A green electricity consumption type honeycomb micro-grid system based on distance adaptive aggregation

Abstract

The application discloses a green electricity consumption type honeycomb micro-grid system based on distance adaptive aggregation, relates to the technical field of distributed energy and smart grid, and comprises a configuration module, a power transmission module, an aggregation module and a control module. The distributed power supply is configured to a centralized load side unit and a decentralized load side unit, and a honeycomb unit is constructed. The honeycomb unit comprises one centralized load side unit and n decentralized load side units. The power transmission module is used for connecting the centralized load side unit and the decentralized load side unit through a booster station and a power transmission line suitable for different voltage grades, so as to form a honeycomb micro-grid architecture. The aggregation module links the honeycomb units through a virtual power plant or a physical link, so as to form a honeycomb micro-grid architecture. The control module collects power data, regulates and controls the power flow direction and connects with a public grid.

Description

Technical Field

[0001] This invention relates to the field of distributed energy and smart grid technology, and in particular to a green energy consumption-type honeycomb microgrid system based on distance adaptive aggregation. Background Technology

[0002] In recent years, with the popularization of distributed photovoltaic power, centralized load-side units (such as industrial parks and large commercial complexes) and decentralized load-side units (such as farmer clusters and small businesses) have respectively faced the problems of insufficient green electricity self-sufficiency and high midday curtailment rate.

[0003] Existing source-grid-load-storage technologies mostly focus on scheduling optimization or general equipment combinations within the same type of load, lacking dedicated physical architecture designs for cross-type load aggregation. The main shortcomings of existing technologies are as follows: 1) Microgrids in centralized load-side units typically only utilize rooftop resources within the region, resulting in limited photovoltaic capacity and a low green electricity self-sufficiency rate, usually below 30%. If ultra-high voltage transmission of 110kV or higher is used for long-distance green electricity transmission, there are issues of high investment costs and poor operational flexibility. 2) Distributed photovoltaic systems in decentralized load-side units mostly use low-voltage grid connection, leading to a severe mismatch between peak midday power output and local evening peak electricity consumption, resulting in significant midday curtailment. Existing source-grid-load-storage technologies primarily rely on scheduling algorithms and increased energy storage capacity to achieve green electricity allocation, which is costly and economically inefficient, failing to achieve complementary absorption of cross-type loads through architectural innovation. 3) When faced with the problem of green electricity consumption in environmental protection parks and factories, the conventional approach for those skilled in the art is to increase energy storage capacity or adopt ultra-high voltage long-distance green electricity direct connection technology. Existing technologies do not disclose or suggest the use of the "nearest minimum distributed energy unit aggregation - multi-level voltage boost interconnection - surplus power direct connection - global real-time balance" architecture to solve this technical problem. Summary of the Invention

[0004] The technical problem addressed by this invention is that existing source-grid-load-storage technologies mostly focus on scheduling optimization or general equipment combinations within the same type of load, lacking a dedicated physical architecture design for cross-type load aggregation. The main shortcomings of existing technologies are as follows: 1) Microgrids in centralized load-side units typically only utilize rooftop resources within the region, resulting in limited photovoltaic capacity and a low green electricity self-sufficiency rate, usually below 30%. If ultra-high voltage (e.g., 110kV and above) is used for long-distance transmission of green electricity, there are problems of high investment costs and poor operational flexibility. 2) Distributed photovoltaic systems in decentralized load-side units mostly use low-voltage grid connection, leading to a severe mismatch between peak power output (midday) and local peak electricity consumption (evening peak), resulting in significant curtailment of solar power during midday. Existing "source-grid-load-storage" technologies primarily rely on scheduling algorithms and increased energy storage capacity to achieve green electricity allocation, which is costly and economically inefficient, failing to achieve complementary absorption of cross-type loads through architectural innovation. 3) When faced with the problem of green electricity consumption in environmental parks / factories, the conventional approach for those skilled in the art is to "increase energy storage capacity" or "adopt the technical solution of "ultra-high voltage long-distance green electricity direct connection". The existing technology does not disclose or suggest the use of the architecture of "aggregation of nearby minimum distributed energy units - multi-level voltage boost interconnection - surplus power direct connection - global real-time balance" to solve this technical problem.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a green energy consumption type honeycomb microgrid system based on distance adaptive aggregation, including a configuration module, a transmission module, an aggregation module and a control module; The configuration module configures the distributed power supply in centralized load-side units and decentralized load-side units, and constructs honeycomb units, each of which includes one centralized load-side unit and n decentralized load-side units. The power transmission module is used to connect centralized load-side units and decentralized load-side units through a step-up substation and transmission lines adapted to different voltage levels to form a honeycomb microgrid architecture. The aggregation module connects the honeycomb units through virtual power plants or physical links to form a honeycomb microgrid architecture; The control module collects power data, regulates the flow of electrical energy, and connects to the public power grid.

[0006] As a preferred embodiment of the distance-adaptive aggregation-based green energy consumption honeycomb microgrid system described in this invention, the architecture of the honeycomb unit includes a basic architecture, an expansion architecture, and an optimization architecture. The basic architecture is a direct power supply type, which is suitable for low-load power consumption scenarios at midday for distributed load-side units; When the honeycomb unit has a direct power supply architecture, it is equipped with distributed power supply, combiner equipment, step-up equipment, smart metering switch and power transmission interface. Distributed power sources are aggregated and voltage-converted, and then directly transmitted to centralized load-side units via corresponding transmission lines; The extended architecture is a local balancing type, which is suitable for distributed power load units with low load during midday. When the architecture of the honeycomb unit is local balancing type, the distributed load side unit side includes distributed load and local load balancing equipment. The local load balancing equipment includes smart meters, smart switches and energy storage units. When the centralized load-side unit is in peak electricity consumption, the distributed power source prioritizes supplying the centralized load-side unit, combining and converting the electricity, and then transmitting it to the centralized load-side unit through the corresponding transmission line; When the centralized load-side unit is in a low-power period, the distributed power source of the decentralized load-side unit will prioritize supplying the decentralized load-side unit. The remaining power will be combined and voltage converted, and then transmitted to the centralized load-side unit and energy storage device through the corresponding transmission line. The optimized architecture is designed for overall absorption, making it suitable for scenarios with large fluctuations in distributed power output and high demand for spatiotemporal complementarity among different types of loads. When the architecture of the honeycomb unit is an overall absorption type, both the centralized load-side unit and the decentralized load-side unit are equipped with bidirectional transmission links, smart metering switches and local energy storage units; The microgrid central controller generates real-time dispatch commands to control the on / off state of smart metering switches, thereby executing control actions, specifically including... When the centralized load-side unit is in peak electricity consumption, the distributed power source prioritizes supplying the centralized load-side unit. The power is combined and the voltage is converted, and then transmitted to the centralized load-side unit through the corresponding transmission line. When the centralized load-side unit is in a low-power period, the surplus power generated by the distributed power source of the centralized load-side unit is transmitted in reverse through a bidirectional transmission link to the decentralized load-side unit.

[0007] As a preferred embodiment of the distance-adaptive aggregation-based green electricity consumption honeycomb microgrid system described in this invention, the distributed load-side unit is equipped with a distribution box and user incoming line switch, forming a dual-link topology of local consumption and surplus power transmission with local load balancing equipment and distributed power sources. Real-time data on local load power and distributed power output are collected through intelligent metering devices.

[0008] As a preferred embodiment of the distance-adaptive aggregation-based green energy consumption honeycomb microgrid system described in this invention, in the honeycomb unit of the overall consumption type, the local energy storage unit configured in the distributed load-side unit is an enhanced energy storage unit, and the centralized load-side unit is equipped with a power prediction and dynamic control module. The power prediction and dynamic control module is connected to the smart metering switch.

[0009] As a preferred embodiment of the distance-adaptive aggregation-based green energy consumption honeycomb microgrid system described in this invention, the distributed power sources of the decentralized load-side units supply power to the centralized load-side units through dedicated direct connection lines. The dedicated direct connection line selects the corresponding access voltage according to the connection distance. Specifically, when the connection distance is less than or equal to 500 meters, a 380V low-voltage access is used. When the connection distance is greater than 500 meters and less than 10 kilometers, a 10kV medium-voltage connection should be used. When there are special requirements for power supply stability or line capacity, a voltage level higher than 10kV should be used for connection.

