Multi-energy agent double-layer energy sharing optimization method and system

Abstract

This invention relates to power system and integrated energy system optimization and scheduling technology, specifically to a multi-energy agent two-layer energy sharing optimization method and system. The method includes: constructing an integrated energy model comprising multiple energy users, various types of energy conversion equipment, and composite energy storage resources; constructing an inner-layer energy sharing cluster composed of energy users and composite energy storage providers, and an outer-layer multi-energy agent collaborative framework composed of the inner-layer cluster, integrated energy supply stations, photovoltaic-hydrogen storage-hydrogen refueling integrated stations, and energy aggregators; establishing coupling constraints for electricity, heat, oxygen, and hydrogen, as well as consistency constraints for multi-user energy sharing; establishing a two-layer energy sharing optimization model with the goal of minimizing collaborative operating costs; and employing an improved alternating direction multiplier method with dynamic penalty factors for distributed solution, outputting a multi-energy agent sharing scheduling scheme. This method can improve the multi-energy mutual assistance capability and renewable energy absorption level, while reducing the system's collaborative operating costs.

Description

Technical Field

[0001] This invention belongs to the field of power system and integrated energy system optimization and dispatching technology, and specifically relates to a multi-energy agent two-layer energy sharing optimization method and system. Background Technology

[0002] With the rapid development of renewable energy, integrated energy systems, and hydrogen infrastructure, the coupling relationships among different energy entities within a region, encompassing electricity, heat, oxygen, hydrogen, and other energy types, are increasingly strengthened. In areas with weak grid connections, far from the main grid, abundant renewable energy resources, or limited transmission channels, energy entities such as pastoral areas, villages, renewable energy power plants, integrated energy supply stations, and photovoltaic hydrogen production and refueling stations may simultaneously have multiple energy supply and demand relationships. Due to differences in resource endowment, load characteristics, energy storage configuration, and energy conversion capabilities among these entities, independent operation by a single entity is insufficient to fully meet the demands of multi-energy collaborative supply and renewable energy consumption. High-altitude pastoral areas and villages are a typical application of the aforementioned multi-energy agent collaborative operation scenario.

[0003] In multi-energy agent collaborative operation scenarios, the energy loads of different entities typically exhibit multi-energy coupling, time-varying fluctuations, and local complementarity. For example, some energy-consuming entities may be equipped with wind power, photovoltaic thermal power, oxygen production, electric boilers, and controllable loads, while others may be equipped with rooftop photovoltaic thermal power, electric vehicles, oxygen production equipment, and thermal energy conversion equipment. Composite energy storage providers can offer shared energy storage services to multiple entities through resources such as electrical energy storage, thermal energy storage, and oxygen energy storage. Due to the intermittent and fluctuating nature of renewable energy output, and the potential limitations of external grid trading or energy transmission capacity, the lack of a collaborative mechanism among multiple entities can easily lead to problems such as insufficient energy supply, increased wind and solar curtailment, and redundant allocation of energy storage resources.

[0004] Existing integrated energy system scheduling methods often focus on optimization within a single region or energy station. They typically require centralized acquisition of equipment parameters, load information, and cost information from various entities, making it difficult to simultaneously address the needs of multiple energy entities, multi-energy flow coupling, and multi-level energy sharing. Traditional centralized optimization methods may also suffer from insufficient protection of entity privacy, heavy communication burdens, and low coordination efficiency in practical applications. While existing energy sharing methods can achieve some degree of electricity sharing, they do not adequately consider the coupling relationships between multiple energy sources such as electricity, heat, oxygen, and hydrogen. They struggle to effectively utilize surplus electricity under conditions of limited renewable energy transmission and also fail to achieve coordinated operation among energy-consuming entities, integrated energy supply stations, and photovoltaic hydrogen production and refueling stations. Summary of the Invention

[0005] This invention provides a multi-energy agent, two-layer energy sharing optimization method and system to address the shortcomings of existing technologies, such as the difficulty in effectively utilizing surplus electricity under conditions of limited renewable energy transmission, and the difficulty in achieving coordinated operation among energy users, integrated energy supply stations, and photovoltaic hydrogen production and refueling stations. It achieves coordinated supply of multiple energy sources, including electricity, heat, oxygen, and hydrogen, among various energy agents such as multiple energy users, composite energy storage providers, integrated energy supply stations, and photovoltaic hydrogen production and refueling stations. Furthermore, it improves load supply capacity, renewable energy absorption level, and system operating economy while protecting the operational information of each entity.

[0006] This invention provides a two-layer energy sharing optimization method using multiple energy agents. It constructs a comprehensive energy model comprising multiple energy users, various types of energy conversion equipment, and composite energy storage resources. An inner-layer energy sharing cluster is built, consisting of energy users and composite energy storage providers. An outer-layer multi-energy agent collaborative framework is constructed, comprising the inner-layer cluster, integrated energy supply stations, photovoltaic-hydrogen storage-hydrogen refueling integrated stations, and energy aggregators. Considering the transaction relationships with the power grid and the hydrogen energy market, coupling constraints on electricity, heat, oxygen, and hydrogen, as well as consistency constraints on multi-entity energy sharing, are established. A two-layer energy sharing optimization model is established with the goal of minimizing collaborative operating costs. An improved alternating direction multiplier method with dynamic penalty factors is used for distributed solution, outputting a multi-energy agent sharing scheduling scheme.

[0007] Includes the following steps: S1. Construct a comprehensive energy model with multiple energy entities; S2. Construct an inner-layer energy-sharing cluster; S3. Construct an outer-layer multi-energy agent collaborative framework; S4. Establish a two-tier energy sharing optimization model; S5. An improved ADMM distributed solution is adopted and a shared scheduling scheme is output.

[0008] According to the multi-energy agent two-layer energy sharing optimization method provided by the present invention, step S1, which involves constructing a multi-energy entity integrated energy model, includes constructing a HAR model of a high-altitude pastoral integrated energy system and a HAV model of a high-altitude village integrated energy system; the specific steps are as follows: S1.1. HAR modeling of integrated energy systems in high-altitude pastoral areas; The High Altitude Integrated Energy System (HAR) in pastoral areas includes electrical load, thermal load, and oxygen load. The energy supply side of the High Altitude Pastoral Integrated Energy System (HAR) includes wind turbines (WT), photovoltaic / thermal energy (PV / T), oxygen generators (OM), and electric boilers (EF). The wind turbines (WT) and photovoltaic / thermal energy (PV / T) provide local renewable energy output; the oxygen generators (OM) convert electricity into oxygen; and the electric boilers (EF) convert electricity into heat. The High Altitude Pastoral Integrated Energy System (HAR) shares electricity, heat, and oxygen with the integrated energy storage provider CESP through an inner-layer energy sharing cluster. S1.2. High-altitude village integrated energy system HAV modeling; The High Altitude Village Integrated Energy System (HAV) includes the needs of residents' daily life, agricultural planting, public services, and electric vehicle charging. The energy supply side of the High Altitude Village Integrated Energy System (HAV) includes rooftop photovoltaic / thermal equipment (PV / T), oxygen generators (OM), electric boilers (EF), and electric vehicles (EV). The rooftop photovoltaic / thermal equipment (PV / T) is used to provide local electricity and heat. The oxygen generators (OM) and electric boilers (EF) are used to supply oxygen and heat, respectively. The electric vehicles (EV) participate in the village's power balance as adjustable electricity users. S1.3. Construction of a unified balance relationship between electricity, heat, and oxygen; The unified energy balance relationship between the High Altitude Pastoral Integrated Energy System (HAR) and the High Altitude Village Integrated Energy System (HAV) is expressed as follows:

[0009] In the formula, i Indicates HAR or HAV; t Indicates the scheduling period; m ∈{ e , h , o}, , , They represent electrical energy, thermal energy, and oxygen, respectively. For the amount of energy imported or purchased locally, for i During the period t The m Net shared energy input, with positive values ​​indicating shared input and negative values ​​indicating shared output. and These are energy release and energy charging, respectively. To meet load demand, For adjustable load, This refers to energy conversion or equipment consumption.

