A hybrid energy storage system, device and storage medium for new energy consumption

Abstract

The present invention proposes a hybrid energy storage system, device and storage medium for new energy consumption, which belongs to the field of energy storage technology. The system includes: the input end of the LC filter device is electrically connected to the power supply line, and the output end of the LC filter device is electrically connected to the hybrid energy storage device through the AC / DC conversion device; a switch control module is provided in the hybrid energy storage device, which is used to control the on-off of the input circuit and the output circuit inside the hybrid energy storage device; a data acquisition module is used to collect the operating parameters of the LC filter device and the AC / DC conversion device and the SOC value of the hybrid energy storage device in real time; the intelligent power distribution module is connected to the switch control module data, which is used to predict the input and output power of the hybrid energy storage device according to the collected operating parameters and SOC values, using the built-in LSTM algorithm model, to obtain the average power of the input and output and its fluctuation, determine the control strategy and send it to the switch control module to control the power direction.

Description

Technical Field

[0001] The present invention relates to the field of energy storage technology, and more specifically to a hybrid energy storage system, device and storage medium for new energy consumption. Background Art

[0002] At present, the use of renewable energy such as wind and solar energy has become an important force in promoting green development. However, the inherent volatility of these new energy generation methods has brought unprecedented challenges to the stable operation of the power grid. The limitations of traditional new energy consumption and power grid dispatching methods are becoming increasingly prominent in the face of such volatility.

[0003] First, although traditional energy storage facilities, such as pumped storage power stations, can balance the grid load to a certain extent, their construction costs are high and they are severely restricted by geographical conditions, making it difficult to flexibly deploy them in a wide area. In addition, the response speed of pumped storage power stations is relatively slow, making it difficult to quickly respond to the instantaneous fluctuations of renewable energy generation, thus limiting their application effect in dealing with the volatility of renewable energy generation.

[0004] Secondly, the existing grid dispatching methods often adopt conservative strategies when facing the uncertainty of renewable energy generation, such as limiting the scale of renewable energy grid connection or increasing backup power sources, which not only reduces the utilization rate of renewable energy, but also increases the operating cost of the grid. At the same time, the traditional dispatching methods lack intelligence and foresight, and it is difficult to make dynamic adjustments according to the real-time changes of renewable energy generation, thus affecting the stability and economy of the grid.

[0005] In summary, existing technologies have many shortcomings in dealing with the volatility of renewable energy generation, such as high cost of energy storage facilities, slow response speed, large geographical restrictions, conservative grid dispatching methods, and lack of intelligence. These problems have seriously restricted the large-scale development and utilization of renewable energy, and also hindered the optimization and upgrading of energy structure. Therefore, developing a new technology that can efficiently deal with the volatility of renewable energy generation is of great significance to ensure the stable operation of the power grid and improve the utilization rate of renewable energy. Summary of the invention

[0006] In view of the above problems, the purpose of the present invention is to provide a hybrid energy storage system, device and storage medium for new energy consumption, which can not only realize the efficient consumption of new energy, but also effectively regulate the load fluctuations of the power grid, improve the system's support capacity for large fluctuating loads, that is, the primary frequency regulation capability, and effectively improve the stability of the power grid.

[0007] In order to achieve the above object, the present invention is implemented through the following technical solutions:

[0008] In a first aspect, the present invention discloses a hybrid energy storage system for new energy consumption, comprising: an LC filter device, an AC / DC conversion device, a hybrid energy storage device, a data acquisition module and an intelligent power distribution module;

[0009] The input end of the LC filter device is electrically connected to the power supply line of the power grid or the new energy power plant, and the output end of the LC filter device is electrically connected to the hybrid energy storage device through the AC / DC conversion device;

[0010] A switch control module is provided in the hybrid energy storage device to control the on / off of the input circuit and the output circuit inside the hybrid energy storage device;

[0011] The data acquisition module is respectively connected to the LC filter device, the AC / DC conversion device, the hybrid energy storage device and the intelligent power distribution module for real-time acquisition of the operating parameters of the LC filter device and the AC / DC conversion device, and the SOC value of the hybrid energy storage device, and sends them to the intelligent power distribution module;

[0012] The intelligent power distribution module is connected to the switch control module for predicting the input and output power of the hybrid energy storage device based on the received operating parameters using the built-in LSTM algorithm model, determining the average input and output power and its fluctuation, and combining the SOC value of the hybrid energy storage device to determine the control strategy and send it to the switch control module to control the power trend of the hybrid energy storage device.

[0013] Furthermore, the hybrid energy storage device is provided with a battery pack and two supercapacitor packs, both of which are provided with input circuits and output circuits, the battery pack is electrically connected to the two supercapacitor packs respectively, and a plurality of battery packs are provided in the battery pack.

