Operation control method, device and equipment of direct current power distribution network

Abstract

The application provides a kind of operation control method, device and equipment of direct current power distribution network, it is related to power distribution network control technical field.The method comprises: based on the photovoltaic prediction data of the direct current power distribution network to be controlled, initial power deviation data is corrected, and power deviation data is obtained;According to power deviation data, photovoltaic prediction data and energy storage system state parameters, determine the adjustment requirement data;Based on the communication state data of the direct current power distribution network to be controlled, the synchronous time difference of the issued instruction of each control device is determined;According to adjustment requirement data and energy storage charge-discharge preparation parameters, generate control instruction, and based on the synchronous time difference of the issued instruction of each control device, control instruction is issued to each control device;Wherein, energy storage charge-discharge preparation parameters are determined according to photovoltaic prediction data and equipment operation data.The application can improve the operation stability and power supply reliability of direct current power distribution network.

Description

Technical Field

[0001] This invention relates to the field of power distribution network control technology, and in particular to a method, apparatus and equipment for the operation control of a DC power distribution network. Background Technology

[0002] A DC distribution network is a power distribution system that transmits, distributes, and supplies electrical energy in the form of direct current. It is a next-generation power distribution technology adapted to the high penetration of new energy sources and the explosive growth of DC loads. A DC distribution network mainly consists of DC buses, voltage source converters, DC transformers, DC circuit breakers, distributed power sources such as photovoltaics, DC loads, and control systems.

[0003] After photovoltaic distributed power sources are connected to the DC distribution network, their output power is easily affected by factors such as sunlight intensity and ambient temperature, exhibiting significant intermittency and volatility. This random fluctuation in output can directly cause the DC bus voltage to deviate from its rated operating range, leading to problems such as voltage drops, transient impacts, and power imbalances, thus reducing the operational stability of the DC distribution network.

[0004] Currently, DC distribution networks primarily rely on real-time monitoring data for reactive adjustments during operation. However, this control model suffers from significant regulatory lag. When photovoltaic power output experiences drastic changes, it can cause DC bus voltage fluctuations to exceed permissible limits, impacting the stability of the distribution network. Furthermore, this control model also suffers from asynchronous control actions among various devices, leading to increased power distribution deviations and a reduction in the overall effectiveness of power regulation. Summary of the Invention

[0005] This invention provides a method, apparatus, and equipment for the operation control of a DC distribution network, in order to solve the problem of lag in current control methods.

[0006] In a first aspect, embodiments of the present invention provide an operation control method for a DC distribution network, comprising: The initial power deviation data is corrected based on the photovoltaic prediction data of the DC distribution network to be controlled, and the power deviation data is obtained. Based on the power deviation data, the photovoltaic forecast data, and the energy storage system status parameters, the adjustment demand data is determined; Based on the communication status data of the DC distribution network to be controlled, the synchronization time difference of the instructions issued by each control device is determined; Based on the regulation demand data and energy storage charging and discharging preparation parameters, control commands are generated, and based on the synchronization time difference of the commands issued by each regulation device, the control commands are issued to each regulation device; wherein, the energy storage charging and discharging preparation parameters are determined based on the photovoltaic forecast data and the equipment operation data.

[0007] In one possible implementation, determining the adjustment demand data based on the power deviation data, the photovoltaic forecast data, and the energy storage system state parameters includes: Acquire DC bus voltage data and parallel operation parameters of each converter; Based on the DC bus voltage data and the parallel operation parameters of each converter, determine the voltage fluctuation risk level, the power allocation compliance status of each converter, and the power allocation deviation of each converter. Based on the power deviation data, determine the power imbalance type and power imbalance level corresponding to the power deviation data; Based on the power imbalance type, the power imbalance level, the voltage fluctuation risk level, the power allocation compliance status of each converter, the power allocation deviation of each converter, and the energy storage system status parameters, the adjustment demand data is determined.

[0008] In one possible implementation, determining the voltage fluctuation risk level, the power allocation compliance status of each converter, and the power allocation deviation of each converter based on the DC bus voltage data and the parallel operation parameters of each converter includes: Based on the DC bus voltage data and the DC bus rated voltage, determine the DC bus voltage deviation data; Based on the DC bus voltage deviation data and the power imbalance level, the voltage fluctuation risk level is determined; Based on the parallel operation parameters of each converter, the power distribution deviation and circulating current value of each converter are determined, and based on the power distribution deviation and circulating current value of each converter, the power distribution compliance status of each converter is determined; wherein, the power distribution compliance status of each converter includes whether the power distribution of each converter meets the standard or whether the power distribution of each converter does not meet the standard.

