Multi-region micro-grid resource unified electrical network interface modeling method

Abstract

The invention discloses a multi-region micro-grid resource unified electrical network interface modeling method which comprises the steps of providing powerful support for resource optimization configuration, cooperative control and stable operation of a multi-region micro-grid through a micro-grid technology, power system modeling, a data communication and interface technology and the like. According to the method, efficient integration and collaborative management of resources among a plurality of micro-grid regions are realized, and optimal configuration and efficient utilization of the multi-region micro-grid resources are realized through technical means such as data collection and analysis, model construction, algorithm design and implementation, interface standardization and the like. Therefore, the absorption capability of the power grid on renewable energy sources can be improved, and the flexibility and reliability of the power grid are enhanced; and meanwhile, the development of intelligent power grid and micro-grid technologies is promoted, and the construction of the energy internet is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-regional microgrid resources, and in particular to a unified electrical network interface modeling method for multi-regional microgrid resources. Background Art

[0002] The unified power network of multi-regional microgrid resources refers to connecting multiple microgrids distributed in different geographical regions through advanced power technology and information systems to form a unified power network. This network can realize the centralized management and optimized scheduling of distributed energy, energy storage equipment and loads in multiple microgrids, thereby improving the energy efficiency, reliability and economy of the entire system.

[0003] However, the unified management and coordinated control of multi-regional microgrid resources face many challenges, one of which is how to build an efficient and unified power network interface model. Through data analysis, interface design, model construction, simulation verification, and optimization adjustment, we can achieve effective integration and optimized scheduling of multi-regional microgrid resources and contribute to the development of smart grids. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a unified electrical network interface modeling method for multi-regional microgrid resources.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A unified electrical network interface modeling method for multi-regional microgrid resources includes providing strong support for optimal resource allocation, coordinated control and stable operation of multi-regional microgrids through microgrid technology, power system modeling, and data communication and interface technology, and realizing efficient integration and coordinated management of resources among multiple microgrid areas. The method realizes optimal allocation and efficient utilization of multi-regional microgrid resources through technical means such as data collection and analysis, model construction, algorithm design and implementation, and interface standardization.

[0007] S1 Data collection and analysis:

[0008] ① Data collection: List and collect all necessary data, including resource distribution, power generation capacity, energy storage status, load demand and relevant parameters of the main power grid in each region.

[0009] ② Structural analysis: Analyze the overall structure of multi-regional microgrids to clarify the basic conditions of each microgrid, such as its geographical location, energy composition, and load demand.

[0010] S2 model construction:

[0011] ① Physical model: Based on the actual situation of multi-regional microgrids, a physical model including renewable energy generation units, energy storage systems, loads and other components is constructed.

[0012] ② Mathematical model: Based on the physical model, a mathematical model describing the operating characteristics of the multi-regional microgrid is established. The model should be able to reflect the interaction between components and the law of energy flow.

[0013] ③ Interface model: formulate a unified electrical network interface standard, including communication protocols, data formats, interface parameters, etc. At the same time, establish an interface model to describe the process of information interaction and energy transmission between microgrids in different regions through the electrical network interface.

[0014] S3 algorithm design:

[0015] ① Intelligent dispatching algorithm: Based on real-time operation data and forecast information, formulate the optimal dispatching strategy to meet load demand and maximize energy utilization efficiency

[0016] ②Communication protocol design: Design efficient and secure communication protocols to ensure real-time transmission and security of data.

[0017] ③Combination of cloud computing and edge computing: Utilize the powerful computing power of cloud computing and the low latency characteristics of edge computing to improve the overall performance of the system.

[0018] S4 interface standardization:

[0019] ① Module interface definition: clarify the interface specifications between modules, including data exchange format and communication protocol.

[0020] ②Interface docking: Ensure that the interfaces between modules are correctly docked to achieve real-time transmission and sharing of data.

[0021] Preferably, the implementation steps are:

[0022] S1 demand analysis:

[0023] ① Determine the overall demand for multi-regional microgrids, including power demand, energy storage demand, and renewable energy access demand.

