Compressed air energy storage control system based on all-digital real-time simulation

Abstract

The invention relates to the field of energy storage control, in particular to a compressed air energy storage control system based on all-digital real-time simulation, which comprises the following steps of: extracting power utilization distribution characteristics in a plurality of preset periods by updating electric energy storage and release data of a power grid in real time; calculating a storage and release stability characterization value for the power grid in combination with the switching frequency of the transmission power so as to divide the storage and release stability degree category of the power grid; when the storage and release stability degree category is a weak stability degree category, evaluating and analyzing the power grid; judging whether the power transmission efficiency of the power grid region needs to be limited or not based on the power flow utilization rate of the power transmission channels in the plurality of power grid regions and the residual capacity of the energy storage equipment; and in response to the judgment result, adjusting a control instruction for the energy storage equipment. By capturing the power utilization characteristics of the power grid, optimizing the energy storage opportunity, adaptively planning the energy storage operation in advance, improving the storage and release response efficiency, and ensuring the power transmission stability of the power grid.

Description

Technical Field

[0001] This invention relates to the field of energy storage control, and in particular to a compressed air energy storage control system based on fully digital real-time simulation. Background Technology

[0002] Compressed air energy storage technology, as a large-capacity, long-duration physical energy storage technology, has advantages such as fast response speed, long service life, and strong environmental adaptability. It can store excess electrical energy during off-peak hours and release electrical energy during peak hours, effectively smoothing out fluctuations in new energy power generation and maintaining grid frequency stability, thus becoming one of the key technologies supporting the construction of new power systems.

[0003] As the scale of power grid operation continues to expand and the demand for power transmission between regions continues to grow, the distribution of electricity load in different regions shows significant differences, and the spatiotemporal distribution of peak and off-peak electricity demand is becoming increasingly complex. At the same time, the power flow distribution of power grid transmission channels is affected by factors such as load changes, which can easily lead to local power flow congestion or uneven utilization, further increasing the difficulty of power grid storage-release balance control.

[0004] Therefore, developing a control system that can capture grid operation data in real time and dynamically adjust energy storage strategies has become an important development direction for improving grid energy storage and release response efficiency and ensuring power transmission stability.

[0005] Chinese Patent Application Publication No. CN117748544A discloses a compressed air energy storage system control system and a power system frequency regulation method, belonging to the field of power system frequency stability control. The compressed air energy storage system control system includes a grid frequency acquisition module, a working mode discrimination module, and a compressed air energy storage system power regulation module connected in series. It also discloses a power system frequency regulation method based on the aforementioned system, which constructs a frequency regulation model according to the working state and runs the corresponding frequency regulation sub-model according to the working state parameters. By employing the aforementioned compressed air energy storage system control system and power system frequency regulation method, the regulation potential of the compressed air energy storage system in bidirectional working states is fully utilized. Furthermore, the frequency domain is transformed into the time domain through differential discretization, which facilitates the analysis of the mathematical model when compressed air energy storage participates in grid frequency regulation, providing a foundation for the coordinated control of other types of energy storage or new energy power plants.

[0006] However, the following problems still exist in the existing technology. The limited perspective makes it impossible to comprehensively quantify the dynamic balance between power grid transmission and energy storage operations, leading to a one-sided assessment of storage and release stability, which in turn affects the accuracy of subsequent control measures. Summary of the Invention

[0007] To address this issue, the present invention provides a compressed air energy storage control system based on fully digital real-time simulation, which overcomes the problem in the prior art that the consideration dimension is singular and cannot comprehensively quantify the dynamic balance between power grid transmission operation and energy storage operation, resulting in a one-sided judgment of storage and release stability, and thus affecting the accuracy of subsequent control implementation.

[0008] To achieve the above objectives, the present invention provides a compressed air energy storage control system based on fully digital real-time simulation, comprising: The data update module is used to update the power grid's energy storage and release data in real time and extract the power consumption distribution characteristics within a certain predetermined period. The power consumption distribution characteristics include the frequency of occurrence of the power consumption off-peak time period and the average duration of the off-peak. The storage-release analysis module, which is connected to the data update module, is used to calculate the storage-release stability characterization value of the power grid by combining the power consumption distribution characteristics and the switching frequency of transmission power, so as to classify the storage-release stability level of the power grid. A stability assessment module, connected to the storage-release analysis module, is used to assess and analyze the power grid based on the storage-release stability category being classified as weak stability. This includes... The system identifies time-domain segments with concentrated low electricity consumption, constructs time-domain variation curves of electricity consumption for these segments, extracts corresponding electricity fluctuation characteristics, evaluates the transmission fluctuation level of the power grid, and determines whether the power grid meets the storage-release balance benchmark. A network analysis module, connected to the stability assessment module, is used to determine whether it is necessary to limit the transmission efficiency of the power grid area based on the power flow utilization rate of the transmission channels in several power grid areas and the remaining capacity of the energy storage devices. A control adjustment module, which is connected to the network analysis module, adjusts the control commands for the energy storage device in response to the determination result of the network analysis module; The electricity consumption fluctuation characteristics include the fluctuation frequency and the maximum difference in electricity consumption.