[0010] According to the multi-energy agent two-layer energy sharing optimization method provided by the present invention, step S2, of constructing the inner layer energy sharing cluster, includes forming the inner layer energy sharing cluster by combining the adjacent high-altitude pastoral integrated energy system (HAR), the high-altitude village integrated energy system (HAV), and the composite energy storage provider (CESP); the specific steps are as follows: S2.1. CESP structural modeling for composite energy storage providers; The CESP (Comprehensive Energy Storage Provider) comprises electrical energy storage (ES), thermal energy storage (TS), and oxygen energy storage (OS). ES is used to balance the fluctuations in electricity supply and demand within the inner cluster; TS is used to balance the fluctuations in thermal energy supply and demand; and OS is used to balance the fluctuations in oxygen supply and demand. Based on the energy demand, storage status, and equipment operating boundaries of the High Altitude Pastoral Integrated Energy System (HAR) and the High Altitude Village Integrated Energy System (HAV) at different times, the CESP determines the charging and discharging plans for electricity, thermal energy, and oxygen, and provides shared energy storage services to both systems. S2.2. Composite energy storage state update model; The status update relationships of various energy storage types in the CESP (Comprehensive Energy Storage Provider) are represented as follows:

[0011] In the formula, For the first k Energy storage-like periods t The energy storage status, and These are charging and releasing energy, respectively. and These are charging efficiency and discharging efficiency, respectively; various energy storage states and charging / discharging energies satisfy their corresponding upper capacity limit, lower capacity limit, and charging / discharging boundary; S2.3. Inner layer information interaction and energy sharing; High-altitude pastoral integrated energy system (HAR), high-altitude village integrated energy system (HAV), and composite energy storage provider (CESP) register, report information, confirm permissions, and share and schedule data through a cloud platform. The cloud platform receives energy demand, equipment status, energy storage status, and sharing intentions uploaded by each entity, calculates the energy sharing plan within the cluster, and returns the scheduling results to each entity. Each entity retains complete operating parameters and cost parameters locally and only uploads information such as sharing volume, boundary variables, or information required for iteration. In the process of inner-layer energy sharing, the composite energy storage provider CESP serves as a common support node for the interaction of electricity, heat and oxygen between the high-altitude pastoral integrated energy system HAR and the high-altitude village integrated energy system HAV. When the output of renewable energy is high, it stores surplus electricity, heat and oxygen, and releases the stored energy when the load is at its peak or when external energy purchases are limited.

[0012] According to the multi-energy agent two-layer energy sharing optimization method provided by the present invention, step S3, which involves constructing an outer multi-energy agent collaborative framework, includes building an outer multi-energy agent collaborative framework composed of a high-altitude pastoral integrated energy system (HAR), a high-altitude village integrated energy system (HAV), a composite energy storage provider (CESP) inner energy sharing cluster, an integrated energy supply station (IESS), a photovoltaic-hydrogen storage-hydrogen refueling integrated station (PV-HSRIS), and an energy aggregator (EA), and completing external energy transactions through the power grid and the hydrogen energy market; the specific steps are as follows: S3.1. Modeling of Integrated Energy Supply Station (IESS); The Integrated Energy Supply Station (IESS) comprises a renewable energy power plant, an oxygen generator (OM), and an electricity-oxygen sharing channel. Under conditions of limited grid transmission capacity, the IESS inputs a portion of surplus renewable energy into the OM, converting the electricity into oxygen, and provides electricity or oxygen sharing services to other entities within the outer multi-energy agent collaborative framework. The electricity-oxygen conversion relationship of the OM is expressed as follows:

[0013] In the formula, O Indicates oxygen production capacity. P Indicates the power consumption of the oxygen concentrator, subscript I and t These represent IESS and the scheduling period, respectively. The superscript or subscript OM indicates the oxygen generator. η Indicates the oxygen production efficiency coefficient; S3.2. PV-HSRIS modeling of integrated photovoltaic-hydrogen storage-hydrogen refueling stations; The photovoltaic-hydrogen storage-hydrogen refueling integrated station PV-HSRIS includes photovoltaic power generation equipment (PV), electric hydrogen production equipment (P2H), hydrogen energy storage (HS), oxygen energy storage (OS), hydrogen refueling station (HRS), and hydrogen refueling load for heavy-duty hydrogen trucks (HHT). PV-HSRIS uses photovoltaic power to provide electricity for the electric hydrogen production equipment and the load within the station. Hydrogen is produced through the electric hydrogen production equipment (P2H), and the hydrogen energy storage (HS) and hydrogen refueling station (HRS) meet the hydrogen refueling needs of heavy-duty hydrogen trucks (HHT). The oxygen produced during the hydrogen production process enters the oxygen energy storage (OS) and participates in the outer layer oxygen sharing and scheduling. The relationship between hydrogen production via electro-hydrogen and oxygen production in the hydrogen production process is expressed as follows:

[0014] In the formula, H Indicates hydrogen production capacity. P This indicates the power consumption of the electro-hydrogen production equipment. O Indicates the oxygen production related to the hydrogen production process, subscript H and t These represent PV-HSRIS and the scheduling period, respectively. The superscript P2H indicates the electro-hydrogen production process. η Indicates the efficiency of hydrogen production via electro-hydrogen conversion. κ Indicates the oxygen production conversion factor; S3.3. Energy Aggregator (EA) Coordination Mechanism; Energy aggregator EA is used to coordinate the energy sharing relationships between the High Altitude Pastoral Integrated Energy System (HAR), the High Altitude Village Integrated Energy System (HAV), the CESP inner layer energy sharing cluster, the Integrated Energy Supply Station (IESS), and the PV-HSRIS photovoltaic-hydrogen storage-hydrogen refueling integrated station. Energy aggregator EA gathers information on the willingness and boundaries of various energy agents to participate in sharing, coordinates electricity, oxygen, and hydrogen energy trading plans, and issues collaborative dispatch schemes to various energy agents.

[0015] According to the multi-energy agent two-layer energy sharing optimization method provided by the present invention, step S4, establishing a two-layer energy sharing optimization model, includes establishing an inner-layer energy sharing optimization model and an outer-layer multi-energy agent collaborative optimization model; the specific steps are as follows: S4.1. Inner layer energy sharing optimization model; The inner-layer energy sharing optimization model aims to minimize the collaborative operating costs of the High-Altitude Pastoral Integrated Energy System (HAR), the High-Altitude Village Integrated Energy System (HAV), and the Composite Energy Storage Provider (CESP), coordinating the sharing relationships of electricity, heat, and oxygen among HAR, HAV, and CESP; its objective function can be expressed as:

[0016] In the formula, C Indicates operating costs, superscript in The inner layer energy sharing cluster is represented by HAR, HAV, and CESP, which respectively represent the high-altitude pastoral integrated energy system, the high-altitude village integrated energy system, and the composite energy storage provider. The inner layer constraints include the energy balance constraints of electricity, heat, and oxygen, the equipment operation boundary constraints, the energy storage charging and discharging constraints, the adjustable load constraints, and the energy sharing consistency constraints among HAR, HAV, and CESP.

[0017] S4.2. Outer Layer Multi-Energy Agent Collaborative Optimization Model; The outer-layer multi-energy agent collaborative optimization model aims to minimize the collaborative operating costs of the high-altitude pastoral integrated energy system (HAR), the high-altitude village integrated energy system (HAV), the inner-layer energy sharing cluster of the composite energy storage provider (CESP), the integrated energy supply station (IESS), the photovoltaic-hydrogen storage-hydrogen refueling integrated station (PV-HSRIS), and the energy aggregator (EA), while coordinating the interaction relationships of electricity, oxygen, and hydrogen among the outer-layer entities. Its objective function can be expressed as:

[0018] In the formula, C Indicates operating costs, superscript outThe outer multi-energy agent collaborative framework represents the outer layer; CL represents the inner HAR / HAV-CESP cluster; IESS represents the integrated energy supply station; PV-HSRIS represents the photovoltaic-hydrogen storage-hydrogen refueling integrated station; EA represents the energy aggregator; grid represents grid trading; H2 represents hydrogen energy market trading. S4.3. Two-layer collaborative optimization model and shared consistency constraints; Integrating the inner and outer layer optimization relationships, the two-layer energy sharing optimization model uses the inner layer optimization results as the equivalent operating costs of the outer layer high-altitude pastoral integrated energy system (HAR), high-altitude village integrated energy system (HAV), and composite energy storage provider (CESP) cluster, and also considers the outer layer multi-energy agent collaborative operating costs. Minimize as the objective; where, The two-tier energy sharing optimization model is expressed as:

[0019] The constraints of the two-layer energy sharing optimization model include the inner layer of electricity, heat, and oxygen energy balance constraints, the outer layer of electricity, oxygen, and hydrogen energy interaction constraints, equipment operation constraints, grid trading constraints, hydrogen energy market trading constraints, and energy sharing consistency constraints. For entities with a two-way sharing relationship m and main body n The consistency of energy and oxygen sharing can be expressed as:

[0020] In the formula, P and O These represent the shared amount of electricity and the shared amount of oxygen, respectively; subscript m , n and t These represent the two sharing entities and the scheduling period, respectively; superscript sh Represent shared variables; for inner-layer heat energy sharing relationships, establish corresponding heat energy sharing consistency constraints.

