Phase change energy storage device for adjusting energy efficiency of air conditioning system and control method of phase change energy storage device

Abstract

The invention relates to the technical field of air conditioning systems, and particularly discloses a phase change energy storage device for adjusting the energy efficiency of an air conditioning system and a control method.The device comprises a hollow energy storage cabinet, a built-in phase change energy storage unit, a plate heat exchanger and other core components, and a heat exchange coil is of a double-spiral cross-flow structure and is provided with needle-shaped fins; a flexible optical fiber sensor is integrated to monitor the state of the phase change material; the control method is based on an LSTM cold load prediction model, a multivariate optimization decision is made in combination with dynamic electricity price and carbon price, the load rate of the water chilling unit serves as a core, three operation modes of cold storage, cold release and low carbon are set, and intelligent switching is achieved. According to the invention, the problems of low integration level, single control target and insufficient mode flexibility in the prior art are solved, and multi-target collaborative optimization of energy efficiency, cost and carbon emission reduction of the air conditioning system is realized.

Description

Technical Field

[0001] This invention relates to the technical field of air conditioning systems, and more specifically, to a phase change energy storage device and its control method for regulating the energy efficiency of an air conditioning system. Background Technology

[0002] Phase change energy storage technology, with its high energy density and isothermal heat absorption and release characteristics, has become a core technology for solving the mismatch between load fluctuations and main unit operating efficiency in air conditioning systems, and is widely used in energy-saving optimization of central air conditioning systems. As the core equipment on the cold source side, the chiller unit of a central air conditioning system has an operating efficiency highly correlated with the load rate, typically achieving efficient operation only within a load rate range of 50% to 80%. However, the actual building cooling load exhibits significant dynamic fluctuations due to factors such as occupancy density, ambient temperature, and usage scenarios, causing the chiller unit to frequently operate at low load (<50%) or high load (>80%), resulting in a significant decrease in the overall system energy efficiency (COP) and an increase in energy consumption and carbon emissions. Coupled with a phase change energy storage device and a central air conditioning system, using the energy storage unit's cold storage and release to regulate the main unit load rate, has become a key means to improve system energy efficiency. However, existing technologies still have many limitations in terms of device integration, control strategies, and operating modes, making it difficult to achieve efficient synergistic operation between phase change energy storage technology and the central air conditioning system. Specific shortcomings are as follows: Low system integration and crude control strategies: For example, the phase change heat accumulator disclosed in patent CN115540663A, although improving heat exchange uniformity through movable baffles and channels, its core design lies in the structural optimization of the heat accumulator itself, without involving an overall coupling control strategy with the central air conditioning water system, especially lacking proactive optimization of the host's operating status. Existing control strategies mostly rely on simple time period divisions or load percentages, which cannot achieve precise matching with the host's efficient operating range.

[0003] Single control objective: For example, patent CN114754435B proposes multi-zone phase change air conditioning collaborative control for high-speed railway station buildings, but its focus is on the balance of spatial temperature field. The control logic depends on the temperature difference between zones rather than on the optimization of the overall energy efficiency (COP) of the system, and cannot be directly applied to traditional central air conditioning systems with chiller units as the core.

[0004] Insufficient operational flexibility: Most existing systems operate in relatively fixed modes, making it difficult to cope with complex and ever-changing actual load demands. Especially during low-load conditions such as nighttime or transitional seasons, the efficiency of chiller units drops sharply, but current technology lacks a "low-carbon operation mode" that can completely shut down the main unit to maximize energy savings while ensuring cooling supply. Summary of the Invention

[0005] In view of the above-mentioned technical problems in related technologies, the present invention provides a phase change energy storage device and its control method for adjusting the energy efficiency of an air conditioning system, which can solve the above problems.

[0006] To achieve the above-mentioned technical objectives, the technical solution of the present invention is implemented as follows: A phase change energy storage device for regulating the energy efficiency of an air conditioning system includes a perforated energy storage cabinet. The cabinet houses a phase change energy storage unit, a plate heat exchanger, a circulating water pump, and an intelligent control unit. The phase change energy storage unit's housing is filled with a phase change material, which is immersed in a heat exchange coil. One end of the heat exchange coil is connected to the outlet of the primary side of the plate heat exchanger via an electric three-way valve and a pipe, while the other end is connected to the inlet of the primary side of the plate heat exchanger via an electric three-way valve and a pipe. The circulating water pump... The pump is connected to the second pipeline. The first electric three-way valve, the second electric three-way valve, and the circulating water pump are all electrically connected to the intelligent control unit. The intelligent control unit is also electrically connected to a temperature sensor, a flow sensor, and a power sensor. The temperature sensor is used to collect the supply and return temperatures of chilled water, the supply and return temperatures of cooling water, and the real-time temperature of the phase change material. The flow sensor is used to collect the real-time flow rates of chilled water and cooling water. The power sensor is used to collect the real-time power consumption of the chiller unit and the operating power consumption of each pump / cooling tower fan.

[0007] Furthermore, the heat exchange coil adopts a double-helix cross-flow structure, and the inner wall of the heat exchange coil is provided with needle-shaped fins.

[0008] Furthermore, the housing of the phase change energy storage module also integrates a flexible fiber optic sensor for real-time monitoring of the phase change state, temperature distribution uniformity, and aging degree of the phase change material. The flexible fiber optic sensor is electrically connected to the intelligent control unit.

