Air conditioner energy storage system based on intelligent control and peak-valley electricity price utilization method

Abstract

This invention discloses an air conditioning energy storage system based on intelligent control and a method for utilizing peak-valley electricity prices, relating to the field of air conditioning control technology. It includes an energy storage module, an air conditioning module, and an intelligent control module. The intelligent control module is configured to: based on predicted environmental parameters, electricity price information, and user demand for a future period, and with the goal of minimizing operating costs, dynamically solve for the optimal control sequence using a model predictive control algorithm, and control the start / stop and power of the valves, the refrigeration unit, and associated water pumps. This ensures that the system prioritizes charging the energy storage module during off-peak electricity price periods and prioritizes utilizing the energy storage module for power supply during peak electricity price periods. It can accurately predict load, perceive energy storage status in real time, and dynamically and intelligently coordinate the air conditioning unit and energy storage device throughout the entire operating cycle based on a multi-objective optimization algorithm, thereby maximizing the utilization of peak-valley electricity price differences while ensuring user comfort.

Description

Technical Field

[0001] This invention relates to the field of air conditioning control technology, and in particular to an air conditioning energy storage system based on intelligent control and a method for utilizing peak-valley electricity pricing. Background Technology

[0002] With the development of the social economy and the improvement of people's living standards, the proportion of building energy consumption in total social energy consumption continues to rise. Among them, air conditioning systems, as major energy-consuming equipment in buildings, play an important role in reducing total social energy consumption and achieving the "dual carbon" goal through energy-saving operation. To optimize power resource allocation and alleviate the peak-valley difference in the power grid, the power sector has generally implemented peak-valley time-of-use pricing policies, aiming to guide users to consume electricity during off-peak hours and reduce power supply pressure during peak hours. Against this backdrop, air conditioning energy storage technology has emerged. The basic principle of this technology is to start the cooling equipment during off-peak hours at night, storing the generated cooling energy in energy storage devices (such as water storage tanks or ice storage devices); during peak hours in the daytime, the stored cooling energy is released to provide cooling services for buildings, thereby significantly reducing the power consumption of air conditioning during peak hours, saving users' electricity bills, and achieving "peak shaving and valley filling" for the power grid.

[0003] However, existing air conditioning energy storage systems still face numerous technical bottlenecks in practical applications, hindering their full realization of energy-saving effects and economic efficiency. These bottlenecks are mainly reflected in the following aspects: 1) Currently, most systems rely on simple timed control or control based on fixed temperature thresholds. For example, the system activates cold storage during fixed off-peak hours and releases cold during fixed peak hours. This pre-programmed control method lacks foresight and dynamic adjustment capabilities, and cannot optimize based on future weather changes, actual building load fluctuations, and real-time electricity price signals. 2) Existing systems often use simple temperature point measurements or time integration methods to estimate energy storage, resulting in significant errors and making it difficult for the control system to formulate precise energy release strategies. 3) Traditional systems typically operate according to fixed temperature setpoints, lacking the ability to learn and adapt to users' personalized comfort needs and behavioral habits. The system cannot dynamically and intelligently balance the conflicting goals of "cost" and "comfort." 4) Lack of self-learning and adaptive capabilities: The thermal characteristics of buildings, the performance of air conditioning systems, and user habits all change over time. Existing fixed-parameter control systems cannot adapt to these changes, leading to a decline in control performance after long-term operation and an inability to maintain optimal operating conditions. To address this, an air conditioning energy storage system based on intelligent control and a peak-valley electricity pricing method are proposed. Summary of the Invention

[0004] The main objective of this invention is to provide an air conditioning energy storage system based on intelligent control and a method for utilizing peak-valley electricity prices. This system can accurately predict load, perceive energy storage status in real time, and dynamically and intelligently coordinate the air conditioning unit and energy storage device throughout the entire operating cycle based on a multi-objective optimization algorithm. This allows for the maximum utilization of peak-valley electricity price differences while ensuring user comfort, effectively solving the problems in the background technology.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] An air conditioning energy storage system based on intelligent control includes:

[0007] An energy storage module includes an energy storage cabinet and a phase change material filled therein, wherein a heat exchange coil is provided inside the energy storage cabinet;

[0008] An air conditioning module includes a refrigeration unit and an indoor terminal unit, the refrigeration unit and the indoor terminal unit being connected by pipes to form a circulation loop; and,

[0009] Intelligent control module;

[0010] The heat exchange coil of the energy storage module is connected in parallel or in series with the piping of the air conditioning module through pipes and valves.