[0009] In one possible implementation, determining the regulation demand data based on the power imbalance type, the power imbalance level, the voltage fluctuation risk level, the power allocation compliance status of each converter, the power allocation deviation of each converter, and the energy storage system state parameters includes: Based on the power imbalance type, the power imbalance level, the voltage fluctuation risk level, and the power allocation compliance status of each converter, the adjustment target is determined; Based on the power imbalance level, the voltage fluctuation risk level, and the power allocation deviation of each converter, the adjustment priority of the adjustment target is determined; Based on the state parameters of the energy storage system, determine the energy storage adjustability; The regulation demand data is determined based on the energy storage adjustability, the regulation target, and the regulation priority of the regulation target.

[0010] In one possible implementation, the initial power deviation data is corrected based on the photovoltaic prediction data of the DC distribution network to be controlled to obtain the power deviation data, including: Based on the photovoltaic forecast data of the DC distribution network to be controlled, the photovoltaic output fluctuation trend characteristics, fluctuation amplitude and predicted fluctuation time nodes are extracted. Based on the photovoltaic power output fluctuation trend characteristics, the fluctuation amplitude, and the predicted fluctuation time nodes, the photovoltaic power output change is obtained; Based on the change in photovoltaic output, a power deviation correction amount is determined; wherein, the magnitudes of the change in photovoltaic output and the power deviation correction amount are positively correlated. The total power supply is determined based on the photovoltaic output power, the total output power of each parallel converter, and the discharge power of the energy storage system. The total power demand is determined based on the total load demand and the charging power of the energy storage system. The initial power deviation data is determined based on the total power supplied and the total power demand. The initial power deviation data is corrected based on the power deviation correction amount to obtain the power deviation data.

[0011] In one possible implementation, determining the synchronization time difference of the commands issued by each control device based on the communication status data of the DC distribution network to be controlled includes: Based on the communication status data of the DC distribution network to be controlled, the delay data set of each control device is determined; Based on the delay dataset of each control device, a benchmark control device and a benchmark total delay value are determined. Based on the delay dataset of each control device and the baseline total delay value, the synchronization time difference of the instructions issued by each control device is determined.

[0012] In one possible implementation, the delay dataset includes communication link transmission delay, controller processing delay, and power unit execution delay; The determination of the benchmark control device and the benchmark total delay value based on the delay dataset of each control device includes: Based on the delay dataset of each control device, the total delay value of each control device is determined, the control device with the smallest total delay value is determined as the benchmark control device, and the total delay value corresponding to the benchmark control device is determined as the benchmark total delay value; The determination of the command synchronization time difference for each control device based on the delay dataset of each control device and the baseline total delay value includes: Based on the difference between the total delay value of each control device and the benchmark total delay value, the synchronization time difference of the instructions issued by each control device is determined; wherein, if the difference is positive, the instructions are issued earlier, and if the difference is negative, the instructions are issued later.

[0013] In one possible implementation, generating control commands based on the adjustment demand data and energy storage charging / discharging preparation parameters includes: From the photovoltaic forecast data, extract the photovoltaic output fluctuation trend characteristics, fluctuation amplitude, predicted fluctuation time nodes, and predicted fluctuation duration; Extract the current remaining capacity of the energy storage system, the rated power limit of energy storage charging and discharging, the energy storage charging and discharging response rate, and the DC bus voltage data from the equipment operation data. Based on the characteristics of the photovoltaic power output fluctuation trend and the fluctuation amplitude, the charging and discharging preparation action type of the energy storage system is determined; Based on the current remaining capacity of the energy storage system and the rated power limit of the energy storage charging and discharging, the safe adjustable range of the energy storage system is determined, and based on the energy storage charging and discharging response rate, the actual rechargeable power and actual dischargeable power of the energy storage system are determined. Based on the predicted fluctuation time point and the predicted fluctuation duration, the pre-response start-up time and the preparatory state duration of the energy storage system are determined respectively. Based on the charge / discharge preparation action type, the actual rechargeable power, the actual dischargeable power, the pre-response start time, the duration of the preparation state, and the DC bus voltage data, the energy storage charge / discharge preparation parameters are determined.