[0014] Furthermore, the data acquisition module is specifically used for:

[0015] Collect the operating parameters of the LC filter device and AC / DC converter in real time and upload them to the intelligent power distribution module;

[0016] The SOC values ​​of the battery pack and the two supercapacitor packs are collected in real time and uploaded to the intelligent power distribution module.

[0017] Furthermore, the intelligent power distribution module is specifically used for:

[0018] According to the SOC values ​​of the battery pack and the two supercapacitor packs, the operating status of the hybrid energy storage device is determined in real time;

[0019] After the charging instruction is obtained, if the operating state of the hybrid energy storage device meets the charging condition, the charging control of the hybrid energy storage device is executed through the first intelligent power scheduling control strategy;

[0020] After receiving the discharge command, if the operating state of the hybrid energy storage device meets the discharge conditions, the discharge control of the hybrid energy storage device is performed through the second intelligent power scheduling control strategy.

[0021] Furthermore, the first intelligent power scheduling control strategy includes:

[0022] When the SOC value of the battery pack reaches the preset standard value and the SOC values of both supercapacitor banks are within the preset standard range, the supercapacitor bank with the smaller SOC value is denoted as the No. 1 supercapacitor, and the other supercapacitor is denoted as the No. 2 supercapacitor;

[0023] Turn on the input circuit of the No. 1 supercapacitor to receive the external charging power and charge the No. 1 supercapacitor;

[0024] Collect the operating parameters of the LC filtering device and the AC / DC conversion device in real time, predict the first average power (Pcd) and the first fluctuation difference (Pcdset) of the charging through the LSTM algorithm model, and send the data of the current time period into the LSTM model for correction to predict the parameters of the next time period;

[0025] Collect the SOC states of the supercapacitor bank and the battery pack in real time, and calculate the current first power setting value (Pcdset), the first fluctuation upper limit (σcdset1) and the second fluctuation upper limit (σcdset2);

[0026] If the prediction result is stable power within the energy storage power station's consumption capacity (Pcd < Pcdset and σcd < σcdset1), the charging power is allocated to the battery pack to charge the battery;

[0027] If the prediction result is power within the energy storage power station's consumption capacity but with large fluctuations (Pcd < Pcdset and σcdset1 < σcd < σcdset2), continue to charge the No. 1 supercapacitor until the SOC value of the No. 1 supercapacitor reaches the SOC upper limit value, and automatically switch to only charging the No. 2 supercapacitor;

[0028] While charging the No. 2 supercapacitor, control the No. 1 supercapacitor to charge the battery pack at a constant current or constant voltage with power PI;

[0029] When the SOC value of the No. 2 supercapacitor reaches the SOC upper limit value or the SOC value of the No. 1 supercapacitor reaches the SOC lower limit value, switch to the No. 1 supercapacitor to receive the input power from the grid side, and the No. 2 supercapacitor charges the battery pack;

[0030] Execute the above steps cyclically until the battery pack is fully charged and the SOC values of the No. 1 capacitor and the No. 2 supercapacitor both reach the upper SOC limit during charging, or the input power on the grid side disappears, then the charging is completed;

[0031] After the charging is completed, disconnect the transmission line of the input power, and control the No. 1 supercapacitor or the No. 2 supercapacitor to perform floating charge on the battery pack.

[0032] If the prediction result is that the power is outside the absorbable capacity range of the energy storage power station (Pcd > Pcdset or σcd > σcdset2), then discard part of the power, control the charging time ratio during charging, only use the No. 1 supercapacitor to receive the grid-side power, and only use the No. 2 supercapacitor to charge the battery pack. The two perform intelligent switching and charge-discharge power control according to the upper limit of the battery charging power, the SOC values of the supercapacitor bank and the battery.

[0033] Furthermore, the second intelligent power scheduling control strategy includes:

[0034] Collect the SOC values of the two supercapacitor banks, record the supercapacitor bank with the larger SOC value as the No. 1 supercapacitor, and record the other supercapacitor as the No. 2 supercapacitor;

[0035] Conduct the output circuit of the No. 1 supercapacitor to perform discharge; conduct the output circuit of the battery pack and the input circuit of the No. 2 supercapacitor to charge the No. 2 supercapacitor to the upper SOC limit during charging.

[0036] Collect the operating parameters of the LC filtering device and the AC / DC conversion device in real time, predict the second average power (Pfd) and the second fluctuation difference (σfd) required by the current grid side through the LSTM algorithm model, and send the data of the current time period into the LSTM model for correction to be used to predict the parameters of the next time period; collect the SOC status of the supercapacitor bank and the battery pack in real time, and calculate the current second power setting value (Pfdset), the third fluctuation upper limit (σfdset1) and the fourth fluctuation upper limit (σfdset2);

