Multi-level DC microgrid energy storage control method, system and storage medium for cigarette factories

Abstract

This invention relates to the fields of power systems and industrial automation technology, specifically to a method, system, and storage medium for coordinated control of energy storage in a multi-level DC microgrid in a cigarette factory. The method includes: acquiring production scheduling information from a production management system; generating a baseline load prediction sequence; performing a first optimization calculation using a first processing unit to obtain a long-term energy dispatch plan and a reference SOC trajectory for the main energy storage system; performing a second optimization calculation using a second processing unit based on the long-term energy dispatch plan, the reference SOC trajectory, and short-term prediction information to obtain a real-time power setting command; monitoring the local network voltage in real time, and performing a third optimization calculation using a third processing unit based on the real-time power setting command to obtain control signals for the local energy storage converter; and performing real-time dual-mode switching and intelligent coordinated control based on the control signals. This invention achieves coordinated control of a multi-voltage level hierarchical architecture, improving the adaptability and economy of energy management in industrial production.

Description

Technical Field

[0001] This invention relates to the fields of power systems and industrial automation technology, specifically to an energy storage collaborative control method, system, and storage medium applicable to multi-level DC microgrids in cigarette factories. Background Technology

[0002] In the cigarette manufacturing industry, production processes place extremely high demands on the stability of the production environment (such as temperature, humidity, and cleanliness). These environmental parameters are precisely controlled primarily by efficient air conditioning and ventilation systems. To improve energy efficiency and control precision, modern cigarette factories are increasingly adopting motor-driven equipment based on DC buses or capable of being efficiently connected to DC buses via frequency converters in their process air conditioning systems, automated production lines, and logistics conveying systems. Due to their excellent speed regulation performance and high efficiency, these devices have become major energy-consuming units and power regulation targets.

[0003] Against this backdrop, constructing a plant-level DC microgrid, integrating distributed DC power sources such as photovoltaics, energy storage systems, and the aforementioned production and environmental control loads into a unified DC power distribution architecture, can bring several key advantages: First, by reducing AC-DC conversion layers, it significantly improves the overall plant's power utilization efficiency; second, it provides high-quality DC power to precision production equipment sensitive to voltage fluctuations, effectively avoiding interference from AC power quality issues on continuous production; third, by utilizing the flexible adjustment capabilities of energy storage, it achieves friendly interaction with the grid, enabling peak shaving and valley filling, demand management, and reducing overall energy costs.

[0004] The production and environmental control loads connected to the DC bus are directly related to production plans and process requirements, exhibiting typical dynamism, diversity, and potential for shocks. This new power supply system centered on the DC bus has fundamentally different operating characteristics from traditional AC power distribution, bringing new control challenges. Currently, control technologies for DC microgrids have a certain foundation, mainly including voltage regulation methods based on droop control, closed-loop control strategies based on bus voltage deviation or energy storage state of charge, and energy management methods based on historical data or simple load forecasting. Among these, while droop control can achieve power distribution, it suffers from static voltage deviation, making it difficult to meet the high voltage stability requirements of precision process loads in cigarette factories; closed-loop control based on voltage deviation is essentially a post-event response, unable to effectively intervene in the early stages of disturbances, especially in dealing with predictable but large power surges such as the start-up and shutdown of large power equipment; and general energy management algorithms do not fully consider the load characteristics deeply coupled with production scheduling in high-end manufacturing scenarios, lacking sufficient adaptability to the dynamic loads that exhibit both regularity and suddenness.

[0005] In summary, existing methods generally suffer from poor architectural adaptability, difficulty in coordinating energy flow and fault isolation between buses of different voltage levels, lack of feedforward control mechanisms oriented towards production scheduling, and lagging control criteria. There is an urgent need to develop a control method that adapts to the DC microgrid architecture of cigarette factories, integrates production planning information, and has the ability to actively intervene. Summary of the Invention

[0006] The purpose of this invention is to provide an energy storage collaborative control method, system, and storage medium applicable to multi-level DC microgrids in cigarette factories, in order to solve the problems of poor architecture adaptability, difficulty in coordinating energy flow and fault isolation between buses of different voltage levels, lack of feedforward control mechanism for production scheduling, and lagging control criteria in existing DC microgrid control methods.

