Source-network-load-storage integrated flexible regulation and control method for industrial enterprise

Abstract

The invention discloses a source-network-load-storage integrated flexible regulation and control method for an industrial enterprise, and aims to solve the problems that production scheduling and energy regulation and control of an existing regulation and control system are disjointed, emergency response is slow, light abandoning loss is large and the like. The method comprises a flexible adjustment step and a quasi-flexible emergency adjustment step: the flexible adjustment step is based on a four-level time scale intelligent production scheduling plan with deducible and invertible functions, combines real-time operation data of each subsystem, cooperatively regulates and controls photovoltaic, energy storage, waste heat power generation and load subsystems, preferentially stores energy and charges when photovoltaic is over-generating, and carries out emergency adjustment when the photovoltaic is over-generating. After the stored energy is saturated, the output of the photovoltaic inverter is limited in proportion; the quasi-flexible emergency adjustment step is started when an emergency situation is monitored, a minimum branch removal combination is solved through a branch removal algorithm, an instruction is quickly issued by means of IO equipment connected with a specific optical fiber link, emergency logic can be reused to all subsystems, and output is gradually recovered according to a preset strategy after the emergency is finished. The method improves the new energy consumption rate, reduces the light abandoning loss and the power consumption cost, guarantees the system stability and the production continuity, is suitable for various high-energy-consumption industrial scenes, and is low in improvement cost.

Description

Technical Field

[0001] This invention relates to a control method for an industrial enterprise energy management system. More specifically, it relates to a method for achieving flexible regulation and quasi-flexible emergency control in an integrated energy source-grid-load-storage system for industrial enterprises. It is applicable to various high-energy-consuming industrial production scenarios such as cement and chemical industries. Background Technology

