Air conditioner coupling energy storage system based on multi-sensor fusion and control method

Abstract

This invention provides an air conditioning coupled energy storage system and control method based on multi-sensor fusion, belonging to the field of power system technology. Specifically, it includes: determining the historical adjustment matching data of the optimized control weather type using an operation control method; determining the matching adjustment strategy for the optimized control weather type based on the adjustment matching data and monitoring data from multiple sensors; determining the variation data of energy storage adjustment deviation periods within different temperature control intervals under different optimized control weather types based on the matching adjustment strategy under different optimized control weather types; and using the variation data to define an update management method for the optimized control weather type. By adjusting the air conditioning in advance, the temperature control requirements during the energy storage adjustment process are reduced, and the adaptability of temperature adjustment is improved.

Description

Technical Field

[0001] This invention belongs to the field of power system technology, and in particular relates to an air conditioning coupled energy storage system and control method based on multi-sensor fusion. Background Technology

[0002] Temperature anomalies are inevitable during the operation of energy storage power stations. Therefore, air conditioning systems are needed to regulate the temperature of the energy storage system to ensure its operational reliability. A similar technical solution is given in invention patent application CN202011369177.0 "Containerized Energy Storage Device", but it has the following technical problems: During energy storage regulation, overheating often occurs, forcing air conditioning systems to face both temperature regulation and energy storage regulation needs. This significantly limits the energy storage regulation capacity of energy storage systems. Consequently, determining advance control methods for air conditioning systems under different weather conditions by combining electricity price data and energy storage regulation data, and implementing temperature control in advance under low electricity prices, reduces the temperature control requirements during energy storage regulation periods and improves the adaptability of energy storage regulation, has become an urgent technical problem to be solved.

[0003] Therefore, there is an urgent need for an air conditioning coupled energy storage system and control method based on multi-sensor fusion. Summary of the Invention

[0004] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a control method, which includes: S1 uses the adjustment data of the energy storage system to determine the response matching data of the energy storage power of the energy storage system under the weather type. The response matching data is used to determine the identification method of the optimal control weather type of the air conditioning device of the energy storage system. Based on the optimal control weather type data and the degree of overlap between the energy storage adjustment deviation period in the optimal control weather type and the energy storage adjustment deviation period of the energy storage system, the operation control method of the air conditioning device of the optimal control weather type is determined. S2 uses the operation control method to determine the adjustment matching data of the optimized control weather type in history, and determines the matching adjustment strategy of the optimized control weather type based on the adjustment matching data and the monitoring data of multi-source sensors. S3, based on the matching adjustment strategy under different optimal control weather types, determines the variation data of energy storage adjustment deviation periods within different temperature control ranges under different optimal control weather types, and uses the variation data to determine the update management method for the optimal control weather type.

[0005] The beneficial effects of this invention are as follows: Based on optimized control weather type data and the degree of overlap between the energy storage regulation deviation period in the optimized control weather type and the energy storage regulation deviation period of the energy storage system, the operation control method of the air conditioning unit under the optimized control weather type is determined. According to the quantitative characteristics of the identified optimized control weather types and the degree of overlap between their deviation periods and the overall deviation periods of the system, a multi-level, multi-condition dynamic decision tree is constructed to adaptively determine the specific operation control strategy of the air conditioning unit under different optimized control weather types. Thus, while meeting the improvement needs of the energy storage regulation deviation period, the corresponding electricity price threshold for advance temperature control is adjusted, thereby improving economic benefits and achieving the optimal balance between improving the regulation capacity of the energy storage system and effectively controlling the energy efficiency of air conditioning operation.

[0006] Based on the adjustment matching data and monitoring data from multiple sources of sensors, the matching adjustment strategy for the optimized control weather type is determined. According to the distribution characteristics of abnormal temperature periods in the historical operation data under the optimized control weather type and the statistical data of air conditioning adjustment frequency under different electricity prices, the optimal electricity price start threshold of the air conditioning unit under each weather type is dynamically determined. This is to avoid the situation where frequent temperature adjustment has a significant impact on the energy consumption and service life of the air conditioning system while ensuring the normal battery temperature. It balances the adjustment effect and the reliability of equipment operation, and avoids the shortened lifespan or increased energy consumption of the air conditioning unit due to excessively frequent adjustment.

[0007] Furthermore, the regulation data of the energy storage system includes the energy storage regulation period of the energy storage system under the weather type, and the degree of matching between the output power of the energy storage system and the energy storage regulation demand power during the energy storage regulation period.

[0008] Furthermore, the response matching data of the energy storage power under the weather type is determined based on whether the energy storage power under the weather type can adapt to the energy storage regulation demand power.

[0009] Furthermore, the method for determining the weather type identification method for the optimized control of the air conditioning unit of the energy storage system is as follows: Based on the response matching data of the energy storage power of the energy storage system under the weather type, determine the degree of matching between the energy storage power and the energy storage regulation demand power of the energy storage system during different energy storage regulation periods under the weather type; Based on the degree of matching, the matching deviation adjustment period in the energy storage adjustment period is determined; By utilizing matching deviation adjustment time period data under different weather types, a method for identifying the optimal control weather type for the air conditioning unit of the energy storage system is determined.

[0010] Furthermore, by utilizing matching deviation adjustment period data under different weather types, a method for identifying the optimal control weather type for the air conditioning unit of the energy storage system is determined, specifically including: If there are matching deviation adjustment periods under different weather types, then the weather type identification method for the optimized control of the air conditioning unit of the energy storage system is determined to be the preset identification method.

[0011] Furthermore, the preset identification method is to determine the weather type as an optimized control weather type if the proportion of time in the weather type where the electricity price is less than a preset electricity price threshold is greater than a preset duration proportion threshold.

[0012] Furthermore, the method for determining the optimized control weather type update management method is as follows: Using the aforementioned variation data, the duration of energy storage regulation deviation periods on different dates within the optimized controlled weather type is determined; The improvement coefficient for the energy storage adjustment deviation period on a given date is determined by the ratio of the duration of the energy storage adjustment deviation period on different dates to the average duration of the energy storage adjustment deviation period in the optimized control weather type. The mean improvement coefficient for the optimized control weather type is determined based on the average improvement coefficient of the energy storage regulation deviation period on different dates. Based on the mean improvement coefficient in different optimized control weather types, the update management method for the optimized control weather type is determined.

[0013] Furthermore, if the average improvement demand in different optimized control weather types is greater than the preset improvement demand threshold, then in order to ensure the improvement effect of the energy storage adjustment deviation period, the updated management method for the optimized control weather type is determined to be that if there are no dates in the future target duration where the number of energy storage adjustment periods of the air conditioning system meets the requirements, then the weather type is determined to no longer belong to the optimized control weather type.

[0014] Secondly, the present invention provides an air conditioning coupled energy storage system based on multi-sensor fusion, employing one of the control methods described above, specifically including: The system includes a runtime control module, an adjustment strategy determination module, and an update management module. The operation control module is responsible for determining the operation control method of the air conditioning unit for optimizing the control of the weather type. The adjustment strategy determination module is responsible for determining the matching adjustment strategy for the optimized control weather type. The update management module is responsible for determining the update management method for the optimized control weather type.