[0021] According to the multi-energy agent two-layer energy sharing optimization method provided by the present invention, step S5 adopts an improved ADMM distributed solution and outputs a shared scheduling scheme, which includes decomposing the centralized optimization problem into local optimization sub-problems of multiple energy agents. Each agent completes the optimization calculation locally, and coordination is achieved through updates of shared variables, Lagrange multipliers, and penalty factors. The specific steps are as follows: S5.1 Solving the inner and outer layer local optimization subproblems; In the inner-layer optimization, the High Altitude Pastoral Integrated Energy System (HAR), the High Altitude Village Integrated Energy System (HAV), and the Composite Energy Storage Provider (CESP) solve the local main optimization sub-problems respectively, and upload shared variables or boundary information to the cloud platform; the cloud platform updates the inner-layer consistency variables and Lagrange multipliers, and determines whether convergence is achieved based on the original residuals and dual residuals; Once the inner-layer optimization reaches convergence, the cloud platform treats the High Altitude Pastoral Integrated Energy System (HAR), the High Altitude Village Integrated Energy System (HAV), and the CESP cluster as equivalent outer-layer energy agents, and publishes the outer-layer sharing intention and boundary information to the Energy Aggregator (EA). The Energy Aggregator (EA) coordinates the High Altitude Pastoral Integrated Energy System (HAR), the High Altitude Village Integrated Energy System (HAV), the CESP cluster, the Integrated Energy Supply Station (IESS), and the Photovoltaic-Hydrogen Storage-Hydrogen Refueling Station (PV-HSRIS) to solve the outer-layer local optimization subproblems respectively, and updates the outer-layer shared variables, Lagrange multipliers, and penalty factors. If the outer-layer original residual and dual residual satisfy the convergence threshold, the final energy sharing scheduling scheme is output; otherwise, the updated information is fed back to the relevant energy agents and the iteration continues. During the distributed solution process, each energy agent exchanges and shares information such as quantities, boundary variables, multiplier updates, or residual information, but does not disclose the complete operating model and cost parameters. S5.2. Dynamic penalty factor update; A dynamic penalty factor update strategy is adopted, which adjusts the penalty factor based on the relative magnitudes of the original residual and the dual residual; the update relationship is expressed as:

[0022] In the formula, For the first r The original residual from +1 iteration, For the first r The dual residual of +1 iteration, and These are the penalty factors before and after the update, respectively. To increase the penalty factor coefficient, To reduce the coefficient of the penalty factor, This is the residual balance coefficient. When both the original residual and the dual residual satisfy the preset convergence threshold, the iteration stops; otherwise, local optimization, shared variable update, multiplier update, and penalty factor adjustment continue. S5.3. Shared scheduling scheme output; Once the improved alternating direction multiplier method meets the convergence condition, the operation plan of each energy agent and the energy sharing scheduling scheme are output.

[0023] This invention also provides a two-layer energy sharing optimization system for energy agents, the system comprising: a comprehensive energy model construction module, an inner-layer energy sharing cluster construction module, an outer-layer collaborative framework construction module, a two-layer optimization modeling module, a distributed solution module, and a scheduling output module; The integrated energy model building module is used to build integrated energy models of electricity, heat, and oxygen for the high-altitude pastoral integrated energy system HAR and the high-altitude village integrated energy system HAV, and to set the variables corresponding to unconfigured equipment to 0 according to the differences in equipment configuration between HAR and HAV. The inner layer energy sharing cluster construction module is used to construct the high-altitude pastoral integrated energy system HAR, the high-altitude village integrated energy system HAV, and the composite energy storage provider CESP into an inner layer energy sharing cluster of the high-altitude pastoral integrated energy system HAR, the high-altitude village integrated energy system HAV, and the composite energy storage provider CESP, and to establish the corresponding energy sharing relationships of the public power bus, public heating pipeline, and public oxygen transmission pipeline. The outer layer collaborative framework construction module is used to build the high-altitude pastoral integrated energy system HAR, the high-altitude village integrated energy system HAV, the CESP composite energy storage provider inner layer energy sharing cluster, the integrated energy supply station IESS, the photovoltaic-hydrogen storage-hydrogen refueling integrated station PV-HSRIS, and the energy aggregator EA into an outer layer multi-energy agent collaborative framework, and set the power grid and hydrogen energy market as external trading objects. The two-layer optimization modeling module is used to establish an inner-layer energy sharing optimization model and an outer-layer multi-energy agent collaborative optimization model, and to generate energy balance constraints, equipment operation boundary constraints, controllable load adjustment constraints, energy storage charging and discharging constraints, and energy sharing consistency constraints. The distributed solution module is used to decompose the two-layer energy sharing optimization model into local optimization subproblems of the high-altitude pastoral integrated energy system (HAR), high-altitude village integrated energy system (HAV), composite energy storage provider (CESP), energy supply station (IESS), photovoltaic-hydrogen storage-hydrogen refueling integrated station (PV-HSRIS), and shared consistency variable and Lagrange multiplier update subproblems executed by the cloud platform or energy aggregator (EA). The distributed solver module includes an inner solver unit, an outer solver unit, and a penalty factor update unit. The inner solver unit is used to coordinate the local optimization of HAR, HAV, and CESP. The outer solver unit is used to coordinate the local optimization of the HAR / HAV-CESP inner energy sharing cluster, IESS, and PV-HSRIS. The penalty factor update unit is used to dynamically adjust the penalty factor according to the relative magnitude of the original residual and the dual residual. The scheduling output module is used to output the electricity, heat, and oxygen operation plans of the high-altitude pastoral integrated energy system HAR, the electricity, heat, and oxygen operation plans of the high-altitude village integrated energy system HAV, the electricity storage, heat storage, and oxygen storage charging and discharging plans of the composite energy storage provider CESP, the electricity sharing and oxygen sharing plans of the energy supply station IESS, and the photovoltaic consumption, electric hydrogen production, hydrogen storage, oxygen storage, and hydrogen refueling service plans of the photovoltaic-hydrogen storage-hydrogen refueling integrated station PV-HSRIS, after the original residual and dual residual meet the preset convergence threshold.

[0024] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-energy agent two-layer energy sharing optimization method as described above.

[0025] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-energy agent two-layer energy sharing optimization method as described above.

[0026] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the multi-energy agent two-layer energy sharing optimization method as described above.

[0027] Compared with the present invention, the beneficial effects of this application are: (1) The method and system provided by the present invention construct an inner energy sharing cluster composed of multiple energy users and composite energy storage provider CESP. Through CESP, it provides electric energy storage, thermal energy storage and oxygen energy storage sharing services to multiple energy users, which can reduce the pressure of individual entities independently configuring composite energy storage resources and improve the supply capacity of electric, thermal and oxygen loads.

[0028] (2) The method and system provided by the present invention construct an outer multi-energy agent collaborative framework between the integrated energy supply station IESS, the photovoltaic-hydrogen storage-hydrogen refueling integrated station PV-HSRIS and the inner energy sharing cluster, so that the surplus electricity of renewable energy under the condition of limited external transmission can be consumed locally through power sharing, oxygen production and hydrogen production, etc., thereby improving the utilization capacity of renewable energy in the multi-energy agent collaborative scenario.