[0024] ② Analyze the characteristics of microgrids in each region, such as geographical location, climate conditions, and load characteristics.

[0025] S2 Resource Assessment:

[0026] ① Conduct a detailed assessment of renewable energy resources within each regional microgrid, including power generation forecast and stability analysis.

[0027] ② Evaluate the capacity, efficiency and charging and discharging strategies of energy storage devices, analyze load characteristics, and determine peak and valley periods and demand-side response potential.

[0028] S3 interface design:

[0029] ① Design standardized electrical network interface protocols, including communication protocols, data formats, and control strategies.

[0030] ② Determine the physical connection method of the interface, such as fiber optic communication, wireless communication, consider the security, stability and reliability of the interface, and formulate corresponding protection measures.

[0031] S4 model construction:

[0032] ① Based on demand analysis, resource assessment and interface design, a unified power network interface model for multi-regional microgrid resources is constructed.

[0033] ②The model should include renewable energy power generation model, energy storage device model, load model and interface control model.

[0034] ③ Use simulation software to verify and optimize the model to ensure its accuracy and reliability.

[0035] S5 system integration and testing:

[0036] ① Integrate the models of microgrids in each region to form a unified multi-regional microgrid resource management system.

[0037] ② Conduct comprehensive testing on the system, including functional testing, performance testing and security testing.

[0038] S6 parameter optimization and adjustment:

[0039] ①Optimize and adjust the model parameters according to the simulation results to improve the operating efficiency and stability of the system.

[0040] ② Repeat the simulation verification until the design requirements are met.

[0041] Preferably, the interface design:

[0042] Physical interface: defines the electrical connection method and parameters between each regional microgrid and the main grid, including voltage level, current limit, and protection configuration.

[0043] Communication interface: Establish unified communication protocols and data exchange standards to ensure real-time transmission and sharing of information between microgrids in different regions and between microgrids and the main grid.

[0044] Control interface: Design coordinated control strategies to achieve power balance and scheduling optimization between microgrids in different regions. The control interface should support both remote control and local control modes to meet the needs of different scenarios.

[0045] Preferably, the model is constructed as follows:

[0046] Modular design: The multi-regional microgrid system is divided into several modules, including power generation modules (wind power, photovoltaic, etc.), energy storage modules, load modules, and interface modules. Each module is independently modeled, and then interconnected and coordinated through the interface module.

[0047] Agent-based modeling: Using the autonomy, interaction, and adaptability of agents, a multi-agent system is constructed to simulate and control multi-regional microgrids. Agents can represent different resources or subsystems and achieve optimal resource allocation and collaborative control through communication and negotiation.

[0048] Stochastic modeling: Taking into account the uncertainty of renewable energy generation and load demand, stochastic modeling methods (such as Monte Carlo simulation, random process model, etc.) are used to simulate and evaluate the operating status and performance of the system.

[0049] Preferably, the system is integrated and tested:

[0050] Model integration: Integrate the module models according to the system architecture to form a complete unified power network interface model for multi-regional microgrid resources.

[0051] Set simulation parameters: including the volatility of renewable energy generation, the randomness of load demand, etc.

[0052] Run simulation: Use simulation software to simulate the integrated model.

[0053] Result analysis: Observe and record the simulation results, and analyze the performance of the model, including resource optimization configuration effect, collaborative control capability, power supply reliability, etc.

[0054] Preferably, the parameters are optimized and adjusted:

[0055] Parameter optimization: Apply the optimized model to the actual multi-region microgrid system for real-time monitoring and control, and continuously collect feedback data for iterative optimization of the model.

[0056] Repeat validation: Rerun the simulation to validate the performance of the optimized model.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The present invention uses data analysis, interface design, model construction, simulation verification, and optimization adjustment to achieve effective integration and optimized scheduling of multi-regional microgrid resources, contributing to the development of smart grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flowchart of a unified electrical network interface modeling method for multi-regional microgrid resources proposed by the present invention.