[0037] When the average power of the predicted load does not exceed the long-term maximum output power of the battery pack and its fluctuation range is within the adjustment capacity of one supercapacitor bank (Pfd < Pfdset and σfd < σfdset1), conduct the output circuits of the battery pack and the No. 1 supercapacitor, distribute the load power to the battery pack, set the No. 1 supercapacitor to the only discharge mode, and use the No. 1 supercapacitor to smooth the output power; when the SOC value of the No. 1 supercapacitor reaches the lower SOC limit during discharge, disconnect the input circuit of the No. 1 supercapacitor, and conduct the input circuit of the No. 2 supercapacitor to switch to the No. 2 supercapacitor to smooth the output power, and at the same time use the battery pack to charge the No. 1 supercapacitor as a backup;

[0038] When the average power of the predicted grid-side load does not exceed the long-term maximum output power of the battery pack, but its fluctuation range exceeds the regulation range of one supercapacitor bank and does not exceed the adjustable fluctuation range of two supercapacitor banks (Pfd < Pfdset and σfdset1 < σfd < σfdset2), control the output power of the battery pack, set the No. 1 capacitor as the output capacitor, and only discharge to the grid side, which is used to supplement the power difference when the output power of the battery pack is less than the grid-side load power; at the same time, set the No. 2 supercapacitor as the energy storage capacitor, which only accepts the charging of the battery pack and is used to absorb the power of the battery pack when the output power of the battery pack to the grid side is higher than the load power; detect the SOC values of the No. 1 supercapacitor and the No. 2 supercapacitor in real time. When the SOC value of the No. 1 supercapacitor is lower than the SOC lower limit value or the SOC value of the No. 2 supercapacitor is higher than the SOC upper limit value, switch the No. 1 supercapacitor to the energy storage capacitor and set the No. 2 supercapacitor as the output capacitor.

[0039] When the average value of the output power required for discharging exceeds the maximum continuous output power of the battery pack or the fluctuation exceeds the adjustment range of two supercapacitors (Pfd > Pfdset or σfd > σfdset2), control the battery pack to supply power to the grid side as much as possible at the maximum output power, and intelligently switch the supercapacitors.

[0040] In a second aspect, the present invention also discloses a hybrid energy storage device for new energy consumption, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it realizes the functions of the hybrid energy storage system for new energy consumption as described in any one of the above.

[0041] In a third aspect, the present invention also discloses a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it realizes the functions of the hybrid energy storage system for new energy consumption as described in any one of the above.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] 1. The present invention predicts the power trend of new energy power generation through the LSTM (Long Short-Term Memory) algorithm and intelligently schedules the power flow to the hybrid energy storage device according to the prediction results. Specifically, when the new energy power generation fluctuates, the system allocates the relatively stable part of the power to charge the battery pack, while the part of the power with larger fluctuations is allocated to the supercapacitor; when the capacity of one supercapacitor is insufficient, the system can automatically switch to another supercapacitor and discharge the supercapacitor with insufficient full capacity through a certain intelligent control strategy, so as to achieve the balance and maximum utilization of new energy power.

[0044] 2. The present invention predicts the power trend through LSTM, determines the average power of input and output and its fluctuation, realizes efficient scheduling of power flow, and optimizes the working state of the energy storage system.

[0045] 3. The two supercapacitors of the present invention can achieve seamless switching, ensuring that the system can still work stably when the power grid fluctuates greatly.

[0046] 4. The present invention utilizes the battery pack to preferentially store stable power, while the supercapacitor is responsible for processing instantaneous power fluctuations, thereby improving the response speed and energy storage efficiency of the system.

[0047] 5. The present invention can automatically adjust the energy storage charging and discharging strategy to ensure efficient use of energy and the stability of the power grid.

[0048] It can be seen that compared with the prior art, the present invention has outstanding substantive features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0050] Figure 1 It is a system structure diagram of a specific implementation mode of the present invention.

[0051] Figure 2 It is a flow chart of the first intelligent power dispatching control strategy according to a specific implementation mode of the present invention.

[0052] Figure 3 It is a flow chart of the second intelligent power dispatching control strategy according to a specific implementation mode of the present invention. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0054] See also Figure 1As shown, this embodiment provides a hybrid energy storage system for new energy consumption, characterized in that it includes: an LC filter device, an AC / DC conversion device, a hybrid energy storage device, a data acquisition module and an intelligent power distribution module.

[0055] The input end of the LC filter device is electrically connected to the power supply line of the power grid or the new energy power plant, and the output end of the LC filter device is electrically connected to the hybrid energy storage device through the AC / DC conversion device.

[0056] The hybrid energy storage device is provided with a switch control module, a battery pack and two supercapacitor packs. The switch control module is used to control the on and off of the input circuit and the output circuit inside the hybrid energy storage device. The battery pack and the two supercapacitor packs are both provided with input circuits and output circuits. The battery pack is electrically connected to the two supercapacitor packs respectively, and a plurality of battery packs are provided in the battery pack. For example, the two supercapacitor packs can be divided into supercapacitor No. 1 and supercapacitor No. 2, and the two can be seamlessly switched through the scheduling of the intelligent power distribution system. The battery pack is composed of a plurality of battery packs, and the battery packs for charging and discharging can be freely selected.