[0010] As a preferred embodiment of the distance-adaptive aggregation-based green energy consumption honeycomb microgrid system described in this invention, the microgrid central controller can be connected to a virtual power plant aggregation platform to receive platform scheduling instructions. Direct power supply type honeycomb units, local balancing type honeycomb units, and overall absorption type honeycomb units all retain independent metering and power balancing capabilities.

[0011] As a preferred embodiment of the distance-adaptive aggregation-based green electricity consumption honeycomb microgrid system described in this invention, multiple honeycomb units constitute a honeycomb microgrid architecture through at least one of the following methods: virtual power plant aggregation, physical transmission and distribution bus aggregation, or secondary aggregation. When a honeycomb microgrid architecture is constructed through virtual power plant aggregation, each honeycomb unit is connected to the virtual power plant aggregation platform via a communication link. When aggregated via physical transmission and distribution bus, each honeycomb unit is interconnected through the transmission and distribution bus or transmission and distribution ring network and connected to the regional energy management platform; The honeycomb unit includes at least one of the following: direct power supply type honeycomb unit, local balancing type honeycomb unit, and overall absorption type honeycomb unit, with each honeycomb unit configured in a mixed manner. The regional energy management platform includes a unit layer, an overall coordination layer, and a multi-level aggregated coordination layer; The unit layer is connected to each honeycomb unit and is used to balance the power of each honeycomb unit; The overall coordination layer is connected to the unit layer and is used to coordinate the overall photovoltaic green electricity consumption in the region; The multi-level aggregation coordination layer is connected to the overall coordination layer and is used to balance the power distribution of the remote secondary aggregates; The multi-level aggregation coordination layer also includes a cross-side power coordination module, which is connected to the overall absorption type honeycomb unit and is used to coordinate the bidirectional power of the overall absorption type honeycomb unit.

[0012] As a preferred embodiment of the distance-adaptive aggregation-based green energy consumption honeycomb microgrid system described in this invention, the grid connection interface unit configures the grid connection voltage level according to the estimated total capacity of the system. Specifically, when the total load is greater than 10MW, the grid connection voltage level is 35kV. The scheduling optimization module is connected to the microgrid central controller and is used to perform the following actions: at any time, prioritize meeting the real-time power demand of centralized load-side units; For the overall absorption type honeycomb unit, when the output of the distributed power source of the centralized load side unit is greater than the power load of the centralized load side unit, the surplus power generated by the distributed power source of the centralized load side unit is preferentially transmitted to the decentralized load side unit that is in the peak power consumption period, and the remaining part is transmitted to the energy storage unit for charging. When the centralized load-side unit is at its peak electricity consumption, the power generated by the distributed power source throughout the region is preferentially delivered to the centralized load-side unit, and the remaining part is delivered to the decentralized load-side unit. When the total load is less than or equal to 10MW, the grid connection voltage level is 10kV.

[0013] As a preferred embodiment of the distance-adaptive aggregation-based green energy consumption honeycomb microgrid system described in this invention, the transmission lines in the transmission module are AC or DC. When the transmission line is DC, the transmission module includes an AC / DC conversion device, which is connected to the transmission line; The scheduling optimization module is also used to regulate DC power; The secondary polymer is equipped with a 35kV step-up device, which is connected to the centralized load-side unit via a 35kV high-voltage busbar. The secondary aggregate is formed by multiple decentralized load-side units being aggregated twice via a 10kV line, then stepped up, and finally connected to a remote centralized load-side unit via a 35kV transmission line.

[0014] As a preferred embodiment of the distance-adaptive aggregation-based green electricity consumption honeycomb microgrid system described in this invention, the multi-level aggregation coordination layer of the regional energy management system is connected to the secondary aggregate to balance the power of the secondary aggregate. When the secondary aggregate includes honeycomb cells of the overall absorption type, the multi-level aggregation coordination layer also includes a cross-cell tidal power coordination submodule. The cross-cell tidal power coordination submodule is connected to the honeycomb cells of the overall absorption type and is used to coordinate the power of the honeycomb cells of the overall absorption type. The target confirmation and preliminary feasibility assessment stage is used to determine the green energy consumption target of centralized load-side units and assess distributed power resources; The architecture selection and system solution design phase is used to select the architecture of the honeycomb unit and design the system topology scheme; The key equipment deployment and system infrastructure construction phase is used to install distributed power sources, transmission lines and control equipment and establish communication networks. The system initialization and basic policy loading phase is used to power on and debug the system and load the basic scheduling policy. The data-driven optimization and performance verification phase is used to collect runtime data and optimize scheduling strategies.

[0015] The beneficial effects of this invention are as follows: Through a dedicated physical architecture of "honeycomb unit - multi-level aggregation - honeycomb system," it achieves spatiotemporal complementarity and efficient green energy consumption across different load types, resulting in the following benefits: First, it significantly improves the green energy self-sufficiency rate of centralized loads while simultaneously reducing the overall curtailment rate. The system uses centralized loads as its core, aggregating surrounding distributed power sources. Through a "tidal flow" scheduling logic, surplus green energy is prioritized for consumption by distributed loads during peak demand periods, eliminating the need for large-scale energy storage and achieving real-time balance of photovoltaic output at midday. This increases the green energy self-sufficiency rate of centralized loads to over 50% and significantly reduces the overall curtailment rate. Second, it breaks through traditional technological path dependence, significantly reducing system construction and operation costs. Through "distance-voltage" adaptation rules and multi-level aggregation design, it replaces ultra-high-voltage long-distance transmission with low-voltage / medium-voltage direct connection and replaces large-capacity energy storage configuration with architectural innovation. The core equipment consists of mature mass-produced products, allowing for flexible expansion, and significantly reducing the total lifecycle cost compared to conventional solutions. Third, a modular and scalable standardized topology is constructed to lay the foundation for advanced intelligence. The "Honeycomb Unit" serves as the smallest autonomous node, possessing independent metering and power balancing capabilities, and supporting mixed configurations and multi-level aggregation of various architectures. The dual-level node design is naturally adapted to digital twins and graph computing models, and can deeply integrate physical information neural networks and causal discovery algorithms to achieve precise scheduling and continuous optimization in extreme scenarios. Attached Figure Description

[0016] Figure 1 This is a basic flowchart of a distance-adaptive aggregation-based green energy consumption honeycomb microgrid system provided as an embodiment of the present invention.

[0017] Figure 2This is a topology diagram of the first architecture (direct power supply type) of the honeycomb unit in this invention.

[0018] Figure 3 This is a topology diagram of the second architecture (locally balanced type) of the honeycomb house unit in this invention.

[0019] Figure 4 This is a topology diagram of the third architecture (overall absorption type) of the honeycomb house unit in this invention.

[0020] Figure 5 This is a virtual power plant aggregation topology diagram of the honeycomb microgrid system in this invention.

[0021] Figure 6 This is a regional autonomous microgrid (bus type) topology diagram of the honeycomb microgrid system in this invention.

[0022] Figure 7 This is a regional autonomous microgrid (loop type) topology diagram of the honeycomb microgrid system in this invention.

[0023] Figure 8 This is a secondary aggregation topology diagram of the honeycomb microgrid system in this invention.

[0024] Figure 9 A schematic diagram of a honeycomb microgrid system for green electricity consumption between factories and farmers.

[0025] Figure 10 The load curves for centralized loads, decentralized loads, and distributed photovoltaic systems change throughout the day. Detailed Implementation

[0026] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0027] Example, refer to Figure 1 As an embodiment of the present invention, a green energy consumption-type honeycomb microgrid system based on distance adaptive aggregation is provided, including a configuration module, a transmission module, an aggregation module and a control module; The configuration module configures the distributed power supply in centralized load-side units and decentralized load-side units, and constructs honeycomb units, each of which includes one centralized load-side unit and n decentralized load-side units. The power transmission module is used to connect centralized load-side units and decentralized load-side units through a step-up substation and transmission lines adapted to different voltage levels to form a honeycomb microgrid architecture. The aggregation module connects the honeycomb units through virtual power plants or physical links to form a honeycomb microgrid architecture; The control module collects power data, regulates the flow of electrical energy, and connects to the public power grid.