[0011] The intelligent control module is signal-connected to the energy storage module and the air conditioning module, and is configured to: based on predicted environmental parameters, electricity price information and user demand in the future period, with the goal of minimizing operating costs, dynamically solve the optimal control sequence through model predictive control algorithm, and control the start-up, shutdown and power of the valve, the refrigeration unit and associated water pump, so that the system prioritizes charging the energy storage module during the off-peak electricity price period and prioritizes using the energy storage module to supply energy during the peak electricity price period.

[0012] A peak-valley electricity pricing method based on intelligent control is applied to the aforementioned air conditioning energy storage system based on intelligent control. The method includes:

[0013] Receive future peak-valley electricity price signals and user-set temperature preferences;

[0014] Based on the model predictive control framework, the following steps are executed in a rolling manner:

[0015] Predict building load and system status for future periods;

[0016] With the goal of minimizing the weighted sum of the deviation between total operating cost and comfort level, the optimal control strategy that minimizes the system operating cost is obtained. The optimal control strategy defines the cooperative working mode of the refrigeration unit and the energy storage module.

[0017] The optimal control strategy is executed to control the air conditioning module and the energy storage module, thereby achieving peak shaving and valley filling of electrical energy.

[0018] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the intelligent control-based peak-valley electricity pricing utilization method.

[0019] Furthermore, the intelligent control module is specifically configured to perform the following steps:

[0020] Within each control cycle,

[0021] Collects indoor and outdoor temperatures, system flow and temperature, real-time and predicted electricity prices, and user settings;

[0022] The building thermodynamics model is used to predict the load in future periods, and the parameter estimation algorithm is used to correct the model parameters online.

[0023] Based on system measurement data, the remaining available energy of the energy storage module is estimated using a state observer;

[0024] Using the weighted sum of total operating cost and comfort deviation as the objective function, the optimal control sequence in the future finite time domain is solved under the condition of satisfying system constraints.

[0025] Execute the first control instruction in the optimal control sequence;

[0026] Record user manual intervention behaviors and update user preference models based on historical data.

[0027] Furthermore, the building thermodynamic model is specifically described as follows: ,in Let K be the indoor temperature at time k. Let K be the outdoor temperature at time k. The amount of heat or cooling provided by the air conditioning system at time k. The lumped disturbance term at time k. All constant coefficients were obtained through online correction using the recursive least squares method.

[0028] Furthermore, the remaining usable energy of the energy storage module is estimated using a Kalman filter based on the dynamic model of the energy storage cabinet. The state equation of the dynamic model of the energy storage cabinet is specifically expressed as follows: ,in Let be the remaining usable energy of the energy storage cabinet in the k-th time period. The charging power of the energy storage cabinet in the k-th time period is... For charging efficiency, Let be the energy release power of the energy storage cabinet in the k-th time period. For energy release efficiency, To control the length of the time period.

[0029] Furthermore, the objective function, which is to minimize the weighted sum of the deviations between total operating cost and comfort level, is specifically expressed as follows: ;

[0030] in, Let be the objective function. Let k be the electricity price at time k. Let k be the power consumption of the compressor. Let be the deviation of indoor temperature from the dynamic comfort range at time k, defined as: , To obtain Maximum value operation in These are the lower and upper limits of the dynamic comfort range defined for the k-th time period, respectively. This is a comfort weighting factor.

[0031] Furthermore, the lower limit of the dynamic comfort range and The upper limit value, and the aforementioned comfort weighting factor All of these are dynamic parameters that are adaptively adjusted based on time period, user occupancy status, and historical user behavior.

[0032] Furthermore, the collaborative working modes include at least two of the following:

[0033] Energy storage mode: During off-peak electricity prices, the cooling unit is controlled to operate and the generated cold / heat energy is stored in the energy storage module;

[0034] Energy release mode: During peak electricity price periods, the refrigeration unit is controlled to stop or operate at reduced power, and the cold / heat energy stored in the energy storage module is used for cooling / heating.

[0035] Combined energy supply mode: When the load demand exceeds a preset threshold, the refrigeration unit and the energy storage module are controlled to supply energy simultaneously.