[0007] To achieve the above objectives, embodiments of the present invention provide an energy storage coordinated control method for a multi-level DC microgrid in a cigarette factory, comprising: Obtain production scheduling information from the production management system; A baseline load prediction sequence is generated based on the production scheduling information of the production management system, and a first optimization calculation is performed using the first processing unit to obtain the long-term energy dispatch plan and reference SOC trajectory of the main energy storage system. Based on the long-term energy scheduling plan, the reference SOC trajectory, and short-term forecast information, a second processing unit performs a second optimization calculation to obtain a real-time power setting instruction. The local network voltage is monitored in real time, and based on the real-time power setting command, a third processing unit performs a third optimization calculation to obtain the control signal of the local energy storage converter. Based on the voltage change rate, real-time dual-mode switching and intelligent coordinated control are performed according to the control signal.

[0008] Optionally, a baseline load forecast sequence is generated based on the production scheduling information of the production management system, and a first optimization calculation is performed using a first processing unit to obtain the long-term energy dispatch plan and reference SOC trajectory of the main energy storage system, including: The objective function for the first optimization calculation is obtained according to formula (1). (1) in, For long-term electricity price signals, for Power purchased from the grid during the specified time period The duration of each time period, This represents the total number of time periods; The constraints on the objective function of the first optimization calculation include: ; ; ; ; ; in, for Time period baseline load forecast For the charging and discharging power of the energy storage system in the long-term plan, for The energy corresponding to the state of charge of a time-limited energy storage system. for The energy corresponding to the state of charge of a time-limited energy storage system. The energy storage charge / discharge efficiency coefficient. , The upper and lower limits of energy storage capacity. , These are the upper and lower limits of charge and discharge power. , The upper and lower limits are the power purchase capacity; A long-term energy scheduling plan is obtained based on the objective function and the constraints. Compared with reference SOC trajectory .

[0009] Optionally, based on the long-term energy scheduling plan, the reference SOC trajectory, and short-term forecast information, a second processing unit performs a second optimization calculation to obtain real-time power setting instructions, including: The objective function for the second optimization calculation is obtained according to formula (2). (2) in, for Electricity price during specific time periods for The amount of electricity purchased from the grid during a given period. This is the weighting factor for the power deviation. The weighting coefficient for SOC deviation, To optimize the discharge power of energy storage in the short term, For long-term energy dispatch planning, To optimize the energy state of medium-energy storage in the short term, For reference to the SOC trajectory, This represents the short-term forecast window length.

[0010] Optionally, the constraints on the objective function of the second optimization calculation include: ; ; ; ; ; ; in, For the revised load forecast, for Energy stored over a period of time Energy stored for the next period of time To optimize the time interval in the short term, The energy storage charge / discharge efficiency coefficient. , The upper and lower limits of energy storage capacity. , These are the upper and lower limits of charge and discharge power. , The upper and lower limits of the power purchase capacity, This represents the energy storage capacity of the previous time period. This represents the maximum power change rate of energy storage.

[0011] Optionally, the local network voltage is monitored in real time, and based on the real-time power setting command, a third processing unit performs a third optimization calculation to obtain the control signal for the local energy storage converter, including: The control signal is obtained according to formula (3). (3) in, For control signals, For real-time power setting commands, This is the voltage-power droop factor. This is the local rated voltage. To monitor local network voltage in real time.

[0012] Optionally, based on the voltage change rate, real-time dual-mode switching and intelligent coordinated control according to the control signal includes: Calculate the voltage change rate in real time and determine whether the voltage change rate is less than a threshold. If the voltage change rate is determined to be less than a threshold, a steady-state optimized mode control law is executed. If the voltage change rate is determined to be greater than or equal to a threshold, the dynamic emergency support modal control law is executed.

[0013] Optionally, executing the steady-state optimization modal control law includes: The steady-state optimal modal control law is obtained according to formula (4). (4) in, Steady-state optimal modal control law, For economical power command, This is the reference value for the rated voltage of the DC bus. This is the real-time voltage of the DC bus. This is the steady-state modal proportionality coefficient. These are the steady-state modal integral coefficients.