[0002] With the increasing demand for clean energy from industrial enterprises and the growing pressure on power grid peak regulation, integrated source-grid-load-storage coordinated control technology has become an important means to improve the absorption capacity of new energy sources and ensure the safe and stable operation of the power grid. This technology achieves optimized configuration and intelligent control of multiple energy systems by coordinating photovoltaic, waste heat power generation, energy storage, and load operation. Currently, there are relevant technical solutions in the field of source-grid-load-storage coordinated control. Chinese patent CN114142532B discloses a method and system for distributed photovoltaic power participating in source-grid-load-storage coordinated control. This solution establishes a distribution network operation status prediction model that includes source, grid, load, and storage, with the control objective of prioritizing the full absorption of distributed photovoltaic power while considering the economic operation of energy storage. It determines a collaborative optimization scheduling and control strategy according to the principle of hierarchical zoning. Chinese patent CN120377377A discloses a source-grid-load-storage coordinated control system and method for large-capacity multi-network energy storage intermodal operation. Through a hierarchical architecture of master station layer, substation layer, and terminal layer, it achieves economic optimization scheduling and rapid coordinated control of photovoltaic and energy storage resources. Chinese patent CN120810790A discloses a source-grid-load-storage coordinated control method for multi-grid energy storage collaborative operation. It generates new energy output and load change trends based on a dynamic prediction model and decomposes the power generation plan into various control commands according to grid security priorities. Chinese patent CN119362592B discloses an incremental distribution network source-grid-load-storage coordinated control system. Through data acquisition, analysis, security assessment, and control modules, it ensures full utilization of photovoltaic power generation while meeting the requirements of safe and stable operation. Chinese patent CN116093931A discloses a source-grid-load-storage coordinated control system based on edge computing. It adopts an architecture of a master station terminal and multiple edge computing substation terminals, and performs optimization decisions based on various collected data. The existing technology has the following drawbacks: 1. Traditional industrial enterprises' source-grid-load-storage control systems do not incorporate energy factors such as photovoltaic output forecast curves, time-of-use electricity price tiers, carbon emissions during grid curtailment periods, green electricity, and power supply reliability into specific industrial production optimization scheduling decisions. This results in low absorption rates of new energy sources such as photovoltaics, high peak-hour electricity purchase ratios, and failure to achieve optimal energy cost control. 2. Existing anti-backflow processing methods are inefficient. When a large production load is suddenly cut off, traditional anti-backflow devices will directly disconnect all photovoltaic inverters and restore grid access, which takes a long time and results in huge losses from a single curtailment. 3. The current source-grid-load-storage control system lacks a complete closed-loop control system, which cannot achieve full-process management from macro-production scheduling to micro-control, and is difficult to adapt to multi-energy coexistence scenarios. 4. Existing source-grid-load-storage collaborative optimization scheduling methods still have shortcomings in terms of optimizing objective functions and constraints, making it difficult to meet the complex needs of industrial production. Summary of the Invention To address the technical problems in existing industrial enterprise power generation, grid, load, and storage control systems, such as the disconnect between production scheduling and energy regulation, inefficient backflow prevention methods, lack of a complete closed-loop control system, insufficient emergency response speed, and poor reusability of control logic, and to achieve the technical effects of increasing the renewable energy consumption rate, reducing curtailment losses, enhancing system flexibility, improving emergency response capabilities, and unifying the control of the entire system, this invention provides an integrated flexible control method for power generation, grid, load, and storage in industrial enterprises. The technical solution adopted by this invention to solve its technical problem is: to provide an integrated flexible regulation method for energy source, grid, load and storage in industrial enterprises, including flexible regulation steps and quasi-flexible emergency regulation steps. Moreover, this solution does not modify the original energy storage and photovoltaic systems, but achieves emergency control by adding independent remote IO devices, thereby reducing the cost of modification. The flexible adjustment step is based on intelligent production scheduling and coordinates the photovoltaic subsystem, energy storage subsystem, waste heat power generation subsystem, and load subsystem. The intelligent production scheduling plan has both extrapolation and reversal capabilities: extrapolation means that after adjusting constraints such as electricity price, weather, green electricity price, and grid curtailment, the system can quickly regenerate an optimized production scheduling plan; reversal means that based on the deviation between actual operating data and the plan, the production scheduling model parameters can be retrospectively optimized to improve the accuracy of subsequent plans. The intelligent production scheduling plan operates on four time scales: monthly, weekly, daily, and hourly. Its constraints include order demand, electricity price trends during specific periods, equipment maintenance plans, weather forecasts, carbon emission control indicators, monthly green electricity price fluctuation data, and grid curtailment instructions. The quasi-flexible emergency regulation step is activated when a sudden load change, a sharp drop in photovoltaic output, a grid fault, or a sudden situation where the energy storage SOC falls below a preset threshold is detected. This includes generating an emergency disconnection command based on a branch disconnection algorithm to disconnect at least one energy branch, and sending the emergency disconnection command to the corresponding energy subsystem via an I / O device. The I / O device in the energy subsystem responds to the emergency disconnection command and executes the corresponding branch disconnection operation. The regulation logic of the quasi-flexible emergency regulation step (branch disconnection algorithm + rapid response + gradual recovery) can be reused in emergency regulation scenarios of photovoltaic subsystem, energy storage subsystem, and waste heat power generation system to achieve unified control of the entire system. Preferably, the flexible adjustment step includes prioritizing the charging of the energy storage subsystem to absorb the excess power when the real-time output of the photovoltaic system exceeds the real-time load demand. Furthermore, if the energy storage subsystem still generates excess power after being fully charged, the output of some photovoltaic inverters will be dynamically limited proportionally. This proportional dynamic limitation of the output of some photovoltaic inverters includes controlling the single adjustment range within a preset range. Optionally, the flexible adjustment step further includes combining time-of-use electricity pricing and photovoltaic output forecasting to coordinate the output of the waste heat power generation system and the operating status of the load subsystem. Preferably, the branch cutoff algorithm is configured to solve for branch cutoff combinations. The criteria for determining the branch cutoff combination are: ① the number of branches N is minimized; ② the total real-time current of the cutoff branches is greater than or equal to the reverse current deficit. The optimal combination is solved in real time by the core controller. The determining factors of the branch cutoff combination include at least one of the following: reverse current magnitude, real-time output of each energy unit, current load status, and remaining energy storage capacity. Furthermore, the I / O device is built on a fiber optic link, and the instruction transmission latency is less than 1 millisecond. Optionally, the time for the I / O device to perform latch-up and exit disconnection is less than or equal to 15 milliseconds, and the function of the I / O device is limited to receiving emergency commands and performing fault disconnection operations only. Preferably, while the quasi-flexible emergency adjustment step is being executed, the energy storage subsystem is controlled to charge and discharge to compensate for the power shortage. Furthermore, after the quasi-flexible emergency adjustment step is completed, a recovery step is also included, in which the system automatically switches back to the flexible adjustment step and gradually restores the output of the disconnected energy branch according to a preset strategy. Preferably, the emergency response strategy for the sudden situation includes: 1. Sudden load change / sudden drop in photovoltaic output: Perform branch cut-off combined cut-off operation, and simultaneously control the charging and discharging of the energy storage subsystem to compensate for the power gap; 2. Grid failure: The system switches to islanded mode, coordinating the waste heat generation system and energy storage subsystem to jointly supply power, ensuring the continuous operation of primary loads; 3. When the energy storage SOC is lower than the preset threshold: stop the energy storage subsystem from discharging before peak hours, and prioritize the charging and capacity recovery of the energy storage subsystem during off-peak hours. The beneficial effects of this invention are as follows: 1. This invention achieves the organic integration of production scheduling and energy regulation by constructing a closed-loop control system of intelligent production scheduling, flexible adjustment, and quasi-flexible emergency support. It incorporates energy elements such as photovoltaic output forecast curves, time-of-use electricity price tiers, and grid curtailment periods, effectively improving the absorption rate of new energy sources such as photovoltaics, reducing the proportion of peak-hour electricity purchases, and significantly improving energy utilization efficiency. 2. The present invention adopts a strategy of prioritizing the charging of the energy storage system. When the real-time output of photovoltaic power exceeds the real-time load demand, the excess power can be absorbed in time, avoiding the problem of all photovoltaic inverters being cut off in the traditional solution, greatly reducing the single curtailment loss and improving the system operating efficiency. 3. This invention achieves precise matching between photovoltaic output and load demand by dynamically limiting the output of a portion of the photovoltaic inverter in a proportional manner, effectively solving the limitations of traditional fixed power limitation and improving the system's flexibility and adjustment accuracy. 4. In response to emergencies such as sudden load changes and sharp drops in photovoltaic output, this invention employs a "remote IO device + optimal branch cutoff algorithm" to achieve precise control within 15 milliseconds, utilizes photovoltaic resources, avoids the activation of anti-reverse flow devices, and ensures the continuity and stability of production. 5. This invention optimizes production scheduling plans through the four-level time scale full constraint optimization of the intelligent scheduling module, combined with the multi-energy collaborative precise control of the flexible adjustment module, realizing full-process monitoring and optimization from monthly to hourly, forming a complete closed-loop control system, effectively solving the problem of missing control closed loop in the prior art. Attached Figure Description Figure 1 This is a schematic diagram of the overall structure of this solution; Figure 2 This is the flow chart of the flexible adjustment method in this scheme; Figure 3 This is the flowchart of the quasi-flexible adjustment method in this scheme; Figure 4 This is a flowchart illustrating the intelligent scheduling and optimization process of this solution. Detailed Implementation