[0036] Furthermore, the process of solving the optimal control strategy is a constrained optimization problem, with constraints including: upper and lower limits of the energy storage module's capacity, upper and lower limits of its charging and discharging power, upper and lower limits of the cooling unit's power, the comfort range of indoor temperature, and the system's energy balance, specifically: ; ; ; ; ; ;

[0037] in, This represents the maximum energy storage capacity of the energy storage cabinet. These are the maximum charging power and maximum discharging power of the energy storage cabinet, respectively. This represents the compressor's maximum power consumption. The energy generated by the compressor.

[0038] The present invention has the following beneficial effects:

[0039] Compared with existing technologies, this solution optimizes energy storage through intelligent algorithms, not just by storing energy during off-peak hours and releasing energy during peak hours, but by dynamically and accurately calculating the optimal timing and power for energy storage / release. This avoids unnecessary energy storage or premature depletion of stored energy under flat electricity prices, achieving more economical electricity cost savings than traditional timed control.

[0040] Compared with existing technologies, this solution, based on Kalman filter state estimation, achieves high-precision, real-time sensing of the remaining energy in the energy storage cabinet, solves the problem of insufficient dynamism in traditional systems, improves the accuracy of energy dispatch, significantly enhances the utilization rate of energy storage devices, and avoids energy waste or supply interruptions.

[0041] Compared with existing technologies, this solution introduces a dynamic comfort range, utilizes the system's adaptive learning function to continuously learn users' behavioral preferences and habits, and automatically adjusts the control strategy, thus balancing users' comfort needs while taking into account economy. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the structure of the air conditioning energy storage system and peak-valley electricity pricing method based on intelligent control of the present invention;

[0043] Figure 2 This is a flowchart illustrating the peak-valley electricity pricing method based on intelligent control according to the present invention.

[0044] Figure 3 This is a schematic diagram of the process steps of one possible embodiment of the present invention. Detailed Implementation

[0045] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. Example 1

[0046] See Figure 1 The structural diagram of the intelligent control-based air conditioning energy storage system of the present invention shown herein includes:

[0047] An energy storage module includes an energy storage cabinet and a phase change material filled therein, and a heat exchange coil is installed inside the energy storage cabinet.

[0048] An air conditioning module includes a refrigeration unit and indoor terminals, which are connected by piping to form a circulation loop; and,

[0049] Intelligent control module;

[0050] The heat exchange coil of the energy storage module is connected in parallel or in series with the air conditioning module through pipes and valves.

[0051] The intelligent control module is connected to the energy storage module and the air conditioning module and is configured to: based on predicted environmental parameters, electricity price information and user demand in the future period, with the goal of minimizing operating costs, dynamically solve the optimal control sequence through model predictive control algorithm, and control the start-up and shutdown and power of valves, refrigeration unit and associated water pumps, so that the system prioritizes charging the energy storage module during the off-peak electricity price period and prioritizes using the energy storage module to supply energy during the peak electricity price period.

[0052] The intelligent control module is specifically configured to perform the following steps:

[0053] Within each control cycle,

[0054] 1) Collect indoor and outdoor temperatures, system flow and temperature, real-time and predicted electricity prices, and user settings;

[0055] 2) Predict future loads using building thermodynamics models and perform online calibration of model parameters using parameter estimation algorithms;

[0056] The building thermodynamic model is specifically described as follows: ;

[0057] in Let K be the indoor temperature at time k. Let K be the outdoor temperature at time k. The amount of heat or cooling provided by the air conditioning system at time k. The lumped disturbance term at time k. All constant coefficients were obtained through online correction using the recursive least squares method.

[0058] 3) Based on system measurement data, estimate the remaining usable energy of the energy storage module using a state observer;

[0059] The remaining available energy is estimated based on the dynamic model of the energy storage tank using a Kalman filter. The state equation of the dynamic model of the energy storage tank is specifically expressed as follows: ,in Let K represent the remaining usable energy of the energy storage cabinet over k time periods. The charging power of the energy storage cabinet in the k-th time period is... For charging efficiency, Let be the energy release power of the energy storage cabinet in the k-th time period. For energy release efficiency, To control the length of the time period.

