Precise power control method for DC microgrid

Abstract

The present invention discloses a power precision control method for a DC microgrid, which relates to the field of power control technology, including: identifying distributed power equipment of a DC microgrid, generating a power control network; identifying distributed energy storage equipment of a DC microgrid, generating an energy storage control network; obtaining a dispatching area of ​​the energy storage equipment, establishing a connection relationship between the corresponding energy storage equipment, and connecting the energy storage control network with the power control network; performing power control response analysis on each power equipment in the power control network according to a preset power demand, and identifying a tagged power equipment whose response effect is less than a preset threshold; connecting the energy storage control network to identify the matching energy storage equipment of the tagged power equipment, and preferentially performing power control on the tagged power equipment. The present invention solves the technical problem that the prior art is difficult to adapt to the dynamic and diversified load changes and energy storage management requirements in modern power grids, and has low control precision for power distribution and energy storage equipment, and achieves the technical effect of improving the operating efficiency of the microgrid.

Description

Technical Field

[0001] The present invention relates to the technical field of power control, and in particular to a power precision control method for a direct current microgrid. Background Art

[0002] With the continuous growth of energy demand and the rapid development of renewable energy technology, DC microgrid technology has been widely used in many fields. DC microgrids are able to integrate a variety of distributed power equipment and energy storage equipment, such as solar power generation, wind power generation, and battery energy storage systems, to provide users with flexible and efficient power supply. However, with the expansion of microgrid scale and the increase of system complexity, traditional power control methods face increasing challenges. Traditional DC microgrid power control methods usually rely on fixed power allocation schemes, lack flexible response to real-time load and equipment status, and have the problem of low control accuracy. These methods often have difficulty in coping with dynamic changes in the power grid, resulting in poor operation efficiency of the microgrid. Summary of the invention

[0003] The present application provides a precise power control method for a DC microgrid, which is used to solve the technical problem that the existing technology is difficult to adapt to the dynamic and diverse load changes and energy storage management requirements in modern power grids, and has low control accuracy for power distribution and energy storage equipment.

[0004] In view of the above problems, the present application provides a power precision control method for a DC microgrid.

[0005] The present application provides a power precision control method for a DC microgrid, the method comprising:

[0006] Identify the distributed power equipment of the DC microgrid, and generate a power control network from the distributed power equipment; identify the distributed energy storage equipment of the DC microgrid, and generate an energy storage control network from the distributed energy storage equipment; obtain the scheduling area of ​​each energy storage equipment in the energy storage control network, establish a connection relationship with the corresponding energy storage equipment through the power equipment in the scheduling area, and connect the energy storage control network with the power control network; perform power control response analysis on each power equipment in the power control network according to the preset power demand, and identify the tagged power equipment whose response effect is less than the preset threshold; connect the energy storage control network to identify the matching energy storage device of the tagged power equipment, and use the matching energy storage device to preferentially perform power control on the tagged power equipment.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The present application identifies the distributed power equipment of the DC microgrid, and generates a power control network from the distributed power equipment; identifies the distributed energy storage equipment of the DC microgrid, and generates an energy storage control network from the distributed energy storage equipment; obtains the dispatching area of ​​each energy storage equipment in the energy storage control network, establishes a connection relationship with the corresponding energy storage equipment with the power equipment in the dispatching area, and connects the energy storage control network with the power control network; performs power control response analysis on each power equipment in the power control network according to the preset power demand, and identifies the label power equipment whose response effect is less than the preset threshold; connects the matching energy storage equipment of the energy storage control network to identify the label power equipment, and preferentially performs power control on the label power equipment with the matching energy storage equipment. The present invention solves the technical problem that the prior art is difficult to adapt to the dynamic and diversified load changes and energy storage management requirements in the modern power grid, and has low control accuracy for power distribution and energy storage equipment. By identifying the distributed power equipment and energy storage equipment of the DC microgrid, a power control network and an energy storage control network are established, and control analysis is performed according to the preset power demand, the power equipment with poor response effect is identified, and the matching energy storage equipment is preferentially controlled to achieve accurate power distribution and energy storage management, so as to achieve the technical effect of improving the operation efficiency of the microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of a power precision control method for a DC microgrid provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of a flow chart for performing power regulation response analysis in a power precision control method for a DC microgrid provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The present application provides a precise power control method for a DC microgrid, which is used to solve the technical problem that the existing technology is difficult to adapt to the dynamic and diverse load changes and energy storage management requirements in modern power grids, and has low control accuracy for power distribution and energy storage equipment. By identifying the distributed power equipment and energy storage equipment of the DC microgrid, a power control network and an energy storage control network are established, and control analysis is performed according to preset power requirements. The power equipment with poor response effect is identified, and the matching energy storage equipment is used for priority control to achieve precise power distribution and energy storage management, thereby achieving the technical effect of improving the operating efficiency of the microgrid.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0015] like Figure 1 As shown, the present application provides a power precision control method for a DC microgrid, the method comprising:

[0016] Step S100: identifying distributed power equipment of a DC microgrid, and generating a power control network from the distributed power equipment.

[0017] In the embodiment of the present application, in the process of identifying the distributed power equipment of the DC microgrid and generating the power control network, firstly, the data of the equipment operation parameters of each distributed power equipment, such as photovoltaic power generation group, wind power generation equipment, etc., are collected through the installed sensors and power metering equipment, and the data are transmitted to the central control system through the industrial communication protocol. Then, the central control system identifies the type of each distributed power equipment through the identification information of the equipment, such as equipment ID, model, etc., and classifies it as power generation equipment or load equipment.

[0018] After identifying the device type, the connection relationship between the devices is determined by reading the pre-stored physical wiring diagram or topology diagram. The physical wiring diagram shows the actual power transmission path between the power generation equipment and the load equipment. Combined with the topology analysis and wiring information, the power transmission path is determined. Finally, based on the identified device information and the connection relationship between the devices, the power control network is generated.

[0019] Step S200: Identify the distributed energy storage devices of the DC microgrid, and generate an energy storage control network using the distributed energy storage devices.

[0020] In the embodiment of the present application, the operating parameters of the energy storage device are first collected through sensors and power metering devices installed on the energy storage device, including information such as battery voltage, current, charge and discharge rate, and energy storage capacity. Next, the collected data is transmitted to the central control system through the industrial communication protocol. Then, based on the identification information of the device, such as device ID, device model, rated capacity of the energy storage device, etc., the type of each energy storage device is identified through a preset device database. According to the identified type, the device is classified into different energy storage types, such as battery energy storage system, flywheel energy storage system, etc.

[0021] Then, by reading the physical wiring diagram or the topological structure diagram of the energy storage device, the connection relationship between the energy storage devices and between the energy storage devices and other power devices, such as power generation equipment or load equipment, is determined. By combining topological analysis and wiring information, the power flow relationship between the energy storage device and other devices is determined. Finally, based on the real-time status of the energy storage device, the device type and the connection relationship between the devices, a functional control network is generated.

[0022] Step S300: Obtain the dispatching area of ​​each energy storage device in the energy storage control network, establish a connection relationship with the corresponding energy storage device through the power equipment in the dispatching area, and connect the energy storage control network with the power control network.

[0023] In the embodiment of the present application, in order to obtain the dispatching area of ​​each energy storage device in the energy storage control network, the geographical location information of the energy storage device is first imported through the GIS system to determine its specific coordinates. Next, according to the service capability of the energy storage device, the service range is delineated using GIS tools. Finally, the dispatching area is generated by calculating the coverage radius of the energy storage device.

[0024] Next, by reading the physical wiring diagram or power system topology diagram in the dispatching area, use the topology analysis tool to identify the power equipment in the dispatching area. Specifically, analyze the physical connection relationship between the energy storage device and the power equipment, determine the power equipment that is directly or indirectly connected to the energy storage device, and confirm its effective transmission range through the distance calculation method to ensure that the power equipment can obtain stable power support. Then use the power transmission path analysis tool to analyze the connection path between the energy storage device and the power equipment.

[0025] Finally, the energy storage control network is connected to the power control network through the energy management system.

[0026] Step S400: performing power regulation response analysis on each power device in the power regulation network according to a preset power demand, and identifying a tagged power device whose response effect is less than a preset threshold.

[0027] In the embodiment of the present application, the preset power demand is first obtained, and the power is allocated to each power device in the power control network according to the demand, and the real-time power information of each power device is obtained. Next, the load balancing is evaluated for the real-time power information. If the load balancing is less than the preset value, the preset load balancing is used as the target, and each device is analyzed to obtain the power control parameters of each power device. Then, based on the obtained power control parameters, the power control response analysis is performed on each power device, and finally the labeled power devices whose response effect is less than the preset threshold are identified.

