Flexible aggregate evaluation method considering temperature and concentration inertia in clean room

Abstract

The present application provides a flexible polymerization evaluation method considering the temperature and concentration inertia of a clean room, comprising: constructing an operation model of the target clean room, the operation model comprising operation parameter equations of a production system, an energy storage system, a ventilation system and an air conditioning system of the target clean room, the operation model comprising a temperature inertia model and a concentration inertia model, the temperature inertia model and the concentration inertia model reflecting dynamic changes of temperature and particulate matter concentration in the target clean room respectively; based on the operation model and preset operation constraint conditions of the target clean room, the operation parameters of the target clean room are optimized and solved to obtain a baseline state of the target clean room; and based on the baseline state, a flexibility evaluation result of the target clean room is determined, the flexibility evaluation result reflecting the demand response capability of the target clean room to the power grid. The present application can realize the flexibility evaluation of the clean room system.

Description

Technical Field

[0001] This invention relates to the field of industrial control technology, and in particular to a flexible aggregation evaluation method that takes into account the temperature and concentration inertia of cleanrooms. Background Technology

[0002] The large-scale grid connection of new energy sources has brought significant volatility and uncertainty, posing challenges to the safe and stable operation of the power grid. The limitations of traditional thermal and hydropower units in terms of regulation capacity make it difficult for them to undertake this regulation task alone. Therefore, it is necessary to continue to explore flexibility resources on the load side as an important support for balancing supply and demand.

[0003] In emerging industrial cities, the industrial structure is highly concentrated in high-value-added industries, such as electronic equipment manufacturing and biomedicine. These enterprises rely on cutting-edge technologies to achieve value-added exceeding that of traditional products, and have extremely stringent requirements for the cleanliness and stability of their production environment. Their energy consumption is mainly concentrated in cleanroom systems, used to maintain the stability of key environmental parameters such as temperature, humidity, airflow, and pressure. As a core energy-consuming component of these enterprises, cleanrooms have a large load base and high adjustment potential.

[0004] With the increase in renewable energy and the gradual opening of the electricity market, demand response technology has become an important means of balancing electricity supply and demand. Existing demand response technologies are mainly applied to large industrial loads and commercial buildings, adjusting the operating time of electrical equipment to cope with electricity price fluctuations and achieve peak shaving and valley filling. However, in the cleanroom sector of the pharmaceutical industry, the application of demand response has not yet been fully developed. Existing optimal scheduling technologies still have shortcomings and cannot fully utilize these resources. Currently, there is an urgent need for more refined and comprehensive control strategies to achieve optimized scheduling of cleanroom air conditioning systems while ensuring a stable production environment, thereby providing effective load regulation support for the power grid, reducing operating costs for enterprises, and improving energy efficiency.

[0005] Current load flexibility assessment methods mainly focus on high-energy-consuming industries such as steel and cement. The energy consumption behavior of these industries is less affected by the external environment, so the assessment models often assume that energy consumption patterns are relatively stable. However, for high-value-added industries, their energy consumption characteristics are closely coupled with production processes and environmental control, making traditional assessment methods difficult to apply directly.

[0006] In summary, there is no existing technology for evaluating the flexibility of cleanroom systems. Summary of the Invention

[0007] This invention provides a flexibility evaluation method for cleanroom systems that takes into account temperature and concentration inertia, thereby addressing the deficiency in existing technologies that lack a flexibility evaluation method for cleanroom systems and achieving the effect of providing a flexibility evaluation method for cleanroom systems.

[0008] This invention provides a flexible aggregation evaluation method that considers the temperature and concentration inertia of cleanrooms, the method comprising: An operational model of the target cleanroom is constructed. The operational model includes the operational parameter equations of the production system, energy storage system, ventilation system, and air conditioning system of the target cleanroom. The operational model includes a temperature inertia model and a concentration inertia model, which respectively reflect the dynamic changes of temperature and particulate matter concentration within the target cleanroom. Based on the operating model and the preset operating constraints of the target cleanroom, the operating parameters of the target cleanroom are optimized and solved to obtain the baseline state of the target cleanroom. The flexibility evaluation result of the target cleanroom is determined based on the baseline status, and the flexibility evaluation result reflects the target cleanroom's ability to respond to the power grid's demand.

[0009] According to the present invention, a flexible aggregation evaluation method considering the temperature and concentration inertia of cleanrooms is provided, wherein the temperature inertia model is as follows: ; in, Equivalent heat capacity; Equivalent thermal resistance; The heat generation power produced inside the target cleanroom; This refers to the cooling capacity of the fresh air handling unit for the cleanroom. The coefficient of performance (COP) for cleanroom production. For clean indoor air temperature, Outdoor air temperature For the cooling capacity of the dry cooling coil, The specific heat capacity of air; For fresh air quality flow rate, The temperature of the circulating air in the cleanroom. For time intervals, This refers to the heating power during cleanroom production. Production capacity for cleanrooms.