[0009] Furthermore, the storage-release analysis module is used to calculate the storage-release stability characterization value for the power grid, including: The sum of the ratio of the frequency of occurrence of the off-peak electricity consumption period to the threshold of the frequency of occurrence of the off-peak electricity consumption period and the ratio of the average duration of the off-peak electricity consumption period to the threshold of the average duration of the off-peak electricity consumption period is used as the first storage-release stability feature. The ratio of the switching frequency of transmission power to the switching frequency threshold is used as the second storage-release stability feature; The first storage-release stability feature and the second storage-release stability feature are weighted and summed to determine the storage-release stability characterization value.

[0010] Furthermore, the storage-release analysis module is used to classify the storage-release stability level of the power grid, including: If the energy storage and release stability characterization value of the power grid is greater than or equal to the energy storage stability characterization threshold, then the energy storage stability category of the power grid is classified as the weak stability category.

[0011] Furthermore, the stability assessment module is used to identify time periods with concentrated low electricity consumption, including: Used to obtain the time interval corresponding to the time domain segment where the electricity consumption is at its lowest within a unit period; Used to identify overlapping time domain segments in each time interval; This is used to determine the overlapping time domain segment as the dense time domain segment of low electricity consumption.

[0012] Furthermore, the stability assessment module is used to construct a time-domain variation curve of electricity consumption for the densely populated low-consumption period, including: Used to obtain electricity consumption data at certain times within a dense period of low electricity consumption; Used to construct a rectangular coordinate system with time as the horizontal axis and electricity consumption as the vertical axis; The coordinate points used to mark the electricity consumption at each moment in the rectangular coordinate system; This is used to connect the coordinate points through a smooth curve to obtain the time-domain variation curve of the electricity consumption.

[0013] Furthermore, the stability assessment module is used to assess the transmission fluctuation characteristics of the power grid, including: The ratio of the fluctuation frequency to the fluctuation frequency threshold is used as the first transmission fluctuation feature; The ratio of the maximum difference in electricity consumption to the threshold value of the maximum difference in electricity consumption is used as the second transmission fluctuation feature; The sum of the first transport feature and the second transport feature is used as the transport fluctuation level characterization value.

[0014] Furthermore, the stability assessment module is used to determine whether the power grid meets the storage-release balance benchmark, including: If the value representing the transmission fluctuation of the power grid is less than the threshold value representing the transmission fluctuation, then the power grid is determined to meet the storage-release balance benchmark.

[0015] Furthermore, the network analysis module is used to determine whether it is necessary to limit the transmission efficiency of the power grid area, including: If any power grid region meets the transmission restriction conditions, it is determined that the transmission efficiency of the power grid region needs to be restricted. The power transmission limiting conditions include that the power flow utilization rate of the power transmission channel is greater than the power flow utilization rate threshold, and the remaining capacity of the energy storage device is less than the remaining capacity threshold.

[0016] Furthermore, in response to the determination result of the network analysis module that it is necessary to limit the power transmission efficiency of the power grid area, the control adjustment module adjusts the control command for the energy storage device.

[0017] Furthermore, the control adjustment module is used to adjust the control commands for the energy storage device, including: During the period of concentrated low electricity demand, control commands are sent to the energy storage device to perform compressed air energy storage operations.

[0018] Compared with existing technologies, this invention includes a data update module for real-time updates of power grid energy storage and release data, extracting electricity consumption distribution characteristics within several predetermined periods; a storage and release analysis module for calculating the storage and release stability characterization value of the power grid by combining electricity consumption distribution characteristics and transmission power switching frequency, thereby classifying the power grid's storage and release stability level; a stability assessment module for evaluating and analyzing the power grid based on the storage and release stability level category being weakly stable; a network analysis module for determining whether to limit the transmission efficiency of the power grid area based on the power flow utilization rate of transmission channels in several power grid areas and the remaining capacity of energy storage devices; and a control adjustment module for adjusting control commands for energy storage devices in response to the determination results of the network analysis module. This invention improves storage and release response efficiency and ensures the stability of power grid transmission by capturing the power grid's electricity consumption characteristics, optimizing energy storage timing, and adaptively planning energy storage operations in advance.

