Layered collaborative energy cross-microgrid scheduling system, method and control device

Abstract

The invention provides a hierarchical collaborative energy cross-microgrid scheduling system and method and a control device, and belongs to the technical field of distributed microgrid energy scheduling. Comprising a micro-grid central controller MGCC and a plurality of lower-layer micro-grid controllers MGC, and the plurality of micro-grid controllers MGC are in one-to-one correspondence with a plurality of sub-micro-grids. Through the centralized optimization of the micro-grid central controller MGCC, the power of the surplus sub-micro-grids can be dynamically dispatched to the sub-micro-grids in shortage of electric energy, so that photovoltaic power generation can be maximally utilized in situ, and cross-sub-micro-grid flexible dispatching of energy in a charging station with a multi-transformer structure is realized. The energy island phenomenon in a traditional single-point control mode is broken through, photovoltaic energy and energy storage energy which are possibly limited originally are guided to the most needed places, the consumption rate of renewable energy sources and the peak-valley arbitrage income of the energy storage system are remarkably improved, and meanwhile, the safety and reliability of the system are ensured through a layered cooperative control framework.

Description

Technical Field

[0001] This invention belongs to the field of distributed microgrid energy dispatching technology, specifically relating to a hierarchical collaborative energy dispatching system, method, and control device across microgrids. Background Technology

[0002] With the rapid development of renewable energy technologies and the widespread adoption of electric vehicles, microgrid systems, comprised of distributed photovoltaic (PV) systems, energy storage systems (ESS), and electric vehicle charging stations, have been widely applied in scenarios such as commercial parks and parking lots. Traditional microgrid energy management systems (MG-EMS) are typically designed and optimized based on a key assumption: that the entire microgrid system is downstream of a single point of common coupling (PCC) or a single transformer. Under this architecture, the core function of the controller focuses on energy balance and optimization within this single control point. Typical strategies include: Anti-reverse flow control: By adjusting the charging of energy storage or limiting photovoltaic output, excess power in the microgrid is prevented, especially during peak photovoltaic power generation periods, from being fed back to the upper-level distribution network, thus ensuring grid safety.

[0003] Peak-valley arbitrage: guiding energy storage to charge during off-peak hours and discharge during peak hours, thereby reducing overall electricity costs.

[0004] Overload management: When the charging load increases suddenly, use energy storage to discharge or appropriately limit non-critical loads to avoid transformer overload operation.

[0005] Local photovoltaic consumption: Prioritize the use of photovoltaic power generation to meet local real-time load demand and improve the self-consumption rate of renewable energy.

[0006] However, in practical applications, especially in large-scale electric vehicle charging stations, the limitations of the aforementioned traditional control mode are becoming increasingly apparent. These stations are typically large in scale with more complex internal electrical structures, often powered by multiple transformers in separate zones. Each transformer, along with its downstream loads, photovoltaic (PV) systems, energy storage, and other assets, constitutes a relatively independent power supply unit, which can be considered a sub-microgrid. For example, PV and energy storage systems may only be installed on the output line of a specific transformer, while charging piles are distributed and connected to different transformers within the station. This physical segmentation creates barriers to energy flow. When the area where the transformers connecting PV and energy storage—i.e., the source-side transformers—are located is during peak PV power generation or peak grid electricity prices, a severe operational bottleneck can occur if the area happens to have low charging vehicle load demand. Limited energy release: The local load downstream of the source-side transformer cannot absorb the surplus energy generated by photovoltaic in time, and the traditional anti-reverse flow strategy will actively limit the output of photovoltaic in order to avoid backfeeding power to the grid, or even curtail photovoltaic power, resulting in the waste of renewable energy.

[0007] Economic loss: The energy stored by the energy storage system during peak electricity price periods cannot be effectively discharged due to the lack of local load, missing the best opportunity to generate revenue through peak-valley arbitrage and reducing the economics of energy storage investment.

[0008] Low resource utilization: At the same time, other transformers in the station, i.e., downstream of the load-side transformers, may have high charging demand, but they cannot use the clean, low-cost electricity generated by the station itself, which is nearby. Instead, they need to purchase electricity from the grid, which increases operating costs.

