Multi-energy Cooperative Optimization Method and System for SST DC Microgrids Based on EMS

Abstract

This invention discloses a multi-energy collaborative optimization method and system for SST DC microgrids based on EMS, belonging to the field of new energy technology. The method includes: collecting real-time operating data of the DC microgrid; generating charging and discharging scheduling commands and absorption control strategies based on the real-time operating data and preset revenue and reliability principles; executing the absorption control strategies in conjunction with the charging and discharging scheduling commands to dynamically manage output power; and when real-time operating data indicates that the AC grid is de-energized, executing off-grid power supply control logic based on backup power constraints to control the energy storage system to switch its operating mode to maintain the DC bus voltage and provide uninterrupted power supply to critical loads. This application solves the technical problem in the prior art where SST DC microgrids lack full-link collaborative management and control, and where backup power safety, revenue, equipment safety, and power supply reliability cannot be simultaneously addressed.

Description

Technical Field

[0001] This invention relates to the field of new energy technology, specifically to a multi-energy collaborative optimization method and system for SST DC microgrids based on EMS. Background Technology

[0002] Driven by the continuous advancement of dual-carbon goals and the rapid development of the digital economy, new DC loads such as computing centers, V2G charging and swapping stations, and DC charging piles are experiencing rapid growth. High-voltage DC microgrids, with their core advantages of low line loss, high power density, absence of reactive power and harmonic issues, and strong DC source-load adaptability, have become the mainstream power supply and distribution architecture for industrial and commercial parks, computing hubs, and charging and swapping stations. Among them, SST, with its high-frequency isolation, high efficiency, and high integration, can directly achieve voltage conversion and electrical isolation from 10kV AC to 800V DC, replacing the traditional chain-type power supply and distribution architecture of power frequency transformer + UPS + HVDC, and becoming the core energy conversion equipment of DC microgrid systems.

[0003] Current mainstream SST DC microgrid technology still has many significant shortcomings, making it difficult to adapt to the core needs of industry development: First, it lacks unified and coordinated management and control across the entire EMS system. SST, photovoltaic, energy storage, load, and switching equipment operate independently, and strategy execution lacks unified scheduling, failing to achieve the core objective of optimal overall system profitability. Second, energy storage operation strategies cannot balance rigid backup power demand with maximizing peak shaving and valley filling benefits. The depth of charge and discharge is not clearly constrained, easily leading to insufficient backup power capacity or excessive reserve of backup power during grid outages, resulting in wasted arbitrage opportunities. Third, SST capacity utilization lacks rigid constraints and there is no redundant charge and discharge. The design of the path coordination is prone to causing SST full-load overload operation, shortened equipment life, or revenue loss due to interruption of charging and discharging plans when the load suddenly increases; fourth, the photovoltaic consumption strategy is too extensive, and it does not carry out refined hierarchical management in combination with the total microgrid load and energy storage charging and discharging plans, which cannot maximize the photovoltaic self-consumption rate and is prone to problems such as curtailment or high-cost power purchase during periods of high electricity prices; fifth, the source-load-storage coordination and operating condition switching response capabilities are insufficient. In scenarios such as photovoltaic and energy storage power interaction and grid power failure, the switching of switching equipment and system strategies is lagging behind, which cannot guarantee the stability of DC bus voltage and uninterrupted power supply to critical loads such as computing centers. This application can specifically solve the above-mentioned industry pain points and ultimately achieve the core goal of optimizing the project investment return cycle and maximizing IRR. Summary of the Invention

[0004] This application provides a multi-energy collaborative optimization method and system for SST DC microgrids based on EMS, aiming to solve the technical problems in the existing technology of SST DC microgrids that lack full-link collaborative management and control, and cannot simultaneously ensure backup power safety, benefits, equipment safety and power supply reliability.

[0005] In view of the above problems, this application provides a method and system for multi-energy collaborative optimization of SST DC microgrid based on EMS.

[0006] The first aspect disclosed in this application provides a multi-energy collaborative optimization method for SST DC microgrids based on EMS. This method includes: collecting real-time operating data of the DC microgrid, including photovoltaic power generation, total DC bus load power, energy storage system state of charge, grid time-of-use pricing signals, and grid connection / disconnection status signals of the AC grid; generating charging and discharging scheduling instructions for the energy storage system and a absorption control strategy for the photovoltaic power generation units based on the real-time operating data and preset revenue and reliability principles, wherein the charging and discharging scheduling instructions include backup power constraints to ensure power supply reliability and capacity constraints to ensure the safety of the SST equipment; executing the absorption control strategy in conjunction with the charging and discharging scheduling instructions to dynamically manage the output power of the photovoltaic power generation units, the dynamic management including controlling the distribution ratio of photovoltaic power among the DC bus load, the energy storage system, and the AC grid; and when the real-time operating data indicates a power outage in the AC grid, executing off-grid power supply control logic based on the backup power constraints to control the energy storage system to switch its operating mode to maintain the DC bus voltage and provide uninterrupted power supply to critical loads.

[0007] Another aspect of this application discloses a multi-energy collaborative optimization system for an SST DC microgrid based on an EMS. The DC microgrid includes an AC grid connected via a solid-state transformer (SST) and a DC bus connected to the DC side of the SST. At least one DC load, a photovoltaic power generation unit, and an energy storage system are connected to the DC bus. The system includes: an operation data acquisition module for acquiring real-time operation data of the DC microgrid, including photovoltaic power generation, total DC bus load power, energy storage system state of charge, grid time-of-use pricing signals, and grid connection / disconnection status signals of the AC grid; and a dispatch instruction generation module for generating charging and discharging dispatch instructions for the energy storage system based on the real-time operation data and preset revenue and reliability principles. The system includes a control strategy for the absorption of photovoltaic power generation units, wherein the charge and discharge scheduling command includes a backup power constraint to ensure power supply reliability and a capacity constraint to ensure the safety of the SST equipment; an output power management module is used to execute the absorption control strategy in conjunction with the charge and discharge scheduling command, and dynamically manage the output power of the photovoltaic power generation units, wherein the dynamic management includes controlling the distribution ratio of photovoltaic power among the DC bus load, the energy storage system and the AC grid side; and a working mode switching module is used to execute off-grid power supply control logic based on the backup power constraint when the real-time operating data indicates that the AC grid is out of power, to control the energy storage system to switch its working mode to maintain the DC bus voltage and provide uninterrupted power supply to critical loads.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] This solution employs an EMS as the core of end-to-end management and control, collects all real-time operational data of the SST DC microgrid, and generates charging and discharging scheduling and photovoltaic consumption strategies with dual constraints of backup power and capacity based on the principles of revenue and reliability. It dynamically manages photovoltaic power allocation and executes off-grid backup logic when the grid loses power. This solution solves the technical problems in existing technologies where SST DC microgrids lack end-to-end collaborative management and control, and cannot simultaneously ensure backup power safety, revenue, equipment safety, and power supply reliability. It achieves the technical effect of synergistic optimization of the entire microgrid system, maximizing revenue throughout the entire life cycle, ensuring equipment safety and stability, and providing highly reliable power supply to critical loads.

