Hybrid energy storage joint scheduling method in new energy consumption scene

Abstract

The invention relates to the technical field of data processing, in particular to a hybrid energy storage joint scheduling method in a new energy consumption scene. The method comprises the following steps: collecting multi-source monitoring data and a current consumption rate; based on the multi-source monitoring data, the residual energy storage capacity of the energy storage equipment and the switching frequency of historical energy storage modes are determined, and a mode switching threshold value between compressed air energy storage and electrochemical energy storage is determined by combining the absorption rate; determining a real-time load intensity index and charge-discharge frequency of electrochemical energy storage based on the multi-source monitoring data, and predicting a load intensity index of electrochemical energy storage in the future in combination with the predicted charge state influence coefficient of the battery; the mode switching threshold value is corrected according to the predicted load intensity index of future electrochemical energy storage, and combined scheduling of compressed air energy storage and electrochemical energy storage is completed; and after the joint scheduling is finished, executing a preset consumption ending operation. The long-term stable operation capability of the charging and discharging system is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically to a hybrid energy storage joint scheduling method in new energy consumption scenarios. Background Technology

[0002] Renewable energy consumption refers to the entire process of generating electricity from renewable energy sources such as wind, solar, hydro, and biomass, and then effectively utilizing it through grid connection, local use, and energy storage, thus forming a closed loop between electricity production and consumption. When generating new energy, due to its strong volatility, intermittency, and randomness, it cannot be as stable and controllable as traditional thermal power or nuclear power. To ensure the stability of the power grid, consumption methods must be used to smooth power fluctuations and match electricity demand. To address the issue that a single energy storage method cannot handle the complex power output of new energy sources during the consumption process, existing methods combine new energy storage methods with different characteristics, such as combining compressed air energy storage with electrochemical energy storage.

[0003] Existing joint scheduling methods primarily prioritize rapid charging of electrochemical energy storage during the charging phase. Once the lithium battery is fully charged, compressed air energy storage is used. Subsequently, during the discharging phase, electrochemical energy storage provides initial power, followed by compressed air energy storage to provide stable power, with electrochemical energy storage also assisting in regulating fluctuations. However, existing methods heavily rely on real-time monitoring of both energy storage states during the compressed air and electrochemical energy storage processes. Delays in monitoring signals can lead to overcharging or over-discharging, potentially causing equipment damage. Furthermore, because electrochemical energy storage is consistently responsible for startup and regulation in existing scheduling methods, its energy consumption is significantly higher than that of compressed air energy storage, resulting in substantial load differences between different systems and hindering long-term system operation. Summary of the Invention

[0004] To address the technical problem that electrochemical energy storage has consistently been responsible for startup and regulation in existing scheduling methods, leading to significantly higher energy consumption than compressed air energy storage and resulting in substantial load differences between different systems, which is detrimental to long-term system operation, this invention aims to provide a hybrid energy storage joint scheduling method for new energy consumption scenarios. The specific technical solution adopted is as follows: In a first aspect, embodiments of the present invention provide a hybrid energy storage joint scheduling method for new energy consumption scenarios, applicable to systems including energy storage devices. The method includes the following steps: Collect multi-source monitoring data and the current absorption rate; Based on multi-source monitoring data, the remaining energy storage capacity of the energy storage device and the number of times the historical energy storage mode has been switched are determined. Combined with the absorption rate, the mode switching threshold between compressed air energy storage and electrochemical energy storage is determined. Based on multi-source monitoring data, the real-time load intensity index and charge / discharge frequency of electrochemical energy storage are determined. Combined with the predicted state-of-charge influence coefficient of the battery, the future load intensity index of electrochemical energy storage is predicted. The mode switching threshold is corrected based on the predicted future load intensity index of electrochemical energy storage to complete the joint scheduling of compressed air energy storage and electrochemical energy storage. After the joint scheduling is completed, the preset absorption and cleanup operations are executed.

[0005] In some embodiments, the multi-source monitoring data includes new energy source data, electrochemical energy storage data, compressed air energy storage data, regional environmental data, and regional power grid data.

[0006] In some embodiments, the current absorption rate is determined by: Real-time power output data of new energy equipment is determined based on new energy side data from multi-source monitoring data. Real-time electricity demand data of the regional power grid is determined based on regional power grid data from multi-source monitoring data; By comparing the differences between real-time power output data and real-time electricity demand data, the current absorption rate can be determined.

[0007] In some embodiments, the energy storage device includes an electrochemical energy storage device and a compressed air energy storage device, and the collection of multi-source monitoring data includes collecting electrochemical energy storage data based on the electrochemical energy storage device and collecting compressed air energy storage data based on the compressed air energy storage device.