[0014] Optionally, executing the dynamic emergency support modal control law includes: The dynamic emergency support modal control law is obtained according to formula (5). (5) in, For dynamic emergency support modal control law, For economical power command, This is the reference value for the rated voltage of the DC bus. This is the real-time voltage of the DC bus. This is the dynamic emergency support modal proportionality coefficient. This provides dynamic emergency support modal differential gain.

[0015] On the other hand, the present invention also provides an energy storage collaborative control system for a multi-level DC microgrid in a cigarette factory, implementing the energy storage collaborative control method for a multi-level DC microgrid in a cigarette factory as described in any of the above claims, wherein the system includes: The data acquisition unit is used to obtain production scheduling information from the production management system; The first processing unit is used to generate a baseline load prediction sequence based on the production scheduling information of the production management system, and to perform a first optimization calculation to obtain the long-term energy dispatch plan and reference SOC trajectory of the main energy storage system. The second processing unit is used to perform a second optimization calculation based on the long-term energy scheduling plan, the reference SOC trajectory and short-term forecast information, in order to obtain real-time power setting instructions. The third processing unit is used to perform a third optimization calculation based on the real-time power setting command in order to obtain the control signal of the local energy storage converter. The mode switching and intelligent control unit is used to perform real-time dual-mode switching and intelligent coordinated control based on the voltage change rate and the control signal.

[0016] In another aspect, the present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement any of the methods described above.

[0017] The beneficial effects of this invention are: This invention addresses complex DC equipment in industrial environments by employing a multi-voltage DC bus architecture and proposing a hierarchical collaborative control strategy that logically maps to the physical level. This method effectively coordinates bidirectional power support and autonomous operation between the main bus and sub-buses, resolving the applicability issues of traditional single-bus control methods in similar architectures. It achieves rapid independent voltage self-stabilization relying on local energy storage, significantly improving the reliability, flexibility, and scalability of the system power supply.

[0018] This invention utilizes a precisely predictable production schedule as a key input, deeply integrating it into a multi-timescale prediction and optimization model. This allows the energy storage system's scheduling behavior to proactively align with the production line's start-up and shutdown rhythms and load variation patterns. On one hand, this significantly improves the local absorption rate of fluctuating power sources such as photovoltaics within the factory area; on the other hand, by anticipating and preparing in advance for planned shock events such as the start-up and shutdown of large equipment, it ensures both production continuity and power quality.

[0019] This invention innovatively uses the bus voltage change rate (dV / dt) as one of the core control criteria, establishing a dual-mode switching control mechanism based on this criterion. This upgrades the voltage stabilization control action from a traditional passive, delayed response based on the absolute value of voltage deviation to an active, proactive intervention based on voltage change trends, fundamentally improving the power supply quality for sensitive loads and the dynamic stability of the system.

[0020] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 A flowchart of an energy storage collaborative control method for a multi-level DC microgrid in a cigarette factory according to an embodiment of the present invention; Figure 2 A diagram of a multi-level DC microgrid architecture for a cigarette factory according to an embodiment of the present invention; Figure 3 This is a flowchart of a method for real-time dual-mode switching and intelligent coordinated control based on control signals according to an embodiment of the present invention. Detailed Implementation

[0022] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0023] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0024] like Figure 1 The diagram shows a flowchart of an energy storage coordinated control method for a multi-level DC microgrid in a cigarette factory according to an embodiment of the present invention. Figure 1 The control method may include the following steps: In step S10, the production scheduling information from the production management system is obtained; In step S11, a baseline load prediction sequence is generated based on the production scheduling information of the production management system, and the first processing unit performs the first optimization calculation to obtain the long-term energy dispatch plan and reference SOC trajectory of the main energy storage system. In step S12, based on the long-term energy scheduling plan, the reference SOC trajectory and short-term forecast information, the second processing unit performs a second optimization calculation to obtain real-time power setting instructions. In step S13, the local network voltage is monitored in real time, and based on the real-time power setting command, the third processing unit performs the third optimization calculation to obtain the control signal of the local energy storage converter. In step S14, based on the voltage change rate, real-time dual-mode switching and intelligent coordinated control are performed according to the control signal.

