Photovoltaic lithium motor room tail end energy consumption evaluation and intelligent tuning integrated platform

Abstract

The invention relates to the technical field of energy management, in particular to a photovoltaic lithium motor room tail end energy consumption evaluation and intelligent tuning integrated platform, and aims to improve the accuracy of load prediction and reduce power distribution errors by collecting power consumption data of equipment in real time, accurately analyzing fluctuation characteristics of loads and identifying energy consumption abnormal points. Based on the dynamic power supply capability of the energy storage system, the load demand is matched, the charging and discharging strategy is optimized, the flexibility of energy allocation is enhanced, and the electric energy utilization is more balanced. Factors such as photovoltaic power generation power and sunlight intensity are combined, the future energy consumption deviation amplitude is corrected, the accuracy of energy consumption prediction is improved, and energy waste is reduced. The energy storage discharge priority is dynamically adjusted according to the load distribution condition, load distribution is optimized, the use efficiency of energy storage equipment is improved, and energy storage redundancy or insufficient power supply is avoided. For abnormal energy consumption fluctuation, historical data and an energy storage scheduling plan are comprehensively evaluated, a future abnormal deviation trend is predicted, and an energy supply scheme is adjusted in advance.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to an integrated platform for terminal energy consumption assessment and intelligent optimization in a photovoltaic lithium-ion power plant. Background Art

[0002] The field of energy management technology includes the efficient use, optimized scheduling and dynamic control of energy resources. Its core content is to monitor and manage energy production, transmission, storage and consumption in real time through information technology, so as to achieve energy conservation and emission reduction and improve energy efficiency. This field includes intelligent scheduling and optimization of various energy systems, involving renewable energy, energy storage technology, power systems and energy management of buildings, industrial parks, etc.

[0003] Among them, the integrated platform for terminal energy consumption assessment and intelligent adjustment of photovoltaic lithium-ion power room refers to an integrated system that aims to accurately assess and intelligently adjust the terminal energy consumption of photovoltaic power generation and lithium battery energy storage systems. In view of the energy efficiency assessment and adjustment problems of photovoltaic power generation systems and lithium battery energy storage equipment in practical applications, a comprehensive solution based on data collection, energy efficiency analysis and intelligent adjustment is proposed. Specifically, by real-time monitoring of photovoltaic power generation, lithium battery charging and discharging, and electricity demand, combined with energy management algorithms, the use and storage of electricity can be automatically adjusted.

[0004] The existing technology has the problem of coarse data collection granularity in energy consumption assessment, which fails to fully capture the characteristics of load fluctuations, resulting in large errors in load forecasting and affecting the accuracy of energy distribution. The lack of dynamic evaluation of the power supply capacity of energy storage equipment makes it impossible to effectively adjust the charging and discharging strategy, which may cause insufficient discharge or charging overload of energy storage equipment, affecting the life and power supply stability of the energy storage system. In the prediction of photovoltaic power generation, the impact of short-term fluctuations in sunlight intensity on power generation is not fully considered, resulting in large deviations in power generation estimation, affecting energy utilization efficiency. The ability to identify abnormal energy consumption is weak, and the high energy consumption time period cannot be accurately located, making it difficult to effectively respond to sudden load changes, increasing the difficulty of energy consumption management in the computer room. The lack of a flexible load optimization mechanism fails to fully match the energy storage power supply capacity with the load demand, resulting in unbalanced energy configuration, which may lead to local power shortages or waste of energy storage resources, reducing the overall efficiency of the energy system. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose an integrated platform for terminal energy consumption assessment and intelligent optimization of photovoltaic lithium-ion power room.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: A photovoltaic lithium-ion power room terminal energy consumption assessment and intelligent tuning integrated platform includes: The load monitoring and evaluation module obtains the power consumption data of the terminal equipment in the photovoltaic lithium-ion power room, analyzes the peak period and the mean fluctuation range, determines the fluctuation intensity and distribution ratio, and obtains the load fluctuation characteristics of the terminal equipment room; The energy storage intelligent scheduling module calls the energy storage battery data based on the load fluctuation characteristics of the terminal of the computer room, calculates the power supply ratio, compares the demand and available power, screens the charging and discharging intervals, and obtains the energy storage intelligent scheduling plan; The abnormal energy consumption prediction module selects abnormal fluctuation time points based on the energy storage intelligent scheduling plan, predicts the future abnormal energy consumption deviation amplitude, and obtains the abnormal energy consumption deviation information of the computer room; The photovoltaic power generation correction module analyzes the influence of sunshine intensity based on the abnormal energy consumption offset information of the computer room, corrects the future abnormal energy consumption offset, and obtains photovoltaic power generation correction data; The computer room energy consumption tuning module determines the compatibility between the energy storage power supply capacity and the load demand based on the photovoltaic power generation correction data, optimizes the load distribution, and obtains the computer room energy consumption tuning result.

[0007] As a further solution of the present invention, the load fluctuation characteristics at the terminal of the computer room are specifically the load peak period, the mean fluctuation range, the load fluctuation intensity, and the load distribution ratio. The energy storage intelligent scheduling plan includes the proportion of energy storage power supply, the matching degree of energy storage available power and load demand, the charging and discharging adjustment range, and the dynamic charging and discharging adjustment parameters. The abnormal energy consumption offset information of the computer room specifically refers to the load deviation value, the time point exceeding the fluctuation range, and the future abnormal energy consumption offset amplitude. The photovoltaic power generation correction data includes the photovoltaic power generation power impact ratio, the sunshine change impact range, and the corrected future energy consumption offset amplitude. The energy consumption optimization result of the computer room is specifically the energy storage discharge priority, the load distribution optimization parameters, and the future energy consumption optimization of the computer room.

