A refrigeration control strategy optimization method based on cold storage and electricity price change

Abstract

The present application relates to the technical field of power regulation, in particular to a refrigeration control strategy optimization method based on cold storage and electricity price change, comprising the following steps: through collecting refrigeration capacity and refrigerant temperature difference, calculating the difference between cold demand and refrigeration capacity, combining real-time electricity price and energy efficiency parameter normalization to generate energy efficiency parameter correction result, analyzing power correction coefficient and electricity price segmentation, setting cold storage and cold release instructions, adjusting cold storage or cold release strategy combined with electricity price trend, generating electricity price response control strategy set, and optimizing cold storage power regulation; in the present application, through the difference between cold demand and refrigeration capacity calculation, combined with real-time electricity price and energy efficiency parameter normalization, fitting energy efficiency parameter correction result, based on electricity price segmentation analysis, setting appropriate cold storage or cold release instructions, improving the energy efficiency of the equipment in the peak-valley electricity price interval, adjusting the cold storage or cold release strategy according to the electricity price trend to avoid energy waste, finally improving the electricity utilization efficiency, reducing the cost, and enhancing the flexibility and adaptability of the refrigeration trolley.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power regulation, in particular to a refrigeration control strategy optimization method based on cold storage and electricity price changes. BACKGROUND

[0002] The technical field of power regulation includes related methods and devices for managing power distribution and electricity price regulation in the generation, transmission, and use of electrical energy. The core content includes setting the operation mode of the power load and the adjustment method of the power output, so that electrical energy is distributed according to demand at different times, thereby achieving the rational use of electrical energy during peak and valley periods. It systematically covers power grid power control, user-side energy storage device management, and power regulation strategies based on time or electricity price, forming a complete system with electrical energy allocation as the main line.

[0003] Among them, a refrigeration control strategy optimization method based on cold storage and electricity price changes refers to the use of a small car equipped with energy storage devices and refrigeration functions as a user-side energy carrier to participate in electricity price regulation. For the role of energy storage cars in power demand management, it covers the use of energy storage units to store and release electrical energy, combines the working characteristics of refrigeration devices to regulate power at different electricity price periods, and sets the energy storage charging and discharging time and the refrigeration load operation mode to implement the electricity price regulation method.

[0004] The deficiencies of the prior art in electricity price regulation and electrical energy management mainly manifest in two aspects. First, the prior art cannot make detailed adjustments in response to real-time electricity price changes, and the electricity price regulation is relatively rough, failing to achieve accurate response to electricity price fluctuations, resulting in suboptimal power regulation of energy storage devices and refrigeration systems during electricity price changes. Second, the electricity price segmentation processing in the prior art is simple, and the differentiation and management of valley, flat, and peak segments of the electricity price are not sufficient, making it difficult to reasonably distribute power in different electricity price intervals, which may cause unnecessary excessive energy consumption during the peak period of electrical energy or ineffective energy storage during the low valley electricity price. Due to the lack of dynamic monitoring and response to electricity price trends, the prior art often lags behind in adjusting energy storage strategies, failing to quickly respond to changes in electricity prices, resulting in inefficient energy storage and failure to fully utilize low electricity price periods, or failure to release stored energy in a timely manner when electricity prices rise, causing unnecessary cost increases. SUMMARY

[0005] To achieve the above purpose, the present application adopts the following technical scheme: a refrigeration control strategy optimization method based on cold storage and electricity price changes, comprising the following steps:

[0006] S1: Collect the refrigeration capacity of the energy storage type refrigeration trolley and the temperature difference of the refrigerant at the inlet and outlet, derive the cooling capacity demand and the energy efficiency parameter, calculate the difference between the cooling capacity demand and the refrigeration capacity, obtain the real-time electricity price, and combine the difference and the energy efficiency parameter to normalize, input the linear regression model for fitting and error analysis, and generate an energy efficiency parameter correction result;

[0007] S2: Based on the energy efficiency parameter correction result and the refrigeration capacity, a refrigeration power correction coefficient is analyzed by weighting, and a product calculation is performed with the real-time electricity price, and the electricity price is divided into a valley price area, a flat section and a peak area, and a time-of-use electricity price strategy interval is generated;

[0008] S3: Based on the time-of-use electricity price strategy interval and the refrigeration power correction coefficient, compare multiple electricity price stages in combination with the cold storage capacity threshold, assign cold storage, cold release or stable instructions to multiple stages of the refrigeration trolley, and integrate them into a running strategy instruction set;

[0009] S4: Real-time monitoring of the current energy storage level of the refrigeration trolley, calculation of the electricity price prediction value and analysis of the electricity price trend, when the electricity price shows a downward trend, the cold storage capacity and the refrigeration power correction coefficient are added and the cold storage is enhanced, when the electricity price shows an upward trend, the difference operation is performed and the cold release is enhanced, the cold storage and cold release instructions are combined with the running strategy instruction set to generate an electricity price response control strategy set;

[0010] S5: Based on the electricity price response control strategy set, the instantaneous cooling capacity demand is obtained, when executing the cold storage or cold storage enhancement instruction, if the cold storage capacity parameter does not reach the instantaneous cooling capacity demand, the cold storage power is increased, if the cold storage capacity exceeds the instantaneous cooling capacity demand, the cold release power is slowed down, and an electricity price adjustment optimization result is generated.

[0011] As a further scheme of the present application, the energy efficiency parameter correction result includes the cooling capacity demand difference, the linear regression fitting error, and the energy efficiency parameter correction parameter, the time-of-use electricity price strategy interval includes the valley price area, the electricity price flat section, and the electricity price peak area, the running strategy instruction set includes the cold storage instruction, the cold release instruction, and the stable instruction, the electricity price response control strategy set includes the electricity price prediction value, the cold storage enhancement instruction, and the cold release enhancement instruction, and the electricity price adjustment optimization result includes the instantaneous cooling capacity demand, the cold storage power adjustment, and the cold release power adjustment.

[0012] As a further scheme of the present application, the specific steps of S1 are:

[0013] S101: Collect the refrigeration capacity of the energy storage type refrigeration trolley and the temperature difference of the refrigerant at the inlet and outlet, perform difference operation on the two, and derive the cooling capacity demand and the energy efficiency parameter in combination with the refrigeration cycle thermodynamic formula, then calculate the difference between the cooling capacity demand and the refrigeration capacity to obtain a cooling capacity difference sequence;

[0014] S102: Based on the cooling capacity difference sequence and energy efficiency parameters, normalize both, aggregate them with the real-time electricity price, and input them into a linear regression model to calculate the residuals, thereby obtaining the normalized residual sequence.

[0015] S103: Call the normalized residual sequence, compare the multiple values ​​with the output value of the fitting function item by item, calculate the corresponding error and correct the deviation to obtain the energy efficiency parameter correction result.

[0016] As a further aspect of the present invention, the linear regression model consists of input feature variables, weight coefficients, bias terms, and output predicted values.

[0017] As a further aspect of the present invention, the specific steps of S2 are as follows:

[0018] S201: Based on the energy efficiency parameter correction results, collect the cooling capacity of the energy storage refrigeration vehicle, assign weights to the two values ​​and perform a weighted calculation, and superimpose the weighted power parameter with the cooling capacity output value to generate a cooling power correction coefficient.

