A distributed collaborative scheduling method for rural photovoltaic and hydrogen energy storage system

Abstract

The present application relates to the field of power system, specifically discloses a kind of dispersion collaborative scheduling method of rural photovoltaic and hydrogen energy storage system, comprising: the joint system of rural photovoltaic and hydrogen energy storage cooperation operation is constructed;Based on asymmetric Nash bargaining theory, the collaborative scheduling model of the joint system constructed is established;The collaborative scheduling model established is converted into electric energy transaction amount determination submodel and electric energy transaction cost determination submodel;Using improved alternating direction multiplier method, electric energy transaction amount determination submodel and electric energy transaction cost determination submodel are solved respectively, and the scheduling scheme of joint system is obtained.The present application can effectively improve the on-site utilization level of rural photovoltaic, and relieve the power grid consumption pressure.Moreover, the present application can not only guarantee RPV-HES joint system efficient and stable operation, but also protect the privacy of each subject.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more particularly to a decentralized collaborative scheduling method for rural photovoltaic and hydrogen energy storage systems. Background Technology

[0002] In recent years, my country has proposed to implement a rural clean energy construction project, consolidate and improve the level of rural power security, strive to develop clean energy sources such as solar, wind and hydropower, and explore the construction of multi-energy complementary distributed low-carbon integrated energy systems in suitable areas.

[0003] In recent years, rural photovoltaic (PV) systems have developed rapidly, with an estimated installed capacity potential of approximately 1 billion kilowatts by 2025. However, the large-scale integration of rural PV systems will increase the pressure on the power grid in terms of stable operation and renewable energy consumption. Therefore, effectively improving the level of local utilization is a major issue for the sustainable development of rural PV systems. Summary of the Invention

[0004] To address this, the present invention provides a decentralized collaborative scheduling method for rural photovoltaic and hydrogen energy storage systems, in an effort to solve or at least alleviate the problems mentioned above.

[0005] According to one aspect of the present invention, a decentralized collaborative scheduling method for a rural photovoltaic and hydrogen energy storage system is provided, comprising: constructing a joint system for the cooperative operation of rural photovoltaic and hydrogen energy storage; establishing a collaborative scheduling model for the joint system based on the asymmetric Nash bargaining theory; converting the established collaborative scheduling model into a power trading volume determination sub-model and a power trading cost determination sub-model; and solving the power trading volume determination sub-model and the power trading cost determination sub-model respectively using an improved alternating direction multiplier method to obtain a scheduling scheme for the joint system.

[0006] Optionally, in the decentralized collaborative scheduling method of the rural photovoltaic and hydrogen energy storage system according to the present invention, the joint system includes a photovoltaic aggregator that aggregates multiple photovoltaic objects in a target rural area, and one or more hydrogen energy storage entities, which include hydrogen electrolysis equipment, compression equipment, electrical energy storage equipment, hydrogen storage equipment, and hydrogen load.

[0007] Optionally, in the distributed collaborative scheduling method for rural photovoltaic and hydrogen energy storage systems according to the present invention, the collaborative scheduling model includes a first objective function, which includes: , in, Represents the total benefits of the joint system. These represent photovoltaic aggregators and hydrogen energy storage entities, respectively. h Cost value when running independently These represent photovoltaic aggregators and hydrogen energy storage entities, respectively.h Decision variables when running independently This represents the cost value of photovoltaic aggregators and all hydrogen energy storage entities in the combined system operating collaboratively. This represents the decision variables for the cooperative operation of photovoltaic aggregators and all hydrogen energy storage entities in the combined system. This represents the electricity sales volume negotiated by the photovoltaic aggregator and all hydrogen storage entities within the combined system. This indicates that the electricity sold by the photovoltaic aggregator is The corresponding cost at that time These represent photovoltaic aggregators and hydrogen energy storage entities, respectively. h Negotiation power ， Indicates the main body of hydrogen energy storage h The cost of operating in cooperation with photovoltaic aggregators Indicates the main body of hydrogen energy storage h Decision variables when collaborating with photovoltaic aggregators Indicates the main body of hydrogen energy storage h The electricity purchase agreement negotiated with the photovoltaic power aggregator Indicates the main body of hydrogen energy storage h Purchase volume is The corresponding electricity purchase cost at that time This indicates the total number of hydrogen energy storage units in the combined system.

[0008] Optionally, in the decentralized collaborative scheduling method for rural photovoltaic and hydrogen energy storage systems according to the present invention, the collaborative scheduling model includes a first constraint condition, which includes the operating constraints of the photovoltaic aggregator, the operating constraints of the hydrogen energy storage entity, and the cooperative operating cost constraint.

[0009] Optionally, in the decentralized collaborative scheduling method for rural photovoltaic and hydrogen energy storage systems according to the present invention, the electricity trading volume determination sub-model includes a second objective function, which includes: , in, This represents the total benefit of the joint system when only electricity trading volume is considered. This represents the cost value when photovoltaic aggregators and all hydrogen energy storage entities in the joint system operate collaboratively, considering only electricity trading volume. This indicates the main body of hydrogen energy storage when only considering electricity trading volume. h Cost of operating in cooperation with photovoltaic aggregators.

[0010] Optionally, in the decentralized collaborative scheduling method for rural photovoltaic and hydrogen energy storage systems according to the present invention, the sub-model for determining electricity trading volume further includes a second constraint, which includes the operational constraints of the photovoltaic aggregator and the operational constraints of the hydrogen energy storage entity.

[0011] Optionally, in the decentralized collaborative dispatch method for rural photovoltaic and hydrogen energy storage systems according to the present invention, the electricity trading cost determination sub-model includes a third objective function, which includes: , in, This represents the total electricity trading cost of the integrated system. This indicates the cost savings achieved when the photovoltaic aggregator operates in collaboration with all hydrogen energy storage entities in the combined system, compared to when the photovoltaic aggregator operates independently. Indicates the main body of hydrogen energy storage h When operating in cooperation with photovoltaic aggregators, relative to the main hydrogen energy storage system h Cost savings when running independently.