[0025] In such Figure 1 The energy storage collaborative control method for a multi-level DC microgrid in a cigarette factory, as shown, proposes a specific physical architecture for cigarette factories, such as... Figure 2As shown, this is specifically a hierarchical DC power distribution system built based on the characteristics of cigarette factory equipment. The system includes a DC main bus (high voltage) and DC sub-buses (low voltage), connected by isolated DC / DC converters. The DC main bus connects to the factory's main impact loads, typically connecting DC production drive equipment, photovoltaic systems, main energy storage batteries, and the mains power grid; the DC sub-buses supply power to loads with high reliability requirements, such as control systems, instrumentation, and intelligent lighting, and are equipped with independent secondary energy storage batteries. Furthermore, in this example, based on the electrical heterogeneity of the cigarette factory's loads and the power supply reliability requirements, a trunk-branch type DC power distribution network is constructed, allowing bidirectional energy flow and fault isolation. The first power supply network (main DC bus) is configured to connect the main production drive equipment with high power demand and significant dynamic changes within the plant area, as well as the grid connection interface, main photovoltaic power generation unit, and main energy storage unit. The voltage level of this network needs to be determined by comprehensively considering the voltage levels of the main equipment group on the bus. The second power supply network (sub DC bus) is configured to connect the industrial control system and auxiliary equipment with extremely high requirements for power supply continuity and power quality, and is equipped with an independent local energy storage unit. The voltage level of this network is determined based on the industrial control equipment. An inter-network interconnection device (isolation power converter) is configured to connect the first power supply network and the second power supply network to realize bidirectional, controllable transmission and electrical isolation of power between the two networks. It also has a fast shutdown capability and can realize the electrical disconnection of the two power supply networks when necessary to ensure the independent operation of the sub-network.

[0026] Based on this DC microgrid architecture, a three-layer model predictive control framework integrating multi-dimensional predictive information is adopted. This framework integrates predictive information across three time scales: short-term random signals, medium-term planning signals, and long-term economic signals. The main energy storage system formulates medium- and long-term charging and discharging plans based on production schedules and weather data; these plans are then executed on a rolling basis with shorter cycles, incorporating the latest predictive data to dynamically correct the plans and generate power setting commands for lower-level controllers. Energy storage systems deployed on each voltage bus side maintain the stability of the corresponding bus voltage.

[0027] Specifically, in this implementation, a hierarchical rolling optimization system is employed, aiming to transform uncertainties at long, medium, and short time scales into deterministic control sequences. Its core lies in constructing three optimization problems with different time resolutions and connecting them through information flow.

[0028] Step S10 is used to obtain production scheduling information from the production management system. Step S11 is used to generate a long-term energy dispatch plan and a reference SOC trajectory for the main energy storage system. In this embodiment, the specific method for generating the long-term energy dispatch plan and reference SOC trajectory for the main energy storage system in step S11 can be of various forms known to those skilled in the art. In one example of the present invention, the first processing unit (long-term planning unit) can be configured to periodically receive production scheduling information and long-term electricity price signals from the production management system, and generate a baseline load forecast sequence synchronized with production activities for a future long period (e.g., 24 hours) based on the scheduling information. , ,in This represents the total number of time periods in the long cycle.

[0029] Based on baseline load forecasting and long-term electricity price signals Using the input as an example, and taking the system's operational constraints as a prerequisite, the first optimization calculation is performed, and its objective function can be expressed as: (1) in, For long-term electricity price signals, for Power purchased from the grid during the specified time period The duration of each time period, This represents the total number of time periods.

[0030] The constraints include: ; ; ; ; ; in, for Time period baseline load forecast This refers to the charging and discharging power of the energy storage system in the long-term plan (discharging is positive, charging is negative). for The energy corresponding to the state of charge (SOC) of a time-limited energy storage system. for The energy corresponding to the state of charge of a time-limited energy storage system. The energy storage charge / discharge efficiency coefficient. , The upper and lower limits of energy storage capacity. , These are the upper and lower limits of charge and discharge power. , These are the upper and lower limits for the amount of electricity that can be purchased.

[0031] By solving the above optimization problem, a long-term energy dispatch plan for the main energy storage system is generated. Compared with reference SOC trajectory .