[0008] As a further solution of the present invention, the load monitoring and evaluation module includes: The equipment load data collection submodule obtains the power consumption data of the terminal equipment in the photovoltaic lithium-ion power room, collects the equipment rated power, operating hours, and real-time load changes, calls the room load records, filters the data missing intervals, corrects the operating hours data, analyzes the load change trend of each device over time, and obtains the equipment load time series data; The load fluctuation rate calculation submodule is based on the equipment load time series data and adopts the formula: ; Calculate the load fluctuation rate , combined with the load rate distribution in the time period, the load rate change interval is obtained; in, represents the load fluctuation rate, Representative The equipment load value for a time period, Represents the equipment load value of the previous time period, Represents the total number of time periods; The load distribution analysis submodule analyzes the load peak period and mean fluctuation corresponding to the load rate change interval, determines the load fluctuation intensity and load distribution ratio, and obtains the load fluctuation characteristics at the terminal of the computer room.

[0009] As a further solution of the present invention, the energy storage intelligent scheduling module includes: The energy storage power supply analysis submodule collects the rated capacity, discharge power, and charge and discharge cycle number of the energy storage battery based on the load fluctuation characteristics of the terminal of the equipment room, calculates the current remaining power supply capacity of the energy storage battery, screens the sustainable power supply time within the energy storage power supply cycle, and obtains the energy storage power supply capacity parameters; The charge-discharge matching submodule adopts the formula based on the energy storage power supply capacity parameters, photovoltaic power generation power and room load power requirements: ; Calculate the proportion of energy provided by the energy storage battery to the total energy demand of the computer room , combined with the energy storage adjustment parameters in each time period, the energy storage charging and discharging matching results are obtained; in, Represents the energy storage system in the time period The energy available within Represents the computer room in the time period The energy demand within Represents the photovoltaic system in the time period The power generation energy inside is the total number of time periods in the calculation cycle; The dynamic scheduling optimization submodule is based on the energy storage charging and discharging matching results, calls the load fluctuation characteristics of the computer room, assigns priority to the energy storage discharge in each time period, adjusts the combined energy supply of photovoltaic and energy storage, and optimizes the energy storage scheduling adjustment parameters to obtain an energy storage intelligent scheduling plan.

[0010] As a further solution of the present invention, the abnormal energy consumption prediction module includes: The load deviation calculation submodule obtains the real-time load data after the application of the charge and discharge adjustment parameters based on the energy storage intelligent scheduling plan, including the load mean value, load fluctuation range, and equipment operation status in the time period, using the formula: ; Calculation time period Load deviation value , and obtain the load deviation analysis results; in, represents the load in the corresponding time period, Represents load average record; The abnormal time period screening submodule screens the duration of the abnormal load period based on the load deviation result and with reference to all load fluctuation range records, determines whether the abnormal load is an instantaneous fluctuation or a continuous trend, and obtains abnormal load time period distribution information; The future energy consumption deviation prediction submodule adopts the formula based on the abnormal load time period distribution information, referring to the overall load change trend and energy storage charging and discharging records: ; Calculating future time Load forecast value at , get the abnormal energy consumption deviation information of the computer room; in, Represents the current moment The load, Indicates the past time point before (i.e. time point ) actual load value, Relative to the current time Push forward A point in time, i.e. a point in the past, Indicates the past Before a time point, that is, at a time point The historical load average, is the number of past time points referenced when predicting future loads. is the load offset influence coefficient.

[0011] As a further solution of the present invention, the photovoltaic power generation correction module includes: The photovoltaic power acquisition submodule obtains the abnormal energy consumption offset information of the computer room, calls the current photovoltaic power generation power, sunshine intensity, and ambient temperature, monitors the real-time output power of the photovoltaic power generation equipment, and screens the abnormal offset interval of the photovoltaic power in the target period according to the change amplitude of the photovoltaic power in the time period to obtain the photovoltaic power change data; The sunshine impact analysis submodule calls sunshine intensity and ambient temperature based on the photovoltaic power change data, analyzes the influence of sunshine intensity fluctuation on photovoltaic power generation, selects the interval with obvious influence of sunshine intensity change in the specified time period, and obtains the sunshine impact analysis result; The future energy consumption correction submodule is based on the sunshine impact analysis results and adopts the formula: ; Calculate the revised future load forecast , generate photovoltaic power generation correction data; in, represents the sunshine intensity influence coefficient, Represents the current moment The sunshine intensity, represents the reference sunshine intensity, represents the ambient temperature influence coefficient, Represents the current moment The ambient temperature, Stands for standard reference temperature.