[0003] Example 1 like Figure 1 As shown, this invention provides a flexible control method integrating power generation, grid, load, and energy storage for industrial enterprises. It is applied to a large, high-energy-consuming industrial enterprise equipped with multiple power sources, including mains power, waste heat generators, distributed photovoltaic power generation systems, and energy storage systems. Loads are categorized according to importance and regulation characteristics into primary core loads (such as rotary kilns in cement production), secondary adjustable loads (such as cement mill production lines), and tertiary start-stop loads (such as non-core auxiliary equipment). This solution does not modify the existing energy storage or photovoltaic systems; emergency control is achieved by adding independent remote I / O devices, thus reducing modification costs. S1: Flexible Adjustment Steps like Figure 2As shown, based on intelligent production scheduling, the photovoltaic subsystem, energy storage subsystem, waste heat power generation subsystem, and load subsystem are coordinated and controlled. The system collects data such as source-side output, load consumption, and environmental conditions in real time, uploads them to the core decision-making layer via high-speed fiber optic links, stores them in a distributed database, and updates operating condition information synchronously. The platform combines various constraints, including order demand, time-based electricity price trends, equipment maintenance plans, weather forecasts, carbon emission control indicators, and monthly green electricity price fluctuation data, to generate production plans at four time scales: monthly, weekly, daily, and hourly. This clearly defines the coordinated control direction of power generation, load, and storage for each time period. The intelligent production scheduling plan has predictive and reversible functions: when green electricity prices fluctuate monthly, peak-valley electricity prices adjust, or weather forecasts change, the platform can quickly regenerate an optimized production scheduling plan by adjusting corresponding parameters; at the end of each month, based on the deviation between actual operating data and the plan, the platform backtracks and optimizes parameters such as load allocation coefficients and renewable energy consumption weights in the production scheduling model, improving the accuracy of the plan for the following month. In constructing the optimized production scheduling model, the core decision-making layer adopts a time-discrete scheduling model, dividing the scheduling cycle into T time periods, each with a duration of Δt. The system aims to minimize the total cost of the entire cycle, and the following objective function is constructed: in: The electricity purchase price from the grid during time period t (yuan / kWh); The amount of electricity purchased from the grid during time period t; Compensation cost per unit of load interruption; The power interrupted by the flexible load during time period t; The charging, discharging, operation, and maintenance costs of energy storage units; These are the energy storage charging and discharging power, respectively; This is the curtailment penalty coefficient, used to constrain curtailment of photovoltaic and waste heat power generation. Contribute to photovoltaic theory; This refers to the power of abandoned photovoltaic power. If carbon emission control targets are taken into account, a multi-objective economic-low-carbon optimization model can be constructed: Where λ is the carbon emission conversion factor (tCO2 / kWh), which is transformed into a single objective in engineering through a weighted method: α∈(0,1) is the economic weighting coefficient, which can be dynamically adjusted according to the enterprise's carbon emission control requirements. During off-peak season regulation, the power supply on the source side mainly relies on grid-purchased electricity, photovoltaic self-consumption, and waste heat power generation in tandem. On the load side, secondary loads are arranged to operate at full capacity, making full use of low-priced off-peak electricity and green energy resources. The energy storage side is charged at full power as planned (SOC rises to an appropriate capacity level). During flat-peak season regulation, the power supply on the source side prioritizes waste heat power generation and photovoltaic self-consumption, with the grid supplementing energy as needed. Secondary loads on the load side operate normally, while tertiary loads are started and stopped as needed according to production rhythm. The energy storage side flexibly charges and discharges based on real-time power deviations to balance the supply and demand differences between the source and load sides. During peak season regulation, the power supply on the source side mainly relies on waste heat power generation, photovoltaic self-consumption, and energy storage discharge to replace high-priced peak-hour electricity purchases. Secondary loads on the load side operate at a reasonably reduced load range, while tertiary loads are shut down in advance before the peak season. The energy storage side discharges at full power. When the real-time output of photovoltaic power exceeds the real-time load demand, priority is given to controlling the charging of the energy storage subsystem to absorb the excess power. If there is still excess power after the energy storage subsystem is fully charged, the output of some photovoltaic inverters is dynamically limited proportionally, with each adjustment controlled within a preset small range of the current output to avoid sudden power fluctuations affecting system stability. Combining time-of-use pricing and photovoltaic output forecasting, the output of the waste heat power generation system and the operating status of the load subsystem are coordinated to achieve efficient absorption of new energy and cost optimization. S2: Quasi-flexible emergency regulation steps like Figure 3 As shown, when a sudden change in load, a sharp drop in photovoltaic output, a grid fault, or a sudden event where the energy storage SOC falls below a preset threshold is detected, a quasi-flexible