[0012] Optionally, in the decentralized collaborative scheduling method for rural photovoltaic and hydrogen energy storage systems according to the present invention, the sub-model for determining electricity trading costs includes a third constraint, which includes the electricity trading cost constraint of the photovoltaic aggregator and the electricity trading cost constraint of the hydrogen energy storage entity.

[0013] Optionally, in the decentralized collaborative scheduling method for rural photovoltaic and hydrogen energy storage systems according to the present invention, an improved alternating direction multiplier method is used to solve the electricity trading volume deterministic sub-model, including: obtaining the augmented Lagrange function of the second objective function by introducing Lagrange multipliers and iteration step size; decomposing the augmented Lagrange function of the second objective function to obtain the distributed optimization operation model of photovoltaic aggregators and hydrogen energy storage entities; and solving the distributed optimization operation model of photovoltaic aggregators and hydrogen energy storage entities by updating the introduced Lagrange multipliers and iteration step size respectively until the calculation results of photovoltaic aggregators and hydrogen energy storage entities reach the convergence condition, thereby obtaining the scheduling result of electricity trading volume.

[0014] Optionally, in the decentralized collaborative scheduling method for rural photovoltaic and hydrogen energy storage systems according to the present invention, an improved alternating direction multiplier method is used to solve the electricity trading cost determination sub-model, including: obtaining the augmented Lagrange function of the third objective function by introducing Lagrange multipliers and iteration step size; decomposing the augmented Lagrange function of the third objective function to obtain a distributed optimization trading model for photovoltaic aggregators and hydrogen energy storage entities; and solving the distributed optimization trading model for photovoltaic aggregators and hydrogen energy storage entities by updating the introduced Lagrange multipliers and iteration step size until the calculation results of photovoltaic aggregators and hydrogen energy storage entities reach the convergence condition, thereby obtaining the electricity trading cost under the scheduling result.

[0015] According to another aspect of the present invention, a computing device is provided, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be executed by the at least one processor, the program instructions including instructions for executing a distributed collaborative scheduling method for a rural photovoltaic and hydrogen energy storage system according to the present invention.

[0016] According to another aspect of the 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 distributed collaborative scheduling method for a rural photovoltaic and hydrogen energy storage system according to the invention.

[0017] In summary, this invention constructs a combined system for rural photovoltaic (PV) and hydrogen energy storage, enabling a portion of the PV output to be used for hydrogen production, storage, and utilization. This effectively improves the local utilization level of rural PV, alleviates grid absorption pressure, and significantly reduces hydrogen production costs, promoting the utilization and widespread adoption of hydrogen energy. Furthermore, since the combined system is primarily driven by solar and hydrogen energy, it can also promote the low-carbon transition of rural energy.

[0018] Furthermore, for the constructed joint system of rural photovoltaic and hydrogen energy storage, this invention establishes a collaborative scheduling model based on asymmetric Nash bargaining theory. This reflects the bargaining power of the participants and their contributions to the cooperation, thus ensuring the rationality of the distribution of benefits among the entities in the joint system. Moreover, an improved alternating direction multiplier algorithm is used to solve the electricity trading volume determination sub-model and the electricity trading cost determination sub-model in a distributed manner, which can protect the privacy of each entity and improve convergence efficiency. Attached Figure Description

[0019] To achieve the foregoing and related objectives, certain illustrative aspects are described herein in conjunction with the following description and accompanying drawings. These aspects indicate various ways in which the principles disclosed herein may 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 structural block diagram of a computing device 100 according to an embodiment of the present invention is shown; Figure 2 A flowchart of a decentralized collaborative scheduling method 200 for a rural photovoltaic and hydrogen energy storage system according to an embodiment of the present invention is shown; Figure 3 A schematic diagram of a combined system according to an embodiment of the present invention is shown. Detailed Implementation

[0021] 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.

[0022] Hydrogen energy storage is an emerging energy storage technology that efficiently achieves hydrogen-based electricity storage by utilizing processes such as hydrogen production and storage. Hydrogen energy has the characteristics of high energy density, no pollution, and zero carbon emissions, therefore, hydrogen energy storage can be used for renewable energy consumption.

[0023] Based on this, this invention proposes to organically combine Rural Photovoltaic (RPV) with Hydrogen Storage Systems (HES) to form an RPV-HES joint system. This allows a portion of the photovoltaic output to be used for hydrogen production, storage, and utilization, effectively improving the local utilization of RPV, alleviating grid absorption pressure, significantly reducing hydrogen production costs, and promoting the utilization and promotion of hydrogen energy. Furthermore, the RPV-HES joint system is primarily driven by solar and hydrogen energy, thus also playing a significant role in promoting a clean and low-carbon energy transition.

[0024] The realization of these potential benefits depends on the scientific optimization of the RPV-HES joint system. However, the operation of this joint system faces two challenges: first, the photovoltaic output and load within the system are highly random, affecting its stable operation; second, RPV and HES belong to different stakeholders, each possessing only their own information and rationally pursuing the maximization of their own interests, which restricts the efficient operation of the RPV-HES joint system. Therefore, how to coordinate the relationships between the stakeholders within the RPV-HES joint system, taking into account multiple uncertainties such as photovoltaic output and load, as well as energy trading, to ensure the stable operation of the RPV-HES joint system, has become an urgent problem to be solved.

[0025] Therefore, this invention provides a decentralized collaborative scheduling method for rural photovoltaic and hydrogen energy storage systems. Specifically, this decentralized collaborative scheduling method for rural photovoltaic and hydrogen energy storage systems can be executed on a computing device.

[0026] Figure 1A block diagram of the physical components (i.e., hardware) of a computing device 100 is shown. In a basic configuration, the computing device 100 includes at least one processing unit 102 and a system memory 104. According to one aspect, depending on the configuration and type of the computing device, the processing unit 102 may be implemented as a processor. The system memory 104 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, the system memory 104 includes an operating system 105 and a program module 106, the program module 106 including a distributed cooperative scheduling module 120 configured to execute the distributed cooperative scheduling method 200 of the present invention.