[0028] Further, such as Figure 2 As shown, in the method provided in the embodiment of the application, power regulation response analysis is performed on each power device in the power regulation network according to the preset power demand, and further includes:

[0029] Obtain a preset power demand, distribute power to each power device in the power control network according to the preset power demand, and obtain real-time power information of each power device; perform load balancing evaluation on the real-time power information, and if the load balancing is less than the preset load balancing, analyze each power device with the preset load balancing as the goal to obtain power control parameters of each power device; perform power control response analysis on each power device with the power control parameters.

[0030] In the embodiment of the present application, the pre-stored preset power demand is first obtained. The preset power demand is set in advance by technical experts based on the operation demand of the power grid, historical data and other factors. Next, the power allocation algorithm is used to allocate power to each power device in the power control network according to the preset power demand. After that, the power output, voltage, current and other operating status information of the equipment are obtained in real time through the intelligent sensors and power metering equipment installed on each power device, and this information is integrated to obtain the real-time power information of each power device.

[0031] Next, the real-time power information is evaluated for load balancing. Specifically, the load of each power device is calculated based on the real-time power information of the device, and the difference between the actual power output of the device and its preset power requirement is compared to evaluate whether the device is operating within the target range. Load balancing reflects the uniformity of power output between devices. Ideally, the load of all devices should be as close to their preset power as possible. When calculating load balancing,

[0032] The calculated load balance is then compared with the preset load balance. When the load balance is lower than the preset load balance, each power device is further analyzed with the preset load balance as the goal. At this time, it is no longer dependent on a single power distribution, but the operating status of the device is analyzed by calculating the load deviation of the device. The load deviation represents the difference between the actual power output of each device and its preset power. When calculating the load deviation, the load deviation is obtained by subtracting the preset power from the actual power output value and finally removing the preset power. By calculating the load deviation, it is identified which devices have a load that is significantly higher or lower than expected. Overloaded devices refer to devices with a positive load deviation that exceeds expectations, while inefficient devices refer to devices with a negative load deviation. After identifying these unbalanced load devices, a power control algorithm is used to generate power control parameters. The power control parameters include the upper and lower limits of power output and the voltage adjustment value, which are intended to restore the load balance of the power grid by adjusting the power output of the device.

[0033] Finally, the power control response of each power equipment is analyzed based on the power control parameters.

[0034] Furthermore, in the method provided in the embodiment of the application, the power control response analysis of each power device is performed using the power control parameter, and further includes:

[0035] The response of each power device is predicted using the power control parameters to obtain the power response time and the power fluctuation stability; the response effect of each power device is calculated according to the power response time and the power fluctuation stability, and the labeled power devices whose response effect is less than a preset threshold are identified.

[0036] In an embodiment of the present application, when predicting the response of each power device with power control parameters, the random forest model is first used to predict the response of each power device based on historical operation data and power control parameters. Specifically, multiple features are extracted from the historical operation data of the device, including data such as the power output, response time, and power fluctuation amplitude of the device under different load and power control conditions. These historical data constitute a training data set for training the random forest model. Through training, the random forest model can predict the power response time and power fluctuation stability of the device based on the control parameters.

[0037] When predicting the power response time of a device, the power control parameters are input into the random forest model to obtain the power response time of the device. The power response time refers to the time required for the device to actually adjust the power output after receiving the control command. The model uses the historical operation data of the device to learn the relationship between the power control parameters and the response time, and then makes a prediction based on the current control parameters through the model to output the response time of the device. Then, the power fluctuation stability of the device is predicted through the same random forest model. Power fluctuation stability refers to the fluctuation amplitude of the power output of the device after the power adjustment is completed.

[0038] After obtaining the power response time and power fluctuation stability, the weighted average method is used to calculate the comprehensive response effect of the equipment. First, technical experts set different weight values ​​for response time and fluctuation stability according to the operation requirements of the power grid. For example, the importance of response time may be higher than fluctuation stability, so a higher weight is set for response time, such as 0.6, and a lower weight is set for fluctuation stability, such as 0.4. The response effect is then calculated using the formula: Among them, T max is the maximum acceptable response time, S max is the maximum acceptable power fluctuation stability, T max and S max For the preset, T 设备 and S 设备 is the actual response time and fluctuation stability of the device, w 1 and w 2 are weights set by experts.