[0014] Secondly, embodiments of the present invention provide an operation control device for a DC distribution network, comprising: The correction module is used to correct the initial power deviation data based on the photovoltaic prediction data of the DC distribution network to be controlled, so as to obtain the power deviation data. The first determining module is used to determine the adjustment demand data based on the power deviation data, the photovoltaic prediction data, and the energy storage system state parameters; The second determining module is used to determine the synchronization time difference of the instructions issued by each control device based on the communication status data of the DC distribution network to be controlled. The adjustment module is used to generate control commands based on the adjustment demand data and energy storage charging and discharging preparation parameters, and to send the control commands to each control device based on the synchronization time difference of the commands issued by each control device; wherein, the energy storage charging and discharging preparation parameters are determined based on the photovoltaic forecast data and the equipment operation data.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0016] In this embodiment of the invention, to address the problems of delayed regulation and asynchronous regulation actions of various devices in the prior art, the invention first corrects the initial power deviation data based on the photovoltaic (PV) forecast data of the DC distribution network to be controlled, obtaining the power deviation data. Next, based on the power deviation data, PV forecast data, and energy storage system state parameters, the regulation demand data is determined. Then, based on the communication status data of the DC distribution network to be controlled, the synchronization time difference for issuing commands from each regulation device is determined. Finally, based on the regulation demand data and energy storage charging / discharging preparation parameters, control commands are generated and issued to each regulation device based on the synchronization time difference for issuing commands from each regulation device. This invention, by correcting the initial power deviation data in advance based on PV forecast data, and thus determining the regulation demand data in conjunction with the energy storage system state parameters, anticipates the fluctuation trend of PV output and, by combining this with the energy storage system to determine the regulation demand data in advance, achieves proactive regulation of PV output changes. This avoids the problem of excessive DC bus voltage fluctuations caused by sudden changes in PV output from the source, improving the stability of the distribution network operation. In addition, to address the issue of asynchronous control, the synchronization time difference of the commands issued by each control device is determined based on the communication status data of the DC distribution network to be controlled. This ensures that the control commands are matched with the communication delay characteristics of each device, allowing the control actions of each distribution device to be precisely matched in time. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the implementation of the DC distribution network operation control method provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the structure of the DC distribution network operation control device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0019] As described in the background section, the intermittent and fluctuating nature of solar irradiance after the integration of distributed photovoltaic (PV) power sources can affect the operational stability and power supply reliability of the distribution network. Currently, DC distribution networks primarily rely on real-time monitoring data for post-event adjustments during operation control. However, this control mode suffers from significant regulatory lag. When PV output experiences drastic changes, it can cause DC bus voltage fluctuations to exceed permissible limits, impacting the operational stability of the distribution network.

[0020] To address the aforementioned technical problems, this application provides a method for the operation control of a DC distribution network.

[0021] See Figure 1 The document illustrates a flowchart of the operation control method for a DC distribution network provided in an embodiment of the present invention, which is described in detail below: S110. Based on the photovoltaic prediction data of the DC distribution network to be controlled, the initial power deviation data is corrected to obtain the power deviation data.

[0022] In some embodiments, firstly, based on the photovoltaic (PV) forecast data of the DC distribution network to be controlled, the PV output fluctuation trend characteristics, fluctuation amplitude, and predicted fluctuation time points are extracted. Next, based on the PV output fluctuation trend characteristics, fluctuation amplitude, and predicted fluctuation time points, the PV output change is obtained. Based on the PV output change, a power deviation correction is determined; wherein the PV output change and the power deviation correction are positively correlated. Then, based on the PV output power, the total output power of each parallel converter, and the discharge power of the energy storage system, the total power supply is determined; based on the total load demand power and the charging power of the energy storage system, the total demand power is determined; next, based on the total power supply power and the total demand power, initial power deviation data is determined. Finally, the initial power deviation data is corrected according to the power deviation correction, resulting in the final power deviation data.

[0023] In this embodiment, the photovoltaic output power, total load demand power, total output power of each parallel converter, charging power of the energy storage system, and discharging power are extracted from the equipment operation data of the DC distribution network. Initial power deviation data refers to the uncorrected difference between the total power supplied and the total demand power, for example, the difference when the total power supplied is 500kW and the total demand power is 400kW. Photovoltaic forecast data refers to the predicted information on photovoltaic output, such as the photovoltaic output fluctuation trend and fluctuation amplitude. Power deviation data refers to the power deviation value after correction by the photovoltaic forecast data. Photovoltaic output fluctuation trend characteristics refer to the pattern of photovoltaic output increase and decrease, such as a trend of rising in the morning and falling in the evening. Fluctuation amplitude refers to the magnitude of the photovoltaic output change, and the predicted fluctuation time node refers to the expected time of the output change. Photovoltaic output change amount refers to the predicted increase or decrease in photovoltaic output, and power deviation correction amount refers to the value used to correct the initial deviation.