emergency regulation will be activated. Emergency triggering scenarios include load shedding caused by the tripping of high-power equipment, a short-term decrease in photovoltaic output exceeding a preset decrease threshold, an energy storage SOC falling below a preset threshold, and emergency situations such as detecting reverse current power approaching the activation threshold of the anti-reverse current device. S21: Based on the branch cutoff algorithm, an emergency cutoff command is generated to cut off at least one energy branch. The branch cutoff algorithm is configured to solve for branch cutoff combinations, and the criteria are: ① the number of branches N is minimized; ② the total real-time current of the cutoff branches is greater than or equal to the reverse current deficit. The core controller uses an integer programming algorithm to solve for the optimal combination in real time. The determining factors include the reverse current magnitude, the real-time output of each energy unit, the current load status, and the remaining energy storage capacity, avoiding the large amount of curtailment loss caused by the traditional full cutoff mode. S22: Emergency cutoff commands are sent to the corresponding energy subsystems via I / O devices. These I / O devices are built on fiber optic links, ensuring command transmission latency of less than 1 millisecond, guaranteeing extremely fast transmission of emergency commands. S23: The I / O devices in the energy subsystem respond to emergency disconnection commands and execute corresponding branch disconnection operations. The I / O devices complete the disconnection operation in less than 15 milliseconds, enabling them to quickly disconnect the specified branch and rapidly achieve source-load balance. The function of the I / O devices is limited to receiving emergency commands and executing fault disconnection operations only, ensuring system safety. The emergency response strategies for different emergencies are as follows: Sudden load change / sudden drop in photovoltaic output: Execute branch disconnection combined disconnection operation (for photovoltaic subsystem), and simultaneously control the charging and discharging of energy storage subsystem to compensate for power gap, avoiding voltage fluctuations in primary load; Grid failure: The system quickly switches to island mode, coordinates the waste heat generation system and energy storage subsystem to jointly supply power, and prioritizes the continuous operation of primary loads such as rotary kilns; When the energy storage SOC is below the preset threshold: the energy storage subsystem is immediately stopped from discharging before peak hours, and the energy storage subsystem is prioritized to charge and restore capacity during off-peak hours to avoid excessive energy storage discharge from affecting equipment lifespan. The control logic of the quasi-flexible emergency regulation step (branch cut-off algorithm + rapid response + gradual recovery) can be reused in emergency regulation scenarios of photovoltaic subsystems, energy storage subsystems, and waste heat power generation systems to achieve unified control of the entire system. After the quasi-flexible emergency regulation step ends, a recovery step is also included: when backflow is eliminated, source-load power is restored to balance, load operation is stable, or energy storage SOC is restored to a safe capacity level, the emergency state is determined to be over, the system automatically switches back to the flexible regulation step, and gradually restores the output of the cut-off energy branches according to a preset strategy. The system initiates a gradual recovery process, with energy storage first maintaining charge-discharge balance, and photovoltaic or waste heat output gradually increasing according to preset time intervals and proportions to avoid sudden power changes. Example 2 like Figure 4 As shown in the figure, this embodiment applies the above-mentioned flexible regulation method of integrated power generation, grid, load and storage in industrial enterprises to a large cement production enterprise. This enterprise is a typical high-energy-consuming industrial scenario, which needs to meet multiple requirements at the same time, such as clinker / cement production targets, continuous operation of primary load, energy conservation and emission reduction and cost optimization. (a) Source-side configuration High-voltage mains power is connected as the basic power supply; medium-capacity waste heat generator sets generate electricity using waste heat from rotary kilns during cement production; large-capacity distributed photovoltaic power generation systems (composed of multiple photovoltaic arrays); energy storage systems with corresponding rated power and capacity are used for peak shaving, emergency energy replenishment, and absorbing excess photovoltaic power; green electricity procurement channels are stable, and monthly prices fluctuate to a certain extent. (ii) Load side configuration Level 1 load: Rotary kiln and its key supporting equipment, requiring continuous and uninterrupted operation, forming the core production load; Level 2 load: Multiple cement mill production lines and related auxiliary equipment, with a preset load adjustment capability of reasonable range; Level 3 load: Office power, non-core auxiliary production equipment, etc., which can be started and stopped as needed. (III) External Constraints The local area implements a four-stage time-of-use electricity pricing policy (peak, flat, and valley periods), and the grid occasionally issues temporary power rationing orders. Enterprises are required to meet relevant policy requirements for carbon emission intensity, and the photovoltaic absorption rate has reached a high level. During the optimization process, the system must meet the following core constraints: Power balance equation constraints: in For actual power generation from waste heat, For rigid loads, This represents the actual power of the flexible load. Power supply side constraints: Waste heat power generation output constraints: Constraints on curtailment of solar power: Non-negative constraints on power grid purchases: Load-side constraints: Flexible load