[0057] The data acquisition module is respectively connected to the LC filter device, the AC / DC conversion device, the hybrid energy storage device and the intelligent power distribution module for real-time acquisition of the operating parameters of the LC filter device and the AC / DC conversion device, the SOC value of the hybrid energy storage device, and sends them to the intelligent power distribution module. Specifically, the data acquisition module can acquire the operating parameters of the LC filter device and the AC / DC conversion device, as well as the SOC values ​​of the battery pack and the two supercapacitor packs in real-time, and upload them to the intelligent power distribution module.

[0058] The intelligent power distribution module is connected to the switch control module for predicting the input and output power of the hybrid energy storage device based on the received operating parameters using the built-in LSTM algorithm model, determining the average input and output power and its fluctuation, and combining the SOC value of the hybrid energy storage device to determine the control strategy and send it to the switch control module to control the power trend of the hybrid energy storage device.

[0059] In a specific implementation, the intelligent power distribution module is specifically used for:

[0060] According to the SOC values ​​of the battery pack and the two supercapacitor packs, the operating status of the hybrid energy storage device is determined in real time.

[0061] After the charging instruction is obtained, if the operating state of the hybrid energy storage device meets the charging condition, the charging control of the hybrid energy storage device is executed through the first intelligent power scheduling control strategy.

[0062] After receiving the discharge command, if the operating state of the hybrid energy storage device meets the discharge conditions, then perform discharge control on the hybrid energy storage device through the second intelligent power scheduling control strategy.

[0063] In the specific implementation manner, the first intelligent power scheduling control strategy includes:

[0064] When the SOC value of the battery pack reaches the preset standard value and the SOC values of both supercapacitor banks are within the preset standard range, record the supercapacitor with the smaller SOC value as the No. 1 supercapacitor, and record the other supercapacitor as the No. 2 supercapacitor.

[0065] At this time, first turn on the input circuit of the No. 1 supercapacitor to receive the external charging power and charge the No. 1 supercapacitor. Predict the first average power (Pcd) and the first fluctuation difference (Pcdset) of the charging through the LSTM algorithm model, and send the data of the current time period into the LSTM model for correction to predict the parameters of the next time period; collect the SOC status of the supercapacitor bank and the battery pack in real time, and calculate the current first power setting value (Pcdset), the first fluctuation upper limit (σcdset1) and the second fluctuation upper limit (σcdset2);

[0066] If the prediction result is a stable power within the absorbable capacity range of the energy storage power station (Pcd < Pcdset and σcd < σcdset1), then allocate the charging power to the battery pack to charge the battery.

[0067] If the prediction result is a power within the absorbable capacity range of the energy storage power station but with large fluctuations (Pcd < Pcdset and σcdset1 < σcd < σcdset2), then continue to charge the No. 1 supercapacitor until the SOC value of the No. 1 supercapacitor reaches the SOC upper limit value, and automatically switch to only charge the No. 2 supercapacitor.

[0068] While charging the No. 2 supercapacitor, control the No. 1 supercapacitor to charge the battery pack with a constant current or constant voltage at power PI;

[0069] When the SOC value of the No. 2 supercapacitor reaches the SOC upper limit value or the SOC value of the No. 1 supercapacitor reaches the SOC lower limit value, switch to the No. 1 supercapacitor to receive the input power from the grid side, and the No. 2 supercapacitor charges the battery pack;

[0070] Loop through the above steps until the battery pack is full and the SOC values of both the No. 1 supercapacitor and the No. 2 supercapacitor reach the SOC upper limit value, or the input power from the grid side disappears, then the charging is completed.

[0071] If the predicted result is power outside the available absorption capacity range of the energy storage power station (Pcd > Pcdset or σcd > σcdset2), then part of the power is abandoned, and the charging time ratio during the charging process is controlled. Only the No. 1 supercapacitor receives the grid-side power, and only the No. 2 supercapacitor charges the battery pack. The two perform intelligent switching and charge-discharge power control according to the upper limit of the battery charging power, the SOC values of the supercapacitor bank and the battery.

[0072] After charging is completed, disconnect the transmission line of the input power, and control the No. 1 supercapacitor or the No. 2 supercapacitor to perform floating charge on the battery pack.

[0073] As an example, such as Figure 2 shown, the specific execution process of the first intelligent power scheduling control strategy is as follows:

[0074] Assume that the available charging capacity of the battery pack of the hybrid energy storage device is sufficient, the No. 1 supercapacitor and the No. 2 supercapacitor store a certain amount of energy, and it is between the upper and lower limits of the SOC.