[0008] In some embodiments, determining the remaining energy storage capacity of the energy storage device and the number of historical energy storage mode switching times based on multi-source monitoring data, and determining the mode switching threshold between compressed air energy storage and electrochemical energy storage in conjunction with the absorption rate, includes: Based on electrochemical energy storage data, determine the real-time state of charge data of the electrochemical energy storage device, and determine the remaining energy storage capacity of the electrochemical energy storage device when switching historical modes based on the real-time state of charge data. The number of times the compressed air energy storage mode and the electrochemical energy storage mode were switched was counted from the historical operation records of the energy storage equipment. Obtain the absorption rate data corresponding to the historical mode switch; The mode switching threshold is calculated based on the number of times the compressed air energy storage mode and the electrochemical energy storage mode are switched, combined with the remaining energy storage capacity of the electrochemical energy storage device and the corresponding absorption rate during historical mode switching.

[0009] In some embodiments, the real-time load intensity index of the electrochemical energy storage is determined as follows: Determine the device temperature data of the electrochemical energy storage equipment based on electrochemical energy storage data; Determine the real-time remaining energy storage capacity of the electrochemical energy storage device; The real-time load intensity index of electrochemical energy storage is calculated based on equipment temperature data and real-time remaining energy storage capacity.

[0010] In some embodiments, the charge / discharge frequency is determined as follows: Determine the real-time output data of new energy equipment and the real-time electricity demand data of the regional power grid; The influence coefficient of the regional environment on the output of new energy sources is determined based on regional environmental data from multi-source monitoring data. Collect future meteorological data and combine it with the influence coefficient of the regional environment on the output of new energy sources to predict the output of new energy sources at various future times; By combining the predicted new energy output with the corresponding regional electricity demand, the energy storage demand index at each time point is calculated, and the energy storage demand change curve is determined based on the energy storage demand index at each time point. Analyze the energy storage demand variation curve to identify the charge-discharge cycle of electrochemical energy storage, and calculate the charge-discharge frequency of electrochemical energy storage based on the average time interval of the charge-discharge cycle.

[0011] In some embodiments, the prediction of the future electrochemical energy storage load intensity index by incorporating the predicted state-of-charge influence coefficient of the battery includes: Obtain the determined charge and discharge frequency of the electrochemical energy storage; The charge and discharge amplitude index of each predicted charge and discharge process of electrochemical energy storage is calculated based on the energy storage demand change curve, and the state of charge influence coefficient of the battery is determined based on the charge and discharge amplitude index. The predicted load intensity component is calculated based on the charging and discharging frequency and the state of charge influence coefficient. By combining the real-time load intensity index of electrochemical energy storage with the predicted load intensity components, the future load intensity index of electrochemical energy storage is predicted.

[0012] In some embodiments, after determining the mode switching threshold between compressed air energy storage and electrochemical energy storage, the method further includes: Compare the predicted energy storage demand index with the actual energy storage demand within the same time period to calculate the prediction error index for the same time period. Calculate the energy storage pressure index of the electrochemical energy storage device based on its real-time remaining energy storage capacity and real-time energy storage demand. Based on the energy storage demand index, predict the energy storage change coefficient of new energy output, and combine the energy storage change coefficient of new energy output with the energy storage pressure index to calculate the necessity of switching energy storage modes at the current moment. By combining the necessity of energy storage mode switching with the prediction error index, an adjustment coefficient for the mode switching threshold is constructed, and the mode switching threshold is corrected for the first time based on the adjustment coefficient.

[0013] In some embodiments, correcting the mode switching threshold based on the predicted future load intensity index of electrochemical energy storage includes: Obtain the current mode switching threshold; Obtain the predicted load intensity index for future electrochemical energy storage; The correction coefficient is determined based on the load intensity index of future electrochemical energy storage. The correction coefficient is then used to correct the current mode switching threshold, and the corrected mode switching threshold is taken as the final mode switching threshold.

[0014] In a second aspect, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements the various possible implementations of the first aspect.

[0015] Thirdly, embodiments of the present invention provide a computer program product comprising: computer program code, which, when run on a computer, causes the computer to perform the method described in the first aspect or any possible implementation thereof.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the various possible implementations of the first aspect.

[0017] The embodiments of the present invention have at least the following beneficial effects: This invention collects multi-source monitoring data and the current absorption rate; based on the multi-source monitoring data, it determines the remaining energy storage capacity of the energy storage device and the number of historical energy storage mode switching times, and determines the mode switching threshold between compressed air energy storage and electrochemical energy storage in conjunction with the absorption rate; based on the multi-source monitoring data, it determines the real-time load intensity index and charge / discharge frequency of electrochemical energy storage, and predicts the future load intensity index of electrochemical energy storage in conjunction with the predicted state of charge influence coefficient of the battery; it corrects the mode switching threshold according to the predicted future load intensity index of electrochemical energy storage, and completes the joint scheduling of compressed air energy storage and electrochemical energy storage; after the joint scheduling is completed, it executes the preset absorption closing operation. In this application, the switching threshold is adjusted by load intensity prediction to reduce the frequency and amplitude of deep charge and discharge, reduce system losses, make the load of electrochemical energy storage and compressed air energy storage more balanced, and ensure the long-term stable operation of the joint system. Attached Figure Description

[0018] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a hybrid energy storage joint scheduling method for a new energy consumption scenario provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a computer device provided in one embodiment of the present invention. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the hybrid energy storage joint scheduling method for new energy consumption scenarios proposed in this invention.