power constraints: Load interruption constraints: 4. Constraints of energy storage systems: Charge / discharge power and state are mutually exclusive: Capacity updates dynamically: Capacity Boundaries and Round-Robin Scheduling: Specific implementation process This embodiment strictly follows the steps of the integrated flexible regulation method for industrial enterprise power generation, grid, load and storage described in this invention, as follows: Step 1: Real-time data acquisition from the system Data acquisition scope: The following data will be collected in real time through remote I / O acquisition and control units deployed in photovoltaic arrays, energy storage stations, waste heat generator sets, substations, and production workshops: Source-side data: output of each inverter in the photovoltaic array, real-time output of the waste heat generator set, SOC and charging / discharging power of the energy storage system, voltage / current / power of the high-voltage mains power supply, and real-time green electricity procurement power; Load-side data: Real-time power of rotary kiln (primary load), power of cement mill production line (secondary load), total power of tertiary load; Environmental and external data: meteorological data (irradiance, temperature), local time-of-use electricity price data, real-time carbon emission monitoring data, and monthly green electricity price data. Data storage and processing: Through a communication network composed of high-speed industrial switches and optical fibers, the collected real-time data, historical operating data over many years, photovoltaic / waste heat output prediction data for the next few hours, production scheduling data, etc. are stored in a distributed database. Objective function construction: Using a linear programming algorithm, the objective function is constructed with the dual objectives of "lowest energy cost for cement production + safe and reliable power supply to primary loads," and its specific form is as follows: The constraints include ensuring that the equipment operating load rate reaches a reasonable level, the photovoltaic absorption rate reaches a high level, the peak-hour electricity purchase ratio is controlled within a low range, and the carbon emission intensity meets the relevant policy requirements. The optimal control scheme is to be found. Step 2: Generate a four-level time-scale production and energy co-scheduling plan Monthly production schedule: Based on the monthly clinker / cement production targets, and with reference to historical energy consumption data, monthly time-of-use electricity price trends, green electricity prices and carbon emission indicators, determine the operating shifts of each production line and the full-load operating periods of waste heat generator units, and set a target for a significant reduction in monthly electricity consumption per ton of cement. Weekly production scheduling plan: Adjust the photovoltaic output forecast curve according to the weekly weather forecast, optimize the synergistic output period of waste heat power generation and photovoltaic, and rationally allocate the operating time of cement mill production line (prioritize scheduling during off-peak / peak hours) to avoid inefficient operation; Daily Precision Production Scheduling Plan: Based on historical data, a predictive algorithm is used to obtain the photovoltaic output curve, waste heat power generation output curve, and load demand curve for the next day. The production scheduling plan for the next day is optimized through a linear programming algorithm to ensure that all constraints are met while minimizing electricity costs. Hourly dynamic adjustment plan: Based on real-time data deviations, formulate hourly source-load-storage coordination strategies, specifying energy storage charging and discharging power, secondary load adjustment range, photovoltaic output limit ratio, etc. Step 3: Perform flexible adjustment Based on the daily production schedule and real-time data, flexible adjustments are implemented according to differentiated strategies for off-peak, flat, and peak periods, as follows: Off-peak period (the period with the lowest electricity price): On the source side, "high-voltage grid power purchase + photovoltaic self-consumption + waste heat power generation operate at full load"; on the load side, secondary loads (cement mills) operate at full load; on the storage side, energy storage systems are charged at rated power, and the SOC is raised to an appropriate capacity level. During flat periods (when electricity prices are moderate): On the source side, "waste heat power generation + photovoltaic self-consumption + grid supplementation (on demand)"; on the load side, secondary loads operate normally, and tertiary loads are started and stopped on demand; on the storage side, charging and discharging are performed on demand to balance power deviations. Peak hours (the period with the highest electricity price): On the source side, "waste heat power generation + photovoltaic self-consumption + full-power discharge of energy storage"; on the load side, secondary loads are reduced by a preset reasonable amount, and tertiary loads are shut down before peak hours; on the storage side, discharges at rated power to replace peak-hour electricity purchases. Anomaly prediction: Real-time monitoring of sudden changes in load power and sudden drops in photovoltaic output; once a set threshold is triggered, emergency protection and control will be immediately activated. Step 4: Prioritize charging of the energy storage system Keep the energy storage charging power within the rated value to avoid overcharging and affecting equipment lifespan; prioritize starting energy storage charging during off-peak hours and periods of excessive photovoltaic output. For example, when the real-time output of photovoltaic exceeds the load demand, the energy storage charges at the corresponding power to absorb the excess power; record the energy storage charging period and charging power value in real time to the distributed database for subsequent production scheduling optimization. Step 5: Dynamically limit the output of some photovoltaic inverters proportionally. The output ratio of each