[0075] At the start of charging, first detect the SOC values of the two supercapacitors, and connect the supercapacitor with the smaller SOC value to the hybrid energy storage terminal (assume it is the No. 1 supercapacitor), and the No. 2 supercapacitor and the battery pack do not work temporarily; after the system runs for a period of time, if the subsequent prediction result of the LSTM model is stable power within the available absorption capacity range of the energy storage power station (Pcd < Pcdset and σcd < σcdset1), it is considered that this is the normal power absorption of the power grid at this time, and the power at this time is directly allocated to the battery pack for charging. The system intelligently controls the number of battery packs to be charged according to the predicted average charging power Pcd to ensure that the charging voltage or current of the battery pack is constant.

[0076] If the predicted result is power within the available absorption capacity range of the energy storage power station but with large fluctuations (Pcd < Pcdset and σcdset1 < σcd < σcdset2), continue to charge the No. 1 supercapacitor until the current SOC value soc1 of the No. 1 supercapacitor reaches the set SOC upper limit value SOC_1max, and the system automatically switches to the No. 2 supercapacitor to control the power flow to charge only the No. 2 supercapacitor.

[0077] While supercapacitor No. 2 is charging, supercapacitor No. 1 is controlled to charge the battery pack at a fixed power PI to maintain constant current or constant voltage. This fixed power value PI is determined by the input power prediction result, the SOC state of different packs of the battery pack, the current SOC value soc1 of supercapacitor No. 1, and the current SOC value soc2 of supercapacitor No. 2. That is, while supercapacitor No. 1 is charging the battery pack, supercapacitor No. 2 is responsible for the current grid-side input power, and at the same time, the charging power of the battery pack is controlled to match the external input power conversion, ensuring that the supercapacitor has a certain remaining capacity margin and the charging voltage or current of the battery pack is stable.

[0078] When the SOC value of supercapacitor No. 2 approaches the upper limit or the SOC value of supercapacitor No. 1 approaches the lower limit, the system switches to the supercapacitor that receives the input power. That is, supercapacitor No. 1 receives the input power from the grid side, and supercapacitor No. 2 charges the battery pack. This cycle continues until the battery pack of the hybrid energy storage system is fully charged and the SOC of supercapacitors No. 1 and No. 2 reaches a certain reserved level.

[0079] If the prediction result is that the power is outside the capacity of the energy storage power station (Pcd>Pcdset or σcd>σcdset2), part of the power will be abandoned, the charging time ratio in the charging process will be controlled, only supercapacitor No. 1 will be used to receive grid-side power, and only supercapacitor No. 2 will be used to charge the battery pack. The two will be intelligently switched and the charging and discharging power will be controlled according to the upper limit of the battery charging power, the SOC value of the supercapacitor group and the battery.

[0080] At the same time, after charging is completed, the grid side is disconnected, and the energy exchange system between the battery pack and the supercapacitor is started. At this time, the battery pack is float-charged using the remaining energy from the supercapacitor. This operation can extend the service life of the battery and ensure that the battery pack can still maintain a certain charging level when not connected to the grid. This allows the hybrid energy storage system to operate completely off-grid, improving the reliability of the system.

[0081] In a specific implementation, the second intelligent power scheduling control strategy includes:

[0082] When the average output power required for discharge does not exceed the maximum continuous output power of the battery pack, first collect the SOC values ​​of the two supercapacitors in real time, record the supercapacitor with the larger SOC value as supercapacitor No. 1, and the other supercapacitor as supercapacitor No. 2. At this time, turn on the output circuit of supercapacitor No. 1 to perform discharge; turn on the output circuit of the battery pack and the input circuit of supercapacitor No. 2 to charge supercapacitor No. 2 to the upper limit of SOC.

[0083] Then, the operating parameters of the LC filtering device and the AC / DC conversion device are collected in real time. The second average power (Pfd) and the second fluctuation difference (σfd) required by the grid side are predicted through the LSTM algorithm model, and the data of the current time period are sent into the LSTM model for correction to predict the parameters of the next time period. The SOC states of the supercapacitor bank and the battery bank are collected in real time, and the current second power setting value (Pfdset), the third fluctuation upper limit (σfdset1), and the fourth fluctuation upper limit (σfdset2) are calculated.

[0084] When the demand of the predicted grid side load does not exceed the current maximum output average power of the hybrid energy storage device and the fluctuation range is within the regulation capacity of one supercapacitor bank (Pfd < Pfdset and σfd < σfdset1), the output circuits of the battery bank and the No. 1 supercapacitor are turned on, the load power is distributed to the battery bank, and the No. 1 supercapacitor is used to smooth the output power. When the SOC value of the No. 1 supercapacitor reaches the SOC lower limit value, the input circuit of the No. 1 supercapacitor is disconnected, and the input circuit of the No. 2 supercapacitor is turned on to switch to the No. 2 supercapacitor to smooth the output power, and at the same time, the battery bank is used to charge the No. 1 supercapacitor as a backup.