[0021] In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0022] In the description of the embodiments of the present invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" means two or more.

[0023] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0025] The embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0026] The following description, in conjunction with the accompanying drawings, details the specific scheme of the hybrid energy storage joint scheduling method for new energy consumption scenarios provided by this invention.

[0027] Example 1: Please see Figure 1 This document illustrates a flowchart of a hybrid energy storage joint scheduling method for a new energy consumption scenario provided by an embodiment of the present invention. Applied to a system including several regional radiation monitoring instruments, the method includes the following steps: S10. Collect multi-source monitoring data and the current absorption rate.

[0028] Specifically, multi-source monitoring data can be collected in real time through sensor networks deployed at renewable energy power plants, energy storage systems, and the power grid. This includes: reading real-time output data from the metering devices of renewable energy power plant grid-connected cabinets (i.e., the real-time power transmission data of renewable energy equipment, i.e., the power supply data on the renewable energy side); obtaining total voltage / current, individual cell voltage / temperature, and state of charge of the battery packs in electrochemical energy storage through the battery management system (i.e., electrochemical energy storage data); collecting pressure / temperature data of compressed air storage tanks using piezoelectric pressure sensors and platinum resistance temperature sensors; collecting compressor / expander power data through power transmitters; collecting temperature / flow rate data of the heat recovery system using thermocouple temperature sensors and electromagnetic flowmeters; and collecting liquid level data of the storage tanks using radar level gauges (i.e., compressed air energy storage data). Simultaneously, regional environmental data is obtained from meteorological platforms, and regional electricity demand data is obtained from the power grid dispatching system (i.e., regional power grid data).

[0029] It should be noted that the acquisition method for each type of data in the multi-source monitoring data, i.e., the specific data category acquired, is not unique and can be selected according to actual needs.

[0030] Furthermore, real-time output data of new energy equipment is obtained based on the collected new energy side data, and real-time electricity demand data is obtained based on regional power grid data. The real-time electricity demand data represents the electricity consumption data required by users and can be directly obtained from the regional power grid. The absorption rate is calculated by comparing the difference between the two. Specifically, the calculation method for the absorption rate is well-known to those skilled in the art and will not be elaborated here. In this embodiment of the invention, the absorption rate is the ratio of real-time electricity demand data to real-time output data.

[0031] Furthermore, the energy storage device includes an electrochemical energy storage device and a compressed air energy storage device, and the collection of multi-source monitoring data includes collecting electrochemical energy storage data based on the electrochemical energy storage device and collecting compressed air energy storage data based on the compressed air energy storage device.

[0032] It should be noted that electrochemical energy storage devices can be battery management systems, while compressed air energy storage devices can be power transmitters.

[0033] S11. Based on multi-source monitoring data, determine the remaining energy storage capacity of the energy storage device and the number of times the historical energy storage mode has been switched, and determine the mode switching threshold between compressed air energy storage and electrochemical energy storage in combination with the absorption rate.

[0034] It should be noted that the remaining energy storage capacity represents the remaining energy stored by the energy storage device. The number of switching times in the historical energy storage mode can be directly obtained and is used to represent the corresponding statistical number of times when switching between two modes. The mode switching threshold is the initial threshold set when switching between two modes is required. When the energy storage capacity of any type reaches the mode switching threshold, the energy storage mode needs to be switched to another energy storage mode. It should be noted that the two switchable modes include compressed air energy storage mode and electrochemical energy storage mode.

[0035] It should be noted that when switching between compressed air energy storage and electrochemical energy storage, the relationship between the remaining energy storage capacity of different energy storage methods and the energy storage variation coefficient of the predicted new energy output needs to be considered in real time. For example, since the capacity of electrochemical energy storage is relatively small, when the predicted required energy storage is much larger than the remaining energy storage capacity of electrochemical energy storage, a lower switching threshold can be set to avoid overcharging. Simultaneously, by comparing the predicted data with subsequently collected real-time data, the error of the predicted data can be quantified. This reduces the reliability of the predicted data when the error is large, maintains higher mode switching tolerance at the adaptive mode switching threshold, and improves the new energy consumption rate.

[0036] Step S11 includes: Based on electrochemical energy storage data, determine the real-time state of charge data of the electrochemical energy storage device, and determine the remaining energy storage capacity of the electrochemical energy storage device when switching historical modes based on the real-time state of charge data. It should be noted that the remaining energy storage capacity of electrochemical energy storage can be recorded multiple times from historical data when the mode is switched.

[0037] The number of times the compressed air energy storage mode and the electrochemical energy storage mode were switched was counted from the historical operation records of the energy storage equipment. Obtain the absorption rate data corresponding to the historical mode switch; The mode switching threshold is calculated based on the number of times the compressed air energy storage mode and the electrochemical energy storage mode are switched, combined with the remaining energy storage capacity of the electrochemical energy storage device and the corresponding absorption rate during historical mode switching.

