market according to some embodiments of the present invention is shown.

[0317] like Figure 9 As shown, the amount of green electricity awarded in bids also varies significantly across different time periods. The trends of green electricity award volume and green electricity output curves are roughly the same; during periods of high green electricity output, the amount of green electricity awarded in bids is also at a high level. Under the background of prioritizing the consumption of renewable energy, green electricity has taken away some of the generation space from thermal power. Figure 5 During certain periods, such as 40-55, green electricity is in its peak generation period and is prioritized for consumption, resulting in a decrease in the amount of thermal power awarded in bidding. Compared to thermal power, the amount of green electricity awarded in bidding is relatively low overall, reflecting the intermittency and instability of renewable energy generation.

[0318] Figure 10 A schematic diagram of the scalar quantity of energy storage clusters in the spot market is shown according to some embodiments of the present invention.

[0319] See Figure 10 ,contrast Figure 8 and Figure 9 Energy storage has a relatively small number of successful bids in the electricity market. Energy storage plays a crucial role in regulating electricity supply and demand and improving the stability of the power system. The number of successful bids for energy storage reflects its value in power regulation. When there is a surplus of electricity, energy storage can store electrical energy; when electricity demand is high, energy storage can release electrical energy, thereby balancing electricity supply and demand.

[0320] Overall, thermal power, green electricity, and energy storage each play different roles in the spot market. Thermal power, with its stability and reliability, remains an important support for electricity supply; green electricity, as a clean energy source, although experiencing output fluctuations, is expected to gradually expand its market share with technological advancements; energy storage plays a crucial role in regulating electricity supply and demand, and is an important means of promoting the consumption of renewable energy and improving the stability of the power system.

[0321] Figure 11A schematic diagram illustrating the bidding process for frequency modulation market capacity is shown in some embodiments of the present invention.

[0322] like Figure 11 As shown, unlike the electricity spot market, energy storage clusters occupy the vast majority of frequency regulation capacity in the frequency regulation market. This is because thermal power incurs higher frequency regulation costs, making it more inclined to participate in the electricity spot market to generate profits. In contrast, energy storage can provide higher frequency regulation capacity in the frequency regulation market. Driven by the goal of maximizing the economic benefits of energy storage, priority is given to calling upon energy storage clusters to participate in the frequency regulation market, thus their probability of winning bids and the amount of winning bids are much higher than those of thermal power. Secondly, energy storage has superior frequency regulation performance, enabling it to respond quickly to the grid's frequency regulation needs and precisely adjust power output compared to thermal power. Thirdly, the frequency regulation market offers high returns, and energy storage clusters are more willing to allocate more capacity to the frequency regulation market to obtain profits. Therefore, energy storage exhibits a relatively stable allocation of winning bid capacity in the frequency regulation market. This stability helps the grid better plan and schedule frequency regulation resources, improving the reliability and stability of grid operation.

[0323] Figure 12 A schematic diagram illustrating the mileage bidding situation in the frequency modulation market according to some embodiments of the present invention is shown.

[0324] like Figure 12 As shown, similar to the trend in frequency regulation capacity bidding, energy storage clusters also account for the vast majority of bids for frequency regulation mileage. The trend in thermal power bidding also follows the same pattern. Figure 9 Similarly, this is because thermal power plants did not exhibit significant differences in frequency regulation performance in the case studies, resulting in roughly similar frequency regulation mileage. Meanwhile, the awarded mileage for energy storage units also remained relatively stable, with minimal differences across different time periods. This further indicates that the continuous participation and contribution of energy storage units in frequency regulation services are relatively stable and consistent. The ability of energy storage units to maintain relatively stable frequency regulation service performance across different time periods is crucial for the stable operation of the power grid. The power grid can more accurately assess the role of energy storage units in frequency regulation services, rationally allocate the use of energy storage resources, and thereby improve the frequency regulation efficiency and stability of the entire power system.

[0325] Analysis of the winning bid capacity and mileage in the frequency regulation markets for thermal power and energy storage reveals that energy storage units outperform thermal power units in both aspects. Thermal power units, limited by factors such as long equipment start-up and shutdown times and relatively poor load adjustment flexibility, experience significant fluctuations in their frequency regulation capabilities across different time periods. In contrast, energy storage technology, with its rapid response and precise adjustment capabilities, better adapts to the rapid changes in grid frequency regulation demands, thus exhibiting higher stability in both winning bid capacity and mileage.