[0024] To improve the accuracy and adaptability of power deviation calculation in DC distribution networks and provide a reliable basis for subsequent regulation, this embodiment first extracts key power parameters from equipment operating data to accurately calculate the current initial power deviation data, considering the intermittent and fluctuating nature of photovoltaic output. This calculation relies on initial power deviation data obtained solely from real-time equipment data, which cannot predict future changes and can easily lead to lag in regulation. Then, photovoltaic forecast data is used to mine output fluctuation characteristics, predict future changes, and correct the power deviation data, resulting in power deviation data that more closely reflects actual operating trends. Finally, combined with equipment operating data, reasonable regulation needs are determined, ensuring the targeted and effective nature of subsequent regulation measures.

[0025] For example, real-time equipment operation data can be collected using Hall effect power sensors, data acquisition terminals supporting the DL / T645 protocol, and smart meters deployed within the distribution network. Specifically, the output power of the photovoltaic array can be collected using a high-precision Hall effect power sensor installed at the photovoltaic combiner box outlet; the total demand power of industrial and residential loads within the area is obtained by aggregating real-time load data from each user's smart meter via the smart fusion terminal of the distribution area; the output power of each of the three parallel converters is collected through the converter's built-in metering module; and the current charging and discharging power of the energy storage system is collected through the status monitoring interface of the energy storage converter. All data is uploaded to the distribution network edge computing gateway using Ethernet + 4G dual-mode transmission, with a collection frequency set to once per second. A data timestamp synchronization mechanism ensures the real-time performance and consistency of data from each device.

[0026] After collecting equipment operation data, preprocessing is required. The 3σ criterion is used to remove outliers exceeding the mean ± 3 standard deviations, and a Kalman filter algorithm is used to filter out interference from grid harmonics. Subsequently, the parameter extraction module built into the edge computing gateway automatically selects key power parameters such as photovoltaic output power and converter output power. Following preset calculation rules, a summation logic is pre-set in the edge computing gateway to accumulate the photovoltaic output power, the total output power of the three parallel converters, and the discharge power of the energy storage system to obtain the total power supply. The total load demand power is then summed with the charging power of the energy storage system to obtain the total demand power. Finally, the initial power deviation data is obtained by calculating the difference between the total power supply and the total demand power, with the result rounded to two decimal places to ensure accuracy.

[0027] In this embodiment, the photovoltaic power prediction subsystem of the meteorological bureau or a third-party commercial photovoltaic prediction platform is accessed through the API interface of the distribution network control platform to obtain photovoltaic prediction data for the next 24 hours. Then, the prediction data is parsed in JSON format through the data parsing module built into the platform to extract key information such as the photovoltaic output fluctuation trend characteristics, such as the exponential upward trend of photovoltaic output in the next two hours, the fluctuation amplitude, which can be calculated by the difference between the maximum and minimum prediction values, such as ±80kW, and the prediction fluctuation time nodes can be accurate to the minute, such as 10:00 am. The parsing results are stored in the time series database of the control platform.

[0028] This embodiment can predict changes in photovoltaic output based on extracted fluctuation characteristic parameters using a long short-term memory network algorithm, combined with photovoltaic operation data from the same period over the past 30 days, including auxiliary data such as irradiance, ambient temperature, and humidity. The prediction model is trained through the AI ​​computing module of the control platform. For example, if the model predicts a 60kW increase in photovoltaic output within 15 minutes after 10:00 AM, the power deviation correction is determined to be +60kW, matching the predicted output change with the power deviation correction. After obtaining the power deviation correction, it is superimposed on the initial power deviation data. A data verification mechanism is activated during the superposition process. If the superposition result exceeds a preset reasonable deviation range, such as ±200kW, a data recalculation process is triggered. If the result is within a reasonable range, the initial data is corrected, the power deviation data is obtained, and the correction time and basis are simultaneously marked.

[0029] S120. Determine the regulation demand data based on the power deviation data, photovoltaic forecast data, and energy storage system status parameters.