[0012] As a further solution of the present invention, the computer room energy consumption tuning module includes: The energy storage adaptation evaluation submodule obtains the remaining power, available discharge power, and current load demand of the energy storage battery in the future time period based on the photovoltaic power generation correction data, analyzes the adaptation of the energy storage power supply capacity to the future load demand, screens the time interval of insufficient power supply or redundant power supply, and obtains energy storage supply and demand matching parameters; The discharge priority adjustment submodule calls the energy storage discharge power, the fluctuation of the load demand in the computer room, and the energy storage discharge threshold based on the energy storage supply and demand matching parameters, analyzes the supply and demand deviation between the energy storage discharge power and the load demand, adjusts the energy storage discharge priority in the corresponding time period, screens the discharge priority adjustment scheme, and obtains the energy storage discharge priority parameter; The load optimization distribution submodule calls the load demand data of the computer room, the equipment operation time period, and the load distribution situation based on the energy storage discharge priority parameters, analyzes the matching degree between the load distribution situation and the energy storage power supply capacity, screens the adjustable load time interval, and obtains the energy consumption tuning result of the computer room.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by collecting the power consumption data of the equipment in real time, accurately analyzing the fluctuation characteristics of the load, identifying the abnormal points of energy consumption, improving the accuracy of load prediction, and reducing the power allocation error. Based on the dynamic power supply capacity of the energy storage system, matching the load demand, optimizing the charging and discharging strategy, enhancing the flexibility of energy allocation, and making the use of electricity more balanced. Combined with factors such as photovoltaic power generation power and sunshine intensity, correct the future energy consumption offset amplitude, improve the accuracy of energy consumption prediction, and reduce energy waste. Dynamically adjust the energy storage discharge priority according to the load distribution, optimize the load distribution, improve the efficiency of energy storage equipment, and avoid energy storage redundancy or insufficient power supply. For abnormal energy consumption fluctuations, comprehensively evaluate historical data and energy storage scheduling plans, predict future abnormal offset trends, adjust the energy supply plan in advance, and prevent power loss caused by load overload. Utilize the photovoltaic power generation correction mechanism to dynamically correct the load forecast, reduce the impact of sunshine fluctuations on energy supply, and improve the utilization rate of photovoltaic power. Through multi-dimensional load optimization strategies, enhance the matching degree between the load of the computer room and the energy storage supply, achieve accurate adaptation of energy supply and demand, and improve the stability of the energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flow chart of the platform of the present invention; Figure 2 It is a flow chart of the load monitoring and evaluation module of the present invention; Figure 3 This is a flow chart of the energy storage intelligent scheduling module of the present invention; Figure 4 This is a flow chart of the abnormal energy consumption prediction module of the present invention; Figure 5 It is a flow chart of the photovoltaic power generation correction module of the present invention; Figure 6 This is a flow chart of the energy consumption optimization module of the computer room of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0017] See also Figure 1 , an integrated platform for terminal energy consumption assessment and intelligent tuning of photovoltaic lithium-ion power room includes: The load monitoring and evaluation module obtains the power consumption data of the terminal equipment in the photovoltaic lithium-ion power room, analyzes the peak period and the mean fluctuation range, determines the fluctuation intensity and distribution ratio, and obtains the load fluctuation characteristics of the terminal equipment room; The energy storage intelligent scheduling module calls the energy storage battery data based on the load fluctuation characteristics at the terminal of the computer room, calculates the power supply ratio, compares the demand and available power, screens the charging and discharging intervals, and obtains the energy storage intelligent scheduling plan; The abnormal energy consumption prediction module is based on the energy storage intelligent scheduling plan, screens the abnormal fluctuation time points, predicts the future abnormal energy consumption deviation range, and obtains the abnormal energy consumption deviation information of the computer room; The photovoltaic power generation correction module analyzes the impact of sunlight intensity based on the abnormal energy consumption offset information of the computer room, corrects the future abnormal energy consumption offset, and obtains photovoltaic power generation correction data; The energy consumption optimization module of the computer room determines the compatibility between the energy storage power supply capacity and the load demand based on the photovoltaic power generation correction data, optimizes the load distribution, and obtains the energy consumption optimization result of the computer room; The load fluctuation characteristics at the terminal of the computer room include load peak period, mean fluctuation range, load fluctuation intensity, and load distribution ratio. The energy storage intelligent scheduling plan includes the proportion of energy storage power supply, the matching degree of energy storage available power and load demand, the charging and discharging adjustment range, and the dynamic charging and discharging adjustment parameters. The abnormal energy consumption offset information of the computer room specifically refers to the load deviation value, the time point exceeding the fluctuation range, and the future abnormal energy consumption offset amplitude. The photovoltaic power generation correction data includes the photovoltaic power generation power impact ratio, the short-term sunshine change impact range, and the corrected future energy consumption offset amplitude. The energy consumption optimization results of the computer room include energy storage discharge priority, load distribution optimization parameters, and future energy consumption optimization of the computer room.

[0018] See also Figure 2 , the load monitoring and evaluation module includes: The equipment load data collection submodule obtains the power consumption data of the terminal equipment in the photovoltaic lithium-ion power room, collects the equipment rated power, operating hours, and real-time load changes, calls the historical load records of the room, filters the data missing intervals, corrects the operating hours data, analyzes the load change trend of each device over time, and obtains the equipment load time series data; For various types of equipment in the computer room, including servers, air conditioners, battery management systems, monitoring systems, etc., first call the equipment's operation log, read the operating status and historical power data of each device, collect the equipment's rated power, operating time, and real-time load changes, and for each device, calculate its power consumption per unit time. For example, the power consumption of server S1 from 8:00 to 9:00 is 3.5kW, and from 9:00 to 10:00 is 3.8kW, then its power change trend per unit time is 3.8kW-3.5kW=0.3kW / h. Call the computer room's historical load records, match the current data with the historical data, and filter out the data missing intervals. For data missing segments, for example, the power data of server S2 during 10:00-11:00 is lost, refer to the data of adjacent time periods (9:00-10:00 and 11:00-12:00) for weighted interpolation, and calculate its missing value. , get the data correction value, and finally calculate the unit time load change trend of each device to form the equipment load time series data.

[0019] The load fluctuation rate calculation submodule is based on the equipment load time series data and uses the formula: ; Calculate the load fluctuation rate , combined with the load rate distribution in the time period, the load rate change interval is obtained; in, represents the load fluctuation rate, Representative The equipment load value for a time period, Represents the equipment load value of the previous time period, Represents the total number of time periods, Used to calculate the absolute value of load changes in adjacent time periods and sum them up.

[0020] For example, if the average load from 8:00 to 9:00 is 4.0kW and the average load from 9:00 to 10:00 is 4.5kW, the load change rate is calculated as follows: .

[0021] Set the load rate change threshold. Assume that the load change threshold set by the data center is 10% / hour. For the load average of 4.0kW between 8:00 and 9:00, the change threshold is 0.4kW / h. , it is determined that the load rate change in this period exceeds the threshold, the time period in which the load rate change exceeds the set threshold is screened, and finally the load rate change interval is calculated for subsequent load fluctuation trend analysis.