[0015] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0018] Figure 1 It is a flowchart of a control method; Figure 2 This is a flowchart illustrating the method for determining the weather type identification method for the optimized control of the air conditioning unit in an energy storage system; Figure 3 This is a flowchart illustrating the method for determining the operation control method of an air conditioning unit that optimizes weather type control; Figure 4 This is a flowchart illustrating the method for determining the matching control strategy for optimizing weather types. Detailed Implementation

[0019] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0020] Example 1 like Figure 1 As shown, this application provides a control method, specifically including: S1 uses the adjustment data of the energy storage system to determine the response matching data of the energy storage power of the energy storage system under the weather type. The response matching data is used to determine the identification method of the optimal control weather type of the air conditioning device of the energy storage system. Based on the optimal control weather type data and the degree of overlap between the energy storage adjustment deviation period in the optimal control weather type and the energy storage adjustment deviation period of the energy storage system, the operation control method of the air conditioning device of the optimal control weather type is determined. S2 uses the operation control method to determine the adjustment matching data of the optimized control weather type in history, and determines the matching adjustment strategy of the optimized control weather type based on the adjustment matching data and the monitoring data of multi-source sensors. S3, based on the matching adjustment strategy under different optimal control weather types, determines the variation data of energy storage adjustment deviation periods within different temperature control ranges under different optimal control weather types, and uses the variation data to determine the update management method for the optimal control weather type.

[0021] Furthermore, the regulation data of the energy storage system includes the energy storage regulation period of the energy storage system under the weather type, and the degree of matching between the output power of the energy storage system and the energy storage regulation demand power during the energy storage regulation period.

[0022] Furthermore, the response matching data of the energy storage power under the weather type is determined based on whether the energy storage power under the weather type can adapt to the energy storage regulation demand power.

[0023] Specifically, such as Figure 2 As shown, the method for determining the weather type identification method for the optimized control of the air conditioning unit of the energy storage system is as follows: Based on the power response matching of the energy storage system under different weather conditions, the weather types that require optimized control of the air conditioning unit are dynamically identified. This ensures the energy storage system's regulation capabilities while also incorporating electricity price data to improve the reliability and economy of the energy storage system's regulation.

[0024] First, based on three meteorological parameters—temperature, humidity, and wind speed—weather conditions are categorized into at least 10 fine-grained types. The matching degree between energy storage capacity and demand power is analyzed for each type, identifying periods with discrepancies (matching deviation adjustment periods). Then, based on the distribution characteristics of these discrepancy periods, a tiered approach is adopted for identification: if discrepancies exist in all weather types, a preset identification method based on low electricity prices is directly used; if significant discrepancies exist only in some weather types, the preset identification method is also applied to types with larger discrepancies; if discrepancies are generally small and the average discrepancy is below a threshold, no optimization is needed; otherwise, a basic identification method is used, considering both the discrepancy value and the proportion of low-electricity-price periods. This strategy aims to combine air conditioning pre-regulation with low-electricity-price periods to reduce adjustment deviations caused by insufficient power.

[0025] S11 uses the response matching data of the energy storage power of the energy storage system under the weather type to determine the degree of matching between the energy storage power and the energy storage regulation demand power of the energy storage system during different energy storage regulation periods under the weather type. Weather types are categorized by temperature, humidity, and wind speed, and are divided into at least 10 groups.

[0026] Temperature: The temperature of the ambient air affects the chemical reaction rate of the battery and its cooling / heating requirements. It can be divided into low temperature (<5℃), medium temperature (5-25℃), and high temperature (>25℃).

[0027] Humidity: The relative humidity of the air affects the battery's heat dissipation efficiency and the risk of condensation. It can be divided into low humidity (<40%), medium humidity (40-70%), and high humidity (>70%) levels.

[0028] Wind speed: Ambient wind speed affects the natural convection cooling effect. It can be divided into low wind (<2m / s), medium wind (2-5m / s), and high wind (>5m / s).

[0029] Weather type: A category of meteorological conditions composed of different combinations of temperature, humidity, and wind speed. For example, "high temperature-high humidity-low wind" is one weather type, and "low temperature-low humidity-moderate wind" is another. Each type represents a specific set of external environmental conditions.

[0030] Temperature, humidity, and wind speed are three core meteorological factors affecting the operating efficiency of energy storage systems. Temperature directly affects battery performance and air conditioning load; humidity affects heat dissipation efficiency and condensation risk; and wind speed affects natural cooling. Combining these three factors allows for a more refined characterization of the operating characteristics of energy storage systems under different environmental conditions, providing a more precise basis for optimized control.

[0031] Multidimensional classification can capture the interactions between different meteorological factors. For example, battery heat dissipation is most difficult under hot, humid, and windless weather, and adjustment deviations may be most severe. This refined classification lays the foundation for subsequent targeted optimization.

[0032] Energy storage power response matching data: This refers to the actual output power data of the energy storage system in response to the power demand given by the dispatch command during actual operation. It typically includes the power demand value and the corresponding actual output power value for each adjustment period.

[0033] Energy storage regulation period: refers to the time interval during which the energy storage system participates in grid regulation, such as the peak and off-peak periods of each day, or a continuous time period divided according to dispatch instructions. Each regulation period has a fixed duration (e.g., 1 hour).

[0034] Matching degree: An indicator that quantifies how close the actual output power is to the required power. A common definition is: Matching degree = Actual output power / Required power, with a value between 0 and 1 (if the actual power exceeds the requirement, it can be truncated to 1 or processed differently). The closer this value is to 1, the better the response.

[0035] Matching degree is a core indicator for measuring the regulation capability of an energy storage system. By calculating the matching degree in each time period, it is possible to identify which periods have insufficient response, providing a data foundation for subsequent deviation analysis.

[0036] S12 determines the matching deviation adjustment period in the energy storage adjustment period based on the matching degree; Matching deviation adjustment period: refers to the energy storage adjustment period in which the duration of energy storage power being less than the energy storage adjustment demand power does not meet the requirements. The specific judgment criteria are as follows: within an adjustment period, if the proportion of the duration of actual power being lower than the demand power to the total duration of the period exceeds a preset threshold, or if the average matching degree of the period is lower than a preset threshold, then the period is marked as a matching deviation adjustment period.

[0037] While a low single-point matching degree may be caused by momentary fluctuations, persistent insufficient power is the real issue that needs attention. By introducing thresholds for "duration percentage" or "average matching degree," occasional and transient deviations can be filtered out, focusing on systemic and persistent insufficient response problems.

[0038] Assuming a regulation period lasts for 1 hour, with a constant power demand of 100kW, and the actual output power is 100kW for the first 30 minutes and drops to 80kW for the next 30 minutes, then the period of output power below demand accounts for 50% of the total time, exceeding the preset threshold. Therefore, this period is determined to be a matching deviation regulation period.

[0039] S13 uses matching deviation adjustment time period data under different weather types to determine the identification method for the optimal control weather type of the air conditioning unit of the energy storage system.

[0040] It should be noted that the matching deviation adjustment period in the energy storage adjustment period refers to the energy storage adjustment period in which the duration of energy storage power being less than the energy storage adjustment demand power does not meet the requirements.

[0041] It is understandable that the method for identifying the optimal control weather type for the air conditioning unit of the energy storage system, using matching deviation adjustment time data under different weather types, specifically includes: If there are matching deviation adjustment periods under different weather types, then the weather type identification method for the optimized control of the air conditioning unit of the energy storage system is determined to be the preset identification method; If adjustment deviations occur under every weather type, it indicates that the insufficient response of the energy storage system is a widespread problem, likely related to global factors such as system capacity and control strategies, rather than specific weather conditions. In this case, there is no need to differentiate between weather conditions; the simplest preset identification method can be used: whenever the proportion of low electricity price periods is high under that weather condition, pre-adjustment of the air conditioning is performed, that is, adjusting the temperature of the energy storage device to a preset temperature range, such as 19.5 to 20.5 degrees Celsius, to improve subsequent response. Under a global problem, this simplifies decision-making and quickly covers all potentially beneficial weather types.