[0010] According to the present invention, a flexible aggregation evaluation method considering the temperature and concentration inertia of cleanrooms is provided, wherein the concentration inertia model is as follows: ; in, Equivalent capacitance; Equivalent resistance; Outdoor particulate matter concentration; Indoor particulate matter concentration; This refers to the concentration of particulate matter in the circulating air. This refers to the circulating air volume; The concentration of particulate matter in fresh air; and These are the purification efficiencies of the recirculated air and the fresh air, respectively. The increase in the concentration of particulate matter released during production in a cleanroom; This is the particulate matter release coefficient. This refers to the total air volume introduced into the cleanroom.

[0011] According to the present invention, a method for evaluating the flexibility of a cleanroom considering temperature and concentration inertia is provided, wherein determining the flexibility evaluation result of the target cleanroom based on the baseline state includes: Based on the baseline status, the load adjustability of the air conditioning system in the target cleanroom during the demand response period is determined as the flexibility evaluation result of the target cleanroom.

[0012] According to the present invention, a flexible aggregation evaluation method considering the temperature and concentration inertia of a cleanroom is provided, wherein optimizing the operating parameters of the target cleanroom includes: Based on the optimization objective, the operating parameters of the target cleanroom are optimized and solved. The optimization objective is to minimize the operating cost of the target cleanroom.

[0013] According to the present invention, a flexible aggregation evaluation method considering the temperature and concentration inertia of cleanrooms is provided, wherein the operating costs include electricity consumption costs and carbon emission costs.

[0014] The present invention also provides a flexible polymerization evaluation device that considers the temperature and concentration inertia of cleanrooms, the device comprising: The model building module is used to build an operational model of the target cleanroom. The operational model includes the operational parameter equations of the production system, energy storage system, ventilation system, and air conditioning system of the target cleanroom. The operational model includes a temperature inertia model and a concentration inertia model, which respectively reflect the dynamic changes of temperature and particulate matter concentration in the target cleanroom. The optimization solution module is used to optimize and solve the operating parameters of the target cleanroom based on the operating model and the preset operating constraints of the target cleanroom, so as to obtain the baseline state of the target cleanroom. A flexibility assessment module is used to determine the flexibility evaluation result of the target cleanroom based on the baseline state, the flexibility evaluation result reflecting the target cleanroom's ability to respond to power grid demands.

[0015] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the flexibility aggregation evaluation method considering the temperature and concentration inertia of the cleanroom as described above.

[0016] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the flexible aggregate evaluation method considering the temperature and concentration inertia of cleanrooms as described above.

[0017] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the flexible aggregate evaluation method considering the temperature and concentration inertia of cleanrooms as described above.

[0018] The present invention provides a flexible aggregation evaluation method considering the temperature and concentration inertia of cleanrooms. This method constructs an operational model comprising the operating parameter equations of the production system, energy storage system, ventilation system, and air conditioning system of the target cleanroom. The operational model includes a temperature inertia model and a concentration inertia model reflecting the dynamic changes in temperature and particulate matter concentration within the cleanroom. Based on the operational model and preset operational constraints of the target cleanroom, the operational parameters of the target cleanroom are optimized and solved to obtain the baseline state of the target cleanroom. Furthermore, based on this baseline state, the flexibility evaluation result of the target cleanroom is determined. This method incorporates the temperature and particulate matter concentration inertia of cleanroom environmental control into the modeling framework, constructs a mathematical model to assess the dynamic response characteristics of the system under different operating conditions, and determines the flexible adjustment capability of the cleanroom by aggregating and evaluating the allowable deviation range of key variables such as temperature and concentration, while ensuring that the operational constraints are met. This achieves a flexible evaluation of the cleanroom system. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the flexible aggregation evaluation method for considering the temperature and concentration inertia of cleanrooms provided by the present invention.

[0021] Figure 2 This is a structural diagram of the cleanroom ventilation and air conditioning system in the flexible aggregation evaluation method that considers the temperature and concentration inertia of the cleanroom provided by the present invention.

[0022] Figure 3 This is a schematic diagram illustrating the application effect of the flexible aggregation evaluation method for considering the temperature and concentration inertia of cleanrooms provided by the present invention. Figure 1 .

[0023] Figure 4 This is a schematic diagram illustrating the application effect of the flexible aggregation evaluation method for considering the temperature and concentration inertia of cleanrooms provided by the present invention. Figure 2 .

[0024] Figure 5 This is a schematic diagram illustrating the application effect of the flexible aggregation evaluation method for considering the temperature and concentration inertia of cleanrooms provided by the present invention. Figure 3 .