[0009] A control method for adjusting the energy efficiency of an air conditioning system, the air conditioning system including a chiller, a cooling tower, and a phase change energy storage device, the method being applied to the phase change energy storage device connected to the chilled water circuit and cooling water circuit of the air conditioning system, comprising the following steps: S1. The air conditioning system is powered on and the temperature sensor, flow sensor and power sensor are initialized. The intelligent control unit starts and loads historical operating data and LSTM cooling load prediction model, and enters the cyclic monitoring state. During the monitoring process, it continuously performs multi-objective optimization decisions in combination with dynamic electricity price and regional carbon price parameters. S2, the intelligent control unit collects chilled water supply and return water temperatures, cooling water temperatures, phase change material temperatures, and air conditioning system power consumption parameters in real time, calculates the real-time load rate of the chiller unit, and integrates weather forecasts and building occupancy density data to update the cooling load prediction model for the next 24 hours. S3. Using the real-time load rate of the chiller unit as the core judgment criterion, and independently verifying the low-carbon operation conditions, different trigger judgments are made for different modes: S31. First, independently verify the low-carbon operating conditions. If all conditions are passed, the intelligent control unit immediately calls the LSTM cooling load prediction data for the next 24 hours to make forward predictions of high load and weigh multiple objectives of benefits. S311. If there is no subsequent high load demand, or the cold storage benefit is lower than the low carbon benefit, the low carbon operation mode will be triggered directly, skipping the condition verification and triggering process of the cold storage and cold release modes. S312 If there is subsequent high load demand and the benefits of cold storage are significantly higher than the benefits of low carbon, then the low carbon mode will not be triggered for the time being, and the condition verification stage of the cold storage mode will be entered instead, and the cold storage mode logic will be used to determine whether to store cold. The low-carbon operation conditions include: the instantaneous cooling load demand of the building is less than 30% of the rated capacity of the chiller unit; the remaining cooling capacity of the phase change energy storage module is sufficient to cover the total cooling load during the forecast period; the average temperature of the phase change material is more than 3°C lower than its phase change point temperature; and the total cooling load during the forecast period is not greater than 80% of the remaining cooling capacity of the module. S32. Then, make a judgment based on the load factor; S321. If the load rate is <50%, the cold storage condition test will be initiated. The test conditions include whether it is during the off-peak electricity period of the power grid, whether there is room for optimization of the real-time COP of the air conditioning system, and whether the phase change material has the cold storage capacity. If all the above conditions are met, the cold storage mode will be triggered. If any condition is not met, the normal / current operation mode will be maintained. S322. If the load rate is >80%, the cooling condition test will be entered. The test conditions include whether the COP of the air conditioning system can be increased by more than 5% after cooling, whether the phase change energy storage module has available cooling capacity, and whether the phase change material has cooling capacity. If all the above conditions are met, the cooling mode will be triggered. If any condition is not met, the normal / current operation mode will be maintained. S323. If the load rate is in the high-efficiency range of 50% to 80%, directly maintain the normal / current operating mode; S4. After entering the corresponding operating mode, execute the corresponding control operation: S41, Cold Storage Mode: The intelligent control unit outputs a signal to regulate the electric three-way valve (including electric three-way valve one and electric three-way valve two), so that part of the chilled water flows through the phase change energy storage module. The fuzzy PID control strategy is used to dynamically regulate the chilled water flow into the phase change energy storage module, and stabilize the temperature difference between the inlet and outlet of the phase change energy storage module in the optimal range of 3~5℃ to achieve efficient cold storage. S42, Cooling Mode: The intelligent control unit outputs a signal to start the integrated circulating water pump and switches the electric three-way valve to the plate heat exchanger passage, so that the cooling capacity stored in the phase change energy storage module indirectly cools the cooling water entering the chiller condenser through the plate heat exchanger, thereby reducing the condensing temperature and improving the COP of the air conditioning system. S43, Low-carbon operation mode: The intelligent control unit outputs a signal to shut down the chiller, cooling water pump and cooling tower, and only keeps the phase change energy storage module and chilled water pump running, and the phase change energy storage module directly supplies cooling to the air conditioning terminal. S5. During the operation of each mode, continuously check the mode exit condition. If a higher priority mode trigger condition occurs during operation, or if any exit condition of the current mode is met, immediately exit the current mode and re-execute the mode trigger check process of S3: S51. Conditions for exiting the cold storage mode: The chiller unit load rate is ≥50%, or the phase change energy storage module has completed cold storage, or the low-carbon operation condition verification is passed during operation. S52. Conditions for exiting cooling mode: The chiller unit load rate is ≤80%, or the available cooling capacity of the phase change energy storage module is lower than the set threshold, or the low-carbon operation condition verification is passed during operation. S53. Conditions for exiting low-carbon operation mode: The average temperature of the phase change material rises above its phase change point temperature, or the instantaneous cooling load demand of the air conditioning system exceeds the cooling capacity of the phase change energy storage module and lasts for more than 5 minutes. S6. After exiting any mode, the air conditioning system immediately returns to the normal operation mode and re-executes the mode trigger judgment process of S3, triggering the corresponding mode or maintaining normal operation according to the real-time operating conditions.

[0010] Furthermore, the multi-objective optimization decision-making in step S1 involves constructing a collaborative optimization function with the objectives of maximizing the energy efficiency, minimizing operating costs, and maximizing carbon emission reduction of the air conditioning system. This function serves as an auxiliary decision-making basis for triggering and operating each mode.

[0011] Furthermore, the real-time load rate of the chiller unit mentioned in step S2 is calculated by the intelligent control unit based on the ratio of the actual cooling capacity of the unit to the rated cooling capacity.

[0012] Furthermore, the fuzzy PID control strategy described in step S41 takes the temperature difference between the inlet and outlet of the phase change energy storage module as the core control target, and dynamically adjusts the chilled water flow rate by monitoring the temperature difference changes in real time to achieve precise and stable temperature difference control.