[0027] According to one aspect, operating system 105 is, for example, suitable for controlling the operation of computing device 100. 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 1 The basic configuration is illustrated by the components within the dashed lines 108. According to one aspect, the computing device 100 has additional features or functions. For example, according to one aspect, the computing device 100 includes additional data storage devices (removable and / or non-removable), such as disks, optical discs, or magnetic tapes. This additional storage... Figure 1 The image is shown by removable storage 109 and non-removable storage 110.

[0028] As stated above, according to one aspect, a program module is stored in system memory 104. According to one aspect, the program module 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.

[0029] 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 1Each 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 operating via the SOC, the functions described herein can be operated via dedicated logic integrated on a single integrated circuit (chip) with other components of the computing device 100. 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.

[0030] According to one aspect, computing device 100 may also have one or more input devices 112, such as a keyboard, mouse, pen, voice input device, touch input device, etc. It may also include output devices 114, such as a display, speaker, printer, etc. The foregoing devices are examples and other devices may also be used. Computing device 100 may include one or more communication connections 116 that allow communication with other computing devices 118. Examples of suitable communication connections 116 include, but are not limited to: RF transmitter, receiver and / or transceiver circuitry; Universal Serial Bus (USB), parallel and / or serial ports.

[0031] 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 104, removable storage 109, and non-removable storage 110 are examples of computer storage media (i.e., memory storage). Computer storage media can include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory 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 computer device 100. According to one aspect, any such computer storage medium can be part of computing device 100. Computer storage media does not include carrier waves or other transmitted data signals.

[0032] 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.

[0033] Figure 2 A flowchart of a decentralized collaborative scheduling method 200 for a rural photovoltaic and hydrogen energy storage system according to an embodiment of the present invention is shown. Method 200 is adapted to be used in computing devices (e.g., Figure 1 The calculation is performed in setting 100 as shown. Figure 2 As shown, method 200 begins at 210.

[0034] In section 210, a joint system for the cooperative operation of rural photovoltaic and hydrogen energy storage will be constructed.

[0035] In some embodiments, considering the relatively small installed capacity of individual photovoltaic households in rural areas, when constructing a joint system for the cooperative operation of rural photovoltaic and hydrogen energy storage, all photovoltaic objects within the target rural area (i.e., a region in the village) can be aggregated, and then a photovoltaic aggregator can act as an agent for transactions between the photovoltaic objects within the target rural area and the hydrogen energy storage entity (i.e., the hydrogen energy storage system).

[0036] Based on this, according to one embodiment of the present invention, the combined system includes a photovoltaic aggregator that aggregates multiple photovoltaic objects in a target rural area, and one or more hydrogen energy storage entities. Each hydrogen energy storage entity includes a hydrogen electrolysis device (e.g., an electrolyzer), a compression device (e.g., a compressor), an electrical energy storage device, a hydrogen storage device (e.g., a hydrogen storage tank), and a hydrogen load.

[0037] like Figure 3 The diagram illustrates a combined system according to an embodiment of the present invention. As can be seen from the diagram, a rural photovoltaic aggregator aggregates multiple rooftop photovoltaic systems (i.e., the aforementioned photovoltaic objects) into a unified system that trades with the main grid and hydrogen energy storage system. In some embodiments, the photovoltaic aggregator can utilize the grid lines for energy exchange with the hydrogen energy storage system by paying grid access fees to the upstream grid. It should be noted that although... Figure 3 The combined system shown depicts only one hydrogen energy storage system, but the present invention does not limit the number of hydrogen energy systems in a combined system. In specific embodiments, those skilled in the art can make adjustments according to actual circumstances.

[0038] Typically, photovoltaic (PV) aggregators and hydrogen energy storage systems belong to different stakeholders. Therefore, in a non-cooperative model, PV aggregators sell their generated electricity to the grid at the feed-in tariff, while hydrogen energy storage systems purchase electricity from the market at market prices to meet their internal needs. However, in the rural PV-hydrogen energy storage cooperative model proposed in this invention, PV aggregators can sign a cooperation agreement with the hydrogen energy storage entity, negotiating and negotiating to determine the transaction volume and corresponding price.

[0039] Furthermore, the operational models for photovoltaic aggregators and hydrogen storage entities are explained below. In rural energy systems, photovoltaic output and hydrogen load are uncertain and difficult to predict accurately. Therefore, rural photovoltaic aggregators and hydrogen storage entities need to consider these uncertainties when formulating operational plans. Based on this, this invention models the uncertainties through probabilistic scenarios, and then constructs an operational model based on stochastic optimization, as detailed below.

[0040] (I) Operating Model of Photovoltaic Aggregators (1) Objective function Operating costs of rural photovoltaic aggregators The minimum expected value is the target, specifically: , in, This represents the maintenance cost of photovoltaics (i.e., all photovoltaic objects aggregated in a photovoltaic aggregator). This represents the revenue that photovoltaic aggregators generate from selling electricity to the grid. This indicates a scenario where photovoltaic power output is specifically... , This represents the total number of photovoltaic power generation scenarios. Representing a scene The probability of occurrence The maintenance cost coefficient per unit of photovoltaic power generation can be taken as 0.0095. This indicates that photovoltaics (i.e., all rooftop photovoltaics) are in t Moment Scene The power generation below, This represents the total number of moments, and its specific value can be 24. Indicates that photovoltaic aggregators are in t Moment Scene Electricity sold to the main grid This indicates the feed-in tariff for photovoltaic (PV) aggregators, which is the price at which PV aggregators sell electricity to the grid.

[0041] (2) Constraints Photovoltaic aggregators t Moment Scene The amount of electricity sold to the main grid can not exceed the amount of electricity generated: , In addition, when photovoltaic aggregators cooperate with hydrogen energy storage entities, they also need to consider the grid access fees and corresponding constraints when conducting direct transactions with hydrogen energy storage entities.