[0029] (3) The method and system provided by the present invention adopt a two-layer collaborative optimization and an improved alternating direction multiplier method to decompose the multi-energy agent optimization problem into local sub-problems and perform distributed solutions, avoiding the direct disclosure of the complete operating parameters and cost parameters of each entity, which is conducive to protecting the operating information of each energy agent.

[0030] (4) The method and system provided by the present invention can output a coordinated scheduling scheme for electricity, heat, oxygen and hydrogen for multiple energy users, composite energy storage companies, integrated energy supply stations and photovoltaic-hydrogen storage-hydrogen refueling stations, thereby improving the regional energy mutual assistance capacity and the economic efficiency of system operation. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0032] Figure 1 This is a flowchart of the multi-energy agent two-layer energy sharing optimization method for high-altitude pastoral areas and villages provided in this embodiment of the invention; Figure 2 This is a schematic diagram of a multi-energy agent two-layer energy sharing framework for high-altitude pastoral areas and villages provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the inner-layer energy-sharing cluster structure for high-altitude pastoral areas and villages provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the CESP (Complex Energy Storage System) structure for high-altitude pastoral areas and villages provided in an embodiment of the present invention; Figure 5 This is a flowchart of the improved alternating direction multiplier method for two-level distributed optimization in high-altitude pastoral areas and villages, provided by an embodiment of the present invention. Figure 6 This is a schematic diagram of the structure of a multi-energy agent two-layer energy sharing optimization system for high-altitude pastoral areas and villages provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0034] This embodiment uses a multi-energy agent collaborative operation scenario consisting of high-altitude pastoral areas, high-altitude villages, integrated energy supply stations, and photovoltaic-hydrogen storage-hydrogen refueling integrated stations as an example to illustrate the multi-energy agent two-layer energy sharing optimization method and system. The high-altitude pastoral integrated energy system (HAR) and the high-altitude village integrated energy system (HAV) are merely specific examples of multiple energy-consuming entities; without departing from the technical concept of this invention, the method and system can also be applied to other regional integrated energy systems with multiple energy coupling needs such as electricity, heat, oxygen, and hydrogen. This embodiment is applicable to multi-entity collaborative operation scenarios such as pastoral areas, villages, integrated energy supply stations, renewable energy power plants, and photovoltaic hydrogen production and refueling stations.

[0035] like Figure 1 As shown, the multi-energy agent two-layer energy sharing optimization method in this embodiment is a multi-energy agent two-layer energy sharing optimization method for high-altitude pastoral areas and villages, including the following steps: Step 1: Construct a comprehensive energy model for high-altitude pastoral areas and villages; Step 2: Construct a high-altitude pastoral integrated energy system (HAR), a high-altitude village integrated energy system (HAV), and a CESP (composite energy storage provider) inner-layer energy sharing cluster. Step 3: Construct an outer-layer multi-energy agent collaborative framework; Step 4: Establish a two-tier energy sharing optimization model; Step 5: Use the improved ADMM distributed solution and output a shared scheduling scheme.

[0036] The specific steps to achieve step 1 are as follows: Step 1.1: HAR modeling of integrated energy systems in high-altitude pastoral areas; The High Altitude Integrated Energy System (HAR) in high-altitude pastoral areas includes electrical load, thermal load, and oxygen load. Electrical load comprises residential load, livestock production equipment load, and management system load; thermal load includes hot water supply and heating load; and oxygen load includes the oxygen demand for living, production, and public services at high altitudes.

[0037] HAR's energy supply side includes wind turbines (WT), photovoltaic / thermal energy (PV / T), oxygen generators (OM), and electric boilers (EF). The WT and PV / T provide local renewable energy output; the OM converts electricity into oxygen; and the EF converts electricity into heat. HAR can also share electricity, heat, and oxygen with the integrated energy storage provider CESP through its inner-layer energy-sharing cluster.

[0038] Step 1.2: High-altitude village integrated energy system (HAV) modeling; The High Altitude Village Integrated Energy System (HAV) encompasses load demands for residential living, agricultural production, public services, and electric vehicle charging. The HAV's electrical load includes residential living load, agricultural production load, and electric vehicle charging load; its heat load includes domestic hot water and heating load; and its oxygen load includes the oxygen supply needs for village living and public services.

[0039] The energy supply side of the HAV includes rooftop photovoltaic / thermal power units (PV / T), oxygen generators (OM), electric boilers (EF), and electric vehicles (EV). The PV / T provides local electricity and heat; the OM and EF supply oxygen and heat, respectively; and the EV participates in the village's energy balance as an adjustable electricity consumer. For equipment not configured in the HAR or HAV, the corresponding variables can be set to 0 to use a unified model to describe the energy balance of different entities.

[0040] Step 1.3: Establishing a unified balance between electricity, heat, and oxygen; The unified energy balance relationship between HAR and HAV is expressed as:

[0041] In the formula, i Indicates HAR or HAV; t Indicates the scheduling period; m ∈{ e , h , o},in, 、 、 These represent the energy types of electricity, heat, and oxygen, respectively. For the amount of energy imported or purchased locally, for i During the period t The m Net shared energy input, with positive values ​​indicating shared input and negative values ​​indicating shared output. and These are energy release and energy charging, respectively. To meet load demand, For adjustable load, This refers to energy conversion or equipment consumption.

[0042] The specific steps to achieve step 2 are as follows: like Figure 3As shown, the adjacent high-altitude pastoral integrated energy system (HAR), high-altitude village integrated energy system (HAV), and composite energy storage provider (CESP) form an inner-layer energy sharing cluster. HAR, HAV, and CESP are connected to the public power bus, public heating pipeline, and public oxygen pipeline, forming an inner-layer energy sharing network oriented towards electricity, heat, and oxygen.

[0043] Step 2.1: CESP structural modeling for composite energy storage providers like Figure 4 As shown, the integrated energy storage provider CESP includes electrical energy storage (ES), thermal energy storage (TS), and oxygen energy storage (OS). Electrical energy storage (ES) is used to balance the fluctuations in electricity supply and demand within the inner cluster; thermal energy storage (TS) is used to balance the fluctuations in thermal energy supply and demand; and oxygen energy storage (OS) is used to balance the fluctuations in oxygen supply and demand. Based on the energy demand, storage status, and equipment operating boundaries of the HAR and HAV at different times, CESP determines the charging and discharging plans for electricity, thermal energy, and oxygen, and provides shared energy storage services to the HAR and HAV.

[0044] Step 2.2, Composite Energy Storage State Update Model The state update relationships of various energy storage types in CESP are represented as follows:

[0045] In the formula, For the first k Energy storage-like periods t The energy storage status, and These are charging and releasing energy, respectively. and These are charging efficiency and discharging efficiency, respectively; various energy storage states and charging / discharging energies meet their corresponding upper capacity limit, lower capacity limit, and charging / discharging boundary.

[0046] Step 2.3: Inner Layer Information Interaction and Energy Sharing HAR, HAV, and CESP register, report information, confirm permissions, and perform shared scheduling through a cloud platform. The cloud platform receives energy demand, equipment status, energy storage status, and sharing intentions uploaded by each entity, calculates the energy sharing plan within the cluster, and returns the scheduling results to each entity. Each entity retains complete operating and cost parameters locally, uploading only the shared amount, boundary variables, or information required for iteration, thereby reducing the reliance of centralized scheduling on entity operating information.

[0047] In the process of inner-layer energy sharing, CESP serves as a common support node for the interaction of electricity, heat, and oxygen between HAR and HAV. When the output of renewable energy is high, it stores surplus electricity, heat, and oxygen, and releases the stored energy when the load is at its peak or when external energy purchases are limited, so as to improve the multi-energy supply capacity of pastoral areas and villages.

[0048] The specific steps to achieve step 3 are as follows: like Figure 2 As shown, an outer multi-energy agent collaborative framework is constructed, consisting of an inner energy-sharing cluster (HAR / HAV-CESP), an integrated energy supply station (IESS), a photovoltaic-hydrogen storage-hydrogen refueling integrated station (PV-HSRIS), and an energy aggregator (EA). External energy transactions are completed through the power grid and the hydrogen market. The outer framework enables point-to-point sharing of electricity and oxygen among the inner cluster, IESS, and PV-HSRIS, and facilitates necessary external transactions through the power grid and the hydrogen market.