[0019] S202: Call the cooling power correction coefficient and the real-time electricity price, multiply the two items one by one, and aggregate all the product results in sequence to obtain the power price sequence;

[0020] S203: Based on the power price sequence, the intervals will be identified and marked according to the division criteria of valley price zone, flat period and peak period. The results of multiple interval marking will be serialized and stored to obtain the time-of-use pricing strategy interval.

[0021] As a further aspect of the present invention, the specific steps of S3 are as follows:

[0022] S301: Based on the time-of-use electricity price strategy range and the cooling power correction coefficient, the values ​​of the two are matched and compared item by item. The power level of the multi-price stage is compared with the cooling capacity threshold. The stage that exceeds the cooling capacity threshold is recorded to obtain the stage threshold determination result.

[0023] S302: Call the stage threshold determination result and combine it with the corresponding electricity price stage. Mark the stage that has not been reached as a cold storage instruction, mark the stage that exceeds the cold storage capacity threshold as a cold release instruction, and mark the stage within the threshold range as a stable instruction, and generate a stage instruction sequence.

[0024] S303: Based on the stage instruction sequence, integrate and serialize the corresponding cold storage, cold release and stabilization instructions for multiple stages to obtain the operating strategy instruction set.

[0025] As a further aspect of the present invention, the cold storage capacity threshold is set by collecting cold storage capacity data of the refrigeration vehicle at multiple operating stages and combining it with the refrigeration power correction coefficient and the time-of-use electricity price strategy range.

[0026] As a further aspect of the present invention, the specific steps of S4 are as follows:

[0027] S401: Monitor the current energy storage level of the refrigeration vehicle in real time, calculate the electricity price forecast based on the cold storage value, and obtain the electricity price trend analysis result by analyzing the changing trend of the electricity price forecast.

[0028] S402: Based on the electricity price trend analysis results, when the electricity price is trending downward, the cold storage capacity and the cooling power correction coefficient are summed and the cold storage capacity is enhanced; when the electricity price is trending upward, the difference between the cold storage capacity and the cooling power correction coefficient is calculated and the cold release is enhanced, thus obtaining the cold storage and cold release adjustment results.

[0029] S403: Based on the cold storage and release adjustment results, and combined with the cold storage and release instructions in the operation strategy instruction set, generate an electricity price response control strategy set.

[0030] As a further aspect of the present invention, the specific steps of S5 are as follows:

[0031] S501: Based on the electricity price response control strategy set, obtain the instantaneous cooling demand data frame, read the time period cooling demand value in the data frame, and match it with the cooling storage capacity parameter to obtain the instantaneous cooling demand value.

[0032] S502: Call the instantaneous cooling demand value. When executing the cooling storage or cooling storage enhancement command, perform differential calculation between the cooling storage parameter and the instantaneous cooling demand value. If the differential result is negative, adjust the cooling storage power parameter by increasing the value. If the differential result is positive, adjust the power adjustment correction value by decreasing the value.

[0033] S503: Based on the power adjustment correction amount and in conjunction with the electricity price response control strategy set, the power parameter fields in the instruction set are corrected and replaced, and the consistency of the cold storage and cold release state fields is maintained during the correction process, generating the electricity price adjustment optimization result.

[0034] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0035] In this invention, by calculating the difference between the cooling demand and cooling capacity of the energy storage refrigeration vehicle, and combining this with the normalization of real-time electricity prices and energy efficiency parameters, the corrected energy efficiency parameters can be accurately fitted. In this way, the system can adjust the cooling power according to real-time electricity price changes, making electricity use more flexible and precise in response to price fluctuations. Based on segmented electricity price analysis, and through precise comparison of different price stages, appropriate cold storage or release commands can be set for the energy storage device, maximizing the energy efficiency of the refrigeration device during peak and off-peak electricity price periods while maintaining stability during price fluctuations. Furthermore, by analyzing electricity price trends and combining cold storage capacity and correction coefficients, cold storage capacity is enhanced when electricity prices decrease, and cold release regulation is strengthened when electricity prices increase. This not only improves the system's response speed but also effectively avoids excessive energy consumption or waste caused by electricity price changes. Ultimately, real-time adjustment of cooling demand and energy storage load improves the overall system's energy utilization efficiency, reduces energy costs, and enhances the flexibility and adaptability of the refrigeration vehicle, forming a more intelligent and efficient electricity price adjustment mechanism. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0037] Figure 1 This is a schematic diagram of the steps of the present invention. Detailed Implementation

[0038] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0039] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0040] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0041] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0042] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0043] Please see Figure 1 This invention provides a method for optimizing a refrigeration control strategy based on cold storage and electricity price changes, comprising the following steps:

[0044] S1: Collect the cooling capacity and the inlet and outlet temperature difference of the refrigerant of the energy storage refrigeration vehicle, derive the cooling demand and energy efficiency parameters, calculate the difference between the cooling demand and the cooling capacity, obtain the real-time electricity price, and combine the difference with the energy efficiency parameters for normalization. Input the data into the linear regression model for fitting and error analysis, and generate the energy efficiency parameter correction results.

[0045] S2: Based on the energy efficiency parameter correction results and cooling capacity, a weighted analysis of the cooling power correction coefficient is performed, and the product is calculated with the real-time electricity price. The electricity price is divided into valley price zone, flat period and peak period to generate time-of-use electricity price strategy interval.

[0046] S3: Based on the time-of-use electricity price strategy range and the cooling power correction coefficient, and combined with the cold storage capacity threshold, compare multiple electricity price stages, assign cold storage, cold release or stabilization commands to multiple stages of the refrigeration vehicle, and integrate them into a set of operation strategy commands.

[0047] S4: Monitor the current energy storage level of the refrigeration vehicle in real time, calculate the predicted electricity price and analyze the electricity price trend. When the electricity price is declining, sum the correction coefficients of the cold storage capacity and the refrigeration power and enhance the cold storage. When the electricity price is rising, perform the difference calculation and enhance the cold release. Combine the cold storage and cold release commands with the set of operation strategy commands to generate a set of electricity price response control strategies.

[0048] S5: Based on the electricity price response control strategy set, obtain the instantaneous cooling demand. When executing the cooling storage or cooling storage enhancement command, if the cooling storage parameter does not meet the instantaneous cooling demand, increase the cooling storage power. If the cooling storage exceeds the instantaneous cooling demand, reduce the cooling release power and generate the electricity price adjustment optimization result.

[0049] The energy efficiency parameter correction results include cooling demand difference, linear regression fitting error, and energy efficiency parameter correction parameters. The time-of-use electricity price strategy range includes the off-peak price range, the flat price range, and the peak price range. The set of operation strategy instructions includes the cooling storage instruction, the cooling release instruction, and the stabilization instruction. The electricity price response control strategy set includes the electricity price forecast, the cooling storage enhancement instruction, and the cooling release enhancement instruction. The electricity price adjustment optimization results include instantaneous cooling demand, cooling storage power adjustment, and cooling release power adjustment.