[0025] Figure 6 This is a schematic diagram of the flexible polymerization evaluation device that takes into account the temperature and concentration inertia of cleanrooms provided by the present invention.

[0026] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0028] The following is combined with Figure 1-5 This invention describes a flexible polymeric evaluation method that considers the temperature and concentration inertia of cleanrooms. For example... Figure 1 As shown, the flexible aggregation evaluation method considering the temperature and concentration inertia of cleanrooms provided by this invention includes the following steps: S110. Construct an operational model for the target cleanroom. The operational model includes the operational parameter equations for the production system, energy storage system, ventilation system, and air conditioning system of the target cleanroom. The operational model includes a temperature inertia model and a concentration inertia model, which respectively reflect the dynamic changes in temperature and particulate matter concentration within the target cleanroom. S120. Based on the operation model and the preset operation constraints of the target cleanroom, optimize and solve the operation parameters of the target cleanroom to obtain the baseline state of the target cleanroom. S130. Determine the flexibility evaluation results of the target cleanroom based on the baseline status. The flexibility evaluation results reflect the target cleanroom's ability to respond to the power grid's demands.

[0029] The method provided by this invention constructs an operational model comprising the operational parameter equations of a production system, energy storage system, ventilation system, and air conditioning system of a target cleanroom. The operational model includes a temperature inertia model and a concentration inertia model reflecting the dynamic changes in temperature and particulate matter concentration within the cleanroom. Based on the operational model and preset operational constraints of the target cleanroom, the operational parameters of the target cleanroom are optimized and solved to obtain the baseline state of the target cleanroom. Furthermore, based on this baseline state, the flexibility evaluation result of the target cleanroom is determined. This method incorporates temperature inertia and particulate matter concentration inertia from cleanroom environmental control into the modeling framework, constructs a mathematical model to assess the dynamic response characteristics of the system under different operational states, and, while ensuring that operational constraints are met, determines the cleanroom's flexible adjustment capability by aggregating and evaluating the allowable deviation ranges of key variables such as temperature and concentration, thereby achieving a flexibility evaluation of the cleanroom system.

[0030] With the continuous advancement of production processes, high-value-added industries (such as integrated circuits, biopharmaceuticals, precision manufacturing, and data centers) are placing increasingly stringent requirements on controlled production environments and their auxiliary systems. To ensure the production of high-quality products, the air cleanliness level in cleanrooms must meet the corresponding standards, while environmental parameters such as temperature, relative humidity, and pressure must also be strictly controlled. A cleanroom is a workshop where the concentration of suspended particles and microorganisms in the air is under controlled conditions.

[0031] As shown in Figure 2, the ventilation and air conditioning system of a cleanroom relies on multi-level coordinated operation to achieve environmental control. The system mainly consists of chillers, pumps, and terminal equipment, including make-up air units (MAUs), dry cooling coils (DCCs), and fan filter units (FFUs). The chillers and pumps provide chilled water, which is then piped to the terminal equipment to regulate the temperature of the fresh and recirculated air. The terminal equipment is typically equipped with cooling coils to precisely regulate the supply air temperature and high-efficiency filters to purify the airflow, thereby ensuring that the particulate matter concentration, air cleanliness, and other key environmental indicators within the cleanroom meet the requirements of the production process.

[0032] The MAU (Main Air Regulator) is responsible for supplying fresh air to the cleanroom. Its function is to draw in air from the outside, perform preliminary filtration and temperature and humidity regulation before delivering it into the system, ensuring a continuous supply of fresh air that meets requirements. Stable operation of the MAU not only maintains the pressure difference between the inside and outside environments but also ensures that air quality and cleanliness standards are met even when external environmental conditions fluctuate.

[0033] DCC is mainly used to process the circulating air in air conditioning systems. It uses chilled water or hot water for heat exchange to regulate the temperature and humidity of the circulating air, maintaining a stable cleanroom environment.

[0034] FFU (Fan Filter Unit) is a stand-alone air purification device, typically installed within the ceiling of a cleanroom. It consists of a fan and a high-efficiency particulate filter (HEPA filter), directly filtering air at the terminal to remove suspended particles and ensure that the air supplied to the cleanroom meets the required cleanliness level. Through the coordinated operation of these devices, cleanrooms can maintain strict environmental control, ensuring that the quality and safety of products during the production process are not affected by external contamination.

[0035] The method provided in this application models the relationships between various parameters during the operation of a cleanroom, resulting in an operational model, a temperature inertia model, and a concentration inertia model. The constructed models are then described below for each system included in the cleanroom.