[0060] Figure 2 This is a block diagram of the interface design in the present invention.

[0061] Figure 3 Construct a block diagram for the model in the present invention.

[0062] Figure 4 This is a system integration and testing block diagram of the present invention.

[0063] Figure 5 This is a block diagram of parameter optimization and adjustment in the present invention. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0065] Reference Figure 1-5 A unified electrical network interface modeling method for multi-regional microgrid resources includes microgrid technology, power system modeling, data communication and interface technology, etc., which provides strong support for the optimal resource allocation, coordinated control and stable operation of multi-regional microgrids, and realizes efficient integration and coordinated management of resources among multiple microgrid areas. This method realizes the optimal allocation and efficient utilization of multi-regional microgrid resources through technical means such as data collection and analysis, model construction, algorithm design and implementation, and interface standardization.

[0066] S1 Data collection and analysis:

[0067] ① Data collection: List and collect all necessary data, including resource distribution, power generation capacity, energy storage status, load demand and relevant parameters of the main power grid in each region.

[0068] ② Structural analysis: Analyze the overall structure of multi-regional microgrids to clarify the basic conditions of each microgrid, such as its geographical location, energy composition, and load demand.

[0069] S2 model construction:

[0070] ① Physical model: Based on the actual situation of multi-regional microgrids, a physical model including renewable energy generation units, energy storage systems, loads and other components is constructed.

[0071] ② Mathematical model: Based on the physical model, a mathematical model describing the operating characteristics of the multi-regional microgrid is established. The model should be able to reflect the interaction between components and the law of energy flow.

[0072] ③ Interface model: formulate a unified electrical network interface standard, including communication protocols, data formats, interface parameters, etc. At the same time, establish an interface model to describe the process of information interaction and energy transmission between microgrids in different regions through the electrical network interface.

[0073] S3 algorithm design:

[0074] ① Intelligent dispatching algorithm: Based on real-time operation data and forecast information, formulate the optimal dispatching strategy to meet load demand and maximize energy utilization efficiency

[0075] ②Communication protocol design: Design efficient and secure communication protocols to ensure real-time transmission and security of data.

[0076] ③Combination of cloud computing and edge computing: Utilize the powerful computing power of cloud computing and the low latency characteristics of edge computing to improve the overall performance of the system.

[0077] S4 interface standardization:

[0078] ① Module interface definition: clarify the interface specifications between modules, including data exchange format and communication protocol.

[0079] ②Interface docking: Ensure that the interfaces between modules are correctly docked to achieve real-time transmission and sharing of data.

[0080] The implementation steps are:

[0081] S1 demand analysis:

[0082] ① Determine the overall demand for multi-regional microgrids, including power demand, energy storage demand, and renewable energy access demand.

[0083] ② Analyze the characteristics of microgrids in each region, such as geographical location, climate conditions, and load characteristics.

[0084] S2 Resource Assessment:

[0085] ① Conduct a detailed assessment of renewable energy resources within each regional microgrid, including power generation forecast and stability analysis.

[0086] ② Evaluate the capacity, efficiency and charging and discharging strategies of energy storage devices, analyze load characteristics, and determine peak and valley periods and demand-side response potential.

[0087] S3 interface design:

[0088] ① Design standardized electrical network interface protocols, including communication protocols, data formats, and control strategies.

[0089] ② Determine the physical connection method of the interface, such as fiber optic communication, wireless communication, consider the security, stability and reliability of the interface, and formulate corresponding protection measures.

[0090] S4 model construction:

[0091] ① Based on demand analysis, resource assessment and interface design, a unified power network interface model for multi-regional microgrid resources is constructed.

[0092] ②The model should include renewable energy power generation model, energy storage device model, load model and interface control model.

[0093] ③ Use simulation software to verify and optimize the model to ensure its accuracy and reliability.

[0094] S5 system integration and testing:

[0095] ① Integrate the models of microgrids in each region to form a unified multi-regional microgrid resource management system.

[0096] ② Conduct comprehensive testing on the system, including functional testing, performance testing and security testing.