[0019] In particular, this invention comprehensively improves the determination of the grid's storage and release stability state by synergistically considering the characteristics of electricity distribution and the switching frequency of transmission power. This includes both the storage and release demand characteristics at the load level and the stability parameters at the transmission operation level. Specifically, the frequency of electricity load in a low-load state is quantified by the frequency of occurrence of low-load periods within a predetermined cycle, reflecting the frequency of excess energy generation in the grid. The average duration of a low-load period quantifies the time span of a single low-load period, reflecting the grid's continuous supply capacity of excess energy. Combined with the switching frequency of transmission power, which quantifies the frequency of fluctuations in grid transmission power, the balance of active power in the grid is directly reflected. Therefore, this invention comprehensively quantifies the scale of electrical energy that the power grid needs to store, the frequency of storage, and the urgency of storage-release adjustment through the above three characteristics. It then calculates the storage-release stability characterization value to characterize the intensity of the power grid's storage-release balance demand, identifies key scenarios that require energy storage intervention, reduces energy loss and equipment ineffective operation caused by unnecessary energy storage operations, and ensures that key states such as weak storage-release stability trigger subsequent adjustments in a timely manner, thereby improving the pertinence and effectiveness of power grid storage-release balance control.

[0020] In particular, this invention focuses on the core scenario of concentrated excess power generation in the power grid by locking onto peak electricity consumption periods. It uses the frequency of fluctuations in electricity consumption within these peak periods—the fluctuation frequency—to reflect the degree of fluctuation in electricity consumption during that period. Furthermore, it uses the maximum difference in electricity consumption to reflect the range of fluctuations in the grid's active power. Therefore, this invention quantifies the dynamic fluctuation state of the power grid's electricity consumption within peak electricity consumption periods from two dimensions: the frequency of fluctuations and the magnitude of fluctuations. It then characterizes the degree of abnormality in the grid's fluctuation state by evaluating the grid's transmission fluctuation level. This invention achieves precise judgment of the grid's energy storage and release balance state through a progressive logical chain of locking onto core time periods, characterizing dynamic changes, quantifying fluctuation levels, and determining balance benchmarks. This ensures that the energy storage system only initiates adjustments when the grid does not meet the balance benchmark, reducing energy waste from unnecessary energy storage operations and ensuring timely response when grid fluctuations exceed permissible ranges, thus enhancing the effectiveness and accuracy of energy storage and release balance control.

[0021] In particular, this invention makes targeted judgments based on the status of transmission channels and energy storage devices within a power grid area, achieving precise adaptation to the operational differences in different power grid areas, and considering the power transmission capacity of transmission channels and the capacity of energy storage devices. The power flow utilization rate of transmission channels reflects the load level and remaining transmission potential of the transmission channels. The remaining capacity of energy storage devices directly reflects the power acceptance capacity of the energy storage system. Therefore, this invention characterizes the supply and demand matching relationship between transmission and energy storage, jointly quantifying the coordinated operation matching status of transmission networks and energy storage systems within a power grid area, providing data support for subsequent determination of transmission efficiency limitations, and ensuring a high degree of adaptation between transmission regulation and energy storage acceptance capacity. Attached Figure Description

[0022] Figure 1 A functional block diagram of a fully digital real-time simulation-based compressed air energy storage control system according to an embodiment of the invention; Figure 2 A logic decision diagram for classifying the storage and release stability levels of the power grid in embodiments of the invention; Figure 3 A logic diagram for determining whether a power grid meets the storage-release balance benchmark in an embodiment of the invention; Figure 4 This is a logic diagram for determining whether it is necessary to limit the power transmission efficiency of the power grid area in an embodiment of the invention. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0026] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral connection; it can refer to a mechanical connection or an electrical connection. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0027] Please see Figure 1 The diagram shown is a functional block diagram of a compressed air energy storage control system based on fully digital real-time simulation according to an embodiment of the present invention. The compressed air energy storage control system based on fully digital real-time simulation according to an embodiment of the present invention includes: The data update module is used to update the power grid's energy storage and release data in real time and extract the power consumption distribution characteristics within a certain predetermined period. The power consumption distribution characteristics include the frequency of occurrence of the power consumption off-peak time period and the average duration of the off-peak. The storage-release analysis module, which is connected to the data update module, is used to calculate the storage-release stability characterization value of the power grid by combining the power consumption distribution characteristics and the switching frequency of transmission power, so as to classify the storage-release stability level of the power grid. A stability assessment module, connected to the storage-release analysis module, is used to assess and analyze the power grid based on the storage-release stability category being classified as weak stability. This includes... The system identifies time-domain segments with concentrated low electricity consumption, constructs time-domain variation curves of electricity consumption for these segments, extracts corresponding electricity fluctuation characteristics, evaluates the transmission fluctuation level of the power grid, and determines whether the power grid meets the storage-release balance benchmark. A network analysis module, connected to the stability assessment module, is used to determine whether it is necessary to limit the transmission efficiency of the power grid area based on the power flow utilization rate of the transmission channels in several power grid areas and the remaining capacity of the energy storage devices. A control adjustment module, which is connected to the network analysis module, adjusts the control commands for the energy storage device in response to the determination result of the network analysis module; The electricity consumption fluctuation characteristics include the fluctuation frequency and the maximum difference in electricity consumption.