[0042] , in, This represents the grid connection fee cost incurred by the photovoltaic aggregator in trading electricity with all hydrogen energy storage entities in the integrated system. and This represents the conversion factor for internet access fees, specifically... 0.00003 can be taken. 0.01 is acceptable. Indicates that photovoltaic aggregators are in t Moment Scene The total electrical energy sold by the photovoltaic aggregator to all hydrogen storage entities in the combined system. Equation (6) represents the total electrical energy sold by the photovoltaic aggregator to all hydrogen storage entities in the combined system. t Moment Scene The amount of electricity sold to all hydrogen storage entities in the downstream integrated system cannot exceed their power generation. Equation (7) indicates that the photovoltaic aggregator... t Moment Scene The sum of the electricity sold to the main grid and the electricity sold to all hydrogen storage entities in the combined system equals its power generation.

[0043] (II) Main Operation Model of Hydrogen Energy Storage The hydrogen energy storage entity aims to minimize operating costs by optimizing its production plan to determine the electricity purchases from the main grid and rural photovoltaic aggregators, thereby meeting the energy demand of the hydrogen energy storage entity the following day. Here, "hydrogen energy storage entity" refers to any single hydrogen energy storage entity.

[0044] (1) Objective function Operating costs of hydrogen energy storage The minimum expected value is the target, specifically: , In the formula, This indicates the cost of purchasing electricity from the main grid for hydrogen energy storage systems. This indicates the operating and maintenance costs of the internal units of the hydrogen energy storage system. This represents the operating loss cost of electrical energy storage in a hydrogen energy storage system. This represents the carbon cost of electricity purchased from the main grid by the hydrogen energy storage entity.

[0045] in: , , In the formula, The scenario representing hydrogen load, specifically, , This represents the total number of scenarios involving hydrogen load. Representing a scene The probability of occurrence This indicates the electricity price sold on the main grid. This indicates that the main body of hydrogen energy storage is in t Moment Scene The electricity purchased from the mainnet and These represent the unit power maintenance cost coefficients for the electrolytic cell and the compressor, respectively. It can be 0.018. It can be taken as 0.02. and These represent the electrolytic cell and the compressor, respectively. t Moment Scene The electrical energy consumed and These represent the investment cost and cycle life of energy storage, respectively. and represent electrical energy storage, respectively. t Moment Scene The amount of charge and discharge is below. This represents the carbon cost coefficient, which can be 0.06.

[0046] (2) Constraints Electrolyzer: Hydrogen production by electrolyzing water involves injecting direct current into the electrolyzer to produce hydrogen and oxygen. The chemical reaction process is as follows: The specific formula for calculating hydrogen production is as follows: , in, express t Moment Scene The hydrogen production rate is as follows: This indicates the efficiency of hydrogen production through water electrolysis. This indicates the lower heating value of hydrogen; in some embodiments, this value can be 119.64. , express t The maximum amount of hydrogen produced at any given time.

[0047] Compressor: To facilitate hydrogen storage, the hydrogen produced in the electrolyzer needs to be compressed. The compressor's operating formula is as follows: , In the formula, Indicates that the compressor is in t Moment Scene The electrical energy consumed This represents the specific heat capacity of hydrogen, typically 14.304. , Indicates that the compressor is in t Moment Scene The hydrogen flow rate under compression, Indicates the temperature of the input hydrogen gas. This represents the isentropic index of hydrogen. This represents the compression efficiency value. This indicates the compressor's maximum power. This indicates the compression ratio of the compressor.

[0048] Hydrogen storage tank: Compressed hydrogen gas can be stored in a hydrogen storage tank for future use. The amount of hydrogen stored in the tank is generally expressed using internal pressure, and its operating formula is as follows: , in, These respectively indicate the hydrogen storage tank at Time and Moment Scene The pressure below, Represents the universal gas constant. This indicates the temperature inside the hydrogen storage tank. This indicates the volume of the hydrogen storage tank. express （t-1） Moment Scene The hydrogen production rate is as follows: Indicates in t Moment Scene The hydrogen consumption under the given conditions, i.e. hydrogen load, is represented by equation (19), which indicates the hydrogen storage balance constraint.

[0049] Electricity storage: When the market price of electricity is low, the hydrogen energy storage system can purchase more electricity and store it using energy storage units. When the market price of electricity is high, the electricity stored in the energy storage can be used first, reducing the amount purchased from the grid and thus optimizing energy costs.

[0050] , in, Indicates energy storage t Moment Scene The energy stored below This indicates the scenario of energy storage at the initial moment. The energy stored below and These represent the charging and discharging efficiencies of electrical energy storage, respectively. and These represent the minimum and maximum stored energy values ​​of electrical energy storage, respectively. and These represent the maximum charging and discharging power of the electrical energy storage, respectively. and These represent electrical energy storage. t Moment Scene The charging and discharging states are represented by values ​​of 0 or 1. Taking 1 indicates that electrical energy storage t Moment Scene It is currently in a charging state. Setting it to 0 indicates that electrical energy storage t Moment Scene It is not in a charging state. Taking 1 indicates that electrical energy storage t Moment Scene It is in a discharge state. Setting it to 0 indicates that electrical energy storage t Moment Scene It is not in a discharged state. These represent the energy stored at the end time (or termination time) and the initial time, respectively.

[0051] Market Interaction: , in, This indicates the maximum amount of electricity that the hydrogen energy storage system can purchase from the main grid.

[0052] Power balance: .

[0053] Thus, the construction of the joint system is complete. The key to the operation of the joint system lies in how the photovoltaic aggregator and the hydrogen energy storage system reach a negotiation agreement to ensure that the benefits of all parties are improved on a fair basis. Based on this, this invention proposes a collaborative scheduling model for the RPV-HES joint system based on the Nash-Harsanyi bargaining game (NHBG). The Nash-Harsanyi bargaining game improves the Nash bargaining game into an asymmetric game model, which can reflect the bargaining power of the participants and their contributions to cooperation, thereby ensuring a reasonable distribution of benefits.