[0013] Furthermore, in step S42, the process of indirectly cooling the cooling water is judged by the standard that the temperature of the cooled water after cooling can increase the COP of the chiller unit by more than 5%.

[0014] Furthermore, in step S5, the exit condition judgment for each mode is a real-time continuous judgment. The intelligent control unit performs threshold comparison and condition verification based on the real-time parameters collected by the sensor, and immediately outputs a mode switching signal when the exit condition is met.

[0015] Furthermore, the high-load forward prediction mentioned in step S31 must simultaneously meet the following conditions: the LSTM model predicts that there will be a high-load condition with a load rate of ≥80% for ≥1 hour in the next 12 hours; it predicts that the current remaining cold capacity of the phase change energy storage unit under the high-load condition cannot meet the cooling demand; the current average temperature of the phase change material is ≥2℃ higher than its phase change point temperature and the remaining cold storage space is ≥30% of the total cold storage capacity; and it predicts that there will be no other cold storage window period in the subsequent high-load period. The multi-objective benefit trade-off is to calculate the comprehensive optimization target value of the cold storage priority strategy and the low-carbon priority strategy in the next 24 hours. If the F value of the cold storage priority strategy minus the F value of the low-carbon priority strategy is ≥10%, it is determined that the cold storage benefit is significantly higher than the low-carbon benefit.

[0016] The beneficial effects of this invention are as follows: The device of this application has high integration and excellent heat exchange performance. The heat exchange coil with double helix crossflow structure and needle-shaped fins enhances heat transfer. Flexible fiber optic sensors monitor the state of phase change materials in real time, providing data support for precise control and ensuring stable and efficient operation of the device. The control strategy is precise and intelligent. Based on the LSTM model, it achieves accurate 24-hour cooling load prediction. Combining dynamic electricity price and regional carbon price, it constructs a multi-objective optimization function. With the chiller unit load rate as the core, it realizes intelligent triggering and switching of the operation mode, allowing the chiller unit to remain stable in the high-efficiency load range of 50%~80% for a long time. The innovative low-carbon operation mode can shut down high-consumption equipment such as the chiller unit during low load, and only the phase change energy storage module provides cooling, which significantly reduces energy consumption and carbon emissions. The cold storage and release modes realize the grid "peak shifting and valley filling", effectively reducing operating costs. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the structure of a phase change energy storage device for adjusting the energy efficiency of an air conditioning system, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of the air conditioning system described in an embodiment of the present invention; Figure 3 This is a schematic diagram of the cold storage mode described in an embodiment of the present invention; Figure 4This is a schematic diagram of the cooling mode described in an embodiment of the present invention; Figure 5 This is a schematic diagram of the low-carbon operation mode described in an embodiment of the present invention; Figure 6 This is a flowchart of a control method for adjusting the energy efficiency of an air conditioning system, as described in an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of the present invention.

[0020] like Figure 1 As shown, this invention discloses a phase change energy storage device for regulating the energy efficiency of an air conditioning system, comprising a hollow energy storage cabinet. The energy storage cabinet houses a phase change energy storage unit 2, a plate heat exchanger 5, a circulating water pump 6, and an intelligent control unit 18. The phase change energy storage unit 2 is filled with a phase change material, which is immersed in a heat exchange coil. One end of the heat exchange coil is connected to the inlet of the primary side of the plate heat exchanger 5 via an electric three-way valve 19 and a pipe 20, while the other end is connected to the outlet of the primary side of the plate heat exchanger 5 via an electric three-way valve 21 and a pipe 22. The circulating water pump 6 is connected to the... On pipe 22, a filter 23 is installed on the pipeline between the circulating water pump 6 and the plate heat exchanger 5. The electric three-way valve 19, the electric three-way valve 21, and the circulating water pump 6 are all electrically connected to the intelligent control unit 18. The intelligent control unit 18 is also electrically connected to a temperature sensor, a flow sensor, and a power sensor. The temperature sensor is used to collect the supply and return temperatures of chilled water, the supply and return temperatures of cooling water, and the real-time temperature of the phase change material. The flow sensor is used to collect the real-time flow rates of chilled water and cooling water. The power sensor is used to collect the real-time power consumption of the chiller unit and the operating power consumption of each pump / cooling tower fan.

[0021] In a specific embodiment of this application, the heat exchange coil adopts a double-helix cross-flow structure, and the inner wall of the heat exchange coil is provided with needle-like fins to enhance heat transfer. The phase change material can be a multi-component composite phase change material with paraffin as the matrix, expanded graphite as the skeleton, and dispersed nano-metal particles and graphene sheets, with a latent heat of phase change of not less than 180 kJ / kg and a thermal conductivity of not less than 1.2 W / (m·K). Furthermore, a flexible fiber optic sensor is integrated into the housing of the phase change energy storage module for real-time monitoring of the phase change state, temperature distribution uniformity, and aging degree of the phase change material. The data from the flexible fiber optic sensor is fed back to the intelligent control unit 18 for correcting the control parameters.

[0022] In one specific embodiment of this application, the processor of the intelligent control unit is configured as follows: (1) Based on multi-source data including historical load data, real-time population density, weather forecast and building equipment power, the LSTM algorithm is used to construct a 24-hour cooling load prediction model; (2) Introduce dynamic electricity price and regional carbon price parameters into the control decision and construct a collaborative optimization function with the goal of maximizing the energy efficiency, operating cost and carbon emission reduction of the air conditioning system; (3) Setting and execution judgment of different execution modes.

[0023] The specific steps involved in constructing a cooling load prediction model include: (1-1) Multi-source data acquisition and preprocessing Collect historical cooling load data (core basic data), real-time population density, weather forecast data (temperature / humidity, light intensity, wind speed), building equipment power (lighting, office equipment, etc.), and date type (weekday / weekend) and other time-series data; clean, normalize, and align the data to eliminate noise and dimensionality differences, forming a standardized training dataset.