[0028] Specifically, the energy storage and release data includes electricity distribution characteristics, transmission power switching frequency, electricity consumption during periods of high electricity demand, electricity fluctuation characteristics, power flow utilization rate of transmission channels in several power grid areas, and remaining capacity of energy storage devices. The updated data mentioned above can be obtained from the relevant subsystems of the power grid system, which will not be elaborated further here.

[0029] It is understandable that the power grid load has a significant weekly cycle characteristic, with obvious differences in the distribution of peak and off-peak periods between weekdays and weekends. For example, on weekdays, industrial load dominates, and off-peak periods are mostly in the early morning; on weekends, the proportion of residential load increases, and the off-peak periods shift. Therefore, in order to adapt to the periodic pattern of power load, the predetermined cycle is set to 7 days.

[0030] Specifically, there is no specific limitation on the way the power grid area is divided. It can be based on the physical operation boundary of the power grid to ensure that the transmission channels and energy storage devices of each power grid area form independent statistical units, so as to avoid cross-regional data interference. Of course, those skilled in the art can also use other division methods, which will not be elaborated here.

[0031] Specifically, there are no restrictions on the specific structure of the data update module, storage and release analysis module, stability assessment module, network analysis module, and control and adjustment module. Each module or its units can be composed of logic components or combinations of logic components. Logic components include field-programmable processors, computers, or microprocessors in computers.

[0032] Specifically, the storage-release analysis module is used to calculate the storage-release stability characterization values ​​for the power grid, including: The sum of the ratio of the frequency of occurrence of the off-peak electricity consumption period to the threshold of the frequency of occurrence of the off-peak electricity consumption period and the ratio of the average duration of the off-peak electricity consumption period to the threshold of the average duration of the off-peak electricity consumption period is used as the first storage-release stability feature. The ratio of the switching frequency of transmission power to the switching frequency threshold is used as the second storage-release stability feature; The first storage-release stability feature and the second storage-release stability feature are weighted and summed to determine the storage-release stability characterization value.

[0033] Specifically, electricity consumption distribution characteristics can directly quantify the frequency of excess power generation in the power grid and the required scale of energy storage. This is the core requirement for energy storage systems to fulfill their storage-release balance function, and its fundamental and decisive role in determining storage-release stability is stronger. Clearly defining the frequency and duration of off-peak periods is crucial to determining whether energy storage is needed and, if so, how much. This aligns with the core storage-release needs of the power grid and has a stronger fundamental impact on storage-release stability. The frequency of transmission power switching, on the other hand, focuses on the fluctuations in power grid operation and is essentially an operational constraint that needs to be adapted during the storage-release process. Therefore, the first storage-release stability characteristic calculated based on electricity consumption distribution characteristics—namely, the frequency of off-peak periods and the average duration of off-peak periods—is more suitable for the core storage-release needs of the power grid and should be given a higher weighting coefficient, set to 0.6. The second storage-release stability characteristic calculated based on the frequency of transmission power switching takes into account the impact of power grid operation fluctuations on storage-release stability, and its weighting coefficient is set to 0.4.

[0034] In this embodiment, the purpose of setting thresholds for the frequency of occurrence of off-peak electricity consumption periods, the average duration of off-peak periods, and the switching frequency is to characterize the fluctuating state of the power grid's energy storage and release balance. For situations with high demand for energy storage and release balance, historical energy storage and release data of the power grid are obtained. Historical data on the frequency of occurrence of off-peak electricity consumption periods, the average duration of off-peak periods, and the switching frequency corresponding to a predetermined period are then used to calculate the average frequency of occurrence of off-peak electricity consumption periods, the average duration of off-peak periods, and the average switching frequency. These are then used as baseline values ​​under normal conditions. Based on the purpose of setting the above three thresholds, the... The threshold for the frequency of occurrence of the electricity off-peak time period is determined as the product of the average frequency of occurrence of the electricity off-peak time period and a first deviation coefficient. The threshold for the average duration of the off-peak is determined as the product of the average duration of the off-peak and a second deviation coefficient. The threshold for the switching frequency is determined as the product of the average switching frequency and a third deviation coefficient. The first deviation coefficient is selected within the interval [1.3, 1.4], preferably 1.3 in practice. The second deviation coefficient is selected within the interval [1.1, 1.2], preferably 1.1 in practice. The third deviation coefficient is selected within the interval [1.2, 1.3], preferably 1.2 in practice.

