Dc micro-grid voltage control method and system based on distributed cooperation

Abstract

This invention relates to the field of microgrid voltage control technology, specifically a distributed collaborative DC microgrid voltage control method and system. The method includes: identifying the DC microgrid; setting a control cycle; and performing the following operations on each of multiple smart energy storage units: calculating the energy storage unit's charge based on its output current value to obtain a state-of-charge estimate for the current cycle; identifying the effective set of adjacent nodes; calculating the unit's adaptive droop coefficient; calculating a reference voltage; calculating a compensation voltage based on the reference voltage, a preset line resistance, and the unit's output current value; controlling the smart energy storage units to obtain the controlled energy storage units; summarizing the controlled energy storage units to obtain a set of controlled energy storage units; and completing the distributed collaborative DC microgrid voltage control based on this set of controlled energy storage units. This invention can achieve high-precision voltage control while autonomously optimizing the power and energy balance among energy storage units.

Description

Technical Field

[0001] This invention relates to the field of microgrid voltage control technology, and in particular to a method and system for voltage control of DC microgrids based on distributed collaboration. Background Technology

[0002] Distributed coordination is a system control architecture and strategy. A DC microgrid is a small, independent power supply network composed of distributed power sources, energy storage devices, DC loads, and converters, which distributes, controls, and manages power in the form of DC electricity. Voltage control is achieved by actively adjusting controllable devices in the system (such as energy storage converters) to maintain the voltage of critical buses within the allowable deviation range of the rated value, thereby ensuring power quality and system stability.

[0003] Existing distributed voltage control methods focus solely on bus voltage recovery without adequately considering the internal state differences of each energy storage unit. This can easily lead to prolonged overcharging or over-discharging of some energy storage units, thus affecting the overall system lifespan and power supply continuity. Furthermore, many methods fail to fully incorporate the impact of uneven line impedance on precise voltage distribution, potentially resulting in excessive voltage deviations or unreasonable power allocation at remote nodes. Therefore, how to achieve high-precision voltage control while autonomously optimizing power and energy balance among energy storage units is a pressing computational problem that needs to be solved. Summary of the Invention

[0004] This invention provides a distributed collaborative DC microgrid voltage control method and a computer-readable storage medium. Its main purpose is to achieve high-precision voltage control while autonomously optimizing the power and energy balance among energy storage units.

[0005] To achieve the above objectives, the present invention provides a DC microgrid voltage control method based on distributed collaborative control, comprising:

[0006] Receive distributed coordination instructions and identify the DC microgrid based on the distributed coordination instructions. The DC microgrid includes multiple smart energy storage units, wherein each smart energy storage unit includes a local controller.

[0007] Set a control cycle and perform the following operations for each of the multiple smart energy storage units:

[0008] According to the control cycle, the local controller in the intelligent energy storage unit collects current and voltage data to obtain the unit output current and unit output voltage values.

[0009] The energy storage unit's charge is calculated based on the unit's output current value to obtain the estimated state of charge for this cycle.

[0010] Based on the estimated state of charge and unit output voltage value of this cycle, the effective adjacent node set is identified, and the unit adaptive droop coefficient is calculated based on the effective adjacent node set.

[0011] The reference voltage is calculated based on the preset bus voltage rating, unit output current value and unit adaptive droop coefficient, and the compensation voltage is calculated based on the reference voltage, preset line resistance and unit output current value.

[0012] The intelligent energy storage unit is controlled by the compensation voltage and the local controller to obtain the controlled energy storage unit. The controlled energy storage units are then aggregated to obtain the controlled energy storage unit set. Based on the controlled energy storage unit set, the voltage control of the DC microgrid based on distributed collaboration is completed.

[0013] Optionally, the step of calculating the energy storage unit's charge based on the unit's output current value to obtain the estimated state of charge for the current cycle includes:

[0014] Obtain the estimated state of charge from the previous cycle, and calculate the charge change in the current cycle based on the control cycle and the unit output current value.

[0015] The rated capacity of the unit battery pack is obtained from the intelligent energy storage unit, and the current state of charge estimate is calculated based on the charge change in this cycle, the state of charge estimate of the previous cycle, and the rated capacity of the unit battery pack.

[0016] If the current state of charge estimate is greater than the preset maximum state of charge estimate, then the current state of charge estimate is replaced by the maximum state of charge estimate to obtain the corrected state of charge estimate.

[0017] If the current state of charge estimate is less than the preset minimum state of charge estimate, the current state of charge estimate is replaced by the minimum state of charge estimate to obtain the corrected state of charge estimate.

[0018] If the current state of charge estimate is not greater than the maximum state of charge estimate and the current state of charge estimate is not less than the minimum state of charge estimate, then the current state of charge estimate is taken as the normal state of charge estimate.

[0019] The state of charge (SOC) estimate for the current period is determined based on either the revised SOC estimate or the normal SOC estimate.

[0020] Optionally, the formula for calculating the current state of charge estimate is as follows:

[0021] ;

[0022] in, This represents the current state of charge estimate. This represents the estimated state of charge in the previous period. This represents the change in charge during the current period. This indicates the rated capacity of the battery pack.

[0023] Optionally, the step of determining the effective neighboring node set based on the estimated state of charge and the unit output voltage value for this cycle includes:

[0024] The key state vector to be coordinated is set based on the state of charge estimate of this cycle. The key state vector to be coordinated includes: the state of charge estimate of this cycle, the unit output voltage value and the operating mode flag.

[0025] The key state vectors to be coordinated are packaged to obtain energy storage unit data packets, and the adjacent node sets are identified from the pre-constructed mesh topology diagram based on the smart energy storage units;

[0026] The transmission period is set according to the control period, and the energy storage unit data packets are sent to the adjacent node set according to the transmission period to obtain the adjacent node set to be responded to.

[0027] The response time set is obtained by timing each neighboring node in the set of neighboring nodes to be responded to;

[0028] If there is a response time in the response time set that is greater than the preset maximum response time, then the adjacent node corresponding to the response time is removed from the adjacent node set to obtain the effective adjacent node set.

[0029] Optionally, the step of calculating the adaptive droop coefficient based on the effective neighbor set includes:

[0030] The system average state of charge and average voltage are estimated based on the effective adjacent node set. The number of battery cycles is obtained based on the smart energy storage unit. The initial unit low-end threshold and initial unit high-end threshold are calculated based on the number of battery cycles, the preset low-end threshold, and the preset high-end threshold.

[0031] The initial low-end threshold and the initial high-end threshold of the unit are adjusted according to the preset hysteresis width to obtain the low-end threshold and the high-end threshold of the unit.

[0032] If the estimated average state of charge of the system is less than the low-end threshold of the cell or greater than the high-end threshold of the cell, then the corrected voltage droop coefficient is calculated based on the average voltage of the system.

[0033] If the system average state of charge (SOC) estimate is greater than or equal to the low-end threshold of the cell and less than or equal to the high-end threshold of the cell, then the SOC difference is calculated based on the SOC estimate for the current period and the system average SOC estimate. The SOC difference is the SOC estimate for the current period minus the system average SOC estimate.

[0034] The compensation droop coefficient is calculated based on the difference in state of charge, and the hybrid droop coefficient is calculated based on the corrected voltage droop coefficient and the compensation droop coefficient.