photovoltaic inverter is calculated in real time. When the energy storage SOC reaches saturation but there is still photovoltaic over-generation, the inverter whose output is restricted is determined according to the real-time output ratio of each inverter. The adjustment range is controlled within a preset small range of the current output to avoid power fluctuations affecting system stability. Step 6: Determine the limiting output ratio The total power of the photovoltaic inverters with restricted output is monitored in real time. If the total power exceeds the preset threshold for real-time total photovoltaic output, the process returns to step 1 to re-collect data and optimize the control strategy; if it does not exceed the threshold, step 7 is executed. Step 7: Emergency Support Control When an emergency such as sudden load change, sudden drop in photovoltaic output, grid fault, or energy storage SOC below a preset threshold is detected, the system is adjusted based on the load deficit by solving the branch cut-off combination algorithm: when a high-power device above the preset power threshold is detected to trip and a load deficit occurs, the branch cut-off combination is solved by integer programming algorithm, and the command is transmitted to the photovoltaic inverter branch switch within 15 milliseconds from the start of the event, avoiding the traditional anti-reverse current device from cutting off all photovoltaic power; Emergency response to sudden drop in photovoltaic output: When the photovoltaic output drops by more than a preset threshold in a short period of time, the energy storage system immediately discharges at the corresponding power to replenish energy, so as to avoid excessive voltage fluctuations in the primary load. Emergency response to power grid failure: When a power grid failure occurs, the system quickly switches to islanded mode, coordinates the waste heat generator set and energy storage system to jointly supply power, and prioritizes the continuous operation of primary loads such as rotary kilns; Emergency response to low remaining energy storage capacity: If the energy storage SOC is lower than the preset threshold during peak hours, discharge will be stopped immediately, and the capacity will be restored by prioritizing charging during off-peak hours. III. Expected Outcomes The application of the method of this invention in this cement enterprise has achieved the following results: Production targets: The power supply reliability for the primary load (rotary kiln) reaches a high level, with very few monthly production interruptions; the equipment operating load rate reaches a reasonable level. Energy indicators: Electricity consumption per ton of cement has decreased significantly; peak electricity purchase ratio has been controlled within a low range; photovoltaic absorption rate has reached a high level; and waste heat power generation utilization rate has reached a high level. Economic indicators: Monthly electricity costs have decreased significantly, maximizing the arbitrage profits from energy storage charging and discharging; Emergency Response: Achieve rapid and precise control within 15 milliseconds or less, reduce the malfunction rate of the anti-backflow device, significantly reduce single-time light curtailment loss compared to traditional solutions, effectively respond to various abnormal situations, and ensure production continuity and stability. Through the coordinated operation of the aforementioned flexible adjustment and quasi-flexible emergency adjustment, a complete collaborative closed loop is formed, encompassing basic support, flexible control, emergency reversal, and recovery optimization. After implementation, it fully adapts to the multiple demands of complex industrial production scenarios, requiring no modification to existing equipment, thus reducing modification costs while achieving deep coupling and optimization of production and energy. Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A flexible regulation method integrating power generation, grid, load, and storage for industrial enterprises, characterized in that, Includes the following steps: Flexible adjustment steps: Based on an intelligent production scheduling plan with predictable and inverse functions, and combined with real-time operating data of the photovoltaic subsystem, energy storage subsystem, waste heat power generation system and load subsystem, the subsystems are coordinated and regulated through preset control rules; The ability to predict refers to the system regenerating an optimized production schedule within a preset time after adjusting at least one constraint parameter among electricity price, weather, green electricity supply, power grid curtailment orders, or equipment maintenance plans. The term "reversible" refers to the ability to backtrack and optimize the parameters in the production scheduling model based on the deviation between actual operating data and the production scheduling plan, in order to improve the accuracy of subsequent production scheduling plans. The optimized production scheduling scheme is distributed to each subsystem via fiber optic Ethernet through the source-network-load-storage coordination controller, achieving flexible adjustment with a response time of up to seconds; Quasi-flexible emergency control procedure: When an emergency occurs in the system and flexible adjustment cannot meet the requirements for rapid response, quasi-flexible emergency control is activated. The time from the occurrence of the event to the quasi-flexible control command reaching the device is less than 15 milliseconds; The coordination controller generates an emergency interlock or disconnect command for disconnecting at least one energy branch through a branch optimization disconnection algorithm, and sends it to the corresponding energy subsystem via a remote I / O device. The remote I / O device in the energy subsystem responds to the instruction and performs the corresponding branch blocking or disconnection operation. The regulation logic of the quasi-flexible emergency control can be reused in emergency regulation scenarios of photovoltaic subsystems, energy storage subsystems and waste heat power generation systems.

