Resource scheduling optimization method and device, equipment and storage medium

Abstract

The invention relates to the technical field of energy management of an integrated energy system, and discloses a resource scheduling optimization method, device and equipment and a storage medium, and the method comprises the steps: obtaining a node energy price and the output power of a renewable energy unit in each microgrid; according to the node energy price and the output power, performing preliminary resource scheduling on resources in each micro-grid in the household energy management system to obtain an initial resource scheduling result and operation data of each micro-grid; and transmitting the initial resource scheduling result and the operation data to a community energy management system, and carrying out scheduling optimization to obtain a target resource scheduling strategy. A resource scheduling strategy is optimized by developing a two-stage management framework, and compromise between operation cost and operation risk caused by uncertainty related to renewable energy sources is considered, so that the operation cost of a multi-microgrid system is reduced to the maximum extent, and the flexibility of the whole power grid is improved.

Description

Technical Field

[0001] This application relates to the field of energy management technology for integrated energy systems, and in particular to a resource scheduling optimization method, apparatus, equipment, and storage medium. Background Technology

[0002] Currently, distributed hydrogen mixing can lead to uneven gas composition across the network. Traditional node energy pricing schemes are designed for uniform gas compositions and cannot reflect the impact of non-uniform gas compositions on carbon emission reduction. There is also no new framework to address new requirements that enables local system operators to effectively manage available resources to cope with the limitations of flexibility ramps. Summary of the Invention

[0003] The main objective of this application is to provide a resource scheduling optimization method, apparatus, equipment, and storage medium, aiming to address the issue of uneven gas composition across the network caused by distributed hydrogen mixing. Traditional node energy pricing schemes are designed for uniform gas compositions and cannot reflect the impact of non-uniform gas compositions on carbon emission reduction. Furthermore, there is no new framework to address the technical challenges of managing available resources effectively to meet new requirements and overcome the limitations of flexibility ramps.

[0004] To achieve the above objectives, this application proposes a resource scheduling optimization method, which includes: Obtain node energy prices and the output power of renewable energy units in each microgrid; Based on the node energy price and the output power, preliminary resource scheduling is performed on the resources in each microgrid in the home energy management system to obtain the initial resource scheduling results and operating data of each microgrid. The initial resource scheduling results and the operational data are transmitted to the community energy management system for scheduling optimization to obtain the target resource scheduling strategy.

[0005] Optionally, the step of transmitting the initial resource scheduling results and the operational data to the community energy management system for scheduling optimization to obtain the target resource scheduling strategy includes: The initial resource scheduling results and the operating data are transmitted to the community energy management system as input data through a preset communication link management strategy. The total power shortage and remaining power of the flexible power unit and the home energy management system are calculated based on the operating data. The flexible power unit is the operating data of the dispatchable unit that receives the operating costs between the natural gas supply point and the natural gas mixing point from each microgrid. Based on a preset target minimum operating cost function, the target minimum operating cost is calculated by taking the flexible power unit, the total power shortage, the remaining power, and the storage unit of the community energy management system as target constraints. The initial resource scheduling result is optimized based on the target minimum operating cost to generate a target resource scheduling strategy.

[0006] Optionally, before the step of transmitting the initial resource scheduling result and the operating data as input data to the community energy management system through a preset communication link management strategy, the method further includes: The information exchange between the microgrids and the home energy management system is restricted to the cumulative operating constraints of the flexible power unit, thereby generating privacy constraints. Based on the aforementioned privacy constraints, the communication link management policy is configured to send the accumulated deviation of the initial planned operating point of the flexible generator set to the community energy management system without exchanging their respective predetermined operating points.

[0007] Optionally, the step of performing preliminary resource scheduling on the resources in each microgrid based on the node energy price and the output power to obtain the initial resource scheduling results and operating data of each microgrid includes: The node energy price and the output power are loaded into a preset primary resource scheduling optimization model to calculate the initial minimum operating cost that satisfies the primary constraints in each microgrid. Preliminary resource scheduling is performed based on the initial minimized operating cost to obtain the initial resource scheduling results and operating data of each microgrid.

[0008] Optionally, before the step of loading the node energy price and the output power into a preset primary resource scheduling optimization model to calculate the initial minimum operating cost that satisfies the primary constraints in each microgrid, the method further includes: Determine the primary minimum operating cost function for each microgrid, taking into account the operating costs of the home energy management system. The primary constraints of the primary minimum operating cost function are established based on supply and demand balance constraints, power and gas grid exchange constraints, power generation constraints, energy storage constraints, and adjustable load constraints. The primary minimum operating cost function and the primary constraints are combined to generate the primary resource scheduling optimization model.

[0009] Optionally, the step of obtaining the node energy price and the output power of the renewable energy units in each microgrid includes: Obtain forecasted wind speed and energy demand, and derive gas safety constraints based on a hydrogen-electricity-natural gas integrated system; Based on the wind speed, energy demand, and gas safety constraints, calculate the node energy price that minimizes the total operating cost of the hydrogen-electricity-natural gas integrated system during its operating cycle. A test scenario for renewable energy devices is generated considering the correlation of the renewable resource units, and the output power of the renewable resource units in each microgrid is specified according to the test scenario.

[0010] Optionally, the step of generating test scenarios for renewable energy devices by considering the correlation of the renewable resource units, and specifying the output power of each renewable resource unit in the microgrid according to the test scenarios, includes: The rank correlation coefficient is used to measure the degree of dependence between the output power of different renewable energy units, and the correlation between the output power of different renewable energy units is simulated based on the degree of dependence and the Gaussian joint multivariate function. Based on the aforementioned correlation, a Gaussian joint multivariable function is used to generate multiple initial scenarios in a multidimensional space, wherein each initial scenario represents a power combination of a renewable energy output unit; Clustering algorithms are used to cluster the initial scene into different categories, and the resulting set of scenes with similar characteristics is used as the test scene for each of the renewable energy devices. The output power of the renewable resource units in each microgrid is specified according to the test scenario.