[0035] The adaptive droop coefficient of the cell is determined based on the modified voltage droop coefficient, the compensated charge droop coefficient, or the hybrid droop coefficient.

[0036] Optionally, the step of calculating the corrected voltage droop factor based on the system average voltage includes:

[0037] Obtain the maximum output current of the intelligent energy storage unit and calculate the droop coefficient based on the maximum output current;

[0038] The voltage deviation is calculated based on the system average voltage and the unit output voltage value. The voltage deviation is the value obtained by subtracting the system average voltage from the unit output voltage value.

[0039] The voltage deviation is normalized to obtain the normalized voltage deviation, which is the value obtained by dividing the voltage deviation by the preset system rated voltage.

[0040] The voltage droop coefficient is calculated based on the droop coefficient, the preset voltage equalization factor, and the normalized voltage deviation.

[0041] The voltage droop coefficient is adaptively corrected based on the unit output current value and the preset standard current value to obtain the corrected voltage droop coefficient.

[0042] Optionally, the formula for calculating the voltage droop coefficient is as follows:

[0043] ;

[0044] in, This represents the voltage droop coefficient. Indicates the droop coefficient. This represents the natural exponential function. This represents the voltage equalization factor. This indicates the normalized voltage deviation.

[0045] Optionally, the calculation of the compensation droop coefficient based on the difference in state of charge includes:

[0046] The state of charge difference is normalized to obtain the normalized state of charge difference, which is the value obtained by dividing the state of charge difference by 100.

[0047] The charge balance droop coefficient is calculated based on the droop coefficient, the normalized charge difference, and the preset charge balance factor.

[0048] Obtain the cell health state factor, and use the cell health state factor to compensate for the charge balance droop coefficient to obtain the compensated charge droop coefficient.

[0049] Optionally, the step of calculating the hybrid droop coefficient based on the corrected voltage droop coefficient and the compensated charge droop coefficient includes:

[0050] If the system average state of charge (SOC) estimate is greater than the initial unit low threshold and the system average SOC estimate is greater than the unit low threshold, or if the system average SOC estimate is greater than the initial unit high threshold and the system average SOC estimate is greater than the unit high threshold, then the midpoint value of the plateau period is calculated based on the initial unit low threshold and the initial unit high threshold.

[0051] Calculate the voltage balance weighting coefficient based on the midpoint value of the platform period and the estimated average state of charge of the system.

[0052] The hybrid droop coefficient is calculated based on the voltage balance weighting coefficient, the corrected voltage droop coefficient, and the compensated charge droop coefficient.

[0053] To achieve the above objectives, the present invention also provides a distributed collaborative DC microgrid voltage control system, comprising:

[0054] The system initialization module is used to receive distributed coordination instructions and identify the DC microgrid based on the distributed coordination instructions. The DC microgrid includes multiple smart energy storage units, wherein each smart energy storage unit includes a local controller.

[0055] The state of charge estimation module is used to set the control cycle and perform the following operations on each of the multiple smart energy storage units: according to the control cycle, the local controller in the smart energy storage unit is sampled for current and voltage to obtain the unit output current value and unit output voltage value; the energy storage unit is charged based on the unit output current value to obtain the state of charge estimation value for this cycle.

[0056] The droop coefficient acquisition module is used to identify the effective adjacent node set based on the estimated state of charge and the unit output voltage value in this cycle, and to calculate the unit's adaptive droop coefficient based on the effective adjacent node set.

[0057] The local control execution module is used to calculate the reference voltage based on the preset bus voltage rating, unit output current value and unit adaptive droop coefficient, calculate the compensation voltage based on the reference voltage, preset line resistance and unit output current value, control the smart energy storage unit using the compensation voltage and the local controller, obtain the controlled energy storage unit, summarize the controlled energy storage unit to obtain the controlled energy storage unit set, and complete the voltage control of the DC microgrid based on distributed collaboration based on the controlled energy storage unit set.

[0058] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0059] Memory, storing at least one instruction;

[0060] The processor executes the instructions stored in the memory to implement the distributed collaborative DC microgrid voltage control method described above.

[0061] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the aforementioned distributed cooperative DC microgrid voltage control method.

[0062] To address the problems described in the background art, this invention receives distributed coordination instructions and identifies a DC microgrid based on these instructions. The DC microgrid includes multiple smart energy storage units, each including a local controller. A control cycle is set, and the following operations are performed on each of the multiple smart energy storage units: This invention establishes a unified time base to ensure the synchronicity and orderliness of all units' collaborative work, enabling distributed decisions to be aligned in time and avoiding control conflicts or oscillations caused by information asynchrony. This is a prerequisite for effective distributed coordination. The control cycle is used to perform the following operations on each of the smart energy storage units: The local controller collects current and voltage data to obtain the unit output current and voltage values. This invention achieves localized and real-time data acquisition. Based on the unit output current value, the energy storage unit's charge is calculated to obtain the estimated state of charge (SOC) for the current cycle. This invention incorporates SOC into control decision-making, which helps to consider the remaining energy levels of each energy storage unit in voltage control, facilitating subsequent energy balance management among units. Based on the estimated SOC and unit output voltage values ​​for the current cycle, a set of effective adjacent nodes is identified. The unit's adaptive droop coefficient is calculated based on the effective adjacent node set. This invention performs SOC based on the smart energy storage unit's own SOC. Dynamic adjustment enables on-demand load allocation. While regulating voltage, it automatically optimizes power distribution. A reference voltage is calculated based on preset bus voltage ratings, unit output current values, and unit adaptive droop coefficients. A compensation voltage is calculated based on the reference voltage, preset line resistance, and unit output current values. This invention uses current feedback for compensation, effectively offsetting bus voltage deviations caused by power transmission, helping to maintain the stability of critical bus voltages, improving voltage control accuracy and power quality. The compensation voltage and local controller are used to control the intelligent energy storage units, resulting in controlled energy storage units. In this invention, each intelligent energy storage unit only... Relying on local information and limited information from neighboring units, decision-making and action execution can be completed independently. Through network collaboration among units, the individual behaviors of all intelligent energy storage units will eventually combine to form a stable global system objective. By summarizing the controlled energy storage units, a set of controlled energy storage units is obtained. Based on this set, distributed collaborative DC microgrid voltage control is achieved. This invention introduces a state-of-charge adaptive mechanism and line voltage drop compensation function, which, while achieving the control objective, further considers voltage stability, power distribution rationality, and energy balance among energy storage units, forming a multi-dimensional collaborative optimization effect. Therefore, this invention can autonomously optimize the power and energy balance among energy storage units while achieving high-precision voltage control. Attached Figure Description

[0063] Figure 1 A schematic flowchart of a DC microgrid voltage control method based on distributed cooperation provided in an embodiment of the present invention;

[0064] Figure 2 A functional block diagram of a distributed collaborative DC microgrid voltage control system provided in an embodiment of the present invention;

[0065] Figure 3 This is a schematic diagram of the structure of an electronic device that implements the distributed collaborative DC microgrid voltage control method according to an embodiment of the present invention.

[0066] Explanation of reference numerals in the attached figures:

[0067] 10. Electronic device; 11. Processor; 12. Memory; 13. Bus.