2. The method according to claim 1, characterized in that, The intelligent production scheduling plan is a four-level time scale production scheduling plan of monthly, weekly, daily and hourly. Its constraints include at least one of the following: order demand, time-of-use electricity price trend, photovoltaic output forecast, load demand curve, carbon emission control indicators, equipment maintenance plan and weather forecast data.

3. The method according to claim 1, characterized in that, The flexible adjustment step further includes: During off-peak electricity prices, secondary loads are controlled to operate at full capacity, and energy storage systems are charged according to plan. During periods of normal electricity prices, secondary loads are controlled to operate normally, tertiary loads are started and stopped as needed, and the energy storage system is flexibly charged and discharged according to power deviations. During peak electricity price periods, secondary loads are controlled to operate at reduced load, tertiary loads are shut down in advance, and energy storage systems discharge at full power.

4. The method according to claim 1, characterized in that, The intelligent production scheduling plan is constructed based on a time-discrete scheduling model, with the objective function being the minimum total cost of the entire cycle operation. The expression for the objective function is as follows: in: The electricity purchase price from the grid during time period t (yuan / kWh); The amount of electricity purchased from the grid during time period t; Compensation cost per unit of load interruption; The power interrupted by the flexible load during time period t; The charging, discharging, operation, and maintenance costs of energy storage units; These are the energy storage charging and discharging power, respectively; This is the curtailment penalty coefficient, used to constrain curtailment of photovoltaic and waste heat power generation. Contribute to photovoltaic theory; This refers to the power of abandoned photovoltaic power.