[0009] Against this backdrop, traditional microgrid controllers treat the downstream system of each transformer as an isolated optimization unit. Their strategy boundaries cannot overcome the limitations of the transformer, lacking the ability to coordinate and schedule energy among multiple transformers, i.e., multiple sub-microgrids. This creates energy islands between the sub-microgrids, preventing the achievement of global energy optimization at the plant level. Therefore, a new type of control device or system is needed that can break down transformer barriers and achieve flexible, safe, and economical energy scheduling among multiple sub-microgrids. Summary of the Invention

[0010] The purpose of this invention is to overcome the above-mentioned shortcomings and provide a hierarchical collaborative energy scheduling system, method and control device across microgrids.

[0011] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a hierarchical collaborative energy dispatching system across microgrids, comprising a microgrid central controller (MGCC) and several lower-level microgrid controllers (MGCs), wherein each MGC corresponds one-to-one with a plurality of sub-microgrids; wherein... The microgrid central controller MGCC communicates with the high-voltage side main switch meter connected to the upper-level power grid to obtain the total grid connection point power data of the system, and communicates with all lower-level microgrid controllers MGC; The microgrid controller MGC communicates with the distributed energy units and load units within its sub-microgrid and collects the meter data at the gateway of the sub-microgrid. The distributed energy unit includes a photovoltaic power generation unit and an energy storage unit, and the load unit includes a charging pile.

[0012] The microgrid central controller (MGCC) performs cross-sub-microgrid scheduling as follows: When the MGCC detects that the first sub-microgrid has an energy surplus while the second sub-microgrid has a load demand, the MGCC sends a command to the microgrid controller (MGC) corresponding to the first sub-microgrid to control the output power of the distributed energy units of the first sub-microgrid. Part of the output power flows back to the high-voltage bus via the point of common coupling, and then is transmitted to the second sub-microgrid via the high-voltage bus for its consumption.

[0013] The microgrid controller MGC executes the local energy management strategy of its sub-microgrid and uploads the operating status and adjustability of its sub-microgrid to the microgrid central controller MGCC in real time.

[0014] The local energy management strategy includes at least one of anti-reverse flow control, peak-valley arbitrage, and maximum photovoltaic absorption; the adjustable capability uploaded by the microgrid controller MGC to the microgrid central controller MGCC in real time includes the residual discharge capacity of the sub-microgrid, which is the difference between the maximum power that the distributed energy unit of the sub-microgrid can provide and the local load power under its operating state.

[0015] The microgrid central controller (MGCC) calculates the overall power interaction target of the system with the upper-level power grid based on the data from the main switch meter on the high-voltage side, and generates power scheduling instructions for each microgrid controller (MGC) to achieve cross-sub-microgrid scheduling of energy among multiple sub-microgrids.

[0016] When calculating power dispatch instructions, the microgrid central controller (MGCC) presets a regulation dead zone; when the reverse power value measured by the main switch meter on the high-voltage side is less than the regulation dead zone threshold, the microgrid central controller (MGCC) does not perform dispatch intervention.

[0017] Secondly, this invention provides a hierarchical collaborative energy scheduling method across microgrids, including online and offline modes. When the microgrid central controller (MGCC) is online, each microgrid controller (MGC) reports the operating status and remaining discharge capacity of its corresponding sub-microgrid to the MGCC in real time. The MGCC collects power data from the main meter on the high-voltage side and, with the constraint of preventing reverse flow to the upper layer of the grid, dynamically calculates and sends the following instructions to each microgrid controller (MGC) to achieve global energy optimization. Each microgrid controller (MGC) executes power control according to the instructions of the MGCC. When the microgrid central controller (MGCC) goes offline or communication is interrupted, each microgrid controller (MGC) automatically switches to independent operation and executes local energy management strategies.

[0018] In the online mode, the microgrid central controller (MGCC) aims to achieve global energy optimization. Specifically, the MGCC dynamically allocates the power value that allows reverse flow based on the remaining discharge capacity reported by each microgrid controller (MGC). This allows sub-microgrids with surplus energy to dispatch their excess energy to other sub-microgrids with energy shortages via the high-voltage bus, thereby achieving peak-valley arbitrage and photovoltaic power consumption at the power station level.