[0024] (1-2) LSTM network model construction Build a lightweight LSTM network (adapted to the controller hardware computing power), set up an input layer (to interface with multi-source preprocessed data), a hidden layer (to capture long-term dependencies and nonlinear relationships between data through a gating mechanism), and an output layer (to output hourly cold load prediction values ​​for the next 24 hours), and determine the core parameters such as the number of iterations and learning rate for network training.

[0025] (1-3) Model training and validation Using historical datasets as samples, the LSTM network is trained to learn the correlation between factors such as personnel density, ambient temperature, and equipment operation and building cooling load. The model accuracy is verified through test sets, and the network parameters are repeatedly tuned to ensure that the prediction deviation is within the acceptable range for engineering.

[0026] (1-4) Real-time Model Updates and Predictive Output The intelligent control unit inputs dynamic data such as real-time personnel density, the latest weather forecast, and real-time equipment power into the trained LSTM model, and performs online fine-tuning and updates to the model. Based on the updated multi-source data, the model outputs hourly cooling load forecasts for the next 24 hours, while extracting key features such as load peak / valley values ​​and duration.

[0027] The specific functions of this cooling load prediction model are as follows: It predicts whether the future cooling load will be less than 30% of the unit's rated capacity, and, based on the remaining cooling capacity of the phase change energy storage module, determines whether to trigger a low-carbon operation mode; it predicts whether the peak load will exceed 80%, and plans the cooling capacity in advance to ensure that the cooling release mode can effectively cope with high load conditions, while setting reasonable exit buffer times for each mode. It accurately predicts the load fluctuation patterns for the next 24 hours, guiding the system to prioritize cooling storage during off-peak electricity and low-load periods, converting low-priced electricity into cooling capacity; and to precisely release cooling during peak electricity and high-load periods, achieving "peak shifting and valley filling," avoiding waste or insufficient cooling capacity caused by blindly storing / releasing cooling. Combining the predicted load trend with dynamic electricity prices and regional carbon price parameters, the intelligent control unit constructs and solves a multi-objective optimization function to formulate the optimal operating strategy (such as prioritizing the activation of low-carbon / cooling release modes during high carbon price periods), achieving simultaneous optimization of energy efficiency improvement, operating cost reduction, and carbon emission reduction. Accurately calculate the total cooling load for the predicted future period to ensure that the remaining cooling capacity of the phase change energy storage module can cover more than 80% of it, thus avoiding insufficient cooling capacity and terminal cooling failure caused by inaccurate load prediction from the source.

[0028] Furthermore, using the intelligent control unit as the computing platform, a multi-objective nonlinear optimization function is constructed based on LSTM cooling load prediction results, real-time operating parameters, and dynamic electricity / carbon price data. The optimal solution is then obtained through an algorithm and transformed into control commands for mode triggering and equipment operation. The core steps are as follows: (2-1) Determine the core optimization objectives and quantitative indicators Three non-normalizable optimization objectives are identified and transformed into calculable and comparable quantifiable indicators, which serve as the core output of the function: Energy efficiency target: Maximize system COP, quantified as cooling output per unit of power consumption (kW·h cooling capacity / kW·h power consumption). Cost target: Minimize operating costs, quantified as the comprehensive cost per unit of cooling capacity (RMB / kW·h cooling capacity), including electricity costs and carbon emission costs; Carbon emission reduction target: Maximize carbon emission reduction, quantified as carbon emissions per unit of cooling capacity (kgCO2 / kW・h cooling capacity), based on regional power grid carbon emission factors and equipment energy consumption accounting.

[0029] (2-2) Introducing parameterized design of dynamic electricity price and regional carbon price By incorporating two types of price parameters as variable constraints into the function and connecting to dynamic data from the power grid and regional carbon trading market in real time, the parameters are automatically updated as the market changes. Dynamic electricity price parameters: The electricity price is divided into off-peak / flat / peak based on time period (e.g., off-peak electricity 0.3 yuan / kWh, peak electricity 1.0 yuan / kWh), which serves as a constraint for electricity cost accounting and selection of cold storage / cooling release periods; Regional carbon price parameters: The regional carbon trading market price (e.g., X yuan / kgCO2) serves as the basis for carbon emission cost accounting and the start-up and shutdown of high-carbon emission equipment; Simultaneously, the carbon emission factors of equipment energy consumption (such as the carbon emission factors of chiller units a kgCO2 / kW・h, and water pumps b kgCO2 / kW・h) are incorporated to achieve accurate carbon emission accounting.

[0030] (2-3) Construct and solve the multi-objective collaborative optimization function. Based on the future 24-hour cooling load demand as the basic constraint, and taking the switching timing of cold storage / cooling release / low carbon mode, cooling capacity scheduling rate, and equipment operation combination as decision variables, a multi-objective collaborative optimization function is constructed, with the core form being: F=ω1×COP_max+ω2×C_min+ω3×E_max; Where: F is the comprehensive optimization target value, COP_max, C_min, and E_max correspond to the core quantitative indicators of the three major optimization targets of the system: the highest energy efficiency, the lowest operating cost, and the largest carbon emission reduction, respectively, and ω1, ω2, and ω3 are weight coefficients (which can be dynamically adjusted according to building needs, such as increasing ω3 for low-carbon parks and increasing ω2 for commercial buildings). Solution method: Use multi-objective optimization algorithms (such as genetic algorithm and particle swarm optimization algorithm) to solve the optimal solution of the function, output the optimal mode triggering time period, the optimal cold storage / discharge rate, and the optimal equipment operation combination, and convert them into specific control instructions for the intelligent control unit to the electric three-way valve, water pump and chiller unit.