[0036] (1) Production system In cleanroom systems, the production system is considered a crucial component of cleanroom environmental control, and its operating power is regarded as one of the key variables in the model. It needs to be adjusted within specific upper and lower limits to ensure the continuity and stability of the production process. By optimizing the power usage of the production system, the model can appropriately reduce system energy consumption during peak grid load periods, reducing grid pressure, while increasing energy consumption during off-peak periods, thereby lowering overall electricity costs.

[0037] To ensure the continuity and efficiency of the production process, the model introduces inventory management between production systems. Inventory settings and management are directly affected by production rate and energy consumption rate. By incorporating inventory constraints into the model, production interruptions can be avoided, ensuring the economic benefits of production.

[0038] 1. Production scheduling constraints Given the constraints of task continuity, personnel allocation, and the complexity of process changes in the production system, power adjustments are made on an hourly basis. Under these conditions, the selection of shifts for personnel allocation can be expressed as:

[0039] In the formula: It is a 0-1 variable, indicating whether to select shift s for production.

[0040] 2. Production power range constraints Production power constraints are a key factor in ensuring that a production system optimizes energy consumption while meeting process requirements. Production power constraints stipulate that the operating power of the production system must remain within a certain range during each time period, fluctuating between a given upper and lower limit. Under production power constraints, the model dynamically optimizes the production rhythm by appropriately reducing production power during periods of high electricity prices and increasing production power during periods of low electricity prices. Production power is always kept within the allowable range, thus ensuring process continuity and product quality, and significantly reducing enterprise energy costs and improving overall energy efficiency. See the following formula for details:

[0041] In the formula: and For the first The upper and lower limits of the production capacity of each cleanroom; For the first A cleanroom at all times Production capacity. That is: only on the selected shifts. Internal production power It can only operate within a certain range if it is within a certain range; otherwise, it is 0.

[0042] 3. Workshop and warehouse constraints In each production workshop, the inventory management system plays a crucial role in ensuring the continuity and efficiency of the production process. The selected production power of the cleanroom directly determines the product production rate; therefore, inventory management requirements must be fully considered when formulating power selection and scheduling strategies. To avoid production interruptions, the inventory management system needs to be closely coordinated with production power to ensure sufficient intermediate product inventory at each stage to support subsequent production activities. See the following formula for details:

[0043] In the formula: Indicates time period cleanroom Inventory quantity; The product output rate of the cleanroom is r. The product consumption rate of the cleanroom is r.

[0044] (2) Energy storage system A centralized energy storage system provides unified power to multiple cleanrooms to improve overall power efficiency. It must also ensure a balance between grid power supply, energy storage charging / discharging power, and production power, which can be expressed as:

[0045] In the formula: For cleanroom Production system during time period Power supply from the power grid at that time; and For cleanroom During the period The energy storage charging and discharging power at that time.

[0046] The change in electrical power in a cleanroom energy storage system can be expressed as:

[0047] In the formula: For the time period Energy storage capacity at that time; and For cleanroom Energy storage charging and discharging efficiency.

[0048] (3) Ventilation system In a cleanroom air handling system, it is essential to compensate for exhaust air, maintain a positive pressure differential, and meet the fresh air requirements of personnel. The fresh air volume can be expressed as:

[0049] In the formula: For fresh air volume; The number of air changes required to maintain positive pressure; This refers to the volume of the cleanroom. The exhaust ratio; This refers to the number of staff working in the cleanroom.

[0050] The power of a MAU fan can be expressed as:

[0051] In the formula: For MAU fan power; For MAU blower residual pressure; For MAU efficiency.

[0052] Cleanrooms reduce the concentration of particulate matter within the room to achieve the required cleanliness level by using a large volume of air. The total air volume required for a cleanroom can be expressed as:

[0053] In the formula: Total air volume; Average wind speed; This represents the cross-sectional area.

[0054] The power of an FFU fan can be expressed as:

[0055] In the formula, For FFU fan power; For FFU fan residual pressure; For FFU efficiency.

[0056] The dynamic changes in particulate matter concentration within cleanrooms are influenced by multiple factors, including the infiltration of external particulate matter, the release of indoor particulate matter, the dilution effect of ventilation, the purification of recirculated air, and the introduction of fresh air. An equivalent circuit analogy method can be introduced, using equivalent capacitance and equivalent resistance to characterize these processes. The equivalent capacitance represents the cleanroom's capacity to store particulate matter, reflecting the hysteretic response of concentration changes to disturbances; the equivalent resistance describes the concentration exchange capacity between the cleanroom and the external environment or internal purification system, reflecting the dynamic characteristics of particulate matter during transport and dilution. This method abstracts the change in cleanroom particulate matter concentration into a combined effect of input, storage, and dissipation, thus establishing a dynamic model suitable for flexibility analysis. This process can be represented as:

[0057] In the formula: Equivalent capacitance; Equivalent resistance; Outdoor particulate matter concentration; Indoor particulate matter concentration; This refers to the concentration of particulate matter in the circulating air. This refers to the circulating air volume; The concentration of particulate matter in fresh air; and These are the purification efficiencies of the recirculated air and the fresh air, respectively. The increase in the concentration of particulate matter released during production in a cleanroom; Particulate matter release coefficient (4) Air conditioning system The MAU (Main Air Unit) is responsible for regulating the temperature and humidity of outdoor air. In summer, outdoor air needs to be cooled to its dew point temperature for dehumidification, and then reheated to the supply air temperature. The corresponding cooling and reheating power are expressed as follows:

[0058] In winter, when the outdoor air temperature is low, it can be directly heated to the set temperature of the air supply. Its heating power is expressed as:

[0059] In the formula: For MAU cooling capacity; Heating power of MAU; The specific heat capacity of air; For fresh air quality flow rate; air density; Outdoor air temperature; To set the dew point temperature; The temperature of clean indoor air.

[0060] The cooling power required by a DCC can be expressed as:

[0061] In the formula: DCC cooling capacity; The circulating air mass flow rate; The temperature of the circulating air in the cleanroom; The thermal equilibrium process of a cleanroom can be modeled and described using equivalent thermal parameters. Equivalent heat capacity reflects the thermal inertia of the cleanroom, manifested as the lag effect of room temperature changes relative to heat input or output; equivalent thermal resistance characterizes the heat transfer capacity between the cleanroom and the external environment, reflecting the rate of heat exchange between the interior and exterior spaces. Based on these parameters, the overall thermal equilibrium process of the cleanroom can be abstracted as a dynamic relationship between input, storage, and dissipation, and its mathematical expression can be represented as:

[0062] In the formula: Equivalent heat capacity; Equivalent thermal resistance; To generate heat power inside the cleanroom; The cooling capacity of the MAU for the cleanroom. The coefficient of performance for cleanroom production.

[0063] The terminal equipment itself does not have the ability to generate cooling capacity; instead, it cools the air using chilled water supplied by a chiller unit. Its cooling capacity and power consumption can be expressed as follows:

[0064] In the formula: The cooling capacity provided to the chiller unit; This refers to the cooling power loss of the chiller unit; This refers to the electrical power consumption of the chiller unit; This refers to the energy efficiency ratio of the chiller unit.

[0065] Heat pumps, as the primary heat source for cleanroom heating, regulate the supply air temperature by delivering hot water to terminal equipment. The heating capacity and electrical power consumption of a heat pump can be expressed as follows:

[0066] In the formula: The heating power provided to the heat pump; This represents the heating power loss of the heat pump; This refers to the electrical power required for the heat pump. This refers to the heat pump's energy efficiency ratio.

[0067] The method provided in this application, based on existing air conditioning and environmental control models, comprehensively considers the dynamic changes in key environmental parameters such as temperature and cleanliness to construct a cleanroom model suitable for industries such as biomedicine and integrated circuits. By combining actual operating data with production environment requirements, this model can more accurately depict the energy consumption patterns of cleanrooms, providing a foundation for subsequent optimization scheduling and flexibility assessment.

[0068] The operation of a cleanroom needs to meet certain constraints, which include the following:

[0069] The total power consumption of the cleanroom must meet the following constraints:

[0070] Warehousing must meet the following constraints:

[0071] In the formula: and Cleanroom Upper and lower limits of storage capacity.

[0072] The required fresh air volume for cleanrooms must meet the following constraints:

[0073] In the formula: and Cleanroom Upper and lower limits of fresh air volume.

[0074] The total air volume required for a cleanroom must meet the following constraints:

[0075] In the formula: and Cleanroom Total air volume upper and lower limits.

[0076] The temperature of the cleanroom must meet the following constraints:

[0077] In the formula: and Cleanroom Temperature requirements: upper and lower limits.

[0078] The particulate matter concentration in cleanrooms must meet the following constraints:

[0079] In the formula: and These are the upper and lower limits for particulate matter concentration requirements, respectively.

[0080] The cleanroom production capacity of enterprises must meet the following constraints:

[0081] In the formula: For the required product quantity in the order, This represents the product output rate in the cleanroom.

[0082] The method provided in this application achieves unified optimization of the production process and air conditioning system operation by introducing coupled constraints of production process parameters and environmental control variables into the model. This method utilizes the thermal inertia and cleanliness inertia of cleanrooms to ensure environmental conditions and production continuity, while also exploring potential flexible adjustment capabilities across multiple time scales, thereby improving the overall operating efficiency and adjustability of the system.