[0022] The load distribution analysis submodule analyzes the load peak period and mean fluctuation corresponding to the load rate change interval, determines the load fluctuation intensity and load distribution ratio, and obtains the load fluctuation characteristics at the terminal of the computer room; Analyze the load peak time period and average fluctuation, that is, in a certain day, calculate the load average of 24 time periods, and filter out the maximum load time period. For example, the power changes of a server in a certain computer room are as follows: 8:00-9:00 load average: 4.0kW, 9:00-10:00 load average: 4.5kW, 10:00-11:00 load average: 5.0kW, 11:00-12:00 load average: 5.3kW. Among them, the load at 11:00-12:00 is the highest, which is determined as the peak time period. Calculate the change of this time period relative to the daily average load. If the daily average load is 4.5kW, the peak load change rate is calculated as follows: ,in, Represents the peak load change rate, that is, the ratio of the load in a certain period of time to the daily average load, in percentage (%); Represents peak load, which is the maximum load value monitored within a selected time period (such as a day or a week), in kW; Represents the average daily load, that is, the average load of all time periods within the time period, in kW; the numerator What is calculated is the increase in peak load relative to the average daily load; the denominator As a reference value, it is used to normalize the increment to measure the relative change amplitude of load fluctuation; If the load fluctuation threshold is set to 15%, it is judged that the load fluctuation is large during the period of 11:00-12:00, and the load fluctuation intensity and load distribution ratio are judged, and finally the load fluctuation characteristics of the terminal of the computer room are obtained.

[0023] See also Figure 3 , the energy storage intelligent scheduling module includes: The energy storage power supply analysis submodule collects the rated capacity, discharge power, and number of charge and discharge cycles of the energy storage battery based on the load fluctuation characteristics at the terminal of the computer room, calculates the current remaining power supply capacity of the energy storage battery, screens the sustainable power supply duration within the energy storage power supply cycle, and obtains the energy storage power supply capacity parameters; Collect the rated capacity, discharge power, and number of charge and discharge cycles of the energy storage battery. First, read the current storage capacity through the energy storage battery management system. The storage capacity is collected by the battery monitoring device and is usually expressed in kilowatt-hours (kWh). For an energy storage battery with a rated capacity of 500kWh, the remaining capacity at a certain point in time is 300kWh, then the storage capacity is 300kWh. Then read the rated capacity to determine the theoretical maximum storage limit of the energy storage battery, and read the discharge power to determine the maximum instantaneous discharge capacity of the battery. For example, a battery with a maximum discharge power of 100kW has a maximum discharge capacity of 100kWh per unit time (such as 1 hour). Combined with the number of charge and discharge cycles, the health status of the battery is judged to determine its sustainable power supply capacity. If the design life of a battery is 3000 cycles and the current number of cycles is 2500, its remaining life coefficient can be calculated as (i.e., about 16.67% of the remaining lifespan). Combined with the above data, the current remaining power supply capacity of the energy storage battery is calculated, and the sustainable power supply duration within the energy storage power supply cycle is screened. When the storage power is 300kWh and the load demand is 50kW, the available time is calculated as hours, and obtain the energy storage power supply capacity parameters.

[0024] The charge-discharge matching submodule is based on the energy storage power supply capacity parameters, with reference to the photovoltaic power generation power and the power demand of the equipment room load, and adopts the formula: ; Calculate the proportion of energy provided by the energy storage battery to the total energy demand of the computer room , combined with the energy storage adjustment parameters in each time period, the energy storage charging and discharging matching results are obtained; in, Represents the energy storage system in the time period The energy available in kWh, Represents the computer room in the time period Energy demand in kWh, Represents the photovoltaic system in the time period The power generation energy in kWh, To calculate the total number of time periods in the cycle, when calculating the matching degree, the energy storage power supply in different time periods and the energy demand of the computer room are gradually accumulated to ensure unit matching and determine whether additional adjustment of the charging and discharging strategy is needed.

[0025] If the current storage capacity of the energy storage system is 300kWh, the maximum discharge power is 100kW, and the discharge time is 3 hours, then its maximum available energy is 300kWh. On the other hand, the load demand of the computer room is determined by real-time monitoring data. For example, during the period of 8:00-12:00, the total power demand of the server, refrigeration equipment, lighting, etc. in the computer room is 30kW, 40kW, and 10kW respectively. The total load demand of the computer room is 30+40+10=80kW, and the corresponding energy demand is 320kWh (assuming that the period is 4 hours). The matching ratio is calculated as follows: , the value is greater than 1, indicating that the energy storage system can meet the current load demand of the computer room. , it means that the energy storage power supply is insufficient, and it is necessary to increase photovoltaic power generation or external power grid supplementation. Combined with the energy storage adjustment parameters in each time period, the energy storage charging and discharging matching results are obtained.

[0026] The dynamic scheduling optimization submodule is based on the energy storage charging and discharging matching results, calls the load fluctuation characteristics of the computer room, assigns priority to energy storage discharge in each time period, adjusts the combined energy supply of photovoltaic and energy storage, and optimizes the energy storage scheduling adjustment parameters to obtain an energy storage intelligent scheduling plan; First, according to the fluctuation of load demand in the computer room in different time periods, high-load periods and low-load periods are determined. For example, from 10:00 to 14:00, the server load is 50kW, the air conditioning load is 70kW, the lighting load is 10kW, and the total load is 130kW. From 18:00 to 22:00, the total load drops to 80kW. Then 10:00-14:00 is defined as a high-load period, and energy storage is discharged first. From 18:00 to 22:00, energy storage discharge is reduced. The energy storage discharge priority is adjusted, and the combination strategy of photovoltaic power generation and energy storage discharge is optimized. For example, from 10:00 to 14:00, the photovoltaic power generation power is 60kW, and the energy storage discharge power is 70kW, totaling 130kW to meet the load demand. From 18:00 to 22:00, the photovoltaic power generation power is insufficient, only 20kW, and the energy storage discharge is increased to 60kW, totaling 80kW to maintain power supply stability. Finally, the energy storage scheduling adjustment parameters are optimized to obtain the energy storage intelligent scheduling plan.

[0027] See also Figure 4 ,The abnormal energy consumption prediction module includes: The load deviation calculation submodule obtains the real-time load data after the application of the charging and discharging adjustment parameters based on the energy storage intelligent scheduling plan, including the load mean value, load fluctuation range, and equipment operation status in the time period, using the formula: ; Calculation time period Load deviation value , and obtain the load deviation analysis results; in, Represents the actual load during this time period (unit: kW), Represents the historical load average (unit: kW), using absolute value calculation to ensure that both positive and negative load changes are recorded.