[0042] S131 If there are uneven matching deviation adjustment periods under different weather types, determine the matching deviation value under the weather type by the proportion of the matching deviation adjustment period under different weather types in the adjustment period, and determine whether there is a weather type with a matching deviation value greater than the preset deviation value. If so, determine the identification method of the optimized control weather type of the air conditioning device of the energy storage system as the preset identification method, so that the air conditioning device can be adjusted in advance under certain weather types when the electricity price is low, thereby reducing the matching deviation adjustment period of the energy storage system. If not, proceed to step S132. Matching deviation value: For a given weather type, this is the proportion of the total duration of the matching deviation adjustment period to the total duration of all adjustment periods under that weather condition. This value reflects the severity of the adjustment deviation under that weather type.

[0043] Deviation preset value: A pre-set percentage value used to determine whether the deviation of a certain weather type is significant enough to require special handling.

[0044] If certain weather types exhibit particularly severe deviations, it indicates that these weather conditions are the primary factors causing the regulation deviations and should be addressed first. For these severely deviating weather conditions, a pre-defined identification method is employed: optimization is performed whenever low electricity price periods constitute a high proportion, focusing on resolving the main issues.

[0045] S132 determines whether the average value of the matching deviation value in different weather types is less than a preset deviation threshold based on the average value of the matching deviation value in different weather types. If so, then in order to reduce the energy consumption of the air conditioning system, there is no need to optimize the identification of the weather type. If not, then the identification method of the optimized control weather type of the air conditioning device of the energy storage system is determined as the basic identification method.

[0046] If the deviations for all weather types are not severe, but the overall average level is still high, it indicates a general, slight deviation, requiring further refined screening. The basic identification method requires two conditions to be met simultaneously: the deviation value for that weather type is not less than a threshold (indicating that the weather is relatively severe), and the proportion of low-electricity-price periods during that weather is high (indicating economic viability). This allows for the screening of weather types that are both necessary and valuable for optimization.

[0047] In cases of generally slight deviations, the system uses dual criteria to select the weather conditions that are truly worth optimizing, thus avoiding energy waste caused by optimizing even slightly deviated weather conditions.

[0048] Specifically, the basic identification method is to determine the weather type as an optimized control weather type if the matching deviation value in the weather type is not less than a preset deviation threshold and the proportion of time when the electricity price is less than a preset electricity price threshold is greater than a preset duration proportion threshold.

[0049] Specifically, the preset identification method is to determine the weather type as an optimized control weather type if the proportion of time in the weather type where the electricity price is less than a preset electricity price threshold is greater than a preset duration proportion threshold.

[0050] A certain energy storage power station is located in a coastal area with an installed capacity of 50MW / 100MWh. To optimize air conditioning control, the system categorizes weather into 27 types based on three parameters: temperature, humidity, and wind speed. For simplicity, 10 actual weather types are selected for explanation. Historical operating data is collected over a one-year period.

[0051] Step 1: Weather Type Classification: Set the level of each parameter: Temperature T: Low temperature (<5℃), medium temperature (5-25℃), high temperature (>25℃); Humidity H: Low humidity (<40%), medium humidity (40-70%), high humidity (>70%); Wind speed W: Low wind (<2m / s), Medium wind (2-5m / s), High wind (>5m / s); Step 2: Identify the matching deviation adjustment period (S12) Set the criteria for determining the matching deviation adjustment period: if the average matching degree is lower than 0.9 within an adjustment period, it is marked as a deviation period.

[0052] Based on the above distribution, the number and duration of deviation periods under each weather type were counted (each period is 3 hours, and the deviation duration = number of deviation periods × 3 hours).

[0053] Matching deviation value = Total duration of the deviation period / Total duration of the weather adjustment period; T1: 30 / 720 = 0.0417 (4.17%); T2: 90 / 960 = 0.0938 (9.38%); T3: 15 / 600 = 0.0250 (2.50%); T4: 120 / 840 = 0.1429 (14.29%); T5: 60 / 1080 = 0.0556 (5.56%); T6: 24 / 480 = 0.0500 (5.00%); T7: 30 / 1200 = 0.02500 (2.5%); T8: 18 / 360 = 0.05000 (5.00%); T9: 15 / 720 = 0.02083 (2.83%); T10:36 / 600 = 0.0600 (6.00%).

[0054] Step 3: Execute the S13 decision: S131: Determine if there is a matching deviation adjustment period for all weather types: The deviation duration for all 10 weather types is greater than 0, therefore the condition "all exist" is met (note: existence is defined as long as the number of deviation periods is greater than 0). According to S131, a preset identification method is adopted.

[0055] Preset identification method execution: For each weather type, calculate the percentage of time during which the electricity price is below 0.5 yuan / kWh (this needs to be combined with the actual electricity price data on the day the weather occurred). Assume the percentage of low electricity price periods for each weather type is as follows: T1: 25%, T2: 32%, T3: 40%, T4: 28%, T5: 35%, T6: 45%, T7: 30%, T8: 20%, T9: 38%, T10: 42%.

[0056] The preset duration threshold is 30%. Therefore, the weather types with a low electricity price percentage greater than 30% are: T2 (32%), T3 (40%), T5 (35%), T6 (45%), T9 (38%), and T10 (42%). Thus, T2, T3, T5, T6, T9, and T10 are identified as optimized control weather types. Under these weather conditions, the system will activate air conditioning in advance during low electricity price periods to regulate temperature and improve energy storage response capabilities.

[0057] Specifically, such as Figure 3 As shown, the method for determining the operation control method of the air conditioning unit that optimizes weather type control is as follows: Based on the quantitative characteristics of the identified optimal control weather types and the degree of overlap between their deviation periods and the overall system deviation periods, a multi-level, multi-condition dynamic decision tree is constructed to adaptively determine the specific operation control strategy of the air conditioning unit under different optimal control weather types, so as to achieve the optimal balance between improving the regulation capacity of the energy storage system and effectively controlling the energy consumption of air conditioning operation.

[0058] First, by calculating the sum of the overlap ratios of all optimized control weather types (total overlap ratio), the overall optimization potential is assessed from a macro perspective. When the total overlap ratio is low, it indicates that even with optimization of these weather types, the improvement in overall deviation is still limited. In this case, a uniform, lenient electricity price control strategy is adopted to capture limited optimization opportunities with minimal decision-making costs. When the total overlap ratio is high, a more refined decision-making level is adopted: first, the number of optimized weather types is considered to determine whether further differentiation is needed; if the number of types is small, a uniform, lenient strategy is still used to maintain simplicity and efficiency; if the number of types is large, further differentiated processing is carried out based on the overlap ratio of each type—a lenient electricity price strategy is adopted for major contributing types with high overlap ratios to ensure optimization effects, while a stricter target electricity price strategy is adopted for minor contributing types with low overlap ratios to control energy consumption costs. Through this multi-level, progressive decision-making mechanism, step-by-step optimization from macro to micro and from extensive to intensive is achieved.