[0068] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0069] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0070] This application provides a distributed collaborative DC microgrid voltage control method. The executing entity of the distributed collaborative DC microgrid voltage control method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the distributed collaborative DC microgrid voltage control method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0071] Reference Figure 1 The diagram shown is a flowchart illustrating a distributed collaborative DC microgrid voltage control method according to an embodiment of the present invention. In this embodiment, the distributed collaborative DC microgrid voltage control method includes:

[0072] S1. Receive distributed coordination instructions and identify the DC microgrid based on the distributed coordination instructions. The DC microgrid includes multiple smart energy storage units, wherein each smart energy storage unit includes a local controller.

[0073] It should be explained that distributed coordination commands are control commands generated by the distributed control system to guide the various smart energy storage units in the DC microgrid to work collaboratively, thereby jointly maintaining the voltage stability of the DC microgrid. A DC microgrid is a small-scale, localized power network that transmits and distributes electricity in the form of direct current. A smart energy storage unit is an energy storage system in a DC microgrid that possesses sensing, communication, decision-making, and execution capabilities. It should be noted that a smart energy storage unit also includes energy storage batteries. The local controller is the hardware platform that executes the distributed control algorithm.

[0074] S2. Set the control cycle and perform the following operations on each of the multiple smart energy storage units.

[0075] It should be explained that the control cycle is a fixed time interval set in the distributed collaborative control system, within which the local controller of each intelligent energy storage unit completes one complete control cycle. It should be noted that this invention ensures consistency of the clock reference for data acquisition, state calculation, information interaction, and control command execution of each unit by setting a unified and synchronized control cycle for all intelligent energy storage units. This avoids information staleness, decision conflicts, or system oscillations that may result from asynchronous and independent operation of each unit, and is an important timing foundation for achieving the aforementioned efficient and stable distributed collaborative control.

[0076] S3. According to the control cycle, the local controller in the intelligent energy storage unit is used to collect current and voltage data to obtain the unit output current value and unit output voltage value.

[0077] It should be explained that the unit output current value is the current value generated by the intelligent energy storage unit when transferring electrical energy from its internal energy storage battery to the DC bus within the current control cycle. Taking the transmission of current from the energy storage battery to the DC bus as the positive direction of the current, a positive unit output current value indicates that the energy storage battery is in a discharging state, and a negative unit output current value indicates that the energy storage battery is in a charging state. The unit output voltage value is the voltage value measured at the DC output port of the power converter within the current control cycle. The DC output port is the physical interface through which the device outputs DC power to the outside.

[0078] S4. Calculate the charge of the energy storage unit based on the unit output current value to obtain the estimated state of charge for this cycle.

[0079] In detail, the step of calculating the energy storage unit's charge based on the unit's output current value to obtain the estimated state of charge for this cycle includes:

[0080] Obtain the estimated state of charge from the previous cycle, and calculate the charge change in the current cycle based on the control cycle and the unit output current value.

[0081] The rated capacity of the unit battery pack is obtained from the intelligent energy storage unit, and the current state of charge estimate is calculated based on the charge change in this cycle, the state of charge estimate of the previous cycle, and the rated capacity of the unit battery pack.

[0082] If the current state of charge estimate is greater than the preset maximum state of charge estimate, then the current state of charge estimate is replaced by the maximum state of charge estimate to obtain the corrected state of charge estimate.

[0083] If the current state of charge estimate is less than the preset minimum state of charge estimate, the current state of charge estimate is replaced by the minimum state of charge estimate to obtain the corrected state of charge estimate.

[0084] If the current state of charge estimate is not greater than the maximum state of charge estimate and the current state of charge estimate is not less than the minimum state of charge estimate, then the current state of charge estimate is taken as the normal state of charge estimate.

[0085] The state of charge (SOC) estimate for the current period is determined based on either the revised SOC estimate or the normal SOC estimate.

[0086] It should be explained that the previous cycle state of charge (SOC) estimate is the battery charge value estimated at the end of the previous control cycle, reflecting the remaining charge level of the energy storage battery at the previous moment. For example, if the control cycle estimates the battery SOC every 10 seconds, and the current control cycle is 10:00:10, then the previous control cycle was from 10:00:00 to 10:00:10. The current cycle charge change is the value obtained by multiplying the control cycle by the unit output current value. The current cycle charge change represents the increase or decrease in the energy storage battery charge within the current control cycle. The rated capacity of the unit battery pack is the total charge that the battery can store after being fully charged. The maximum SOC estimate is a pre-set value used to protect the energy storage battery from overcharging and ensure that the battery operates within a safe range. For example, the maximum SOC estimate is 90%. The minimum SOC estimate is a pre-set value used to protect the energy storage battery from over-discharge and prevent battery damage. For example, the minimum SOC estimate is 15%. The corrected state of charge (SOC) estimate is obtained by replacing the current SOC estimate with the maximum or minimum SOC estimate when the current SOC estimate is greater than the maximum SOC estimate or less than the minimum SOC estimate. The purpose of replacing the current SOC estimate with the minimum or maximum SOC estimate is to limit the SOC estimate to the range defined by the minimum and maximum SOC estimates, thus preventing overcharging and discharging of the energy storage battery due to abnormal SOC estimates. The normal SOC estimate is the current SOC estimate that is neither greater than the maximum SOC estimate nor less than the minimum SOC estimate. This periodic SOC estimate is either the corrected SOC estimate or the normal SOC estimate.

[0087] It should be noted that if the current estimated state of charge (SOC) is greater than the preset maximum SOC, it may be due to current measurement errors, integral accumulation errors, or actual overcharging, causing the current SOC to exceed the safe charging limit (maximum SOC). Therefore, the battery may face the risk of overcharging, and charging needs to be limited to protect the energy storage battery. If the current SOC is less than the preset minimum SOC, it may be due to measurement errors, calculation errors, or actual over-discharging, causing the current SOC to exceed the safe charging limit (minimum SOC). Therefore, the energy storage battery may face the risk of over-discharging, and discharging needs to be limited to protect the energy storage battery.

[0088] In detail, the formula for calculating the current state of charge estimate is as follows:

[0089] ;

[0090] in, This represents the current state of charge estimate. This represents the estimated state of charge in the previous period. This represents the change in charge during the current period. This indicates the rated capacity of the battery pack.

[0091] It should be explained that the above formula uses the estimated state of charge (SOC) value from the previous cycle as a benchmark, adds the percentage of charge change relative to the battery pack's rated capacity during the current cycle, and thus derives the estimated SOC value for the current cycle. This formula not only enables continuous and dynamic updates to the SOC estimate, but also avoids performing a full calculation every time the SOC is calculated through iterative accumulation, reducing the complexity of the SOC calculation process, while also reflecting the real-time charge and discharge status of the energy storage battery.

[0092] S5. Based on the estimated state of charge and unit output voltage value for this cycle, determine the effective adjacent node set, and calculate the unit adaptive droop coefficient based on the effective adjacent node set.

[0093] Specifically, the process of determining the effective neighboring node set based on the estimated state of charge and the unit output voltage value for this cycle includes:

[0094] The key state vector to be coordinated is set based on the state of charge estimate of this cycle. The key state vector to be coordinated includes: the state of charge estimate of this cycle, the unit output voltage value and the operating mode flag.

[0095] The key state vectors to be coordinated are packaged to obtain energy storage unit data packets, and the adjacent node sets are identified from the pre-constructed mesh topology diagram based on the smart energy storage units;

[0096] The transmission period is set according to the control period, and the energy storage unit data packets are sent to the adjacent node set according to the transmission period to obtain the adjacent node set to be responded to.