[0011] Furthermore, to achieve the above objectives, this application also proposes a resource scheduling optimization device, which includes: The resource acquisition module is used to acquire node energy prices and the output power of renewable energy units in each microgrid. The resource scheduling module is used to perform preliminary resource scheduling on the resources in each microgrid based on the energy price of the node and the output power, and to obtain the initial resource scheduling results and operating data of each microgrid. The strategy adjustment module is used to transmit the initial resource scheduling results and the operating data to the community energy management system for scheduling optimization to obtain the target resource scheduling strategy.

[0012] In addition, to achieve the above objectives, this application also proposes a resource scheduling optimization device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the resource scheduling optimization method as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the resource scheduling optimization method described above.

[0014] This application discloses a method for obtaining nodal energy prices and the output power of renewable energy units in each microgrid; performing preliminary resource scheduling in a home energy management system based on nodal energy prices and output power to obtain initial resource scheduling results and operational data for each microgrid; and transmitting the initial resource scheduling results and operational data to a community energy management system for scheduling optimization to obtain a target resource scheduling strategy. By developing a two-stage management framework to optimize the resource scheduling strategy, this method considers the trade-off between operating costs and operational risks caused by uncertainties related to renewable energy, aiming to minimize the operating costs of multi-microgrid systems and improve the flexibility of the entire power grid. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the first embodiment of the resource scheduling optimization method of this application; Figure 2 This is a multi-microgrid management framework diagram for the resource scheduling optimization method of this application; Figure 3 This is a flowchart illustrating the second embodiment of the resource scheduling optimization method of this application; Figure 4 This is a diagram showing the two-level management structure and communication links in the multi-microgrid system model of the resource scheduling optimization method in this application; Figure 5 This is a flowchart illustrating the third embodiment of the resource scheduling optimization method of this application; Figure 6 This is a flowchart illustrating the fourth embodiment of the resource scheduling optimization method of this application; Figure 7 This is a schematic diagram of the module structure of the resource scheduling optimization device according to an embodiment of this application; Figure 8This is a schematic diagram of the hardware operating environment involved in the resource scheduling optimization method in this application embodiment.

[0018] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0020] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0021] The main solution of this application embodiment is: to obtain the node energy price and the output power of the renewable energy units in each microgrid; to perform preliminary resource scheduling in the home energy management system based on the node energy price and the output power to obtain the initial resource scheduling results and operating data of each microgrid; and to transmit the initial resource scheduling results and the operating data to the community energy management system for scheduling optimization to obtain the target resource scheduling strategy.

[0022] While most existing research considers the cooperation between power and gas networks, the impact of their interaction has not been studied from the perspective of flexibility ramps. With the distributed injection of hydrogen, the gas composition at different locations becomes inconsistent. Heterogeneous gas composition alters the physical properties of the gas mixture (such as specific gravity), which are constant in homogeneous gas systems. This significantly affects the energy flow state of the Hydrogen Integrated Electricity and Gas System (H-IEGS), increasing the complexity of the overall optimization problem. Secondly, with the emergence of various forms of carbon emission-related costs (such as carbon emission budgets / taxes / penalties), the value of hydrogen blending in reducing external carbon emission costs should also be quantified in energy market settlements. However, the introduction of Renewable Energy Sources (RESs) into independently operating Microgrid Multigrid (MMG) systems presents new challenges to power system management. The uncertainties associated with RESs and the strong ramp of system flexibility constraints imposed on the network pose new challenges to the stable operation of the power system.

[0023] Therefore, this application provides a two-stage management framework for an electric-gas-hydrogen hybrid system that considers the interaction between hydrogen co-firing and decarbonization and the gas pipeline network. In this framework, resource scheduling of the microgrid is handled by its control unit in the first stage, while the second-stage program is primarily responsible for coordination between microgrids, taking into account flexibility constraints. Furthermore, the interaction with the gas network as a potential flexible resource is optimized in the second-stage program. Finally, considering flexibility constraints, a local resource scheduling scheme for a flexibility-based three-microgrid test system is implemented.

[0024] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, or an electronic device capable of performing the above functions. The following electric-gas-hydrogen hybrid system will be used as an example to illustrate this embodiment and the subsequent embodiments.

[0025] Based on this, embodiments of this application provide a resource scheduling optimization method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the resource scheduling optimization method of this application.

[0026] In this embodiment, the resource scheduling optimization method includes: Step S10: Obtain the node energy price and the output power of the renewable energy units in each microgrid.

[0027] It should be noted that nodal energy prices refer to the electricity prices formed in the electricity spot market mechanism, reflecting a specific node (geographical location). They typically include energy, congestion, and grid loss components, reflecting real-time changes in electricity supply and demand, as well as the impact of grid constraints on electricity flow. A renewable energy unit refers to a device or system that uses continuously regenerated, regularly replenished, or reused energy from nature (such as solar, wind, and hydropower) to generate electricity or heat. The output power of a renewable energy unit refers to the amount of electricity generated by renewable energy power generation equipment, such as wind or solar power, within a specific time period. A microgrid is a small-scale power generation, transmission, and distribution system that organically integrates distributed generation, loads, energy storage devices, converters, and monitoring and protection devices.

[0028] Understandably, nodal energy prices are typically determined by electricity market operators through a market clearing process. Price calculations can take into account factors such as generation costs, transmission line usage, grid congestion, and grid losses. The output power of renewable energy units can be obtained through predictive models, which estimate future output power based on historical data and weather forecasts.

[0029] Step S20: Based on the node energy price and the output power, perform preliminary resource scheduling in the home energy management system to obtain the initial resource scheduling results and operating data of each microgrid.

[0030] It's important to understand that a home energy management system (EMS) is a system that integrates energy monitoring, control, optimization, and scheduling functions. It can monitor energy usage within a home in real time, including electricity, gas, and water, and automatically schedule resources according to preset goals and strategies. The initial resource scheduling result is a preliminary resource allocation plan formulated by the EMS based on real-time data. Operational data refers to the data generated by the EMS during actual operation, including energy usage data such as electricity consumption, related to energy usage, equipment status, and environmental monitoring.