[0010] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0011] Figure 1 A flowchart illustrating the multi-energy collaborative optimization method for SST DC microgrids based on EMS is provided for embodiments of this application.

[0012] Figure 2 A schematic diagram of the structure of an EMS-based SST DC microgrid multi-energy collaborative optimization system is provided for the embodiments of this application.

[0013] Figure labeling: 11 is the data acquisition module, 12 is the scheduling instruction generation module, 13 is the output power management module, and 14 is the working mode switching module. Detailed Implementation

[0014] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0015] The overall concept of the technical solution provided in this application is as follows:

[0016] This application provides a method and system for multi-energy collaborative optimization of SST DC microgrids based on EMS. Using EMS as the core of end-to-end management, it collects full real-time operational data on photovoltaics, loads, energy storage, electricity prices, and grid connection / off-grid status of the SST DC microgrid. Based on the principles of profitability and reliability, it generates charging and discharging scheduling and photovoltaic absorption strategies with dual constraints of backup power and capacity, dynamically manages photovoltaic power allocation, and executes off-grid supply guarantee logic when the grid loses power, achieving safe, economical, and stable operation of the microgrid.

[0017] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0018] Example 1, as Figure 1 As shown in the embodiment of this application, a multi-energy collaborative optimization method for SST DC microgrids based on EMS is provided. The DC microgrid includes an AC grid connected via a solid-state transformer (SST) and a DC bus connected to the DC side of the SST. At least one DC load, a photovoltaic power generation unit, and an energy storage system are connected to the DC bus. The method includes:

[0019] Step S100: Collect real-time operating data of the DC microgrid. The real-time operating data includes photovoltaic power generation, total load power of the DC bus, state of charge of the energy storage system, time-of-use electricity price signal of the grid, and grid connection / disconnection status signal of the AC grid.

[0020] Specifically, the DC microgrid uses the SST (Solar Transmission Station) as its core energy interface and the 800V DC bus as its power carrier. It integrates photovoltaic power generation units, energy storage systems, and various DC loads into a small, independent DC power supply and distribution system, enabling both grid-connected and off-grid operation. The DC bus refers to the rated 800V DC common distribution channel output from the SST DC side, serving as the power convergence and transmission node for photovoltaic power generation units, energy storage systems, and all DC loads. DC loads refer to electrical equipment connected to the DC bus and powered by DC electricity, primarily including computing power containers, DC charging piles, V2G bidirectional charging systems, and other DC loads within the plant area. The state of charge (SOC) of the energy storage system refers to the percentage of remaining usable energy in the energy storage batteries relative to their rated total capacity; it is a core indicator for measuring the energy storage's charge status, ranging from 0% to 100%. The AC grid connection / off-grid status signal is a switching signal reflecting the energized operation status of the 10kV municipal power grid connected to the SST AC side, categorized into grid-connected and off-grid status.

[0021] Specifically, the EMS system pre-configures standardized distributed acquisition terminals for all devices within the microgrid. These terminals connect to the SST's measurement and control unit, the photovoltaic DC / DC controller of the photovoltaic power generation unit, the BMS and bidirectional DC / DC controller of the energy storage system, the local measurement and control modules of each DC load, and the smart meters and relay protection devices on the grid side. A high-frequency synchronous acquisition frequency of 100ms / time is preset to meet the real-time control response requirements of the microgrid. The EMS system sends synchronous acquisition commands to all acquisition terminals via an industrial-grade communication link using industrial Ethernet or CAN bus. Each terminal synchronously executes the acquisition action and transmits data back. Specifically, for the photovoltaic power generation unit, the acquisition terminal acquires the output DC voltage and current data of the photovoltaic array in real time through the photovoltaic DC / DC controller, and calculates the photovoltaic power generation in real time using the power calculation formula P=UI. For the DC bus side, the acquisition terminal synchronously acquires the real-time operating voltage and current data of all DC loads connected in parallel to the DC bus through the bus intelligent measurement and control device, and calculates the total load power of the DC bus after summarizing and superimposing the data. For the energy storage system, the acquisition terminal acquires the data through the energy storage... The BMS collects core data from the battery cluster in real time, including individual cell voltage, total voltage, charging and discharging current, remaining usable capacity, and rated total capacity, to calculate the State of Charge (SOC) of the energy storage system. On the grid side, the data acquisition terminal obtains real-time time-of-use pricing signals from the grid through smart meters and pricing modules connected to the grid marketing system. Simultaneously, it collects three-phase voltage and current data from the 10kV AC grid in real time through relay protection devices and electronic undervoltage release devices on the SST AC side. Based on preset voltage threshold judgment logic, it outputs AC data in real time. The grid connection and off-grid status signals are collected. For example, if the three-phase voltage on the 10kV grid side is stable at 10.2kV, the grid connection status signal is determined and output. If the grid side voltage drops to 0V and lasts for more than 2ms, the off-grid status signal is immediately determined and output. All real-time operation data collected and transmitted is verified in real time, anomaly cleaning is performed, and timestamp synchronization calibration is performed before being stored in the real-time database of the EMS system. This provides full, accurate, and synchronous basic data support for subsequent generation of charging and discharging scheduling instructions, optimization of photovoltaic consumption strategies, and control of grid connection and off-grid operation switching.

[0022] This step breaks down the data silos formed by the independent operation of SST, photovoltaic, energy storage, load, and switchgear in the existing SST DC microgrid. Through the EMS system, it realizes the unified collection and centralized management of the entire link and all equipment operation data of the microgrid, solving the core pain point of the lack of data support for the entire link collaborative management and control in the existing technology.

[0023] Step S200: Based on the real-time operating data and the preset revenue and reliability principles, generate charging and discharging scheduling instructions for the energy storage system and absorption control strategies for the photovoltaic power generation unit. The charging and discharging scheduling instructions include backup power constraints to ensure power supply reliability and capacity constraints to ensure the safety of the SST equipment.