[0097] The response time set is obtained by timing each neighboring node in the set of neighboring nodes to be responded to;

[0098] If there is a response time in the response time set that is greater than the preset maximum response time, then the adjacent node corresponding to the response time is removed from the adjacent node set to obtain the effective adjacent node set.

[0099] It should be explained that the operating mode flag is a coded identifier of the internal state of the intelligent energy storage unit, used to indicate the current control mode of the intelligent energy storage unit. For example, the operating mode flag 0x00 indicates voltage balancing mode, 0x01 indicates state of charge balancing mode, 0x02 indicates standby mode, and 0x03 indicates fault mode. The step of packaging the key state vector to be coordinated is as follows: this set of data (state of charge estimate for this cycle, unit output voltage value, operating mode flag) is encapsulated according to a predefined communication protocol and data format (such as adding header information such as node ID, timestamp, and checksum), forming a standardized communication data unit that can be transmitted in the network. The energy storage unit data packet is a data packet formed after packaging and encapsulation that can be transmitted in the network. The mesh topology diagram is a pre-constructed network map describing the logical connections between all smart energy storage units in a DC microgrid. In this network map, each smart energy storage unit corresponds to a network node, and each node establishes direct logical connections with multiple other nodes, ultimately forming an interwoven mesh structure. This mesh structure possesses high redundancy due to the multi-path connections between nodes. The neighboring node set is the set of all other energy storage units that have a direct communication link with this node (smart energy storage unit). The transmission period is the time interval at which this node sends energy storage unit data packets to the neighboring node set, used to ensure the frequency of synchronized updates of status information. The set of neighboring nodes awaiting response is the set of neighboring nodes from which this node expects to receive a reply message after sending an energy storage unit data packet. The response time set is the set of all response times. The response time is the time taken for this node (smart energy storage unit) to receive a reply message from each neighboring node awaiting response after sending an energy storage unit data packet. The maximum response time is a pre-set time threshold that defines the maximum allowable delay in network communication. Exceeding this time is considered a communication timeout or an anomaly in the neighboring node awaiting a response. The effective neighboring node set is the set of neighboring nodes whose response time is no greater than the maximum response time.

[0100] Importantly, this invention first achieves state synchronization between nodes by periodically sending their own state information, and then verifies whether the communication between the two parties is normal by judging whether the neighboring nodes reply to messages within a specified time. Then, it dynamically selects effective neighboring nodes that are currently in a reliable and active state from the set of neighboring nodes based on a static mesh topology diagram, thereby building a real-time and stable local communication network foundation for subsequent distributed collaborative control (such as power distribution between energy storage units and state of charge equalization regulation).

[0101] Specifically, the step of calculating the adaptive droop coefficient of the unit based on the effective neighboring node set includes:

[0102] The system average state of charge and average voltage are estimated based on the effective adjacent node set. The number of battery cycles is obtained based on the smart energy storage unit. The initial unit low-end threshold and initial unit high-end threshold are calculated based on the number of battery cycles, the preset low-end threshold, and the preset high-end threshold.

[0103] The initial low-end threshold and the initial high-end threshold of the unit are adjusted according to the preset hysteresis width to obtain the low-end threshold and the high-end threshold of the unit.

[0104] If the estimated average state of charge of the system is less than the low-end threshold of the cell or greater than the high-end threshold of the cell, then the corrected voltage droop coefficient is calculated based on the average voltage of the system.

[0105] If the system average state of charge (SOC) estimate is greater than or equal to the low-end threshold of the cell and less than or equal to the high-end threshold of the cell, then the SOC difference is calculated based on the SOC estimate for the current period and the system average SOC estimate. The SOC difference is the SOC estimate for the current period minus the system average SOC estimate.

[0106] The compensation droop coefficient is calculated based on the difference in state of charge, and the hybrid droop coefficient is calculated based on the corrected voltage droop coefficient and the compensation droop coefficient.

[0107] The adaptive droop coefficient of the cell is determined based on the modified voltage droop coefficient, the compensated charge droop coefficient, or the hybrid droop coefficient.

[0108] It should be explained that the steps for calculating the system average state of charge (SOC) estimate and system average voltage based on the effective neighbor node set are as follows: SOC estimate and output voltage value are extracted from each effective neighbor node in the effective neighbor node set, resulting in multiple SOC estimates and multiple output voltage values. A distributed average consensus algorithm is then executed on these multiple SOC estimates and multiple output voltage values ​​until they converge to a global average value, thus obtaining the system average SOC estimate and system average voltage. The system in the system average SOC estimate and system average voltage refers to a local cooperative subsystem composed of the current node and all its effective neighbor nodes. The distributed average consensus algorithm is existing technology and will not be elaborated upon here. The system average SOC estimate is the average of the SOC estimates of the current node and its effective neighbor nodes, representing the average remaining energy level of the local node group composed of the current node and its effective neighbor nodes, and thus serving as an indicator for judging the overall operating status of this local node group. The system average voltage is the average of the output voltages of the current node and its effective neighbor nodes. The battery cycle count is the total number of complete charge-discharge cycles that the battery pack has undergone since its manufacture, used to measure the degree of battery aging.

[0109] Importantly, the low-end threshold is a pre-set lower limit of a percentage value at the system level (local collaborative subsystem). For example, the low-end threshold is 30%. When the estimated average state of charge (SOC) of the system is less than the low-end threshold, the system is determined to be in a low energy reserve state. At this time, the primary objective of the system control strategy switches to system-level discharge protection and energy recovery, triggering a priority charging command to prevent the risk of collective over-discharge of all energy storage units in the system and ensure the sustainability of the system's power supply. The high-end threshold is a pre-set upper limit of a percentage value at the system level. For example, the high-end threshold is 80%. When the estimated average SOC of the system is greater than the high-end threshold, the system is determined to be in a high energy reserve state. At this time, the primary objective of the system control strategy switches to system-level charging protection and lifespan maintenance, triggering a limited charging or priority discharging command to avoid the risk of collective overcharging of all energy storage units in the system, while extending the battery pack's lifespan by appropriately suppressing the time the battery is in a fully charged state. The hysteresis width is a pre-set value. The purpose of setting the hysteresis width in this invention is to establish a buffer zone when the system's average state of charge (SOC) estimate approaches the policy threshold (such as the initial unit low-end threshold and the initial unit high-end threshold). This avoids frequent charge / discharge switching operations triggered by small fluctuations in the SOC estimate near the threshold, thereby improving the stability of system control. The unit low-end threshold is obtained by subtracting the hysteresis width from the initial unit low-end threshold. The unit high-end threshold is obtained by adding the hysteresis width to the initial unit low-end threshold.