[0031] Step S30: The initial resource scheduling results and the operating data are transmitted to the community energy management system for scheduling optimization to obtain the target resource scheduling strategy.

[0032] It should be noted that a Community Energy Management System (CEMS) is a broader, higher-level energy management system that integrates data from multiple household energy management systems, aiming to achieve efficient energy utilization and optimization across the entire community. The target resource scheduling strategy is the optimal or near-optimal resource scheduling scheme derived after system optimization, which can achieve specific energy management objectives, such as maximizing renewable energy utilization, minimizing energy costs, and reducing carbon emissions.

[0033] For ease of understanding, the following examples are provided, but they do not limit this application. In one example, refer to... Figure 2 , Figure 2 This diagram illustrates the multi-microgrid management framework for the resource scheduling optimization method proposed in this application. Within this framework, the two-tiered management structure of the multi-microgrid system includes a Home Energy Management System (EMS) agent and a Community Energy Management System (CEMS) agent. At the EMS agent level, input data includes electricity prices exchanged with the main grid (e.g., GBP, GSP, etc.) and operational data of local resources (e.g., distributed generation (DG), renewable energy sources (RES), energy storage systems (ESS, etc.). The EMS agent is responsible for performing local resource scheduling optimization and sending scheduling data to the CEMS. At the CEMS agent level, input data includes the main grid's electricity prices and flexibility ramp limits, operational data of the microgrid central controller (MGC) resources controlled by the CEMS, and preliminary scheduling data for each microgrid. The CEMS agent integrates this information, considering the flexibility ramp provided by the main grid, and performs cost-effective operational optimization for the entire MMG system.

[0034] In this embodiment, the energy prices at nodes and the output power of renewable energy units in each microgrid are obtained. Based on these prices and output power, preliminary resource scheduling is performed in the home energy management system to obtain initial resource scheduling results and operational data for each microgrid. These initial resource scheduling results and operational data are then transmitted to the community energy management system for scheduling optimization to obtain the target resource scheduling strategy. A two-stage management framework is developed to optimize the resource scheduling strategy, considering the trade-off between operating costs and risks caused by uncertainties related to renewable energy, in order to minimize the operating costs of the multi-microgrid system and improve the flexibility of the entire power grid.

[0035] Reference Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the resource scheduling optimization method of this application. Based on the first embodiment described above, a second embodiment of the resource scheduling optimization method of this application is proposed.

[0036] In the second embodiment, step S30 includes: Step S301: The initial resource scheduling result and the operation data are transmitted to the community energy management system as input data through a preset communication link management strategy.

[0037] It should be noted that communication link management strategy is a strategy used to manage and optimize data transmission paths, ensuring that data can be transmitted efficiently and accurately between different network nodes or systems, including selecting the best communication path, managing bandwidth, and handling issues such as data loss or delay.

[0038] It should be understood that, according to the preset communication link management strategy, one or more communication links suitable for data transmission can be selected. These links may be optimized based on factors such as network topology, bandwidth, latency, and security. During transmission, technologies such as data encryption, compression, and segmentation need to be considered to ensure data security and transmission efficiency.

[0039] Step S302: Calculate the total power shortage and remaining power of the flexible power unit and the home energy management system based on the operating data. The flexible power unit is the operating data of the dispatchable unit that receives the operating costs between the natural gas supply point and the natural gas mixing point from each microgrid.

[0040] It should be noted that the flexible power unit is a dispatchable unit that receives preliminary dispatching data from each microgrid, which is related to the operating costs between natural gas supply points and natural gas mixing points. It can take into account the cost-effectiveness of natural gas supply during dispatching to optimize power production and distribution.

[0041] It should be understood that in the second-level microgrid optimization, considering flexibility, it is necessary to optimize the amount of electricity exchange between each grid, the amount of electricity trading between each grid and the main grid, and the interaction with the gas grid. Therefore, it is necessary to calculate the power shortage and surplus in each scenario by comparing power demand and power supply, and to determine the preliminary scheduling-related operational data of dispatchable units related to the operating costs between natural gas supply points and natural gas mixing points by extracting keywords.

[0042] It is understood that the total power shortage and surplus power can be calculated by comparing the power demand data and dispatch output data of the power system where the home energy management system and flexible power unit are located, comparing the power demand with the power supply for each time period, and accumulating the power shortage for each time period to obtain the total power shortage. For each time period with a power surplus, the difference between the power supply and the power demand is taken as the surplus power.

[0043] Step S303: Based on the preset target minimum operating cost function, the target minimum operating cost is calculated by taking the flexible power unit, the total power shortage, the remaining power, and the storage unit of the community energy management system as target constraints.

[0044] It's important to understand that the objective minimum operating cost function is used to calculate and minimize the operating cost of a system over a specific time period. It typically considers multiple factors, such as electricity prices and equipment maintenance costs, and includes various constraints, such as electricity supply and demand balance and charging / discharging limitations of energy storage devices. Storage units are devices used to store electrical energy; they can be charged during periods of low electricity demand and discharged during periods of high demand to balance electricity supply and demand and optimize operating costs.