[0028] In one embodiment, to address the technical problems of limited available green electricity resources for centralized loads, which cannot meet their green electricity needs, and the high cost and poor flexibility resulting from relying on ultra-high voltage long-distance green electricity transmission, this invention achieves local aggregation and power supply of distributed power sources through honeycomb units: The honeycomb unit uses the centralized load as its core, utilizing surrounding idle and available space resources to deploy distributed power sources. After convergence and inversion processing, the distributed power sources are aggregated locally based on the transmission distance and the appropriate voltage level of the line. The aggregated green electricity is then directly supplied to the centralized load bus via a dedicated direct connection line, significantly improving the green electricity self-sufficiency rate of the centralized load. This design does not require... Large-scale civil engineering projects, core equipment are all mature mass-produced products, which can be flexibly expanded, and the cost of obtaining green electricity is significantly reduced compared with ultra-high voltage transmission schemes, taking into account both practicality and economy. Specifically, the above three types of honeycomb units include the following basic components: (1) centralized load-side units, which are set in areas with concentrated loads (such as industrial parks and large commercial buildings) and serve as stable load centers, serving as the main core nodes for the green electricity consumption of the present invention. The centralized load-side units include centralized power loads and distributed power sources and energy storage devices configured on the centralized power load side; (2) decentralized load-side units, which are distributed within 10-15km of the centralized load-side units and are intended to serve the surrounding areas. Within the scope of services (such as farmer clusters, small businesses), it forms a cross-type aggregation entity that is spatially and temporally complementary with the centralized load-side unit, and has both green electricity generation and local consumption functions. The decentralized load-side unit includes n (n≥2) decentralized load-side units and distributed power sources and energy storage devices located on the decentralized load-side unit side; (3) Distributed power sources are respectively installed on the decentralized load-side unit side and the centralized load-side unit side, and can be compatible with rooftop photovoltaic and small distributed power station forms, providing the core source of green electricity for the architecture; (4) Energy storage units are connected to the beehive unit to quickly smooth cross-side power fluctuations, store surplus photovoltaic green electricity, and support the "tidal flow" function; (5) ) Transmission and voltage conversion equipment for different distances, including medium and high voltage busbars (10kV / 35kV), low voltage transmission lines (380V), transformers, combiners, step-up and step-down devices, to achieve low-loss transmission of green electricity and adapt to voltage level requirements for different distances; (6) Combiner / step-up station (device), divided into local combiner device and remote step-up station: Local combiner device: configured on the distributed load side unit side (both architectures require it), integrating combiner box, inverter, medium and low voltage step-up module, the core function is to combine the output of multiple distributed power sources and convert it into a voltage level (380V / 10kV) suitable for transmission distance, laying the foundation for low-loss transmission of green electricity;Remote booster station: Dedicated configuration on the secondary aggregation side of >10km, the core equipment is a 35kV booster transformer and supporting protection device, used to further boost the 10kV primary aggregation green electricity of multiple decentralized sides to the 35kV high voltage level, and connect to the centralized load side unit through the high voltage bus to solve the problem of large transmission loss at the remote end; (7) Intelligent metering switch, deployed at key nodes of the microgrid (distributed power output end, energy storage device access end, bus connection end, public grid interface), is the core integrated device of the topology map, with dual functions of "precise metering + intelligent control": on the one hand, it collects full-link power data to provide a basis for control decision; on the other hand, it receives instructions from the microgrid central controller, dynamically adjusts the current transmission amount, realizes precise control of power flow, and provides hardware support for power balance of cross-type load aggregation; (8) Microgrid central controller (including energy management and control integrated machine), electrically connected to all intelligent metering switches, static switches, energy storage units and other equipment in the system, dynamically coordinates the output of distributed power, the power demand of two types of loads and storage It can charge and discharge power, provide data support and control instructions for the power balance of the physical architecture, and ensure the efficient operation of the architecture; (9) Distribution box, mainly configured on the distributed load side, as the connection node between local load and power supply link, integrates line protection and overload protection functions to ensure local power safety; (10) User incoming line switch, mainly configured on the distributed load side, one end connects to the distribution box and the other end connects to the user terminal. Its core function is to realize the safe isolation of user power, access control and fault disconnection. It can be linked with the data of the smart metering switch to accurately manage the green power access of local load; (11) Static switch, deployed at the interface between the honeycomb unit and the public power grid, and the AC bus connection node (all three architectures are configured). It has the ability to quickly isolate faults and switch to island operation. Its core function is: to ensure stable connection with the public power grid during normal operation, and to complete the isolation with the public power grid within ≤300ms during faults (such as power grid fluctuations and line overloads) to avoid fault propagation, support the island operation of the system or rapid grid connection recovery, and comply with the safety specifications for distributed power access to the distribution network;(12) Bidirectional transmission link (dedicated to overall absorption type), consisting of bidirectional cable, bidirectional inverter, and transformer and protection circuit for voltage conversion. Its working voltage level is fixed in the system planning and design stage, supporting the bidirectional flow of green electricity on the centralized load side and the decentralized load side, providing a physical transmission channel for the "tidal flow" function. The architecture is based on the spatiotemporal complementarity of cross-type loads. The key is to organically interconnect the distributed power sources, decentralized loads and centralized loads through transmission lines with adapted voltage levels, forming a topology system with "honeycomb units" as the smallest autonomous unit (n≥1, n is the number of honeycomb units) - the unit has independent metering and power balancing capabilities, can be flexibly combined and expanded, and lays the foundation for stable system operation. At the system operation level, by combining intelligent switches and metering equipment with the hierarchical coordination of the microgrid central controller and the regional energy management platform, the direction of green electricity is dynamically adjusted according to preset time period priorities. This allows green electricity to be flexibly and rationally adjusted between centralized and decentralized load-side units through local balancing or "tidal flow" methods. Simultaneously, the grid connection interface unit ensures stable connection with the public power grid, requiring only a small amount of energy storage to mitigate extreme fluctuations. It also eliminates the need for ultra-high-voltage long-distance transmission, ultimately achieving low-loss and maximized local consumption of regional green electricity, simultaneously addressing the two core industry pain points of insufficient green electricity self-sufficiency for centralized loads and midday curtailment of solar power for decentralized loads.

[0029] The architecture of a honeycomb unit includes basic architecture, expansion architecture, and optimization architecture; The basic architecture is a direct power supply type, which is suitable for low-load power consumption scenarios at midday for distributed load-side units; When the honeycomb unit has a direct power supply architecture, it is equipped with distributed power supply, combiner equipment, step-up equipment, smart metering switch and power transmission interface. Distributed power sources are aggregated and voltage-converted, and then directly transmitted to centralized load-side units via corresponding transmission lines; The extended architecture is a local balancing type, which is suitable for distributed power load units with low load during midday. When the architecture of the honeycomb unit is local balancing type, the distributed load side unit side includes distributed load and local load balancing equipment. The local load balancing equipment includes smart meters, smart switches and energy storage units. When the centralized load-side unit is in peak electricity consumption, the distributed power source prioritizes supplying the centralized load-side unit, combining and converting the electricity, and then transmitting it to the centralized load-side unit through the corresponding transmission line; When the centralized load-side unit is in a low-power period, the distributed power source of the decentralized load-side unit will prioritize supplying the decentralized load-side unit. The remaining power will be combined and voltage converted, and then transmitted to the centralized load-side unit and energy storage device through the corresponding transmission line. The optimized architecture is designed for overall absorption, making it suitable for scenarios with large fluctuations in distributed power output and high demand for spatiotemporal complementarity among different types of loads. When the architecture of the honeycomb unit is an overall absorption type, both the centralized load-side unit and the decentralized load-side unit are equipped with bidirectional transmission links, smart metering switches and local energy storage units; The microgrid central controller generates real-time dispatch commands to control the on / off state of smart metering switches, thereby executing control actions, specifically including... When the centralized load-side unit is in peak electricity consumption, the distributed power source prioritizes supplying the centralized load-side unit. The power is combined and the voltage is converted, and then transmitted to the centralized load-side unit through the corresponding transmission line. When the centralized load-side unit is in a low-power period, the surplus power generated by the distributed power source of the centralized load-side unit is transmitted in reverse through a bidirectional transmission link to the decentralized load-side unit.