[0028] Collect the load average, load fluctuation range, and operating status of the equipment in the computer room in each time period, compare the deviation of the current load data with the historical load, calculate the load deviation value of each time period, and filter the time point when the deviation value is greater than the set threshold. In actual applications, the load data of each terminal equipment in the photovoltaic lithium-ion power room, including servers, communication base stations, power conversion devices, etc., needs to be sampled once a second to obtain high-frequency data points to ensure the accuracy of load fluctuations. Specifically, the historical load fluctuation range of server A in the past 24 hours was 8.5kW to 11.2kW, and at a certain point in time , the actual load reaches 12.5kW, and the value is: , the calculated load deviation value is 2.65kW. If the load deviation threshold set in the computer room is kW, then this time point is identified as an abnormal load period. Through this calculation, all periods where load fluctuations exceed the normal range can be screened out and recorded in the load deviation results.

[0029] The abnormal time period screening submodule screens the duration of the abnormal load period based on the load deviation results and refers to all load fluctuation range records, determines whether the abnormal load is an instantaneous fluctuation or a continuous trend, and obtains the abnormal load time period distribution information; Filter the duration of abnormal load periods to determine whether the abnormal load is a short-term fluctuation or a long-term trend. For example, the load fluctuation data of the air-conditioning load in the computer room in the past 30 days shows that fluctuations of more than 5 minutes are usually related to equipment failure or abnormal ambient temperature. Therefore, the abnormal load time period duration threshold is set to 5 minutes. When the load exceeds the threshold, the load anomaly is classified as a long-term abnormal load, otherwise it is classified as a short-term abnormal load. For example, the load of a power conversion device fluctuates between 18:30 and 18:40 and exceeds the historical load average by 3.2kW for 10 minutes. Minutes, greater than the set threshold Minutes, so the load anomaly is classified as long-term abnormal load and recorded in the abnormal load time period distribution.

[0030] The future energy consumption deviation prediction submodule is based on the abnormal load time period distribution information, referring to the overall load change trend and energy storage charging and discharging records, and adopts the formula: ; Calculating future time Load forecast value at , get the abnormal energy consumption deviation information of the computer room; in, Represents the current moment The actual load (unit: kW), Indicates the past time point before (i.e. time point ) actual load value, Relative to the current time Push forward A point in time, i.e. a point in the past, Indicates the actual load value at that time point, which is different from the historical average load (average of past data), e.g. Represents the previous time point The actual load, Represents the first two time points The actual load, Indicates the past time point before (i.e. time point ) is the historical load average, Relative to the current time Push forward A point in time, i.e. a point in the past , Indicates the historical load mean at that time point, that is, the average value calculated based on the data of multiple same time points in the past, rather than the actual load currently observed. For example: represents 10:00 am today, then It may be the average load at 10:00 a.m. every day in the past week, month, or year. This value can be used to measure whether the current load is abnormal: (Actual load) ratio If it is much higher or much lower, there may be abnormal energy consumption. is the number of past time points referenced when predicting future loads. is the historical load deviation influence coefficient, which depends on the time point Importance and influence coefficient in historical load trends According to the historical data regression analysis setting, usually using exponential decay or moving average weighting, for example, if the load fluctuation in the last hour has the greatest impact on the future trend in the historical data, while the impact of 3 hours ago is smaller, then set , , , ensuring that the closer the time is to the present, the greater the impact coefficient, so that future load forecasts are more consistent with recent trends.

[0031] Assume that the current load is 10.5kW, the loads at the past three time points (5-minute intervals) were 9.8kW, 10.2kW, and 10.7kW, and the historical averages were 9.5kW, 9.7kW, and 10.1kW, respectively. Then: , The calculated future abnormal energy consumption deviation is 10.92kW, which is recorded in the abnormal energy consumption deviation information of the computer room.

[0032] See also Figure 5 , the photovoltaic power generation correction module includes: The photovoltaic power acquisition submodule obtains the abnormal energy consumption offset information of the computer room, calls the current photovoltaic power generation power, sunshine intensity, and ambient temperature, monitors the real-time output power of the photovoltaic power generation equipment, and screens the abnormal offset interval of the photovoltaic power in a specific period of time according to the change amplitude of the photovoltaic power in the time period to obtain the photovoltaic power change data; First, the current photovoltaic power generation is obtained. This value is collected by the photovoltaic inverter, including the output DC power and AC power, and converted to obtain the output power value of the current photovoltaic power generation system. At the same time, the sunshine intensity data of the light sensor is collected. This data is usually measured in W / m² and comes from the solar radiation meter around the photovoltaic module. The ambient temperature around the computer room is obtained synchronously. The temperature data unit is °C and is obtained by the ambient temperature sensor. Then, the power change of the photovoltaic system in different time periods is monitored. The power output of the photovoltaic system is recorded at intervals of 15 minutes, and the power change rate of each time period is calculated. The sliding window method is used to determine the short-term power change range, and the time period when the photovoltaic power change amplitude exceeds the set threshold is screened. The threshold can be set by the historical power output curve of the photovoltaic system. For example, the maximum power change rate of the 95% confidence interval in the past 30 days is set as the benchmark. The time period beyond this range is the abnormal fluctuation interval. Finally, all abnormal fluctuation intervals are sorted to obtain the photovoltaic power change data.

[0033] The sunshine impact analysis submodule uses the sunshine intensity and ambient temperature based on the photovoltaic power change data to analyze the impact of sunshine intensity fluctuations on photovoltaic power generation, and selects the intervals with obvious sunshine intensity changes within the specified period to obtain the sunshine impact analysis results; First, the changes in the sunshine intensity of the photovoltaic power generation system throughout the day are obtained, and the sunshine intensity data in multiple time periods are collected to monitor its fluctuation trend. The changes in the photovoltaic power generation power output in different time periods are compared to determine whether the changes in sunshine intensity directly affect the photovoltaic power. Then, combined with the ambient temperature data, the impact of temperature on the rated output power of the photovoltaic power generation system is analyzed, and the time periods with obvious sunshine changes are screened out. The additional impact caused by the change in ambient temperature is screened out, and only the time periods significantly affected by the fluctuations in sunshine intensity are retained. On this basis, the historical data is compared to analyze whether the sunshine intensity fluctuations in this period have periodic characteristics, such as similar lighting conditions in the same time period every day or sudden changes due to the influence of short-term clouds. For different types of sunshine fluctuations, different types of impacts are classified, marked and summarized, such as short-term cloud cover, morning-dusk transition, long-term cloudy days, etc., to further evaluate the impact of different types of sunshine fluctuations on the output of the photovoltaic power generation system. Finally, combined with the impact data of different types of sunshine changes, the results of the sunshine impact analysis are sorted out.