[0054] Next, in section 220, a cooperative scheduling model for the constructed joint system is established based on asymmetric Nash bargaining theory. According to one embodiment of the present invention, the established cooperative scheduling model includes a first objective function and first constraints.

[0055] Specifically, the first objective function is: , in, Represents the total benefits of the joint system. These represent photovoltaic aggregators and hydrogen energy storage entities, respectively. h Cost value when running independently These represent photovoltaic aggregators and hydrogen energy storage entities, respectively. h Decision variables when running independently This represents the cost value of photovoltaic aggregators and all hydrogen energy storage entities in the combined system operating collaboratively. This represents the decision variables for the cooperative operation of photovoltaic aggregators and all hydrogen storage entities in the combined system, including at least the electricity sold by the photovoltaic aggregator to the main grid. Similarly), This represents the total electricity sales negotiated by the photovoltaic aggregator and all hydrogen storage entities in the combined system. This indicates that the electricity sold by the photovoltaic aggregator is The corresponding cost, i.e., the revenue generated from electricity trading between photovoltaic aggregators and all hydrogen energy storage entities in the joint system. These represent photovoltaic aggregators and hydrogen energy storage entities, respectively. h Negotiation power (i.e., bargaining power). ， Indicates the main body of hydrogen energy storage h The cost of operating in cooperation with photovoltaic aggregators Indicates the main body of hydrogen energy storage h The decision variables for operating in cooperation with photovoltaic aggregators include at least the hydrogen energy storage entity. h Electricity purchased from the main grid and hydrogen energy storage unit h Output values ​​of each unit ( Similarly), Indicates the main body of hydrogen energy storage h The electricity purchase agreement negotiated with the photovoltaic power aggregator Indicates the main body of hydrogen energy storage h Electricity purchased from photovoltaic aggregators The corresponding electricity purchase cost, i.e., the main body of hydrogen energy storage. h and The electricity purchase cost negotiated by the photovoltaic aggregator This indicates the total number of hydrogen energy storage units in the combined system.

[0056] The first constraints corresponding to the first objective function mentioned above include: the operational constraints of the photovoltaic aggregator, the operational constraints of the hydrogen energy storage entity, and the cooperative operation cost constraints. Further, the operational constraints of the photovoltaic aggregator include the above equations (2)-(7), the operational constraints of the hydrogen energy entity include the above equations (9)-(27), and the cooperative operation cost constraints include the following equations (29) and (30).

[0057] .

[0058] In other words, the first constraint includes the above equations (2)-(7), (9)-(27), (29) and (30).

[0059] The aforementioned collaborative scheduling model is a non-convex nonlinear problem, which is difficult to solve directly. Furthermore, in multi-agent cooperation, it is crucial to protect the privacy of each agent's information. Therefore, according to one embodiment of the present invention, the established collaborative scheduling model can be first transformed into easily solvable subproblems, and then solved using the improved ADMM (Alternating Direction Multiplier Method) distributed solution algorithm. This approach protects the privacy of each agent.

[0060] Furthermore, the solution to the energy trading problem is the optimal solution for operating costs. Therefore, in some embodiments, the established collaborative scheduling model can be first transformed into two sub-problems: determining the electricity trading volume and determining the electricity trading cost. Then, the improved ADMM algorithm is used to solve them in a distributed manner, as detailed below.

[0061] In step 230, the established collaborative scheduling model is converted into a sub-model for determining electricity trading volume and a sub-model for determining electricity trading cost. According to one embodiment of the present invention, the sub-model for determining electricity trading volume includes a second objective function and a second constraint, and the sub-model for determining electricity trading cost includes a third objective function and a third constraint. The second objective function, the second constraint, the third objective function, and the third constraint are described in detail below.

[0062] The second objective function is: , in, This represents the total benefit of the joint system when only electricity trading volume is considered (i.e., the total benefit of the joint system without considering transaction costs). This represents the cost value of photovoltaic aggregators and all hydrogen storage entities in the joint system operating collaboratively, considering only electricity trading volume. This indicates the main body of hydrogen energy storage when only considering electricity trading volume. h Cost of operating in cooperation with photovoltaic aggregators.

[0063] The second constraints corresponding to the second objective function mentioned above include: the operational constraints of the photovoltaic aggregator and the operational constraints of the hydrogen energy storage entity. In this embodiment, the operational constraints of the photovoltaic aggregator include the above equations (2)-(7), and the operational constraints of the hydrogen energy storage entity include the above equations (9)-(27).

[0064] The third objective function is: , In the formula, This represents the total electricity trading cost of the integrated system. This indicates the cost savings achieved when the photovoltaic aggregator operates in collaboration with all hydrogen energy storage entities in the combined system, compared to when the photovoltaic aggregator operates independently. Indicates the main body of hydrogen energy storage h When operating in cooperation with photovoltaic aggregators, relative to the main hydrogen energy storage system h Cost savings when running independently.

[0065] in: .

[0066] The third constraints corresponding to the third objective function mentioned above include: the electricity trading cost constraints for photovoltaic aggregators and the electricity trading cost constraints for hydrogen energy storage entities, as detailed below.

[0067] The cost constraints of electricity trading for photovoltaic aggregators include: .

[0068] The cost constraints of electricity trading for hydrogen energy storage systems include: .

[0069] As is known above, in order to protect the privacy of each entity within the joint system and ensure the normal operation of the joint system, this invention employs a distributed algorithm—the improved alternating direction multiplier method (ADMM)—to solve the above-mentioned NHBG problem.

[0070] Specifically, in 240, the improved alternating direction multiplier method is used to solve the power trading volume determination sub-model and the power trading cost determination sub-model respectively, so as to obtain the scheduling scheme of the joint system.