[0060] 4) Using the weighted sum of the deviation between total operating cost and comfort level as the objective function, and under the condition of satisfying system constraints, solve for the optimal control sequence in the future finite time domain;

[0061] The objective function is specifically expressed as: ;

[0062] in, Let be the objective function. Let k be the electricity price at time k. Let k be the power consumption of the compressor. Let be the deviation of indoor temperature from the dynamic comfort range at time k, defined as: , To obtain Maximum value operation in These are the lower and upper limits of the dynamic comfort range defined for the k-th time period, respectively. Comfort weighting factor;

[0063] The constraints include:

[0064] Constraint 1: Upper and lower limits of the energy storage module's capacity. ;

[0065] Constraint 2: Upper and lower limits of charge and discharge power. ;

[0066] Constraint 3: Upper and lower power limits of the refrigeration unit. ;

[0067] Constraint 4: The comfortable range of indoor temperature. ;

[0068] Constraint 5: System energy balance. ;

[0069] in, This represents the maximum energy storage capacity of the energy storage cabinet. These are the maximum charging power and maximum discharging power of the energy storage cabinet, respectively. is the maximum power consumption of the compressor; is the energy produced by the compressor.

[0070] 5) Execute the first control instruction in the optimal control sequence;

[0071] 6) Record user manual intervention behaviors and update the user preference model based on historical data.

[0072] See Figure 2The flowchart shown is a method for utilizing peak-valley electricity pricing based on intelligent control, including:

[0073] 1) Receive future peak and off-peak electricity price signals and user-set temperature preferences, including the lower limit of the dynamic comfort range. and Upper limit value, and comfort weighting factor All of these are dynamic parameters that are adaptively adjusted based on time period, user occupancy status, and historical user behavior.

[0074] 2) Based on the model predictive control framework, execute the following steps in a rolling manner:

[0075] 2.1) Predict building load and system status for future periods;

[0076] 2.2) With the goal of minimizing the weighted sum of the deviation between total operating cost and comfort level, the optimal control strategy that minimizes the system operating cost is obtained. The optimal control strategy defines the cooperative working mode of the refrigeration unit and the energy storage module.

[0077] Collaborative work modes include at least two of the following:

[0078] Energy storage mode: During off-peak electricity prices, the chiller is controlled to operate and the generated cold / heat energy is stored in the energy storage module;

[0079] Energy release mode: During peak electricity price periods, the chiller is controlled to shut down or operate at reduced power, and the cold / heat energy stored in the energy storage module is used for cooling / heating.

[0080] Combined energy supply mode: When the load demand exceeds the preset threshold, the refrigeration unit and the energy storage module are controlled to supply energy simultaneously.

[0081] 3) Execute the optimal control strategy to control the air conditioning module and energy storage module to achieve peak shifting and valley filling of electricity.

[0082] Based on the above system and method, this embodiment provides an implementable process step, specifically as follows:

[0083] Phase 1: System Initialization and Parameter Self-Tuning

[0084] Step 1.1: Hardware System Check and Startup

[0085] Check that the connections of hardware devices such as energy storage cabinet, refrigeration unit, water pump, valves, and sensors are normal.

[0086] When the system is powered on, the intelligent control module starts up and establishes communication connections with all sensors and actuators.

[0087] Perform the equipment self-test procedure to ensure there are no fault alarms.

[0088] Step 1.2: Basic Parameter Settings

[0089] User basic settings: Users input basic information in the app or human-computer interface, such as:

[0090] Building location (used to obtain weather forecasts).

[0091] Building type (residential, office, etc., used for initial load model).

[0092] The expected default comfortable temperature range (e.g., 24-26℃).

[0093] Electricity pricing strategy input: The system presets or the user inputs the local peak, flat, and valley electricity pricing periods and prices.

[0094] Equipment parameter settings: Input the rated power of the refrigeration unit, the nominal capacity of the energy storage cabinet, the maximum flow rate of the water pump, and other equipment nameplate parameters.

[0095] Step 1.3: Initial self-tuning of model parameters

[0096] The system entered a self-learning phase that lasted for several days.

[0097] During this period, the system operated in normal mode while simultaneously collecting data intensively. Data such as...

[0098] Using the collected data, online calibration was performed using the recursive least squares method to preliminarily calibrate the parameters in the building thermodynamic model. .

[0099] Initial energy state of the energy storage tank Charging efficiency and energy release efficiency Make a preliminary estimate.