[0059] S21 determines the number of optimized control weather types based on the optimized control weather type data; Optimized control weather types: These refer to weather types identified through previous steps that require targeted optimization control of the energy storage system's air conditioning unit. These weather types are characterized by significant deviations in the energy storage system's power response under specific combinations of temperature, humidity, and wind speed, necessitating proactive adjustments to the air conditioning load to improve regulation capabilities. In this embodiment, weather types are categorized into 10 specific types, from T1 to T10, based on three parameters: temperature, humidity, and wind speed.

[0060] Number of weather types requiring optimized control: This refers to the total number of identified weather types that need to be included in the scope of air conditioning optimized control, and is a non-negative integer. This value reflects the scale and complexity of the optimized control task.

[0061] Statistical analysis of the number of weather types requiring optimized control is essentially a quantitative assessment of the scale of the optimization task. A small number indicates that the problems are relatively concentrated, meaning that even a uniformly lenient electricity price control will have a minimal impact on overall energy consumption and operational stability. A large number indicates that the problems are more dispersed, requiring more refined differentiation and handling to avoid excessive energy consumption and operational instability caused by overly frequent pre-emptive adjustments to the air conditioning system. This step is the starting point of the entire decision-making process, laying the foundation for all subsequent judgments.

[0062] A certain energy storage power station defined 10 weather types, T1 to T10, based on a fine division of temperature, humidity, and wind speed. After preliminary identification steps, it was found that the power response deviation of the energy storage system was significant under 6 of these weather types, requiring inclusion in the air conditioning optimization control scope. Therefore, the number of weather types to be optimized and controlled is 6.

[0063] S22 determines the proportion of the energy storage adjustment deviation period in the optimized control weather type within the energy storage adjustment deviation period of the energy storage system based on the degree of overlap between the energy storage adjustment deviation period in the optimized control weather type and the energy storage adjustment deviation period of the energy storage system. The proportion of the energy storage adjustment deviation period in the optimized control weather type within the energy storage adjustment deviation period of the energy storage system is taken as the overlap ratio of the optimized control weather type. Energy storage regulation deviation period: refers to the time interval during which the actual output power of the energy storage system cannot meet the dispatch demand. The specific criteria are: within this period, the duration of actual power being lower than the demand power exceeds a preset threshold, or the average matching degree is lower than a preset threshold. The energy storage regulation deviation period is a core indicator for measuring the insufficient regulation capability of the energy storage system.

[0064] Overlap: The overlap between the time periods of energy storage regulation deviation that occur during a specific optimized control weather type and the time periods of energy storage regulation deviation that occur under all weather types on the time axis.

[0065] Overlap ratio: This refers to the proportion of the total duration of deviation periods for a specific weather type under optimal control to the total duration of deviation periods for all weather types. The calculation formula is: Overlap ratio for a specific weather type = Total duration of deviation periods for that weather type / Total duration of deviation periods for all weather types. This ratio is a value between 0 and 1, reflecting the contribution or responsibility weight of that weather type to the overall control deviation.

[0066] The impact of different weather types on the operation of energy storage systems varies significantly. Some weather types may be the main factors causing regulation deviations, while the impact of others is negligible. By calculating the overlap ratio of each optimal control weather type, the qualitative "magnitude of impact" can be transformed into a quantitative "responsibility weight," providing a scientific basis for subsequent resource allocation and strategy differentiation.

[0067] The overlap ratio serves as a bridge connecting weather types with the actual operational performance of energy storage systems. It reveals which weather types are the "primary issues" that truly require priority resolution, and which are "secondary issues" that can be moderately mitigated. This quantitative indicator ensures that subsequent decisions are not based on subjective assumptions but on evidence-based data. Weather types with a high overlap ratio should receive more optimization resources and more proactive control strategies; weather types with a low overlap ratio should, while ensuring basic effectiveness, minimize costs and avoid over-optimization that leads to resource waste.

[0068] Statistics from an energy storage system show that, across all weather types from T1 to T10, a total of 438 hours of energy storage regulation deviation occurred throughout the year. Among these, weather type T4, which is the optimal control weather type, contributed 120 hours of deviation. Therefore, the overlap ratio for this weather type is approximately 120 / 438 ≈ 0.274, indicating that weather type T4 contributes 27.4% to the overall deviation and is a type requiring close monitoring.

[0069] S23 determines the operation control method of the air conditioning unit for the optimized control weather type based on the number of optimized control weather types and the overlap ratio of the optimized control weather types.

[0070] Operational control methods refer to a set of rules that guide when and how air conditioning units should start under specific optimized weather conditions. In this scheme, the core of the operational control method is setting the electricity price trigger condition for early temperature adjustment. Different control methods correspond to different electricity price thresholds, thereby achieving differentiated control over the timing and intensity of adjustment.

[0071] The number of weather types and the overlap ratio between them provide two dimensions of decision-making information—scale and importance. Combining these two dimensions allows for the construction of a multi-level, multi-condition decision-making framework, enabling the selection of the most suitable control strategy under different data characteristics and achieving an optimal balance between optimization effectiveness and energy consumption costs.

[0072] In the above steps, based on the overlap ratio of the optimized control weather types, the sum of the overlap ratios of the optimized control weather types is determined and used as the total overlap ratio. If the total overlap ratio is less than the preset ratio threshold, then even if the air conditioning is controlled in advance under the optimized control weather type, the improvement on the overall energy storage regulation deviation period is still low. Therefore, in all optimized control weather types, as long as the electricity price is less than the preset electricity price threshold, the temperature control of the air conditioning can be controlled in advance, thereby ensuring that there is enough space for energy storage regulation during the energy storage regulation deviation period.

[0073] Total overlap ratio: This value is obtained by summing the overlap ratios of all optimized control weather types. This value reflects the overall contribution of all weather types that need to be optimized to the overall regulation deviation.

[0074] Preset Proportion Threshold: A pre-defined critical proportion value used to judge the overall optimization potential. This threshold is set based on factors such as operational experience and cost-benefit analysis, representing the minimum contribution threshold that system administrators deem "worth investing optimization resources in."

[0075] Before deciding on a specific control strategy, it is necessary to first assess the overall optimization value from a macro perspective. If the combined contribution of all weather types requiring optimization to the total deviation is small (the total overlap is below the threshold), then even if significant resources are invested in fine-tuning these weather conditions, the improvement to the overall performance of the energy storage system will be negligible. In this case, the most lenient strategy should be adopted to maximize the improvement effect.

[0076] Additionally, it should be noted that if the total overlap ratio is not less than a preset ratio threshold, the following content is also included: Case 1: Obtain the number of optimized control weather types. If the number of optimized control weather types is less than the preset weather type number threshold, then as long as the electricity price is less than the preset electricity price threshold among all optimized control weather types, the air conditioner can be controlled in advance to ensure that there is enough space for energy storage regulation during the energy storage regulation deviation period.

[0077] Preset weather type quantity threshold: A pre-defined integer, such as 4. This threshold is used to determine the number of weather types to be optimized and controlled, representing the dividing point from "few types" to "multiple types".

[0078] Assuming the overall optimization value is sufficient, it is necessary to further determine whether different weather types need to be treated differently. If the number of weather types involved is small (less than the threshold), then even unified management will not cause frequent start-ups of the air conditioning system; on the contrary, it can improve the energy storage regulation deviation.