[0071] The original ADMM algorithm only updates the Lagrange multipliers, and the step size generally remains unchanged. However, the convergence speed of the ADMM algorithm is affected not only by the Lagrange multipliers but also by the step size. Therefore, the performance of the original ADMM algorithm usually deteriorates gradually in the later stages of iteration. To address this, this invention proposes a two-stage dynamic step size correction method to improve the original ADMM algorithm, as detailed below.

[0072] Phase 1: As shown in equation (37), the original residual is calculated during each iteration. r and dual residuals s The value of changes. Specifically, if the minimum change (i.e., the change in square norm) of both the original and dual residuals is greater than the set value Δ (e.g., 0.15), the step size remains unchanged because the current step size is geared towards a decrease in the original and dual residuals. Otherwise, the current step size may worsen the convergence of the algorithm; in this case, the step size is updated according to Phase Two.

[0073] .

[0074] Phase 2: Update the step size based on the current values ​​of the original and dual residuals, as shown in equation (38). If the original residual is much larger than the dual residual, the step size will increase, resulting in a severe penalty for violating the original feasibility. If the dual residual is much larger than the original residual, the step size will decrease due to the convergence of dual feasibility. In this case, the convergence of the original feasibility and the dual feasibility can be balanced alternately.

[0075] , in, Let these represent the iteration step size for the (k+1)th and kth iterations, respectively. Let the infinite norm of the original residual in the k-th iteration be denoted as . Let represent the infinite norm of the dual residual in the k-th iteration.

[0076] Having clearly understood the improved alternating direction multiplier algorithm, the following steps will be explained in detail. First, the solution process for the electricity trading volume determination sub-model will be explained. In one embodiment of the present invention, when solving the electricity trading volume determination sub-model, it can be assumed that the electricity that the photovoltaic aggregator expects to sell to any hydrogen energy storage entity is consistent with the electricity that the hydrogen energy storage entity expects to purchase from the photovoltaic aggregator, as shown in the following equation (39).

[0077] , In the formula, Indicates that photovoltaic aggregators are in t time s In this scenario, the expectation is to move towards hydrogen energy storage as the main body. h The electricity sold Indicates the main body of hydrogen energy storage h exist t time s In this scenario, the expected purchase of electricity from photovoltaic aggregators is indicated by the following equation: when this equation holds, it signifies the presence of both rural photovoltaic aggregators and hydrogen storage entities. h A cooperation agreement was reached.

[0078] Once the photovoltaic aggregator reaches a cooperation agreement with each hydrogen energy storage entity in the joint system, according to an embodiment of the present invention, the sub-model for determining the electricity trading volume can be solved in the following manner.

[0079] First, by introducing Lagrange multipliers and the iteration step size, the augmented Lagrange function of the second objective function is obtained. Specifically, the augmented Lagrange function of the second objective function... for: , In the formula, Indicates the main body of hydrogen energy storage h exist t Lagrange multipliers of time, Indicates the main body of hydrogen energy storage h The iteration step size.

[0080] Next, the augmented Lagrangian function of the second objective function is decomposed to obtain a distributed optimal operation model for photovoltaic aggregators and hydrogen energy storage entities. The decomposition of equation (40) can be based on the ADMM algorithm principle, which will not be elaborated further in this application.

[0081] Specifically, the distributed operation optimization model for the photovoltaic aggregator obtained from the decomposition includes a fourth objective function and a fourth constraint, while the distributed optimization operation model for the hydrogen energy storage entity includes a fifth objective function and a fifth constraint. The fourth objective function, fourth constraint, fifth objective function, and fifth constraint are explained below.

[0082] The fourth objective function is: .

[0083] The fourth constraint condition corresponding to the fourth objective function mentioned above includes the above equations (2)-(7).

[0084] The fifth objective function is: .

[0085] The fifth constraint condition corresponding to the fifth objective function mentioned above includes the above equations (9)-(27).

[0086] Finally, for the distributed optimization operation model of photovoltaic aggregators and hydrogen energy storage entities, the Lagrange multipliers and iteration step size are updated and solved separately until the calculation results of photovoltaic aggregators and hydrogen energy storage entities reach the convergence condition, and the scheduling results of electricity trading volume are obtained. Specifically, the following steps can be included.

[0087] (1) Set convergence accuracy The iteration step size of each hydrogen energy storage unit (for each hydrogen energy storage unit) h Then it is ), and the number of initial iterations. Rural photovoltaic aggregators expect to sell electricity to various hydrogen storage entities (for hydrogen storage entities) h Then it is ), various hydrogen energy storage entities t Lagrange multipliers at time (for hydrogen energy storage main body) h Then it is Furthermore, in some embodiments, it is possible to set... ,initialization Of course, this is just an example, and the present invention does not limit it.

[0088] (2) For each hydrogen energy storage entity, the expected electricity sales volume is received from the photovoltaic aggregator (for each hydrogen energy storage entity) h Then it is Solve the distributed optimization operation model of the hydrogen energy storage system (for the hydrogen energy storage system). h Then it becomes equation (42), which gives the expected electricity purchase volume for each hydrogen energy storage entity (for each hydrogen energy storage entity) h Then it is ).

[0089] (3) For photovoltaic aggregators, the expected electricity purchase volume is received from various hydrogen energy storage facilities (for hydrogen energy storage entities) h Then it is Solve the distributed operation optimization model of the photovoltaic aggregator (i.e., equation (41)) to obtain the expected electricity sales volume of the photovoltaic aggregator to each hydrogen storage unit (for the hydrogen storage unit). h Then it is ).

[0090] (4) Calculate the original residual of the ()th iteration according to the following formula (43). and dual residuals The square norm is calculated, and all main calculation results are checked to see if they meet the convergence criterion. If convergence is achieved, the iteration ends and the scheduling result is output; otherwise, proceed to (5).

[0091] , in, They represent the first k Next and first ( k+1 All hydrogen energy storage entities during the next iteration t Always expecting to purchase total electricity from photovoltaic aggregators, Indicates the first k Photovoltaic aggregator in the next iteration t The total amount of electricity expected to be sold to all hydrogen energy storage entities at all times. represents the judgment thresholds for the original residual and the dual residual, respectively, which are the convergence precisions set above. Wherein, when equation (43) holds, it means that all main calculation results satisfy the convergence criterion.