[0100] Phase Two: Daily Rolling Optimization and Operation (Core System Workflow)

[0101] This stage is a closed-loop process that automatically cycles through each control period (e.g., 15 minutes). See below for specific steps. Figure 3 As shown, the specific steps include the following:

[0102] Step 2.1: Data Sensing and Fusion

[0103] Read sensor network data, including indoor and outdoor temperatures, system supply and return water temperatures, flow rate, key point temperatures of the energy storage cabinet, and real-time electrical power.

[0104] Obtain external forecast data: Obtain weather forecasts (temperature, humidity, sunshine intensity) and power grid price information (especially time-of-use price signals) for the next 24-48 hours via Internet API.

[0105] Invoke the user preference strategy library: Based on information such as the current time and whether it is a weekday, retrieve the corresponding dynamic comfort range from the learned strategy library. and comfort weight .

[0106] Step 2.2: Model Prediction and State Update

[0107] Load forecasting: Predicting future outdoor temperatures Using schedule information and a calibrated building thermodynamics model, the building's cooling / heating loads for the next N time periods are predicted. .

[0108] Online model parameter calibration: The recursive least squares method compares the model's predicted temperature from the previous cycle with the actual measured temperature, and fine-tunes the parameters. This allows the model to adapt to weather changes and the dynamic characteristics of the building itself.

[0109] Energy storage state estimation: The Kalman filter estimates the remaining usable energy in the energy storage tank based on the actual supply and return water temperatures and flow rate measurements of the system. Perform optimal estimation and perceive the energy storage status of phase change materials in real time.

[0110] Step 2.3: Optimization Problem Solving

[0111] Constructing the optimization problem: Based on the current state and prediction information, the controller constructs an optimization problem for the next 24 hours (N=96).

[0112] Objective function: .

[0113] Constraints include the upper and lower limits of the energy storage module's capacity, the upper and lower limits of its charging and discharging power, the upper and lower limits of the cooling unit's power, the comfort range of indoor temperature, and the system's energy balance.

[0114] Numerical solution: The built-in numerical solver (such as QP or MIP solver) solves this constrained optimization problem to obtain the optimal control sequence starting from the current time step. .

[0115] Step 2.4: Execution of Control Commands

[0116] A rolling time-domain strategy is adopted, taking only the optimal sequence. The first step of the control command.

[0117] The controller translates the initial instruction into specific device actions:

[0118] Controls the start-up, shutdown, and operating frequency of the refrigeration unit.

[0119] Adjust the speed of the water pump.

[0120] Switch the direction and opening of the electric valve to achieve seamless switching between modes such as "energy storage", "energy release", "direct supply from main unit" and "joint energy supply".

[0121] For example, the instruction might be: "Close valve V1 at the end of the ventilation room, open valve V2 of the energy storage cabinet, and start the compressor to operate at 50% power (energy storage mode)."

[0122] Step 2.5: Waiting and Looping

[0123] After the system waits for one control cycle (e.g., 15 minutes), it returns to step 2.1 to start a new round of perception, prediction, optimization, and execution.

[0124] This rolling optimization mechanism enables the system to continuously utilize the latest information, cope with prediction errors and random disturbances, and always implement the current optimal control strategy.

[0125] Phase 3: Long-term adaptive learning (running continuously in the background)

[0126] Step 3.1: User Behavior Recording

[0127] The system continuously monitors and records all manual intervention behaviors of users (such as manually adjusting the temperature and setting scene modes through the APP), and records the context of the intervention (time, indoor and outdoor temperature, and personnel status).

[0128] Step 3.2: Policy Library Update

[0129] The system periodically (e.g., during low-load periods in the early morning) clusters and analyzes the recorded user behavior data.

[0130] If a stable pattern is detected (e.g., "Every Wednesday at 8 PM, users have an 80% probability of setting the temperature to 23°C"), the system will automatically update the user preference policy library and adjust the corresponding time period. or value.

[0131] Example 2:

[0132] 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 program to implement the steps of a peak-valley electricity pricing method based on intelligent control.