[0045] For ease of understanding, the following example is provided, but it does not limit this application. In one example, an optimization model is implemented through a CEMS agent to achieve efficient coordination of a multi-microgrid (MMG) system. The objective function for minimizing operating costs in the optimization model is expressed as follows:

[0046] in, and To represent the amount of electricity purchased / sold from / to the main grid during upper-level optimization, This represents the discharge power of the storage unit of the i-th microgrid central controller (MGCs) at time t. This represents the load shedding situation of the k-th MG (Multigrid) at time t in the upper-level optimization. Let represent the change in power generation of the i-th flexible unit in the k-th MG at time t. This represents the unit power generation change cost of the k-th flexible generator unit at time t. It is the unit load shedding cost of the k-th flexible generator unit at time t. This represents the unit discharge cost of the storage unit of the i-th microgrid central controller (MGCs) at time t. and These represent the prices for purchasing and selling electricity from the main power grid, respectively. This represents the probability of scenario S occurring. Describes the set of scenarios S. Represents a collection of microgrids. This represents the set of storage units in the central controller of a microgrid. and These are metrics for the bus, scheduling interval, and system components, respectively. These are the gas source, generator, and PTG specifications, respectively. and These are the bus set and the dispatch interval, respectively; the above cost function is subject to:

[0047] in, This represents the power generation of gas-fired power plant i at time t. and Let represent the shortage and remaining power of the k-th MG at time t. Let represent the charging power of the i-th microgrid central controller (MGCs) storage unit at time t. The sum of the changes in power generation of the flexible generator set, load shedding, power exchange between each MG energy storage unit and the main grid, discharge changes of the MG energy storage units, and changes in the G2P unit equals the sum of the electricity stored in the P2G, the charging electricity of the MG energy storage units, and the difference between the remaining electricity and the power shortage published by the MG. Consider the power exchange between the entire multi-microgrid MMG system and the main grid, as well as the relevant constraints of possible load shedding in the multi-microgrid MMG. The possible changes in the operating point of the flexible power unit are as follows:

[0048]

[0049] in, and These represent the minimum and maximum values ​​of the electricity purchased from the main grid during the upper-level optimization. and These represent the minimum and maximum values ​​of the electricity purchased from the main grid during the upper-level optimization. This represents the load shedding and short-load value of scenario S at time t for the k-th MG in the upper-level optimization. and Let represent the decrease and increase in power generation of scenario S at time t for the k-th MG. and This represents the maximum and minimum values ​​of the change in power generation in scenario S at time t for the k-th MG. The above formula constructs the ramp limit for the flexible generator set in each time interval, and changes in the operating point of the flexible generator set will affect the scheduling and related operating costs in subsequent time intervals. The amount of natural gas stored in the natural gas network and their respective capacity limits are as follows:

[0050]

[0051] in, and Let represent the maximum and minimum discharge power of the storage unit of the i-th microgrid central controller (MGCs) at time t. and Let represent the maximum and minimum charging power of the storage unit of the i-th microgrid central controller (MGCs) at time t. Let represent the stored energy of the i microgrid central controllers (MGCs) at time t. and This represents the charging and discharging efficiency of the i-th energy storage unit in the k-th braking unit.

[0052]

[0053] in, and This represents the maximum and minimum capacity of the i-th storage unit in the k-th memory module.

[0054]

[0055] in This represents the energy stored in the natural gas grid at time t. and This represents the amount of charge and discharge stored in the gas network at time t.

[0056]

[0057] in, This represents the amount of charge that P2G cell i converts into gas at time t. Indicates the energy efficiency ratio of the power-to-gas conversion device. and This represents the minimum and maximum energy stored in the natural gas grid at time t.

[0058]

[0059]

[0060]

[0061] in, This represents the energy efficiency ratio of gas-fired power plant i. This represents the amount of electricity converted into gas by gas-fired power generation unit i at time t. and This represents the minimum and maximum values ​​of the charge stored in the gas network at time t. and This represents the minimum and maximum discharge amounts stored in the gas network at time t. This represents the flexibility limitations at time t.

[0062] Step S304: Optimize the initial resource scheduling result based on the target minimum operating cost to generate a target resource scheduling strategy.

[0063] It should be noted that the target resource scheduling strategy refers to the optimized resource scheduling scheme, which aims to achieve the minimum operating cost. It minimizes operating costs by taking into account all relevant constraints and adjusting the scheduling of resources (such as flexible power units, storage units, etc.).

[0064] It should be understood that when optimizing the initial resource scheduling result based on the target minimum operating cost to generate the target resource scheduling strategy, appropriate optimization algorithms (such as linear programming, nonlinear programming, heuristic algorithms, etc.) can be used to solve the optimization problem. By comparing the operating costs of different schemes, the algorithm will gradually converge to the optimal solution or a near-optimal solution.

[0065] Of course, in order to avoid privacy issues arising from transmitting detailed operational data of each flexible power unit in the multi-microgrid to the CEMS during information exchange between EMSs and CEMS by setting new constraints, the process also includes the following steps before step S301: Information exchange between the microgrids and the home energy management system is restricted to the cumulative operating constraints of the flexible power units, generating privacy constraints. Based on the privacy constraints, the communication link management strategy is set to send the accumulated deviation of the preliminary planned operating point of the flexible generator sets to the community energy management system when the home energy management system agent does not exchange their respective predetermined operating points.

[0066] It should be noted that cumulative operating constraints refer to limitations on the total operating time, total output power, or other relevant parameters of flexible power units within a certain period. These constraints aim to protect equipment from overuse and ensure the stability and security of the power grid. Privacy constraints refer to restrictions set in energy management systems to protect user privacy and data security, involving aspects such as data transmission, storage, processing, and sharing. Communication link management policies refer to the rules and methods used to manage communication between microgrids, home energy management systems, and other related systems.

[0067] For ease of understanding, the following examples are provided, but they do not limit this application. In one example, refer to... Figure 4 , Figure 4 This diagram illustrates the two-level management structure and communication links in the multi-microgrid system model of the resource scheduling optimization method proposed in this application. The diagram includes multiple microgrids (MG1 to MGn), each with its own Energy Management System (EMS) agent. The CEMS is responsible for coordinating and managing the energy resources of these microgrids to optimize energy use across the entire community. Microgrid communities (MGCs) exchange information with the CEMS and adjust their energy strategies through interaction with the gas grid. MGCs also manage their resources, including renewable energy and energy storage devices. Dashed arrows in the diagram represent information exchange, and solid arrows represent energy flow. Based on constraints, each home energy management system (EMS) agent sends the potential accumulated deviations in the initial planned operating points of the flexible generator units to the CEMS without exchanging their respective predetermined operating points. Therefore, in the second-level optimization, considering the operating constraints of each MG flexible power unit, the operating points of each MG flexible power unit can be rescheduled. This benefits the system by minimizing operating costs and increasing system flexibility. Specific constraints are as follows:

[0068]

[0069]

[0070] Where, constant This represents the optimal power scheduling of flexible power unit i calculated by the EMS agent of the home energy management system in MGk. , , and This represents the ramp-up, ramp-down, maximum capacity, and minimum capacity constraints of flexible power supply unit i.