[0092] (5) Update the Lagrange multipliers according to equation (44), and update the step size and the number of iterations according to equations (37) and (38). Then return (2).

[0093] , In the formula, They represent the ( ) k+1 ) and the firstk The hydrogen energy storage main body in the next iteration h exist t Lagrange multipliers of time, Indicates the ( k+1 In the next iteration, the photovoltaic aggregator was t Always looking forward to becoming the main body of hydrogen energy storage h The electricity sold Indicates the ( k+1 The main body of hydrogen energy storage during the second iteration h exist t Always looking forward to purchasing electricity from photovoltaic aggregators Indicates the first k The hydrogen energy storage main body in the next iteration h The iteration step size.

[0094] Thus, the scheduling results of electricity trading volume have been obtained, namely, the electricity trading volume between entities at each time point. Next, the solution process of the electricity trading cost determination sub-model will be explained.

[0095] Specifically, firstly, by introducing Lagrange multipliers and the iteration step size, the augmented Lagrange function of the third objective function is obtained. The augmented Lagrange function of the third objective function... for: , In the formula, Indicates photovoltaic aggregator t Constantly feeding hydrogen energy storage entities h The price of selling electricity Indicates photovoltaic aggregator t Always looking forward to becoming the main body of hydrogen energy storage h The price of selling electricity Indicates photovoltaic aggregator t Constantly feeding hydrogen energy storage entities h Electricity sold.

[0096] Then, the augmented Lagrangian function of the third objective function is decomposed to obtain distributed optimal trading models for photovoltaic aggregators and hydrogen energy storage entities. Specifically, the resulting distributed optimal trading model for photovoltaic aggregators includes a sixth objective function and a sixth constraint, while the distributed optimal trading model for hydrogen energy storage entities includes a seventh objective function and a seventh constraint. The sixth objective function, sixth constraint, seventh objective function, and seventh constraint are explained below.

[0097] The sixth objective function is: , in, This represents the electricity trading cost for photovoltaic aggregators. Additionally, the sixth constraint corresponding to the sixth objective function mentioned above includes equations (33) and (35) above, and equation (47) below.

[0098] .

[0099] The seventh objective function is: , in, Indicates the main body of hydrogen energy storage h The electricity trading cost. Additionally, the seventh constraint corresponding to the seventh objective function includes equations (34), (36), and (47). Finally, for the distributed optimization trading model of photovoltaic aggregators and hydrogen energy storage entities, the introduced Lagrange multipliers and iteration step size are updated and solved separately until the calculation results of photovoltaic aggregators and hydrogen energy storage entities reach the convergence condition, thus obtaining the electricity trading cost under the scheduling results of the electricity trading volume obtained above.

[0100] The solution process for the sub-model for determining electricity trading costs will not be elaborated here; please refer to the relevant description of the sub-model for determining electricity trading volume mentioned above.

[0101] Thus, it is evident that in this invention, the solution of each sub-model, the calculation of iterative operators, and the acquisition of residuals are all performed within their respective entities. Interaction with other entities occurs only during the extraction of reference values ​​for connection line variables and the determination of residual convergence. In other words, the collaborative scheduling method of this invention involves minimal interaction information and low communication requirements, thereby protecting the information privacy of each entity. Therefore, the scheduling model proposed in this invention does not require central coordination; it achieves distributed convergence of the entire system's scheduling strategy solely through iterative interactions between entities, realizing distributed optimization with entity autonomy and decentralized coordination.

[0102] Furthermore, the method of acquiring bargaining power is explained below. Under asymmetric conditions, if a participant's bargaining power increases, that participant will receive more profit in the distribution of benefits. According to one embodiment of the present invention, bargaining power can be quantified from marginal contribution rate and main equipment utilization rate, as detailed below.

[0103] Marginal Contribution Ratio (MC): The contribution of each participant to the cooperative operation. Each participant's contribution can be reflected by comparing the cost changes before and after joining (costs in SP1). Specifically, the marginal contribution ratio... The calculation method is as follows: , In the formula, SP1 represents the cost per participant n during the collaborative operation. This represents the revenue of each participant when running independently.

[0104] Equipment Utilization Rate (UR): The efficiency of equipment utilization for each participant. Utilization rate... The calculation is as follows: , In the formula, For the equipment capacity of each participant, The actual output of each participant's equipment.

[0105] Based on the above two factors, the total score for each participant can be obtained: , In the formula, and These are the weights of MC and UR respectively when reflecting bargaining power, and they satisfy... By normalizing the above formula, the bargaining power of each participant can be obtained. : .

[0106] Finally, regarding To clarify, this represents the operating costs of photovoltaic aggregators under different circumstances (i.e., with and without cooperation), and the calculation method is the same. Similarly, This represents the operating cost of the hydrogen energy storage system under different conditions, and the calculation method is the same.

[0107] In summary, this invention constructs a combined system for rural photovoltaic (PV) and hydrogen energy storage, enabling a portion of the PV output to be used for hydrogen production, storage, and utilization. This effectively improves the local utilization level of rural PV, alleviates grid absorption pressure, and significantly reduces hydrogen production costs, promoting the utilization and widespread adoption of hydrogen energy. Furthermore, since the combined system is primarily driven by solar and hydrogen energy, it can also promote the low-carbon transition of rural energy.

[0108] Furthermore, for the constructed joint system of rural photovoltaic and hydrogen energy storage, this invention establishes a collaborative scheduling model based on asymmetric Nash bargaining theory. This reflects the bargaining power of the participants and their contributions to the cooperation, thus ensuring the rationality of the distribution of benefits among the entities in the joint system. Moreover, an improved alternating direction multiplier algorithm is used to solve the electricity trading volume determination sub-model and the electricity trading cost determination sub-model in a distributed manner, which can protect the privacy of each entity and improve convergence efficiency.

[0109] 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.