[0133] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control based air conditioning energy storage system, characterized in that, include: An energy storage module includes an energy storage cabinet and a phase change material filled therein, wherein a heat exchange coil is provided inside the energy storage cabinet; An air conditioning module includes a refrigeration unit and an indoor terminal unit, the refrigeration unit and the indoor terminal unit being connected by pipes to form a circulation loop; and, Intelligent control module; The heat exchange coil of the energy storage module is connected in parallel or in series with the piping of the air conditioning module through pipes and valves. The intelligent control module is signal-connected to the energy storage module and the air conditioning module, and is configured to: based on predicted environmental parameters, electricity price information and user demand in the future period, with the goal of minimizing operating costs, dynamically solve the optimal control sequence through model predictive control algorithm, and control the start-up and shutdown and power of the valve, the refrigeration unit and associated water pump, so that the system prioritizes charging the energy storage module during the off-peak electricity price period and prioritizes using the energy storage module to supply energy during the peak electricity price period; The intelligent control module is specifically configured to perform the following steps: Within each control cycle, Collects indoor and outdoor temperatures, system flow and temperature, real-time and predicted electricity prices, and user settings; The building thermodynamics model is used to predict the load in future periods, and the parameter estimation algorithm is used to correct the model parameters online. Based on system measurement data, the remaining available energy of the energy storage module is estimated using a state observer; Using the weighted sum of total operating cost and comfort deviation as the objective function, the optimal control sequence in the future finite time domain is solved under the condition of satisfying system constraints. Execute the first control instruction in the optimal control sequence; Record user manual intervention behaviors and update user preference models based on historical data; The building thermodynamic model is specifically expressed as: wherein is the indoor temperature at time k, is the outdoor temperature at time k, is the heat or cold provided by the air conditioning system at time k, is the lumped disturbance term at time k, and a, b, and c are constant coefficients obtained by online correction through the recursive least square method. The remaining usable energy of the energy storage module is estimated based on the dynamic model of the energy storage cabinet using a Kalman filter. The state equation of the dynamic model of the energy storage cabinet is specifically expressed as follows: ,in Let be the remaining usable energy in the energy storage cabinet during the k-th time period. The charging power of the energy storage cabinet in the k-th time period is... For charging efficiency, Let be the energy release power of the energy storage cabinet in the k-th time period. For energy release efficiency, To control the length of the time period; The objective function is targeted at minimizing the weighted sum of total operating cost and comfort deviation, which is specifically expressed as: ; in, Let be the objective function. Let k be the electricity price at time k. Let k be the power consumption of the compressor. Let be the deviation of indoor temperature from the dynamic comfort range at time k, defined as: , To obtain Maximum value operation in These are the lower and upper limits of the dynamic comfort range defined for the k-th time period, respectively. This is a comfort weighting factor.

2. The smart control based air conditioning energy storage system according to claim 1, wherein, a lower limit value of the dynamic comfort range and an upper limit value, and the comfort weight factor are all dynamically adapted parameters according to time period, occupancy status and historical user adaptation behavior.

3. A peak and valley electricity price utilization method based on intelligent control, characterized by, Applied to the intelligent control-based air conditioning energy storage system as described in any one of claims 1-2, the method includes: Receive future peak-valley electricity price signals and user-set temperature preferences; Based on the model predictive control framework, the following steps are executed in a rolling manner: Predict building load and system status for future periods; With the goal of minimizing the weighted sum of the deviation between total operating cost and comfort level, the optimal control strategy that minimizes the system operating cost is obtained. The optimal control strategy defines the cooperative working mode of the refrigeration unit and the energy storage module. The optimal control strategy is executed to control the air conditioning module and the energy storage module, thereby achieving peak shaving and valley filling of electrical energy. 4.The smart control based peak and off-peak electricity price utilization method according to claim 3, characterized in that, The collaborative work modes include at least two of the following: Energy storage mode: During off-peak electricity prices, the cooling unit is controlled to operate and the generated cold / heat energy is stored in the energy storage module; Energy release mode: During peak electricity price periods, the refrigeration unit is controlled to stop or operate at reduced power, and the cold / heat energy stored in the energy storage module is used for cooling / heating. Combined energy supply mode: When the load demand exceeds a preset threshold, the refrigeration unit and the energy storage module are controlled to supply energy simultaneously. 5.The smart control based peak and off-peak electricity price utilization method according to claim 3, characterized in that, The process of solving the optimal control strategy is a constrained optimization problem. The constraints include: the upper and lower limits of the energy storage module's capacity, the upper and lower limits of its charging and discharging power, the upper and lower limits of the cooling unit's power, the comfort range of the indoor temperature, and the system's energy balance.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the peak-valley electricity pricing method based on intelligent control as described in any one of claims 3 to 5.

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