[0110] It should be noted that in the state of charge control of the local cooperative subsystem (composed of this node and all effective adjacent nodes), on the one hand, the initial low threshold and the initial high threshold are the benchmarks for the system to determine whether to trigger extreme protection (e.g., if it is lower than the initial low threshold, discharge protection is triggered, or if it is higher than the initial high threshold, charging protection is triggered). However, in actual operation, the estimated average state of charge of the system will fluctuate slightly due to factors such as load fluctuations and changes in charging and discharging efficiency (e.g., it will fluctuate slightly up and down around the initial low threshold of 30%). If no buffer mechanism is set and the initial low threshold and the initial high threshold are used as the judgment criteria, the system will frequently switch between triggering protection and deactivating protection. This frequent switching will cause repeated fluctuations in voltage and current, which will affect the stability of power supply and aggravate battery wear. Since the system may frequently switch between triggering protection and deactivating protection, this invention sets the low-end threshold and high-end threshold of the unit by adjusting the hysteresis width. First, to address the problem of frequent policy switching, the hysteresis interval (composed of the low-end threshold and the high-end threshold) provides a buffer space for the fluctuation of the system's state of charge by widening the threshold boundaries (e.g., the initial low-end threshold is reduced from 30% to 28%, and the initial high-end threshold is increased from 80% to 82%). This ensures that protection is only triggered when the estimated state of charge exceeds the wider unit threshold, thereby effectively avoiding the problem of the system frequently switching between triggering protection and deactivating protection.

[0111] Understandably, if the system's average state of charge (SOC) estimate is less than the low-end threshold of the cell or greater than the high-end threshold, it indicates that the SOC estimate is in an abnormal range (too low or too high). In this case, the primary goal of the system is overall safety protection, requiring coordinated action from all effective adjacent nodes to bring the SOC estimate back to the normal range (e.g., if the SOC estimate is too low, the effective adjacent node set should reduce discharge) to avoid systemic over-discharge risk. If the SOC estimate is greater than or equal to the low-end threshold of the cell and less than or equal to the high-end threshold, it indicates that the SOC estimate is in the normal range. In this case, the system can switch to internal equalization control, allowing batteries with higher SOC estimates to discharge more or charge less, and batteries with lower SOC estimates to discharge less or charge more, thus aligning the SOC of all batteries and extending overall battery life. The cell adaptive droop coefficient refers to the coefficient ultimately calculated for droop control in this smart energy storage unit.

[0112] Specifically, the calculation of the corrected voltage droop factor based on the system average voltage includes:

[0113] Obtain the maximum output current of the intelligent energy storage unit and calculate the droop coefficient based on the maximum output current;

[0114] The voltage deviation is calculated based on the system average voltage and the unit output voltage value. The voltage deviation is the value obtained by subtracting the system average voltage from the unit output voltage value.

[0115] The voltage deviation is normalized to obtain the normalized voltage deviation, which is the value obtained by dividing the voltage deviation by the preset system rated voltage.

[0116] The voltage droop coefficient is calculated based on the droop coefficient, the preset voltage equalization factor, and the normalized voltage deviation.

[0117] The voltage droop coefficient is adaptively corrected based on the unit output current value and the preset standard current value to obtain the corrected voltage droop coefficient.

[0118] It should be explained that the maximum output current is the upper limit of the current that the power converter of a single smart energy storage unit can safely and continuously output. The droop coefficient is the value obtained by dividing the preset maximum voltage deviation by the maximum output current. The droop coefficient characterizes the response characteristics of the smart energy storage unit's output voltage to changes in output current, reflecting the magnitude of the change in the unit's output voltage caused by changes in the unit's output current. The larger the unit's output current, the greater the voltage drop of the smart energy storage unit. The maximum voltage deviation is the largest deviation between the unit's output voltage and the system's average voltage, actively introduced by droop control. For example, the maximum voltage deviation is 5V. The system rated voltage is the nominal voltage of the entire DC bus. For example, the system rated voltage is 400V. The normalized voltage deviation is the voltage deviation after normalization. The voltage equalization factor is a preset proportional coefficient used to adjust the weight of the voltage deviation on the droop coefficient. The larger the voltage equalization factor, the greater the adjustment range of the system to the droop coefficient under the same voltage deviation, thereby driving the unit output voltage of each smart energy storage unit to converge to the system average voltage more quickly, achieving bus voltage equalization. The standard current value is the rated operating current of the smart energy storage unit. The corrected voltage droop factor is the value obtained by dividing the unit's output current value by the standard current value.

[0119] In detail, the formula for calculating the voltage droop coefficient is as follows:

[0120] ;

[0121] in, This represents the voltage droop coefficient. Indicates the droop coefficient. This represents the natural exponential function. This represents the voltage equalization factor. This indicates the normalized voltage deviation.

[0122] It should be explained that during the operation of voltage regulation systems (such as energy storage systems and microgrids), the voltage control accuracy requirements vary across different scenarios. Furthermore, changes in the normalized voltage deviation can easily cause system voltage fluctuations or shocks due to abrupt changes in the voltage droop coefficient. Simultaneously, it is necessary to ensure that the voltage droop coefficient can adapt to the dynamic demands of the system under different normalized voltage deviations. Therefore, this invention introduces a calculation formula for the voltage droop coefficient. By constructing a natural exponential function relationship and leveraging the smoothing characteristics of the natural exponential function, it avoids abrupt changes in the voltage droop coefficient as the normalized voltage deviation changes, thereby ensuring the system voltage... The stability of the voltage regulation process is improved, reducing voltage fluctuations or shocks caused by sudden changes in the voltage droop coefficient. Secondly, by introducing a flexibly settable voltage balancing factor into the formula, the sensitivity of the voltage droop coefficient to the normalized voltage deviation can be adjusted according to the voltage control accuracy requirements of different application scenarios. This allows the coefficient to remain stable when the normalized voltage deviation is small to maintain the basic operating state of the system, and also to enhance the voltage balancing regulation capability through reasonable changes in the coefficient when the deviation increases. Ultimately, this achieves precise and dynamic control of the voltage droop coefficient to adapt to the system requirements under different normalized voltage deviations.

[0123] Specifically, the calculation of the compensation droop coefficient based on the difference in state of charge includes:

[0124] The state of charge difference is normalized to obtain the normalized state of charge difference, which is the value obtained by dividing the state of charge difference by 100.

[0125] The charge balance droop coefficient is calculated based on the droop coefficient, the normalized charge difference, and the preset charge balance factor.

[0126] Obtain the cell health state factor, and use the cell health state factor to compensate for the charge balance droop coefficient to obtain the compensated charge droop coefficient.

[0127] It should be explained that the original numerical range of the state-of-charge (SOC) difference can fluctuate significantly depending on battery specifications and application scenarios (such as energy storage systems and power battery packs) (e.g., an SOC difference of 5 or 20). Directly using this value to calculate the charge balancing droop factor would result in a lack of a unified standard for the responsiveness of the charge balancing droop factor to SOC differences, making it difficult to achieve controllable adjustment through a fixed charge balancing factor. Therefore, dividing the SOC difference by 100 to obtain the normalized SOC difference allows for a unified mapping of the SOC difference to the (0,1) numerical range, eliminating computational interference caused by differences in the magnitude of SOC differences across different scenarios. This ensures consistent computational logic when subsequently calculating the charge balancing droop factor based on the normalized SOC difference, droop factor, and charge balancing factor, using standardized input. The charge balancing factor is a pre-defined proportional coefficient used to adjust the intensity of the SOC difference's influence on the droop factor correction. The formula for calculating the charge balance droop coefficient in the step of calculating the charge balance droop coefficient based on the droop coefficient, the normalized charge difference, and the preset charge balance factor is as follows:

[0128] ;

[0129] in, This represents the charge balance droop coefficient. Indicates the charge balance factor. This represents the normalized charge difference. The cell health status factor is a coefficient reflecting the degree of aging or performance degradation of this cell pack. Optionally, the cell health status factor can range from 0.5 to 1.0. The charge droop compensation factor is obtained by dividing 1 by the cell health status factor and then multiplying it by the charge droop compensation factor.