[0035] Specifically, this invention comprehensively improves the determination of the grid's storage and release stability by synergistically considering the characteristics of electricity distribution and the switching frequency of transmission power. This covers both the storage and release demand characteristics of the electricity load dimension and incorporates stability parameters of the transmission operation dimension. Specifically, the frequency of occurrence of electricity off-peak periods within a predetermined cycle quantifies the frequency of electricity load being in a low-peak state, reflecting the frequency of excess energy generation in the grid. A higher frequency of off-peak periods indicates a more normalized demand for energy storage. The average duration of an off-peak period quantifies the time span of a single off-peak, reflecting the grid's continuous supply capacity of excess energy. A longer duration means a larger energy storage capacity required by the energy storage system. Combined with the switching frequency of transmission power, which quantifies the frequency of fluctuations in grid transmission power, this directly reflects the balance of active power in the grid. A higher switching frequency of transmission power indicates weaker grid operational stability. Therefore, this invention comprehensively quantifies the scale of electrical energy that the power grid needs to store, the frequency of storage, and the urgency of storage-release adjustment through the above three characteristics. It then calculates the storage-release stability characterization value to characterize the intensity of the power grid's storage-release balance demand, identifies key scenarios that require energy storage intervention, reduces energy loss and equipment ineffective operation caused by unnecessary energy storage operations, and ensures that key states such as weak storage-release stability trigger subsequent adjustments in a timely manner, thereby improving the pertinence and effectiveness of power grid storage-release balance control.

[0036] Specifically, please refer to Figure 2 As shown, this is a logic decision diagram for classifying the storage-release stability level of the power grid according to an embodiment of the present invention. The storage-release analysis module is used to classify the storage-release stability level of the power grid, including: If the energy storage and release stability characterization value of the power grid is greater than or equal to the energy storage stability characterization threshold, then the energy storage stability category of the power grid is classified as the weak stability category. If the energy storage and release stability characterization value of the power grid is less than the energy storage stability characterization threshold, then the energy storage stability category of the power grid is classified as a strong stability category.

[0037] The storage-release stability characterization threshold is predetermined. It is determined by calculating the storage-release stability characterization value when the frequency of occurrence of the power consumption off-peak time period is equal to the power consumption off-peak time period frequency threshold, the average duration of the off-peak is equal to the average duration of the off-peak, and the switching frequency of the transmission power is equal to the switching frequency threshold.

[0038] Specifically, if the energy storage stability level of the power grid is classified as a strong stability level, the time interval of the real-time update frequency will be adjusted according to the storage-release stability characterization value. The time interval for adjusting the real-time update frequency includes: The time interval is shortened, and the amount of shortening is positively correlated with the storage-release stability characterization value.

[0039] In this embodiment, optionally, The storage-release stability characterization value is compared with the preset first storage-release stability characterization comparison threshold and the second storage-release stability characterization comparison threshold. When the storage-release stability characterization value is greater than the second storage-release stability characterization comparison threshold, the amount of time interval reduction is determined as the first reduction amount, which is set to 0.3 times the initial time interval. When the storage-release stability characterization value is greater than or equal to the first storage-release stability characterization comparison threshold and less than or equal to the second storage-release stability characterization comparison threshold, the amount of time interval reduction is determined to be the second reduction amount, which is set to be 0.2 times the initial time interval. When the storage-release stability characterization value is less than the first storage-release stability characterization comparison threshold, the amount of time interval reduction is determined as the third reduction amount, which is set to 0.1 times the initial time interval. The first comparison threshold for storage-release stability characterization is 1.1 times the threshold for storage-release stability characterization value, and the second comparison threshold for storage-release stability characterization value is 1.3 times the threshold for storage-release stability characterization value.

[0040] It is understandable that the purpose of shortening the time interval is to dynamically improve the timeliness of data and adapt to the stable state of energy storage and release. The larger the value of the energy storage and release stability, the weaker the energy storage and release stability of the power grid. In this case, shortening the time interval can collect and update data more quickly, so that the subsequent related modules can capture changes in the power grid status in a timely manner and avoid untimely adjustment of energy storage control due to data lag. In addition, the amount of time interval shortening can also be adjusted by those skilled in the art under different circumstances. In order to balance the hardware processing pressure and real-time requirements, the initial time interval is set to 1 second, which will not be elaborated further.