[0110] 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.

[0111] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not others, combinations of features from different embodiments are meant to be within the scope of the invention and form different embodiments.

[0112] As used herein, 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.

[0113] Although the invention has been described with reference to a limited number of embodiments, those skilled in the art will understand from the foregoing description that other embodiments are conceivable within the scope of the invention described herein. Furthermore, it should be noted that the language used in this specification has been chosen primarily for readability and edibility purposes, and not for the purpose of explaining or limiting the subject matter of the invention. Therefore, many modifications and variations will be apparent to those skilled in the art. The disclosure of the invention regarding its scope is illustrative and not restrictive.

Claims

1. A decentralized collaborative scheduling method for a rural photovoltaic and hydrogen energy storage system, comprising: Construct a joint system for the cooperative operation of rural photovoltaic and hydrogen energy storage. The joint system includes a photovoltaic aggregator that aggregates multiple photovoltaic objects in a target rural area, and one or more hydrogen energy storage entities. The hydrogen energy storage entities include hydrogen electrolysis equipment, compression equipment, electric energy storage equipment, hydrogen storage equipment, and hydrogen load. Based on the asymmetric Nash bargaining theory, a cooperative scheduling model for the joint system is established. The collaborative scheduling model is converted into a power trading volume determination sub-model and a power trading cost determination sub-model. The improved alternating direction multiplier method is used to solve the power trading volume determination sub-model and the power trading cost determination sub-model respectively, so as to obtain the scheduling scheme of the joint system. The cooperative scheduling model includes a first objective function, which includes: , in, Represents the total benefits of the joint system. These represent photovoltaic aggregators and hydrogen energy storage entities, respectively. h Cost value when running independently These represent photovoltaic aggregators and hydrogen energy storage entities, respectively. h Decision variables when running independently This represents the cost value of photovoltaic aggregators and all hydrogen energy storage entities in the combined system operating collaboratively. This represents the decision variables for the cooperative operation of photovoltaic aggregators and all hydrogen energy storage entities in the combined system. This represents the electricity sales volume negotiated by the photovoltaic aggregator and all hydrogen storage entities within the combined system. This indicates that the electricity sold by the photovoltaic aggregator is The corresponding cost at that time These represent photovoltaic aggregators and hydrogen energy storage entities, respectively. h Negotiation power ， Indicates the main body of hydrogen energy storage h The cost of operating in cooperation with photovoltaic aggregators Indicates the main body of hydrogen energy storage h Decision variables when collaborating with photovoltaic aggregators Indicates the main body of hydrogen energy storage h The electricity purchase agreement negotiated with the photovoltaic power aggregator Indicates the main body of hydrogen energy storage h Purchase volume is The corresponding electricity purchase cost at that time This indicates the total number of hydrogen energy storage units in the combined system.

2. The method as described in claim 1, wherein, The collaborative scheduling model includes a first constraint, which includes operational constraints of photovoltaic aggregators, operational constraints of hydrogen energy storage entities, and cooperative operation cost constraints.

3. The method as described in claim 1 or 2, wherein, The electricity trading volume determination sub-model includes a second objective function, which includes: ， in, This represents the total benefit of the joint system when only electricity trading volume is considered. This represents the cost value when photovoltaic aggregators and all hydrogen energy storage entities in the joint system operate collaboratively, considering only electricity trading volume. This indicates the main body of hydrogen energy storage when only considering electricity trading volume. h Cost of operating in cooperation with photovoltaic aggregators.

4. The method of claim 3, wherein, The sub-model for determining electricity trading volume also includes a second constraint, which includes operational constraints on photovoltaic aggregators and operational constraints on hydrogen energy storage entities.

5. The method of claim 3, wherein, The electricity trading cost determination sub-model includes a third objective function, which includes: ， in, This represents the total electricity trading cost of the integrated system. This indicates the cost savings achieved when the photovoltaic aggregator operates in collaboration with all hydrogen energy storage entities in the combined system, compared to when the photovoltaic aggregator operates independently. Indicates the main body of hydrogen energy storage h When operating in cooperation with photovoltaic aggregators, relative to the main body of hydrogen energy storage h Cost savings when running independently.

6. The method of claim 5, wherein, The sub-model for determining electricity trading costs includes a third constraint, which includes the electricity trading cost constraints for photovoltaic aggregators and the electricity trading cost constraints for hydrogen energy storage entities.

7. The method of claim 5, wherein, The method of solving the electricity trading volume determination sub-model using the improved alternating direction multiplier method includes: By introducing Lagrange multipliers and iteration step size, the augmented Lagrange function of the second objective function is obtained; The augmented Lagrangian function of the second objective function is decomposed to obtain a distributed optimal operation model for photovoltaic aggregators and hydrogen energy storage entities; For the distributed optimization operation model of photovoltaic aggregators and hydrogen energy storage entities, the Lagrange multipliers and iteration step size are updated and introduced to solve the model respectively until the calculation results of photovoltaic aggregators and hydrogen energy storage entities reach the convergence condition, and the scheduling results of electricity trading volume are obtained.

8. The method of claim 7, wherein, The method of solving the electricity trading cost determination sub-model using the improved alternating direction multiplier method includes: By introducing Lagrange multipliers and iteration step size, the augmented Lagrange function of the third objective function is obtained; The augmented Lagrangian function of the third objective function is decomposed to obtain a distributed optimization trading model for photovoltaic aggregators and hydrogen energy storage entities; For the distributed optimization trading model of photovoltaic aggregators and hydrogen energy storage entities, the Lagrange multipliers and iteration step size are updated and introduced to solve the model respectively until the calculation results of photovoltaic aggregators and hydrogen energy storage entities reach the convergence condition, and the electricity trading cost under the scheduling result is obtained.

9. A computing device, comprising: At least one processor; and A memory storing program instructions configured to be executed by the at least one processor, the program instructions including instructions for performing the method as described in any one of claims 1-8.

10. A readable storage medium storing program instructions that, when read and executed 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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