[0071] In this embodiment, the initial resource scheduling results are optimized based on the target minimum operating cost to generate a target resource scheduling strategy. Under the new constraints, CEMS makes efforts in the second phase to modify its initial scheduling to maximize the economic benefits of the entire system.

[0072] Reference Figure 5 , Figure 5 This is a flowchart illustrating the third embodiment of the resource scheduling optimization method of this application. Based on the second embodiment described above, a third embodiment of the resource scheduling optimization method of this application is proposed.

[0073] In the third embodiment, step S20 includes: Step S201: Load the node energy price and the output power into a preset primary resource scheduling optimization model, and calculate the initial minimum operating cost that satisfies the primary constraints in each microgrid.

[0074] It should be noted that a primary resource scheduling optimization model is a mathematical model or algorithm used to optimize the scheduling of energy resources under certain constraints. A primary resource scheduling optimization model may include an objective function (such as minimizing operating costs, maximizing energy efficiency, etc.) and a series of constraints (such as supply and demand balance, equipment capacity limitations, energy quality requirements, etc.). Primary constraints are the basic conditions and limitations that must be met in the primary resource scheduling optimization model, and may include energy supply and demand balance, equipment operating limitations, energy quality requirements, etc. Minimizing initial operating costs refers to achieving the lowest total operating cost by optimizing the scheduling of energy resources, while satisfying energy demand and constraints in the first-level optimization.

[0075] Of course, in order to establish resource scheduling optimization modeling for each household energy management system agent with the goal of minimizing grid operating costs, it is determined to minimize operating costs and risks from the perspective of the k-th grid. In this manner, before step S201, the following steps are also included: Considering the operating cost of the home energy management system, determine the primary minimum operating cost function for each microgrid; establish primary constraints for the primary minimum operating cost function based on supply and demand balance constraints, power and gas grid exchange constraints, power generation constraints, energy storage constraints, and adjustable load constraints; combine the primary minimum operating cost function and the primary constraints to generate the primary resource scheduling optimization model.

[0076] It should be noted that the primary minimum operating cost function is a mathematical model used to represent the minimum operating cost of a microgrid under specific conditions.

[0077] For ease of understanding, the following example is provided, but it does not limit this application. In one example, at the lower level, the home energy management system (EMS) agent in each MG optimizes the resource scheduling of the MG to achieve the previous day's operation. To this end, the resource scheduling optimization model performed by each home energy management system (EMS) agent with the objective of minimizing the operating cost of MG k is as follows:

[0078]

[0079] in, Represents the production cost of kG schedulable unit i. This represents the output power of the k-th scheduling unit i at time t. This represents the discharge amount of the i-th energy storage unit in the k-th MG at time t. This represents the load reduction cost of the k-th MG. This represents the load shedding of the k-th MG at time t. and These represent the prices for buying and selling electricity from the main grid, respectively. and C1, C2, and C3 represent the electricity purchased or sold to the main grid by the k-th MG, respectively. C1, C2, and C3 represent the total production cost, total load reduction cost, and total electricity cost, respectively. Represents conditional risk value. Let represent the total cost; where α (i.e., the confidence factor) is a parameter representing the right-tail probability of the density function. β These are parameters that simulate the MG's perception of risk. The above formula is constrained by the Conditional Risk Value (CVaR) and considers the impact of CVaR on the MG's operational scheduling risk, as follows:

[0080] in This represents the auxiliary variable used to calculate CVaR. and Indicates the start and end times, and calculates the energy consumed by the adjustable load. This represents the confidence coefficient. Meanwhile, the supply-demand balance equation below ensures that the sum of injected power from local DGs, renewable energy RESs, and ESSs discharges, as well as the sum of electricity purchased from the main grid excluding load reductions, matches the sum of the total ESSs load, ESSs charging power, and electricity sold to the grid.

[0081]

[0082] in, This represents the power output of the i-th renewable energy unit in the k-th MG at time t. Let represent the i-th type of load in the k-th MG at time t. The relevant constraints for exchanging power with the main grid are as follows:

[0083] in This indicates the maximum and minimum amount of electricity MGk can purchase from the main grid. This represents the maximum and minimum amount of electricity that MG k can sell to the main grid. The constraints on the generation, rise, and fall limits of DGs are as follows:

[0084] in This represents the maximum and minimum power generation of generator set i in the k-th MG. This represents the on / off state of the k-th scheduling unit i at time t. This represents the set of all dispatchable generators.

[0085]

[0086] in , This represents the maximum boost and deboost amplitude of dispatchable unit i within dispatchable unit k. The formula for determining the number of hours unit t is in the ON or OFF state at time k (MG) is as follows:

[0087] in Indicates the minimum running time. This indicates the minimum downtime. This represents the on / off state of the k-th scheduling unit i at time t. This represents the startup status of the i-th unit in the k-th MG at time t. This indicates that unit i of the k-th MG shuts down at time t.

[0088] The constraints related to storage operations are as follows: 1. The operating limits of the storage unit in discharge and charge modes are as follows:

[0089] in , This represents the maximum and minimum possible displacement of storage unit i in the microgrid central controller MGCs. , This represents the maximum and minimum charge amount of storage unit i in the microgrid central controller MGCs.

[0090] 2. The energy stored in the storage unit during each time period is as follows:

[0091] in, Indicates the first i Energy storage unit in the first k The charging and discharging efficiency of each braking unit. Let the energy stored in the i-th energy storage unit of the k-th MG at time t satisfy: ,in, This represents the maximum and minimum capacity of the i-th storage unit in the k-th memory module.