[0038] A lower absorption rate indicates that there is a situation where electricity cannot be stored, which may be due to late mode switching, leading to overcharging of electrochemical energy storage. Therefore, the lower the absorption rate, the higher the mode switching threshold, meaning that there is more remaining energy storage capacity of electrochemical energy storage at the time of switching.

[0039] It should be noted that the mode switching threshold The calculation method can be: in, This indicates the number of records of historical mode switching, or the number of switching times. Indicates the first The remaining energy storage capacity of the electrochemical energy storage recorded this time. Indicates the first The absorption rate of the previous historical records.

[0040] Specifically, when the absorption rate When the capacity is small, the threshold may be twice the remaining energy storage capacity. If the remaining energy storage capacity is greater than or equal to half of the total capacity, the threshold will not match the actual capacity. Therefore, the maximum threshold for mode switching is specified to be no more than 0.7 times the total capacity.

[0041] S12. Based on multi-source monitoring data, determine the real-time load intensity index and charge / discharge frequency of electrochemical energy storage, and combine the predicted state-of-charge influence coefficient of the battery to predict the future load intensity index of electrochemical energy storage.

[0042] It should be noted that the real-time load intensity index represents the load intensity before the energy storage mode switch at the current moment, and the charge / discharge frequency represents the frequency of charging and discharging under the current energy storage mode, which can be once per minute, without any specific limitation. The state of charge influence coefficient is used to characterize the magnitude of energy change during charging and discharging at any given time. Combining the above three factors, the load intensity index of future electrochemical energy storage can be predicted.

[0043] The real-time load intensity index of the electrochemical energy storage is determined as follows: Determine the device temperature data of the electrochemical energy storage equipment based on electrochemical energy storage data; Determine the real-time remaining energy storage capacity of the electrochemical energy storage device; The real-time load intensity index of electrochemical energy storage is calculated based on equipment temperature data and real-time remaining energy storage capacity.

[0044] It should be noted that, taking time t as an example, the real-time load intensity index of electrochemical energy storage... The calculation method can be: in, Here is the device temperature data at time t. Let be the real-time remaining energy storage capacity at time t, and norm() represent the normalization function, which can be a maximum or minimum value normalization.

[0045] It should be noted that the electrochemical energy storage load intensity index analyzed at this time is the load intensity before the mode switch. Therefore, the approximate load intensity index of the current electrochemical energy storage can only be determined by the equipment temperature and the remaining energy storage capacity.

[0046] The charge / discharge frequency is determined in the following manner: Determine the real-time output data of new energy equipment and the real-time electricity demand data of the regional power grid.

[0047] The influence coefficient of the regional environment on the output of new energy sources is determined based on regional environmental data from multi-source monitoring data.

[0048] It should be noted that by analyzing historical renewable energy output data and regional environmental factors, it is possible to predict renewable energy output in the future. Combined with possible changes in regional electricity demand, it is possible to predict the change in the amount of electricity that needs to be stored, i.e., the energy storage change coefficient of renewable energy output, and thus provide data support for the adaptive adjustment of subsequent energy storage switching thresholds.

[0049] It should be noted that, taking the regional environment of the i-th type of new energy equipment as an example, the i-th type of new energy equipment... The influence coefficient of the environment in which a new energy device is located on the output of new energy. The calculation method can be: in, Indicates the first At this moment The output value of a new energy device, that is, real-time output data; Indicates the first At this moment Environmental values ​​corresponding to the environment in which a new energy device is located, i.e., regional environmental data, such as wind speed for wind power generation equipment and sunlight intensity for photovoltaic power generation equipment. This indicates the number of moments in the historical data collected. Real-time power output data and regional environmental data are standardized or normalized before calculation to eliminate dimensions.

[0050] By collecting future meteorological data and combining it with the influence coefficient of the regional environment on the output of new energy sources, the output of new energy sources at various future times can be predicted.

[0051] It should be noted that, taking the i-th type of new energy equipment at time t as an example, the new energy output at each future time... The prediction results can be calculated as follows: in, Indicates the first The impact coefficient of the environment on the output of new energy sources. This refers to future meteorological data, which is forecasted by meteorological stations for the next few hours or day. For example, future meteorological data might show that due to cloud cover, the light intensity or range will decrease at time t. Here, the future meteorological data refers to the affected light intensity; that is, for photovoltaic power generation equipment, the corresponding future meteorological data is light intensity. Other future meteorological data follow the same logic; for example, for wind power generation equipment, the corresponding future meteorological data is wind speed. New energy sources are highly susceptible to external factors when generating power, such as a cloud blocking sunlight from above the photovoltaic panels. In this case, no meteorological data exists, so the power output forecast results for new energy sources are usually ideal values. The calculation of all new energy types at time t... Current New Energy Output Forecast Results The sum of , to obtain the first Real-time renewable energy output forecast results .

[0052] By combining the predicted new energy output with the corresponding regional electricity demand, the energy storage demand index at each time point is calculated, and the energy storage demand change curve is determined based on the energy storage demand index at each time point.