[0039] Finally, the response effect of the device is compared with a preset response effect threshold. If the response effect of the device is lower than the preset response effect threshold, the device is marked as a tagged power device.

[0040] Furthermore, in the method provided in the embodiment of the application, before connecting the energy storage control network to identify the matching energy storage device of the tagged power device, it also includes:

[0041] Monitor the remaining capacity and charge and discharge rate of each energy storage device in the energy storage control network; identify the health status of each energy storage device in the energy storage control network according to the remaining capacity and the charge and discharge rate, and obtain a healthy-energy storage control network that can be used for matching; identify the matching energy storage device of the tagged power device according to the healthy-energy storage control network.

[0042] In the embodiment of the present application, the remaining capacity and charge and discharge rate of each energy storage device are monitored in real time through the built-in sensors and power metering devices of the energy storage device. The remaining capacity represents the amount of electricity available in the energy storage device, which is calculated by the coulomb metering method, measuring the current and time of the device to estimate the current remaining capacity. The charge and discharge rate is calculated by monitoring the voltage and current changes of the device to calculate the charging or discharging speed of the device per unit time.

[0043] Next, the health status of the energy storage device is evaluated using the preset health status threshold. The health status threshold is a standard pre-set by technical experts based on the design parameters, nominal capacity, nominal charge and discharge rate, and equipment life curve of the equipment to form a standard comparison table. For example, the standard stipulates that when the remaining capacity of the equipment is less than 30% of its nominal capacity or the charge and discharge rate is less than 20% of its nominal rate, the health status of the equipment will be considered poor. The real-time remaining capacity and charge and discharge rate of each energy storage device are compared with these standards to determine whether the device meets the health standards. If the status of the device is within the standard range, it will be marked as a healthy device; otherwise, the device will be marked as an unhealthy device. Through this process, the health status of the energy storage device is identified, and a healthy-energy storage control network composed of healthy devices is constructed.

[0044] On this basis, the health-energy storage control network is used to identify the energy storage device that best matches the power device according to its power demand. Specifically, the candidate energy storage devices for each tagged power device are first screened out based on the connection relationship between the power devices and the energy storage devices in the dispatching area, and then the energy storage response analysis is performed on these candidate energy storage devices to evaluate their response time, power output and other indicators, and the energy storage device with the best response effect is selected as the matching energy storage device.

[0045] Furthermore, in the method provided in the embodiment of the application, identifying the matching energy storage device of the tagged power device according to the health-energy storage control network also includes:

[0046] According to establishing a connection relationship between the power equipment in the dispatching area and the corresponding energy storage device, candidate energy storage devices of the tagged power device are obtained; energy storage response analysis is performed among the candidate energy storage devices, and a matching energy storage device of the tagged power device is selected, wherein the matching energy storage device is an energy storage device with the highest energy storage response effect among the candidate energy storage devices.

[0047] In an embodiment of the present application, in order to obtain candidate energy storage devices for tagged power devices, first, an association between power devices and energy storage devices is established based on the physical connection relationship and power demand between the power devices and the energy storage devices in the scheduling area. The scheduling area refers to a specific power grid area where the power equipment is located, and based on the geographical location, power demand, and power grid topology of the power equipment, energy storage devices that can be connected to these power devices physically or through the network are found. Based on the power grid topology in the scheduling area, the connection information between the power equipment and the energy storage device is obtained, and a connection relationship diagram is formed to mark which power devices each energy storage device can provide power support for. Through this step, candidate energy storage devices corresponding to each tagged power device are screened out from the energy storage control network.