[0041] Specifically, the stability assessment module is used to identify time periods with concentrated low electricity consumption, including: Used to obtain the time interval corresponding to the time domain segment where the electricity consumption is at its lowest within a unit period; Used to identify overlapping time domain segments in each time interval; This is used to determine the overlapping time domain segment as the dense time domain segment of low electricity consumption.

[0042] It is understandable that in the process of identifying the dense time period of low electricity consumption, it is necessary to ensure the accurate capture of the distribution pattern of low electricity consumption within a certain time range and a sufficient number of samples. Based on this, the unit period is set to 1 day.

[0043] Specifically, the stability assessment module is used to construct a time-domain variation curve of electricity consumption for the densely populated low-consumption period, including: Used to obtain electricity consumption data at certain times within a dense period of low electricity consumption; Used to construct a rectangular coordinate system with time as the horizontal axis and electricity consumption as the vertical axis; The coordinate points used to mark the electricity consumption at each moment in the rectangular coordinate system; This is used to connect the coordinate points through a smooth curve to obtain the time-domain variation curve of the electricity consumption.

[0044] Specifically, there are no restrictions on the method for constructing the time-domain variation curve of electricity consumption. For example, the time-domain variation curve of electricity consumption can be fitted using Matlab correlation fitting software, which will not be elaborated further.

[0045] Specifically, the stability assessment module is used to assess the transmission fluctuation characteristics of the power grid, including: The ratio of the fluctuation frequency to the fluctuation frequency threshold is used as the first transmission fluctuation feature; The ratio of the maximum difference in electricity consumption to the threshold value of the maximum difference in electricity consumption is used as the second transmission fluctuation feature; The sum of the first transport feature and the second transport feature is used as the transport fluctuation level characterization value.

[0046] In this embodiment, the purpose of setting the fluctuation frequency threshold and the maximum difference in electricity consumption threshold is to characterize situations where the abnormality of the power grid fluctuation state is high. By acquiring historical energy storage and release data of the power grid, calling historical fluctuation frequency data and historical data of maximum difference in electricity consumption, the average fluctuation frequency and the average maximum difference in electricity consumption are calculated and used as the benchmark values ​​under normal conditions. Based on the purpose of setting the above two thresholds, the fluctuation frequency threshold is determined as the product of the average fluctuation frequency and the fluctuation deviation coefficient, and the maximum difference in electricity consumption threshold is determined as the product of the average maximum difference in electricity consumption and the electricity consumption deviation coefficient. The fluctuation deviation coefficient is selected within the interval [1.2, 1.4], preferably 1.2 in practice, and the electricity consumption deviation coefficient is selected within the interval [1.25, 1.35], preferably 1.25 in practice.

[0047] Specifically, this invention focuses on the core scenario of concentrated excess power generation in the power grid by targeting peak periods of low electricity consumption. It measures the frequency of fluctuations in electricity consumption within these peak periods, indicating the degree of fluctuation. Higher frequency fluctuations suggest weaker grid load stability and greater difficulty in achieving dynamic balance of active power. Furthermore, the maximum difference in electricity consumption reflects the range of active power fluctuations; a larger maximum difference indicates a wider range of load fluctuations. Therefore, this invention quantifies the dynamic fluctuation state of power grid consumption within peak periods of low electricity consumption from two dimensions: frequency and amplitude. Finally, it characterizes the degree of abnormality in grid fluctuation by evaluating the grid's transmission fluctuation level. This invention achieves accurate judgment of the power grid's energy storage and release balance state through a logical chain that locks in the core time period, characterizes dynamic changes, quantifies the degree of fluctuation, and determines the balance benchmark. This ensures that the energy storage system only initiates adjustments when the power grid does not meet the energy storage and release balance benchmark, thereby reducing energy waste from unnecessary energy storage operations and ensuring timely response when power grid fluctuations exceed the allowable range, thus enhancing the effectiveness and accuracy of energy storage and release balance control.

[0048] Specifically, please refer to Figure 3 As shown, this is a logic diagram for determining whether a power grid meets the storage-release balance benchmark in an embodiment of the present invention. The stability assessment module is used to determine whether the power grid meets the storage-release balance benchmark, including: If the value representing the transmission fluctuation of the power grid is less than the threshold value representing the transmission fluctuation, then the power grid is determined to meet the storage-release balance benchmark. If the power grid's transmission fluctuation level characterization value is greater than or equal to the transmission fluctuation level characterization threshold, then the power grid is determined to be non-compliant with the storage-release balance benchmark.