[0053] It should be noted that, taking one hour as a time period, and based on the historical electricity consumption of different time periods in the region, the average electricity consumption of any given time period across different days is calculated to obtain the first... Regional electricity demand during a certain time period ; with the first The time period corresponding to a given moment is denoted as the [number]. The time period, combined with the predicted first time period New energy output at present and the Regional electricity demand during a certain time period The prediction yielded the first Energy storage demand index at the moment ; in, Indicates the predicted first The current output of new energy sources Indicates the first Regional electricity demand over a specific time period. When This indicates that there is unabsorbed electricity, therefore, when the energy storage demand index... The larger the value, the more electricity needs to be stored. It should be noted that, due to the predicted [number]th [year], [the amount of electricity stored] is [increased / ... New energy output at present Because of its inherent uncertainty, it is necessary to further adjust it by setting weights. That is, the further back in time the forecast is, the more difficult it is to predict changes in the weather environment. Therefore, the further back in time the forecast is, the lower the corresponding demand index will be.

[0054] It should be noted that, based on the energy storage demand index obtained at different times... Perform a line fitting on the discrete data and calculate the slope of the fitted line. This represents the trend of energy storage change; (obtained by constructing a two-dimensional coordinate system, where the fitted curves corresponding to each discrete data point are denoted as the energy storage demand change curves). Thus, the energy storage demand change curve can be obtained. Simultaneously, the exponential gradient of energy storage demand at adjacent time points is acquired. And calculate the mean of all energy storage demand exponential gradients, denoted as the i-th Complexity of predicted changes in energy storage demand at any given time Finally, calculate the first... Complexity of predicted changes in energy storage demand at any given time With the changing trend of energy storage The product of, i.e., the first Energy storage variation coefficient of predicted new energy output at any given time : It should be noted that a larger energy storage variation coefficient indicates a greater demand for energy storage when absorbing new energy sources in the future, and stronger fluctuations in the output of new energy sources. norm() represents the normalization function, which can be a maximum or minimum value normalization function.

[0055] Analyze the energy storage demand variation curve to identify the charge-discharge cycle of electrochemical energy storage, and calculate the charge-discharge frequency of electrochemical energy storage based on the average time interval of the charge-discharge cycle.

[0056] It should be noted that, based on the predicted energy storage demand change curve, the possible replay cycle of electrochemical energy storage is predicted, resulting in the [number]th [period / cycle]. Predicted charge / discharge frequency of electrochemical energy storage .

[0057] It should be noted that when energy storage demand fluctuates, it indicates that electrochemical processes need to be frequently charged and discharged to regulate and ensure the stability of compressed air energy storage. For changes in energy storage demand, the transition from one trough to another represents a cycle of charge and discharge for electrochemical energy storage. By obtaining the average of the charge and discharge time intervals, the charge and discharge frequency can be determined. The calculation method is as follows: in, This represents the average time interval between charging and discharging in the energy storage demand change curve.

[0058] In step S12, the prediction of the future electrochemical energy storage load intensity index, based on the predicted state-of-charge influence coefficient of the battery, includes: Obtain the determined charge and discharge frequency of the electrochemical energy storage; The charge and discharge amplitude index of each predicted charge and discharge process of electrochemical energy storage is calculated based on the energy storage demand change curve, and the state of charge influence coefficient of the battery is determined based on the charge and discharge amplitude index.

[0059] It should be noted that, regarding the energy storage demand change curve, the calculation and prediction of the first... The charge / discharge amplitude index of the first charge / discharge cycle When analyzing electrochemical energy storage load, the increase in battery fatigue load caused by deep charge-discharge is much greater than that caused by shallow charge-discharge. in, Indicates the first The increase in the secondary energy storage demand index, electrochemical energy storage charging; Indicates the first The decrease in the energy storage demand index, electrochemical energy storage discharge, norm() represents the normalization function, which can be the maximum and minimum value normalization function.

[0060] Based on the above predictions, the first... The charge / discharge amplitude index of the first charge / discharge cycle Calculate the mean of the amplitude exponents of all charge and discharge cycles. , recorded as the number The influence coefficient of the battery's state of charge predicted at any time .

[0061] The predicted load intensity component is calculated based on the charging and discharging frequency and the state of charge influence coefficient. By combining the real-time load intensity index of electrochemical energy storage with the predicted load intensity components, the future load intensity index of electrochemical energy storage is predicted.

[0062] It should be noted that, according to the first Predicted charge / discharge frequency of electrochemical energy storage and the influence coefficient of battery state of charge The load intensity index of future electrochemical energy storage is predicted. ; It should be noted that the predicted load intensity index at this time is the load intensity after the mode switch, that is, based on the real-time load intensity, combined with the subsequent adjustment demand of electrochemical energy storage.

[0063] in, The predicted electrochemical energy storage load intensity index after mode switching is represented, which is also the load intensity component. norm() represents the normalization function, which can be the maximum and minimum value normalization function.

[0064] The process, after determining the mode switching threshold between compressed air energy storage and electrochemical energy storage as described in step S11, also includes an initial correction of the obtained mode switching threshold. Specifically: By comparing the predicted energy storage demand index with the actual energy storage demand within the same time period, the prediction error index for the same time period is calculated.