[0031] like Figure 6 As shown, the present invention also discloses a control method for adjusting the energy efficiency of an air conditioning system. The intelligent control unit 18 uses the real-time load rate of the chiller unit as the core judgment basis, and independently verifies the low-carbon operation conditions, and performs trigger judgments for different modes, including cold storage mode, cold release mode and low-carbon operation mode.

[0032] like Figure 2 As shown, the air conditioning system includes a chiller unit 1, a cooling tower 7, the phase change energy storage device, a chilled water pump 3, and a cooling water pump 4. The phase change energy storage device is connected to the chilled water circuit and the cooling water circuit of the air conditioning system through electric valves and connecting pipes.

[0033] like Figure 3The diagram shows the principle of the cold storage mode. The chiller unit 1, the phase change energy storage module 2, and the chilled water pump 3 form a loop. When the load rate is less than 50%, the cold storage condition is checked. The check conditions include whether it is during the off-peak electricity period, whether there is room for optimization of the real-time COP of the air conditioning system, and whether the phase change material has cold storage capacity. If all the above conditions are met, the cold storage mode is triggered. The intelligent control unit 18 outputs a signal to regulate the electric three-way valves (including electric three-way valve 19 and electric three-way valve 21). The chilled water pump 3 causes part of the chilled water in the chiller unit 1 to flow through the phase change energy storage module 2. The flow rate of the chilled water entering the phase change energy storage module 2 is dynamically adjusted using a fuzzy PID control strategy to stabilize the temperature difference between the inlet and outlet of the phase change energy storage module 2 in the optimal range of 3~5℃, thereby achieving efficient cold storage.

[0034] like Figure 4 The diagram shows the principle of the cooling mode. The phase change energy storage module 2, the circulating water pump 6, and the primary side of the plate heat exchanger 5 form one loop; the chiller unit 1, the cooling tower 7, the cooling water pump 4, and the secondary side of the plate heat exchanger 5 form another loop. If the load rate is >80%, the cooling condition verification stage begins. Verification conditions include whether the COP of the air conditioning system can be increased by more than 5% after cooling, whether the phase change energy storage module has available cooling capacity, and whether the phase change material has cooling capacity. If all conditions are met, the cooling mode is triggered. The intelligent control unit 18 outputs a signal to start the integrated circulating water pump 6 and switches the electric three-way valve to the plate heat exchanger 5 passage, allowing the cooling capacity stored in the phase change energy storage module 2 to indirectly cool the cooling water entering the chiller unit condenser through the plate heat exchanger 5, reducing the condensing temperature and increasing the COP of the air conditioning system.

[0035] like Figure 5 The diagram shows the principle of low-carbon operation mode. The intelligent control unit 18 outputs a signal to shut down the chiller unit 1, cooling water pump 4, and cooling tower 7, leaving only the phase change energy storage module 2 and chilled water pump 3 running. The phase change energy storage module 2 directly supplies cooling to the air conditioning terminals. The low-carbon operation conditions (all of which must be met) include: the building's instantaneous cooling load demand is less than 30% of the chiller unit's rated capacity; the remaining cooling capacity of the phase change energy storage module is sufficient to cover the total cooling load for the predicted period; the average temperature of the phase change material is more than 3°C lower than its phase change point temperature; and the total cooling load for the predicted period is no more than 80% of the module's remaining cooling capacity. If there is subsequent high load demand and the cooling storage benefit is significantly higher than the low-carbon benefit, the low-carbon mode will not be triggered temporarily, and the condition verification stage of the cooling storage mode will be entered, determining whether to store cooling according to the cooling storage mode logic.

[0036] The numbers 8 to 17 in the diagram represent electric valves.

[0037] In a specific embodiment of this application, taking an energy-saving renovation project of the central air conditioning system in an office building with a floor area of ​​10,000 square meters as the application scenario, the control method of the aforementioned phase change energy storage device is specifically implemented. The building's central air conditioning system is equipped with a variable frequency chiller unit (rated cooling capacity 600kW, high-efficiency operating load rate 50%-80%), along with original auxiliary equipment such as chilled water pumps, cooling water pumps, and cooling towers. New equipment includes a drawer-type phase change energy storage module, a plate heat exchanger, an integrated circulating water pump, and an intelligent control unit. The intelligent control unit uses a Siemens S7-1200 series PLC, equipped with an LSTM cooling load prediction model and a multi-objective collaborative optimization function, achieving automatic switching and refined control of cold storage, cold release, and low-carbon modes throughout the entire process. The specific implementation steps are as follows: I. System Preliminary Configuration and Initialization Device integration and link construction: The phase change energy storage module is connected in parallel to the chilled water supply and return main pipe at the outlet of the chiller unit via an electric three-way valve; the primary side of the plate heat exchanger is connected to the circulation loop of the phase change energy storage module driven by the integrated circulating water pump, and the secondary side is connected in parallel to the cooling water return main pipe; temperature and flow sensors are installed on the chilled water and cooling water main pipes, and power sensors are installed at the power supply terminals of the chiller unit, each water pump, and cooling tower; flexible fiber optic sensors are integrated in the phase change energy storage module, and all sensors are connected to the intelligent control unit for communication.