[0083] Based on the above operating model, concentration inertia model, temperature inertia model, and constraints, the operating parameters of the target cleanroom are optimized and solved, including: Based on the optimization objective, the operating parameters of the target cleanroom are optimized and solved. The optimization objective is to minimize the operating cost of the target cleanroom.

[0084] The operating costs of the target cleanroom include electricity consumption costs and carbon emission costs. The optimization objective can be expressed as:

[0085] In the formula: Total power of the cleanroom; For electricity price; For carbon price; It is a carbon emission factor.

[0086] The model first establishes a baseline by minimizing the company's operating costs, representing the normal operating state without demand response participation. Then, if the company needs to participate in demand response, this baseline is used as a reference to determine how much the air conditioning system load can be increased or decreased within a specific time period. This fluctuation in power represents the peak-shaving and valley-filling capacity that the company can provide, contributing to grid balance and reflecting the company's own adjustment potential.

[0087] Therefore, peak shaving capacity and valley filling capacity constitute the key indicators for evaluating the flexible aggregation of power consumption in the system, which can be expressed as:

[0088] In the formula: and From the start time Begin, Continue Peak shaving and valley filling capabilities during specific time periods; The specific time period required by the peak-shaving market to respond; The peak-shaving market requires a set of time periods within the response period; A set of start times; For the collection of durations of participation in peak shaving services, The baseline power is the power that lasts for h time periods starting from time s. This is the lower limit of the power range for a duration of h time periods, starting from the initial time s. This represents the upper limit of the power range for h time periods starting from the initial time s.

[0089] The above problem can be viewed as an optimization scheduling model. Under the premise that constraints such as production power, airflow regulation, temperature and humidity, and particulate matter concentration must be met, the model searches for the optimal solution that minimizes the total operating cost within the feasible region formed by the equipment operating range and the environmental boundary, thus obtaining the optimal operating baseline of the cleanroom system under normal conditions. Based on this, by imposing power adjustments within specific time windows, the model further calculates the load variation range that the system can achieve without disrupting the process environment, i.e., the flexibility assessment result. In this way, the model provides both the most economical baseline operating strategy and quantifies the peak-shaving and valley-filling capabilities available in each time period.

[0090] The method provided in this application constructs evaluation indicators covering multiple dimensions such as regulation capacity, duration, and economic efficiency, and combines real-time electricity price signals with grid demand response mechanisms to achieve flexible operation of cleanroom air conditioning systems. By reducing load during peak electricity price periods and increasing operation during off-peak electricity price periods, the system can not only minimize enterprise costs but also provide peak shaving and valley filling support to the power grid while ensuring a stable production environment.

[0091] The method provided in this application can coordinate and optimize production and air conditioning systems to reduce operating costs. Specifically, the model constructed in the method provided in this application dynamically adjusts the production plan by coordinating and optimizing production scheduling and air conditioning system operation, combined with electricity-carbon coupling price signals, to effectively reduce system operating costs. Figure 3As shown, under a fixed shift schedule and with only in-shift optimization, the overall system power exhibits a clear intraday fluctuation pattern. From early morning to dawn, only FFU and MAU fans maintain the basic ventilation load of the cleanroom, while the chiller units and hot water pumps operate at lower power levels, keeping the total power at a relatively low level. As production shifts begin, the production system power rises rapidly, driving a synchronous increase in the load of the chiller units and hot water pumps, forming the peak period around noon. At this time, the air conditioning system needs to provide additional cooling to compensate for the heat released during production, with the chiller units reaching their highest power level of the day. The energy storage system discharges at the beginning of production to smooth out peak loads and recharges after production to balance energy levels. Overall, the production system determines the peak period of the total load, the air conditioning system changes synchronously with it, and the energy storage system plays a certain role in peak shaving and valley filling, making the power curve relatively smooth. However, due to the fixed shift schedule, the peak total power is still concentrated during peak electricity price periods.

[0092] Building on this, the model further incorporates adjustable scheduling optimization, such as... Figure 4 As shown, by dynamically adjusting production shifts in conjunction with electricity-carbon price signals, some production tasks are shifted to periods with lower electricity prices, achieving a coordinated match between production plans and energy prices. Optimization results show that peak total power is significantly brought forward, load during periods of high electricity prices decreases significantly, and operational economics are further improved. Overall, this strategy effectively reduces high-priced electricity consumption and lowers operating costs while ensuring process environment requirements such as cleanroom temperature and humidity and air cleanliness, fully demonstrating the synergistic optimization effect of the production system and air conditioning system in the time dimension.