[0130] Importantly, batteries in different health states have fundamentally different requirements for charge balancing: batteries in poor health (cell health state factor close to 0.5) have weaker charging and discharging capabilities. If an uncompensated charge balancing droop coefficient is still used for adjustment, the adjustment intensity may not match the actual battery performance, resulting in the balancing speed failing to eliminate charge differences in time or exacerbating battery damage. Batteries in good health (cell health state factor close to 1.0) have stronger balancing tolerance. Therefore, this invention uses a compensation method of dividing 1 by the cell health state factor and multiplying by the charge balancing droop coefficient. This allows for dynamic adjustment of the final compensated charge droop coefficient based on the battery's aging level. That is, the worse the battery's health, the larger the compensated coefficient, which can appropriately increase the balancing adjustment intensity to adapt to the battery's deteriorating performance. Conversely, the better the battery's health, the closer the compensated coefficient is to the original charge droop coefficient, maintaining a regular and efficient balancing effect. Ultimately, this achieves on-demand compensation, avoiding problems such as low balancing efficiency or battery damage caused by ignoring battery health differences.

[0131] Specifically, the calculation of the hybrid droop coefficient based on the corrected voltage droop coefficient and the compensated charge droop coefficient includes:

[0132] If the system average state of charge (SOC) estimate is greater than the initial unit low threshold and the system average SOC estimate is greater than the unit low threshold, or if the system average SOC estimate is greater than the initial unit high threshold and the system average SOC estimate is greater than the unit high threshold, then the midpoint value of the plateau period is calculated based on the initial unit low threshold and the initial unit high threshold.

[0133] Calculate the voltage balance weighting coefficient based on the midpoint value of the platform period and the estimated average state of charge of the system.

[0134] The hybrid droop coefficient is calculated based on the voltage balance weighting coefficient, the corrected voltage droop coefficient, and the compensated charge droop coefficient.

[0135] It should be explained that the midpoint value of the plateau period is the average of the initial unit's low-end threshold and the initial unit's high-end threshold. The formula for calculating the voltage balance weighting coefficient in the step of calculating the voltage balance weighting coefficient based on the midpoint value of the plateau period and the system's average state of charge estimate is as follows:

[0136] ;

[0137] in, This represents the voltage balancing weighting coefficient. This represents the estimated average state of charge of the system. This represents the midpoint value of the platform period. This represents the initial low-end threshold. This indicates taking the absolute value. The formula for calculating the mixed droop coefficient in the step of calculating the mixed droop coefficient based on the voltage equalization weighting coefficient, the corrected voltage droop coefficient, and the compensated charge droop coefficient is as follows:

[0138] ;

[0139] in, Indicates the mixed droop coefficient. This indicates the corrected voltage droop factor. This represents the compensating charge droop coefficient.

[0140] Importantly, during the operation of the local collaborative subsystem (composed of the current node and all its effective adjacent nodes), when the estimated average state of charge (SOC) of the system is between the low-end and high-end thresholds of the cells, and further judgment is required based on the initial low-end or high-end thresholds, relying solely on the correction voltage droop coefficient or the compensation charge droop coefficient as a single adjustment dimension will result in the following drawbacks: while the correction voltage droop coefficient can ensure stable voltage output, it is difficult to ensure the charge consistency of the batteries in each smart energy storage unit; while the compensation charge droop coefficient can promote SOC equilibrium, it is difficult to ensure voltage stability of the local collaborative subsystem. This single adjustment defect will cause the system to fail to simultaneously meet the dual requirements of voltage stability and charge consistency. Therefore, this invention introduces a hybrid droop coefficient to solve the defects of single adjustment.

[0141] S6. Calculate the reference voltage based on the preset bus voltage rating, unit output current value, and unit adaptive droop coefficient. Calculate the compensation voltage based on the reference voltage, preset line resistance, and unit output current value.

[0142] It should be explained that the rated bus voltage is the nominal voltage value ideally maintained by the entire DC microgrid system. For example, for a 400V DC microgrid, the rated bus voltage is 400V. The step of calculating the reference voltage based on the preset rated bus voltage, unit output current value, and unit adaptive droop coefficient is as follows: multiply the unit output current value and the unit adaptive droop coefficient to obtain a value, and use the value obtained by subtracting this value from the rated bus voltage as the reference voltage. The line resistance is the equivalent resistance of the connecting cable from the output terminal of the smart energy storage unit to the DC common bus. The step of calculating the compensation voltage based on the reference voltage, the preset line resistance, and the unit output current value is as follows: multiply the line resistance and the unit output current value to obtain a value, and add the reference voltage to the obtained value as the compensation voltage. The compensation voltage is used to offset the voltage drop caused by the line resistance.

[0143] S7. Use the compensation voltage and local controller to control the smart energy storage unit to obtain the controlled energy storage unit. Summarize the controlled energy storage units to obtain the controlled energy storage unit set. Based on the controlled energy storage unit set, complete the voltage control of the DC microgrid based on distributed collaboration.

[0144] It should be explained that a controlled energy storage unit is an intelligent energy storage unit that uses a voltage command (compensation voltage) with line compensation to drive a power converter through its local controller, enabling its output voltage to track the command in real time, thereby completing the control actions within the control cycle. A controlled energy storage unit set is a collection of all controlled energy storage units.

[0145] To address the problems described in the background art, this invention receives distributed coordination instructions and identifies a DC microgrid based on these instructions. The DC microgrid includes multiple smart energy storage units, each including a local controller. A control cycle is set, and the following operations are performed on each of the multiple smart energy storage units: This invention establishes a unified time base to ensure the synchronicity and orderliness of all units' collaborative work, enabling distributed decisions to be aligned in time and avoiding control conflicts or oscillations caused by information asynchrony. This is a prerequisite for effective distributed coordination. The control cycle is used to perform the following operations on each of the smart energy storage units: The local controller collects current and voltage data to obtain the unit output current and voltage values. This invention achieves localized and real-time data acquisition. Based on the unit output current value, the energy storage unit's charge is calculated to obtain the estimated state of charge (SOC) for the current cycle. This invention incorporates SOC into control decision-making, which helps to consider the remaining energy levels of each energy storage unit in voltage control, facilitating subsequent energy balance management among units. Based on the estimated SOC and unit output voltage values ​​for the current cycle, a set of effective adjacent nodes is identified. The unit's adaptive droop coefficient is calculated based on the effective adjacent node set. This invention performs SOC based on the smart energy storage unit's own SOC. Dynamic adjustment enables on-demand load allocation. While regulating voltage, it automatically optimizes power distribution. A reference voltage is calculated based on preset bus voltage ratings, unit output current values, and unit adaptive droop coefficients. A compensation voltage is calculated based on the reference voltage, preset line resistance, and unit output current values. This invention uses current feedback for compensation, effectively offsetting bus voltage deviations caused by power transmission, helping to maintain the stability of critical bus voltages, improving voltage control accuracy and power quality. The compensation voltage and local controller are used to control the intelligent energy storage units, resulting in controlled energy storage units. In this invention, each intelligent energy storage unit only... Relying on local information and limited information from neighboring units, decision-making and action execution can be completed independently. Through network collaboration among units, the individual behaviors of all intelligent energy storage units will eventually combine to form a stable global system objective. By summarizing the controlled energy storage units, a set of controlled energy storage units is obtained. Based on this set, distributed collaborative DC microgrid voltage control is achieved. This invention introduces a state-of-charge adaptive mechanism and line voltage drop compensation function, which, while achieving the control objective, further considers voltage stability, power distribution rationality, and energy balance among energy storage units, forming a multi-dimensional collaborative optimization effect. Therefore, this invention can autonomously optimize the power and energy balance among energy storage units while achieving high-precision voltage control.