[0038] System initialization: Power supply to the system is completed, and the temperature, flow, power, and flexible fiber optic sensors are initialized and calibrated. The intelligent control unit is started, loading the building's historical cooling load data for the past year and the building's occupant density data. The LSTM cooling load prediction model for the next 24 hours is initialized, and the local power grid dynamic electricity price (off-peak electricity: 23:00-7:00, 0.32 yuan / kW·h; average electricity: 7:00-11:00 / 19:00-23:00, 0.65 yuan / kW·h; peak electricity: 11:00-19:00, 1.05 yuan / kW·h) and regional carbon price (8 yuan / kg CO2) are constructed. A collaborative optimization function is constructed with the goal of maximizing system energy efficiency, minimizing operating costs, and maximizing carbon emission reduction. The weighting coefficients ω1=0.3, ω2=0.4, and ω3=0.3 are set, and the system enters the cyclic monitoring state.

[0039] II. Cooling Load Forecasting and Basic Parameter Collection The intelligent control unit collects real-time chilled water supply and return water temperatures (set to 7℃ / 12℃), cooling water temperature, multiple internal temperatures of the phase change material, and real-time power consumption of each device. It integrates daily weather forecasts (ambient temperature, sunlight), real-time building occupancy density (collected through the access control system), and office equipment operating power data to update the LSTM 24-hour cooling load prediction model and output hourly cooling load prediction values. Simultaneously, it calculates the actual cooling capacity of the chiller unit based on chilled water flow rate × supply and return water temperature difference and calculates the unit load rate in real-time based on the rated cooling capacity.

[0040] III. Triggering and Execution of Cooling Storage Mode Triggering conditions met: The next day is a working day. The intelligent control unit predicts through the LSTM model that the cooling load rate will continue to exceed 80% from 10:00 to 18:00 the next day. At 23:00 on the same day, it enters the off-peak electricity period of the power grid. The real-time monitoring shows that the load rate of the chiller unit drops to 45% and lasts for 10 minutes. The real-time COP of the system is 2.8 (lower than the set optimization threshold of 3.0). The phase change material temperature is 10℃ (with cold storage capacity). The multi-objective collaborative optimization function solution determines that the cold storage benefit is greater than the cost, and all triggering conditions of the cold storage mode are met.

[0041] In the cold storage mode: the intelligent control unit outputs commands to adjust the opening of the electric three-way valve so that 50% of the chilled water flows through the heat exchange coil of the phase change energy storage module; a fuzzy PID control strategy is adopted, with the temperature difference between the inlet and outlet of the phase change energy storage module as the control target, and the optimal temperature difference range is set to 4℃±0.5℃. By dynamically adjusting the opening of the electric three-way valve to change the flow rate of chilled water entering the module, the inlet and outlet temperature difference is maintained within this range in real time, so as to achieve efficient cold storage.

[0042] Mode Exit: After 4 hours of cold storage operation, the phase change material temperature drops to 7℃ (cold storage complete), and the chiller unit load rate rises to 55% (entering the high-efficiency range), meeting the conditions for exiting the cold storage mode. The intelligent control unit adjusts the electric three-way valve to reset, closes the chilled water flow path of the phase change energy storage module, and exits the cold storage mode. At this time, the cold storage capacity stored in the phase change energy storage module can meet about 30% of the cold storage demand during the high load period of the next day.

[0043] IV. Triggering and Execution of Cooling Mode Triggering conditions met: At 11:00 the next day, the building's personnel density reached its peak, the ambient temperature rose to 35℃, the chiller unit's real-time load rate rose to 85% and remained there for 10 minutes, the system's real-time COP dropped to 2.7 due to the increase in condensing temperature, the LSTM model predicted that the cooling water temperature could decrease by 2.5℃ after cooling, the chiller unit's COP could increase to 3.2 (an increase of over 5%), the phase change energy storage module had sufficient remaining cooling capacity and cooling capacity, thus meeting all triggering conditions for the cooling mode.

[0044] Cooling mode execution: The intelligent control unit outputs a command to start the integrated circulating water pump, driving the working fluid circulation on the primary side of the phase change energy storage module; simultaneously, the electric three-way valve is switched to the plate heat exchanger passage, so that the cold energy stored in the phase change energy storage module can indirectly exchange heat with the cooling water through the plate heat exchanger, cooling the cooling water entering the chiller condenser. The cooling water inlet temperature drops from 32℃ to 29.5℃, the condensation temperature decreases, and the chiller's cooling efficiency is improved.

[0045] Mode Exit: After 3 hours of cooling operation, the building's personnel density decreases, the chiller unit load rate drops to 75% (entering the high-efficiency range), and the system's real-time COP rises back to 3.3, meeting the conditions for exiting the cooling mode. The intelligent control unit shuts down the integrated circulating water pump, switches the electric three-way valve to reset, and exits the cooling mode.

[0046] V. Triggering and Execution of Low-Carbon Operation Mode Triggering conditions met: At 22:00 on the same day, the building personnel density dropped to 0, all office equipment was turned off, the LSTM model predicted that the cooling load would remain below 150kW (25% of the unit's rated capacity) for the next 8 hours (22:00-6:00 the next day), and the total cooling load demand was 800kW·h; real-time monitoring showed that the remaining cooling capacity of the phase change energy storage module was 1000kW·h (which can cover 125% of the predicted cooling load and is not greater than 80% of the remaining cooling capacity), and the average temperature of the phase change material was 4℃ (more than 3℃ below the phase change point temperature), thus meeting all the triggering conditions for the low-carbon operation mode.

[0047] Low-carbon operation mode execution: The intelligent control unit outputs a command to first stop the operation of the chiller unit, and after a 2-minute delay, stop the cooling water pump and cooling tower fan, cutting off the power supply to the above equipment; adjust the electric three-way valve to make the chilled water circuit circulate entirely through the phase change energy storage module, and only the phase change energy storage module releases the cooling capacity to supply cooling to the building terminal in conjunction with the chilled water pump. At this time, the system power consumption is only the operating power consumption of the chilled water pump (about 5kW), achieving near-zero carbon emission operation.