[0092] 3. Finally, the adjustable load operation constraints are as follows:

[0093] in, This represents the maximum and minimum adjustable load per hour for the k-th dispatch station. This represents the adjustable load of the k-th dispatcher at time t. This represents the energy required by the adjustable load in the k-th braking unit. It is worth noting that the home energy management system (EMS) agent determines the initial scheduling of local resources through an optimization model. The scheduling and operation results required by this layer will be passed to the upper layer as input data.

[0094] Step S202: Based on the initial minimized operating cost, perform preliminary resource scheduling to obtain the initial resource scheduling results and operating data of each microgrid.

[0095] It should be understood that in the process of initial resource scheduling to minimize operating costs and obtain initial resource scheduling results and operational data for each microgrid, the objective of resource scheduling needs to be clearly defined as minimizing operating costs while ensuring energy supply and demand balance and stable operation of each microgrid. Based on data analysis results, appropriate scheduling methods, such as distributed robust optimization methods, should be selected. After scheduling is completed, a cost-benefit analysis of the scheduling results is also required to evaluate the effectiveness of the scheduling strategy.

[0096] In this embodiment, by loading the node energy price and the output power into a preset primary resource scheduling optimization model, the initial minimum operating cost satisfying the primary constraints in each microgrid is calculated. Based on the initial minimum operating cost, preliminary resource scheduling is performed to obtain the initial resource scheduling results and operational data for each microgrid. This helps each microgrid achieve effective cost control while ensuring supply-demand balance and stable operation, avoiding energy shortages or surpluses, improving system stability and reliability, and providing strong support and basis for subsequent decision-making.

[0097] Reference Figure 6 , Figure 6 This is a flowchart illustrating the fourth embodiment of the resource scheduling optimization method of this application. Based on the third embodiment described above, the fourth embodiment of the resource scheduling optimization method of this application is proposed.

[0098] In the fourth embodiment, step S10 includes: Step S101: Obtain the forecasted wind speed and energy demand, and obtain the gas safety constraints based on the integrated hydrogen-electricity-natural gas system.

[0099] It should be noted that energy demand refers to the total demand for energy resources in social, economic, and technological activities, including various forms of energy such as electricity, natural gas, and oil. A hydrogen-electricity-gas integrated system (or a "hydrogen-electricity-gas integrated energy system") is a comprehensive energy system that integrates multiple energy forms such as hydrogen, electricity, and natural gas. This system uses electricity generated from renewable energy sources (such as solar and wind power) to electrolyze water to produce hydrogen, uses hydrogen as an energy storage medium, and converts the hydrogen into electricity or heat energy through fuel cells or gas turbines when needed. Gas safety constraints refer to a series of constraints set in a hydrogen-electricity-gas integrated system to ensure the safe, stable, and efficient use of gases (including natural gas and hydrogen). These constraints include limitations on physical parameters such as gas pressure, flow rate, and temperature, as well as requirements regarding gas quality, purity, and safety.

[0100] Step S102: Calculate the node energy price that minimizes the total operating cost of the hydrogen-electricity-natural gas integrated system during its operating cycle based on the wind speed, energy demand, and gas safety constraints.

[0101] It should be understood that the nodal energy price for the next day is determined through the joint market settlement of the hydrogen hybrid electricity and natural gas integrated system (H-IEGS) based on the forecasted wind speed and energy demand. This nodal energy price can be determined based on the minimum cost function or by analyzing historical data.

[0102] For ease of understanding, the following example is provided, but it does not limit this application. In one example, the formula for calculating the total operating cost over the operating cycle is as follows, including gas production cost, power generation cost, and carbon emission cost.

[0103]

[0104] in, and These are metrics for the bus, scheduling interval, and system components, respectively. These are the gas source, generator, and PTG specifications, respectively. and These are respectively bus assembly and dispatch interval; and They are respectively Bus gas source group, traditional fossil power plant group, and PTGs group; among them, For scheduling interval Down On-line air supply Gas production; For scheduling interval Generator 1 at the bus Electricity generation at the location; For scheduling interval PTG h at time Methane production on the bus; and Gas source Compared to traditional fossil fuel power plants Carbon emission coefficients on the bus; they represent how much carbon dioxide will be generated by consuming a unit of gas supply or the electricity supplied by a gas source or generator. This is the carbon capture factor for the PTG methanation process; it is typically taken as zero, considering that the produced methane will eventually be completely burned in the gas network. When subsidies are applied to the electrolysis and methanation processes, corresponding values ​​can be used. gas source On the bus The price of gas produced at the location; among which, and For traditional thermal power plants Power generation cost coefficient on the bus; It is the price of the penalty for carbon dioxide emissions.

[0105] Because gas composition fluctuates significantly during operation, excessive hydrogen injection may jeopardize the normal operation and even safety of the hydrogen-integrated power and natural gas system (H-IEGS). Therefore, it should be controlled within certain limits. The Wobbe index (WI), flame speed factor (FS), relative density, and the mole fraction of hydrogen can serve as indicators for regulating gas safety. WI measures the thermal output of gas appliances by consuming the same volume of mixed gas under identical conditions. FS quantifies the speed at which the flame front passes through the fuel-air mixture. Therefore, FS constraints are crucial for ensuring stable combustion and preventing flashback. Thus, the following formula applies:

[0106]

[0107]

[0108] In the formula, , and The scheduling intervals are respectively Down The relative density, WI, and FS of the mixed gas in the bus; gaseous components Molecular weight; It is the molecular weight of air; , and The scheduling intervals are respectively hour The mole fractions of hydrogen, nitrogen, and oxygen in the bus; gaseous components Flame speed factor; Air-fuel ratio; and These are the upper bounds for the hydrogen mole fraction and the relative density, respectively. , , and These are the lower and upper bounds of WI and FS, respectively. The energy price at the target node can be determined using the aforementioned cost function and its corresponding constraints.