[0030] In one embodiment, the honeycomb unit has three architectural forms: The first architecture (direct power supply type): Distributed power sources on the distributed load-side unit side are aggregated and voltage-converted, then directly supply power to the centralized load-side unit via adaptive transmission lines. No additional local load balancing equipment is configured on the distributed load-side unit side. This is suitable for scenarios with extremely low distributed midday electricity load and is a simplified architecture for the honeycomb unit. This architecture is suitable for scenarios where distributed load-side units have extremely low midday electricity load (such as clusters of farmers with severe population outflow). The simplified architecture reduces engineering and operating costs, while relying on aggregated links to achieve direct green electricity transmission, solving the pain point that conventional architectures cannot achieve low-cost direct green electricity connection. The second architecture (local balancing type): Local load balancing equipment is configured on the distributed load-side unit side, prioritizing the output of distributed power sources to local midday loads. Surplus power is then transferred to the centralized load-side unit via adaptive transmission lines after current collection and voltage conversion. This architecture is suitable for scenarios where centralized load units experience significant load troughs during midday, and distributed load units have pre-set power absorption capacity during midday. It is an optimized architecture for the honeycomb unit. This architecture is suitable for scenarios where distributed load-side units have midday load absorption capacity (such as clusters of farmers or small businesses with large numbers of residents). Leveraging the dual-link architecture to fully utilize spatiotemporal complementarity, this architecture significantly improves the local absorption rate of photovoltaic green electricity and reduces energy storage capacity, outperforming the absorption effect of conventional single-link architectures. The third architecture (overall absorption type): Designed based on the real-time spatiotemporal complementarity of distributed power sources and cross-type loads, both the distributed and centralized load sides are equipped with bidirectional transmission links, high-performance smart metering switches, and enhanced energy storage units. Through real-time scheduling commands from the microgrid central controller, the smart metering switches are dynamically switched on and off, enabling tidal flow of green electricity between the two sides. This architecture is suitable for complex scenarios with large fluctuations in distributed power output and high demand for spatiotemporal complementarity among cross-type loads, representing an advanced optimized architecture for the honeycomb unit. This architecture overcomes the limitations of traditional unidirectional power supply through bidirectional transmission links and real-time control logic, achieving dynamic cross-side distribution of green electricity. Relying on tidal flow logic, global real-time balance can be achieved without large-scale energy storage, further reducing curtailment rates and system costs, and improving the flexibility and stability of green electricity absorption.

[0031] The distributed load-side unit is equipped with a distribution box and user incoming line switch, forming a dual-link topology with local load balancing equipment and distributed power sources for local consumption and surplus power transmission. Real-time data on local load power and distributed power output are collected through intelligent metering devices.

[0032] In one embodiment, the distributed load side unit is also equipped with a distribution box and user incoming line switch, forming a "local consumption - surplus power transmission" dual-link topology with the local load balancing equipment and distributed power sources. The smart metering equipment collects local load power and distributed power output data in real time, providing a basis for power allocation decisions.

[0033] In the overall absorption type of honeycomb unit, the local energy storage unit configured in the distributed load-side unit is an enhanced energy storage unit, and the centralized load-side unit is equipped with a power prediction and dynamic control module; The power prediction and dynamic control module is connected to the smart metering switch.

[0034] In one embodiment, the overall absorption-type honeycomb unit is equipped with a bidirectional transmission link, the distributed load side is equipped with an enhanced energy storage unit (charge and discharge response time ≤50ms), and the centralized load side is equipped with a power prediction and dynamic control module, forming a closed-loop control with the smart metering switch to ensure the stability and real-time performance of the "tidal flow" of green electricity, and can smooth cross-side power fluctuations without the need for additional large-capacity energy storage.

[0035] The distributed power supply of the distributed load-side unit supplies power to the centralized load-side unit through a dedicated direct connection line; The dedicated direct connection line selects the corresponding access voltage according to the connection distance. Specifically, when the connection distance is less than or equal to 500 meters, a 380V low-voltage access is used. When the connection distance is greater than 500 meters and less than 10 kilometers, a 10kV medium-voltage connection should be used. When there are special requirements for power supply stability or line capacity, a voltage level higher than 10kV should be used for connection.

[0036] In one embodiment, the distributed power supply on the distributed load side unit supplies power to the designated centralized load side unit through a dedicated direct connection line. Based on the goal of green electricity transmission with low loss, the corresponding access voltage is selected according to the connection distance: 380V low voltage access is preferred when the connection distance is ≤500 meters, and 10kV medium voltage access is preferred when the connection distance is 500 meters-10km. If there are special requirements for power supply stability and line capacity, a higher voltage level access method can be adapted without deviating from the core architecture of the present invention.

[0037] The microgrid central controller can connect to the virtual power plant aggregation platform and receive platform dispatch instructions; Direct power supply type honeycomb units, local balancing type honeycomb units, and overall absorption type honeycomb units all retain independent metering and power balancing capabilities.

[0038] In one embodiment, the microgrid central controller is connected to the virtual power plant aggregation platform and can accept platform scheduling instructions to participate in grid demand response. The direct power supply type, local balancing type and overall absorption type honeycomb units all retain independent metering and power balancing capabilities, without affecting cross-regional resource coordination.

[0039] Building upon the established honeycomb units, this invention further provides three aggregation methods to expand and construct the honeycomb system. Mode 1, virtual power plant aggregation, is a cross-regional expansion mode. Its core feature is an autonomous architecture based on honeycomb units. Each honeycomb unit connects to the virtual power plant aggregation platform via a communication link, retaining the independent metering and power balancing capabilities of each individual unit while achieving cross-regional resource coordination. Its core value lies in the system's ability to respond to virtual power plant dispatch commands and participate in grid demand response, breaking through regional boundary limitations and optimizing cross-regional green energy allocation.

[0040] Multiple honeycomb units can form a honeycomb microgrid architecture through at least one of the following methods: virtual power plant aggregation, physical transmission and distribution bus aggregation, or secondary aggregation. When a honeycomb microgrid architecture is constructed through virtual power plant aggregation, each honeycomb unit is connected to the virtual power plant aggregation platform via a communication link. When aggregated via physical transmission and distribution bus, each honeycomb unit is interconnected through the transmission and distribution bus or transmission and distribution ring network and connected to the regional energy management platform; The honeycomb unit includes at least one of the following: direct power supply type honeycomb unit, local balancing type honeycomb unit, and overall absorption type honeycomb unit, and the honeycomb units are mixed and configured. The regional energy management platform includes a unit layer, an overall coordination layer, and a multi-level aggregated coordination layer; The unit layer is connected to each honeycomb unit and is used to balance the power of each honeycomb unit; The overall coordination layer is connected to the unit layer and is used to coordinate the overall photovoltaic green electricity consumption in the region; The multi-level aggregation coordination layer is connected to the overall coordination layer and is used to balance the power distribution of the remote secondary aggregates; The multi-level aggregation coordination layer also includes a cross-side power coordination module, which is connected to the overall absorption type honeycomb unit and is used to coordinate the bidirectional power of the overall absorption type honeycomb unit.