[0049] S3.1 Integrated Energy Supply Station (IESS) Modeling The Integrated Energy Supply Station (IESS) comprises a renewable energy power plant, an oxygen generator (OM), and an electricity-oxygen sharing channel. Under conditions of limited grid transmission capacity, the IESS can input surplus renewable energy into the OM, converting the electricity into oxygen, and providing electricity or oxygen sharing services to other entities within the outer multi-energy agent collaborative framework. The electricity-oxygen conversion relationship of the OM is expressed as follows:

[0050] In the formula, O Indicates oxygen production capacity. P Indicates the power consumption of the oxygen concentrator, subscript I and t These represent IESS and the scheduling period, respectively. The superscript or subscript OM indicates the oxygen generator. η This represents the oxygen production efficiency coefficient.

[0051] S3.2 PV-HSRIS Modeling of Integrated Photovoltaic-Hydrogen Storage-Hydrogen Refueling Station The PV-HSRIS (Photovoltaic-Hydrogen Storage-Hydrogen Refueling Integrated Station) comprises a photovoltaic (PV) power generation unit, a hydrogen production unit (P2H), a hydrogen storage unit (HS), an oxygen storage unit (OS), a hydrogen refueling station (HRS), and a hydrogen refueling load for heavy-duty hydrogen trucks (HHT). The PV-HSRIS utilizes photovoltaic power to supply electricity to the P2H unit and the station's load. Hydrogen is produced through the P2H unit, and the hydrogen storage unit (HS) and the HRS meet the refueling needs of the heavy-duty hydrogen trucks (HHT). Oxygen generated during hydrogen production enters the oxygen storage unit (OS) and participates in the outer-layer oxygen sharing and dispatching system.

[0052] The relationship between hydrogen production via electro-hydrogen and oxygen production in the hydrogen production process is expressed as follows:

[0053] In the formula, H Indicates hydrogen production capacity. P This indicates the power consumption of the electro-hydrogen production equipment. O Indicates the oxygen production related to the hydrogen production process, subscript H and tThese represent PV-HSRIS and the scheduling period, respectively. The superscript P2H indicates the electro-hydrogen production process. η Indicates the efficiency of hydrogen production via electro-hydrogen conversion. κ This represents the oxygen production conversion factor.

[0054] S3.3 Energy Aggregator (EA) Coordination Mechanism Energy aggregator (EA) coordinates energy sharing relationships between the HAR / HAV-CESP inner-layer energy sharing cluster, IESS, and PV-HSRIS. EA gathers information on the willingness and boundaries of various energy agents to participate in sharing, coordinates electricity, oxygen, and hydrogen trading plans, and publishes collaborative scheduling schemes to each energy agent. EA does not directly obtain complete equipment models and cost details from each entity, thereby reducing the information exposure risks associated with centralized optimization.

[0055] The specific steps for implementing step S4 are as follows: An inner-layer energy-sharing optimization model and an outer-layer multi-energy agent collaborative optimization model are established. The inner-layer optimization objects are HAR, HAV, and CESP, while the outer-layer optimization objects are the HAR / HAV-CESP inner-layer energy-sharing cluster, IESS, PV-HSRIS, and EA.

[0056] S4.1 Inner Layer Energy Sharing Optimization Model The inner-layer energy-sharing optimization model aims to minimize the collaborative operating cost of HAR, HAV, and CESP, coordinating the sharing of electricity, heat, and oxygen among HAR, HAV, and CESP. Its objective function can be expressed as:

[0057] In the formula, C Indicates operating costs, superscript in This represents the inner-layer energy sharing cluster. HAR, HAV, and CESP represent the integrated energy system for high-altitude pastoral areas, the integrated energy system for high-altitude villages, and the composite energy storage provider, respectively. Inner-layer constraints include energy balance constraints for electricity, heat, and oxygen; equipment operating boundary constraints; energy storage charging and discharging constraints; adjustable load constraints; and energy sharing consistency constraints among HAR, HAV, and CESP.

[0058] S4.2 Outer Layer Multi-Energy Agent Collaborative Optimization Model The outer-layer multi-energy agent collaborative optimization model aims to minimize the collaborative operating cost of the HAR / HAV-CESP inner-layer energy-sharing cluster, IESS, PV-HSRIS, and EA, while coordinating the interactions of electricity, oxygen, and hydrogen among the outer-layer entities. Its objective function can be expressed as:

[0059] In the formula, C Indicates operating costs, superscript out The outer layer represents the multi-energy agent collaborative framework; CL represents the inner layer HAR / HAV-CESP cluster; IESS represents the integrated energy supply station; PV-HSRIS represents the photovoltaic-hydrogen storage-hydrogen refueling integrated station; EA represents the energy aggregator; grid represents grid trading; and H2 represents hydrogen energy market trading.

[0060] S4.3 Two-layer collaborative optimization model and shared consistency constraints Integrating the optimization relationships between the inner and outer layers, the two-layer energy-sharing optimization model uses the inner-layer optimization results as the equivalent operating cost of the outer-layer HAR / HAV-CESP cluster, and also considers the collaborative operating cost of the outer-layer multi-energy agent. Minimize as the objective; where, The two-tier energy-sharing optimization model can be expressed as:

[0061] The constraints of the two-layer energy sharing optimization model include the inner layer's energy balance constraints for electricity, heat, and oxygen, the outer layer's interaction constraints for electricity, oxygen, and hydrogen, equipment operation constraints, grid trading constraints, hydrogen market trading constraints, and energy sharing consistency constraints.

[0062] For entities with a two-way sharing relationship m and main body n The consistency of energy and oxygen sharing can be expressed as:

[0063] In the formula, P and O These represent the shared amount of electricity and the shared amount of oxygen, respectively; subscript m , n and t These represent the two sharing entities and the scheduling period, respectively; superscript sh This represents a shared variable. For the inner layer's heat energy sharing relationship, corresponding heat energy sharing consistency constraints can be established.

[0064] The specific steps to achieve step 5 are as follows: like Figure 5 As shown, an improved alternating direction multiplier method is used to solve the two-layer energy sharing optimization model in a distributed manner. This method decomposes the centralized optimization problem into multiple local optimization subproblems of energy agents. Each agent completes the optimization calculation locally, and coordination is achieved through shared variables, Lagrange multipliers, and penalty factor updates.

[0065] S5.1 Solving the inner and outer layer local optimization subproblems In the inner-layer optimization, HAR, HAV, and CESP solve the local main optimization subproblems respectively and upload shared variables or boundary information to the cloud platform; the cloud platform updates the inner-layer consistency variables and Lagrange multipliers, and determines whether convergence has occurred based on the original residuals and dual residuals.

[0066] Once the inner-layer optimization reaches convergence, the cloud platform treats the HAR / HAV-CESP cluster as an equivalent outer-layer energy agent and publishes the outer-layer sharing intention and boundary information to the EA. Subsequently, the EA coordinates the HAR / HAV-CESP cluster, IESS, and PV-HSRIS to solve the outer-layer local optimization subproblems respectively, and updates the outer-layer shared variables, Lagrange multipliers, and penalty factors. If the outer-layer original residual and dual residual satisfy the convergence threshold, the final energy-sharing scheduling scheme is output; otherwise, the updated information is fed back to the relevant energy agents and the iteration continues.

[0067] In the distributed solution process, each energy agent only exchanges shared quantities, boundary variables, multiplier updates, or residual information, without needing to directly disclose the complete operating model and cost parameters, thereby protecting the main operating information while achieving collaborative optimization.