[0085] When the demand of the predicted grid side load does not exceed the current maximum output average power of the hybrid energy storage device, and the power fluctuation range exceeds the regulation range of one supercapacitor bank but does not exceed the adjustable fluctuation range of two supercapacitor banks (Pfd < Pfdset and σfdset1 < σfd < σfdset2), the output power of the battery bank is controlled according to the prediction result, and the No. 1 capacitor is set as the output capacitor, and only the No. 1 capacitor is turned on for the output circuit to supplement the power difference when the output power of the battery bank is less than the load power. At the same time, the No. 2 supercapacitor is set as the energy storage capacitor, and only the input circuit of the No. 2 supercapacitor is turned on to absorb the power of the battery bank when the output power of the battery bank is higher than the load power. At the same time, the SOC values of the No. 1 supercapacitor and the No. 2 supercapacitor are detected in real time. When the SOC value of the No. 1 supercapacitor is lower than the SOC lower limit value or the SOC value of the No. 2 supercapacitor is higher than the SOC upper limit value, the No. 1 supercapacitor is set as the energy storage capacitor, and the No. 2 supercapacitor is set as the output capacitor.

[0086] When the average value of the output power required by the grid side load exceeds the maximum continuous output power of the battery bank, or the power fluctuation exceeds the adjustable range of two supercapacitors (Pfd > Pfdset or σfd > σfdset2), the battery bank is controlled to supply power to the grid side as much as possible at the maximum output power, and the supercapacitor bank is intelligently switched.

[0087] As an example, assume that even when the supercapacitor is at the lower limit of the SOC value, it is sufficient to support the output power for a certain period of time, and the average value of the required output power does not exceed the maximum continuous output power of the battery pack. As Figure 3 shown, the specific execution process of the second intelligent power scheduling control strategy is as follows:

[0088] At the beginning of discharging, detect the SOC values of the battery pack and the supercapacitor, compare the SOC values of the No. 1 supercapacitor and the No. 2 supercapacitor, and connect the supercapacitor with the higher SOC value to the system (assumed to be the No. 1 supercapacitor) to support the external load. The battery pack charges the No. 2 supercapacitor to its SOC upper limit at a certain power, and the power size depends on the SOCs of the No. 1 supercapacitor, the No. 2 supercapacitor, the battery, and the average output power required by the grid side.

[0089] Collect the operation parameters of the LC filter device and the AC / DC conversion device in real time, predict the second average power (Pfd) and the second fluctuation difference (σfd) required by the current grid side through the LSTM algorithm model, and send the data of the current time period into the LSTM model for correction to be used to predict the parameters of the next time period; collect the SOC states of the supercapacitor bank and the battery pack in real time, and calculate the current second power setting value (Pfdset), the third fluctuation upper limit (σfdset1), and the fourth fluctuation upper limit (σfdset2).

[0090] After the system runs for a period of time, if the predicted load power in the subsequent period is a stable power within the consumption capacity of the energy storage power station (Pfd < Pfdset and σfd < σfdset1), it is determined that this is the normal power supply mode at this time (the working mode can also be set manually). The system gradually distributes the power to the battery pack and uses the supercapacitor to supplement the fluctuating load demand. At this time, the fluctuating load is guaranteed by the No. 1 supercapacitor connected to the system. The No. 1 supercapacitor acts as both a power source and a load to smooth the output power. When the SOC of the No. 1 supercapacitor reaches the lower limit, switch to the No. 2 supercapacitor, and at the same time charge the No. 1 supercapacitor as a backup.

[0091] If the grid-side fluctuates greatly, but the long-term average power does not exceed the long-term maximum output power range of the battery pack (Pfd < Pfdset and σfdset1 < σfd < σfdset2), the system will automatically switch to the primary frequency modulation mode at this time (the working mode can also be set manually). The system controls the output power of the battery pack according to the average output power Pfd required at this time or predicted by the LSTM model, sets the No. 1 supercapacitor as the output capacitor, and only the output circuit is turned on, which is responsible for quickly supplementing the required power difference when the power of the battery pack is less than the output power required by the grid-side; at the same time, the No. 2 supercapacitor is set as the energy storage capacitor, and only the input circuit is turned on, which is responsible for absorbing the power of the battery pack when the output power of the battery pack is higher than the output power required by the grid-side.

[0092] During the above process, the SOC states of the No. 1 supercapacitor and the No. 2 supercapacitor are detected in real time, and the working states and modes of the two supercapacitors are automatically switched when the SOC value of the No. 1 supercapacitor is lower than the lower limit or the SOC value of the No. 2 supercapacitor is higher than the upper limit.

[0093] When the average value of the output power required for discharging exceeds the maximum continuous output power of the battery pack or the fluctuation exceeds the adjustment ability range of the two supercapacitors (Pfd > Pfdset or σfd > σfdset2), control the battery pack to supply power to the grid-side as much as possible with the maximum output power, and intelligently switch the supercapacitors.