[0093] Furthermore, the method provided in this application, by fully considering thermal inertia and concentration inertia, can balance energy saving and flexibility. Specifically, cleanrooms inherently possess thermal inertia and concentration inertia: the former stems from the heat capacity of walls, equipment, and air, causing temperature changes to lag behind cooling input, thus allowing for reduced cooling power during peak electricity price periods and the accumulation of cooling capacity through pre-cooling during off-peak periods; the latter manifests as the slow change in air particulate matter concentration due to infiltration, release, filtration, and dilution, enabling the system to adjust airflow or filtration efficiency within a short period without immediately affecting cleanliness levels. Introducing these two types of inertia not only expands the energy-saving space in environmental control but also quantifies the adjustment potential of cleanrooms at different time scales, enabling them to provide flexible resources to the power grid while ensuring a stable process environment, achieving the dual goals of energy saving and peak shaving.

[0094] Furthermore, the method provided in this application establishes a flexibility assessment method to quantify the adjustable capacity of a cleanroom. Specifically, a flexibility assessment method based on baseline operating conditions is established, capable of quantifying the adjustable capacity of the cleanroom air conditioning system under different durations and start times. Figure 5As shown, the evaluation results indicate that peak-shaving capacity is mainly concentrated during periods of high production load, when the system has significant downward adjustment potential, which can be achieved by reducing the power of chillers and fans. Valley-filling capacity, on the other hand, is prominent at night and during production breaks, demonstrating significant upward adjustment potential, which can be achieved by pre-cooling or increasing airflow to shift the load. Overall, both peak-shaving and valley-filling capacities gradually decrease with increasing duration, indicating that the longer the adjustment period, the more limited the adjustable space becomes. These results reveal the flexible response characteristics of cleanroom air conditioning systems at different time scales, providing a quantitative basis for subsequent demand response and peak-shaving / valley-shaving strategies.

[0095] The following describes the flexible polymerization evaluation device considering the inertia of cleanroom temperature and concentration provided by the present invention. The flexible polymerization evaluation device considering the inertia of cleanroom temperature and concentration described below can be referred to in correspondence with the flexible polymerization evaluation method considering the inertia of cleanroom temperature and concentration described above. For example... Figure 6 As shown, the flexible aggregation evaluation device considering the temperature and concentration inertia of cleanrooms provided by the present invention includes the following modules: The model building module 610 is used to build the operation model of the target cleanroom. The operation model includes the operation parameter equations of the production system, energy storage system, ventilation system and air conditioning system of the target cleanroom. The operation model includes a temperature inertia model and a concentration inertia model. The temperature inertia model and the concentration inertia model reflect the dynamic changes of temperature and particulate matter concentration in the target cleanroom, respectively. The optimization solution module 620 is used to optimize and solve the operating parameters of the target cleanroom based on the operating model and the preset operating constraints of the target cleanroom, so as to obtain the baseline state of the target cleanroom. The flexibility assessment module 630 is used to determine the flexibility evaluation results of the target cleanroom based on the baseline status. The flexibility evaluation results reflect the target cleanroom's ability to respond to the power grid's demands.

[0096] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other through the communication bus 740. The processor 710 can call logical instructions in the memory 730 to execute a flexibility aggregation evaluation method considering the temperature and concentration inertia of the cleanroom. This method includes: constructing an operating model of the target cleanroom, the operating model including the operating parameter equations of the production system, energy storage system, ventilation system, and air conditioning system of the target cleanroom, the operating model including a temperature inertia model and a concentration inertia model, the temperature inertia model and the concentration inertia model reflecting the dynamic changes of temperature and particulate matter concentration in the target cleanroom, respectively; optimizing and solving the operating parameters of the target cleanroom based on the operating model and preset operating constraints of the target cleanroom to obtain the baseline state of the target cleanroom; and determining the flexibility evaluation result of the target cleanroom based on the baseline state, the flexibility evaluation result reflecting the target cleanroom's ability to respond to the power grid demand.

[0097] Furthermore, the logic instructions in the aforementioned memory 730 can be implemented as a software-powered unit and, when sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0098] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the flexibility aggregation evaluation method considering the temperature and concentration inertia of the cleanroom provided by the above methods. The method includes: constructing an operating model of the target cleanroom, the operating model including the operating parameter equations of the production system, energy storage system, ventilation system and air conditioning system of the target cleanroom, the operating model including a temperature inertia model and a concentration inertia model, the temperature inertia model and the concentration inertia model respectively reflecting the dynamic changes of temperature and particulate matter concentration in the target cleanroom; optimizing and solving the operating parameters of the target cleanroom based on the operating model and the preset operating constraints of the target cleanroom to obtain the baseline state of the target cleanroom; and determining the flexibility evaluation result of the target cleanroom based on the baseline state, the flexibility evaluation result reflecting the target cleanroom's ability to respond to the power grid demand.