[0041] In one embodiment, the honeycomb microgrid system of the present invention employs an innovative dual-level node design in its topology. This design clarifies the organizational logic of the system at different scales and forms the basis for achieving efficient aggregation and precise control. System-level topology: At the macroscopic system interconnection level, the honeycomb system defines and constructs each honeycomb unit as a core node of the system, with honeycomb cells as the core nodes. Regardless of whether the honeycomb cell is a direct power supply type, a local balancing type, or a global absorption type, it exists as the smallest standardized autonomous unit (i.e., a "honeycomb") with independent metering and power balancing capabilities. The voltage level transmission lines (e.g., 10kV medium-voltage lines or 35kV high-voltage busbars) connecting these core nodes at appropriate distances constitute the edges of the system layer. The scheduling of the regional energy management platform at this level focuses on coordinating the power interaction between these "honeycomb" nodes, for example, scheduling the surplus green electricity from multiple dispersed honeycomb cells to the centralized load honeycomb cells, which serve as the main absorption core, through the transmission and distribution network. Unit-level topology: With loads and internal power supply components as core nodes, when focusing on the interior of a single honeycomb unit, it constitutes a more refined micro-topology network. At this level, the specific physical components of centralized power loads, distributed power loads, distributed power sources, and energy storage units are defined as the core nodes within the unit. The power lines, bidirectional transmission links, and logical paths controlled by smart metering switches within the unit constitute the edges of the unit layer. The task of the microgrid central controller at this level is to manage the flow of power between these internal nodes; for example, controlling whether the output of distributed power sources is prioritized for centralized loads or for local consumption by distributed loads, or for transmission to distributed loads via bidirectional links. Mode 2: Regional autonomous microgrids are a specific implementation of this architecture. This mode is a multi-unit interconnection mode, with the core feature being that at least two honeycomb units are networked through transmission and distribution buses or transmission and distribution loops, connecting to a regional energy management platform to form a honeycomb system with autonomous operation capabilities. The key feature of this system lies in its islanded operation switching capability. The energy management platform employs an optimization strategy of "unit-level autonomy + overall coordination layer coordination" to ensure maximum power balance and green energy consumption within the region, with strong architectural redundancy. This clear dual-level node topology is one of the core innovations of this invention, bringing outstanding technical effects: Modularity and scalability: Abstracting the system into interconnected "honeycomb unit" nodes allows for flexible expansion, much like building blocks. Adding a new distributed power cluster simply requires constructing it as a new standardized honeycomb unit node and connecting it to the system, greatly reducing the complexity of planning and expansion. Simplified control logic: The hierarchical topology separates "unit-level autonomy" from "overall coordination layer coordination." Each honeycomb unit node maintains its internal power balance independently, and the system layer only needs to focus on power exchange between nodes, thus decomposing the control challenges of a complex system and improving system stability and response speed.Laying the foundation for advanced intelligence: This clear topology and well-defined nodes and edges make it naturally easy to map to digital twin models or domain-specific knowledge graphs. The "honeycomb" nodes at the system layer and the "component" nodes at the unit layer can be linked through "inclusion" relationships, providing perfect underlying model support for future implementation of more advanced scheduling strategies based on graph computing and artificial intelligence (such as optimal power flow calculation and fault impact simulation).

[0042] The grid connection interface unit is configured with a grid connection voltage level according to the estimated total capacity of the system. Specifically, when the total load is greater than 10MW, the grid connection voltage level is 35kV. The scheduling optimization module is connected to the microgrid central controller and is used to perform the following actions: at any time, prioritize meeting the real-time power demand of centralized load-side units; For the overall absorption type honeycomb unit, when the output of the distributed power source of the centralized load side unit is greater than the power load of the centralized load side unit, the surplus power generated by the distributed power source of the centralized load side unit is preferentially transmitted to the decentralized load side unit that is in the peak power consumption period, and the remaining part is transmitted to the energy storage unit for charging. When the centralized load-side unit is at its peak electricity consumption, the power generated by the distributed power source throughout the region is preferentially delivered to the centralized load-side unit, and the remaining part is delivered to the decentralized load-side unit. When the total load is less than or equal to 10MW, the grid connection voltage level is 10kV.

[0043] In one embodiment, during the system planning and design phase, the grid connection interface unit is fixed to the grid connection voltage level based on the estimated total capacity: 35kV is selected when the total load is >10MW, and 10kV is selected when the total load is ≤10MW. To address the supply-demand mismatch between the peak output of distributed photovoltaic power generation at midday and the load trough caused by the operational characteristics of concentrated loads during specific midday periods, and to avoid the high investment and complex operation and maintenance drawbacks of existing technologies that rely on large-capacity energy storage to store surplus green electricity, this invention designs a complementary absorption scheme based on the spatiotemporal characteristics differences of cross-type loads: the honeycomb unit, through topology reconstruction, incorporates concentrated loads and surrounding distributed loads into the same green electricity absorption network, with the system operation prioritizing the real-time green electricity demand of concentrated loads. Based on this, when centralized loads are at a low point, the surplus distributed photovoltaic power that cannot be fully absorbed is actively dispatched to the decentralized load side during peak electricity demand through local consumption or "tidal flow" methods for priority consumption, thereby achieving real-time absorption of distributed photovoltaic power output during midday. During peak electricity demand periods of centralized loads, the demand of centralized loads is prioritized, and power is supplemented by distributed power generation and energy storage discharge. This design does not require large-capacity energy storage, only a small amount of energy storage to smooth extreme power fluctuations, which can significantly reduce the midday distributed photovoltaic curtailment rate and reduce the total life cycle cost of green electricity consumption. For scenarios where there are no usable distributed power resources in the vicinity of the centralized load side unit, this invention also provides a secondary aggregator as a supplementary form of the honeycomb microgrid architecture. The secondary aggregator is formed by aggregating the distributed power of multiple decentralized load side units after convergence and primary voltage boosting, and then connected to the centralized load side unit through a transmission line with a voltage level of 35kV or above after secondary voltage boosting.

[0044] The power transmission lines in the power transmission module are either AC or DC. When the transmission line is DC, the transmission module includes an AC / DC conversion device, which is connected to the transmission line; The scheduling optimization module is also used to regulate DC power; The secondary polymer is equipped with a 35kV step-up device, which is connected to the centralized load-side unit via a 35kV high-voltage busbar. The secondary aggregate is formed by multiple decentralized load-side units being aggregated twice via a 10kV line, then stepped up, and finally connected to a remote centralized load-side unit via a 35kV transmission line.

[0045] In one embodiment, the adaptable transmission line can be selected as AC or DC. Under the DC system, corresponding AC-DC conversion equipment is configured, and the scheduling optimization module adapts to the DC power regulation logic to meet the different transmission requirements of direct power supply type, local balance type and overall absorption type honeycomb unit.

[0046] Mode 3: Secondary aggregation power supply is a remote adaptation mode. Its core feature is that for remote distributed generation units exceeding 10km, it first interconnects them via 10kV medium-voltage transmission lines to form a primary aggregation unit, then achieves secondary aggregation via 35kV step-up equipment, and finally connects to the centralized load-side unit via a high-voltage bus. This system, through "two-stage aggregation + voltage upgrade," solves the pain points of high losses and high costs in long-distance, cross-type green energy transmission, adapting to remote needs in scenarios with distances ranging from 3-15km.

[0047] Specifically, based on the aforementioned secondary aggregation power supply mode, when the total aggregated load is less than or equal to 10MW and the transmission distance is within a medium-to-short range, the system also provides a secondary aggregation form of the same voltage level as a supplementary implementation of this mode. In this implementation, multiple adjacent distributed load-side units, after being locally stepped up to 10kV, are not directly connected to the centralized load-side unit individually. Instead, they are first interconnected and combined via a nearby 10kV medium-voltage transmission line to form a secondary aggregation body of the same voltage level. Subsequently, this secondary aggregation body is uniformly connected to the 10kV busbar of the centralized load-side unit via a 10kV main line.

[0048] Unlike the aforementioned boost-type secondary aggregation, this implementation does not involve a voltage level increase during the secondary aggregation process; that is, the 10kV current is still transmitted at the 10kV voltage level after aggregation. This same voltage level aggregation topology effectively avoids the waste of channel resources and excessive cable investment caused by laying multiple independent medium-voltage lines in parallel. It is particularly suitable for scenarios where the individual capacity of dispersed nodes is small and the geographical locations are clustered, maximizing the economic benefits of near-terminal clustered green electricity aggregation without the need for additional booster stations.

[0049] Thus, the secondary aggregation power supply mode forms a complete technical system that adaptively selects based on distance and capacity: for remote scenarios with a total load exceeding 10MW or a transmission distance greater than 10km, a 10kV converter is used followed by a 35kV boost for long-distance transmission; for scenarios with a total load not exceeding 10MW and a transmission distance within the medium to short range, a 10kV converter is used followed by direct 10kV transmission. These two implementation methods together constitute the complete distance adaptive capability of the secondary aggregation system.