[0094] The present invention provides a hybrid energy storage system for new energy consumption. While achieving efficient new energy consumption, it can also effectively regulate the grid load fluctuation, improve the system's support ability for large-fluctuation loads, that is, the primary frequency modulation ability, and can effectively improve the stability of the power grid.

[0095] The present invention also provides a hybrid energy storage device for new energy consumption, including a memory, a processor, and a computer program stored in the memory and executable on the above processor. When the processor executes the computer program, it realizes the functions of the hybrid energy storage system for new energy consumption described in any one of the above.

[0096] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it realizes the functions of the hybrid energy storage system for new energy consumption described in any one of the above.

[0097] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors, application-specific integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0098] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the dairy data early warning analysis system in the invention by running or executing the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0099] If the hybrid energy storage system for new energy consumption is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be stored in a computer-readable storage medium through a computer program. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, point carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0100] The present invention has described the basic concepts. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0101] In addition, it will be understood by those skilled in the art that various aspects of this specification may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of this specification may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of this specification may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0102] A computer storage medium may include a propagated data signal containing computer program code, for example, in baseband or as part of a carrier wave. The propagated signal may be in a variety of forms, including electromagnetic, optical, etc., or a suitable combination. A computer storage medium may be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, device or apparatus to communicate, propagate or transmit the program for use. The program code on the computer storage medium may be transmitted via any suitable medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0103] The computer program code required for the operation of each part of this specification can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages ​​such as C language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages ​​such as Python, Ruby and Groovy, or other programming languages, etc. The program code can be run entirely on the user's computer, or run on the user's computer as an independent software package, or run partially on the user's computer and partially on a remote computer, or run entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0104] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0105] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0106] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0107] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A hybrid energy storage system for new energy consumption, characterized in that: include: LC filter device, AC / DC conversion device, hybrid energy storage device, data acquisition module and intelligent power distribution module; The input end of the LC filter device is electrically connected to the power supply line of the power grid or the new energy power plant, and the output end of the LC filter device is electrically connected to the hybrid energy storage device through the AC / DC conversion device; A switch control module is provided in the hybrid energy storage device, which is used to control the on and off of the input circuit and the output circuit inside the hybrid energy storage device; a battery pack and two supercapacitor groups are provided in the hybrid energy storage device, both of which are provided with an input circuit and an output circuit, and the battery pack is electrically connected to the two supercapacitor groups respectively, and a plurality of battery packs are provided in the battery pack; The data acquisition module is respectively connected to the LC filter device, the AC / DC conversion device, the hybrid energy storage device and the intelligent power distribution module for real-time acquisition of the operating parameters of the LC filter device and the AC / DC conversion device, and the SOC value of the hybrid energy storage device, and sends them to the intelligent power distribution module; The intelligent power distribution module is connected to the switch control module in data connection, and is used to predict the input and output power of the hybrid energy storage device based on the received operating parameters using the built-in LSTM algorithm model, determine the average power of the input and output and its fluctuation, and determine the control strategy in combination with the SOC value of the hybrid energy storage device and send it to the switch control module to control the power trend of the hybrid energy storage device; The intelligent power distribution module is specifically used for: According to the SOC values ​​of the battery pack and the two supercapacitor packs, the operating status of the hybrid energy storage device is determined in real time; After the charging instruction is obtained, if the operating state of the hybrid energy storage device meets the charging condition, the charging control of the hybrid energy storage device is executed through the first intelligent power scheduling control strategy; After the discharge instruction is obtained, if the operating state of the hybrid energy storage device meets the discharge condition, the discharge control of the hybrid energy storage device is executed through the second intelligent power dispatch control strategy; The first intelligent power dispatching control strategy includes: When the SOC value of the battery group reaches the preset standard value, and the SOC values ​​of the two supercapacitor groups are both within the preset standard range, the supercapacitor group with the smaller SOC value is recorded as supercapacitor No. 1, and the other supercapacitor group is recorded as supercapacitor No. 2; The input circuit of supercapacitor No. 1 is turned on to receive external charging power and charge supercapacitor No. 1; The operating parameters of the LC filter device and the AC / DC converter are collected in real time, and the first average power and the first fluctuation difference of charging are predicted through the LSTM algorithm model. The data of the current