[0032] Step S12 involves using a second processing unit to perform a second optimization calculation based on a long-term energy dispatch plan, a reference SOC trajectory, and short-term forecast information to obtain real-time power setting instructions. Specifically, in this embodiment, the second processing unit (short-term correction unit) is configured to operate at a shorter cycle (e.g., 15 minutes) than the first unit. It receives long-term plans, updated short-term forecast information (e.g., meteorological data), and real-time system status feedback (e.g., energy storage SOC) from the first unit. In a finite prediction window Within this framework, using the current real-time state as the initial condition, updated forecast information (such as corrected load forecasts) is integrated. ), and with reference to the aforementioned long-term plan, perform the second optimization calculation, the objective function of which is: (2) in, for Electricity price during specific time periods for The amount of electricity purchased from the grid during a given time period; This is the weighting factor for the power deviation. This is the weighting coefficient for SOC deviation, used to balance economic efficiency with tracking of long-term plans; To optimize the discharge power of energy storage in the short term, For long-term energy dispatch planning, To optimize the energy state of medium-energy storage in the short term, For reference to the SOC trajectory, This represents the short-term forecast window length.

[0033] The constraints include: ; ; ; ; ; ; in, For the revised load forecast, for Energy stored over a period of time Energy stored for the next period of time To optimize the time interval in the short term, The energy storage charge / discharge efficiency coefficient. , The upper and lower limits of energy storage capacity. , These are the upper and lower limits of charge and discharge power. , The upper and lower limits of the power purchase capacity, This represents the energy storage capacity of the previous time period. This represents the maximum power change rate of energy storage.

[0034] After optimization, the output is a dynamically corrected real-time power setting command for the next execution period (e.g., the next 5 minutes). And distribute it to each power supply network.

[0035] Step S13 is used to monitor the local network voltage in real time and, based on the real-time power setting command, perform a third optimization calculation using the third processing unit to obtain the control signal for the local energy storage converter. Specifically, in this embodiment, the third processing unit (local execution unit) is deployed locally on the power supply network at each voltage level and is configured to receive the real-time power setting command generated by the second processing unit. Simultaneously, it monitors the local network voltage in real time. Based on this voltage information and power setting command This generates the final control signal that acts on the local energy storage converter, and its control law can be designed as follows: (3) in, For real-time power setting commands, This is the voltage-power droop factor. This is the local rated voltage. To monitor local network voltage in real time, To ultimately issue power commands to the converter, and simultaneously track power commands from the higher level and accurately stabilize the local voltage, this control signal directly acts on the local energy storage converter, enabling rapid and adaptive power regulation and voltage support.

[0036] Step S14 is used to perform real-time dual-mode switching and intelligent coordination control based on the voltage change rate and according to the control signal. Real-time dual-mode switching and intelligent coordination based on the voltage change rate (dV / dt) is a hybrid control strategy combining event triggering and state feedback. This control method is implemented collaboratively by two core functional modules: a mode decision module and a power allocation and execution module. In this embodiment, the specific method for performing real-time dual-mode switching and intelligent coordination control based on the control signal in step S14 can be of various forms known to those skilled in the art. In one example of the present invention, step S14 may include, for example... Figure 3 The steps shown are described in this. Figure 3 In this context, step S14 may include: In step S20, the voltage change rate is calculated in real time; In step S21, it is determined whether the voltage change rate is less than a threshold. In step S22, if the voltage change rate is less than the threshold, the steady-state optimized mode control law is executed; If the voltage change rate is greater than or equal to the threshold, the dynamic emergency support modal control law is executed.

[0037] In such Figure 3 In the method shown, step S20 is used to calculate the voltage change rate in real time using a modal decision module. In this example, the modal decision module is configured to monitor the voltage of the DC bus in real time. And its voltage change rate is calculated in real time using a first-order differential or state observer algorithm. The set security threshold This is crucial for triggering control mode switching. Its numerical design must match the system's dynamic tolerance to power disturbances. A basic tuning principle can be referenced based on the system's maximum instantaneous power support requirements. Equivalent support capacitor of bus Relationship: ; threshold This reflects the minimum voltage change rate sensing sensitivity required by the system to cope with the most severe power surges. In practical engineering, Final fine-tuning is required, taking into account specific bus impedance characteristics, load sensitivity, and power quality standards, to achieve the best balance between response sensitivity and measurement noise immunity.