[0050] The multi-level aggregation and coordination layer of the regional energy management system is connected to the secondary aggregation body to balance the power of the secondary aggregation body; When the secondary aggregate includes honeycomb cells of the overall absorption type, the multi-level aggregation coordination layer also includes a cross-cell tidal power coordination submodule. The cross-cell tidal power coordination submodule is connected to the honeycomb cells of the overall absorption type and is used to coordinate the power of the honeycomb cells of the overall absorption type. The target confirmation and preliminary feasibility assessment stage is used to determine the green energy consumption target of centralized load-side units and assess distributed power resources; The architecture selection and system solution design phase is used to select the architecture of the honeycomb unit and design the system topology scheme; The key equipment deployment and system infrastructure construction phase is used to install distributed power sources, transmission lines and control equipment and establish communication networks. The system initialization and basic policy loading phase is used to power on and debug the system and load the basic scheduling policy. The data-driven optimization and performance verification phase is used to collect runtime data and optimize scheduling strategies.

[0051] In one embodiment, the construction of the green energy consumption-type honeycomb microgrid system of the present invention, which aggregates loads across different types, is a goal-oriented, phased-stage systems engineering project. Its core lies in combining the dedicated physical architecture of "honeycomb unit - multi-level aggregation - honeycomb system" with dynamic optimization based on actual operating data to ensure that the system can reliably achieve the preset green energy consumption target. The construction process includes the following five stages: Stage 1: Target confirmation and preliminary feasibility assessment. The starting point of this stage is to clarify the green energy consumption target of centralized load-side units (such as industrial parks), for example, "achieving a green energy self-sufficiency rate of 60% of the annual total electricity consumption." Based on this target, the power generation potential of its internal distributed power sources (such as rooftop photovoltaics) is first assessed, and the specific green energy gap is calculated. Only when a positive gap exists is the subsequent construction process initiated. Subsequently, centered on this centralized unit, a reasonable aggregation radius (e.g., 10-15 kilometers) is initially delineated on the Geographic Information System (GIS). Distributed load units (e.g., villages) within this radius are qualitatively identified, and their available distributed power resources (e.g., idle rooftop area, available idle land) are assessed. Based on their type (e.g., farmers, businesses), a preliminary judgment is made on their midday electricity consumption characteristics, providing directional guidance for subsequent architecture selection. The core of this stage is to complete the top-level demonstration from business objectives to technical feasibility, avoiding blind resource investment. The second stage: Architecture selection and system solution design. After confirming project feasibility, the core task of this stage is to complete the detailed technical solution design of the system. First, based on the preliminary judgment of the distributed unit's electricity consumption characteristics in stage S1, a matching honeycomb unit architecture is selected. For scenarios with extremely low midday loads, a simplified direct power supply architecture is chosen; for scenarios with self-consumption capacity at midday, a local balancing architecture is chosen; and for complex scenarios with large fluctuations in supply and demand and high complementary needs on both the centralized and distributed sides, an advanced overall consumption architecture is chosen. Next, based on the transmission distance, the "distance-voltage-aggregation method" adaptation rule is applied to design the backbone network topology. For example, 380V direct connection lines are planned for adjacent units, 10kV aggregation transmission is used for shorter distances, and a scheme is designed for remote units to access the network via a 35kV step-up substation after 10kV aggregation. Finally, a complete and executable plan is formed, including architecture selection, network topology, a list of key equipment, and economic evaluation. The third stage: Deployment of key equipment and construction of the system infrastructure. This stage is the engineering construction phase of transforming the design plan into a physical system. All activities revolve around hardware installation and the implementation of basic functions. According to the plan determined in S2, distributed photovoltaic arrays, combiner boxes, and inverters are installed on the distributed unit side, and distribution rooms and access bays are modified or constructed on the centralized unit side; cables or overhead lines of various voltage levels are laid according to the design path, and corresponding transformers, switchgear, ring main units, and pole tower facilities are installed.Simultaneously, core control equipment such as the microgrid central controller, smart metering switches, and static switches are deployed, and an industrial-grade communication network connecting all key nodes is established to ensure the system has basic remote data monitoring and equipment start-up and shutdown control capabilities. Finally, grid-connected interface units are installed and a complete relay protection system is configured to ensure the system can safely connect to the grid and quickly switch to islanded operation mode in the event of a grid fault. The goal of this stage is to build a basic physical system with complete hardware, interconnected communication, and safe start-up and shutdown capabilities. The fourth stage: System initialization and basic strategy loading. After the system hardware is built, this stage aims to inject initial operating logic, transforming it from a "static network" into a "runnable system." First, the system is powered on and initialized to ensure all equipment is working properly and communication links are unobstructed. Next, a basic scheduling strategy based on the "spatiotemporal complementarity" principle is loaded, such as setting a simple rule to prioritize power supply to distributed loads during midday and to prioritize power supply to centralized loads outside midday. At this point, the system begins trial operation and initiates comprehensive and continuous data acquisition, recording the actual output curves of distributed power sources, the actual power consumption curves of the two types of loads, and the electrical parameters of key nodes, thereby establishing a performance baseline for system operation. The real data collected in this stage provides an indispensable data foundation for the next stage of in-depth optimization. Fifth Stage: Data-Driven Optimization and Performance Verification. This stage is crucial to ensuring the system ultimately achieves the goals set in S1. The core lies in utilizing the operational data collected in S4, combined with the system's preset physical topology parameters (such as line impedance, transformer capacity, and transmission line length), to perform refined debugging and algorithm optimization of the system. First, the collected historical system operation data is integrated with historical data from external meteorological data (including light intensity, temperature, and humidity) and social calendar data (including holiday dates and time-of-use pricing policies) to train and calibrate the load forecasting and photovoltaic output forecasting models. To improve the accuracy and robustness of the models, advanced algorithms of Physical Information Neural Networks (PINNs) can be introduced, embedding the physical laws of the power grid as constraints into the model training. During the upgrade of the dynamic scheduling strategy, the causal discovery algorithm is integrated to complete three core operations: First, time window alignment and resampling of multi-source heterogeneous data are performed to uniformly map load characteristics, photovoltaic output, and meteorological data to a preset common time granularity (such as 15 minutes / 30 minutes, adapted to the actual scenario), and interpolation algorithms are used to fill in missing points to prevent causal pseudo-correlation caused by asynchronous sampling frequencies; Second, core causal variables such as light intensity, component temperature, time period type, and load characteristics are screened to eliminate redundant data with purely statistical correlations; Third, a directed acyclic graph (DAG) of "variable-photovoltaic output-load demand" is constructed to clarify the causal transmission path between various factors, thereby replacing the traditional purely data-driven scheduling logic and ensuring the scheduling accuracy of green electricity "tidal flow" in sudden scenarios such as extreme weather and electricity price adjustments.Then, using the trained model and more refined algorithms (such as Model Predictive Control, MPC), the basic scheduling strategy loaded by S4 is optimized and upgraded to dynamically adjust the power and timing of tidal flows to maximize the overall green energy consumption rate. Afterwards, comprehensive system commissioning is conducted, sequentially testing the autonomy of individual honeycomb units, the coordination of multi-unit joint operation, and the stability of the remote secondary aggregation, while continuously monitoring key indicators such as green energy self-sufficiency rate and curtailment rate. Finally, through an iterative cycle of "operation-monitoring-optimization," system parameters are continuously adjusted until they stably reach or exceed the preset green energy consumption target, completing the construction and acceptance of the entire system.

[0052] This invention describes a specific application scenario for a "factory-farmer" green energy consumption honeycomb microgrid system, which is a concrete application of its dedicated physical architecture in a typical scenario. The entire construction process strictly follows a closed-loop logic from goal definition to data-driven optimization. The implementation steps of its five stages will be detailed below.