[0050] The specific steps of S1 are as follows:

[0051] S101: Collect the cooling capacity and refrigerant inlet and outlet temperature difference of the energy storage refrigeration vehicle, perform differential calculation on the two and derive the cooling demand and energy efficiency parameters by combining the thermodynamic formula of the refrigeration cycle, and then calculate the difference between the cooling demand and the cooling capacity to obtain the cooling capacity difference sequence.

[0052] Continuous data acquisition was performed on the cooling capacity and the temperature difference between the refrigerant inlet and outlet of an energy storage refrigeration vehicle. First, PT100 platinum resistance temperature sensors with an accuracy of 0.1 degrees Celsius were installed at the inlet and outlet of the evaporator of the refrigeration vehicle, and a turbine flow meter was used to monitor the refrigerant mass flow rate in real time. In a specific data acquisition cycle, the sensors measured the refrigerant (R134a) temperature at the evaporator inlet to be 1.0 degrees Celsius and the temperature at the outlet to be 6.0 degrees Celsius. The difference between these two values ​​was calculated to obtain a refrigerant inlet and outlet temperature difference of 5.0 degrees Celsius at that moment. Simultaneously, the flow meter measured the refrigerant mass flow rate to be 0.02 kg / s. Next, the physical properties of refrigerant R134a at the corresponding pressure and average temperature (3.5 degrees Celsius) were retrieved, and its specific heat capacity at constant pressure was found to be 1.42 kJ / (kg·Kelvin). The cooling capacity was calculated based on the thermodynamic formula of the refrigeration cycle. The process was as follows: the collected mass flow rate of 0.02 kg / s was multiplied by the obtained specific heat capacity at constant pressure of 1.42 kJ / (kg·Kelvin) and the calculated temperature difference of 5.0 Kelvin, i.e., 0.02 × 1.42 × 5.0, resulting in a cooling capacity of 0.142 kW. Subsequently, the cooling demand under the operating conditions was derived. The outer surface area of ​​the vehicle's box is 4.5 square meters, and the comprehensive heat transfer coefficient of its wall material is experimentally calibrated to be 0.5 W / (m²·Kelvin). The temperature difference between the inside and outside of the box is the difference between the ambient temperature of 25 degrees Celsius and the target temperature inside the box of 4 degrees Celsius, i.e., 21 degrees Celsius. The cooling demand was calculated as follows: the comprehensive heat transfer coefficient of 0.5 W / (m²·Kelvin) was multiplied by the outer surface area of ​​4.5 square meters and the temperature difference of 21 Kelvin, i.e., 0.5 × 4.5 × 21, resulting in a cooling demand of 47.25 W. Meanwhile, the actual input power of the compressor was measured to be 0.06 kW using a power meter. The Coefficient of Performance (COP) was calculated by dividing the actual cooling capacity by the compressor input power. The calculation process was as follows: dividing the previously calculated actual cooling capacity of 0.142 kW by the measured compressor input power of 0.06 kW yielded an COP of approximately 2.37. Finally, the difference between the calculated cooling demand and the actual cooling capacity was calculated: subtracting the cooling demand of 47.25 watts from the actual cooling capacity of 142 watts resulted in a cooling capacity difference of 94.75 watts. This process was repeated continuously at 1-minute intervals to form a cooling capacity difference sequence.

[0053] S102: Based on the cooling capacity difference sequence and energy efficiency parameters, normalize both, aggregate them with the real-time electricity price, and input them into the linear regression model to calculate the residuals, thus obtaining the normalized residual sequence.

[0054] The system retrieves the acquired cooling capacity difference sequence and energy efficiency parameter sequence, and incorporates real-time electricity price data. In one embodiment, the data collection time is set to the off-peak period of the power grid, and the real-time electricity price is 0.80 yuan / kWh. First, normalization is performed on the cooling capacity difference sequence and energy efficiency parameter sequence. This processing uses the min-max normalization method. Based on historical data records, the fluctuation range of the cooling capacity difference sequence is -50 watts to 150 watts, and the fluctuation range of the energy efficiency parameters is 1.80 to 3.00. For the current cooling capacity difference of 94.75 watts, its normalization calculation process is: 94.75 ( 50) / 150 ( 50), yielding a normalized cooling capacity difference of 0.7238. For the current energy efficiency parameter of 2.37, the normalization calculation process is as follows: (2.37) 1.80) / (3.00 1.80), yielding a normalized energy efficiency parameter of 0.4750. Next, numerical aggregation is performed, combining the normalized cooling capacity difference, the normalized energy efficiency parameter, and the real-time electricity price into a comprehensive evaluation index. Aggregation uses a weighted summation method. The weights are set based on a 30-day calibration experiment aimed at determining the impact of each factor on the overall operating cost of the energy storage refrigeration vehicle. The real-time electricity price is also normalized before use, with a daily fluctuation range of 0.40 yuan / kWh to 1.20 yuan / kWh. The normalized value of the current electricity price of 0.80 yuan / kWh is (0.80). 0.40) / (1.20 The input was 0.40), and the result was 0.5. The aggregation calculation process was: 0.5 × 0.7238 + 0.2 × 0.4750 + 0.3 × 0.5, resulting in an aggregated value of 0.6069. Finally, this aggregated value was input into a pre-trained linear regression model, which was constructed based on historical aggregated values ​​and corresponding optimal operating state parameters. For an input of 0.6069, the model's fitted function output was set to 0.5819. Residual calculation was then performed, subtracting the model's fitted value (0.6069) from the actual aggregated value. 0.5819, the residual at that time is 0.0250. This process is repeated for each data point in the sequence to generate a normalized residual sequence.

[0055] S103: Call the normalized residual sequence, compare the multi-valued data with the output value of the fitted function item by item, calculate the corresponding error and correct the deviation to obtain the energy efficiency parameter correction result;

[0056] The generated normalized residual sequence is called, and the values ​​in the sequence are compared item by item with the output value of a preset fitting function, and deviation correction is performed accordingly. First, a deviation judgment threshold needs to be set. The threshold setting is based on statistical analysis of historical residual data from 1000 consecutive sampling periods under fault-free and stable operation. By calculating the standard deviation of these 1000 historical residual values, the standard deviation σ is found to be 0.045. To cover 99.7% of normal fluctuations, the deviation judgment threshold is set to 3 times the standard deviation, i.e., 3 × 0.045 = 0.135. When the absolute value of the normalized residual is greater than 0.135, a significant deviation is judged. Next, the normalized residual sequence [-0.085, 0.045, -0.150, 0.142, 0.090] is compared item by item. The third residual value, -0.150, has an absolute value of 0.150, which is greater than the threshold of 0.135. At this point, calculate the corresponding error, that is, the portion of the residual absolute value that exceeds the threshold: | 0.150 | 0.135 = 0.015. The negative residual exceeds the limit, indicating that the operating performance is worse than the model's prediction. At this point, a correction for the deviation of the energy efficiency parameters is initiated. The calculation of the correction amount is related to the error value and is adjusted through a correction coefficient. The correction coefficient is determined based on a systematic fault injection and performance calibration experiment. By simulating faults of different degrees of performance degradation, the resulting residual errors and the actual degradation values ​​of the energy efficiency parameters are recorded. The average value of the correction coefficients calculated from multiple experiments is taken and set to -4.2. The third data point is corrected, with an error of 0.015. The correction amount for the energy efficiency parameters is calculated as: 0.015 × ( 4.2)= 0.063. If the original calculated energy efficiency parameter at this moment is set to 2.25, then the corrected energy efficiency parameter is 2.25. 0.063 = 2.187. After the above steps, a set of energy efficiency parameter correction results are finally obtained.