5. The method according to claim 4, characterized in that, The scheduling model also supports constructing a multi-objective optimization model with minimizing carbon emissions from grid power purchases as the secondary objective. The multi-objective model is then transformed into a single objective using a weighted method for solution, as expressed in the following expression: Where: α is the economic weighting coefficient, and λ is the carbon emission conversion factor.

6. The method according to claim 4 or 5, characterized in that, The scheduling model satisfies at least one of the following constraints: Power balance constraints; Upper and lower limits of waste heat power generation output; Solar curtailment power constraints; Flexible load power and interruption constraints; Energy storage charging and discharging mutual exclusion constraints, capacity dynamic constraints, and cyclic scheduling constraints.

7. The method according to claim 1, characterized in that, The branch cutoff algorithm is used to solve the branch cutoff combination. Its judgment criteria include: the minimum number of cutoff branches, and the sum of the real-time currents of the cutoff branches is greater than or equal to the reverse current deficit.

8. The method according to claim 7, characterized in that, The factors determining the branch cut-off combination include at least one of the following: the magnitude of the backflow, the real-time output of each energy unit, the current load status, and the remaining energy storage capacity. Each factor participates in the calculation according to a preset weight, and the optimal combination is solved in real time using an integer programming algorithm.

9. The method according to claim 1, characterized in that, The remote I / O device is built on an optical fiber link, and the transmission delay is no more than 1 millisecond.

10. The method according to claim 1, characterized in that, After the quasi-flexible emergency control ends, the system automatically switches back to the flexible adjustment step and gradually restores the output of the cut-off energy branches in a preset order, wherein the restoration order is photovoltaic branch, energy storage branch, and waste heat power generation branch.

Images