[0024] Furthermore, step S200 also includes: setting backup power constraints to ensure power supply reliability and capacity constraints to ensure the safety of the SST equipment; based on the time-of-use electricity price signal in the real-time operating data and the state of charge of the energy storage system, under the premise of satisfying the backup power constraints and the capacity constraints, solving an optimization model with the goal of maximizing economic benefits throughout the entire cycle, and generating charging and discharging scheduling instructions that include planned charging and discharging periods; based on the photovoltaic output and DC bus load demand in the real-time operating data, and under the guidance of preset benefit and reliability principles, constructing graded consumption paths for two scenarios: photovoltaic power generation exceeding capacity and photovoltaic power generation falling capacity, and generating consumption control strategies.

[0025] Furthermore, the backup power constraint is: setting a state of charge lock-in range for the energy storage system for emergency power supply, allowing the energy storage system to execute charging and discharging plans related to grid energy interaction only within the state of charge range outside the lock-in range; the capacity constraint is: setting an upper limit on the power of the energy storage system for charging and discharging through the SST, which is a preset proportion of the rated capacity of the SST.

[0026] Specifically, the pre-defined principles of revenue and reliability refer to the top-level core criteria for formulating all operational strategies of this SST DC microgrid EMS system. Its core essence is optimal revenue and maximum benefit, while rigidly adhering to two bottom lines: first, the uninterrupted power supply reliability of critical loads such as the computing center; and second, the operational safety of SST core equipment. These are the premise and boundary for the generation of all scheduling strategies. The charge / discharge scheduling command is the core execution command issued by the EMS system to the bidirectional DC / DC converter of the energy storage system. It clarifies the planned charge / discharge periods, target charge / discharge power, and charge / discharge depth limits for energy storage, and is the core carrier for realizing peak shaving and valley filling and electricity trading revenue from energy storage. The absorption control strategy refers to the full-link power allocation and absorption control rules formulated by the EMS system for photovoltaic power generation units, clarifying the tiered allocation logic of photovoltaic power among DC bus load, energy storage charging, plant backfeed, and reduced power operation. Backup power constraints are rigid charging and discharging limits imposed on energy storage systems to ensure uninterrupted power supply to critical loads during grid outages. Specifically, a State of Charge (SOC) lockout range is set for the energy storage system for emergency power supply. Charging and discharging plans for grid interaction are only permitted within the SOC range outside this lockout range. The standard setting is a rigid lockout of SOC < 20% for the backup power range, with only the SOC 20%-100% range open. 80% DOD (Deepest Discharge) is allowed for peak-valley arbitrage. Reserved capacity can be dynamically adjusted based on the duration of the power supply guarantee. Capacity constraints are rigid upper limits imposed on the power of energy storage systems charged and discharged via SST to prevent SST overload and ensure equipment safety and lifespan. Specifically, the maximum power of energy storage systems charged and discharged via SST is a preset percentage of the SST's rated capacity, typically set to no more than 95% of the SST's rated capacity, with a 5% capacity margin reserved to prevent equipment overload. The optimization model uses the grid time-of-use electricity price as the core variable, maximizes the arbitrage profit of energy storage throughout its entire charging and discharging cycle as the objective function, and employs standby power constraints, capacity constraints, maximum charging and discharging power of energy storage, and upper and lower limits of SOC as rigid constraints. A mathematical model for the optimal charging and discharging plan is solved through linear programming. The scenario of high photovoltaic power generation is one of the two core scenarios for photovoltaic (PV) consumption, referring to the condition where PV power generation is greater than or equal to the total load power of the DC bus, i.e., a scenario where there is still surplus power after PV output covers the current full load. The scenario of low PV power generation refers to the condition where PV power generation is less than the total load power of the DC bus, i.e., a scenario where PV output cannot cover the current full load and there is a power deficit. A tiered consumption path, namely a four-level tiered consumption path of load self-consumption → energy storage charging → power plant consumption → power reduction control, is used to maximize PV self-consumption rate and minimize curtailment rate.

[0027] Specifically, after acquiring the full real-time operational data of the microgrid, the EMS system performs pre-setting constraint conditions. Based on the supply duration requirements of the microgrid's critical loads and the rated parameters of the SST equipment, it sets backup power constraints to ensure power supply reliability and capacity constraints to ensure the safety of the SST equipment. The backup power constraint rigidly locks the energy storage system's SOC < 20% as the emergency backup power range, allowing only the SOC range of 20%-100% to participate in the charging and discharging plan for energy interaction with the grid. The capacity constraint sets the upper limit of the energy storage system's power for charging and discharging through the SST to 95% of the SST's rated capacity, reserving a 5% safety margin to prevent SST overload operation. The EMS system uses the grid's time-of-use price signal and the energy storage system's real-time SOC from the real-time operational data as core input variables. Under the premise of strictly meeting the pre-set backup power constraints and capacity constraints, it calls the built-in full-cycle economic benefit maximization optimization model, performing linear programming to solve for the maximum peak-valley price arbitrage profit, and calculates the energy storage system's... The system generates optimal charging and discharging plans that include clearly defined charging and discharging periods and target charging and discharging power during off-peak hours with low electricity prices. Simultaneously, the EMS system compares and determines the matching relationship between photovoltaic output and load demand based on real-time operational data of photovoltaic power generation and total DC bus load. Guided by preset principles of profitability and reliability, it constructs tiered consumption paths for two core scenarios: high photovoltaic power generation and low photovoltaic power generation. In the high photovoltaic power generation scenario, control rules are formulated according to a four-level path: "Prioritize DC bus load self-consumption → Charge energy storage under constraints → If there is still surplus, feed it back to the plant for consumption → If consumption is ultimately impossible, control photovoltaic power reduction operation." In the low photovoltaic power generation scenario, control rules are formulated based on the logic of prioritizing whether the current period is within a planned charging and discharging period; if so, energy storage executes the dispatch instructions; otherwise, it prioritizes drawing power from the grid via SST to supplement the load shortfall. Ultimately, a complete photovoltaic power generation unit consumption control strategy is generated.