[0057] The specific steps of S2 are as follows:

[0058] S201: Based on the energy efficiency parameter correction results, collect the cooling capacity of the energy storage refrigeration vehicle, assign weights to the two values ​​and perform a weighted calculation, and superimpose the weighted power parameter with the cooling capacity output value to generate a cooling power correction coefficient.

[0059] Based on the obtained energy efficiency parameter correction results, and simultaneously collecting the cooling capacity of the energy storage refrigeration vehicle at that moment, weights are assigned to these two values ​​and a weighted calculation is performed. Continuing with the aforementioned embodiment, at a specific data point, the energy efficiency parameter correction result is 2.187, while the corresponding cooling capacity, after sensor collection and calculation, is 0.142 kW. The weighting coefficients are set based on a calibration experiment lasting 100 operating cycles. The experiment aims to quantify the combined impact of energy efficiency parameters and cooling capacity fluctuations on the compressor's target adjustment power. In the experiment, the operating conditions were changed through human intervention, and the target power adjustment range required to maintain stable operation was recorded when both the energy efficiency parameter and cooling capacity demand changed independently by 10%.

[0060] Table 1. Experimental Data for Power Parameter Weighting Calibration

[0061]

[0062] As shown in Table 1, the experimental data reveals that the target power is more sensitive to changes in energy efficiency parameters. The average influence factor of the energy efficiency parameters is obtained by averaging the influence factors of the two sets of experiments. The average influencing factor of cooling capacity is These two influencing factors are normalized to serve as weights, with the energy efficiency parameter having a weight of [weight missing]. The weight of cooling capacity is In this embodiment, for ease of calculation, the energy efficiency parameter weight is set to 0.6, and the cooling capacity weight is set to 0.4. Then, a weighted calculation is performed. The energy efficiency parameter correction result of 2.187 is multiplied by the weight of 0.6 to obtain the weighted energy efficiency parameter. Multiplying the cooling capacity of 0.142 kW by its weight of 0.4, we obtain the weighted cooling capacity as follows: Finally, the weighted energy efficiency parameters are superimposed with the weighted cooling capacity to generate a cooling power correction coefficient. The calculation process is as follows: The cooling power correction factor is a dimensionless value that integrates current operating efficiency and load status. This process is repeated every control cycle (e.g., 1 minute), generating a series of continuous cooling power correction factor values. The advantage of this approach is that by dynamically weighting the energy efficiency parameters, which have been corrected for real-time deviations, with the actual cooling capacity, a comprehensive correction factor that reflects both energy efficiency level and load magnitude is generated. This allows subsequent power regulation to be based not only on a single load or efficiency indicator, but also on a more comprehensive assessment of the operating status.

[0063] S202: Call the cooling power correction coefficient and the real-time electricity price, multiply the two items one by one, and aggregate all the product results to obtain the power price sequence.

[0064] The generated cooling power correction coefficient sequence and the real-time electricity price data introduced in step S102 are used to perform a product calculation on these two data sequences. In a continuously running example, the cooling power correction coefficient sequence [1.369, 1.382, 1.355, 1.410, 1.398] is generated over five consecutive time steps (each step is 1 minute). This sequence is obtained by repeatedly performing all the calculation processes from S101 to S201 on the real-time sensor data for each time step. Within the corresponding five time steps, the real-time electricity price sequence [0.80, 0.80, 1.20, 1.20, 0.80] (unit: yuan / kWh) is obtained by accessing the real-time electricity price data interface provided by the power sector. This reflects the process of the vehicle's operation from off-peak hours to peak hours and back to off-peak hours. Next, the corresponding items in the cooling power correction coefficient sequence and the real-time electricity price sequence are multiplied. The calculation process is performed item by item: Calculation of the first time step: Calculation of the second time step: The calculation of the third time step: Calculation of the fourth time step: Calculation of the fifth time step: The product results obtained from all the item-by-item calculations are aggregated sequentially in chronological order to form a new data sequence. This sequence is the power price sequence. In this embodiment, the generated power price sequence is [1.0952, 1.1056, 1.6260, 1.6920, 1.1184]. Each value in the sequence integrates the overall operating status of the cooling system at that moment (reflected by the cooling power correction coefficient) and the external economic cost (reflected by the real-time electricity price). The higher the value of the sequence, the greater the overall cost pressure of maintaining cooling operation at that moment. The advantage of this approach is that by multiplying the correction coefficient reflecting internal operating conditions with the electricity price reflecting external economic factors, an indicator sequence that can quantify instantaneous operating cost pressure is created, allowing decisions to be made based on a unified, quantified cost dimension.

[0065] S203: Based on the power price sequence, the intervals will be identified and marked according to the division criteria of valley price zone, flat period and peak period. The results of multiple interval marking will be serialized and stored to obtain the time-of-use pricing strategy intervals.

[0066] Based on the generated power price sequence [1.0952, 1.1056, 1.6260, 1.6920, 1.1184] and the corresponding real-time price at each time point [0.80, 0.80, 1.20, 1.20, 0.80], interval discrimination and labeling are performed. This process first requires establishing clear standards for dividing electricity price intervals. The standards are based on the time-of-use pricing policy for commercial electricity issued by the local power department. The standards for dividing electricity price intervals are set as follows: Off-peak zone: Real-time price is below 0.60 yuan / kWh. Flat zone: Real-time price is between 0.60 yuan / kWh (inclusive) and 1.00 yuan / kWh (exclusive). Peak zone: Real-time price is equal to or higher than 1.00 yuan / kWh. Simultaneously, to facilitate subsequent data processing, each electricity price interval is quantitatively labeled. The specific labeling rule is: Off-peak zone is labeled with the integer "1". Flat zone is labeled with the integer "2". The peak range is marked with the integer "3". Next, each price data point in the real-time electricity price sequence is analyzed according to the above criteria and assigned a corresponding range label: the electricity price at the first time point is 0.80 yuan / kWh. According to the standard, The price at the first time point was in a flat range, so it was marked "2". The electricity price at the second time point was 0.80 yuan / kWh. Again, the price was in a flat range, so it was marked "2". The electricity price at the third time point was 1.20 yuan / kWh. According to the standard, The price at the fourth time point is 1.20 yuan / kWh. Again, the price is in the peak range and is marked "3". The price at the fifth time point is 0.80 yuan / kWh. The price is in the flat range and is marked "2". All the marking results for each interval are arranged in their original time order to form a multi-interval marking result sequence. In this embodiment, the sequence is [2, 2, 3, 3, 2]. Finally, this marking sequence is serialized and stored, for example, in a database or memory array, to obtain the final time-of-use pricing strategy interval. This result, in the form of a numerical sequence, intuitively represents the changes in the electricity price environment experienced by the energy storage refrigeration vehicle during the monitoring period. The results show that, within five consecutive time steps, the operating cost pressure of the refrigeration vehicle experienced a change pattern of "flat range-flat range-peak range-peak range-flat range".