[0146] like Figure 2 The diagram shown is a functional block diagram of a DC microgrid voltage control system based on distributed collaboration provided in an embodiment of the present invention.

[0147] The distributed collaborative DC microgrid voltage control system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the distributed collaborative DC microgrid voltage control system 100 may include a system initialization module 101, a state of charge estimation module 102, a droop coefficient acquisition module 103, and a local control execution module 104. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and which are stored in the memory of the electronic device.

[0148] The system initialization module 101 is used to receive distributed coordination instructions and confirm the DC microgrid according to the distributed coordination instructions. The DC microgrid includes multiple smart energy storage units, wherein the smart energy storage unit includes a local controller.

[0149] The state of charge estimation module 102 is used to set a control cycle and perform the following operations on each of the multiple smart energy storage units: according to the control cycle, the local controller in the smart energy storage unit is sampled for current and voltage to obtain the unit output current value and the unit output voltage value; the energy storage unit is charged based on the unit output current value to obtain the state of charge estimation value for this cycle.

[0150] The droop coefficient acquisition module 103 is used to identify the effective adjacent node set based on the estimated state of charge and the unit output voltage value in this cycle, and to calculate the unit adaptive droop coefficient based on the effective adjacent node set.

[0151] The local control execution module 104 is used to calculate a reference voltage based on a preset bus voltage rating, unit output current value, and unit adaptive droop coefficient; calculate a compensation voltage based on the reference voltage, preset line resistance, and unit output current value; control the intelligent energy storage unit using the compensation voltage and the local controller; obtain the controlled energy storage units; summarize the controlled energy storage units to obtain a set of controlled energy storage units; and complete the distributed collaborative DC microgrid voltage control based on the set of controlled energy storage units. Specifically, in this embodiment of the invention, the modules in the distributed collaborative DC microgrid voltage control system 100 adopt the same methods as described above. Figure 1 The method uses the same techniques as the distributed collaborative DC microgrid voltage control method described in the article and can produce the same technical effects, so it will not be repeated here.

[0152] like Figure 3 The diagram shown is a schematic representation of an electronic device that implements a distributed collaborative DC microgrid voltage control method according to an embodiment of the present invention.

[0153] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a distributed collaborative DC microgrid voltage control method program.

[0154] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as the portable hard drive of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device 1. Furthermore, the memory 11 includes both internal storage units and external storage devices of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code of a DC microgrid voltage control method program based on distributed collaboration, but also to temporarily store data that has been output or will be output.

[0155] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., a distributed collaborative DC microgrid voltage control method program) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0156] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11 and at least one processor 10, etc.

[0157] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0158] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0159] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0160] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0161] The distributed collaborative DC microgrid voltage control method program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0162] Receive distributed coordination instructions and identify the DC microgrid based on the distributed coordination instructions. The DC microgrid includes multiple smart energy storage units, wherein each smart energy storage unit includes a local controller.

[0163] Set a control cycle and perform the following operations for each of the multiple smart energy storage units:

[0164] According to the control cycle, the local controller in the intelligent energy storage unit collects current and voltage data to obtain the unit output current and unit output voltage values.

[0165] The energy storage unit's charge is calculated based on the unit's output current value to obtain the estimated state of charge for this cycle.

[0166] Based on the estimated state of charge and unit output voltage value of this cycle, the effective adjacent node set is identified, and the unit adaptive droop coefficient is calculated based on the effective adjacent node set.

[0167] The reference voltage is calculated based on the preset bus voltage rating, unit output current value and unit adaptive droop coefficient, and the compensation voltage is calculated based on the reference voltage, preset line resistance and unit output current value.

[0168] The intelligent energy storage unit is controlled by the compensation voltage and the local controller to obtain the controlled energy storage unit. The controlled energy storage units are then aggregated to obtain the controlled energy storage unit set. Based on the controlled energy storage unit set, the voltage control of the DC microgrid based on distributed collaboration is completed.

[0169] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 3 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0170] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0171] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0172] Receive distributed coordination instructions and identify the DC microgrid based on the distributed coordination instructions. The DC microgrid includes multiple smart energy storage units, wherein each smart energy storage unit includes a local controller.

[0173] Set a control cycle and perform the following operations for each of the multiple smart energy storage units:

[0174] According to the control cycle, the local controller in the intelligent energy storage unit collects current and voltage data to obtain the unit output current and unit output voltage values.

[0175] The energy storage unit's charge is calculated based on the unit's output current value to obtain the estimated state of charge for this cycle.

[0176] Based on the estimated state of charge and unit output voltage value of this cycle, the effective adjacent node set is identified, and the unit adaptive droop coefficient is calculated based on the effective adjacent node set.

[0177] The reference voltage is calculated based on the preset bus voltage rating, unit output current value and unit adaptive droop coefficient, and the compensation voltage is calculated based on the reference voltage, preset line resistance and unit output current value.

[0178] The intelligent energy storage unit is controlled by the compensation voltage and the local controller to obtain the controlled energy storage unit. The controlled energy storage units are then aggregated to obtain the controlled energy storage unit set. Based on the controlled energy storage unit set, the voltage control of the DC microgrid based on distributed collaboration is completed.

[0179] In the embodiments provided by this invention, it should be understood that the disclosed devices, systems, and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and actual implementations may have other classification methods.

[0180] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0181] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0182] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0183] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A voltage control method for a DC microgrid based on distributed collaborative operation, characterized in that, The method includes: Receive distributed coordination instructions and identify the DC microgrid based on the distributed coordination instructions. The DC microgrid includes multiple smart energy storage units, wherein each smart energy storage unit includes a local controller. Set a control cycle and perform the following operations for each of the multiple smart energy storage units: According to the control cycle, the local controller in the intelligent energy storage unit collects current and voltage data to obtain the unit output current and unit output voltage values. The energy storage unit's charge is calculated based on the unit's output current value to obtain the estimated state of charge for this cycle. Based on the estimated state of charge and unit output voltage value of this cycle, the effective adjacent node set is identified, and the unit adaptive droop coefficient is calculated based on the effective adjacent node set. The reference voltage is calculated based on the preset bus voltage rating, unit output current value and unit adaptive droop coefficient, and the compensation voltage is calculated based on the reference voltage, preset line resistance and unit output current value. The intelligent energy storage unit is controlled by the compensation voltage and the local controller to obtain the controlled energy storage unit. The controlled energy storage units are then aggregated to obtain the controlled energy storage unit set. Based on the controlled energy storage unit set, the voltage control of the DC microgrid based on distributed collaboration is completed.