[0019] In the offline mode, the local energy management strategies executed by each microgrid controller (MGC) include: an anti-reverse flow strategy to prevent the microgrid from flowing back to the high-voltage side, as well as charging and discharging control based on time-of-use pricing and local priority consumption control of photovoltaic power generation.

[0020] Thirdly, the present invention provides a control device, the control device comprising: a memory, a processor, and a hierarchical collaborative energy cross-microgrid scheduling system stored in the memory and capable of running on the processor, the hierarchical collaborative energy cross-microgrid scheduling system being configured to implement a hierarchical collaborative energy cross-microgrid scheduling method.

[0021] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a hierarchical collaborative energy dispatching system, method, and control device across microgrids, including a microgrid central controller (MGCC) and several lower-level microgrid controllers (MGCs), each MGC corresponding to a specific sub-microgrid. Through centralized optimization by the MGCC, surplus power from sub-microgrids can be dynamically dispatched to those with power shortages. This maximizes the local utilization of photovoltaic power generation and enables flexible energy dispatching across sub-microgrids within a multi-transformer charging station. It breaks the energy island phenomenon of traditional single-point control modes, guiding potentially limited photovoltaic and energy storage energy to where it is most needed, significantly improving the renewable energy absorption rate and peak-valley arbitrage benefits of energy storage systems. Simultaneously, the hierarchical collaborative control architecture ensures the system's safety and reliability.

[0022] Furthermore, a dual-mode operation mechanism of online and offline MGCC is adopted. When MGCC is online, global optimal scheduling is achieved; when MGCC or communication network fails, each sub-microgrid controller (MGC) can immediately switch to local autonomous mode and independently execute basic protection strategies such as anti-reverse current. This design leverages both the global optimization efficiency of centralized control and the robustness and high reliability of distributed systems, ensuring that each sub-microgrid can still operate independently and safely without impacting the external power grid when the central controller fails.

[0023] Furthermore, with the anti-backflow measure at the high-voltage side main gate as the highest constraint, the overall station's friendliness to the upstream power grid is ensured. MGCC provides precise control from a global perspective, effectively preventing the entire system from sending power back to the upstream power grid. Simultaneously, the introduction of a power regulation dead zone design smooths out power fluctuations caused by random start-stop of charging piles, avoiding frequent command actions, thus protecting equipment, reducing disturbances to the power grid, and improving power quality. Attached Figure Description

[0024] Figure 1 This is a system topology diagram of the present invention; Figure 2 This is an application effect diagram of Embodiment 3 of the present invention. Detailed Implementation

[0025] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0026] Example 1 A hierarchical and collaborative energy scheduling system across microgrids has the following structure: like Figure 1 As shown, a hierarchical collaborative energy dispatching system across microgrids includes a microgrid central controller (MGCC) and several lower-level microgrid controllers (MGCs), with each MGC corresponding to a specific sub-microgrid.

[0027] The power supply to the substation comes from the 10kV busbar of the power grid company, connecting via the high-voltage property boundary point. A high-voltage side main gate meter P0 is installed after the property boundary point and before the substation's main switch to accurately measure the power exchange between the entire substation and the upstream 10kV grid. The substation contains several relatively independent sub-microgrids, each powered by an independent transformer and connected to the 10kV busbar through its own sub-microgrid gate meter. The microgrid central controller (MGCC) is deployed at the substation monitoring layer. It connects to the internet via a Cat5e or higher shielded cable, through a router in the substation monitoring room, and interacts with the cloud platform. Simultaneously, the MGCC establishes a reliable wired communication connection (such as Ethernet) with the lower-level microgrid controllers via an industrial switch. Each MGCC is installed within its corresponding sub-microgrid and communicates with the distributed energy and load units within the sub-microgrid via communication lines (such as LAN bus, RS485, or Ethernet).