[0067] The specific steps for S3 are as follows:

[0068] S301: Based on the time-of-use electricity pricing strategy range and the cooling power correction coefficient, the values ​​of the two are matched and compared item by item. The power level of the multi-price stage is compared with the cooling capacity threshold. The stage that exceeds the cooling capacity threshold is recorded to obtain the stage threshold determination result.

[0069] Based on the generated time-of-use electricity price strategy interval [2, 2, 3, 3, 2] and the cooling power correction coefficient sequence [1.369, 1.382, 1.355, 1.410, 1.398] generated in S201, the corresponding values ​​at each time point in these two sequences are paired. The paired data sets are (2, 1.369), (2, 1.382), (3, 1.355), (3, 1.410), (2, 1.398), where the first element of each tuple is the electricity price interval marker, and the second element is the cooling power correction coefficient. Subsequently, the power levels of the multi-price stage are compared according to the cold storage capacity threshold. The cold storage capacity threshold here is not a single value, but a set of tiered thresholds dynamically set according to different electricity price stages. The setting of the tiered thresholds is based on a 60-day operation experiment of an energy storage refrigeration vehicle, which aims to determine the optimal power state trigger point for initiating cold storage or cold release operations under different electricity price costs.

[0070] Table 2. Experimental Data for Electricity Price Tiering and Cold Storage Capacity Threshold Calibration

[0071]

[0072] As shown in Table 2, the experiment shows that in the off-peak price zone, when the cooling power correction coefficient is below 1.20, there is sufficient margin for efficient cold storage; in the flat price zone, a coefficient below 1.40 indicates stable operation, and large-scale cold storage / release is unnecessary; in the peak price zone, when the coefficient exceeds 1.30, it indicates a significant increase in cost pressure from relying solely on the compressor, and the stored cold capacity should be used preferentially. Therefore, the cold storage capacity thresholds are set as follows: 1.20 for the off-peak price zone (marked 1); 1.40 for the flat price zone (marked 2); and 1.30 for the peak price zone (marked 3). Next, the paired data groups are compared item by item: The first data group (2, 1.369): the electricity price phase is the flat price zone (marked 2), and the corresponding threshold is 1.40. The cooling power correction coefficient of 1.369 is compared with the threshold of 1.40. The power level during this phase did not exceed the cold storage capacity threshold for the corresponding electricity price phase. The second set of data (2, 1.382): The electricity price phase is a flat period (marked 2), with a threshold of 1.40. Comparison This stage did not exceed the threshold. The third set of data (3, 1.355): The electricity price stage is in the peak region (marked 3), with a corresponding threshold of 1.30. Comparison The power level of this phase exceeded the cold storage capacity threshold for that electricity price phase. Record this phase. Fourth set of data (3, 1.410): The electricity price phase is in the peak region (marked 3), with a threshold of 1.30. Compare. This stage exceeds the threshold. Recording stage. Fifth data set (2, 1.398): Electricity price stage is in the flat range (marked 2), threshold is 1.40. Comparison The stage did not exceed the threshold. In the above comparison operation, all stages where the power level exceeded the corresponding electricity price stage cold storage capacity threshold are recorded and output in the form of a binary sequence. Exceeding the threshold is recorded as "1", and not exceeding it is recorded as "0". The stage threshold determination result is [0, 0, 1, 1, 0].

[0073] S302: Call the stage threshold determination result, and combine it with the corresponding electricity price stage. Mark the stage that has not been reached as a cold storage instruction, mark the stage that exceeds the cold storage capacity threshold as a cold release instruction, and mark the stage within the threshold range as a stable instruction, and generate a stage instruction sequence.

[0074] The generated stage threshold determination result [0, 0, 1, 1, 0] is invoked, and combined with the corresponding electricity price stage marker sequence [2, 2, 3, 3, 2], to generate a corresponding operation instruction for each stage. The instruction generation follows a set of preset rules, which logically combine the stage threshold determination result with the electricity price stage. The instruction generation rules are defined as follows: 1. When the stage threshold determination result of a stage is "1" (i.e., the power level exceeds the threshold), regardless of the electricity price stage, it will be marked as a "cooling release instruction". The corresponding numerical marker for this instruction is "2". 2. When the stage threshold determination result of a stage is "0" (i.e., the power level has not reached the threshold), and the corresponding electricity price stage is a valley price zone (marked 1), it will be marked as a "cooling storage instruction". The corresponding numerical marker for this instruction is "1". 3. When the stage threshold determination result of a stage is "0", and the corresponding electricity price stage is a flat zone (marked 2) or a peak zone (marked 3), it will be marked as a "stable instruction". The corresponding numerical marker for this instruction is "3". Based on the above rules, each stage in the sequence is processed item by item: First stage: The stage threshold determination result is "0", and the electricity price stage is in a flat range (marked 2). This meets the conditions of rule 3. Therefore, the stage is marked as "stable instruction" and the value is marked as "3". Second stage: The stage threshold determination result is "0", and the electricity price stage is in a flat range (marked 2), also meeting rule 3. The stage is marked as "stable instruction" and the value is marked as "3". Third stage: The stage threshold determination result is "1", and the electricity price stage is in a peak range (marked 3). This meets the conditions of rule 1. Therefore, the stage is marked as "cooling instruction" and the value is marked as "2". Fourth stage: The stage threshold determination result is "1", and the electricity price stage is in a peak range (marked 3), also meeting rule 1. The stage is marked as "cooling instruction" and the value is marked as "2". Fifth stage: The stage threshold determination result is "0", and the electricity price stage is in a flat range (marked 2), meeting rule 3. The stage is marked as a "stable instruction" and its numerical value is "3". By performing the above marking process on all stages, the numerical values ​​of the generated stage instructions are arranged in chronological order to generate a stage instruction sequence. In this embodiment, the obtained stage instruction sequence is [3, 3, 2, 2, 3]. Each number in this sequence represents a specific operating instruction that combines economy and state.

[0075] S303: Based on the stage instruction sequence, integrate and serialize the corresponding cold storage, cold release and stabilization instructions for multiple stages to obtain the set of operation strategy instructions;