[0068] S5.2 Dynamic Penalty Factor Update To improve the convergence performance of the alternating direction multiplier method, this embodiment employs a dynamic penalty factor update strategy, adjusting the penalty factor based on the relative magnitudes of the original and dual residuals. Specifically, when the original residual is significantly larger than the dual residual, the penalty factor is increased to enhance the convergence strength of the shared consistency constraint; when the dual residual is significantly larger than the original residual, the penalty factor is decreased to improve the stability of local variable updates; when the two are in a relatively balanced state, the penalty factor remains unchanged. The update relationship is expressed as follows:

[0069] In the formula, For the first r The original residual from +1 iteration, For the first r The dual residual of +1 iteration, and These are the penalty factors before and after the update, respectively. To increase the penalty factor coefficient, To reduce the coefficient of the penalty factor, This is the residual balance coefficient. The iteration stops when both the original residual and the dual residual satisfy the preset convergence threshold; otherwise, local optimization, shared variable updates, multiplier updates, and penalty factor adjustments continue.

[0070] S5.3 Shared Scheduling Scheme Output Once the improved alternating direction multiplier method meets the convergence condition, the operation plans and energy sharing scheduling schemes for each energy agent are output. The shared scheduling schemes include the electricity, heat, and oxygen operation plans for HAR, HAV, and CESP; the electricity, heat, and oxygen storage charging and discharging plans for CESP; the electricity and oxygen sharing plans for IESS; the photovoltaic consumption, hydrogen production, hydrogen storage, oxygen storage, and hydrogen refueling service plans for PV-HSRIS; and the multi-energy agent sharing coordination results generated by EA.

[0071] S5.4 Feasibility Domain Assessment for Renewable Energy Uncertainty In a further embodiment, a feasible region assessment method for renewable energy uncertainties based on Information Gap Decision Theory (IGDT) can be introduced to evaluate the impact of fluctuations in wind power, photovoltaic (PV), and PV-thermal output on the results of two-tier energy-sharing dispatch. This assessment method is not a necessary limitation of the claims, but rather a further implementation of the robustness analysis of the dispatch scheme of this invention.

[0072] set up r Indicates the type of renewable energy. r Let {WT, PV, PV / T} represent wind power, photovoltaic power, and photovoltaic thermal power, respectively. The actual output of renewable energy can be expressed as:

[0073] In the formula, P Indicates renewable energy output, subscript r and t These represent the type of renewable energy and the dispatch period, respectively. The superscript RE indicates renewable energy; the capped symbol indicates the forecast value. α The radius of uncertainty; ξ This assessment aims to normalize the output bias. Through this evaluation, the risk aversion boundary and opportunity seeking boundary of a multi-energy agent, two-tiered energy sharing framework in high-altitude regions can be obtained under different renewable energy fluctuation conditions.

[0074] The following describes a multi-energy agent two-layer energy sharing optimization system provided by the present invention. The multi-energy agent two-layer energy sharing optimization system described below can be referred to in correspondence with the multi-energy agent two-layer energy sharing optimization method described above.

[0075] The system includes a comprehensive energy model construction module, an inner-layer energy sharing cluster construction module, an outer-layer collaborative framework construction module, a two-layer optimization modeling module, a distributed solution module, and a scheduling output module.

[0076] The integrated energy model building module is used to execute step S1, build integrated energy models of electricity, heat and oxygen for HAR and HAV, and set the variables corresponding to unconfigured equipment to 0 according to the equipment configuration differences between HAR and HAV, so as to achieve unified modeling.

[0077] The inner layer energy sharing cluster construction module is used to execute step S2, which constructs HAR, HAV and CESP into HAR / HAV-CESP inner layer energy sharing cluster, and establishes the energy sharing relationship corresponding to the public power bus, public heating pipeline and public oxygen transmission pipeline.

[0078] The outer layer collaborative framework construction module is used to execute step S3, which constructs the HAR / HAV-CESP inner layer energy sharing cluster, IESS, PV-HSRIS and EA into an outer layer multi-energy agent collaborative framework, and sets the power grid and hydrogen energy market as external trading objects.

[0079] The two-layer optimization modeling module is used to execute step S4, establish the inner layer energy sharing optimization model and the outer layer multi-energy agent collaborative optimization model, and generate energy balance constraints, equipment operation boundary constraints, controllable load adjustment constraints, energy storage charging and discharging constraints, and energy sharing consistency constraints.

[0080] The distributed solution module is used to execute step S5, which uses the improved alternating direction multiplier method to decompose the two-layer energy sharing optimization model into HAR local optimization subproblems, HAV local optimization subproblems, CESP local optimization subproblems, IESS local optimization subproblems, PV-HSRIS local optimization subproblems, and shared consistency variable and Lagrange multiplier update subproblems executed by the cloud platform or energy aggregator EA.

[0081] The distributed solver module includes an inner solver unit, an outer solver unit, and a penalty factor update unit. The inner solver unit coordinates the local optimizations of HAR, HAV, and CESP; the outer solver unit coordinates the local optimizations of the HAR / HAV-CESP inner energy sharing cluster, IESS, and PV-HSRIS; and the penalty factor update unit dynamically adjusts the penalty factor based on the relative magnitudes of the original residuals and dual residuals.

[0082] The scheduling output module is used to output the following plans after the original residual and the dual residual meet the preset convergence threshold: the electricity, heat and oxygen operation plan of HAR, the electricity, heat and oxygen operation plan of HAV, the electricity storage, heat storage and oxygen storage charge and discharge plan of CESP, the electricity sharing and oxygen sharing plan of IESS, and the photovoltaic consumption, electric hydrogen production, hydrogen storage, oxygen storage and hydrogen refueling service plan of PV-HSRIS.

[0083] The system can be deployed on a cloud platform, an energy aggregator's EA server, or an edge computing device. During operation, the system only receives energy sharing data, boundary variables, energy storage status, multiplier updates, or residual information uploaded by each energy agent, without directly obtaining the complete equipment parameters and cost parameters of each energy agent.

[0084] Through the above steps, this embodiment can achieve coordinated supply of electricity, heat, oxygen, and hydrogen among high-altitude pastoral areas, villages, integrated energy supply stations, and photovoltaic-hydrogen storage-hydrogen refueling stations, while protecting the operation information of each energy agent. This improves the local absorption capacity of renewable energy and reduces the cost of coordinated operation of multiple energy agents.

[0085] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7 As shown, the electronic device may include a processor, a communications interface, memory, and a communication bus. The processor, communications interface, and memory communicate with each other via the communication bus. The processor can invoke logical instructions from the memory to execute a multi-energy agent two-layer energy sharing optimization method. Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0086] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the multi-energy agent two-layer energy sharing optimization method provided by the above methods.

[0087] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the multi-energy agent two-layer energy sharing optimization method provided by the above methods.

[0088] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0089] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-energy agent two-layer energy sharing optimization method, characterized in that, Includes the following steps: S1. Construct a comprehensive energy model with multiple energy entities; S2. Construct an inner-layer energy-sharing cluster; S3. Construct an outer-layer multi-energy agent collaborative framework; S4. Establish a two-tier energy sharing optimization model; S5. An improved ADMM distributed solution is adopted and a shared scheduling scheme is output.

2. The multi-energy agent two-layer energy sharing optimization method according to claim 1, characterized in that, S1 describes the construction of a multi-energy entity integrated energy model, which includes constructing a HAR model of a high-altitude pastoral integrated energy system and a HAV model of a high-altitude village integrated energy system; the specific steps are as follows: S1.

1. HAR modeling of integrated energy systems in high-altitude pastoral areas; The High Altitude Integrated Energy System (HAR) in pastoral areas includes electrical load, thermal load, and oxygen load. The energy supply side of the High Altitude Pastoral Integrated Energy System (HAR) includes wind turbines (WT), photovoltaic / thermal energy (PV / T), oxygen generators (OM), and electric boilers (EF). The wind turbines (WT) and photovoltaic / thermal energy (PV / T) provide local renewable energy output; the oxygen generators (OM) convert electricity into oxygen; and the electric boilers (EF) convert electricity into heat. The High Altitude Pastoral Integrated Energy System (HAR) shares electricity, heat, and oxygen with the integrated energy storage provider CESP through an inner-layer energy sharing cluster. S1.

2. High-altitude village integrated energy system HAV modeling; The High Altitude Village Integrated Energy System (HAV) includes the needs of residents' daily life, agricultural planting, public services, and electric vehicle charging. The energy supply side of the High Altitude Village Integrated Energy System (HAV) includes rooftop photovoltaic / thermal equipment (PV / T), oxygen generators (OM), electric boilers (EF), and electric vehicles (EV). The rooftop photovoltaic / thermal equipment (PV / T) is used to provide local electricity and heat. The oxygen generators (OM) and electric boilers (EF) are used to supply oxygen and heat, respectively. The electric vehicles (EV) participate in the village's power balance as adjustable electricity users. S1.