[0028] The microgrid central controller (MGCC) communicates with the high-voltage side main switch meter connected to the upper-level power grid to obtain the total power data of the system's grid connection points, and also communicates with all lower-level microgrid controllers (MGCs). The MGCC is deployed in the combiner station or central distribution room of the clustered microgrid. It establishes a connection with the high-voltage side main switch meter connected to the upper-level power grid via a communication network to obtain real-time total power data exchanged between the entire microgrid cluster and the upper-level power grid. Simultaneously, the MGCC establishes bidirectional communication links with all lower-level microgrid controllers (MGCs) through reliable communication methods such as fiber optics, industrial Ethernet, or private wireless networks.

[0029] The MGCC continuously monitors the operating status of sub-microgrids reported by each MGC, such as photovoltaic output, energy storage SOC, load power, and adjustability, such as remaining discharge capacity. When the MGCC determines that the first sub-microgrid has an energy surplus while the second sub-microgrid has a load demand, the MGCC generates a coordinated dispatch command and sends it to the MGC corresponding to the first sub-microgrid, controlling its distributed energy units to increase discharge power or allowing full photovoltaic power generation. This increased output power, in addition to supplying the local load of the first sub-microgrid, will have its surplus flowed back to the cluster's common connection point (PCC) and high-voltage bus via the sub-microgrid's gateway. Then, this energy is transmitted through the high-voltage bus to the second sub-microgrid with load demand for its consumption. This process realizes the transfer of energy within the cluster, rather than directly sending it to the upper-level grid. The microgrid central controller (MGCC) calculates the overall power interaction target of the system with the upper-level power grid based on the data from the main switch meter on the high-voltage side, and generates power scheduling instructions for each microgrid controller (MGC) to achieve cross-sub-microgrid scheduling of energy among multiple sub-microgrids.

[0030] Each microgrid controller (MGC) is deployed within its corresponding sub-microgrid, typically located in the sub-microgrid's distribution room or energy management box. The MGC communicates with each distributed energy unit and load unit within its sub-microgrid via fieldbus (e.g., RS485, CAN), power line carrier, or local area network (LAN) communication methods to achieve unit-level monitoring and control. Furthermore, each MGC collects meter data from the connection point between its sub-microgrid and the cluster's high-voltage bus, reflecting the power exchange between the sub-microgrid and the cluster's common connection point. The MGC executes the local energy management strategy for its sub-microgrid and uploads its sub-microgrid's operating status and adjustability to the microgrid central controller (MGCC) in real time. Adjustability includes the sub-microgrid's residual discharge capacity, which is the difference between the maximum power available from its distributed energy units and the local load power under its operating conditions.

[0031] Furthermore, the local energy management strategy is executed by the MGC after receiving instructions from the MGCC in offline or online mode. This includes, but is not limited to: anti-reverse flow control: preventing microgrids from flowing back to the high-voltage bus; peak-valley arbitrage: charging during off-peak hours and discharging during peak hours; maximum photovoltaic absorption: prioritizing the use of photovoltaic power, with any shortfall supplemented by energy storage or the grid. The MGC coordinates and controls the photovoltaic, energy storage, and charging piles under its jurisdiction according to preset logic or instructions from higher authorities.

[0032] In a preferred embodiment of the present invention, the distributed energy unit mainly includes a photovoltaic power generation unit and an energy storage unit. The photovoltaic power generation unit consists of photovoltaic modules, a combiner box, and a photovoltaic inverter. The energy storage unit consists of an energy storage battery pack and an energy storage converter (PCS). The load unit mainly includes electric vehicle charging piles, which can be AC ​​slow charging piles or DC fast charging piles. Each MGC can issue power limiting commands to the photovoltaic inverter, charge / discharge power commands to the energy storage PCS, and commands to the charging pile management system to adjust the charging power.

[0033] Furthermore, to reduce frequent controller actions and improve system stability, the Microgrid Central Controller (MGCC) presets a dead zone when calculating power dispatch commands. For example, the dead zone threshold is set to 2% of the rated capacity of the main grid. When the reverse current power measured by the main grid meter on the high-voltage side is less than this threshold, the MGCC considers the current reverse current to have a negligible impact on the grid and does not intervene in dispatch, allowing minor power fluctuations. Only when the reverse current power exceeds the dead zone threshold does the MGCC activate the cross-microgrid dispatch logic, calculate, and issue commands to eliminate the reverse current.