[0076] Based on the generated stage instruction sequence [3, 3, 2, 2, 3], the cold storage, cold release, and stabilization instructions corresponding to each stage in the sequence are sequentially integrated and serialized to obtain the final set of operating strategy instructions. This process is the final encapsulation of the aforementioned calculation and judgment results, becoming a set of commands that can be directly executed by the energy storage refrigeration vehicle control system. The integration process associates the numerical markers in the sequence with their physical meanings and corresponding execution actions. Value 1 associated with the cold storage instruction: When the control system executes this instruction, it will drive the refrigeration compressor to operate at a power higher than the current basic refrigeration demand, and store the excess cold energy in the phase change energy storage material (PCM) carried by the vehicle through forced circulation until the energy storage material is completely solidified or reaches the preset cold storage termination temperature. Value 2 associated with the cold release instruction: When the control system executes this instruction, it will stop or significantly reduce the operating power of the refrigeration compressor, and at the same time start the circulation pump of the energy storage system, flowing the low-temperature refrigerant through the solidified phase change energy storage material, absorbing the stored cold energy, and then sending it to the evaporator inside the vehicle to maintain the temperature inside the container. Value 3 associated with a stable command: When this command is executed, the compressor operates according to standard temperature control logic, that is, based on feedback from the internal temperature sensor, it starts / stops or adjusts the frequency accordingly. The power output is only used to offset the current heat load, without performing additional cold storage or active cold release operations. The serialization process converts the integrated command sequence into a standardized data format for easy storage, transmission, and parsing. In this embodiment, the stage command sequence [3, 3, 2, 2, 3] is appended with timestamp information and a device identifier to form a structured data object. For example, for a sequence with a start time of 14:01 and a time step of 1 minute, the serialization result can be represented as a JSON array: {timestamp: 2025-09-29, T14:01:00Z, command-code: 3, command-desc: stationary instruction}, {timestamp: 2025-09-29, T14:02:00Z, command-code: 3, command-desc: stationary instruction}, {timestamp: 2025-09-29T14 The data structure {{timestamp: 2025-09-29T14:04:00Z, command-code: 2, command-desc: release instruction}, {timestamp: 2025-09-29T14:05:00Z, command-code: 3, command-desc: stable instruction} after integration and serialization is the final set of execution strategy instructions.The instruction set is sent to the vehicle's local controller. The controller parses the instruction set and, based on the instruction code corresponding to each timestamp, precisely controls the start-up, shutdown, and operating power of the compressor, circulating pump, and other actuators.

[0077] The specific steps of S4 are as follows:

[0078] S401: Monitor the current energy storage level of the refrigeration vehicle in real time, calculate the electricity price forecast based on the cold storage value, and obtain the electricity price trend analysis result by analyzing the changing trend of the electricity price forecast.

[0079] The real-time cooling capacity of the phase change material (PCM) inside the energy storage refrigeration vehicle is obtained. The cooling capacity is measured by three T-type thermocouple temperature sensors evenly arranged inside the energy storage module. The controller collects the average temperature of these three points and retrieves the current percentage of cooling capacity based on the pre-calibrated temperature-state of charge (SoC) relationship curve of the PCM material. In a specific embodiment, the measured average temperature of the PCM is -2 degrees Celsius, and after consulting the calibration curve, the corresponding cooling capacity is 75%. Subsequently, the electricity price prediction for the next 30 minutes is calculated based on this cooling capacity value. The calculation of the electricity price prediction calls a prediction model based on historical data. The model stores the electricity price for the same time period (at 10-minute intervals) for each day of the past year and categorizes it according to the day of the week (weekday / weekend). The model first extracts the historical average electricity price for the next three 10-minute time periods. For example, for Monday 14:10-14:40, the historical average electricity price sequence is [1.20, 1.15, 0.80] yuan / kWh. Then, the baseline forecast is corrected using the current cooling capacity. The correction logic is: when the cooling capacity is higher, the tolerance for high electricity prices decreases, and vice versa. The formula for calculating the correction factor is as follows: Substituting 75% of the current cold storage capacity, the correction factor is calculated as follows: Multiply this correction factor by the base electricity price forecast series to obtain the final electricity price forecast series: The values ​​are [1.05, 1.006, 0.70]. Finally, the trend of the electricity price forecast series is analyzed to obtain the electricity price trend analysis results. The analysis process involves calculating the slope of the linear regression of the series. With the time step (0, 1, 2) as the x-axis and the electricity price forecast value as the y-axis, the slope is calculated to be -0.175. A trend judgment threshold is set: a slope less than -0.05 is defined as a downward trend, a slope greater than 0.05 is defined as an upward trend, and a slope in between is defined as a stable trend. Since -0.175 is less than -0.05, the electricity price trend analysis result is determined to be a "downward trend".

[0080] S402: Based on the electricity price trend analysis results, when the electricity price is trending downward, the cold storage capacity and cooling power correction coefficients are summed and the cold storage capacity is enhanced; when the electricity price is trending upward, the difference between the cold storage capacity and cooling power correction coefficients is calculated and the cold release is enhanced, thus obtaining the cold storage and cold release adjustment results.

[0081] Based on the electricity price trend analysis results ("downward trend"), corresponding cold storage or cold release adjustment calculations are performed. The adjustment rules are as follows: when the electricity price is trending downward, an addition operation is performed; when the electricity price is trending upward, a difference operation is performed. In this embodiment, since the electricity price trend is downward, an addition operation is performed on the cold storage capacity and the cooling power correction coefficient. First, the current cold storage capacity obtained in S401 is called, which is 75%, represented as a normalized value of 0.75 in the calculation. At the same time, the cooling power correction coefficient calculated in S201 at this time point is called, which has a value of 1.398. The specific process of the addition operation is as follows: the normalized cold storage capacity of 0.75 is added to the cooling power correction coefficient of 1.398. Calculation The result is 2.148. This result serves as an adjustment signal to enhance cold storage. In contrast, in another scenario, if the electricity price trend analysis shows an "upward trend," the difference between the cold storage capacity and the cooling power correction factor will be calculated. The calculation process is as follows: subtract the cooling power correction factor of 1.398 from the normalized cold storage capacity of 0.75. The result is -0.648. A negative result serves as an adjustment signal to enhance cold release. In the "descending trend" scenario of this embodiment, the final cold storage-cold release adjustment result is a positive value of 2.148. The absolute value of this result is positively correlated with the enhancement strength of the command, and the sign determines the adjustment direction (positive for cold storage, negative for cold release).

[0082] S403: Based on the results of cold storage and release adjustment, and combined with the cold storage and release instructions in the operation strategy instruction set, generate a set of electricity price response control strategies;

[0083] Based on the generated cold storage and release adjustment result 2.148, and combined with the set of operating strategy instructions generated in S303 (the core being the stage instruction sequence [3, 3, 2, 2, 3], corresponding to "stable", "stable", "release", "release", and "stable" instructions respectively), the final electricity price response control strategy set is generated. This process is implemented through a set of preset instruction overwrite logic, which modifies the original instruction set based on the numerical range of the cold storage and release adjustment result. The threshold setting of the instruction overwrite rule is based on the statistical analysis of the numerical distribution of adjustment results over 1000 historical scheduling cycles, selecting the 85th percentile (1.50) and the 15th percentile (-0.50) as the boundaries of the strong adjustment range. The rule is as follows: if the adjustment result is greater than 1.50, the "stable" (marked 3) instruction in the original instruction set is modified to "cold storage" (marked 1), and the "release" (marked 2) instruction is modified to "stable" (marked 3). If the adjustment result is less than -0.50, then "Stable" (marked 3) is changed to "Cold Release" (marked 2), and "Cold Storage" (marked 1) is changed to "Stable" (marked 3). If the adjustment result is between -0.50 and 1.50, then the original instruction set remains unchanged. The current cold storage and cold release adjustment result is 2.148, which is greater than 1.50, triggering the strong cold storage correction logic. The original stage instruction sequence [3, 3, 2, 2, 3] is corrected item by item: the first instruction "3" is changed to "1"; the second instruction "3" is changed to "1"; the third instruction "2" is changed to "3"; the fourth instruction "2" is changed to "3"; and the fifth instruction "3" is changed to "1". The corrected new instruction sequence is [1, 1, 3, 3, 1]. Finally, this new instruction sequence is serialized to form the final electricity price response control strategy set. The instruction set is stored and issued in a structured data format that includes timestamps and device identifiers. The instruction set content is to execute the "cooling storage", "cooling storage", "stabilization", "stabilization", and "cooling storage" operations sequentially over the next five time steps.