[0079] Case 2: If the number of optimized control weather types is not less than the preset weather type number threshold, obtain the overlap ratio under the weather type. If the overlap ratio under the weather type is greater than the preset overlap ratio threshold, then as long as the electricity price is less than the preset electricity price threshold in the optimized control weather type, the air conditioner can be controlled in advance to ensure that there is enough space for energy storage regulation during the energy storage regulation deviation period. Preset overlap ratio threshold: A pre-defined ratio value. This threshold is used to distinguish whether a weather type is a "major contributing type" or a "minor contributing type". Types exceeding the threshold are considered major types and should be prioritized for optimization; types below the threshold are considered minor types and costs should be controlled while ensuring basic effectiveness.

[0080] When there are many weather types, their importance will inevitably differ. By using preset thresholds for binary classification, limited optimization resources can be allocated to the primary types, enabling a concentrated breakthrough in addressing the main challenges while appropriately controlling secondary challenges.

[0081] For the main contributing weather types, their contribution to the overall deviation is significant, and their optimization value is high. Adopting relaxed low electricity price conditions can ensure that low electricity price opportunities are captured to the maximum extent for pre-adjustment under these weather conditions, thereby improving energy storage regulation capabilities as much as possible and reducing the period of deviation.

[0082] Case 3: If the overlap ratio under the weather type is not greater than the preset overlap ratio threshold, in order to ensure energy saving, as long as the electricity price is less than the target electricity price threshold in the optimized control weather type, the air conditioner can be controlled in advance to ensure that there is enough space for energy storage regulation during the energy storage regulation deviation period.

[0083] Target electricity price threshold: A lower electricity price than the preset threshold, for example, 0.3 yuan / kWh. This threshold represents a stricter start-up condition; only when the electricity price drops sufficiently is starting the air conditioner considered economical.

[0084] For secondary weather types, their contribution to the overall bias is small, and their optimization value is limited. If the same lenient conditions as for primary types are still applied, air conditioning may be frequently activated when electricity prices are not low enough, resulting in unnecessary energy waste while yielding minimal optimization benefits. Using stricter low electricity price conditions ensures that air conditioning is only activated when electricity prices are extremely attractive.

[0085] It should be noted that the target electricity price threshold is less than the preset electricity price threshold.

[0086] This solution provides a scientific, precise, and adaptive strategy determination method for the operation and control of air conditioning devices in energy storage systems by constructing a multi-level decision tree that includes macro-level screening, scale judgment, and importance classification. The solution first assesses the overall optimization potential (overlapping proportion) at the macro level, and then refines the distinction of specific weather types at the micro level (overlapping proportion comparison), forming a complete decision chain of "macro first, then micro, overall first, then local", ensuring the comprehensiveness and hierarchy of decision-making.

[0087] By adopting a lenient strategy for major contributing weather events to ensure optimal results, and a strict strategy for minor contributing weather events to control energy consumption costs, a balanced state of maximizing benefits is achieved at the overall level.

[0088] Construct a complete decision tree covering all possible scenarios: The solution covers all possible situations from low-value scenarios to high-value scenarios, from single types to multiple types, and from primary types to secondary types by combining multi-level judgment conditions (overall overlap ratio, number of types, and overlap ratio), ensuring that an appropriate strategy can be output under any data characteristics.

[0089] This solution is a model of the deep integration of energy management in energy storage systems and the control of auxiliary equipment. It realizes the intelligent operation of air-conditioning devices through a data-driven approach, provides strong support for the economic and efficient operation of energy storage systems in the electricity market, and is an important step for energy storage systems to move towards full intelligence.

[0090] Specifically, as Figure 4 shown, the method for determining the matching control strategy for optimizing the controlled weather type is as follows: Based on the distribution characteristics of temperature abnormal periods in historical operation data under the optimized controlled weather type and the statistical data of air-conditioning adjustment frequencies under different electricity prices, dynamically determine the optimal electricity price start threshold for the air-conditioning device under each weather type, so as to balance the adjustment effect and the reliability of equipment operation on the premise of ensuring normal battery temperature, and avoid shortening the life of the air-conditioning device or increasing energy consumption due to overly frequent adjustment.

[0091] First, for each optimized controlled weather type, count the daily temperature anomalies in history and determine whether there are temperature anomalies every day. If so, it means that the temperature problem is common and persistent in this weather, and the basic adjustment strategy should be adopted, that is, within the original electricity price threshold range, select the largest electricity price that makes the average daily adjustment times not exceed the preset quantity threshold as the actual start threshold to ensure sufficient adjustment strength. If there are no temperature anomalies every day, calculate the average proportion of temperature anomaly duration as the adjustment demand value. If the demand value is high, still adopt the basic strategy; otherwise, enter the next overall judgment. Then, count the proportion of types adopting the basic strategy among all optimized types. If this proportion is too high, it means that most types need frequent adjustment. To avoid the overall decline in equipment reliability, all types are uniformly changed to a more stringent preset adjustment strategy; if the proportion is not high, the remaining types adopt a compromise second preset adjustment strategy. Through this hierarchical decision-making, the optimal balance between the adjustment effect and the equipment life is achieved.

[0092] S31 Based on the adjustment matching data of the optimized controlled weather type in history, determine the temperature adjustment periods corresponding to different electricity prices within the electricity price threshold of the operation control method of the corresponding air-conditioning device for the optimized controlled weather type, and use the temperature adjustment period corresponding to the electricity price as the matching adjustment period; Optimized controlled weather type: Refers to the weather types that need to perform air-conditioning optimization control identified through the previous steps. In this embodiment, the weather is divided into 10 types from T1 to T10 based on temperature, humidity, and wind speed, and the optimized control types include T2, T4, T5, T6, T8, and T10.

[0093] The operation control method for air conditioning units refers to the electricity price activation rules determined in the preceding steps for each optimized weather type, including preset electricity price thresholds or target electricity price thresholds. In this embodiment, based on the overlap ratio threshold of 0.05, T2, T4, T5, and T10 are assigned a lenient strategy with an original electricity price threshold of 0.5 yuan / kWh; T6 and T8 are assigned a strict strategy with an original electricity price threshold of 0.3 yuan / kWh.

[0094] Electricity price: Real-time electricity market price, in yuan / kWh.

[0095] Temperature adjustment period: This refers to the time interval during which the air conditioner actually starts to adjust the temperature when the electricity price is lower than a certain value in historical operation. In this embodiment, each adjustment period is set to 15 minutes, i.e., 96 periods per day.

[0096] Matching adjustment periods: For a specific electricity price, all adjustment periods initiated below that price are collectively referred to as the matching adjustment periods corresponding to that price. By statistically analyzing the number of matching adjustment periods under different electricity prices, a correspondence between electricity prices and adjustment frequencies can be established.

[0097] To determine a suitable starting electricity price, it is necessary to quantify the pattern of adjustment frequency as a function of electricity price. Historical data provides this mapping relationship; by statistically analyzing the number of times air conditioners are triggered under different electricity price levels, it is possible to accurately select an electricity price point that meets adjustment needs without being excessively frequent.

[0098] S32 determines, based on the monitoring data from the multi-source sensors, the time periods during which the battery temperature is not within the target range under the optimized air conditioning weather type, and identifies these time periods as abnormal temperature periods. Multi-source sensors: Temperature sensors, humidity sensors, and other devices installed in the battery compartment are used to monitor the battery's operating environment parameters in real time.

[0099] Target range: The safe temperature range for normal battery operation, such as 15℃-35℃. Exceeding this range will affect battery performance and may even cause safety issues.

[0100] Abnormal temperature period: This refers to a period during which the battery temperature continuously or repeatedly exceeds the target range. This period is marked as an abnormal temperature period. In this embodiment, abnormal temperature periods are also recorded in 15-minute increments.