[0097] S6 parameter optimization and adjustment:

[0098] ①Optimize and adjust the model parameters according to the simulation results to improve the operating efficiency and stability of the system.

[0099] ② Repeat the simulation verification until the design requirements are met.

[0100] The interface design includes:

[0101] Physical interface: defines the electrical connection method and parameters between each regional microgrid and the main grid, including voltage level, current limit, and protection configuration.

[0102] Communication interface: Establish unified communication protocols and data exchange standards to ensure real-time transmission and sharing of information between microgrids in different regions and between microgrids and the main grid.

[0103] Control interface: Design coordinated control strategies to achieve power balance and scheduling optimization between microgrids in different regions. The control interface should support both remote control and local control modes to meet the needs of different scenarios.

[0104] The model is constructed as follows:

[0105] Modular design: The multi-regional microgrid system is divided into several modules, including power generation modules (wind power, photovoltaic, etc.), energy storage modules, load modules, and interface modules. Each module is independently modeled, and then interconnected and coordinated through the interface module.

[0106] Agent-based modeling: Using the autonomy, interaction, and adaptability of agents, a multi-agent system is constructed to simulate and control multi-regional microgrids. Agents can represent different resources or subsystems and achieve optimal resource allocation and collaborative control through communication and negotiation.

[0107] Stochastic modeling: Considering the uncertainty of renewable energy generation and load demand, stochastic modeling methods (such as Monte Carlo simulation, random process model, etc.) are used to simulate and evaluate the operating status and performance of the system. System integration and testing:

[0108] Model integration: Integrate the module models according to the system architecture to form a complete unified power network interface model for multi-regional microgrid resources.

[0109] Set simulation parameters: including the volatility of renewable energy generation, the randomness of load demand, etc.

[0110] Run simulation: Use simulation software to simulate the integrated model.

[0111] Result analysis: Observe and record the simulation results, and analyze the performance of the model, including resource optimization configuration effect, collaborative control capability, power supply reliability, etc.

[0112] Parameter optimization and adjustment:

[0113] Parameter optimization: Apply the optimized model to the actual multi-region microgrid system for real-time monitoring and control, and continuously collect feedback data for iterative optimization of the model.

[0114] Repeat validation: Rerun the simulation to validate the performance of the optimized model.

[0115] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A unified electric network interface modeling method for multi-regional microgrid resources, including microgrid technology, power system modeling, and data communication and interface technology, provides strong support for the optimal allocation, coordinated control and stable operation of multi-regional microgrid resources, and realizes efficient integration and coordinated management of resources between multiple microgrid regions. The method realizes the optimal allocation and efficient utilization of multi-regional microgrid resources through technical means such as data collection and analysis, model construction, algorithm design and implementation, and interface standardization; It is characterized in that The method comprises the following steps: S1 Data collection and analysis: ① Data collection: List and collect all necessary data, including resource distribution, power generation capacity, energy storage status, load demand and relevant parameters of the main power grid in each region; ②Structural analysis: Analyze the overall structure of multi-regional microgrids to clarify the basic conditions of each microgrid, such as geographical location, energy composition, and load demand; S2 model construction: ① Physical model: Based on the actual situation of multi-regional microgrids, a physical model including renewable energy generation units, energy storage systems, loads and other components is constructed; ② Mathematical model: Based on the physical model, a mathematical model describing the operating characteristics of the multi-regional microgrid is established. The model should be able to reflect the interaction between the components and the law of energy flow; ③ Interface model: formulate a unified electrical network interface standard, including communication protocols, data formats, interface parameters, etc. At the same time, establish an interface model to describe the process of information interaction and energy transmission between microgrids in different regions through the electrical network interface; S3 algorithm design: ① Intelligent dispatching algorithm: Based on real-time operation data and forecast information, formulate the optimal dispatching strategy to meet load demand and maximize energy efficiency; ② Communication protocol design: Design efficient and secure communication protocols to ensure real-time transmission and security of data; ③ Combination of computing and edge computing: Using the powerful computing power of cloud computing and the low latency characteristics of edge computing to improve the overall performance of the system; S4 interface standardization: ① Module interface definition: clarify the interface specifications between modules, including data exchange format and communication protocol; ②Interface docking: Ensure that the interfaces between modules are correctly docked to achieve real-time transmission and sharing of data.