[0084] The specific steps of S5 are as follows:

[0085] S501: Based on the electricity price response control strategy set, acquire instantaneous cooling demand data frames, read the time period cooling demand values ​​in the data frames, and match them with the cooling storage capacity parameters to obtain instantaneous cooling demand values.

[0086] Based on the generated electricity price response control strategy set, the instruction set is [1, 1, 3, 3, 1], corresponding to the instructions "Cold Storage", "Cold Storage", "Stable", "Stable", and "Cold Storage". At the moment the first instruction, "Cold Storage", is executed, an instantaneous cooling demand data frame is acquired. This data frame is a structured data set generated in real-time by multiple sensors on the vehicle, containing: the external ambient temperature of the enclosure (25.0 degrees Celsius), the average internal temperature of the enclosure (4.2 degrees Celsius), and the number of times the enclosure door was opened in the past acquisition cycle (1 minute). Subsequently, the time-period cooling demand value in the data frame is read and calculated. The calculation of the time-period cooling demand consists of two parts: one is the steady-state heat transfer leakage from the enclosure wall, and the other is the instantaneous heat intrusion caused by door opening. The calculation of the steady-state heat transfer leakage uses the enclosure external surface area of ​​4.5 square meters and the comprehensive heat transfer coefficient of 0.5 W / (m²·Kelvin), which have been calibrated in S101. The calculation process is as follows: The overall heat transfer coefficient is 0.5, the external surface area is 4.5, and the temperature difference between the inside and outside of the box is... Multiplying the three (degrees Celsius, etc.) together, we get the steady-state cooling leakage as follows: The instantaneous intrusion heat load was calculated based on a door-opening heat load calibration experiment. Under standard environmental conditions (25°C, 60% relative humidity), the experiment calibrated the average intrusion heat load of a single door opening operation to be 3000 joules by measuring the additional cooling capacity required to restore the internal temperature to 4.0°C after a single door opening (lasting 5 seconds). Dividing the heat load by the sampling period of 60 seconds yielded the equivalent instantaneous intrusion power. Watts. Adding the steady-state cooling leakage to the instantaneous intrusion power yields the cooling demand value for the given period. Watts. Next, the cooling demand value for this period will be numerically matched with the cooling capacity parameter. The cooling capacity parameter refers to the available cooling capacity within the energy storage material, expressed in energy form (unit: kilowatt-hours). This parameter is obtained by multiplying the cooling capacity percentage (75%) obtained in S401 by the total cooling capacity of the energy storage material. The total cooling capacity of the energy storage material, determined by laboratory calorimetry, is 0.5 kilowatt-hours. Therefore, the current value of the cooling capacity parameter is... Kilowatt-hours. The numerical matching process involves recording these two parameters (power and energy) from different physical dimensions side-by-side to provide input for subsequent power regulation calculations. Ultimately, the instantaneous cooling demand at that moment is determined to be 96.8 watts.

[0087] S502: Call the instantaneous cooling demand value. When executing the cooling storage or cooling storage enhancement command, the cooling storage parameter and the instantaneous cooling demand value are calculated by difference. If the difference result is negative, the cooling storage power parameter is adjusted by increase. If the difference result is positive, the power adjustment is adjusted by decrease to obtain the power adjustment correction amount.

[0088] The calculated instantaneous cooling demand value is 96.8 watts. According to the electricity price response control strategy set [1, 1, 3, 3, 1], the currently executed instruction is the first instruction, "Cold Storage" (marked 1). When the Cold Storage instruction is executed, the differential calculation process is initiated. The process differentially calculates a "baseline Cold Storage Power" parameter with the instantaneous cooling demand value. The baseline Cold Storage Power parameter is a preset value representing the standard power expected to be used to charge the energy storage material with cooling capacity in Cold Storage mode. This value is set based on performance testing of the energy storage material. The test shows that the average charging power required to charge the energy storage material from 0% to 100% (i.e., storing 0.5 kWh of cooling capacity) within 2 hours is: That is, 250 watts. To avoid excessive losses and retain adjustment margin, the baseline cooling power is set to 60% of this value, i.e. The differential calculation process is as follows: subtract the instantaneous cooling capacity requirement of 96.8 watts from the baseline cooling storage power of 150 watts. The calculation process is as follows: The difference result is positive, 53.2. According to the preset adjustment rules, if the difference result is positive, the cooling power parameter is adjusted by decreasing the magnitude; if the difference result is negative, it is adjusted by increasing the magnitude. The positive result of 53.2 here indicates that after meeting the instantaneous cooling demand, the power used for cooling storage (150 watts) still has a surplus, and the total output power of the compressor can be appropriately reduced. The adjustment amount is calculated by multiplying the difference result by an adjustment coefficient. The setting of this adjustment coefficient comes from a dynamic response tuning experiment. By applying small power disturbances under different loads and observing the changes in the energy efficiency ratio, a coefficient that can achieve smooth convergence was finally determined and set to -0.1. The calculation process of the power adjustment correction is: multiply the difference result 53.2 by the adjustment coefficient -0.1, i.e. Watts. This negative value represents the reduction adjustment to the current total cooling power. Ultimately, the power adjustment correction at this moment is -5.32 watts.

[0089] S503: Based on the power adjustment correction amount and combined with the electricity price response control strategy set, the power parameter fields in the instruction set are corrected and replaced, and the consistency of the cold storage and cold release status fields is maintained during the correction process to generate the electricity price adjustment optimization result.

[0090] Based on the calculated power adjustment correction of -5.32 watts, and combined with the generated electricity price response control strategy set, the power parameter fields in the instruction set are corrected and replaced. First, an initial power parameter needs to be set for each instruction in the electricity price response control strategy set. The initial power parameter is calculated according to the instruction type: for a cooling storage instruction (marked 1), the initial power is the sum of the instantaneous cooling demand and the baseline cooling storage power; for a stable instruction (marked 3), the initial power equals the instantaneous cooling demand; for a cooling release instruction (marked 2), the initial power is the base power to maintain the operation of the fan and water pump, set to 15 watts. The first instruction being executed is cooling storage (marked 1), with an instantaneous cooling demand of 96.8 watts and a baseline cooling storage power of 150 watts, therefore the initial power parameter is... Watts. Next, the initial power parameter is corrected using a power adjustment correction. The correction process is as follows: add the initial power parameter of 246.8 watts to the power adjustment correction of -5.32 watts, and calculate... The result is the corrected power parameter. Subsequently, in the corresponding entry of the electricity price response control strategy set, the original initial power parameter of 246.8 watts is replaced with the corrected power parameter of 241.48 watts. During this correction and replacement process, the consistency of the cold storage and release status fields is maintained, that is, the status flag 1 (cold storage) of the instruction does not change, and only the associated power value is fine-tuned. The generated electricity price adjustment optimization result is a more refined instruction set, which not only specifies the operating mode (cold storage, release, or steady) for each time step, but also gives the optimal compressor target output power for that mode. For example, for the first time step, the optimized instruction is updated from {command-code: 1} to {command-code: 1, power-target: 241.48} (unit: watts). This process will be repeated for each instruction in the instruction set at the execution time, thereby achieving dynamic and refined optimization of the power output for the entire scheduling cycle.