[0109] Step S103: Consider the correlation of the renewable resource units to generate a test scenario for renewable energy devices, and specify the output power of the renewable resource units in each microgrid according to the test scenario.

[0110] Understandably, when considering stochastic optimization to address uncertainties associated with the operation of renewable energy RESs, the variables related to the power generation of renewable energy RESs can be transformed into a common domain, namely the rank / uniform domain, through the cumulative distribution function (CDF) transformation. Finally, the correlation between these variables can be modeled using Gaussian Copula to generate the corresponding test environment and determine the output power based on the environment.

[0111] Of course, to address the uncertainty of renewable energy production and the correlations between them, and to help generate possible future scenarios, step S103 includes: The rank correlation coefficient is used to measure the dependence between the output power of different renewable energy units, and the correlation between the output power of different renewable energy units is simulated based on the dependence and a Gaussian joint multivariate function. Based on the correlation, multiple initial scenarios are generated in a multidimensional space using the Gaussian joint multivariate function, where each initial scenario represents a power combination of renewable energy output units. The initial scenarios are clustered into different categories using a clustering algorithm, and a set of scenarios with similar characteristics is used as the test scenarios for each of the renewable energy devices. The output power of renewable resource units in each microgrid is specified according to the test scenarios.

[0112] It should be understood that the rank correlation coefficient is a statistic that measures the degree of dependence between two variables. The Gaussian joint multivariate function, also known as the multivariate normal distribution function, can generate a combination of random variables that conform to these statistical properties based on known mean, variance, and covariance matrices. Clustering algorithms are statistical methods that divide a dataset into several groups (or clusters), such that data points within the same group have high similarity, while data points between different groups have low similarity.

[0113] For ease of understanding, the following example is provided, but it does not limit this application. In one example, a stochastic optimization algorithm is considered in the operation and scheduling of a multi-microgrid (MMG) system to address the uncertainty caused by the dependence of renewable energy resources (RESs) on meteorological parameters. However, meteorological characteristics are often correlated in the geographical area where the MMG system is located. Therefore, considering this correlation, we assume that the CEMS is responsible for generating scenarios for renewable energy installations, using a Gaussian connection model to simulate the correlation between the power generated by renewable energy RESs units in different MGs. Copula functions help establish multivariate functions to simulate the correlation between random variables. Finally, considering the individual correlations of renewable energy RESs units, the following three-step procedure is used to generate scenarios.

[0114] 1. Measurement of random dependence: rank correlation coefficient This is used to measure the degree of dependence between corresponding decision variables. For this, random variables... , With CDFs, , The rank-related considerations are as follows:

[0115] in It is a measurement and A function of linear correlation between them.

[0116] 2. Copula-based association modeling: The Gaussian coupling function C(u1, u2, ..., uN) is coupled to the multivariate joint distribution F(x1, x2, ..., xN) according to the CDF function of its variables, as follows:

[0117] 3. Scene Generation Based on k-means Clustering: In this step, a joint multivariate function is used to generate N scenes in the [0,1]N domain, and then the inverse CDF function is used to transform the variables to their respective principal domains. Finally, a k-means clustering program is developed to distribute the n generated scenes with probabilities. The system is divided into S clusters, serving as the final scenarios for the two-layer operation optimization of the multi-microgrid (MMG) system. Notably, the three-step procedure generates operating scenarios for both photovoltaic (PV) and wind turbine generators. Therefore, the correlation between wind speed and solar irradiance can be considered, and the hierarchical correlation can be modeled to form a Copula function. Finally, each MG calculates the output power of its respective RES unit in each scenario using its associated solar irradiance and wind speed. It is worth noting that the Copula model can also be developed using the cumulative output power of PV and wind turbine generators within the MG. Therefore, the generated scenarios will specify the output power of the renewable energy RES units in each MG and can be easily allocated to their respective resources during the operation optimization process.

[0118] In this embodiment, by acquiring forecasted wind speed and energy demand, and combining this with the gas safety constraints of the hydrogen-electricity-natural gas integrated system, the node energy price that minimizes the total operating cost during the operating cycle can be accurately calculated. Simultaneously, considering the correlation between renewable resource units, test scenarios for renewable energy devices are generated, and output power is assigned to the renewable resource units in each microgrid based on these scenarios. This improves the economy and operating efficiency of the energy system, while also ensuring the system's safety and reliability.

[0119] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the resource scheduling optimization method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0120] This application also provides a resource scheduling optimization device, please refer to... Figure 7 The resource scheduling optimization device includes: Resource acquisition module 10 is used to acquire node energy prices and the output power of renewable energy units in each microgrid; The resource scheduling module 20 is used to perform preliminary resource scheduling on the resources in each microgrid based on the node energy price and the output power, and to obtain the initial resource scheduling results and operating data of each microgrid. The strategy adjustment module 30 is used to transmit the initial resource scheduling results and the operating data to the community energy management system for scheduling optimization to obtain the target resource scheduling strategy.

[0121] The resource scheduling optimization apparatus provided in this application, employing the resource scheduling optimization method in the above embodiments, can solve the technical problems addressed by the resource scheduling optimization method provided in the above embodiments. Compared with the prior art, the beneficial effects of the resource scheduling optimization apparatus provided in this application are the same as those of the resource scheduling optimization method provided in the above embodiments, and other technical features in the resource scheduling optimization apparatus are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0122] This application provides a resource scheduling optimization device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the resource scheduling optimization method in the above embodiment 1.

[0123] The following is for reference. Figure 8 The diagram illustrates a structural schematic of a resource scheduling optimization device suitable for implementing embodiments of this application. The resource scheduling optimization device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 8 The resource scheduling optimization device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0124] like Figure 8As shown, the resource scheduling optimization device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the resource scheduling optimization device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the resource scheduling optimization device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows resource scheduling optimization devices with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0125] In particular, according to the embodiments disclosed in this application, the process described above with reference to the flowchart can be implemented as a computer software program.