time period is sent to the LSTM model for correction to predict the parameters of the next time period. Collect the SOC status of the supercapacitor group and the battery group in real time, and calculate the current first power setting value, the first fluctuation upper limit and the second fluctuation upper limit; If the first average power is less than the first power setting value, and the first fluctuation difference is less than the first fluctuation upper limit, the charging power is allocated to the battery group to charge the battery; If the first average power is less than the first power setting value and the first fluctuation difference is greater than the first fluctuation upper limit and less than the second fluctuation upper limit, supercapacitor No. 1 is continuously charged until the SOC value of supercapacitor No. 1 reaches the SOC upper limit, and then it is automatically switched to charging only supercapacitor No. 2; While supercapacitor No. 2 is being charged, supercapacitor No. 1 is controlled to charge the battery pack at a constant current or constant voltage at power PI; When the SOC value of supercapacitor No. 2 reaches the SOC upper limit or the SOC value of supercapacitor No. 1 reaches the SOC lower limit, supercapacitor No. 1 is switched to receive the input power from the grid side, and supercapacitor No. 2 charges the battery pack; supercapacitor No. 1 and supercapacitor No. 2 are switched cyclically to charge the battery pack until the battery pack is fully charged and the SOC values ​​of supercapacitor No. 1 and supercapacitor No. 2 both reach the SOC upper limit, or the input power from the grid side disappears, then the charging is completed; If the first average power is greater than the first power setting value or the first fluctuation difference is greater than the second fluctuation upper limit, the proportion of charging time in the charging process is controlled, only supercapacitor No. 1 is used to receive grid-side power, and only supercapacitor No. 2 is used to charge the battery pack. Supercapacitor No. 1 and supercapacitor No. 2 are intelligently switched and the charging and discharging power is controlled according to the upper limit of the battery charging power, the SOC values ​​of the supercapacitor group and the battery group; After charging is completed, the transmission line of the input power is disconnected, and supercapacitor No. 1 or supercapacitor No. 2 is controlled to float charge the battery pack; The second intelligent power dispatching control strategy includes: When the average output power required for discharge does not exceed the maximum continuous output power of the battery pack, the SOC values ​​of the two supercapacitors are collected in real time, and the supercapacitor with the larger SOC value is recorded as supercapacitor No. 1, and the other supercapacitor is recorded as supercapacitor No. 2; The output circuit of supercapacitor No. 1 is turned on to discharge; the output circuit of the battery pack is turned on to the input circuit of supercapacitor No. 2 to charge supercapacitor No. 2 to the upper limit of SOC; The operating parameters of the LC filter device and the AC / DC converter are collected in real time, and the second average power and the second fluctuation difference required by the current grid side are predicted through the LSTM algorithm model. The data of the current time period is sent to the LSTM model for correction to predict the parameters of the next time period. Collect the SOC status of the supercapacitor group and the battery group in real time, and calculate the current second power setting value, the third fluctuation upper limit and the fourth fluctuation upper limit; If the second average power is less than the second power setting value, and the second fluctuation difference is less than the third fluctuation upper limit, the battery pack and the No. 1 supercapacitor output circuit are turned on, the load power is allocated to the battery pack, the No. 1 supercapacitor is set to the discharge-only mode, and the No. 1 supercapacitor is used to smooth the output power; when the SOC value of the No. 1 supercapacitor reaches the SOC lower limit, the input circuit of the No. 1 supercapacitor is disconnected, and the input circuit of the No. 2 supercapacitor is turned on, and the No. 2 supercapacitor is switched to smooth the output power, and the battery pack is used to supplement the No. 1 supercapacitor as a backup; If the second average power is less than the second power setting value, and the second fluctuation difference is greater than the third fluctuation upper limit and less than the fourth fluctuation upper limit, the output power of the battery pack is controlled according to the prediction result, and capacitor No. 1 is set as the output capacitor, and only the output circuit of capacitor No. 1 is turned on to discharge to the grid side, so as to supplement the power difference when the output power of the battery pack is less than the load power; at the same time, supercapacitor No. 2 is set as the energy storage capacitor, and only the input circuit of supercapacitor No. 2 is turned on to accept the charge of the battery pack, so as to absorb the power of the battery pack when the output power of the battery pack to the grid side is higher than the load power; the SOC values ​​of supercapacitor No. 1 and supercapacitor No. 2 are detected in real time, and when the SOC value of supercapacitor No. 1 is lower than the SOC lower limit or the SOC value of supercapacitor No. 2 is higher than the SOC upper limit, supercapacitor No. 1 is switched to the energy storage capacitor, and supercapacitor No. 2 is switched to the output capacitor; If the second average power is greater than the second power setting value, or the second fluctuation difference is greater than the fourth fluctuation upper limit, the battery group is controlled to supply power to the grid side at the maximum output power.

2. The hybrid energy storage system for new energy consumption according to claim 1 is characterized in that: The data acquisition module is specifically used for: Collect the operating parameters of the LC filter device and AC / DC converter in real time and upload them to the intelligent power distribution module; The SOC values ​​of the battery pack and the two supercapacitor packs are collected in real time and uploaded to the intelligent power distribution module.

3. A hybrid energy storage device for new energy consumption, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the function of the hybrid energy storage system for new energy consumption as described in any one of claims 1 to 2 is realized.

4. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which, when executed by a processor, implements the functions of the hybrid energy storage system for new energy consumption as described in any one of claims 1 to 2.

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