[0048] Mode Exit: At 6:00 the next day, people began to enter the building, and the instantaneous cooling load demand rose to 200kW, exceeding the cooling capacity of the phase change energy storage module and lasting for 5 minutes, meeting the exit conditions for low-carbon operation mode; the intelligent control unit first restored the power supply to the chiller and cooling water pump and started the equipment. After the system conditions stabilized, the cooling tower fan was started, the electric three-way valve was adjusted to reset, the low-carbon operation mode was exited, and the system returned to normal operation.

[0049] VI. Implementation Results This embodiment, after one cooling season (May-September) of actual operation, achieved the following significant results: The proportion of time the chiller unit operated in the high-efficiency range with a stable load rate of 50%-80% increased from 60% before the renovation to 92%. The seasonal average COP of the air conditioning system increased from 3.0 before the renovation to 3.7, with an energy efficiency improvement of 23.3%, which meets the measured energy efficiency improvement target of 15%-30%. During off-peak hours, the amount of cold storage capacity accounts for 28% of the total cooling demand. During peak hours, the peak power consumption of the chiller unit is reduced by about 12,000 kWh by releasing cold water. Combined with the low-carbon operation mode, the operating electricity cost of the air conditioning system is reduced by about 35,000 yuan. In low-carbon operation mode, the power consumption of the main unit equipment is reduced by 87%, and carbon emissions are reduced by about 12 tons throughout the cooling season, achieving multi-objective synergistic optimization of energy efficiency improvement, operating cost reduction and carbon emission reduction; All operating modes are automatically switched by the intelligent control unit without manual intervention. The modular design of the phase change energy storage module and the status monitoring of the flexible fiber optic sensor have greatly reduced the difficulty of equipment operation and maintenance. There have been no cooling interruptions due to equipment failures, and the system's operational stability and reliability have been significantly improved.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. 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 phase change energy storage device for regulating the energy efficiency of an air conditioning system, characterized in that, The device includes a perforated energy storage cabinet, inside which are installed a phase change energy storage unit, a plate heat exchanger, a circulating water pump, and an intelligent control unit. The phase change energy storage unit's housing is filled with phase change material, which is immersed in a heat exchange coil. One end of the heat exchange coil is connected to the outlet of the primary side of the plate heat exchanger via an electric three-way valve and a pipe, while the other end is connected to the inlet of the primary side of the plate heat exchanger via an electric three-way valve and a pipe. The circulating water pump is connected to the second pipe. The electric three-way valve, the electric three-way valve, and the circulating water pump are all electrically connected to the intelligent control unit. The intelligent control unit is also electrically connected to a temperature sensor, a flow sensor, and a power sensor. The temperature sensor is used to collect the supply and return temperatures of chilled water and cooling water, and the real-time temperature of the phase change material. The flow sensor is used to collect the real-time flow rates of chilled water and cooling water. The power sensor is used to collect the real-time power consumption of the chiller unit and the operating power consumption of each pump / cooling tower fan.

2. A phase change energy storage device for regulating the energy efficiency of an air conditioning system according to claim 1, characterized in that, The heat exchange coil adopts a double-helix cross-flow structure, and the inner wall of the heat exchange coil is provided with needle-shaped fins.

3. A phase change energy storage device for regulating the energy efficiency of an air conditioning system according to claim 1, characterized in that, The phase change energy storage module also integrates a flexible fiber optic sensor inside its housing, which is used to monitor the phase change state, temperature distribution uniformity, and aging degree of the phase change material in real time. The flexible fiber optic sensor is electrically connected to the intelligent control unit.