3. Construction of a unified balance relationship between electricity, heat, and oxygen; The unified energy balance relationship between the High Altitude Pastoral Integrated Energy System (HAR) and the High Altitude Village Integrated Energy System (HAV) is expressed as follows: In the formula, i Indicates HAR or HAV; t Indicates the scheduling period; m ∈{ e , h , o }, , , They represent electrical energy, thermal energy, and oxygen, respectively. For the amount of energy imported or purchased locally, for i During the period t The m Net shared energy input, with positive values ​​indicating shared input and negative values ​​indicating shared output. and These are energy release and energy charging, respectively. To meet load demand, For adjustable load, This refers to energy conversion or equipment consumption.

3. The multi-energy agent two-layer energy sharing optimization method according to claim 1, characterized in that, S2 describes the construction of an inner-layer energy sharing cluster, which includes integrating adjacent high-altitude pastoral integrated energy systems (HAR), high-altitude village integrated energy systems (HAV), and composite energy storage provider (CESP) into an inner-layer energy sharing cluster; the specific steps are as follows: S2.

1. CESP structural modeling for composite energy storage providers; The CESP (Comprehensive Energy Storage Provider) comprises electrical energy storage (ES), thermal energy storage (TS), and oxygen energy storage (OS). ES is used to balance the fluctuations in electricity supply and demand within the inner cluster; TS is used to balance the fluctuations in thermal energy supply and demand; and OS is used to balance the fluctuations in oxygen supply and demand. Based on the energy demand, storage status, and equipment operating boundaries of the High Altitude Pastoral Integrated Energy System (HAR) and the High Altitude Village Integrated Energy System (HAV) at different times, the CESP determines the charging and discharging plans for electricity, thermal energy, and oxygen, and provides shared energy storage services to both systems. S2.

2. Composite energy storage state update model; The status update relationships of various energy storage types in the CESP (Comprehensive Energy Storage Provider) are represented as follows: In the formula, For the first k Energy storage-like periods t The energy storage status, and These are charging and releasing energy, respectively. and These are charging efficiency and discharging efficiency, respectively; various energy storage states and charging / discharging energies meet their corresponding upper capacity limit, lower capacity limit, and charging / discharging boundary. S2.

3. Inner layer information interaction and energy sharing; High-altitude pastoral integrated energy system (HAR), high-altitude village integrated energy system (HAV), and composite energy storage provider (CESP) register, report information, confirm permissions, and share and schedule data through a cloud platform. The cloud platform receives energy demand, equipment status, energy storage status, and sharing intentions uploaded by each entity, calculates the energy sharing plan within the cluster, and returns the scheduling results to each entity. Each entity retains complete operating parameters and cost parameters locally and only uploads information such as sharing volume, boundary variables, or information required for iteration. In the process of inner-layer energy sharing, the composite energy storage provider CESP serves as a common support node for the interaction of electricity, heat and oxygen between the high-altitude pastoral integrated energy system HAR and the high-altitude village integrated energy system HAV. When the output of renewable energy is high, it stores surplus electricity, heat and oxygen, and releases the stored energy when the load is at its peak or when external energy purchases are limited.

4. The multi-energy agent two-layer energy sharing optimization method according to claim 1, characterized in that, S3 describes the construction of an outer-layer multi-energy agent collaborative framework, which includes building an outer-layer multi-energy agent collaborative framework consisting of a High-Altitude Pastoral Integrated Energy System (HAR), a High-Altitude Village Integrated Energy System (HAV), a CESP (Centralized Energy Storage) inner-layer energy sharing cluster, an IESS (Integrated Energy Supply Station), a PV-HSRIS (Photovoltaic-Hydrogen Storage-Hydrogen Refueling Integrated Station), and an EA (Energy Aggregator). External energy transactions are then completed through the power grid and the hydrogen energy market. The specific steps are as follows: S3.

1. Modeling of Integrated Energy Supply Station (IESS); The Integrated Energy Supply Station (IESS) comprises a renewable energy power plant, an oxygen generator (OM), and an electricity-oxygen sharing channel. Under conditions of limited grid transmission capacity, the IESS inputs a portion of surplus renewable energy into the OM, converting the electricity into oxygen, and provides electricity or oxygen sharing services to other entities within the outer multi-energy agent collaborative framework. The electricity-oxygen conversion relationship of the OM is expressed as follows: In the formula, O Indicates oxygen production capacity. P Indicates the power consumption of the oxygen concentrator, subscript I and t These represent IESS and the scheduling period, respectively. The superscript or subscript OM indicates the oxygen generator. η Indicates the oxygen production efficiency coefficient; S3.

2. PV-HSRIS modeling of integrated photovoltaic-hydrogen storage-hydrogen refueling stations; The photovoltaic-hydrogen storage-hydrogen refueling integrated station PV-HSRIS includes photovoltaic power generation equipment (PV), electric hydrogen production equipment (P2H), hydrogen energy storage (HS), oxygen energy storage (OS), hydrogen refueling station (HRS), and hydrogen refueling load for heavy-duty hydrogen trucks (HHT). PV-HSRIS uses photovoltaic power to provide electricity for the electric hydrogen production equipment and the load within the station. Hydrogen is produced through the electric hydrogen production equipment (P2H), and the hydrogen energy storage (HS) and hydrogen refueling station (HRS) meet the hydrogen refueling needs of heavy-duty hydrogen trucks (HHT). The oxygen produced during the hydrogen production process enters the oxygen energy storage (OS) and participates in the outer layer oxygen sharing and scheduling. The relationship between hydrogen production via electro-hydrogen and oxygen production in the hydrogen production process is expressed as follows: In the formula, H Indicates hydrogen production capacity. P This indicates the power consumption of the electro-hydrogen production equipment. O Indicates the oxygen production related to the hydrogen production process, subscript H and t These represent PV-HSRIS and the scheduling period, respectively. The superscript P2H indicates the electro-hydrogen production process. η Indicates the efficiency of hydrogen production via electro-hydrogen conversion. κ Indicates the oxygen production conversion factor; S3.

3. Energy Aggregator (EA) Coordination Mechanism; Energy aggregator EA is used to coordinate the energy sharing relationships between the High Altitude Pastoral Integrated Energy System (HAR), the High Altitude Village Integrated Energy System (HAV), the CESP inner layer energy sharing cluster, the Integrated Energy Supply Station (IESS), and the PV-HSRIS photovoltaic-hydrogen storage-hydrogen refueling integrated station. Energy aggregator EA gathers information on the willingness and boundaries of various energy agents to participate in sharing, coordinates electricity, oxygen, and hydrogen energy trading plans, and issues collaborative dispatch schemes to various energy agents.

5. The multi-energy agent two-layer energy sharing optimization method according to claim 1, characterized in that, The establishment of the two-layer energy sharing optimization model described in S4 includes establishing an inner-layer energy sharing optimization model and an outer-layer multi-energy agent collaborative optimization model; the specific steps are as follows: S4.

1. Inner layer energy sharing optimization model; The inner-layer energy sharing optimization model aims to minimize the collaborative operating costs of the High-Altitude Pastoral Integrated Energy System (HAR), the High-Altitude Village Integrated Energy System (HAV), and the Composite Energy Storage Provider (CESP), coordinating the sharing relationships of electricity, heat, and oxygen among HAR, HAV, and CESP; its objective function can be expressed as: In the formula, C Indicates operating costs, superscript in The inner layer energy sharing cluster is represented by HAR, HAV, and CESP, which respectively represent the high-altitude pastoral integrated energy system, the high-altitude village integrated energy system, and the composite energy storage provider. The inner layer constraints include the balance constraints of electricity, heat, and oxygen energy, the equipment operation boundary constraints, the energy storage charging and discharging constraints, the adjustable load constraints, and the energy sharing consistency constraints among HAR, HAV, and CESP. S4.