[0099] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the flexibility aggregation evaluation method considering the temperature and concentration inertia of cleanrooms provided by the above methods. This method includes: constructing an operating model of the target cleanroom, the operating model including operating parameter equations for the production system, energy storage system, ventilation system, and air conditioning system of the target cleanroom; the operating model including a temperature inertia model and a concentration inertia model, the temperature inertia model and the concentration inertia model respectively reflecting the dynamic changes in temperature and particulate matter concentration within the target cleanroom; optimizing and solving the operating parameters of the target cleanroom based on the operating model and preset operating constraints of the target cleanroom to obtain the baseline state of the target cleanroom; and determining the flexibility evaluation result of the target cleanroom based on the baseline state, the flexibility evaluation result reflecting the target cleanroom's ability to respond to power grid demands.

[0100] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0101] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A flexible aggregation evaluation method considering the temperature and concentration inertia of cleanrooms, characterized in that, The method includes: An operational model of the target cleanroom is constructed. The operational model includes the operational parameter equations of the production system, energy storage system, ventilation system, and air conditioning system of the target cleanroom. The operational model includes a temperature inertia model and a concentration inertia model, which respectively reflect the dynamic changes of temperature and particulate matter concentration within the target cleanroom. Based on the operating model and the preset operating constraints of the target cleanroom, the operating parameters of the target cleanroom are optimized and solved to obtain the baseline state of the target cleanroom. The flexibility evaluation result of the target cleanroom is determined based on the baseline status, and the flexibility evaluation result reflects the target cleanroom's ability to respond to the power grid's demand.

2. The flexible aggregation evaluation method considering the temperature and concentration inertia of cleanrooms according to claim 1, characterized in that, The temperature inertia model is as follows: ； in, Equivalent heat capacity; Equivalent thermal resistance; The heat generation power produced inside the target cleanroom; This refers to the cooling capacity of the fresh air handling unit for the cleanroom. The coefficient of performance (COP) for cleanroom production. For clean indoor air temperature, Outdoor air temperature For the cooling capacity of the dry cooling coil, The specific heat capacity of air; For fresh air quality flow rate, The temperature of the circulating air in the cleanroom. For time intervals, This refers to the heating power during cleanroom production. Production capacity for cleanrooms.

3. The flexible aggregation evaluation method considering the temperature and concentration inertia of cleanrooms according to claim 1, characterized in that, The concentration inertial model is as follows: ； in, Equivalent capacitance; Equivalent resistance; Outdoor particulate matter concentration; Indoor particulate matter concentration; This refers to the concentration of particulate matter in the circulating air. This refers to the circulating air volume; The concentration of particulate matter in fresh air; and These are the purification efficiencies of the recirculated air and the fresh air, respectively. The increase in the concentration of particulate matter released during production in a cleanroom; This is the particulate matter release coefficient. This refers to the total air volume introduced into the cleanroom.

4. The flexible aggregation evaluation method considering the temperature and concentration inertia of cleanrooms according to claim 1, characterized in that, The determination of the flexibility evaluation results of the target cleanroom based on the baseline status includes: Based on the baseline status, the load adjustability of the air conditioning system in the target cleanroom during the demand response period is determined as the flexibility evaluation result of the target cleanroom.

5. The flexible aggregation evaluation method considering the temperature and concentration inertia of cleanrooms according to claim 1, characterized in that, The optimization solution for the operating parameters of the target cleanroom includes: Based on the optimization objective, the operating parameters of the target cleanroom are optimized and solved. The optimization objective is to minimize the operating cost of the target cleanroom.

6. The load control method for the steel industry considering equipment inertia and dynamic data correction according to claim 5, characterized in that, The operating costs include electricity consumption costs and carbon emission costs.

7. A flexible polymerization evaluation device considering the temperature and concentration inertia of cleanrooms, characterized in that, The device includes: The model building module is used to build an operational model of the target cleanroom. The operational model includes the operational parameter equations of the production system, energy storage system, ventilation system, and air conditioning system of the target cleanroom. The operational model includes a temperature inertia model and a concentration inertia model, which respectively reflect the dynamic changes of temperature and particulate matter concentration in the target cleanroom. The optimization solution module is used to optimize and solve the operating parameters of the target cleanroom based on the operating model and the preset operating constraints of the target cleanroom, so as to obtain the baseline state of the target cleanroom. A flexibility assessment module is used to determine the flexibility evaluation result of the target cleanroom based on the baseline state, the flexibility evaluation result reflecting the target cleanroom's ability to respond to the power grid's demands.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the flexible aggregate evaluation method that takes into account the temperature and concentration inertia of the cleanroom as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the flexible aggregate evaluation method that takes into account the temperature and concentration inertia of the cleanroom as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the flexible aggregate evaluation method that takes into account the temperature and concentration inertia of the cleanroom as described in any one of claims 1 to 6.

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