[0048] Next, the energy storage response analysis is performed in the candidate energy storage devices. First, the response time of the candidate energy storage devices is evaluated. At this time, the time series analysis model is trained using historical operation data. The historical operation data includes the response behavior of the energy storage devices under different load conditions, ambient temperatures, and charge and discharge cycles in the past. Through training, the model can predict the response time of the device based on the current real-time monitored load and device status. For example, the response time of one energy storage device may be 5 seconds under a specific environment and load, while another may be 10 seconds. These prediction results are stored for subsequent scoring. The shorter the response time, the better the response effect of the device. Then the charge and discharge rate of the candidate energy storage device is evaluated. For this purpose, the linear regression model is trained by combining historical charge and discharge records with real-time current and voltage data. Historical data includes the charging rate and discharge rate of the device in different time periods, as well as the rate changes when the load fluctuates. Through training, the linear regression model can predict how fast the device can charge and discharge under the current conditions, and obtain the charge and discharge rate results of the device under the current conditions. Finally, the power output capacity of the candidate energy storage device is evaluated. Here, the random forest regression model is used for training. The data used includes the power output curves of each energy storage device under different loads and different control conditions in the past. Through training, the random forest regression model can combine the current health status of the device and the load conditions to predict the power output capacity of the device under the current control requirements.

[0049] After completing the energy storage response analysis, a comprehensive score is given based on the response time, charge and discharge rate, and power output capacity of each energy storage device. First, the response time, charge and discharge rate, and power output capacity of the energy storage device are normalized, for example, by minimum-maximum normalization. After normalization, the overall performance of each energy storage device is comprehensively evaluated by weighted average. Different weights are set for response time, charge and discharge rate, and power output capacity according to the needs of the power grid. The comprehensive response effect of each candidate device is obtained by calculation, and the energy storage device with the highest response effect is selected as the matching energy storage device for the tagged power device.

[0050] Furthermore, in the method provided in the embodiment of the application, selecting a matching energy storage device for the tagged power device also includes:

[0051] If the number of the tagged power devices is greater than or equal to 2, obtain the matching energy storage devices for each tagged power device; determine whether the matching energy storage devices corresponding to each tagged power device are repeated, and detect whether the repeated energy storage devices meet the repeated power regulation requirements; if the repeated power regulation requirements are met, enable the repeated energy storage devices to perform dual-path regulation on the repeated tagged power device group.

[0052] In an embodiment of the present application, when the number of tagged power devices is greater than or equal to 2, the optimal matching energy storage device is first selected for each tagged power device through energy storage response analysis, normalization processing and weighted scoring. In this process, a time series analysis model is used to predict the response time of the energy storage device, a linear regression model is used to evaluate the charging and discharging rate of the device, and a random forest regression model is used to evaluate the power output capacity of the device. By normalizing and weighted averaging these results, the most suitable energy storage device is selected for each tagged power device. However, when there are multiple tagged power devices, it may happen that multiple devices are matched to the same energy storage device, that is, the energy storage device is repeated.

[0053] After obtaining the matching energy storage device for each tagged power device, first check whether there are duplicate energy storage devices through the unique identifier. By traversing the matching energy storage device list of each tagged power device, use the device's unique identifier, such as the device ID, to identify which energy storage devices are selected by multiple power devices. If duplicate energy storage devices are detected, further evaluate whether these duplicate devices can simultaneously meet the power regulation requirements of multiple tagged power devices.

[0054] For each repeated energy storage device, a power demand assessment algorithm is used to evaluate whether it has sufficient charging and discharging capacity and power output capacity to provide power support for multiple devices at the same time. By collecting data such as the voltage, current, and power output of the energy storage device in real time, the remaining capacity of the device is estimated in combination with the coulomb measurement method, and its power output is evaluated using historical data. The power demand assessment algorithm is used to calculate the output of the energy storage device under the current load to determine whether it can support multiple power devices at the same time without overloading. If the remaining capacity and power output capacity of the energy storage device are sufficient, it is confirmed that the device can support dual-channel regulation, that is, provide regulation services for multiple tagged power devices at the same time.

[0055] Once it is confirmed that the energy storage device can meet the needs of multiple devices at the same time, the dual-path control strategy is enabled. At this time, the power output of the energy storage device is dynamically managed through the load balancing algorithm or power allocation algorithm. By monitoring the power demand of each tagged power device in real time and flexibly adjusting the output power of the energy storage device according to the changes in demand, it is ensured that the power demand of each device can be met and the energy storage device will not fail due to overload.

[0056] Furthermore, the method provided in the application embodiment also includes:

[0057] If the repeated power regulation demand is not met, a synchronization instruction is obtained; and according to the synchronization instruction, the repeated-label power equipment group is synchronously regulated by the energy storage regulation network and the power regulation network at the same time.