[0101] Temperature anomalies are a direct indicator of the necessity for air conditioning adjustments. Frequent temperature anomalies under a particular weather condition indicate a high demand for air conditioning system adjustments. By statistically analyzing the distribution of periods of temperature anomalies, the actual adjustment needs can be quantified.

[0102] S33 determines the matching adjustment strategy of the optimized control weather type based on the distribution data of the matching adjustment periods and the distribution data of the temperature anomaly periods in different dates of the optimized control weather type.

[0103] Specifically, based on the distribution data of the temperature anomaly periods in the optimized control weather type, determine the proportion of the duration of the temperature anomaly periods in different dates of the optimized control weather type. If there are temperature anomaly periods in different dates of the optimized control weather type, the matching adjustment strategy of the optimized control weather type is the basic adjustment strategy, that is, within the electricity price threshold corresponding to the operation control method of the air conditioning device of the optimized control weather type, the maximum value of the electricity price when the average daily number of matching adjustment periods is less than the preset quantity threshold, that is, when the average daily number of matching adjustment periods is less than the maximum value of the electricity price of the preset quantity threshold, use the air conditioner in advance to control the temperature.

[0104] There are temperature anomaly periods in different dates: It means that in all the dates when this weather type appears, there is at least one temperature anomaly period every day, indicating that the temperature problem is persistent.

[0105] Basic adjustment strategy: A relatively loose strategy aimed at ensuring sufficient adjustment strength. The specific operation is: within the original electricity price threshold range, find a maximum electricity price P_base such that when the electricity price is lower than P_base, the average daily number of matching adjustment periods is less than the preset quantity threshold.

[0106] Preset quantity threshold: A preset value represents the maximum number of adjustments allowed per day.

[0107] If there is a temperature anomaly every day, it means that the adjustment demand persists, and sufficient adjustment opportunities must be ensured. Therefore, the basic strategy is adopted, allowing a higher adjustment frequency (that is, a higher electricity price threshold) to cover more possible high-temperature periods and ensure that the battery temperature is within the target range.

[0108] This strategy gives priority to meeting the adjustment demand, tolerates a certain adjustment frequency, and is applicable to scenarios where the problem is common and persistent.

[0109] In addition, it can be understood that if there are no temperature anomaly periods in different dates of the optimized control weather type, based on the average value of the proportion of the duration of the temperature anomaly periods in different dates of the optimized control weather type, determine the adjustment demand value under the optimized control weather type, and judge whether the adjustment demand value under the optimized control weather type is greater than the preset demand threshold. If so, the matching adjustment strategy of the optimized control weather type is the basic adjustment strategy. If not, proceed to the next step; Adjustment demand value: For a given weather type, calculate the average percentage of the duration of all days with abnormal temperatures.

[0110] Preset demand threshold: A proportional value used to determine whether the adjustment demand is significant.

[0111] Temperature anomalies don't occur every day, but the average duration of anomalies may still be high, indicating that the problem, while not persistent, is serious. In this case, basic strategies should still be employed to ensure the effectiveness of regulation, and regulation should not be relaxed due to a few days without anomalies.

[0112] By assessing the overall necessity of adjustment through average demand values, discrete abnormal distributions are transformed into continuous quantitative indicators, making decision-making more precise.

[0113] Determine whether the proportion of the optimized control weather type of the basic adjustment strategy is greater than the preset control weather type proportion threshold. If yes, then determine that the adjustment strategy under the optimized control weather type is the preset adjustment strategy. If no, then determine that the adjustment strategy under the optimized control weather type is the second preset adjustment strategy.

[0114] The proportion of weather types for which the basic control strategy is applied: Among all weather types for which the basic control strategy is applied, the proportion of the total number of optimized weather types.

[0115] Preset control weather type ratio threshold: A ratio value used to determine whether the global adjustment frequency needs to be tightened.

[0116] Preset Adjustment Strategy: A more stringent strategy designed to reduce adjustment frequency to protect equipment. It works by finding the maximum electricity price P_preset within the original electricity price threshold, such that the average daily number of matched adjustment periods is less than a second preset quantity threshold. The second preset quantity threshold is smaller than the preset quantity threshold in the basic strategy (e.g., 40 periods / day).

[0117] The second preset adjustment strategy is a compromise strategy, where the third preset quantity threshold is between the basic and the second (e.g., 45 time periods / day). The third preset quantity threshold is greater than the second preset quantity threshold but less than the preset quantity threshold.

[0118] If most optimization types require frequent adjustments, the overall air conditioning unit may operate too frequently, affecting reliability and lifespan. In this case, a stricter preset adjustment strategy needs to be adopted globally, uniformly reducing the adjustment frequency for all types to improve equipment reliability. If only a few types require frequent adjustments, the basic strategy should still be used for these few types, while a compromise second preset strategy should be adopted for the other types to balance adjustment effectiveness and reliability.

[0119] It should be noted that the preset adjustment strategy is that, within the electricity price threshold corresponding to the operation control method of the air conditioning device for the optimized control weather type, the daily average number of matching adjustment periods is less than the maximum value of the electricity price of the second preset number threshold. At this time, frequent adjustments under a large number of weather types have already led to poor operational reliability of the air conditioning device. Therefore, to avoid the air conditioning device being adjusted too frequently, temperature control is performed when the electricity price is less than the second preset number threshold. That is, when the daily average number of matching adjustment periods is less than the maximum value of the electricity price of the second preset number threshold, temperature control is performed in advance using the air conditioning.

[0120] It should be noted that the second preset adjustment strategy is that, within the electricity price threshold corresponding to the operation control method of the air conditioning device for the optimized control weather type, the daily average quantity of the matching adjustment period is less than the maximum value of the electricity price of the third preset quantity threshold, wherein the third preset quantity threshold is greater than the second preset quantity threshold and less than the preset quantity threshold. That is, when the daily average quantity of the matching adjustment period is less than the maximum value of the electricity price of the third preset quantity threshold, the air conditioning is used in advance to control the temperature.

[0121] This solution provides a scientific and precise method for determining the matching control strategy of the air conditioning unit in an energy storage system by constructing a multi-level decision-making mechanism that includes single-type judgment and global adjustment. It quantifies adjustment needs through abnormal temperature data and dynamically selects the electricity price threshold by combining adjustment frequency statistics. This ensures that the battery temperature remains within the target range while avoiding equipment damage caused by excessively frequent start-stop cycles of the air conditioning. In this embodiment, the daily adjustment frequency is controlled at 8-10 hours, which satisfies temperature control requirements while avoiding 24-hour uninterrupted operation.

[0122] By limiting the adjustment frequency, the number of air conditioner start-stop cycles is reduced, extending equipment lifespan and lowering maintenance costs. Reducing the average daily adjustment frequency from 52 to 44 times significantly reduces compressor start-stop wear. As historical data is updated, temperature anomaly distribution and adjustment matching data will change. This solution can automatically recalculate, always maintaining a high degree of match between the strategy and the latest operating conditions, achieving intelligent dynamic optimization.

[0123] Furthermore, the method for determining the optimized control weather type update management method is as follows: Based on the actual operating effect data of the optimized control weather types after the implementation of air conditioning optimization control, the degree of improvement of each weather type is dynamically evaluated. Based on multi-dimensional indicators such as the overall improvement level, the proportion of stable types, and the frequency of adjustment, it is adaptively decided whether to remove certain weather types from the optimized control range, so as to eliminate those types that are frequently adjusted but have poor improvement effects, thereby further reducing the energy consumption of air conditioning and extending the service life of air conditioning.