[0065] It should be noted that if the predicted load intensity of electrochemical energy storage after mode switching is greater, a larger capacity of electrochemical energy storage needs to be reserved before switching to ensure the stability of regulation capability. Therefore, it is necessary to further increase the mode switching threshold. .

[0066] It should be noted that, according to the first Real-time new energy output at any time Real-time electricity demand in the region To obtain real-time energy storage demand By comparing the first Current forecast of energy storage demand index Compared with actual energy storage demand The difference, determine the first Prediction error index at time ; in, Indicates the first The actual demand index at time t is represented by norm(), which is a normalization function. It can be a maximum-minimum normalization function. Here, by comparing the predicted data with the real-time data collected at the same time (e.g., time t), the error of the predicted data can be quantified. This reduces the confidence of the predicted data when the error is large, and retains a higher mode switching fault tolerance when the adaptive mode switching threshold is reached, thereby improving the renewable energy consumption rate.

[0067] Furthermore, based on the prediction error index at multiple consecutive moments (within the same time period) Calculate all prediction error indices The mean value can be used to determine the current time period. Within the prediction error index .

[0068] It should be noted that a larger prediction error index within a given time period indicates a significant discrepancy between the predicted and actual renewable energy output. A positive error indicates that actual energy storage demand is lower than predicted demand, necessitating a delayed mode switch. Conversely, a negative error indicates that actual energy storage demand exceeds predicted demand, requiring an earlier mode switch.

[0069] The energy storage pressure index of the electrochemical energy storage device is calculated based on the real-time remaining energy storage capacity and real-time energy storage demand.

[0070] It should be noted that real-time energy storage demand Real-time remaining energy storage capacity of electrochemical energy storage By comparison, the first number was calculated. Energy storage pressure index of electrochemical energy storage devices at any time The calculation method is as follows: in, This represents the real-time remaining energy storage capacity of electrochemical energy storage, and norm() represents the normalization function, which can be a maximum or minimum value normalization function.

[0071] Based on the energy storage demand index, predict the energy storage change coefficient of new energy output, and combine the energy storage change coefficient of new energy output with the energy storage pressure index to calculate the necessity of switching energy storage modes at the current moment. It should be noted that, in conjunction with the first Energy storage variation coefficient of predicted new energy output at any given time Calculate the first The necessity of switching energy storage modes at any time The calculation method is as follows: in, Indicates the first The energy storage variation coefficient of the predicted new energy output at any given time. Indicates the first The energy storage pressure index of an electrochemical energy storage device at any given time.

[0072] It should be noted that when the first The necessity of switching energy storage modes at any time The larger the value, the higher the value in the first place. At this moment, the remaining energy storage capacity of electrochemical energy storage is insufficient for subsequent energy storage, so it is necessary to switch to compressed air energy storage. norm() represents the normalization function, which can be the maximum and minimum value normalization function.

[0073] By combining the necessity of energy storage mode switching with the prediction error index, an adjustment coefficient is constructed for the initial threshold, and the mode switching threshold is corrected for the first time based on the adjustment coefficient.

[0074] It should be noted that, in conjunction with the first Time period Prediction error index and the The necessity of switching energy storage modes at any time The mode switching threshold is adaptively adjusted to obtain the corrected first... The mode switching threshold at any given time.

[0075] in, This represents the initial threshold, which is also the initial mode switching threshold. This formula represents the first correction process for the initial threshold. This is for adjusting the coefficient.

[0076] S13. Based on the predicted future load intensity index of electrochemical energy storage, the mode switching threshold is corrected to complete the joint scheduling of compressed air energy storage and electrochemical energy storage.

[0077] The step S13, which involves correcting the mode switching threshold based on the predicted future load intensity index of electrochemical energy storage, includes: Obtain the current mode switching threshold; it should be noted that the mode switching threshold referred to here is the mode switching threshold after the initial correction. .

[0078] Obtain the predicted load intensity index for future electrochemical energy storage; The correction coefficient is determined based on the load intensity index of future electrochemical energy storage. The correction coefficient is then used to correct the mode switching threshold after the first correction at the current moment. The mode switching threshold after the second correction is then used as the final mode switching threshold.

[0079] It should be noted that if the predicted load intensity of electrochemical energy storage after mode switching is greater, a larger capacity of electrochemical energy storage needs to be reserved before switching to ensure the stability of regulation capability. Therefore, it is necessary to readjust the mode switching threshold after the initial correction. Finally, based on the predicted future load intensity index of electrochemical energy storage... , for the The mode switching threshold after the first correction at this time The correction is performed to obtain the mode switching threshold after the second correction. .

[0080] Therefore, the revised mode switching threshold is used as the final mode switching threshold. By adaptively adjusting the mode switching thresholds of compressed air energy storage and electrochemical energy storage, joint scheduling of the two is achieved.

[0081] S14. After the joint scheduling is completed, perform the preset elimination and cleanup operations.