[0034] The future energy consumption correction submodule is based on the results of the sunshine impact analysis and uses the formula: ; Calculate the revised future load forecast , generate photovoltaic power generation correction data; in, Represents the sunshine intensity influence coefficient, sunshine intensity influence coefficient It is mainly used to measure the impact of changes in sunlight intensity on photovoltaic power generation. The setting basis is as follows: The change trend of the conversion efficiency of photovoltaic modules: The power output of photovoltaic cells usually increases with the increase of sunlight intensity, but when the sunlight intensity exceeds a certain threshold (usually about 1000W / m2), the power growth tends to saturation and the impact tends to be nonlinear. The setting needs to be based on the power curve of the photovoltaic module. Photovoltaic cell type: Different types of photovoltaic cells (such as monocrystalline silicon, polycrystalline silicon, and thin-film cells) have different sensitivities to sunlight intensity. For example, the sensitivity of monocrystalline silicon modules to sunlight intensity is different. Usually set in , while the thin film components May be in between. Represents the current moment The sunshine intensity (unit: W / m2), Represents the reference sunshine intensity (usually 1000W / m2), Represents the ambient temperature influence coefficient, ambient temperature influence coefficient It mainly measures the impact of temperature changes on photovoltaic power generation efficiency, and is set based on the following: Photovoltaic module temperature coefficient: Different modules have different temperature effects. For example, the temperature power coefficient of common monocrystalline silicon photovoltaic modules is about -0.4% / °C, polycrystalline silicon is about -0.35% / °C, and thin-film modules are lower at about -0.2% / °C. It needs to be set in combination with the temperature coefficient of the photovoltaic module. Ambient temperature range: If the temperature fluctuation range of a certain area is °C, then The impact of this temperature change range must be covered. Historical measurement data: By measuring the power generation efficiency changes of photovoltaic modules at different temperatures, the impact ratio of temperature changes on power generation is calculated and set The specific value of Represents the current moment Ambient temperature (unit: °C), represents the standard reference temperature (usually 25°C). For example, if the previously calculated future load forecast value kW, current sunshine intensity W / m2, ambient temperature °C, sunlight intensity influence coefficient , Temperature influence coefficient ,but: , calculate and obtain the corrected future load forecast value, and obtain the photovoltaic power generation correction data. The dual influence of sunshine intensity and temperature is combined to avoid the problem of error accumulation caused by using sunshine intensity correction alone. In the screening of photovoltaic energy supply fluctuation range, a cross-calibration method based on historical data and future forecast data is introduced to improve the accuracy of forecast correction.

[0035] See also Figure 6 , the computer room energy consumption tuning module includes: The energy storage adaptation assessment submodule obtains the remaining power, available discharge power, and current load demand of the energy storage battery in the future time period based on the photovoltaic power generation correction data, analyzes the adaptation of the energy storage power supply capacity to the future load demand, screens the time intervals of insufficient power supply or redundant power supply, and obtains the energy storage supply and demand matching parameters; First, the photovoltaic power generation correction data is parsed, the power generation time series is extracted, and the power generation for a period of time in the future (such as 24 hours) is predicted by combining historical data with weather forecast parameters. In this process, the historical power generation records and real-time weather parameters (such as radiation intensity, temperature, and cloud cover) of the photovoltaic power station are called. By comparing the photovoltaic output under similar historical meteorological conditions, the corresponding correction factors are screened out and applied to the initial forecast data to obtain the corrected photovoltaic power generation forecast data. At the same time, the current remaining power data is extracted from the energy storage system. The data comes from the energy storage battery management system (BMS), including the voltage, current, SOC (remaining power percentage) and other parameters of the battery pack. Assuming that the current SOC is 60% and the rated capacity is 500kWh, the remaining power is calculated as: Then, combined with the future photovoltaic power generation forecast data and the current state of the energy storage system, the available discharge power is calculated. The discharge power is affected by factors such as the inverter rated power, the maximum discharge rate of the single cell, and the temperature compensation coefficient. Assuming that the inverter rated power is 200kW and the maximum discharge rate of the battery is 1C, the theoretical maximum discharge power is: At the same time, the current load demand data comes from the load monitoring system of the computer room, which records the operating status and power demand of each electrical equipment. Assuming that the load demand of the computer room in the next 4 hours is 180kW, 220kW, 150kW, and 190kW respectively, it is matched with the energy storage discharge capacity to determine whether there is a time interval of insufficient power supply or redundant power supply. For example, in the second hour, the demand is 220kW, and the energy storage can provide a maximum of 200kW, with a gap of 20kW. In the third hour, the demand is 150kW, and the energy storage can provide 200kW, with a redundancy of 50kW. In this way, the power shortage period (the second hour) and the power supply redundancy period (the third hour) are screened out, and finally the energy storage supply and demand matching parameters are obtained, including the power supply gap and surplus in each time period.