[0034] Example 2 A hierarchical collaborative energy scheduling method across microgrids is proposed. Based on the online status of the MGCC, two working modes are designed: online mode and offline mode. This enables seamless switching between centralized coordination and decentralized autonomy, ensuring high system availability.

[0035] 1) Online mode When the microgrid central controller (MGCC) is online, each microgrid controller (MGC) reports the operating status and remaining discharge capacity of its corresponding sub-microgrid to the MGCC in real time. The MGCC collects power data from the main gate meter on the high-voltage side and, with the constraint of preventing reverse current to the upper-level grid and the goal of global energy optimization, dynamically calculates and sends the following instructions to each microgrid controller (MGC). Each microgrid controller (MGC) executes power control according to the instructions from the MGCC, decomposing them into specific control commands for subordinate photovoltaic inverters, energy storage PCS, and charging piles, thereby changing the external power exchange of the sub-microgrid and ultimately affecting the total gate power, bringing it back to the target range.

[0036] The global energy optimization objectives include: a) maximizing the internal absorption of photovoltaic power generation within the cluster; b) minimizing the cost of purchasing electricity from the grid to achieve peak-valley arbitrage.

[0037] In online mode, MGCC can take a holistic view and direct the transfer of energy from surplus sub-microgrids to shortage sub-microgrids, achieving efficient energy allocation at the field station level. Essentially, it virtualizes the entire cluster microgrid as a single, self-balancing intelligent energy system.

[0038] 2) Offline mode When the microgrid central controller (MGCC) goes offline due to fault, maintenance, or communication network interruption, the system automatically and seamlessly switches to offline mode.

[0039] After the switchover, each microgrid controller (MGC) no longer relies on the MGCC's instructions, but instead executes preset local energy management strategies based entirely on its locally collected data, such as sub-microgrid gateway meter data and distributed unit status. These strategies include: anti-reverse flow strategies to prevent backflow from the sub-microgrid to the high-voltage side, as well as charging and discharging control based on time-of-use pricing and local priority consumption control for photovoltaic power generation.

[0040] Offline mode ensures that each sub-micronet can operate independently, safely, stably, and economically even without central coordination, greatly improving the robustness and survivability of the entire cluster system. Once the MGCC returns to online, each MGCC will re-report data, receive instructions, and switch back to online mode.

[0041] Example 3 This embodiment takes a comprehensive energy photovoltaic-storage-charging integrated microgrid system in Zhengzhou as an example. The system includes a microgrid central controller (MGCC) and four lower-level microgrid controllers (MGC1~MGC4), with each MGC managing one sub-microgrid. The system is connected to the upper-level power grid via a 10kV high-voltage line. A main switch meter (P_total) is installed on the high-voltage side. The system has a preset 20kW regulation dead zone to reduce backflow caused by sudden load reduction and ensure grid safety.

[0042] The microgrid central controller MGCC establishes real-time communication connections with microgrid central controllers MGC1, MGC2, MGC3, and MGC4 via fiber optic networks. Simultaneously, the MGCC communicates with the main high-voltage side meter to collect real-time power values ​​at the system's total grid connection point.

[0043] Subnet configuration: Microgrid 1 (managed by MGC1): Connects to photovoltaic power generation units and energy storage units. MGC1 collects the gateway meter data P1 of this subgrid and monitors the operating status of photovoltaic and energy storage.

[0044] Microgrid 2 (managed by MGC2): Connects to photovoltaic power generation units and energy storage units. MGC2 collects the gateway meter data P2 of this subgrid and monitors the operating status of photovoltaic and energy storage.

[0045] Microgrid 3 (managed by MGC3): Its main load is the No. 1 800kW DC charging pile group. MGC3 collects the gateway meter data P3 of this microgrid and monitors the power of the charging piles.

[0046] Microgrid 4 (managed by MGC4): Its main load is the 1# 800kW DC charging pile group. MGC4 collects the gateway meter data P4 of this sub-microgrid and monitors the power of the charging piles.