[0082] It should be noted that after the joint scheduling of energy storage is completed, timely completion operations should be carried out to ensure the complete consumption of new energy.

[0083] Specifically, in renewable energy consumption scenarios, after the joint dispatch ends, a small amount of unconsumed electricity may remain due to factors such as sudden changes in renewable energy output and prediction errors. If not handled promptly, this electricity will be directly abandoned, resulting in energy waste and economic losses. Secondary consumption can reduce the abandonment rate of a single dispatch, thereby improving the overall renewable energy consumption rate. The specific steps are as follows: First, the remaining electricity is consumed in a secondary manner. That is, the remaining renewable energy electricity that has not been consumed after joint dispatch is counted. If there is remaining electricity, the remaining energy storage capacity of electrochemical energy storage is used first to replenish it. If the electrochemical energy storage is full, the CAES off-peak energy replenishment mode is activated, or the adjustable load in the region is coordinated to consume it in time. Then, the status of the energy storage equipment is reset.

[0084] Adjust the SOC of the electrochemical energy storage to the optimal reserve range (40%-60%) to avoid overcharging / over-discharging residues.

[0085] Reset the CAES storage pressure to the rated median value (e.g., 7-8 MPa) to release excess compression heat (to prevent excessive pressure in the storage tank). Check whether the cooling and control systems of both energy storage systems have returned to normal.

[0086] Finally, the curtailment situation is verified and handled urgently, namely, comparing the output data of new energy sources and the charging and discharging data of energy storage before and after dispatch to calculate the actual curtailment rate; if the curtailment rate is >1.5%, the cause is immediately investigated (such as energy storage not being fully activated or grid channels being temporarily restricted), and the grid is coordinated to temporarily open the transmission channels, or the backup energy storage unit is activated to replenish the storage.

[0087] It should be noted that the device provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above.

[0088] Figure 2 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. For example, as shown... Figure 2As shown, the computer device 20 includes: a memory 21, a processor 22, and a computer program 23 stored in the memory 21 and running on the processor 22. When the processor 22 executes the computer program 23, the computer device can execute the hybrid energy storage joint scheduling method under any new energy consumption scenario described above.

[0089] Furthermore, embodiments of the present invention also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to execute the hybrid energy storage joint scheduling method for new energy consumption scenarios provided in embodiments of the present invention.

[0090] In this embodiment of the invention, the device can be divided into functional modules according to the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and is only a logical functional division. In actual implementation, there may be other division methods.

[0091] It should be understood that the apparatus provided in this embodiment of the invention is used to execute the hybrid energy storage joint scheduling method in the above-mentioned new energy consumption scenario, and thus can achieve the same effect as the above-mentioned implementation method.

[0092] When using integrated units, the device may include a processing module and a storage module. When applied to a device, the processing module can be used to control and manage the device's operations. The storage module can be used to support the device in executing program code, etc. The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits as described in this disclosure. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of Digital Signal Processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0093] In addition, the device provided in the embodiments of the present invention may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute the hybrid energy storage joint scheduling method in the new energy consumption scenario provided in the above embodiments.

[0094] This invention also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the aforementioned method steps to implement the hybrid energy storage joint scheduling method for new energy consumption scenarios provided in the above embodiments.

[0095] This invention also provides a computer program product that, when run on a computer, causes the computer to execute the aforementioned steps to realize the hybrid energy storage joint scheduling method for new energy consumption scenarios provided in the above embodiments.

[0096] In this invention, the apparatus, computer-readable storage medium, computer program product, or chip provided in the embodiments are all used to execute the corresponding methods described above. Therefore, the beneficial effects they achieve can be referred to the beneficial effects in the corresponding methods described above, and will not be repeated here. Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by this invention, it should be understood that the disclosed apparatus and method can be implemented in other ways.

[0097] The device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0098] It should also be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0099] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0100] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0101] The above content is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A hybrid energy storage joint scheduling method for new energy consumption scenarios, characterized in that, Applied to systems including energy storage devices, the method includes the following steps: Collect multi-source monitoring data and the current absorption rate; Based on multi-source monitoring data, the remaining energy storage capacity of the energy storage device and the number of times the historical energy storage mode has been switched are determined. Combined with the absorption rate, the mode switching threshold between compressed air energy storage and electrochemical energy storage is determined. Based on multi-source monitoring data, the real-time load intensity index and charge / discharge frequency of electrochemical energy storage are determined. Combined with the predicted state-of-charge influence coefficient of the battery, the future load intensity index of electrochemical energy storage is predicted. The mode switching threshold is corrected based on the predicted future load intensity index of electrochemical energy storage to complete the joint scheduling of compressed air energy storage and electrochemical energy storage. After the joint scheduling is completed, the preset absorption and cleanup operations are executed.

2. The hybrid energy storage joint dispatch method in the new energy consumption scenario according to claim 1, characterized in that, The multi-source monitoring data includes new energy source data, electrochemical energy storage data, compressed air energy storage data, regional environmental data, and regional power grid data.