[0028] The optimization model employs a three-layer closed-loop progressive structure, divided from top to bottom into a core objective layer, a rigid constraint layer, and a dynamic boundary adaptation layer. The core objective layer serves as the core of the model's optimization guidance, with the sole optimization objective being the maximization of the microgrid's comprehensive economic benefits throughout the entire scheduling cycle. It fully covers the two core revenue sources: energy storage peak-valley price arbitrage and the cost savings from photovoltaic self-consumption. Simultaneously, it incorporates the cost of electricity purchased from the grid and the cost of energy storage charging into the full-cycle cost accounting, ensuring that the revenue calculation fully reflects the actual operating conditions of the microgrid. The rigid constraint layer represents the model's inviolable safety operation baseline, integrating six categories of core constraint rules for microgrid operation. Among these, the backup power safety constraint rigidly locks the minimum emergency backup power capacity of energy storage, ensuring uninterrupted power supply to critical loads such as the computing center during grid outages; the SST capacity safety constraint limits the equipment's operating power to no more than 95% of its rated capacity, reserving a fixed safety margin to prevent equipment overload; and it is also equipped with real-time DC bus power balance constraints, energy storage charging and discharging operation constraints, photovoltaic tiered consumption priority constraints, and grid connection compliance constraints. The dynamic boundary adaptation layer is a flexible adjustment module for the model. It can dynamically update the model's input boundary and time period division granularity based on real-time collected photovoltaic output forecasts, DC load demand fluctuations such as computing load, and grid time-of-use price adjustment signals. This adapts to the dynamic changes in the microgrid's real-time operating conditions and solves the core problem of deviation between fixed scheduling plans and actual operating conditions.

[0029] Specifically, the expression for the core target layer function is: ;

[0030] in, The core optimization objective of the model is to maximize the overall economic benefit of the entire microgrid system within a complete scheduling cycle; core benefit item 1 : Corresponding to the net profit from peak-valley arbitrage of energy storage, which is the profit from energy storage discharge during periods of high electricity prices, minus the cost of energy storage charging during periods of low electricity prices; Core profit item 2 : The equivalent benefit of self-consumption of photovoltaic power, i.e., the electricity cost saved by directly supplying photovoltaic power to microgrid loads and replacing grid power purchases; t is the dispatch period number, and T is the total number of periods in the entire cycle. The time-of-use electricity price for the corresponding period. For single time period duration, For energy storage discharge power, For energy storage charging power, This refers to the power supplied directly to the photovoltaic load.

[0031] This model employs a three-tiered engineering-based construction and training method: offline benchmark building, online rolling training, and closed-loop iterative optimization. It adapts to the real-time scheduling requirements of EMS systems and the full-scenario operation of microgrids. The offline benchmark model is constructed and validated. Based on the DC load characteristics of the project's SST rated capacity, energy storage parameters, photovoltaic installation scale, and computing load, the dimensions and time scales of the model variables are unified. A default scheduling cycle of 24 hours and a single step size of 15 minutes are used to match the time-sharing electricity price granularity for industrial and commercial use. The constraint system and objective function are standardized and implemented. Historical time-sharing electricity prices, photovoltaic output, and load operation data from the project location over the past year are imported to complete multi-condition benchmark testing, ensuring that the model's output scheduling plan under normal operating conditions 100% conforms to all constraint rules, while also possessing stable revenue optimization capabilities. Secondly, online rolling training and adaptive optimization are conducted. After the model is deployed to the EMS system, a rolling time-domain optimization framework is adopted, updating real-time photovoltaic output, load power, and energy storage SOC data every 15 minutes to recalculate the optimal scheduling plan for the next 24 hours. Simultaneously, based on the deviation between real-time operating data and planned values, gradient descent is used to dynamically correct photovoltaic output prediction errors and load fluctuation compensation coefficients, continuously optimizing the model's prediction accuracy and scheduling fit. For extreme conditions such as grid outages, sudden load surges, and temporary electricity price adjustments, reinforcement learning training is conducted based on historical abnormal condition datasets to optimize the dynamic adjustment strategy of model constraint boundaries, ensuring that backup power safety and equipment safety are prioritized even under extreme conditions. Finally, a full-cycle closed-loop iterative optimization is completed. The EMS system reviews and verifies the actual revenue, constraint execution, and photovoltaic absorption rate of the model scheduling daily, incorporating the review data into the model training library. A global iterative optimization of model parameters is conducted monthly to continuously improve the model's revenue realization rate and adaptability to all scenarios.

[0032] This step addresses the core pain points of existing SST DC microgrids, such as the difficulty in balancing backup power security and peak shaving and valley filling benefits, unreasonable SST capacity utilization, and low photovoltaic absorption efficiency, through dual-constraint pre-control, profit maximization model solution, and photovoltaic hierarchical absorption path construction. It achieves the goals of optimal economic benefits for the entire microgrid system, equipment operation safety, and power supply reliability.

[0033] Step S300: In conjunction with the charging and discharging scheduling command, execute the absorption control strategy to dynamically manage the output power of the photovoltaic power generation unit. The dynamic management includes controlling the distribution ratio of photovoltaic power among the DC bus load, the energy storage system, and the AC grid side.

[0034] Furthermore, step S300 also includes: determining the relationship between photovoltaic output and the total load demand of the DC bus based on the real-time operating data; if the photovoltaic output is greater than or equal to the total load demand of the DC bus, then according to the absorption control strategy, prioritizing the absorption of photovoltaic power by the DC bus load, secondly controlling the photovoltaic power to charge the energy storage system under the condition of meeting the backup power constraint and capacity constraint in the charge and discharge scheduling command, and thirdly controlling the remaining photovoltaic power to be fed back to the AC grid side; if it still cannot be completely absorbed, then controlling the photovoltaic power generation unit to operate at reduced power; if the photovoltaic output is less than the total load demand of the DC bus, then according to the absorption control strategy, determining whether the current time is within the planned charge and discharge period planned by the charge and discharge scheduling command; if so, controlling the energy storage system to operate according to the charge and discharge scheduling command; if not, controlling the SST to draw power from the AC grid to meet the load deficit.

[0035] Specifically, photovoltaic output refers to the total active power output in real time of the photovoltaic power generation units connected to the 800V DC bus on the SST DC side after converting solar energy into DC power through photovoltaic modules. Load deficit refers to the power shortfall between total load demand and photovoltaic output when photovoltaic output is less than the total load demand of the DC bus. This shortfall needs to be supplemented through energy storage discharge or by SST purchasing power from the grid to maintain the power balance of the DC bus.