[0047] Furthermore, each MGC has a pre-set basic energy management strategy, such as peak-valley arbitrage and anti-backflow.

[0048] 1) Data reporting: MGC1~MGC4 continuously report key data to the microgrid central controller MGCC. For example... Figure 2 As shown, at a certain moment: MGC1 reported: Power P1 = -89.6kW. The negative value indicates that the power is output to the common bus. The operating strategy is peak-valley arbitrage. The remaining discharge capacity, that is, the potential to reverse the flow to the upstream grid, is assumed to be 90kW.

[0049] MGC2 reported: power P2 = -138.6kW, operating strategy is peak-valley arbitrage, and the remaining discharge capacity is assumed to be 140kW.

[0050] MGC3 reported: Power P3 = 89.4kW, a positive value indicates that power is absorbed from the common bus, and the load is a DC charging pile.

[0051] MGC4 reported: Power P4 = 146.5kW, load is DC charging pile.

[0052] 2) The microgrid central controller (MGCC) receives data from the main switch meter on the high-voltage side. Assuming that P_total = -20.5kW, meaning the system as a whole has 20.5kW of power that wants to flow back to the grid. The optimization objective of MGCC is to maximize the clean energy consumption of microgrid 1 and microgrid 2 while ensuring that P_total ≥ -20kW, i.e., the reverse flow power does not exceed the 20kW dead zone.

[0053] The total load of microgrids 3 and 4 is 89.4 + 146.5 ≈ 235.9 kW. The total surplus generating capacity of microgrids 1 and 2 is 90 + 140 = 230 kW. Ideally, the surplus energy can be completely absorbed by microgrids 3 and 4 without needing to flow back to the grid.

[0054] MGCC immediately sends instructions to MGC1 and MGC2, allowing them to increase power output within a certain range, i.e., dynamic reverse current, so that their energy can be transmitted to microgrids 3 and 4 through the high-voltage bus. At the same time, MGCC commands the charging piles under MGC3 and MGC4 to prioritize the use of energy from the bus.

[0055] This system has a preset adjustment dead zone of 20kW. This means that as long as |P_total| < 20kW, the MGCC will not make frequent power command adjustments. This design effectively avoids system oscillations and malfunctions caused by sudden load drops due to vehicles suddenly stopping charging at the charging station, resulting in a momentary drop in P_total to a large reverse current state, thus greatly ensuring the stability and safety of the power grid.

[0056] Furthermore, if communication between the MGCC and a certain MGC is interrupted, the MGC will immediately detect that the MGCC is offline and automatically switch to offline mode. In this mode, the MGC will activate its local anti-reverse flow strategy to strictly ensure that the power of its sub-microgrid does not flow back to the high-voltage side, ensuring that even if the central controller fails, the operation of a single microgrid will not affect the safety of the power grid.

[0057] Example 4 The present invention also proposes a control device, the control device comprising: a memory, a processor, and a hierarchical collaborative energy cross-microgrid scheduling system stored in the memory and capable of running on the processor, the hierarchical collaborative energy cross-microgrid scheduling system being configured to implement a hierarchical collaborative energy cross-microgrid scheduling method.

[0058] It is worth noting that since the control device of the present invention is based on the above-mentioned hierarchical collaborative energy cross-microgrid scheduling method, the embodiments of the control device of the present invention include all the technical solutions of all embodiments of the above-mentioned hierarchical collaborative energy cross-microgrid scheduling method, and the technical effects achieved are exactly the same, so they will not be repeated here.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A hierarchical collaborative energy scheduling system across microgrids, characterized in that, It includes a microgrid central controller (MGCC) and several lower-level microgrid controllers (MGCs), each MGC corresponding to one of multiple sub-microgrids; wherein, The microgrid central controller MGCC communicates with the high-voltage side main switch meter connected to the upper-level power grid to obtain the total grid connection point power data of the system, and communicates with all lower-level microgrid controllers MGC; The microgrid controller MGC communicates with the distributed energy units and load units within its sub-microgrid and collects the meter data at the gateway of the sub-microgrid. The distributed energy unit includes a photovoltaic power generation unit and an energy storage unit, and the load unit includes a charging pile.