2. Outer Layer Multi-Energy Agent Collaborative Optimization Model; The outer multi-energy agent collaborative optimization model aims to minimize the collaborative operating costs of the high-altitude pastoral integrated energy system (HAR), the high-altitude village integrated energy system (HAV), the inner energy sharing cluster of the composite energy storage provider (CESP), the integrated energy supply station (IESS), the photovoltaic-hydrogen storage-hydrogen refueling integrated station (PV-HSRIS), and the energy aggregator (EA), and coordinates the interaction relationships of electricity, oxygen, and hydrogen among the outer entities. Its objective function can be expressed as: In the formula, C Indicates operating costs, superscript out The outer multi-energy agent collaborative framework represents the outer layer; CL represents the inner HAR / HAV-CESP cluster; IESS represents the integrated energy supply station; PV-HSRIS represents the photovoltaic-hydrogen storage-hydrogen refueling integrated station; EA represents the energy aggregator; grid represents grid trading; H2 represents hydrogen energy market trading. S4.

3. Two-layer collaborative optimization model and shared consistency constraints; Integrating the inner and outer layer optimization relationships, the two-layer energy sharing optimization model uses the inner layer optimization results as the equivalent operating costs of the outer layer high-altitude pastoral integrated energy system (HAR), high-altitude village integrated energy system (HAV), and composite energy storage provider (CESP) cluster, and also considers the outer layer multi-energy agent collaborative operating costs. Minimize as the objective; where, The two-tier energy sharing optimization model is expressed as: The constraints of the two-layer energy sharing optimization model include the inner layer of electricity, heat, and oxygen energy balance constraints, the outer layer of electricity, oxygen, and hydrogen energy interaction constraints, equipment operation constraints, grid trading constraints, hydrogen energy market trading constraints, and energy sharing consistency constraints. For entities with a two-way sharing relationship m and main body n The consistency of energy and oxygen sharing can be expressed as: In the formula, P and O These represent the shared amount of electricity and the shared amount of oxygen, respectively; subscript m , n and t These represent the two sharing entities and the scheduling period, respectively; superscript sh Represent shared variables; for inner-layer heat energy sharing relationships, establish corresponding heat energy sharing consistency constraints.

6. The multi-energy agent two-layer energy sharing optimization method according to claim 1, characterized in that, S5 describes an improved ADMM distributed solution and output shared scheduling scheme, which involves decomposing the centralized optimization problem into multiple local optimization subproblems of energy agents. Each agent completes the optimization calculation locally, and coordination is achieved through shared variables, Lagrange multipliers, and penalty factor updates. The specific steps are as follows: S5.1 Solving the inner and outer layer local optimization subproblems; In the inner-layer optimization, the High Altitude Pastoral Integrated Energy System (HAR), the High Altitude Village Integrated Energy System (HAV), and the Composite Energy Storage Provider (CESP) solve the local main optimization sub-problems respectively, and upload shared variables or boundary information to the cloud platform; the cloud platform updates the inner-layer consistency variables and Lagrange multipliers, and determines whether convergence is achieved based on the original residuals and dual residuals; Once the inner-layer optimization reaches convergence, the cloud platform treats the High Altitude Pastoral Integrated Energy System (HAR), the High Altitude Village Integrated Energy System (HAV), and the CESP cluster as equivalent outer-layer energy agents, and publishes the outer-layer sharing intention and boundary information to the Energy Aggregator (EA). The Energy Aggregator (EA) coordinates the High Altitude Pastoral Integrated Energy System (HAR), the High Altitude Village Integrated Energy System (HAV), the CESP cluster, the Integrated Energy Supply Station (IESS), and the Photovoltaic-Hydrogen Storage-Hydrogen Refueling Station (PV-HSRIS) to solve the outer-layer local optimization subproblems respectively, and updates the outer-layer shared variables, Lagrange multipliers, and penalty factors. If the outer-layer original residual and dual residual satisfy the convergence threshold, the final energy sharing scheduling scheme is output; otherwise, the updated information is fed back to the relevant energy agents and the iteration continues. During the distributed solution process, each energy agent exchanges and shares information such as quantities, boundary variables, multiplier updates, or residual information, but does not disclose the complete operating model and cost parameters. S5.

2. Dynamic penalty factor update; A dynamic penalty factor update strategy is adopted, which adjusts the penalty factor based on the relative magnitudes of the original residual and the dual residual; the update relationship is expressed as: In the formula, For the first r The original residual from +1 iteration, For the first r The dual residual of +1 iteration, and These are the penalty factors before and after the update, respectively. To increase the penalty factor coefficient, To reduce the coefficient of the penalty factor, This is the residual balance coefficient. When both the original residual and the dual residual satisfy the preset convergence threshold, the iteration stops; otherwise, local optimization, shared variable update, multiplier update, and penalty factor adjustment continue. S5.

3. Shared scheduling scheme output; Once the improved alternating direction multiplier method meets the convergence condition, the operation plan and energy sharing scheduling scheme of each energy agent are output.

7. A system for implementing the multi-energy agent two-layer energy sharing optimization method according to any one of claims 1-6, characterized in that, The system includes: a comprehensive energy model construction module, an inner-layer energy sharing cluster construction module, an outer-layer collaborative framework construction module, a two-layer optimization modeling module, a distributed solution module, and a scheduling output module; The integrated energy model building module is used to build integrated energy models of electricity, heat, and oxygen for the high-altitude pastoral integrated energy system HAR and the high-altitude village integrated energy system HAV, and to set the variables corresponding to unconfigured equipment to 0 according to the differences in equipment configuration between HAR and HAV. The inner layer energy sharing cluster construction module is used to construct the high-altitude pastoral integrated energy system HAR, the high-altitude village integrated energy system HAV, and the composite energy storage provider CESP into an inner layer energy sharing cluster of the high-altitude pastoral integrated energy system HAR, the high-altitude village integrated energy system HAV, and the composite energy storage provider CESP, and to establish the corresponding energy sharing relationships of the public power bus, public heating pipeline, and public oxygen transmission pipeline. The outer layer collaborative framework construction module is used to build the high-altitude pastoral integrated energy system HAR, the high-altitude village integrated energy system HAV, the CESP composite energy storage provider inner layer energy sharing cluster, the integrated energy supply station IESS, the photovoltaic-hydrogen storage-hydrogen refueling integrated station PV-HSRIS, and the energy aggregator EA into an outer layer multi-energy agent collaborative framework, and set the power grid and hydrogen energy market as external trading objects. The two-layer optimization modeling module is used to establish an inner-layer energy sharing optimization model and an outer-layer multi-energy agent collaborative optimization model, and to generate energy balance constraints, equipment operation boundary constraints, controllable load adjustment constraints, energy storage charging and discharging constraints, and energy sharing consistency constraints. The distributed solution module is used to decompose the two-layer energy sharing optimization model into local optimization subproblems of the high-altitude pastoral integrated energy system (HAR), high-altitude village integrated energy system (HAV), composite energy storage provider (CESP), energy supply station (IESS), photovoltaic-hydrogen storage-hydrogen refueling integrated station (PV-HSRIS), and shared consistency variable and Lagrange multiplier update subproblems executed by the cloud platform or energy aggregator (EA). The distributed solver module includes an inner solver unit, an outer solver unit, and a penalty factor update unit. The inner solver unit is used to coordinate the local optimization of HAR, HAV, and CESP. The outer solver unit is used to coordinate the local optimization of the HAR / HAV-CESP inner energy sharing cluster, IESS, and PV-HSRIS. The penalty factor update unit is used to dynamically adjust the penalty factor according to the relative magnitude of the original residual and the dual residual. The scheduling output module is used to output the electricity, heat, and oxygen operation plans of the high-altitude pastoral integrated energy system HAR, the electricity, heat, and oxygen operation plans of the high-altitude village integrated energy system HAV, the electricity storage, heat storage, and oxygen storage charging and discharging plans of the composite energy storage provider CESP, the electricity sharing and oxygen sharing plans of the energy supply station IESS, and the photovoltaic consumption, electric hydrogen production, hydrogen storage, oxygen storage, and hydrogen refueling service plans of the photovoltaic-hydrogen storage-hydrogen refueling integrated station PV-HSRIS, after the original residual and dual residual meet the preset convergence threshold.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 6.

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