2. A method for modeling a unified electrical network interface of multi-regional microgrid resources according to claim 1, characterized in that The implementation steps are: S1 demand analysis: ① Determine the overall demand of multi-regional microgrids, including power demand, energy storage demand, and renewable energy access demand; ② Analyze the characteristics of microgrids in each region, such as geographical location, climate conditions, and load characteristics; S2 Resource Assessment: ① Conduct detailed assessment of renewable energy resources within each regional microgrid, including power generation forecast and stability analysis; ② Estimate the capacity, efficiency and charging and discharging strategies of energy storage devices, analyze load characteristics, and determine peak and valley periods and demand-side response potential; S3 interface design: ①Design standardized electrical network interface protocols, including communication protocols, data formats, and control strategies; ② Determine the physical connection mode of the interface, such as optical fiber communication and wireless communication, consider the security, stability and reliability of the interface, and formulate corresponding protection measures; S4 model construction: ① Based on demand analysis, resource assessment and interface design, a unified power network interface model for multi-regional microgrid resources is constructed; Type ③ should include renewable energy generation model, energy storage device model, load model and interface control model; ③ Use simulation software to verify and optimize the model to ensure the accuracy and reliability of the model; S5 system integration and testing: ① Integrate the models of microgrids in each region to form a unified multi-regional microgrid resource management system; ③ The system is fully tested, including functional testing, performance testing and safety testing; S6 parameter optimization and adjustment: ①Optimize and adjust the model parameters according to the simulation results to improve the operating efficiency and stability of the system; ③ Repeat the simulation verification until the design requirements are met.

3. A method for modeling a unified electrical network interface of multi-regional microgrid resources according to claim 2, characterized in that The interface design: Physical interface: defines the electrical connection mode and parameters between each regional microgrid and the main grid, including voltage level, current limit, and protection configuration; Communication interface: Establish unified communication protocols and data exchange standards to ensure real-time transmission and sharing of information between microgrids in different regions and between microgrids and the main grid; Control interface: Design coordinated control strategies to achieve power balance and scheduling optimization between microgrids in different regions. The control interface should support both remote control and local control modes to meet the needs of different scenarios.

4. A method for modeling a unified electrical network interface of multi-regional microgrid resources according to claim 2, characterized in that Model construction: Modular design: The multi-regional microgrid system is divided into several modules, including power generation module, energy storage module, load module and interface module; each module is independently modeled, and then interconnected and coordinated through the interface module; Agent-based modeling: Using the autonomy, interaction, and adaptability of agents, a multi-agent system is constructed to simulate and control multi-regional microgrids. Agents represent different resources or subsystems and achieve optimal resource allocation and coordinated control through communication and negotiation. Stochastic modeling: Considering the uncertainty of renewable energy generation and load demand, stochastic modeling methods are used to simulate and evaluate the operating status and performance of the system.

5. A method for modeling a unified electrical network interface of multi-regional microgrid resources according to claim 2, characterized in that System integration and testing: Model integration: Integrate each module model according to the system architecture to form a complete multi-region microgrid resource unified power network interface model; Set simulation parameters: including the volatility of renewable energy generation and the randomness of load demand; Run simulation: Use simulation software to simulate the integrated model; Result analysis: Observe and record the simulation results, and analyze the performance of the model, including resource optimization configuration effect, collaborative control capability, and power supply reliability.

6. A method for modeling a unified electrical network interface of multi-regional microgrid resources according to claim 2, characterized in that Parameter optimization and adjustment: Parameter optimization: Apply the optimized model to the actual multi-region microgrid system for real-time monitoring and control, and continuously collect feedback data for iterative optimization of the model; Repeat validation: Rerun the simulation to validate the performance of the optimized model.

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