[0038] Safety threshold It is the boundary value that distinguishes normal operating conditions from emergency intervention. In one example of this invention, the threshold value is... The method for determining this can be based on threshold tuning calibrated from the system's dynamic characteristics, as detailed below: To conduct an no-load excitation response test, under no-load or light-load conditions, a known-order step power disturbance (e.g., 5% of rated power, lasting 10 ms) is applied to the bus via a programmable electronic load. The dynamic response of the bus voltage is recorded, and the maximum rate of voltage change under this disturbance is measured. This is used as a measure of the system's inherent inertia.

[0039] Statistical learning is performed under normal operating conditions, assuming the system runs continuously for a complete production cycle (e.g., 24 hours) without triggering emergency control, to collect the bus voltage change rate. The real-time value is used to calculate its statistical distribution (such as the 95th percentile or 99th percentile), denoted as . This value represents the typical upper bound reached by the system under normal, permissible dynamic fluctuations.

[0040] Sensitive load tolerance constraints: Based on the voltage sag tolerance curve of the critical load being protected (such as a PLC or controller) (e.g., SEMI F47 standard), calculate the upper limit of the maximum allowable voltage change rate. ,in For the permissible voltage drop depth, For the allowed duration.

[0041] Final decision ,in The reliability factor (typical value 1.2 to 1.5) ensures that the threshold is greater than the upper limit of normal operating conditions but does not exceed the tolerance of sensitive loads.

[0042] Step S21 is used to determine whether the voltage change rate is less than a threshold. Specifically, in this example, the mode switching logic is based on continuous comparison. With threshold And execute the following deterministic logic: when And the bus voltage is within the preset safe operating range. Within this timeframe, the system is determined to be operating in a steady-state optimal mode. Once... If a voltage over-limit is detected, the system immediately enters dynamic emergency support mode. To avoid frequent mode switching due to measurement noise near the threshold, a small hysteresis loop can be introduced. Forming a switching zone To enhance system robustness.

[0043] Step S21 involves the power allocation and execution module receiving decision signals from the modal decision module and upper-level optimization commands, generating final control signals that apply to each energy storage converter. The strategy adaptively switches between modes. In this example, adaptive switching occurs between steady-state optimization mode and dynamic emergency support mode. In steady-state optimization mode, the primary control objective is to accurately and smoothly track the economical power commands issued by the upper-level energy management system. Control output It consists of a command tracking term and a closed-loop fine-tuning term based on voltage deviation: (4) in, Steady-state optimal modal control law, For economical power command, This is the reference value for the rated voltage of the DC bus. This is the real-time voltage of the DC bus. This is the steady-state modal proportionality coefficient. These are the steady-state modal integral coefficients. and The value is chosen to achieve gentle voltage regulation, stabilizing the bus voltage at its rated value while minimizing disruption to the established economic dispatch plan. nearby.

[0044] In the dynamic emergency support mode, when switching to this mode, the economic objective is temporarily downgraded, and the control core switches to suppressing voltage surges at the maximum rate, achieving active damping. The control law employs high gain and incorporates a differential feedback term to directly compensate for the rate of voltage change. (5) in, For dynamic emergency support modal control law, For economical power command, This is the reference value for the rated voltage of the DC bus. This is the real-time voltage of the DC bus. This is the dynamic emergency support modal proportionality coefficient. This provides dynamic emergency support modal differential gain.

[0045] In this control law, the proportional gain Significantly greater than the steady state ( ), to ensure strong voltage recovery capability; differential gain This mode is specifically designed to generate power commands that move in the opposite direction to voltage changes, thus enabling proactive suppression of disturbances. The total output in this mode will be strictly limited to the physical maximum output of the energy storage unit. Within.

[0046] The energy storage collaborative control method for a multi-level DC microgrid in a cigarette factory according to embodiments of the present invention also includes an intelligent coordination and smooth transition mechanism, which includes multi-unit coordination and modal smooth transition. Multi-unit coordination, under steady-state optimization mode, designs a distributed coordination strategy based on a consensus algorithm for multiple energy storage units connected to the same bus. By defining local state variables and designing information exchange rules between adjacent units, the output of each unit can be automatically proportionally allocated according to its available capacity, achieving autonomous equilibrium of the state of charge and reasonable power distribution. Modal smooth transition occurs when an emergency disturbance is suppressed, and the system state (… To avoid secondary power surges caused by sudden changes in control targets when the voltage meets the conditions for returning to steady-state mode, a smooth transition algorithm is designed. This algorithm enables the emergency support component... Instead of immediately returning to zero, it follows a first-order inertial cycle. The voltage decays smoothly and regularly, thus ensuring the stability of the voltage recovery process during control mode switching.