[0049] The threshold for representing the degree of transmission fluctuation is predetermined. The transmission fluctuation degree representation value calculated when the fluctuation frequency is equal to the fluctuation frequency threshold and the maximum difference in electricity consumption is equal to the maximum difference in electricity consumption threshold is determined as the transmission fluctuation degree representation threshold.

[0050] Specifically, please refer to Figure 4 As shown, this is a logic diagram for determining whether to limit the transmission efficiency of the power grid area according to an embodiment of the present invention. The network analysis module is used to determine whether to limit the transmission efficiency of the power grid area, including: If any power grid region meets the transmission restriction conditions, it is determined that the transmission efficiency of the power grid region needs to be restricted. The power transmission limiting conditions include that the power flow utilization rate of the power transmission channel is greater than the power flow utilization rate threshold, and the remaining capacity of the energy storage device is less than the remaining capacity threshold.

[0051] In this embodiment, the purpose of setting the power flow utilization rate threshold and the remaining capacity threshold is to characterize the poor coordination and matching status between the transmission network and the energy storage system within the power grid area. By acquiring historical energy storage and release data of the power grid, calling historical power flow utilization rate data of the transmission channel and historical remaining capacity data of the energy storage device, the average power flow utilization rate and the average remaining capacity are calculated and used as the benchmark values ​​under normal conditions. Based on the purpose of setting the above two thresholds, the power flow utilization rate threshold is determined as the product of the average power flow utilization rate and the power flow deviation coefficient, and the remaining capacity threshold is determined as the product of the average remaining capacity and the capacity deviation coefficient. The power flow deviation coefficient is selected in the interval [1.1, 1.2], preferably 1.1 in practice, and the capacity deviation coefficient is selected in the interval [0.9, 0.95], preferably 0.9 in practice.

[0052] Specifically, this invention makes targeted judgments based on the status of transmission channels and energy storage devices in different power grid areas, achieving precise adaptation to the operational differences in different power grid areas. It considers the power transmission capacity of transmission channels and the capacity of energy storage devices. For example, when the load on a transmission channel in any power grid area is low but the energy storage device has no remaining capacity, the transmission efficiency is limited in a timely manner to avoid waste of transmitted power that cannot be stored. The power flow utilization rate of the transmission channel reflects the load level and remaining transmission potential of the transmission channel. A higher power flow utilization rate indicates that the transmission channel is closer to saturation and has less remaining transmission space; a lower power flow utilization rate indicates that the transmission channel still has idle transmission capacity. The remaining capacity of the energy storage device directly reflects the power acceptance capacity of the energy storage system. A higher remaining capacity indicates that the energy storage device can accept more excess power; a lower remaining capacity indicates that the storage space of the energy storage system is more limited. Therefore, this invention characterizes the supply and demand matching relationship between power transmission and energy storage, and jointly quantifies the coordinated operation matching status of power transmission networks and energy storage systems within the power grid area, providing data support for the subsequent determination of power transmission efficiency limitations, and ensuring a high degree of compatibility between power transmission regulation and energy storage acceptance capacity.

[0053] Specifically, in response to the determination result of the network analysis module that it is necessary to limit the power transmission efficiency of the power grid area, the control adjustment module adjusts the control command for the energy storage device.

[0054] Specifically, the control adjustment module is used to adjust the control commands for the energy storage device, including: During the period of concentrated low electricity demand, control commands are sent to the energy storage device to perform compressed air energy storage operations.

[0055] Specifically, by identifying the most efficient energy storage periods, namely the peak periods of low electricity demand, it is possible to avoid blindly storing energy during non-peak periods while improving the efficiency of energy storage resource utilization. Furthermore, the advance activation of energy storage equipment can allow for preheating, pressure regulation, and other response time, avoiding insufficient energy storage during off-peak periods due to equipment delays. This ensures that the power grid efficiently stores excess electricity during off-peak periods, preparing for peak energy release and enhancing the timeliness and reliability of the power grid's energy storage and release balance.