[0326] Figure 13 A schematic diagram of the standby market bid volume is shown according to some embodiments of the present invention.

[0327] like Figure 13 As shown, unlike the frequency regulation market, thermal power accounts for the majority of reserve capacity in the standby market. This is because energy storage clusters allocate a significant amount of capacity to the spot and frequency regulation markets, leaving relatively little available capacity in the standby market. In contrast, thermal power plants, after participating in the electricity market with a portion of their capacity and the frequency regulation market with a small amount, still have a relatively large capacity available to meet the needs of the standby market. Secondly, driven by the goals of prioritizing the consumption of green electricity and maximizing energy storage revenue, thermal power plants have failed to release a large amount of capacity and reap sufficient profits in the electricity spot and frequency regulation markets, forcing them to allocate some capacity to the less competitive standby market to generate revenue.

[0328] Meanwhile, competition among thermal power units in the standby market is fierce, with frequent fluctuations in market share, reflecting their relatively poor adaptability to changes in market demand. Energy storage units, on the other hand, exhibit a relatively stable market share, indicating their ability to adapt to changes in grid demand for standby capacity to a certain extent, making them competitive in the standby market. With the continuous development of energy storage technology and the reduction in costs, the market share of energy storage units in the standby market is expected to further expand in the future.

[0329] Table 3 shows the revenue analysis of each unit (entity). The spot market revenue of the energy storage cluster is RMB 13,432.73, which is relatively low. This is because prioritizing the consumption of renewable energy means that the energy storage cluster's positioning in the spot market is not primarily focused on charging at the lowest price and discharging at the highest price, but rather on absorbing green electricity and reducing wind and solar curtailment. The charging and discharging characteristics of the energy storage system determine that its participation in the spot market differs from that of traditional generator units. Its main role is to store and release energy when there is an imbalance between power supply and demand, in order to maintain the stable operation of the power system. The energy storage cluster performed exceptionally well in terms of frequency regulation capacity revenue, achieving RMB 246,374.35, far exceeding that of individual thermal power units. This is partly due to the rapid response characteristics of the energy storage system, while thermal power units, due to factors such as mechanical inertia, have a slower response speed. Therefore, the energy storage cluster has a clear competitive advantage in the frequency regulation capacity market and can obtain higher revenue. On the other hand, thermal power focuses on participating in the spot market to maintain the balance between power grid supply and demand. The reserve revenue of the energy storage cluster is RMB 28,527.48. The performance of energy storage clusters in the standby market demonstrates their unique value as backup power sources. Energy storage systems are characterized by rapid response and flexible adjustment, enabling them to provide additional power support to the system in a short time, compensating for the long start-up time of traditional thermal power units. However, due to the relatively limited capacity of energy storage clusters, there may be certain limitations when using them as large-scale backup power sources, which also restricts further increases in their profitability in the standby market.

[0330] Table 3. Analysis of Revenue of Each Unit

[0331]

[0332] In summary, the electricity spot-frequency regulation-reserve joint bidding clearing method 300 of the present invention, oriented towards energy storage cluster participation, firstly, combines idle new energy storage distributed on multiple nodes to form an energy storage cluster, which participates as an independent entity in the spot-frequency regulation-reserve joint market. This can improve the utilization rate of new energy storage, increase the channels for new energy storage to obtain revenue, and increase the revenue of new energy storage. Secondly, an upper-level model is constructed with the dual objectives of minimizing the total cost of the dispatch center in the joint market and maximizing renewable energy consumption, and a lower-level model is constructed with the objective of maximizing the total profit of the energy storage cluster in the joint market. Thus, a joint clearing two-layer model can be constructed. By solving the joint clearing two-layer model, the joint clearing result can be obtained, which can maximize social welfare and renewable energy, while maximizing the profit of the energy storage cluster.