[0053] Phase 1: Target Confirmation and Preliminary Feasibility Assessment. The starting point of this phase is to clarify the green energy consumption target for centralized load-side units. For example, an industrial park sets a target of "50% of its annual total electricity consumption must come from green energy." An assessment reveals that its internal factory rooftop photovoltaic systems can only provide about 15% of the electricity, resulting in a 35% green energy gap. Confirming this gap is a prerequisite for initiating system construction. Subsequently, using the industrial park as the core, a 15-kilometer radius service area is delineated on a Geographic Information System (GIS), initially identifying several villages within this area as potential decentralized load aggregation units. Based on the understanding of the local workers' work schedule of "working in factories during the day and returning to villages to rest at noon," it is qualitatively determined that factories (centralized load) experience a load trough at noon, while villages (decentralized load) experience a peak in electricity consumption due to cooking activities at noon. The two have significant potential for spatiotemporal complementarity, theoretically verifying the feasibility of using the architecture of this invention. The ideal complementary relationship is shown in the figure below, and the construction target is precisely to maximize the utilization of this complementary effect.

[0054] The second phase: Architecture selection and system design. After confirming feasibility, detailed solution design begins. Differentiated architecture selections are made based on the characteristics of different villages surrounding the industrial park: For "hollow villages" with extremely low electricity consumption at midday, a simplified direct-power-supply honeycomb unit architecture is adopted; for villages with permanent residents and stable electricity consumption at midday, a local balancing architecture is used, prioritizing local consumption at midday, with surplus electricity exported, and prioritizing power supply to the park outside midday; for areas close to the park with large supply-demand fluctuations, an advanced overall absorption architecture is planned, reserving capacity for "tidal flow" (power flow during off-peak hours). The basic building blocks of this system are honeycomb units with different characteristics, and its three core architectural forms provide fundamental flexibility to cope with different scenarios.

[0055] Next, based on the precise transmission distance, the "distance-voltage" adaptation rule was applied to design the topology for the "industrial park-village" system: villages less than 500 meters away were directly connected to the park using 380V low voltage; villages within 500 meters to 10 kilometers were connected to the park's 10kV busbar via 10kV lines. Within a 500-meter to 10-kilometer area, a refined topology plan was developed based on the capacity and spatial distribution characteristics of each village. Surveys revealed that the distributed photovoltaic (PV) installed capacity of several villages in this area ranges from 1.5 to 2.5 MW, exhibiting a clustered distribution characteristic of geographical proximity and small individual capacities. To address this, a secondary aggregation method at the same voltage level was adopted: after each village's distributed load-side unit is stepped up to 10kV, a secondary aggregation at the same voltage level is performed at a centrally located junction point via a 10kV interconnection line. This bundles three to four small-capacity units together to form a 10kV secondary aggregation unit with a total capacity of 6 to 8 MW, not exceeding 10 MW. This 10kV main line then crosses the remaining distance to connect to the 10kV busbar of the centralized load-side unit in the park. Compared to the scheme of laying 10kV lines independently from each village to the park, this secondary aggregation method at the same voltage level reduces redundant investment in medium-voltage lines and eliminates the need for an additional 35kV substation, achieving an optimal balance of technical and economic efficiency in near- and mid-range scenarios.

[0056] For villages more than 10 kilometers away, the plan is to first interconnect them via 10kV lines to form a secondary aggregation system, and then connect them to the park via a 35kV step-up substation. Ultimately, this will result in a customized "park-village" honeycomb system technical solution that integrates various honeycomb units, multiple voltage levels, and multi-level aggregation.

[0057] Phase Three: Deployment of Key Equipment and Construction of System Infrastructure. This phase is the engineering construction period, transforming the blueprint into a physical entity. Distributed photovoltaic arrays are installed on selected rooftops in each village, along with matching combiner boxes, inverters, smart meters, and distribution boxes. According to the design plan, power cables of different voltage levels connecting the villages and the industrial park are laid, with transformers, switchgear, and poles installed along the routes. The distribution rooms within the park are renovated, reserving corresponding 10kV and 35kV access bays, and a 35kV step-up substation is constructed for the remote cluster. Simultaneously, microgrid central controllers, smart metering switches, and static switches are deployed at key nodes throughout the network, and a high-speed communication network covering all equipment is established. Finally, grid connection interface units with rapid islanding detection capabilities are installed at the grid connection points in the park, and a complete relay protection system is configured to ensure the basic physical system has the capability for safe grid connection and stable operation.

[0058] Phase Four: System Initialization and Basic Strategy Loading. After the hardware setup is complete, the system enters the power-on initialization phase. For this "factory-farmer" system, comprehensive equipment debugging and communication integration are conducted first. Subsequently, a preset basic dispatch strategy based on the "spatiotemporal complementarity" principle is loaded onto the microgrid central controller. Its core rule is simplified as follows: during midday, electricity is prioritized to meet the village load, with surplus electricity sent to the industrial park; outside midday, the industrial park load demand is prioritized. The system begins trial operation under this strategy and initiates full-network, all-time data acquisition, continuously recording the actual output of photovoltaic power and the real load curves of the factory and villages, thereby establishing the system's operational performance baseline under the initial strategy.

[0059] Phase 5: Data-Driven Optimization and Performance Verification. This is the core phase to ensure the system ultimately achieves the 50% green energy self-sufficiency target. Using real-world operational data collected during Phase S4 of the "factory-farmer" system over a period of six months (or one year, depending on actual project needs), load forecasting and photovoltaic output forecasting models are trained. To improve the model's generalization ability in complex situations such as extreme weather, Physical Information Neural Networks (PINNs) are introduced, embedding electrical and energy conservation laws as constraints into the model training. Subsequently, using the trained forecast data and Model Predictive Control (MPC) algorithm, the initial scheduling strategy is optimized and upgraded, dynamically adjusting the power and timing of "tidal flows." Rigorous system commissioning is then conducted to test the autonomy of individual village units and the coordination of multiple units operating jointly. After the system is put into formal operation, key indicators such as green energy self-sufficiency and curtailment rate are continuously monitored. Through an iterative cycle of "operation-monitoring-optimization," control parameters are fine-tuned until the system stably reaches and exceeds the preset targets, completing the construction and acceptance of the entire system.

[0060] This invention achieves spatiotemporal complementarity and efficient green energy consumption across different load types by constructing a dedicated physical architecture of "honeycomb unit - multi-level aggregation - honeycomb system," resulting in the following beneficial effects: First, it significantly improves the green energy self-sufficiency rate of centralized loads while simultaneously reducing the overall curtailment rate of solar power. The system uses centralized loads as its core, aggregating surrounding distributed power sources. Through a "tidal flow" scheduling logic, surplus green energy is prioritized for consumption by distributed loads during peak demand periods, eliminating the need for large-scale energy storage to achieve real-time balance of photovoltaic output at midday. This increases the green energy self-sufficiency rate of centralized loads to over 50% and significantly reduces the overall curtailment rate of solar power. Second, it breaks through traditional technological path dependence, significantly reducing system construction and operation costs. Through "distance-voltage" adaptation rules and multi-level aggregation design, it replaces ultra-high-voltage long-distance transmission with low-voltage / medium-voltage direct connection and replaces large-capacity energy storage configuration with architectural innovation. The core equipment consists of mature mass-produced products, allowing for flexible expansion, and significantly reducing the total life-cycle cost compared to conventional solutions. Third, a modular and scalable standardized topology is constructed to lay the foundation for advanced intelligence. The "Honeycomb Unit" serves as the smallest autonomous node, possessing independent metering and power balancing capabilities, and supporting mixed configurations and multi-level aggregation of various architectures. The dual-level node design is naturally adapted to digital twins and graph computing models, and can deeply integrate physical information neural networks and causal discovery algorithms to achieve precise scheduling and continuous optimization in extreme scenarios.

[0061] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A distance adaptive aggregation based green power accommodation type honeycomb microgrid system, characterized by, It includes a configuration module, a power transmission module, an aggregation module, and a control module; The configuration module configures the distributed power supply in centralized load-side units and decentralized load-side units, and constructs honeycomb units, each of which includes one centralized load-side unit and n decentralized load-side units. The power transmission module is used to connect centralized load-side units and decentralized load-side units through a step-up substation and transmission lines adapted to different voltage levels to form a honeycomb microgrid architecture. The aggregation module connects the honeycomb units through virtual power plants or physical links to form a honeycomb microgrid architecture; The control module collects power data, regulates the flow of electrical energy, and connects to the public power grid.