[0056] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A compressed air energy storage control system based on fully digital real-time simulation, characterized in that, include: The data update module is used to update the power grid's energy storage and release data in real time and extract the power consumption distribution characteristics within a certain predetermined period. The power consumption distribution characteristics include the frequency of occurrence of the power consumption off-peak time period and the average duration of the off-peak. The storage-release analysis module is used to calculate the storage-release stability characterization value of the power grid by combining the characteristics of electricity distribution and the switching frequency of transmission power, so as to classify the storage-release stability level of the power grid. The stability assessment module is used to assess and analyze the power grid based on the storage-release stability category being classified as weakly stable, including: The system identifies time-domain segments with concentrated low electricity consumption, constructs time-domain variation curves of electricity consumption for these segments, extracts corresponding electricity fluctuation characteristics, evaluates the transmission fluctuation level of the power grid, and determines whether the power grid meets the storage-release balance benchmark. The network analysis module is used to determine whether it is necessary to limit the power transmission efficiency of the power grid area based on the power flow utilization rate of the transmission channels in several power grid areas and the remaining capacity of the energy storage devices. The control adjustment module adjusts the control commands for the energy storage device in response to the determination result of the network analysis module. The electricity consumption fluctuation characteristics include the fluctuation frequency and the maximum difference in electricity consumption.

2. The compressed air energy storage control system based on fully digital real-time simulation according to claim 1, characterized in that, The storage-release analysis module is used to calculate the storage-release stability characterization values ​​for the power grid, including: The sum of the ratio of the frequency of occurrence of the off-peak electricity consumption period to the threshold of the frequency of occurrence of the off-peak electricity consumption period and the ratio of the average duration of the off-peak electricity consumption period to the threshold of the average duration of the off-peak electricity consumption period is used as the first storage-release stability feature. The ratio of the switching frequency of transmission power to the switching frequency threshold is used as the second storage-release stability feature; The first storage-release stability feature and the second storage-release stability feature are weighted and summed to determine the storage-release stability characterization value.

3. The compressed air energy storage control system based on fully digital real-time simulation according to claim 2, characterized in that, The storage-release analysis module is used to classify the storage-release stability level of the power grid, including: If the energy storage stability characterization value of the power grid is greater than or equal to the energy storage stability characterization threshold, then the energy storage stability category of the power grid is classified as the weak stability category.

4. The compressed air energy storage control system based on fully digital real-time simulation according to claim 1, characterized in that, The stability assessment module is used to identify peak electricity consumption periods, including: Used to obtain the time interval corresponding to the time domain segment where the electricity consumption is at its lowest within a unit period; Used to identify overlapping time domain segments in each time interval; This is used to determine the overlapping time domain segment as the dense time domain segment of low electricity consumption.

5. The compressed air energy storage control system based on fully digital real-time simulation according to claim 1, characterized in that, The stability assessment module is used to construct a time-domain variation curve of electricity consumption for the densely populated low-consumption period, including: Used to obtain electricity consumption data at certain times within a dense period of low electricity consumption; Used to construct a rectangular coordinate system with time as the horizontal axis and electricity consumption as the vertical axis; The coordinate points used to mark the electricity consumption at each moment in the rectangular coordinate system; This is used to connect the coordinate points through a smooth curve to obtain the time-domain variation curve of the electricity consumption.

6. The compressed air energy storage control system based on fully digital real-time simulation according to claim 1, characterized in that, The stability assessment module is used to assess the transmission fluctuation characteristics of the power grid, including: The ratio of the fluctuation frequency to the fluctuation frequency threshold is used as the first transmission fluctuation feature; The ratio of the maximum difference in electricity consumption to the threshold value of the maximum difference in electricity consumption is used as the second transmission fluctuation feature; The sum of the first transport feature and the second transport feature is used as the transport fluctuation level characterization value.

7. The compressed air energy storage control system based on fully digital real-time simulation according to claim 6, characterized in that, The stability assessment module is used to determine whether the power grid meets the storage-release balance benchmark, including: If the value representing the transmission fluctuation of the power grid is less than the threshold value representing the transmission fluctuation, then the power grid is determined to meet the storage-release balance benchmark.

8. The compressed air energy storage control system based on fully digital real-time simulation according to claim 1, characterized in that, The network analysis module is used to determine whether it is necessary to limit the transmission efficiency of the power grid area, including: If any power grid region meets the transmission restriction conditions, it is determined that the transmission efficiency of the power grid region needs to be restricted. The power transmission limiting conditions include that the power flow utilization rate of the power transmission channel is greater than the power flow utilization rate threshold, and the remaining capacity of the energy storage device is less than the remaining capacity threshold.

9. The compressed air energy storage control system based on fully digital real-time simulation according to claim 1, characterized in that, The control adjustment module adjusts the control commands for the energy storage device in response to the determination result of the network analysis module that it is necessary to limit the power transmission efficiency of the power grid area.

10. The compressed air energy storage control system based on fully digital real-time simulation according to claim 1, characterized in that, The control adjustment module is used to adjust the control commands for the energy storage device, including: During the period of concentrated low electricity demand, control commands are sent to the energy storage device to perform compressed air energy storage operations.

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