[0036] Specifically, the system calls upon real-time collected data on photovoltaic (PV) power generation and total DC bus load power to compare and determine the relationship between PV output and DC bus load demand. This determines two execution scenarios. When the PV output is determined to be greater than or equal to the DC bus load demand (i.e., PV output covers the current load but still has surplus power), the EMS system strictly follows the four-tiered control path of the absorption control strategy. First, it prioritizes controlling the PV power output to fully supply the DC bus load, meeting the current power demand of all loads. Second, under the strict conditions of backup power constraints and capacity constraints in the charging and discharging scheduling instructions (i.e., the SOC after energy storage charging does not exceed 100%, does not occupy the backup power lockout range below 20%, and the charging power plus the bus load power does not exceed the safety limit of 95% of the SST rated capacity), the remaining PV power is controlled to charge the energy storage system, completing low-cost energy replenishment for the energy storage. Third, when the energy storage can no longer charge, the remaining PV power is controlled to supply AC power through the SST. The grid side feeds back the photovoltaic power to other loads in the plant area. If the grid side is also unable to absorb the remaining photovoltaic power, the photovoltaic power generation unit is controlled to operate at reduced power to avoid DC bus voltage exceeding limits. When it is determined that the photovoltaic output is less than the total load demand of the DC bus, that is, the photovoltaic output cannot cover the current load and there is a power deficit, the EMS system strictly follows the absorption control strategy. First, it determines whether the current time is within the planned charging and discharging period planned by the charging and discharging dispatch command. If it is currently within the peak planned discharging period, the energy storage system is controlled to perform the discharging action according to the charging and discharging dispatch command to supplement the load deficit that the photovoltaic output cannot cover, avoiding additional costs from purchasing electricity from the grid during high electricity price periods. If it is currently within the valley planned charging period or an unplanned charging and discharging period, the SST is controlled to draw power from the AC grid to supplement the load deficit, while ensuring that the energy storage system completes charging as planned without interrupting the preset peak-valley arbitrage charging and discharging plan. The DC bus power balance is checked in real time throughout the process to ensure that all actions do not exceed the preset safety constraints and benefit principles.

[0037] This step addresses the core pain points of existing SST DC microgrid photovoltaic consumption strategies—namely, their extensive nature, low self-consumption rate, high curtailment rate, and high-cost electricity purchase during periods of high electricity prices—by real-time dynamic matching of photovoltaic output and load demand, closed-loop execution of the four-level tiered consumption path, and optimal benefit determination for load deficit supplementation. It achieves multiple objectives, including full tiered utilization of photovoltaic power, optimal cost per kilowatt-hour throughout the day, and complete execution of the pre-set charging and discharging revenue plan.

[0038] Furthermore, when the planned charging and discharging power of the energy storage system exceeds the power limit, the portion of the power exceeding the power limit will be switched to be transmitted through the energy storage converter channel built into the energy storage system.

[0039] Specifically, the planned charge / discharge power refers to the target charge / discharge power value that the energy storage system needs to execute during the corresponding planned charge / discharge period, as specified in the charge / discharge scheduling instructions generated by the EMS system in step S200 through the full-cycle economic benefit maximization optimization model. It is the core execution parameter for maximizing energy storage peak-valley arbitrage benefits and is divided into two categories: planned charging power and planned discharging power, with units of kW / MW. The energy storage converter channel refers to the redundant power transmission channel built into the energy storage system and independent of the SST main power channel. It is composed of a bidirectional energy storage converter as its core and can directly realize bidirectional power conversion and transmission between the energy storage DC side and the 10kV AC grid. It forms double electrical isolation with the SST channel through power frequency isolation and high frequency isolation. The two power transmissions are completely decoupled and do not interfere with each other. It is the backup redundant transmission path of the SST main channel.

[0040] Specifically, during the entire process of the energy storage system executing charge and discharge scheduling commands, the EMS system collects all real-time operating data, including the SST real-time operating power, the total DC bus load power, the current planned charge and discharge power of the energy storage system, and the SST rated capacity parameters. Based on the collected real-time data, it dynamically calculates the upper limit of power available for energy storage charge and discharge on the current SST channel, which is the remaining available capacity after subtracting the current total DC bus load power from the 95% safety threshold of the SST rated capacity. Then, it compares the current planned charge and discharge power of the energy storage system with the calculated upper limit of power on a cycle-by-cycle basis. When it is determined that the planned charge and discharge power of the energy storage system is less than or equal to the upper limit of power, the EMS system controls all charge and discharge power of the energy storage system to be stably transmitted through the SST main channel, strictly following the preset charge and discharge scheduling commands. When it is determined that the planned charge and discharge power of the energy storage system exceeds the upper limit of power, the EMS system immediately triggers the redundancy channel. The seamless switching control logic first locks the basic charging and discharging power, which does not exceed the power limit, to continue stable transmission through the SST main channel. Simultaneously, it sends synchronous start and power following commands to the energy storage converter built into the energy storage system, seamlessly switching the remaining charging and discharging power exceeding the power limit to transmission through the independent channel of the energy storage converter. During the switching process, relying on the dual electrical isolation design of SST high-frequency isolation and energy storage converter power frequency isolation, it ensures that the power transmission of the two channels is completely decoupled and that the actions do not interfere with each other, preventing power coupling oscillations and DC bus voltage fluctuations. At the same time, the EMS system monitors the transmission power of the two channels, the total SST operating power, and the total energy storage charging and discharging power in real time closed loop, dynamically adjusting the power allocation ratio of the two channels to ensure that the total charging and discharging power of the energy storage system fully matches the target value of the charging and discharging scheduling command, without interrupting the execution of the preset charging and discharging plan, and never exceeding the SST capacity safety constraint.

[0041] This step avoids the pain points of existing SST DC microgrids, such as SST overload operation, shortened equipment life, and loss of peak-valley arbitrage revenue caused by load surges, due to real-time dynamic verification of SST capacity rigidity constraints and seamless switching control of redundant dual channels.

[0042] Step S400: When the real-time operating data indicates that the AC power grid is out of power, based on the backup power constraint, the off-grid power supply control logic is executed to control the energy storage system to switch its working mode to maintain the DC bus voltage and provide uninterrupted power supply to the critical load.

[0043] Furthermore, step S400 also includes: when the grid status signal in the real-time operating data indicates that the AC grid is out of power, triggering the closing of a preset tie switch; based on the reserved power corresponding to the backup power constraint, controlling the DC / DC converter of the energy storage system to switch from the grid-connected constant power control mode to the off-grid constant voltage control mode, using the rated voltage of the DC bus as a reference, executing the off-grid power supply control logic to provide uninterrupted power supply to the critical load.