[0124] First, based on the change data after the implementation of optimized control, the improvement coefficient of energy storage regulation deviation duration for each optimized control weather type on different dates is calculated, thus obtaining the average improvement coefficient for each type. Then, at the overall level, it is determined whether the average improvement level of all types meets the preset requirements. If the overall improvement is insufficient, a screening rule based on the frequency of future adjustments is applied to all types: if a weather type has few dates with few adjustment periods within the future target duration, it is removed from optimized control (because there is no opportunity for improvement). If the overall improvement meets the target, further refined judgment is made on individual types: types with significant improvement effects are retained; for types with insignificant improvement effects, different composite judgment rules are applied based on the proportion of stable types—focusing on those types with frequent adjustments (high impact on air conditioner lifespan) but poor improvement effects (low improvement coefficient), removing these "ineffective adjustment" types from optimized control. Through this multi-level, progressive decision-making mechanism, the set of optimized control types is dynamically updated, ensuring the effectiveness of the optimized control strategy and the efficiency of resource utilization.

[0125] S41 uses the variable data to determine the duration of the energy storage adjustment deviation period on different dates within the optimized controlled weather type; Variable data: This refers to new data generated during the operation of the energy storage system after the implementation of the air conditioning optimization control strategy, including the duration of energy storage regulation deviations occurring daily under various weather types. This data reflects the actual effectiveness of the optimization control.

[0126] Optimized control weather type: The set of weather types currently undergoing air conditioning optimization control, identified in the previous steps.

[0127] Energy storage regulation deviation period: refers to the time interval during which the actual output power of the energy storage system cannot meet the demand power, usually measured in hours. This indicator measures the degree of inadequacy in regulation capability.

[0128] To evaluate the effectiveness of the optimized control, it is necessary to collect operational data after implementation, especially the changes in the duration of deviation periods. By comparing the data before and after optimization, the degree of improvement can be quantified.

[0129] For example, for the optimized control weather type T2, it occurred for 10 days within 30 days after the implementation of optimized control (based on historical frequency). The duration of the energy storage adjustment deviation period for each day of these 10 days was recorded, for example: 2.0h, 1.8h, 2.2h, 1.5h, 2.1h, 1.9h, 2.3h, 1.7h, 2.0h, and 1.6h.

[0130] S42 determines the improvement coefficient for the energy storage adjustment deviation period on the given date by the ratio of the duration of the energy storage adjustment deviation period on different dates to the average duration of the energy storage adjustment deviation period in the optimized control weather type. Average duration of energy storage regulation deviation period: This refers to the average daily deviation duration under this weather type before the implementation of optimized control, serving as a baseline value. For example, the average daily deviation duration before optimization of T2 was 2.25 hours.

[0131] Improvement coefficient: For a specific weather type on a given day, the improvement coefficient is defined as the day's deviation duration divided by the average deviation duration before optimization. A coefficient less than 1 indicates that the day's deviation duration is less than the historical average, meaning there has been improvement; a coefficient greater than 1 indicates deterioration; and a coefficient equal to 1 indicates no change. The smaller the improvement coefficient, the more significant the improvement effect.

[0132] By comparing the daily deviation duration with historical benchmarks, the impact of optimized control on the day can be intuitively reflected. The ratio form eliminates the dimensions, facilitating comparisons across dates and types. The improvement coefficient is the core indicator for quantifying the optimization effect; it converts absolute duration into a relative proportion, allowing for comparisons of improvement levels between different benchmark weather types.

[0133] For T2, the average deviation duration before optimization was 2.25 hours. If the deviation duration on a certain day is 1.5 hours, then the improvement coefficient for that day = 1.5 / 2.25 = 0.667, indicating that the deviation has been reduced to 66.7% of its original value, showing significant improvement. If the deviation duration on a certain day is 2.5 hours, then the improvement coefficient = 2.5 / 2.25 = 1.111, indicating that the deviation has increased by 11.1%, and the effect has deteriorated.

[0134] S43 determines the average improvement coefficient of the optimized control weather type based on the average improvement coefficient of the energy storage regulation deviation period in different dates, and determines the update management method of the optimized control weather type based on the average improvement coefficient in different optimized control weather types.

[0135] Mean improvement coefficient: For a given optimally controlled weather type, the arithmetic mean of the improvement coefficients for all days in which the type occurs is taken to obtain the overall improvement level of that type. A mean value less than 1 indicates overall improvement, while a mean value greater than 1 indicates overall deterioration.

[0136] Update management methods: These refer to the rules for determining whether to retain a particular weather type in the optimized control set. This scheme defines several update strategies, including direct removal, retention, and removal based on combined conditions. In particular, types that are frequently adjusted (with many future adjustment opportunities) but have poor improvement effects (mean improvement coefficient close to or greater than 1) are removed.

[0137] Furthermore, if the average improvement demand in different optimized control weather types is greater than the preset improvement demand threshold, then in order to ensure the improvement effect of the energy storage adjustment deviation period, the updated management method for the optimized control weather type is determined to be that if there are no dates in the future target duration where the number of energy storage adjustment periods of the air conditioning system meets the requirements, then the weather type is determined to no longer belong to the optimized control weather type.

[0138] Average improvement demand: This refers to the arithmetic mean of the improvement coefficients of all optimized control weather types, i.e., the overall average improvement level.

[0139] Preset improvement requirement threshold: A pre-set ratio value, such as 1.0, used to determine whether the overall improvement effect meets the standard.

[0140] Future target duration: A pre-defined future time period, such as the next 30 days, used to assess the adjustment capabilities of the air conditioning system.

[0141] The number of days during which the air conditioning system's energy storage regulation period meets the requirements: This refers to a day in which the number of time periods (or total duration) during which the air conditioning system is actually activated for temperature regulation reaches a preset standard, such as a regulation duration exceeding 2 hours in a day. This reflects the air conditioning's regulation activity under this weather type.

[0142] When the overall improvement effect is not good, it means that the optimization strategy may be ineffective for most types. In this case, the weather types with a large number of future adjustment periods should be eliminated first, because these weather types will reduce the service life of the air conditioner.

[0143] Furthermore, if the average improvement demand across different optimized control weather types is not greater than a preset improvement demand threshold, the following applies: S431 Determine whether the average improvement demand in the optimized control weather type is less than a preset threshold. If yes, determine that the update management method for the optimized control weather type does not require update processing. If no, proceed to step S432. Preset threshold: A critical value used to distinguish whether the improvement effect of a single type is significant, for example, 1.0. Types with an improvement greater than this value are considered to have little or no improvement effect and require further analysis; types with an improvement less than or equal to this value are considered to have improved and are retained.

[0144] Provided that the overall effect meets the target, a preliminary screening is performed on individual types. Types with obvious improvement are directly retained to reduce the amount of subsequent calculations. This step quickly filters out high-quality types and focuses on making a more refined judgment on types with no obvious improvement.

[0145] Specific example: If the preset threshold is 0.9, and the average improvement coefficient for one type is 0.8 < 0.9, then it is directly retained. For another type, if the average improvement coefficient is 0.95 > 0.9, then proceed to the next step.