[0126] The resource scheduling optimization device provided in this application, employing the resource scheduling optimization method in the above embodiments, can solve the technical problems described above. Compared with the prior art, the beneficial effects of the resource scheduling optimization device provided in this application are the same as those of the resource scheduling optimization method provided in the above embodiments, and other technical features in this resource scheduling optimization device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0127] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0128] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0129] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the resource scheduling optimization method in the above embodiments.

[0130] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0131] The aforementioned computer-readable storage medium may be included in the resource scheduling and optimization device; or it may exist independently and not assembled into the resource scheduling and optimization device. The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the resource scheduling and optimization device, cause the resource scheduling and optimization device to perform the resource scheduling and optimization method as described above.

[0132] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0133] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described resource scheduling optimization method, and is capable of solving the technical problems described above. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the resource scheduling optimization method provided in the above embodiments, and will not be repeated here.

[0134] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A resource scheduling optimization method, characterized in that, The resource scheduling optimization method includes: Obtain node energy prices and the output power of renewable energy units in each microgrid; Based on the node energy price and the output power, preliminary resource scheduling is performed on the resources in each microgrid in the home energy management system to obtain the initial resource scheduling results and operating data of each microgrid. The initial resource scheduling results and the operational data are transmitted to the community energy management system for scheduling optimization to obtain the target resource scheduling strategy.

2. The resource scheduling optimization method as described in claim 1, characterized in that, The step of transmitting the initial resource scheduling results and the operational data to the community energy management system for scheduling optimization to obtain the target resource scheduling strategy includes: The initial resource scheduling results and the operating data are transmitted to the community energy management system as input data through a preset communication link management strategy. The total power shortage and remaining power of the flexible power unit and the home energy management system are calculated based on the operating data. The flexible power unit is the operating data of the dispatchable unit that receives the operating costs between the natural gas supply point and the natural gas mixing point from each microgrid. Based on a preset target minimum operating cost function, the target minimum operating cost is calculated by taking the flexible power unit, the total power shortage, the remaining power, and the storage unit of the community energy management system as target constraints. The initial resource scheduling result is optimized based on the target minimum operating cost to generate a target resource scheduling strategy.

3. The resource scheduling optimization method as described in claim 2, characterized in that, Before the step of transmitting the initial resource scheduling results and the operating data as input data to the community energy management system through a preset communication link management strategy, the method further includes: The information exchange between the microgrids and the home energy management system is restricted to the cumulative operating constraints of the flexible power unit, thereby generating privacy constraints. Based on the aforementioned privacy constraints, the communication link management policy is configured to send the accumulated deviation of the initial planned operating point of the flexible generator set to the community energy management system without exchanging their respective predetermined operating points.

4. The resource scheduling optimization method as described in claim 1, characterized in that, The step of performing preliminary resource scheduling on resources in each microgrid based on the node energy price and the output power to obtain the initial resource scheduling results and operating data of each microgrid includes: The node energy price and the output power are loaded into a preset primary resource scheduling optimization model to calculate the initial minimum operating cost that satisfies the primary constraints in each microgrid. Preliminary resource scheduling is performed based on the initial minimized operating cost to obtain the initial resource scheduling results and operating data of each microgrid.

5. The resource scheduling optimization method as described in claim 4, characterized in that, Before the step of loading the node energy price and the output power into a preset primary resource scheduling optimization model to calculate the initial minimum operating cost that satisfies the primary constraints in each microgrid, the method further includes: Determine the primary minimum operating cost function for each microgrid, taking into account the operating costs of the home energy management system. The primary constraints of the primary minimum operating cost function are established based on supply and demand balance constraints, power and gas grid exchange constraints, power generation constraints, energy storage constraints, and adjustable load constraints. The primary minimum operating cost function and the primary constraints are combined to generate the primary resource scheduling optimization model.

6. The resource scheduling optimization method as described in claim 1, characterized in that, The steps for obtaining node energy prices and the output power of renewable energy units in each microgrid include: Obtain forecasted wind speed and energy demand, and derive gas safety constraints based on a hydrogen-electricity-natural gas integrated system; Based on the wind speed, energy demand, and gas safety constraints, calculate the node energy price that minimizes the total operating cost of the hydrogen-electricity-natural gas integrated system during its operating cycle. A test scenario for renewable energy devices is generated considering the correlation of the renewable resource units, and the output power of the renewable resource units in each microgrid is specified according to the test scenario.

7. The resource scheduling optimization method as described in claim 6, characterized in that, The step of generating a test scenario for renewable energy devices by considering the correlation of the renewable resource units, and specifying the output power of each renewable resource unit in the microgrid according to the test scenario, includes: The rank correlation coefficient is used to measure the degree of dependence between the output power of different renewable energy units, and the correlation between the output power of different renewable energy units is simulated based on the degree of dependence and the Gaussian joint multivariate function. Based on the aforementioned correlation, a Gaussian joint multivariable function is used to generate multiple initial scenarios in a multidimensional space, wherein each initial scenario represents a power combination of a renewable energy output unit; Clustering algorithms are used to cluster the initial scene into different categories, and the resulting set of scenes with similar characteristics is used as the test scene for each of the renewable energy devices. The output power of the renewable resource units in each microgrid is specified according to the test scenario.

8. A resource scheduling optimization device, characterized in that, The device includes: The resource acquisition module is used to acquire node energy prices and the output power of renewable energy units in each microgrid. The resource scheduling module is used to perform preliminary resource scheduling on the resources in each microgrid based on the energy price of the node and the output power, and to obtain the initial resource scheduling results and operating data of each microgrid. The strategy adjustment module is used to transmit the initial resource scheduling results and the operating data to the community energy management system for scheduling optimization to obtain the target resource scheduling strategy.

9. A resource scheduling optimization device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the resource scheduling optimization method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the resource scheduling optimization method as described in any one of claims 1 to 7.

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