[0044] Specifically, off-grid power supply control logic refers to the full-link coordinated control rules preset by the EMS system to cope with grid outages. Its core includes five major actions: grid-connected channel interlocking, tie switch linkage, energy storage operating mode switching, DC bus voltage stabilization control, and load-level power supply management. It serves as the core execution basis for the stable operation of the microgrid under off-grid conditions. Critical loads refer to core DC loads on the DC bus that have extremely high requirements for power supply continuity and must be guaranteed uninterrupted power supply during grid outages. Tie switches are fast-controllable isolating switches installed between the photovoltaic power generation unit and the DC side of the energy storage system. They have a 10ms-level fast closing response capability, are in the open state during normal grid operation, and are triggered to close when the grid fails, used to achieve off-grid power coordination between photovoltaics and energy storage. Grid-connected constant power control mode refers to the default operating mode of the bidirectional DC / DC converter of the energy storage system during normal grid-connected operation. In this mode, the DC / DC converter strictly follows the charging and discharging scheduling instructions issued by the EMS to perform charging or discharging actions at a set constant power. The core objective is to achieve peak-valley arbitrage profits. Off-grid constant voltage control mode refers to the core operating mode of the energy storage system after the bidirectional DC / DC converter switches when the grid is disconnected. In this mode, the DC / DC converter automatically adjusts the output power with the rated DC bus voltage of 800V as the reference target to maintain the stability of the DC bus voltage. It is the core of the microgrid power balance and stable operation under off-grid conditions.

[0045] Specifically, during the entire lifecycle operation of the microgrid, the EMS system collects three-phase voltage and current data of the 10kV AC grid on the SST side. Relying on electronic undervoltage release devices and relay protection devices, it monitors the grid operation status in real time. When the collected grid status signal indicates that the 10kV AC grid has lost power, or the voltage drops below 10% of the rated value for more than 2ms, it immediately determines the grid power failure and triggers the off-grid power supply control logic. First, it sends a closing command to the preset tie switch, triggering the tie switch to close. At the same time, it immediately blocks the SST grid-connected AC channel to prevent backfeeding safety risks when the grid is restored. Based on the emergency reserve power of SOC ≥ 20% corresponding to the backup power constraint, it sends a mode switching command to the bidirectional DC / DC converter of the energy storage system to control the DC / DC converter from constant power during grid-connected operation. The control mode seamlessly switches to the constant voltage control mode for off-grid operation, using the 800V rated voltage of the DC bus as the voltage stabilization reference. It automatically adjusts the output power of the energy storage to compensate for load power fluctuations in real time, accurately maintaining the DC bus voltage fluctuation within ±5%. At the same time, it executes load hierarchical management logic, prioritizing the power supply needs of critical loads such as computing power containers, cutting off the power supply circuits of non-core loads, and maximizing the extension of emergency backup power time. Throughout the off-grid operation, the EMS system continuously monitors the SOC status of the energy storage, the DC bus voltage, and the operating power of critical loads in real time, strictly controlling the energy storage discharge to not exceed the lower limit threshold of the backup power constraint. At the same time, it links with the photovoltaic power generation unit, prioritizing the use of photovoltaic power to supply critical loads when the photovoltaic has output power, reducing the energy storage backup power consumption, until the grid restores normal power supply, and then controls the microgrid to seamlessly switch back to grid-connected operation mode.

[0046] This step avoids the pain points of existing SST DC microgrids, such as delayed switching of operating conditions, DC bus voltage instability, and power interruption of critical loads, by quickly identifying power grid failures, linking switches, and seamlessly switching energy storage operating modes. It achieves UPS-level uninterrupted power supply switching, meets power availability requirements, and ensures the emergency power supply duration of critical loads when the power grid fails through the rigid execution of backup power constraints, thereby improving the power supply reliability and adaptability to extreme operating conditions of the microgrid.

[0047] Furthermore, the photovoltaic power generation unit and the energy storage system are connected on the DC side via an isolating switch, and the triggering condition for the off-grid power supply control logic includes closing the isolating switch.

[0048] Specifically, the full name of the disconnecting switch is DC-side fast controllable interconnection disconnecting switch. It is a high-speed switching device installed between the DC output terminal of the photovoltaic power generation unit and the DC input terminal of the energy storage system, and connected in parallel with the 800V DC bus. It has the ability to quickly open and close and the electrical interlocking function in grid-connected state. It is in the normally open state when the grid is running normally, and can be controlled to close quickly when the grid loses power, so as to realize the direct DC connection between the photovoltaic power generation unit and the energy storage system.

[0049] Specifically, under normal grid-connected operation of the 10kV AC grid, the EMS system maintains the grid-connected electrical interlock function of the disconnector switch, forcing the disconnector switch to be in the normally open state, prohibiting direct connection between the photovoltaic power generation unit and the energy storage system on the DC side, and avoiding interference with the charging and discharging scheduling plan and photovoltaic consumption strategy during grid connection. The system monitors the real-time operating status of the 10kV AC grid, the open / closed position feedback signal of the disconnector switch, the operating status of the photovoltaic power generation unit, the SOC status of the energy storage system, and the DC bus voltage data. When the EMS system determines that a power outage has occurred in the 10kV AC grid based on the collected grid status signals, it immediately releases the grid-connected electrical interlock of the disconnector switch and simultaneously issues a high-speed closing control command to the disconnector switch to complete rapid closing, thus enabling the photovoltaic power generation unit and the energy storage system to connect. The DC side is directly connected, and then the EMS system collects the feedback signal of the disconnect switch closing in real time. The reliable closing of the disconnect switch is used as one of the core pre-trigger conditions. Together with the power grid failure status signal and the energy storage SOC meeting the backup power constraint threshold signal, it forms a complete triggering logic. When all trigger conditions are met, the system immediately unlocks and executes the preset off-grid power supply control logic. During the entire off-grid operation, the closed disconnect switch enables direct power interaction between the photovoltaic power generation unit and the DC side of the energy storage system. When the photovoltaic system has output power, it prioritizes supplying photovoltaic power to key loads such as computing containers. The remaining power is directly supplied to the energy storage system to reduce the consumption of energy storage backup power capacity. At the same time, it works in conjunction with the constant voltage control mode of the energy storage system to maintain the stability of the DC bus voltage. The backup power constraint requirements are strictly followed throughout the process to ensure the reasonable use of emergency backup power capacity.

[0050] This step, by binding the grid-connected electrical interlock of the disconnecting switch, the high-speed closing of the switch in the event of power failure, and the off-grid logic triggering conditions, fundamentally eliminates the false triggering of the off-grid power supply control logic. At the same time, it realizes efficient coordination between the photovoltaic power generation unit and the energy storage system on the DC side under off-grid conditions, extends the emergency power supply time of critical loads when the grid loses power, and improves the stability and power supply reliability of the SST DC microgrid in off-grid operation.