[0333] Furthermore, the improved Shapley value method of this invention for allocating the total profit of the energy storage cluster can comprehensively consider the contributions of each energy storage unit in various aspects, ensuring that the allocation result is both fair and incentivizing, and further achieving a dual improvement in the economic efficiency of the power system and the utilization efficiency of energy storage resources.

[0334] 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.

[0335] 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.

[0336] 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.

[0337] 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.

[0338] 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.

[0339] 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 method for joint bidding and clearing of electricity spot market, frequency regulation, and reserve markets with the participation of energy storage clusters, executed in a computing device, comprising: With the goal of minimizing the total cost of the dispatch center in the joint market and maximizing renewable energy consumption, a higher-level objective function is constructed. Based on this objective function, a higher-level model is built. The joint market includes a spot market, a frequency regulation market, and a reserve market. The main entities in the joint market include multiple thermal power units, multiple green power units, and energy storage clusters. Each energy storage cluster includes multiple energy storage units distributed across multiple nodes. The total cost of the dispatch center in the joint market includes: the cost of purchasing electricity from each thermal power unit, green power unit, and energy storage cluster in the spot market; the cost of purchasing frequency regulation capacity and mileage from each thermal power unit and energy storage cluster in the frequency regulation market; the cost of purchasing reserve capacity from each thermal power unit and energy storage cluster in the reserve market; and the start-up and shutdown costs of each thermal power unit. The renewable energy consumption is related to the winning bid volume and curtailment volume of each green power unit in the spot market. The lower-level objective function is constructed with the goal of maximizing the total profit of the energy storage cluster in the joint market. A lower-level model is then constructed based on the lower-level objective function. The total profit of the energy storage cluster in the joint market represents the sum of the revenues obtained by the energy storage cluster in the spot market, frequency regulation market, and standby market, minus the variable costs of the energy storage cluster in the spot market, frequency regulation market, and standby market, as well as the fixed costs of the energy storage cluster. Based on the upper-level model and the lower-level model, a joint clearing two-layer model is obtained; The joint clearing two-layer model is solved to obtain the joint clearing result, which is used to indicate the winning bid volume and winning bid price of each entity in the spot market, frequency regulation market and reserve market at each time period.

2. The method as described in claim 1, wherein, The electricity purchase cost of the dispatch center from each thermal power unit and each green power unit in the spot market is determined based on the declared volume and declared price of each thermal power unit and each green power unit in the spot market at each time period; the electricity purchase cost of the dispatch center from the energy storage cluster in the spot market is determined based on the declared discharge volume, declared discharge price, declared charging volume, and declared charging price of the energy storage cluster in the spot market at each time period. The cost of frequency regulation capacity and frequency regulation mileage purchased by the dispatch center from each thermal power unit and energy storage cluster in the frequency regulation market is determined based on the frequency regulation capacity declaration volume, adjusted frequency regulation capacity declaration price, and adjusted frequency regulation mileage declaration price of each thermal power unit and energy storage cluster in the frequency regulation market at each time period. The cost of purchasing standby capacity from each thermal power unit and energy storage cluster in the standby market by the dispatch center is determined based on the standby capacity declaration volume and standby capacity declaration price of each thermal power unit and energy storage cluster in the standby market at each time period. The renewable energy consumption is determined based on the amount of each green power unit won in the spot market, the amount of wind and solar curtailment, its capacity, and its output factor.

3. The method as described in claim 1 or 2, wherein, The revenue obtained by the energy storage cluster in the spot market is suitable for determination based on the discharge volume, discharge price, charging volume, and charging price of the energy storage cluster in the spot market at different times. The revenue obtained by the energy storage cluster in the frequency regulation market is suitable for determination based on the frequency regulation capacity application volume, frequency regulation capacity winning bid price, and frequency regulation mileage winning bid price of the energy storage cluster in the frequency regulation market at different times. The revenue obtained by the energy storage cluster in the standby market is appropriate to be determined based on the amount of standby capacity won by the energy storage cluster in the standby market and the bid price for standby capacity in each time period. The variable cost of the energy storage cluster in the spot market is suitable for determination based on the unit cost and total cost of each energy storage unit in the spot market at different times. The variable cost of the energy storage cluster in the frequency regulation market is suitable for being determined based on the unit cost of frequency regulation capacity, the total cost of frequency regulation capacity, the unit cost of frequency regulation mileage, and the total cost of frequency regulation mileage of each energy storage unit in the frequency regulation market at each time period. The variable cost of the energy storage cluster in the standby market is suitable for being determined based on the standby capacity unit cost and the total standby capacity cost of each energy storage unit in the standby market at each time period. The fixed costs of the energy storage cluster include the initial construction investment cost, annual operation and maintenance costs, replacement costs, and recycling costs.