2. The hierarchical collaborative energy scheduling system across microgrids according to claim 1, characterized in that, The microgrid central controller (MGCC) performs cross-sub-microgrid scheduling. Specifically, when the MGCC detects that the first sub-microgrid has an energy surplus while the second sub-microgrid has a load demand, the MGCC sends a command to the microgrid controller (MGC) corresponding to the first sub-microgrid to control the output power of the distributed energy units of the first sub-microgrid. Part of the output power flows back to the high-voltage bus via the point of common coupling, and then is transmitted to the second sub-microgrid via the high-voltage bus for its consumption.

3. The hierarchical collaborative energy cross-microgrid scheduling system according to claim 2, characterized in that, The microgrid controller MGC executes the local energy management strategy of its sub-microgrid and uploads the operating status and adjustability of its sub-microgrid to the microgrid central controller MGCC in real time.

4. The hierarchical collaborative energy scheduling system across microgrids according to claim 3, characterized in that, The local energy management strategy includes at least one of anti-reverse flow control, peak-valley arbitrage, and maximum photovoltaic absorption; the adjustable capability uploaded by the microgrid controller MGC to the microgrid central controller MGCC in real time includes the residual discharge capacity of the sub-microgrid, which is the difference between the maximum power that the distributed energy unit of the sub-microgrid can provide and the local load power under its operating state.

5. A hierarchical collaborative energy scheduling system across microgrids according to claim 1, characterized in that, The microgrid central controller (MGCC) calculates the overall power interaction target of the system with the upper-level power grid based on the data from the main switch meter on the high-voltage side, and generates power scheduling instructions for each microgrid controller (MGC) to achieve cross-sub-microgrid scheduling of energy among multiple sub-microgrids.

6. A hierarchical collaborative energy scheduling system across microgrids according to claim 5, characterized in that, When calculating power dispatch instructions, the microgrid central controller (MGCC) presets a regulation dead zone; when the reverse power value measured by the main switch meter on the high-voltage side is less than the regulation dead zone threshold, the microgrid central controller (MGCC) does not perform dispatch intervention.

7. A hierarchical and collaborative energy scheduling method across microgrids, characterized in that, Including online and offline modes, when the microgrid central controller MGCC is online, each microgrid controller MGC reports the operating status and remaining discharge capacity of its corresponding sub-microgrid to the MGCC in real time; the microgrid central controller MGCC collects the power data of the main gate meter on the high-voltage side, and with the constraint of preventing reverse current to the upper layer of the grid and the goal of achieving global energy optimization, dynamically calculates and sends the following instructions to each microgrid controller MGC, and each microgrid controller MGC executes power control according to the instructions of the microgrid central controller MGCC; When the microgrid central controller (MGCC) goes offline or communication is interrupted, each microgrid controller (MGC) automatically switches to independent operation and executes local energy management strategies.

8. The hierarchical collaborative energy scheduling method across microgrids according to claim 7, characterized in that, In the online mode, the microgrid central controller (MGCC) aims to achieve global energy optimization. Specifically, the MGCC dynamically allocates the power value that allows reverse flow based on the remaining discharge capacity reported by each microgrid controller (MGC). This allows sub-microgrids with surplus energy to dispatch their excess energy to other sub-microgrids with energy shortages via the high-voltage bus, thereby achieving peak-valley arbitrage and photovoltaic power consumption at the power station level.

9. The hierarchical collaborative energy scheduling method across microgrids according to claim 7, characterized in that, In the offline mode, the local energy management strategies executed by each microgrid controller (MGC) include: an anti-reverse flow strategy to prevent the microgrid from flowing back to the high-voltage side, as well as charging and discharging control based on time-of-use pricing and local priority consumption control of photovoltaic power generation.

10. A control device, characterized in that, The control device includes: a memory, a processor, and a hierarchical collaborative energy cross-microgrid scheduling system stored in the memory and capable of running on the processor, the hierarchical collaborative energy cross-microgrid scheduling system being configured to implement the hierarchical collaborative energy cross-microgrid scheduling method as described in any one of claims 6 to 9.

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