[0058] In an embodiment of the present application, when the energy storage device cannot meet the repeated power regulation requirements of multiple tagged power devices, a synchronization instruction is generated. The purpose of the synchronization instruction is to simultaneously mobilize the power generation equipment in the power regulation network and the energy storage equipment in the energy storage regulation network so that the two networks work together to jointly meet the power requirements of multiple power devices. At this stage, the power grid topology analysis is used to identify which power generation equipment and energy storage equipment can operate in coordination to ensure that there is sufficient communication and power transmission connection between them.

[0059] Then, the target power demand of synchronous regulation is distributed to multiple power sources and energy storage devices through the load distribution algorithm and the power scheduling algorithm. That is, the power demand of each tagged power device is calculated, and the demand is proportionally distributed to the power generation equipment in the power regulation network and the energy storage equipment in the energy storage regulation network. For example, if a tagged power device requires 100kW of power support, it is decided that 70kW of it will be provided by the power generation equipment and the remaining 30kW will be provided by the energy storage equipment to achieve a balanced distribution of power. This process ensures that in the case of insufficient energy storage equipment, the power generation equipment can provide supplementary power support to the power equipment through the power regulation network.

[0060] Finally, synchronous control is performed. Through the synchronous instruction, the power control network and the energy storage control network respond synchronously, that is, the power generation equipment and the energy storage equipment start to output power at the same time, ensuring that the power requirements of multiple tag power equipment can be met at the same time.

[0061] Step S500: connecting to the energy storage control network to identify a matching energy storage device of the tagged power device, and using the matching energy storage device to preferentially control the power of the tagged power device.

[0062] In the embodiment of the present application, the energy storage control network is first connected, and the energy storage device matching the tagged power device is identified through the network. Then, according to the power control parameters of each power device, the matching energy storage device generates corresponding discharge data, and based on this data, the power of the tagged power device is controlled. In this process, the energy storage device in the energy storage control network is used to provide the required power support for each tagged power device on a priority basis, ensuring the stability and response efficiency of the power equipment during operation.

[0063] Furthermore, in the method provided in the embodiment of the application, the matching energy storage device is used to preferentially control the power of the tag power device, and further includes:

[0064] The power control parameters of each power device are obtained; the matching energy storage device generates discharge data according to the power control parameters of each power device, and performs power control on the tagged power device with the discharge data.

[0065] In the embodiment of the present application, power control parameters are first generated for each power device through a power demand prediction model. Specifically, the training of the power demand prediction model is based on the historical power output, load conditions and other data of the power equipment, and is modeled and trained through an LSTM neural network to ensure accurate prediction of the power demand of the power equipment under different load and operating conditions. Through training, the power demand prediction model can predict the power demand of the power equipment in the future in real time and generate power control parameters.

[0066] After obtaining the power control parameters, the power control parameters are passed to the matching energy storage device. The energy storage device generates corresponding discharge data using the PID control algorithm based on the received power control parameters. The PID control algorithm calculates the discharge rate and discharge amount of the energy storage device based on the power requirements of the power equipment to ensure that the output of the energy storage device accurately matches the power requirements of the power equipment. The discharge data includes information such as the discharge voltage, current, and duration.

[0067] Finally, the energy storage device regulates the power of the tag power device according to the generated discharge data. The energy storage device discharges according to the instructions in the discharge data to ensure that the power device obtains the required power output. In this way, the output of the energy storage device can be regulated in real time according to the power demand of the power device to ensure that the power demand of the power device is met, and the regulation process is accurate and efficient.

[0068] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0069] The present application identifies the distributed power equipment of the DC microgrid, and generates a power control network from the distributed power equipment; identifies the distributed energy storage equipment of the DC microgrid, and generates an energy storage control network from the distributed energy storage equipment; obtains the dispatching area of ​​each energy storage equipment in the energy storage control network, establishes a connection relationship with the corresponding energy storage equipment with the power equipment in the dispatching area, and connects the energy storage control network with the power control network; performs power control response analysis on each power equipment in the power control network according to the preset power demand, and identifies the label power equipment whose response effect is less than the preset threshold; connects the matching energy storage equipment of the energy storage control network to identify the label power equipment, and preferentially performs power control on the label power equipment with the matching energy storage equipment. The present invention solves the technical problem that the prior art is difficult to adapt to the dynamic and diversified load changes and energy storage management requirements in the modern power grid, and has low control accuracy for power distribution and energy storage equipment. By identifying the distributed power equipment and energy storage equipment of the DC microgrid, a power control network and an energy storage control network are established, and control analysis is performed according to the preset power demand, the power equipment with poor response effect is identified, and the matching energy storage equipment is preferentially controlled to achieve accurate power distribution and energy storage management, so as to achieve the technical effect of improving the operation efficiency of the microgrid.