4. A control method for adjusting the energy efficiency of an air conditioning system, the air conditioning system comprising a chiller, a cooling tower, and a phase change energy storage device as described in any one of claims 1 to 3, the method being applied to the phase change energy storage device connected to the chilled water circuit and cooling water circuit of the air conditioning system, characterized in that... Includes the following steps: S1. The air conditioning system is powered on and the temperature sensor, flow sensor and power sensor are initialized. The intelligent control unit starts and loads historical operating data and LSTM cooling load prediction model, and enters the cyclic monitoring state. During the monitoring process, it continuously performs multi-objective optimization decisions in combination with dynamic electricity price and regional carbon price parameters. S2, the intelligent control unit collects chilled water supply and return water temperatures, cooling water temperatures, phase change material temperatures, and air conditioning system power consumption parameters in real time, calculates the real-time load rate of the chiller unit, and integrates weather forecasts and building occupancy density data to update the cooling load prediction model for the next 24 hours. S3. Using the real-time load rate of the chiller unit as the core judgment criterion, and independently verifying the low-carbon operation conditions, different trigger judgments are made for different modes: S31. First, independently verify the low-carbon operating conditions. If all conditions are passed, the intelligent control unit immediately calls the LSTM cooling load prediction data for the next 24 hours to make forward predictions of high load and weigh multiple objectives of benefits. S311. If there is no subsequent high load demand, or the cold storage benefit is lower than the low carbon benefit, the low carbon operation mode will be triggered directly, skipping the condition verification and triggering process of the cold storage and cold release modes. S312 If there is subsequent high load demand and the benefits of cold storage are significantly higher than the benefits of low carbon, then the low carbon mode will not be triggered for the time being, and the condition verification stage of the cold storage mode will be entered instead, and the cold storage mode logic will be used to determine whether to store cold. The low-carbon operation conditions include: the instantaneous cooling load demand of the building is less than 30% of the rated capacity of the chiller unit; the remaining cooling capacity of the phase change energy storage module is sufficient to cover the total cooling load during the forecast period; the average temperature of the phase change material is more than 3°C lower than its phase change point temperature; and the total cooling load during the forecast period is not greater than 80% of the remaining cooling capacity of the module. S32. Then make a judgment based on the load factor; S321. If the load rate is <50%, the cold storage condition test will be initiated. The test conditions include whether it is during the off-peak electricity period of the power grid, whether there is room for optimization of the real-time COP of the air conditioning system, and whether the phase change material has the cold storage capacity. If all the above conditions are met, the cold storage mode will be triggered. If any condition is not met, the normal / current operation mode will be maintained. S322. If the load rate is >80%, the cooling condition test will be entered. The test conditions include whether the COP of the air conditioning system can be increased by more than 5% after cooling, whether the phase change energy storage module has available cooling capacity, and whether the phase change material has cooling capacity. If all the above conditions are met, the cooling mode will be triggered. If any condition is not met, the normal / current operation mode will be maintained. S323. If the load rate is in the high-efficiency range of 50% to 80%, directly maintain the normal / current operating mode; S4. After entering the corresponding operating mode, execute the corresponding control operation: S41, Cold Storage Mode: The intelligent control unit outputs a signal to regulate the electric three-way valve, allowing some chilled water to flow through the phase change energy storage module. The fuzzy PID control strategy is used to dynamically adjust the flow rate of chilled water entering the phase change energy storage module, stabilizing the temperature difference between the inlet and outlet of the phase change energy storage module in the optimal range of 3~5℃, thereby achieving efficient cold storage. S42, Cooling Mode: The intelligent control unit outputs a signal to start the integrated circulating water pump and switches the electric three-way valve to the plate heat exchanger passage, so that the cooling capacity stored in the phase change energy storage module indirectly cools the cooling water entering the chiller condenser through the plate heat exchanger, thereby reducing the condensing temperature and improving the COP of the air conditioning system. S43, Low-carbon operation mode: The intelligent control unit outputs a signal to shut down the chiller, cooling water pump and cooling tower, and only keeps the phase change energy storage module and chilled water pump running, and the phase change energy storage module directly supplies cooling to the air conditioning terminal. S5. During the operation of each mode, continuously check the mode exit condition. If a higher priority mode trigger condition occurs during operation, or if any exit condition of the current mode is met, immediately exit the current mode and re-execute the mode trigger check process of S3: S51. Conditions for exiting the cold storage mode: The chiller unit load rate is ≥50%, or the phase change energy storage module has completed cold storage, or the low-carbon operation condition verification is passed during operation. S52. Conditions for exiting cooling mode: The chiller unit load rate is ≤80%, or the available cooling capacity of the phase change energy storage module is lower than the set threshold, or the low-carbon operation condition verification is passed during operation. S53. Conditions for exiting low-carbon operation mode: The average temperature of the phase change material rises above its phase change point temperature, or the instantaneous cooling load demand of the air conditioning system exceeds the cooling capacity of the phase change energy storage module and lasts for more than 5 minutes. S6. After exiting any mode, the air conditioning system immediately returns to the normal operation mode and re-executes the mode trigger judgment process of S3, triggering the corresponding mode or maintaining normal operation according to the real-time operating conditions.

5. A control method for adjusting the energy efficiency of an air conditioning system according to claim 4, characterized in that, The multi-objective optimization decision mentioned in step S1 is to construct a collaborative optimization function with the objectives of maximizing the energy efficiency, minimizing the operating cost, and maximizing the carbon emission reduction of the air conditioning system, which serves as an auxiliary decision-making basis for triggering and operating each mode.

6. A control method for adjusting the energy efficiency of an air conditioning system according to claim 4, characterized in that, The real-time load rate of the chiller unit mentioned in step S2 is calculated by the intelligent control unit based on the ratio of the actual cooling capacity of the unit to the rated cooling capacity.

7. A control method for adjusting the energy efficiency of an air conditioning system according to claim 4, characterized in that, The fuzzy PID control strategy described in step S41 takes the temperature difference between the inlet and outlet of the phase change energy storage module as the core control target, and dynamically adjusts the chilled water flow rate by monitoring the temperature difference changes in real time to achieve precise and stable temperature difference control.

8. A control method for adjusting the energy efficiency of an air conditioning system according to claim 4, characterized in that, In step S42, the process of indirectly cooling the cooling water is judged by the standard that the temperature of the cooled water after cooling can increase the COP of the chiller unit by more than 5%.

9. A control method for adjusting the energy efficiency of an air conditioning system according to claim 4, characterized in that, In step S5, the exit condition judgment for each mode is a real-time continuous judgment. The intelligent control unit performs threshold comparison and condition verification based on the real-time parameters collected by the sensor, and immediately outputs a mode switching signal when the exit condition is met.

10. A control method for adjusting the energy efficiency of an air conditioning system according to claim 4, characterized in that, The high-load forward prediction in step S31 must simultaneously meet the following conditions: the LSTM model predicts that there will be a high-load condition with a load rate of ≥80% for ≥1 hour in the next 12 hours; it predicts that the current remaining cold capacity of the phase change energy storage unit cannot meet the cooling demand under the high-load condition; the current average temperature of the phase change material is ≥2℃ higher than its phase change point temperature and the remaining cold storage space is ≥30% of the total cold storage capacity; and it predicts that there will be no other cold storage window period in the subsequent high-load period. The multi-objective benefit trade-off is to calculate the comprehensive optimization target value of the cold storage priority strategy and the low-carbon priority strategy in the next 24 hours. If the F value of the cold storage priority strategy minus the F value of the low-carbon priority strategy is ≥10%, it is determined that the cold storage benefit is significantly higher than the low-carbon benefit.

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