3. The hybrid energy storage joint dispatch method in the new energy consumption scenario according to claim 1, characterized in that, The current absorption rate is determined in the following ways: Real-time power output data of new energy equipment is determined based on new energy side data from multi-source monitoring data. Real-time electricity demand data of the regional power grid is determined based on regional power grid data from multi-source monitoring data; By comparing the differences between real-time power output data and real-time electricity demand data, the current absorption rate can be determined.

4. The hybrid energy storage joint dispatch method in the new energy consumption scenario according to claim 1, characterized in that, The energy storage devices include electrochemical energy storage devices and compressed air energy storage devices. The collection of multi-source monitoring data includes electrochemical energy storage data collected based on electrochemical energy storage devices and compressed air energy storage data collected based on compressed air energy storage devices.

5. The hybrid energy storage joint dispatch method in the new energy consumption scenario according to claim 4, characterized in that, The method involves determining the remaining energy storage capacity and historical energy storage mode switching frequency of the energy storage device based on multi-source monitoring data, and then determining the mode switching threshold between compressed air energy storage and electrochemical energy storage based on the absorption rate, including: Based on electrochemical energy storage data, determine the real-time state of charge data of the electrochemical energy storage device, and determine the remaining energy storage capacity of the electrochemical energy storage device when switching historical modes based on the real-time state of charge data. The number of times the compressed air energy storage mode and the electrochemical energy storage mode were switched was counted from the historical operation records of the energy storage equipment. Obtain the absorption rate data corresponding to the historical mode switch; The mode switching threshold is calculated based on the number of times the compressed air energy storage mode and the electrochemical energy storage mode are switched, combined with the remaining energy storage capacity of the electrochemical energy storage device and the corresponding absorption rate during historical mode switching.

6. The hybrid energy storage joint scheduling method in the new energy consumption scenario according to claim 5, characterized in that, The real-time load intensity index of the electrochemical energy storage is determined as follows: Determine the device temperature data of the electrochemical energy storage equipment based on electrochemical energy storage data; Determine the real-time remaining energy storage capacity of the electrochemical energy storage device; The real-time load intensity index of electrochemical energy storage is calculated based on equipment temperature data and real-time remaining energy storage capacity.

7. The hybrid energy storage joint dispatch method for new energy consumption scenarios according to claim 3, characterized in that, The charge / discharge frequency is determined in the following manner: Determine the real-time output data of new energy equipment and the real-time electricity demand data of the regional power grid; The influence coefficient of the regional environment on the output of new energy sources is determined based on regional environmental data from multi-source monitoring data. Collect future meteorological data and combine it with the influence coefficient of the regional environment on the output of new energy sources to predict the output of new energy sources at various future times; By combining the predicted new energy output with the corresponding regional electricity demand, the energy storage demand index at each time point is calculated, and the energy storage demand change curve is determined based on the energy storage demand index at each time point. Analyze the energy storage demand variation curve to identify the charge-discharge cycle of electrochemical energy storage, and calculate the charge-discharge frequency of electrochemical energy storage based on the average time interval of the charge-discharge cycle.

8. The hybrid energy storage joint dispatch method in the new energy consumption scenario according to claim 7, characterized in that, The prediction of the future load intensity index of electrochemical energy storage, based on the predicted state-of-charge influence coefficient of the battery, includes: Obtain the determined charge and discharge frequency of the electrochemical energy storage; The charge and discharge amplitude index of each predicted charge and discharge process of electrochemical energy storage is calculated based on the energy storage demand change curve, and the state of charge influence coefficient of the battery is determined based on the charge and discharge amplitude index. The predicted load intensity component is calculated based on the charging and discharging frequency and the state of charge influence coefficient. By combining the real-time load intensity index of electrochemical energy storage with the predicted load intensity components, the future load intensity index of electrochemical energy storage is predicted.

9. The hybrid energy storage joint dispatch method in the new energy consumption scenario according to claim 8, characterized in that, After determining the mode switching threshold between compressed air energy storage and electrochemical energy storage, the method further includes: Compare the predicted energy storage demand index with the actual energy storage demand within the same time period to calculate the prediction error index for the same time period. Calculate the energy storage pressure index of the electrochemical energy storage device based on its real-time remaining energy storage capacity and real-time energy storage demand. Based on the energy storage demand index, predict the energy storage change coefficient of new energy output, and combine the energy storage change coefficient of new energy output with the energy storage pressure index to calculate the necessity of switching energy storage modes at the current moment. By combining the necessity of energy storage mode switching with the prediction error index, an adjustment coefficient for the mode switching threshold is constructed, and the mode switching threshold is corrected for the first time based on the adjustment coefficient.

10. The hybrid energy storage joint scheduling method in the new energy consumption scenario according to claim 1, characterized in that, The correction of the mode switching threshold based on the predicted future load intensity index of electrochemical energy storage includes: Obtain the current mode switching threshold; Obtain the predicted load intensity index for future electrochemical energy storage; The correction coefficient is determined based on the load intensity index of future electrochemical energy storage. The correction coefficient is then used to correct the current mode switching threshold, and the corrected mode switching threshold is taken as the final mode switching threshold.

Images