[0047] On the other hand, the present invention also provides an energy storage collaborative control system for a multi-level DC microgrid in a cigarette factory, implementing the energy storage collaborative control method for a multi-level DC microgrid in a cigarette factory as described in any of the above claims. The system includes a data acquisition unit, a first processing unit, a second processing unit, a third processing unit, and a mode switching and intelligent control unit. The data acquisition unit is used to acquire production scheduling information from the production management system. The first processing unit is used to generate a baseline load prediction sequence based on the production scheduling information from the production management system and perform a first optimization calculation to obtain a long-term energy dispatch plan and a reference SOC trajectory for the main energy storage system. The second processing unit is used to perform a second optimization calculation based on the long-term energy dispatch plan, the reference SOC trajectory, and short-term prediction information to obtain a real-time power setting command. The third processing unit is used to perform a third optimization calculation based on the real-time power setting command to obtain a control signal for the local energy storage converter. The mode switching and intelligent control unit is used to perform real-time dual-mode switching and intelligent coordinated control based on the voltage change rate according to the control signal.

[0048] In another aspect, the present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement any of the methods described in the above-mentioned energy storage collaborative control method for multi-level DC microgrids in cigarette factories.

[0049] The beneficial effects of this invention are: This invention addresses complex DC equipment in industrial environments by employing a multi-voltage DC bus architecture and proposing a hierarchical collaborative control strategy that logically maps to the physical level. This method effectively coordinates bidirectional power support and autonomous operation between the main bus and sub-buses, resolving the applicability issues of traditional single-bus control methods in similar architectures. It achieves rapid independent voltage self-stabilization relying on local energy storage, significantly improving the reliability, flexibility, and scalability of the system power supply.

[0050] This invention utilizes a precisely predictable production schedule as a key input, deeply integrating it into a multi-timescale prediction and optimization model. This allows the energy storage system's scheduling behavior to proactively align with the production line's start-up and shutdown rhythms and load variation patterns. On one hand, this significantly improves the local absorption rate of fluctuating power sources such as photovoltaics within the factory area; on the other hand, by anticipating and preparing in advance for planned shock events such as the start-up and shutdown of large equipment, it ensures both production continuity and power quality.

[0051] This invention innovatively uses the bus voltage change rate (dV / dt) as one of the core control criteria, establishing a dual-mode switching control mechanism based on this criterion. This upgrades the voltage stabilization control action from a traditional passive, delayed response based on the absolute value of voltage deviation to an active, proactive intervention based on voltage change trends, fundamentally improving the power supply quality for sensitive loads and the dynamic stability of the system.

[0052] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0053] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0054] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0055] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0056] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0057] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0058] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0059] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0060] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for coordinated energy storage control of a multi-level DC microgrid in a cigarette factory, characterized in that, The control method includes: Obtain production scheduling information from the production management system; A baseline load prediction sequence is generated based on the production scheduling information of the production management system, and a first optimization calculation is performed using the first processing unit to obtain the long-term energy dispatch plan and reference SOC trajectory of the main energy storage system. Based on the long-term energy scheduling plan, the reference SOC trajectory, and short-term forecast information, a second processing unit performs a second optimization calculation to obtain a real-time power setting instruction. The local network voltage is monitored in real time, and based on the real-time power setting command, a third processing unit performs a third optimization calculation to obtain the control signal of the local energy storage converter. Based on the voltage change rate, real-time dual-mode switching and intelligent coordinated control are performed according to the control signal.

2. The control method according to claim 1, characterized in that, Based on the production scheduling information of the production management system, a baseline load prediction sequence is generated, and a first optimization calculation is performed using the first processing unit to obtain the long-term energy dispatch plan and reference SOC trajectory of the main energy storage system, including: The objective function for the first optimization calculation is obtained according to formula (1). ，（1） in, For long-term electricity price signals, for Power purchased from the grid during the specified time period The duration of each time period, This represents the total number of time periods; The constraints on the objective function of the first optimization calculation include: ； ； ； ； ； in, for Time period baseline load forecast For the charging and discharging power of the energy storage system in the long-term plan, for The energy corresponding to the state of charge of a time-limited energy storage system. for The energy corresponding to the state of charge of a time-limited energy storage system. The energy storage charge / discharge efficiency coefficient. , The upper and lower limits of energy storage capacity. , These are the upper and lower limits of charge and discharge power. , The upper and lower limits are the power purchase capacity; A long-term energy scheduling plan is obtained based on the objective function and the constraints. Compared with reference SOC trajectory .