[0051] In summary, the multi-energy collaborative optimization method for SST DC microgrids based on EMS provided in this application has the following technical effects:

[0052] 1. By collecting full real-time operational data on microgrid photovoltaic power generation, load power, energy storage SOC, time-of-use pricing, and grid connection / off-grid status through EMS, scheduling and absorption strategies with backup power and capacity constraints are generated based on the principles of revenue and reliability. Photovoltaic power allocation is dynamically managed, and off-grid supply protection logic is executed when the grid experiences power outages. This achieves unified and collaborative management of the entire microgrid chain, balancing full-cycle revenue maximization, equipment safety, and power supply reliability, thus addressing the core pain point of existing equipment operating independently and failing to simultaneously achieve multiple objectives.

[0053] 2. By setting dual constraints on backup power and capacity in advance, and based on time-of-use pricing and energy storage SOC, a revenue-maximizing optimization model is solved under these dual constraints to generate a charge-discharge plan. Simultaneously, based on photovoltaic output and load demand, two tiered absorption paths are constructed to generate absorption strategies. This precisely anchors the dual bottom lines of revenue and safety, achieving global optimization of the charge-discharge plan and refined management of the photovoltaic absorption path, thus addressing the industry pain points of the contradiction between backup power and revenue, and the extensive photovoltaic absorption.

[0054] 3. By comparing the photovoltaic output with the total load demand of the DC bus in real time, two major scenarios are divided: high photovoltaic output and low photovoltaic output. When photovoltaic output is high, a four-level tiered consumption logic is implemented. When photovoltaic output is low, the load deficit is supplemented according to the execution of the charging and discharging plan. This achieves full tiered utilization of photovoltaic power, avoids high-cost electricity purchases during periods of high electricity prices, and solves the pain points of existing technologies such as extensive consumption and high curtailment rates.

[0055] Example 2 is based on the same inventive concept as the EMS-based SST DC microgrid multi-energy collaborative optimization method in the previous examples, such as... Figure 2 As shown in the figure, this application provides a multi-energy collaborative optimization system for an SST DC microgrid based on an EMS. The DC microgrid includes an AC grid connected via a solid-state transformer (SST) and a DC bus connected to the DC side of the SST. At least one DC load, a photovoltaic power generation unit, and an energy storage system are connected to the DC bus. The system includes: an operation data acquisition module 11, used to acquire real-time operation data of the DC microgrid, including photovoltaic power generation, total DC bus load power, energy storage system state of charge, grid time-of-use price signal, and grid connection / disconnection status signal of the AC grid; and a dispatch instruction generation module 12, used to generate charging and discharging dispatch instructions for the energy storage system based on the real-time operation data and preset revenue and reliability principles. The photovoltaic power generation unit's absorption control strategy includes a charge / discharge scheduling command that includes backup power constraints to ensure power supply reliability and capacity constraints to ensure the safety of the SST equipment. An output power management module 13 is used to execute the absorption control strategy in conjunction with the charge / discharge scheduling command, dynamically managing the output power of the photovoltaic power generation unit. This dynamic management includes controlling the distribution ratio of photovoltaic power among the DC bus load, the energy storage system, and the AC grid. A working mode switching module 14 is used to execute off-grid power supply control logic based on the backup power constraints when the real-time operating data indicates a power outage in the AC grid. This logic controls the energy storage system to switch its working mode to maintain the DC bus voltage and provide uninterrupted power supply to critical loads.

[0056] Furthermore, the scheduling instruction generation module 12 is also used to perform the following steps: setting backup power constraints to ensure power supply reliability and capacity constraints to ensure the safety of the SST equipment; based on the time-of-use electricity price signal in the real-time operating data and the state of charge of the energy storage system, under the premise of satisfying the backup power constraints and the capacity constraints, solving an optimization model with the goal of maximizing the economic benefits throughout the entire cycle, and generating a charging and discharging scheduling instruction that includes the planned charging and discharging periods; based on the photovoltaic output in the real-time operating data and the DC bus load demand, under the guidance of preset benefit and reliability principles, constructing a graded consumption path for two scenarios of high photovoltaic power generation and low photovoltaic power generation, and generating a consumption control strategy.

[0057] Furthermore, the dispatch instruction generation module 12 is also used to perform the following steps: the backup power constraint is: setting a state of charge lock interval for the energy storage system for emergency power supply, allowing the energy storage system to execute charging and discharging plans related to grid energy interaction only within the state of charge range outside the lock interval; the capacity constraint is: setting an upper limit of the power of the energy storage system for charging and discharging through the SST, the upper limit of which is a preset proportion of the rated capacity of the SST.

[0058] Furthermore, the output power management module 13 is also used to perform the following steps: determine the relationship between photovoltaic output and the total load demand of the DC bus based on the real-time operating data; if the photovoltaic output is greater than or equal to the total load demand of the DC bus, then according to the absorption control strategy, prioritize controlling the photovoltaic power to be absorbed by the DC bus load, then control the photovoltaic power to charge the energy storage system under the condition of meeting the backup power constraint and capacity constraint in the charge and discharge scheduling command, and then control the remaining photovoltaic power to be fed back to the AC grid side. If it still cannot be completely absorbed, control the photovoltaic power generation unit to operate at reduced power; if the photovoltaic output is less than the total load demand of the DC bus, then according to the absorption control strategy, determine whether the current time is within the planned charge and discharge period planned by the charge and discharge scheduling command. If so, control the energy storage system to operate according to the charge and discharge scheduling command. If not, control the SST to draw power from the AC grid to meet the load deficit.

[0059] Furthermore, the working mode switching module 14 is also used to perform the following steps: when the grid status signal in the real-time operating data indicates that the AC grid is out of power, a preset tie switch is triggered to close; based on the reserved power corresponding to the backup power constraint, the DC / DC converter of the energy storage system is controlled to switch from the grid-connected constant power control mode to the off-grid constant voltage control mode, and the off-grid power supply control logic is executed with the rated voltage of the DC bus as a reference to provide uninterrupted power supply to the critical load.

[0060] Furthermore, the output power management module 13 is also used to perform the following steps: when the planned charging and discharging power of the energy storage system exceeds the power limit, the portion of power exceeding the power limit is switched to be transmitted through the energy storage converter channel built into the energy storage system.

[0061] Furthermore, the system is also used to perform the following steps: the photovoltaic power generation unit and the energy storage system are connected on the DC side through an isolating switch, and the triggering condition of the off-grid power supply control logic includes closing the isolating switch.