[0146] S432 takes the optimized control weather type that does not require updating as the stable control weather type, and determines whether the proportion of the number of stable control weather types in the optimized control weather type is greater than the preset stable weather type proportion threshold. If so, it is determined that the update management method of the optimized control weather type is as follows: if the proportion of the number of days in the future target duration that meet the requirements of the energy storage adjustment period of the air conditioning system is less than the preset date proportion threshold, and there are no days with an improvement coefficient less than the preset improvement demand threshold, the adjustment frequency is high and the improvement effect on the energy storage adjustment deviation period is not good. Therefore, it is determined that the weather type no longer belongs to the optimized control weather type. If not, proceed to step S433. Stable weather control type: refers to those types that are determined in S431 to require no update processing, i.e., types with significant improvement effects.

[0147] Preset stable weather type percentage threshold: a percentage value, such as 50%, used to determine whether stable weather types are in the majority.

[0148] Preset threshold for the percentage of dates: for example, 30%, used to measure whether the proportion of future adjustment activity dates is high enough. Here, "greater than the threshold" indicates frequent adjustments.

[0149] Preset improvement requirement threshold: This refers to the standard used to determine whether the improvement effect has been achieved on a certain day.

[0150] If the stable type accounts for the majority, it indicates that the overall situation is good. However, for those types where improvement is not obvious, it is necessary to focus on the frequency of adjustment and the effect of improvement. If these types adjust frequently in the future (i.e., a high percentage of future dates will meet the adjustment requirements), but no day achieves improvement (the improvement coefficient is greater than 1.0, meaning every day is worse or no improvement), it indicates that although the air conditioner starts frequently, it does not have a positive effect on reducing deviation. On the contrary, it may accelerate aging and increase energy consumption. This is considered "ineffective adjustment" and should be removed from the optimization control.

[0151] S433 determines the improvement matching coefficient of the optimized control weather type based on the average improvement demand in the optimized control weather type and the percentage of dates in which the number of energy storage adjustment periods in the optimized control weather type meets the requirements. It then determines whether the improvement matching coefficient of the optimized control weather type is less than a preset improvement matching coefficient threshold. If so, it is determined that the update management method for the optimized control weather type is no longer required. If not, it is determined that the update management method for the optimized control weather type is as follows: if the percentage of dates in which the number of energy storage adjustment periods of the air conditioning system meets the requirements is less than the date percentage threshold within the future target duration, the adjustment frequency is high and the improvement effect on the energy storage adjustment deviation period is poor. Therefore, it is determined that the weather type no longer belongs to the optimized control weather type.

[0152] Improvement Matching Coefficient: A comprehensive indicator determined by the average improvement coefficient and the percentage of future adjustment activity dates. Since a smaller improvement coefficient indicates better results, and frequent adjustments are a negative factor, the matching coefficient can be defined as the average improvement coefficient × (1 - percentage of dates meeting requirements). A larger value indicates worse improvement and more frequent adjustments, making it more likely to be excluded. A critical threshold should be considered when setting the threshold.

[0153] A preset improvement matching coefficient threshold is set as a critical value to determine whether a product should be retained. If the matching coefficient is greater than the threshold, it indicates poor improvement and frequent adjustments, and further removal should be considered. If the matching coefficient is not greater than the threshold, the product may be retained or reassessed.

[0154] When stable types are not the majority, a more refined balance needs to be struck between the improvement effect and future adjustment potential for each type to be judged. The improvement matching coefficient combines both factors. If it is greater than the threshold, it indicates that the type belongs to the candidate of "frequent adjustment and poor improvement", and further verification is needed to see if it meets the condition of frequent adjustment; if it does, it is eliminated.

[0155] Through the above multi-level decision-making, the system can dynamically adjust and optimize the set of control weather types based on actual operating data, ensuring that resources are always allocated to the most needed and effective weather types, especially eliminating "ineffective adjustment" types that are frequently adjusted but have no improvement effect, thereby protecting air conditioning equipment and reducing energy consumption.

[0156] Example 2 Secondly, the present invention provides an air conditioning coupled energy storage system based on multi-sensor fusion, employing one of the control methods described above, specifically including: The system includes a runtime control module, an adjustment strategy determination module, and an update management module. The operation control module is responsible for determining the operation control method of the air conditioning unit for optimizing the control of the weather type. The adjustment strategy determination module is responsible for determining the matching adjustment strategy for the optimized control weather type. The update management module is responsible for determining the update management method for the optimized control weather type.

[0157] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0158] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0159] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A control method, characterized in that, Specifically, it includes: Using the adjustment data of the energy storage system, the response matching data of the energy storage power of the energy storage system under the weather type is determined. The response matching data is used to determine the identification method of the optimal control weather type of the air conditioning device of the energy storage system. Based on the optimal control weather type data and combined with the degree of overlap between the energy storage adjustment deviation period in the optimal control weather type and the energy storage adjustment deviation period of the energy storage system, the operation control method of the air conditioning device of the optimal control weather type is determined. The operation control method is used to determine the adjustment matching data of the optimized control weather type in history. Based on the adjustment matching data and the monitoring data of multi-source sensors, the matching adjustment strategy of the optimized control weather type is determined. Based on the matching adjustment strategy under different optimized control weather types, the variation data of energy storage adjustment deviation time periods in different temperature control ranges under different optimized control weather types are determined, and the update management method for the optimized control weather type is determined using the variation data.

2. The control method as described in claim 1, characterized in that, The regulation data of the energy storage system includes the energy storage regulation period of the energy storage system under the weather type, and the degree of matching between the output power of the energy storage system and the energy storage regulation demand power during the energy storage regulation period.

3. The control method as described in claim 1, characterized in that, The response matching data of the energy storage power under the weather type is determined based on whether the energy storage power under the weather type can adapt to the energy storage regulation demand power.

4. The control method as described in claim 1, characterized in that, The method for determining the weather type identification method for the optimized control of the air conditioning unit of the energy storage system is as follows: Based on the response matching data of the energy storage power of the energy storage system under the weather type, determine the degree of matching between the energy storage power and the energy storage regulation demand power of the energy storage system during different energy storage regulation periods under the weather type; Based on the degree of matching, the matching deviation adjustment period in the energy storage adjustment period is determined; By utilizing matching deviation adjustment time period data under different weather types, a method for identifying the optimal control weather type for the air conditioning unit of the energy storage system is determined.

5. The control method as described in claim 4, characterized in that, The matching deviation adjustment period in the energy storage adjustment period is the energy storage adjustment period in which the duration of energy storage power being less than the energy storage adjustment demand power does not meet the requirements.

6. The control method as described in claim 1, characterized in that, The method for determining the updated management method for optimizing and controlling weather types is as follows: Using the aforementioned variation data, the duration of energy storage regulation deviation periods on different dates within the optimized controlled weather type is determined; The improvement coefficient for the energy storage adjustment deviation period on a given date is determined by the ratio of the duration of the energy storage adjustment deviation period on different dates to the average duration of the energy storage adjustment deviation period in the optimized control weather type. The mean improvement coefficient for the optimized control weather type is determined based on the average improvement coefficient of the energy storage regulation deviation period on different dates. Based on the mean improvement coefficient in different optimized control weather types, the update management method for the optimized control weather type is determined.

7. An air conditioning coupled energy storage system based on multi-sensor fusion, employing a control method according to any one of claims 1-6, characterized in that, Specifically, it includes: The system includes a runtime control module, an adjustment strategy determination module, and an update management module. The operation control module is responsible for determining the operation control method of the air conditioning unit for optimizing the control of the weather type. The adjustment strategy determination module is responsible for determining the matching adjustment strategy for the optimized control weather type. The update management module is responsible for determining the update management method for the optimized control weather type.

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