2. The DC microgrid voltage control method based on distributed collaboration as described in claim 1, characterized in that, The step of calculating the energy storage unit's charge based on the unit's output current value to obtain the estimated state of charge for this cycle includes: Obtain the estimated state of charge from the previous cycle, and calculate the charge change in the current cycle based on the control cycle and the unit output current value. The rated capacity of the unit battery pack is obtained from the intelligent energy storage unit, and the current state of charge estimate is calculated based on the charge change in this cycle, the state of charge estimate of the previous cycle, and the rated capacity of the unit battery pack. If the current state of charge estimate is greater than the preset maximum state of charge estimate, then the current state of charge estimate is replaced by the maximum state of charge estimate to obtain the corrected state of charge estimate. If the current state of charge estimate is less than the preset minimum state of charge estimate, the current state of charge estimate is replaced by the minimum state of charge estimate to obtain the corrected state of charge estimate. If the current state of charge estimate is not greater than the maximum state of charge estimate and the current state of charge estimate is not less than the minimum state of charge estimate, then the current state of charge estimate is taken as the normal state of charge estimate. The state of charge (SOC) estimate for the current period is determined based on either the revised SOC estimate or the normal SOC estimate.

3. The DC microgrid voltage control method based on distributed collaboration as described in claim 2, characterized in that, The formula for calculating the current state of charge estimate is as follows: ； in, This represents the current state of charge estimate. This represents the estimated state of charge in the previous period. This represents the change in charge during the current period. This indicates the rated capacity of the battery pack.

4. The DC microgrid voltage control method based on distributed collaboration as described in claim 3, characterized in that, The process of determining the effective neighbor set based on the estimated state of charge and unit output voltage values ​​for this cycle includes: The key state vector to be coordinated is set based on the state of charge estimate of this cycle. The key state vector to be coordinated includes: the state of charge estimate of this cycle, the unit output voltage value and the operating mode flag. The key state vectors to be coordinated are packaged to obtain energy storage unit data packets, and the adjacent node sets are identified from the pre-constructed mesh topology diagram based on the smart energy storage units; The transmission period is set according to the control period, and the energy storage unit data packets are sent to the adjacent node set according to the transmission period to obtain the adjacent node set to be responded to. The response time set is obtained by timing each neighboring node in the set of neighboring nodes to be responded to; If there is a response time in the response time set that is greater than the preset maximum response time, then the adjacent node corresponding to the response time is removed from the adjacent node set to obtain the effective adjacent node set.

5. The DC microgrid voltage control method based on distributed collaboration as described in claim 4, characterized in that, The step of calculating the adaptive droop coefficient of the unit based on the effective adjacent node set includes: The system average state of charge and average voltage are estimated based on the effective adjacent node set. The number of battery cycles is obtained based on the smart energy storage unit. The initial unit low-end threshold and initial unit high-end threshold are calculated based on the number of battery cycles, the preset low-end threshold, and the preset high-end threshold. The initial low-end threshold and the initial high-end threshold of the unit are adjusted according to the preset hysteresis width to obtain the low-end threshold and the high-end threshold of the unit. If the estimated average state of charge of the system is less than the low-end threshold of the cell or greater than the high-end threshold of the cell, then the corrected voltage droop coefficient is calculated based on the average voltage of the system. If the system average state of charge (SOC) estimate is greater than or equal to the low-end threshold of the cell and less than or equal to the high-end threshold of the cell, then the SOC difference is calculated based on the SOC estimate for the current period and the system average SOC estimate. The SOC difference is the SOC estimate for the current period minus the system average SOC estimate. The compensation droop coefficient is calculated based on the difference in state of charge, and the hybrid droop coefficient is calculated based on the corrected voltage droop coefficient and the compensation droop coefficient. The adaptive droop coefficient of the cell is determined based on the modified voltage droop coefficient, the compensated charge droop coefficient, or the hybrid droop coefficient.

6. The DC microgrid voltage control method based on distributed collaboration as described in claim 5, characterized in that, The calculation of the corrected voltage droop factor based on the system average voltage includes: Obtain the maximum output current of the intelligent energy storage unit and calculate the droop coefficient based on the maximum output current; The voltage deviation is calculated based on the system average voltage and the unit output voltage value. The voltage deviation is the value obtained by subtracting the system average voltage from the unit output voltage value. The voltage deviation is normalized to obtain the normalized voltage deviation, which is the value obtained by dividing the voltage deviation by the preset system rated voltage. The voltage droop coefficient is calculated based on the droop coefficient, the preset voltage equalization factor, and the normalized voltage deviation. The voltage droop coefficient is adaptively corrected based on the unit output current value and the preset standard current value to obtain the corrected voltage droop coefficient.

7. The DC microgrid voltage control method based on distributed collaboration as described in claim 6, characterized in that, The formula for calculating the voltage droop coefficient is as follows: ； in, This represents the voltage droop coefficient. Indicates the droop coefficient. This represents the natural exponential function. This represents the voltage equalization factor. This indicates the normalized voltage deviation.

8. The DC microgrid voltage control method based on distributed collaboration as described in claim 7, characterized in that, The calculation of the compensation droop coefficient based on the difference in state of charge includes: The state of charge difference is normalized to obtain the normalized state of charge difference, which is the value obtained by dividing the state of charge difference by 100. The charge balance droop coefficient is calculated based on the droop coefficient, the normalized charge difference, and the preset charge balance factor. Obtain the cell health state factor, and use the cell health state factor to compensate for the charge balance droop coefficient to obtain the compensated charge droop coefficient.

9. The DC microgrid voltage control method based on distributed collaboration as described in claim 8, characterized in that, The calculation of the hybrid droop coefficient based on the corrected voltage droop coefficient and the compensated charge droop coefficient includes: If the system average state of charge (SOC) estimate is greater than the initial unit low threshold and the system average SOC estimate is greater than the unit low threshold, or if the system average SOC estimate is greater than the initial unit high threshold and the system average SOC estimate is greater than the unit high threshold, then the midpoint value of the plateau period is calculated based on the initial unit low threshold and the initial unit high threshold. Calculate the voltage balance weighting coefficient based on the midpoint value of the platform period and the estimated average state of charge of the system. The hybrid droop coefficient is calculated based on the voltage balance weighting coefficient, the corrected voltage droop coefficient, and the compensated charge droop coefficient.

10. A DC microgrid voltage control system based on distributed collaborative operation, characterized in that, The system includes: The system initialization module is used to receive distributed coordination instructions and identify the DC microgrid based on the distributed coordination instructions. The DC microgrid includes multiple smart energy storage units, wherein each smart energy storage unit includes a local controller. The state of charge estimation module is used to set the control cycle and perform the following operations on each of the multiple smart energy storage units: according to the control cycle, the local controller in the smart energy storage unit is sampled for current and voltage to obtain the unit output current value and unit output voltage value; the energy storage unit is charged based on the unit output current value to obtain the state of charge estimation value for this cycle. The droop coefficient acquisition module is used to identify the effective adjacent node set based on the estimated state of charge and the unit output voltage value in this cycle, and to calculate the unit's adaptive droop coefficient based on the effective adjacent node set. The local control execution module is used to calculate the reference voltage based on the preset bus voltage rating, unit output current value and unit adaptive droop coefficient, calculate the compensation voltage based on the reference voltage, preset line resistance and unit output current value, control the smart energy storage unit using the compensation voltage and the local controller, obtain the controlled energy storage unit, summarize the controlled energy storage unit to obtain the controlled energy storage unit set, and complete the voltage control of the DC microgrid based on distributed collaboration based on the controlled energy storage unit set.

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