2. The distance-adaptive aggregation-based green energy consumption honeycomb microgrid system as described in claim 1, characterized in that, The architecture of the honeycomb unit includes a basic architecture, an expansion architecture, and an optimization architecture; The basic architecture is a direct power supply type, which is suitable for low-load power consumption scenarios at midday for distributed load-side units; When the honeycomb unit has a direct power supply architecture, it is equipped with distributed power supply, combiner equipment, step-up equipment, smart metering switch and power transmission interface. Distributed power sources are aggregated and voltage-converted, and then directly transmitted to centralized load-side units via corresponding transmission lines; The extended architecture is a local balancing type, which is suitable for distributed power load units with low load during midday. When the architecture of the honeycomb unit is local balancing type, the distributed load side unit side includes distributed load and local load balancing equipment. The local load balancing equipment includes smart meters, smart switches and energy storage units. When the centralized load-side unit is in peak electricity consumption, the distributed power source prioritizes supplying the centralized load-side unit, combining and converting the electricity, and then transmitting it to the centralized load-side unit through the corresponding transmission line; When the centralized load-side unit is in a low-power period, the distributed power source of the decentralized load-side unit will prioritize supplying the decentralized load-side unit. The remaining power will be combined and voltage converted, and then transmitted to the centralized load-side unit and energy storage device through the corresponding transmission line. The optimized architecture is designed for overall absorption, making it suitable for scenarios with large fluctuations in distributed power output and high demand for spatiotemporal complementarity among different types of loads. When the architecture of the honeycomb unit is an overall absorption type, both the centralized load-side unit and the decentralized load-side unit are equipped with bidirectional transmission links, smart metering switches and local energy storage units; The microgrid central controller generates real-time dispatch commands to control the on / off state of smart metering switches, thereby executing control actions, specifically including... When the centralized load-side unit is in peak electricity consumption, the distributed power source prioritizes supplying the centralized load-side unit. The power is combined and the voltage is converted, and then transmitted to the centralized load-side unit through the corresponding transmission line. When the centralized load-side unit is in a low-power period, the surplus power generated by the distributed power source of the centralized load-side unit is transmitted in reverse through a bidirectional transmission link to the decentralized load-side unit.

3. A distance-adaptive aggregation-based green energy consumption honeycomb microgrid system as described in claim 2, characterized in that, The distributed load-side unit is equipped with a distribution box and user incoming line switch, forming a dual-link topology with local load balancing equipment and distributed power sources for local consumption and surplus power transmission. Real-time data on local load power and distributed power output are collected through intelligent metering devices.

4. A distance-adaptive aggregation-based green energy consumption honeycomb microgrid system as described in claim 3, characterized in that, In the overall absorption type of honeycomb unit, the local energy storage unit configured in the distributed load-side unit is an enhanced energy storage unit, and the centralized load-side unit is equipped with a power prediction and dynamic control module; The power prediction and dynamic control module is connected to the smart metering switch.

5. A distance-adaptive aggregation-based green energy consumption honeycomb microgrid system as described in claim 4, characterized in that, The distributed power supply of the distributed load-side unit supplies power to the centralized load-side unit through a dedicated direct connection line; The dedicated direct connection line selects the corresponding access voltage according to the connection distance. Specifically, when the connection distance is less than or equal to 500 meters, a 380V low-voltage access is used. When the connection distance is greater than 500 meters and less than 10 kilometers, a 10kV medium-voltage connection should be used. When there are special requirements for power supply stability or line capacity, a voltage level higher than 10kV should be used for connection.

6. A distance-adaptive aggregation-based green energy consumption honeycomb microgrid system as described in claim 5, characterized in that, The microgrid central controller can connect to the virtual power plant aggregation platform and receive scheduling instructions from the platform; Direct power supply type honeycomb units, local balancing type honeycomb units, and overall absorption type honeycomb units all retain independent metering and power balancing capabilities.

7. A distance-adaptive aggregation-based green energy consumption honeycomb microgrid system as described in claim 6, characterized in that, Multiple honeycomb units can form a honeycomb microgrid architecture through at least one of the following methods: virtual power plant aggregation, physical transmission and distribution bus aggregation, or secondary aggregation. When a honeycomb microgrid architecture is constructed through virtual power plant aggregation, each honeycomb unit is connected to the virtual power plant aggregation platform via a communication link. When aggregated via physical transmission and distribution bus, each honeycomb unit is interconnected through the transmission and distribution bus or transmission and distribution ring network and connected to the regional energy management platform; The honeycomb unit includes at least one of the following: direct power supply type honeycomb unit, local balancing type honeycomb unit, and overall absorption type honeycomb unit, with each honeycomb unit configured in a mixed manner. The regional energy management platform includes a unit layer, an overall coordination layer, and a multi-level aggregated coordination layer; The unit layer is connected to each honeycomb unit and is used to balance the power of each honeycomb unit; The overall coordination layer is connected to the unit layer and is used to coordinate the overall photovoltaic green electricity consumption in the region; The multi-level aggregation coordination layer is connected to the overall coordination layer and is used to balance the power distribution of the remote secondary aggregates; The multi-level aggregation coordination layer also includes a cross-side power coordination module, which is connected to the overall absorption type honeycomb unit and is used to coordinate the bidirectional power of the overall absorption type honeycomb unit.

8. A distance-adaptive aggregation-based green energy consumption honeycomb microgrid system as described in claim 7, characterized in that, The grid connection interface unit is configured with a grid connection voltage level according to the estimated total capacity of the system. Specifically, when the total load is greater than 10MW, the grid connection voltage level is 35kV. The scheduling optimization module is connected to the microgrid central controller and is used to perform the following actions: at any time, prioritize meeting the real-time power demand of centralized load-side units; For the overall absorption type honeycomb unit, when the output of the distributed power source of the centralized load side unit is greater than the power load of the centralized load side unit, the surplus power generated by the distributed power source of the centralized load side unit is preferentially transmitted to the decentralized load side unit that is in the peak power consumption period, and the remaining part is transmitted to the energy storage unit for charging. When the centralized load-side unit is at its peak electricity consumption, the power generated by the distributed power source throughout the region is preferentially delivered to the centralized load-side unit, and the remaining part is delivered to the decentralized load-side unit. When the total load is less than or equal to 10MW, the grid connection voltage level is 10kV.

9. A distance-adaptive aggregation-based green energy consumption honeycomb microgrid system as described in claim 8, characterized in that, The power transmission lines in the power transmission module are either AC or DC. When the transmission line is DC, the transmission module includes an AC / DC conversion device, which is connected to the transmission line; The scheduling optimization module is also used to regulate DC power; The secondary polymer is equipped with a 35kV step-up device, which is connected to the centralized load-side unit via a 35kV high-voltage busbar. The secondary aggregate is formed by multiple decentralized load-side units being aggregated twice via a 10kV line, then stepped up, and finally connected to a remote centralized load-side unit via a 35kV transmission line.

10. A distance-adaptive aggregation-based green energy consumption honeycomb microgrid system as described in claim 9, characterized in that, The multi-level aggregation and coordination layer of the regional energy management system is connected to the secondary aggregation body to balance the power of the secondary aggregation body; When the secondary aggregate includes honeycomb cells of the overall absorption type, the multi-level aggregation coordination layer also includes a cross-cell tidal power coordination submodule. The cross-cell tidal power coordination submodule is connected to the honeycomb cells of the overall absorption type and is used to coordinate the power of the honeycomb cells of the overall absorption type. The target confirmation and preliminary feasibility assessment stage is used to determine the green energy consumption target of centralized load-side units and assess distributed power resources; The architecture selection and system solution design phase is used to select the architecture of the honeycomb unit and design the system topology scheme; The key equipment deployment and system infrastructure construction phase is used to install distributed power sources, transmission lines and control equipment and establish communication networks. The system initialization and basic policy loading phase is used to power on and debug the system and load the basic scheduling policy. The data-driven optimization and performance verification phase is used to collect runtime data and optimize scheduling strategies.

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