[0036] The discharge priority adjustment submodule calls the energy storage discharge power, room load demand fluctuation, and energy storage discharge threshold based on the energy storage supply and demand matching parameters, analyzes the supply and demand deviation between the energy storage discharge power and the load demand, adjusts the energy storage discharge priority in different time periods, screens the discharge priority adjustment scheme, and obtains the energy storage discharge priority parameters; First, the supply and demand deviation of each time period is determined according to the energy storage supply and demand matching parameters. Assuming that the power shortage period is the second hour (gap 20kW) and the power supply redundancy period is the third hour (surplus 50kW), the discharge power and load demand of each time period are compared to obtain the supply and demand deviation value, and the energy storage discharge threshold is set at the same time. This threshold is usually determined by the safe operating range of the battery. For example, to avoid over-discharge, the SOC minimum threshold is set to 20%. When the SOC drops to 100kWh, the discharge power should be limited. After analyzing the supply and demand deviation, the discharge priority is adjusted. The adjustment methods include: In the power shortage period, increase the energy storage discharge power, for example, in the second hour, increase the inverter output power limit , adjusted from the original 200kW to 220kW, but the premise is that the battery rate allows. If the maximum discharge rate of the battery is still 1C, the discharge power can reach 500kW, without the risk of over-limit, so the output can be increased by 20kW; - During the power supply redundancy period, reduce the discharge power. For example, in the third hour, reduce the discharge power from 200kW to 150kW to reduce unnecessary energy consumption and avoid a rapid drop in energy storage capacity. At the same time, adjust the load strategy to tilt some adjustable loads to this period, such as postponing some low-priority loads (such as air-conditioning loads) to this period; calculate the adjusted discharge power to form the final discharge priority parameters, including the discharge power setting value and priority adjustment plan for each period.

[0037] The load optimization distribution submodule calls the load demand data of the computer room, the equipment operation time period, and the load distribution situation based on the energy storage discharge priority parameters, analyzes the matching degree between the load distribution situation and the energy storage power supply capacity, selects the adjustable load time interval, optimizes the load distribution strategy to match the energy storage power supply capacity, and obtains the energy consumption tuning result of the computer room; First, extract the operating time and load requirements of each device in the computer room. For example, device A1 has a rated power of 50kW and a current operating time of 08:00-11:00; device B2 has a rated power of 30kW and a current operating time of 09:00-11:00; device C3 has a rated power of 40kW and a current operating time of 10:00-14:00; and device D4 has a rated power of 60kW and a current operating time of 11:00-13:00. Based on these data, calculate the current load distribution and energy storage power supply. Assuming that the energy storage discharge priority parameter requires an increase of 20kW discharge power during the 09:00-10:00 period, the load distribution during this period needs to be optimized to reduce the load demand by 20kW. The optimization methods include: Equipment A1 is currently running from 08:00 to 11:00. In order to reduce the load pressure during the 09:00-10:00 period, some tasks can be run in advance from 07:00 to 08:00, for example, reducing the power demand from 09:00 to 10:00 by 20kW. kW, reducing the load during this period; Equipment B2 operates from 09:00-11:00, and its power demand is 30kW. It can be adjusted to operate from 10:00-12:00, reducing the load from 09:00-10:00 by 30kW, and increasing the load from 10:00-11:00 by 30kW. However, since the energy storage discharge capacity during the period from 10:00-11:00 is sufficient, this adjustment is feasible; Equipment C3 operates from 10:00-14:00. If the load is 10:00 There is still room for optimization of the load during the 0-11:00 period. Its operation can be appropriately delayed so that it starts running from 11:00, thereby reducing the load pressure of 40kW during the 10:00-11:00 period. The operation time of equipment D4 is 11:00-13:00, and its 60kW load can be appropriately adjusted according to the changes in the energy storage discharge priority parameters. For example, if the discharge capacity is strong during the 11:00-12:00 period, it can be kept running during this period to avoid increasing the load from 09:00 to 10:00. After the above optimization, the load time distribution of the computer room has been adjusted to reduce the demand during the high-load period, and at the same time, the adjustable load is moved to the energy storage redundancy period, thereby achieving the best match between the energy storage power supply capacity and the load demand of the computer room, and finally obtaining the energy consumption tuning results of the computer room, including the optimized equipment operation time and load distribution strategy.

[0038] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An integrated platform for terminal energy consumption assessment and intelligent tuning of photovoltaic lithium-ion power plant, characterized in that: The platform includes: The load monitoring and evaluation module obtains the power consumption data of the terminal equipment in the photovoltaic lithium-ion power room, analyzes the peak period and the mean fluctuation range, determines the fluctuation intensity and distribution ratio, and obtains the load fluctuation characteristics of the terminal equipment room; The energy storage intelligent scheduling module calls the energy storage battery data based on the load fluctuation characteristics of the terminal of the computer room, calculates the power supply ratio, compares the demand and available power, screens the charging and discharging intervals, and obtains the energy storage intelligent scheduling plan; The abnormal energy consumption prediction module selects abnormal fluctuation time points based on the energy storage intelligent scheduling plan, predicts the future abnormal energy consumption deviation amplitude, and obtains the abnormal energy consumption deviation information of the computer room; The photovoltaic power generation correction module analyzes the influence of sunshine intensity based on the abnormal energy consumption offset information of the computer room, corrects the future abnormal energy consumption offset, and obtains photovoltaic power generation correction data; The computer room energy consumption tuning module determines the compatibility between the energy storage power supply capacity and the load demand based on the photovoltaic power generation correction data, optimizes the load distribution, and obtains the computer room energy consumption tuning result.

2. The integrated platform for terminal energy consumption assessment and intelligent tuning of photovoltaic lithium-ion power plant according to claim 1 is characterized in that: The load fluctuation characteristics at the terminal of the computer room specifically include the load peak period, the mean fluctuation range, the load fluctuation intensity, and the load distribution ratio. The energy storage intelligent scheduling plan includes the proportion of energy storage power supply, the matching degree of energy storage available power and load demand, the charging and discharging adjustment range, and the dynamic charging and discharging adjustment parameters. The abnormal energy consumption offset information of the computer room specifically refers to the load deviation value, the time point exceeding the fluctuation range, and the future abnormal energy consumption offset amplitude. The photovoltaic power generation correction data includes the photovoltaic power generation power impact ratio, the sunshine change impact range, and the corrected future energy consumption offset amplitude. The energy consumption optimization result of the computer room specifically includes the energy storage discharge priority, the load distribution optimization parameters, and the future energy consumption optimization of the computer room.