[0062] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. An EMS-based multi-energy collaborative optimization method for SST DC microgrid, characterized in that, The DC microgrid includes an AC grid connected via a solid-state transformer (SST), and a DC bus connected to the DC side of the SST. At least one DC load, a photovoltaic power generation unit, and an energy storage system are connected to the DC bus. The method includes: The real-time operation data of the DC microgrid is collected, including photovoltaic power generation, total load power of the DC bus, state of charge of the energy storage system, time-of-use electricity price signal of the grid, and grid connection / disconnection status signal of the AC grid. Based on the real-time operating data and the preset revenue and reliability principles, charging and discharging scheduling instructions for the energy storage system and absorption control strategies for the photovoltaic power generation unit are generated. The charging and discharging scheduling instructions include backup power constraints to ensure power supply reliability and capacity constraints to ensure the safety of the SST equipment. In conjunction with the charging and discharging scheduling instructions, the absorption control strategy is executed to dynamically manage the output power of the photovoltaic power generation unit. The dynamic management includes controlling the distribution ratio of photovoltaic power among the DC bus load, the energy storage system, and the AC grid side. When the real-time operating data indicates that the AC grid is out of power, based on the backup power constraint, the off-grid power supply control logic is executed to control the energy storage system to switch its working mode to maintain the DC bus voltage and provide uninterrupted power supply to critical loads.

2. The EMS-based SST DC microgrid multi-energy collaborative optimization method according to claim 1, wherein, Based on the real-time operating data and preset revenue and reliability principles, charging and discharging scheduling commands for the energy storage system and absorption control strategies for the photovoltaic power generation units are generated, including: Set backup power constraints to ensure power supply reliability and capacity constraints to ensure the safety of the SST equipment; Based on the time-of-use electricity price signal in the real-time operating data and the state of charge of the energy storage system, under the premise of satisfying the backup power constraint and the capacity constraint, an optimization model with the goal of maximizing the economic benefits throughout the entire cycle is solved to generate charging and discharging scheduling instructions that include planned charging and discharging periods. Based on the photovoltaic output and DC bus load demand in the real-time operating data, and guided by the preset principles of revenue and reliability, a tiered consumption path is constructed for two scenarios: high photovoltaic power generation and low photovoltaic power generation, and a consumption control strategy is generated.

3. The EMS-based SST DC microgrid multi-facility coordinated optimization method of claim 1, wherein, The backup power constraint is as follows: a state of charge lock-in interval is set for the energy storage system for emergency power supply, and the energy storage system is only allowed to execute charging and discharging plans related to grid energy interaction within the state of charge range outside the lock-in interval; The capacity constraint is: setting an upper limit on the power of the energy storage system for charging and discharging through the SST, which is a preset ratio of the rated capacity of the SST.

4. The EMS-based SST DC microgrid multi-facility coordinated optimization method of claim 1, wherein, In conjunction with the charging and discharging scheduling instructions, the absorption control strategy is executed to dynamically manage the output power of the photovoltaic power generation unit, including: The relationship between photovoltaic power output and the total load demand of the DC bus is determined based on the real-time operating data. If the photovoltaic output is greater than or equal to the total load demand of the DC bus, then according to the absorption control strategy, the photovoltaic power is first controlled to be absorbed by the DC bus load. Secondly, under the condition of meeting the backup power constraint and capacity constraint in the charging and discharging scheduling command, the photovoltaic power is controlled to charge the energy storage system. Thirdly, the remaining photovoltaic power is controlled to be fed back to the AC grid side. If it still cannot be completely absorbed, the photovoltaic power generation unit is controlled to operate at reduced power. If the photovoltaic output is less than the total load demand of the DC bus, then according to the absorption control strategy, it is determined whether the current time is within the planned charging and discharging period planned by the charging and discharging scheduling command. If so, the energy storage system is controlled to operate according to the charging and discharging scheduling command. If not, the SST is controlled to draw power from the AC grid to meet the load deficit.

5. The EMS-based SST DC microgrid multi-facility coordinated optimization method of claim 1, wherein, When the real-time operating data indicates that the AC grid is experiencing a power outage, based on the backup power constraint, off-grid power supply control logic is executed to control the energy storage system to switch its operating mode to maintain the DC bus voltage and provide uninterrupted power supply to critical loads, including: When the power grid status signal in the real-time operating data indicates that the AC power grid has lost power, a preset interconnection switch is triggered to close. Based on the reserved power capacity corresponding to the backup power constraint, the DC / DC converter of the energy storage system is controlled to switch from the grid-connected constant power control mode to the off-grid constant voltage control mode. With the rated voltage of the DC bus as a reference, the off-grid power supply control logic is executed to provide uninterrupted power supply to the critical load.

6. The multi-energy collaborative optimization method for SST DC microgrids based on EMS as described in claim 3, characterized in that, The method further includes: When the planned charging and discharging power of the energy storage system exceeds the power limit, the portion of the power exceeding the power limit will be switched to be transmitted through the energy storage converter channel built into the energy storage system.

7. The multi-energy collaborative optimization method for SST DC microgrids based on EMS as described in claim 1, characterized in that, The photovoltaic power generation unit and the energy storage system are connected on the DC side through an isolating switch, and the triggering condition of the off-grid power supply control logic includes closing the isolating switch.

8. A multi-energy collaborative optimization system for SST DC microgrids based on EMS, characterized in that, The DC microgrid includes an AC grid connected via a solid-state transformer (SST), and a DC bus connected to the DC side of the SST. At least one DC load, a photovoltaic power generation unit, and an energy storage system are connected to the DC bus. This system is used to execute the EMS-based multi-energy collaborative optimization method for SST DC microgrids as described in any one of claims 1 to 7. The system includes: The data acquisition module is used to collect real-time operating data of the DC microgrid. The real-time operating data includes photovoltaic power generation, total load power of the DC bus, state of charge of the energy storage system, time-of-use electricity price signal of the grid, and grid connection / disconnection status signal of the AC grid. The scheduling instruction generation module is used to generate charging and discharging scheduling instructions for the energy storage system and absorption control strategies for the photovoltaic power generation unit based on the real-time operating data and preset revenue and reliability principles. The charging and discharging scheduling instructions include backup power constraints to ensure power supply reliability and capacity constraints to ensure the safety of the SST equipment. The output power management module is used to combine the charging and discharging scheduling command, execute the absorption control strategy, and dynamically manage the output power of the photovoltaic power generation unit. The dynamic management includes controlling the distribution ratio of photovoltaic power among the DC bus load, the energy storage system, and the AC grid side. The working mode switching module is used to execute off-grid power supply control logic based on the backup power constraint when the real-time operating data indicates that the AC grid is out of power, control the energy storage system to switch working modes to maintain the DC bus voltage and provide uninterrupted power supply to critical loads.

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