[0091] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing refrigeration control strategies based on cold energy storage and electricity price changes, characterized in that, Includes the following steps: S1: Collect the cooling capacity and the inlet and outlet temperature difference of the refrigerant of the energy storage refrigeration vehicle, derive the cooling demand and energy efficiency parameters, calculate the difference between the cooling demand and the cooling capacity, obtain the real-time electricity price, and combine the difference with the energy efficiency parameters for normalization. Input the data into the linear regression model for fitting and error analysis, and generate the energy efficiency parameter correction results. S2: Based on the energy efficiency parameter correction results and cooling capacity, perform weighted analysis of cooling power correction coefficient, multiply it with real-time electricity price, and divide the electricity price into valley price zone, flat period and peak period to generate time-of-use electricity price strategy interval; S3: Based on the time-of-use electricity price strategy range and the cooling power correction coefficient, and combined with the cold storage capacity threshold, compare multiple electricity price stages, assign cold storage, cold release or stabilization commands to multiple stages of the refrigeration vehicle, and integrate them into a set of operation strategy commands. S4: Monitor the current energy storage level of the refrigeration vehicle in real time, calculate the predicted electricity price and analyze the electricity price trend. When the electricity price is trending downward, sum the cold storage capacity and refrigeration power correction coefficients and enhance cold storage. When the electricity price is trending upward, perform difference calculation and enhance cold release. Combine the cold storage and cold release commands with the set of operating strategy commands to generate an electricity price response control strategy set. The specific steps of S4 are as follows: S401: Monitor the current energy storage level of the refrigeration vehicle in real time, calculate the electricity price forecast based on the cold storage value, and obtain the electricity price trend analysis result by analyzing the changing trend of the electricity price forecast. S402: Based on the electricity price trend analysis results, when the electricity price is trending downward, the cold storage capacity and the cooling power correction coefficient are summed and the cold storage capacity is enhanced; when the electricity price is trending upward, the difference between the cold storage capacity and the cooling power correction coefficient is calculated and the cold release is enhanced, thus obtaining the cold storage and cold release adjustment results. S403: Based on the cold storage and release adjustment results, and combined with the cold storage and release instructions in the operation strategy instruction set, generate a set of electricity price response control strategies; S5: Based on the electricity price response control strategy set, obtain the instantaneous cooling demand. When executing the cooling storage or cooling storage enhancement command, if the cooling storage parameter does not meet the instantaneous cooling demand, increase the cooling storage power. If the cooling storage exceeds the instantaneous cooling demand, reduce the cooling release power and generate the electricity price adjustment optimization result. The results of the electricity price adjustment optimization include instantaneous cooling demand, cooling storage power adjustment, and cooling release power adjustment.

2. The refrigeration control strategy optimization method based on cold storage and electricity price changes according to claim 1, characterized in that, The energy efficiency parameter correction results include cooling demand difference, linear regression fitting error, and energy efficiency parameter correction parameters. The time-of-use electricity price strategy range includes off-peak price range, flat price range, and peak price range. The set of operation strategy instructions includes cooling storage instructions, cooling release instructions, and stabilization instructions. The set of electricity price response control strategies includes electricity price forecast, cooling storage enhancement instructions, and cooling release enhancement instructions.

3. The refrigeration control strategy optimization method based on cold storage and electricity price changes according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Collect the cooling capacity and refrigerant inlet and outlet temperature difference of the energy storage refrigeration vehicle, perform differential calculation on the two and derive the cooling demand and energy efficiency parameters by combining the thermodynamic formula of the refrigeration cycle, and then calculate the difference between the cooling demand and the cooling capacity to obtain the cooling capacity difference sequence. S102: Based on the cooling capacity difference sequence and energy efficiency parameters, normalize both, aggregate them with the real-time electricity price, and input them into a linear regression model to calculate the residuals, thereby obtaining the normalized residual sequence. S103: Call the normalized residual sequence, compare the numerical values ​​with the output values ​​of the fitting function item by item, calculate the corresponding errors and correct the deviation to obtain the energy efficiency parameter correction results.

4. The refrigeration control strategy optimization method based on cold storage and electricity price changes according to claim 3, characterized in that, The linear regression model consists of input feature variables, weight coefficients, bias terms, and output predicted values.

5. The method for optimizing refrigeration control strategy based on cold storage and electricity price changes according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the energy efficiency parameter correction results, collect the cooling capacity of the energy storage refrigeration vehicle, assign weights to the two values ​​and perform a weighted calculation, and superimpose the weighted power parameter with the cooling capacity output value to generate a cooling power correction coefficient. S202: Call the cooling power correction coefficient and the real-time electricity price, multiply the two items one by one, and aggregate all the product results in sequence to obtain the power price sequence; S203: Based on the power price sequence, the intervals will be identified and marked according to the division criteria of valley price zone, flat period and peak period. The results of multiple interval marking will be serialized and stored to obtain the time-of-use pricing strategy interval.

6. The method for optimizing refrigeration control strategy based on cold storage and electricity price changes according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the time-of-use electricity price strategy range and the cooling power correction coefficient, the values ​​of the two are matched and compared item by item. The power level of the multi-price stage is compared with the cooling capacity threshold. The stage that exceeds the cooling capacity threshold is recorded to obtain the stage threshold determination result. S302: Call the stage threshold determination result and combine it with the corresponding electricity price stage. Mark the stage that has not been reached as a cold storage instruction, mark the stage that exceeds the cold storage capacity threshold as a cold release instruction, and mark the stage within the threshold range as a stable instruction, and generate a stage instruction sequence. S303: Based on the stage instruction sequence, integrate and serialize the corresponding cold storage, cold release and stabilization instructions for multiple stages to obtain the operating strategy instruction set.

7. The method for optimizing refrigeration control strategy based on cold storage and electricity price changes according to claim 6, characterized in that, The cold storage capacity threshold is set by collecting cold storage capacity data of the refrigeration vehicle at multiple operating stages and combining it with the refrigeration power correction coefficient and the time-of-use electricity price strategy range.

8. The method for optimizing refrigeration control strategy based on cold storage and electricity price changes according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the electricity price response control strategy set, obtain the instantaneous cooling demand data frame, read the time period cooling demand value in the data frame, and match it with the cooling storage capacity parameter to obtain the instantaneous cooling demand value. S502: Call the instantaneous cooling demand value. When executing the cooling storage or cooling storage enhancement command, perform differential calculation between the cooling storage parameter and the instantaneous cooling demand value. If the differential result is negative, adjust the cooling storage power parameter by increasing the value. If the differential result is positive, adjust the power adjustment correction value by decreasing the value. S503: Based on the power adjustment correction amount and in conjunction with the electricity price response control strategy set, the power parameter fields in the instruction set are corrected and replaced, and the consistency of the cold storage and cold release state fields is maintained during the correction process, generating the electricity price adjustment optimization result.

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