[0070] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0071] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0072] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A power precision control method for a DC microgrid, characterized in that: The method comprises: Identifying distributed power equipment of a DC microgrid, and generating a power control network from the distributed power equipment; Identifying a distributed energy storage device of the DC microgrid, and generating an energy storage control network from the distributed energy storage device; Obtaining the dispatching area of ​​each energy storage device in the energy storage control network, establishing a connection relationship with the corresponding energy storage device through the power equipment in the dispatching area, and connecting the energy storage control network with the power control network; Performing power regulation response analysis on each power device in the power regulation network according to the preset power demand, and identifying the labeled power devices whose response effect is less than the preset threshold; Connecting the energy storage control network to identify a matching energy storage device of the tagged power device, and using the matching energy storage device to preferentially control the power of the tagged power device; Performing power regulation response analysis on each power device in the power regulation network according to a preset power demand, the method comprising: Obtaining a preset power demand, allocating power to each power device in the power control network according to the preset power demand, and obtaining real-time power information of each power device; The real-time power information is evaluated for load balancing. If the load balancing is less than the preset load balancing, each power device is analyzed with the preset load balancing as the goal to obtain the power control parameters of each power device. The power control parameters are used to perform power control response analysis on each of the power devices, the method comprising: Using the power control parameters to predict the response of each power device, and obtain the power response time and power fluctuation stability; According to the power response time and the power fluctuation stability, the response effect of each power device is calculated, and the label power device whose response effect is less than a preset threshold is identified. Among them, T max is the maximum acceptable response time, S max is the maximum acceptable power fluctuation stability, T max and S max For the preset, T 设备 and S 设备 is the actual response time and fluctuation stability of the device, and w1 and w2 are the weights set by experts.

2. The power precise control method for a DC microgrid according to claim 1, characterized in that: Before connecting the energy storage control network to identify the matching energy storage device of the tagged power device, the method includes: Monitoring the remaining capacity and charge / discharge rate of each energy storage device in the energy storage control network; Identify the health status of each energy storage device in the energy storage control network according to the remaining capacity and the charge and discharge rate, and obtain a healthy energy storage control network that can be used for matching; The matching energy storage device of the tagged power device is identified according to the health-energy storage regulation network.

3. The power precise control method for a DC microgrid according to claim 2, characterized in that: According to the health-energy storage control network, a matching energy storage device of the tagged power device is identified, and the method includes: Acquire candidate energy storage devices for the tagged power devices according to establishing a connection relationship between the power devices in the dispatching area and the corresponding energy storage devices; An energy storage response analysis is performed among the candidate energy storage devices, and a matching energy storage device of the tagged power device is selected, wherein the matching energy storage device is an energy storage device with the highest energy storage response effect among the candidate energy storage devices.

4. The power precise control method for a DC microgrid according to claim 3, characterized in that: Selecting a matching energy storage device for the tagged power device, the method further includes: If the number of the tagged power devices is greater than or equal to 2, obtain a matching energy storage device for each tagged power device; Determine whether the matching energy storage devices corresponding to each tagged power device are repeated, detect whether the repeated energy storage devices meet the repeated power regulation requirements, and if they meet the repeated power regulation requirements, enable the repeated energy storage devices to perform dual-path regulation on the repeated tagged power device group.

5. The power precise control method for a DC microgrid according to claim 4, characterized in that: If the repeated power control requirement is not met, a synchronization instruction is obtained; According to the synchronization instruction, the energy storage control network and the power control network simultaneously perform synchronous control on the repeated-label power equipment group.

6. The power precision control method for a DC microgrid according to claim 1, characterized in that: The matching energy storage device is used to preferentially control the power of the tag power device, including: Obtain power control parameters of each power equipment; The matching energy storage device generates discharge data according to the power control parameters of each power device, and performs power control on the tag power device with the discharge data.

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