3. The control method according to claim 1, characterized in that, Based on the long-term energy scheduling plan, the reference SOC trajectory, and short-term forecast information, a second processing unit performs a second optimization calculation to obtain real-time power setting instructions, including: The objective function for the second optimization calculation is obtained according to formula (2). ，（2） in, for Electricity price during specific time periods for The amount of electricity purchased from the grid during a given period. This is the weighting factor for the power deviation. The weighting coefficient for SOC deviation, To optimize the discharge power of energy storage in the short term, For long-term energy dispatch planning, To optimize the energy state of medium-energy storage in the short term, For reference to the SOC trajectory, This represents the short-term forecast window length.

4. The control method according to claim 3, characterized in that, The constraints on the objective function of the second optimization calculation include: ； ； ； ； ； ； in, For the revised load forecast, for Energy stored over a period of time Energy stored for the next period of time To optimize the time interval in the short term, The energy storage charge / discharge efficiency coefficient. , The upper and lower limits of energy storage capacity. , These are the upper and lower limits of charge and discharge power. , The upper and lower limits of the power purchase capacity, This represents the energy storage capacity of the previous time period. This represents the maximum power change rate of energy storage.

5. The control method according to claim 1, characterized in that, Real-time monitoring of the local network voltage, and based on the real-time power setting command, a third processing unit performs a third optimization calculation to obtain the control signal for the local energy storage converter, including: The control signal is obtained according to formula (3). ，（3） in, For control signals, For real-time power setting commands, This is the voltage-power droop factor. This is the local rated voltage. To monitor local network voltage in real time.

6. The control method according to claim 1, characterized in that, Based on the voltage change rate, real-time dual-mode switching and intelligent coordinated control according to the control signal includes: Calculate the voltage change rate in real time and determine whether the voltage change rate is less than a threshold. If the voltage change rate is determined to be less than a threshold, a steady-state optimized mode control law is executed. If the voltage change rate is determined to be greater than or equal to a threshold, the dynamic emergency support modal control law is executed.

7. The control method according to claim 6, characterized in that, Executing steady-state optimal modal control laws includes: The steady-state optimal modal control law is obtained according to formula (4). ，（4） in, Steady-state optimal modal control law, For economical power command, This is the reference value for the rated voltage of the DC bus. This is the real-time voltage of the DC bus. This is the steady-state modal proportionality coefficient. These are the steady-state modal integral coefficients.

8. The control method according to claim 6, characterized in that, The execution of dynamic emergency support modal control laws includes: The dynamic emergency support modal control law is obtained according to formula (5). ，（5） in, For dynamic emergency support modal control law, For economical power command, This is the reference value for the rated voltage of the DC bus. This is the real-time voltage of the DC bus. This is the dynamic emergency support modal proportionality coefficient. This provides dynamic emergency support modal differential gain.

9. A collaborative control system for energy storage in a multi-level DC microgrid in a cigarette factory, implementing the collaborative control method for energy storage in a multi-level DC microgrid in a cigarette factory as described in any one of claims 1 to 8, characterized in that, The system includes: The data acquisition unit is used to obtain production scheduling information from the production management system; The first processing unit is used to generate a baseline load prediction sequence based on the production scheduling information of the production management system, and to perform a first optimization calculation to obtain the long-term energy dispatch plan and reference SOC trajectory of the main energy storage system. The second processing unit is used to perform a second optimization calculation based on the long-term energy scheduling plan, the reference SOC trajectory and short-term forecast information, in order to obtain real-time power setting instructions. The third processing unit is used to perform a third optimization calculation based on the real-time power setting command in order to obtain the control signal of the local energy storage converter. The mode switching and intelligent control unit is used to perform real-time dual-mode switching and intelligent coordinated control based on the voltage change rate and the control signal.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed by a processor, implement the method as described in any one of claims 1 to 8.

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