3. The integrated platform for terminal energy consumption assessment and intelligent tuning of photovoltaic lithium-ion power plant according to claim 2 is characterized in that: The load monitoring and evaluation module includes: The equipment load data collection submodule obtains the power consumption data of the terminal equipment in the photovoltaic lithium-ion power room, collects the equipment rated power, operating hours, and real-time load changes, calls the room load records, filters the data missing intervals, corrects the operating hours data, analyzes the load change trend of each device over time, and obtains the equipment load time series data; The load fluctuation rate calculation submodule is based on the equipment load time series data and adopts the formula: ； Calculate the load fluctuation rate , combined with the load rate distribution in the time period, the load rate change interval is obtained; in, represents the load fluctuation rate, Representative The equipment load value for a time period, Represents the equipment load value of the previous time period, Represents the total number of time periods; The load distribution analysis submodule analyzes the load peak period and mean fluctuation corresponding to the load rate change interval, determines the load fluctuation intensity and load distribution ratio, and obtains the load fluctuation characteristics at the terminal of the computer room.

4. The integrated platform for terminal energy consumption assessment and intelligent optimization of photovoltaic lithium-ion power plant room according to claim 3 is characterized in that: The energy storage intelligent scheduling module includes: The energy storage power supply analysis submodule collects the rated capacity, discharge power, and charge and discharge cycle number of the energy storage battery based on the load fluctuation characteristics of the terminal of the equipment room, calculates the current remaining power supply capacity of the energy storage battery, screens the sustainable power supply time within the energy storage power supply cycle, and obtains the energy storage power supply capacity parameters; The charge-discharge matching submodule adopts the formula based on the energy storage power supply capacity parameters, photovoltaic power generation power and room load power requirements: ； Calculate the proportion of energy provided by the energy storage battery to the total energy demand of the computer room , combined with the energy storage adjustment parameters in each time period, the energy storage charging and discharging matching results are obtained; in, Represents the energy storage system in the time period The energy available within Represents the computer room in the time period The energy demand within Represents the photovoltaic system in the time period The power generation energy inside is the total number of time periods in the calculation cycle; The dynamic scheduling optimization submodule is based on the energy storage charging and discharging matching results, calls the load fluctuation characteristics of the computer room, assigns priority to the energy storage discharge in each time period, adjusts the combined energy supply of photovoltaic and energy storage, and optimizes the energy storage scheduling adjustment parameters to obtain an energy storage intelligent scheduling plan.

5. The integrated platform for terminal energy consumption assessment and intelligent tuning of photovoltaic lithium-ion power plant according to claim 4 is characterized in that: The abnormal energy consumption prediction module includes: The load deviation calculation submodule obtains the real-time load data after the application of the charge and discharge adjustment parameters based on the energy storage intelligent scheduling plan, including the load mean value, load fluctuation range, and equipment operation status in the time period, using the formula: ； Calculation time period Load deviation value , and obtain the load deviation analysis results; in, represents the load in the corresponding time period, Represents load mean record; The abnormal time period screening submodule screens the duration of the abnormal load period based on the load deviation result and with reference to all load fluctuation range records, determines whether the abnormal load is an instantaneous fluctuation or a continuous trend, and obtains abnormal load time period distribution information; The future energy consumption deviation prediction submodule adopts the formula based on the abnormal load time period distribution information, referring to the overall load change trend and energy storage charging and discharging records: ； Calculating future time Load forecast value at , get the abnormal energy consumption deviation information of the computer room; in, Represents the current moment The load, Indicates the past The actual load value before the time point, Relative to the current time Push forward A point in time, i.e. a point in the past, Indicates the past Before a time point, that is, at a time point The historical load average, is the number of past time points referenced when predicting future loads. is the load offset influence coefficient.

6. The integrated platform for terminal energy consumption assessment and intelligent tuning of photovoltaic lithium-ion power plant according to claim 5 is characterized in that: The photovoltaic power generation correction module comprises: The photovoltaic power acquisition submodule obtains the abnormal energy consumption offset information of the computer room, calls the current photovoltaic power generation power, sunshine intensity, and ambient temperature, monitors the real-time output power of the photovoltaic power generation equipment, and screens the abnormal offset interval of the photovoltaic power in the target period according to the change amplitude of the photovoltaic power in the time period to obtain the photovoltaic power change data; The sunshine impact analysis submodule calls sunshine intensity and ambient temperature based on the photovoltaic power change data, analyzes the influence of sunshine intensity fluctuation on photovoltaic power generation, selects the interval with obvious influence of sunshine intensity change in the specified time period, and obtains the sunshine impact analysis result; The future energy consumption correction submodule is based on the sunshine impact analysis results and adopts the formula: ； Calculate the revised future load forecast , generate photovoltaic power generation correction data; in, represents the sunshine intensity influence coefficient, Represents the current moment The sunshine intensity, represents the reference sunshine intensity, represents the ambient temperature influence coefficient, Represents the current moment The ambient temperature, Stands for standard reference temperature.

7. The integrated platform for terminal energy consumption assessment and intelligent optimization of photovoltaic lithium-ion power plant room according to claim 6 is characterized in that: The computer room energy consumption tuning module includes: The energy storage adaptation evaluation submodule obtains the remaining power, available discharge power, and current load demand of the energy storage battery in the future time period based on the photovoltaic power generation correction data, analyzes the adaptation of the energy storage power supply capacity to the future load demand, screens the time interval of insufficient power supply or redundant power supply, and obtains energy storage supply and demand matching parameters; The discharge priority adjustment submodule calls the energy storage discharge power, the fluctuation of the load demand in the computer room, and the energy storage discharge threshold based on the energy storage supply and demand matching parameters, analyzes the supply and demand deviation between the energy storage discharge power and the load demand, adjusts the energy storage discharge priority in the corresponding time period, screens the discharge priority adjustment scheme, and obtains the energy storage discharge priority parameter; The load optimization distribution submodule calls the load demand data of the computer room, the equipment operation time period, and the load distribution situation based on the energy storage discharge priority parameters, analyzes the matching degree between the load distribution situation and the energy storage power supply capacity, screens the adjustable load time interval, and obtains the energy consumption tuning result of the computer room.

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