4. The method according to any one of claims 1-3, wherein, With the goals of minimizing the total cost of the dispatch center in the joint market and maximizing renewable energy consumption, a higher-level objective function is constructed. Based on this objective function, a higher-level model is built, including: The primary objective function of the upper layer is to minimize the total cost of the dispatch center in the joint market. With maximizing renewable energy consumption as the upper-level secondary objective, a secondary objective function is constructed. The upper-level model is constructed based on the first upper-level objective function and the second upper-level objective function.

5. The method according to any one of claims 1-4, wherein, Constructing an upper-level model based on the aforementioned upper-level objective function includes: Based on the aforementioned upper-level objective function and multiple upper-level constraints, an upper-level model is constructed. The aforementioned multiple upper-level constraints include: green power unit output constraints, thermal power unit output constraints, thermal power unit ramping capability constraints, thermal power unit start-stop time constraints, thermal power unit frequency regulation capacity constraints, thermal power unit reserve capacity constraints, energy storage cluster charge-discharge constraints, energy storage cluster state of charge constraints, energy storage cluster charge-discharge power constraints, energy storage cluster market application constraints, energy storage cluster bidding constraints, energy storage cluster SOH constraints, market application restriction constraints, frequency regulation price adjustment constraints, and power flow constraints.

6. The method of claim 5, wherein, The green power unit output constraints include green power unit bid quantity constraints, green power unit wind and solar curtailment energy constraints, and green power unit bid quantity composition constraints. The output constraints of the thermal power units include the power generation constraints, the application constraints, the start-up and shutdown constraints, and the winning bid constraints. The start-up and shutdown time constraints of the thermal power units include the start-up time constraints, the shutdown time constraints, the total start-up time constraints, and the total shutdown time constraints. The state of charge constraints of the energy storage cluster include the state of charge constraints of the energy storage cluster at each time period, the state of charge constraints of the energy storage cluster at the initial time period, and the state of charge constraints of the energy storage cluster at the final time period. The application constraints for the energy storage cluster market include application constraints for the energy storage cluster spot market, application constraints for the energy storage cluster frequency regulation market, and application constraints for the energy storage cluster standby market. The bidding constraints for energy storage clusters include the bidding volume constraints for charging and discharging in the spot market, the bidding volume constraints for frequency regulation in the energy storage cluster, the bidding volume constraints for standby in the energy storage cluster, the bidding price constraints for charging and discharging in the spot market, the bidding price constraints for frequency regulation in the energy storage cluster, and the bidding price constraints for standby in the energy storage cluster. The market reporting restrictions and constraints include spot market reporting restrictions, frequency regulation market reporting restrictions, and standby market reporting restrictions; The power flow constraints include node line power flow constraints and upper and lower limits constraints for power flow transmission.

7. The method according to any one of claims 1-6, wherein, Solving the joint clearing two-layer model yields the joint clearing results, including: A hierarchical solution algorithm is used to solve the upper-level model based on multiple upper-level constraints to obtain the unit output plan; Based on the unit output plan, the lower-level model is solved to obtain the joint clearing result.

8. The method according to any one of claims 1-7, wherein, Also includes: Based on the joint clearing results, determine the total profit of the energy storage cluster in the joint market; Using the Shapley value method, the original allocation factor for each energy storage unit is determined based on the total profit of the energy storage cluster in the joint market; The original allocation factor, frequency regulation capacity contribution factor, frequency regulation mileage contribution factor, and reserve capacity contribution factor of each energy storage unit are weighted and summed to obtain the improved allocation factor for each energy storage unit. The total profit of the energy storage cluster in the joint market is allocated to each energy storage unit based on the improved allocation factor of each energy storage unit.

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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