[0030] In some embodiments, the DC bus voltage data and parallel operation parameters of each converter can be acquired first. Then, based on the DC bus voltage data and the parallel operation parameters of each converter, the voltage fluctuation risk level, the power allocation compliance status of each converter, and the power allocation deviation of each converter are determined. Next, based on the power deviation data, the power imbalance type and power imbalance level corresponding to the power deviation data are determined. Finally, based on the power imbalance type, power imbalance level, voltage fluctuation risk level, the power allocation compliance status of each converter, the power allocation deviation of each converter, and the energy storage system state parameters, the regulation demand data are determined.

[0031] In this embodiment, the DC bus voltage deviation data can be determined first based on the DC bus voltage data and the rated DC bus voltage. Then, based on the DC bus voltage deviation data and the power imbalance level, the voltage fluctuation risk level can be determined. Finally, based on the parallel operation parameters of each converter, the power distribution deviation and circulating current value of each converter can be determined, and based on the power distribution deviation and circulating current value of each converter, the power distribution compliance status of each converter can be determined. The power distribution compliance status of each converter includes whether the power distribution of each converter meets the standard or does not meet the standard.

[0032] Power imbalance types include power surplus or power deficit; for example, total power supply exceeding total power demand constitutes a power surplus. Power imbalance level refers to the severity of the power imbalance; the larger the absolute value of the power deviation data, the higher the corresponding power imbalance level. DC bus voltage data refers to the real-time voltage value of the DC distribution network bus, such as the real-time monitoring value of a 380V DC bus. Power distribution deviation refers to the difference between the actual distributed power and the theoretical distributed power of each parallel converter. Circulating current value refers to the DC current value formed between the converters without load power supply significance. Power distribution compliance means that the power distribution deviation of each converter does not exceed a first preset proportion of the rated power of the DC distribution network, and the circulating current value does not exceed a second preset proportion of the converter's rated current. Power distribution non-compliance means that the power distribution deviation of at least one converter exceeds a preset proportion of the rated power of the DC distribution network and / or the circulating current value exceeds a second preset proportion of the converter's rated current.

[0033] Furthermore, in this embodiment, the regulation target can first be determined based on the power imbalance type, power imbalance level, voltage fluctuation risk level, and the power allocation compliance status of each converter. Then, the regulation priority of the regulation target is determined based on the power imbalance level, voltage fluctuation risk level, and power allocation deviation of each converter. Next, the energy storage adjustability is determined based on the energy storage system state parameters. Finally, the regulation demand data is determined based on the energy storage adjustability, the regulation target, and the regulation priority of the regulation target.

[0034] In this embodiment, the power deviation data can be compared with a preset imbalance threshold to determine whether there is a power surplus or deficit. Then, combined with real-time DC bus voltage data and converter operating parameters, the power balance of the distribution network is comprehensively evaluated. Finally, the adjustment demand data is determined, including the magnitude of the power to be adjusted, the direction of adjustment, and the adjustment response time. The direction of adjustment includes absorbing surplus power through energy storage charging and supplementing the power deficit through energy storage discharging.

[0035] Among them, the parallel operation parameters of multiple converters refer to the operating data when multiple converters are operating in parallel, such as the output power of each converter. The energy storage system status parameters refer to the operating status data of the energy storage system, such as the remaining capacity. The DC bus voltage deviation data refers to the difference between the DC bus voltage and the rated voltage. The voltage fluctuation risk level refers to the degree of danger of voltage fluctuations, such as high risk or low risk. The first preset ratio and the second preset ratio refer to pre-set judgment thresholds, such as the first preset ratio being 5% and the second preset ratio being 3%. The regulation target refers to the effect to be achieved by regulation, such as eliminating the power deficit. The regulation priority refers to the execution order of the regulation target, such as voltage stability taking precedence over power distribution balancing. The energy storage adjustability data refers to the achievable adjustment range of the energy storage system, such as the maximum discharge power.

[0036] In this embodiment, the initial power deviation is calculated by accurately collecting equipment operating data, and then corrected by combining photovoltaic prediction data to obtain the power deviation data. This effectively improves the accuracy and foresight of the power deviation data, avoiding the problem of insufficient prediction caused by relying solely on real-time data. Based on this power deviation data, photovoltaic prediction data, and energy storage system status parameters, the determined adjustment demand data is more in line with actual operating needs, providing a reliable basis for subsequent distribution network regulation and control. This helps reduce the impact of photovoltaic output fluctuations on the distribution network and improves the stability and reliability of distribution network operation. By comprehensively considering the multi-dimensional operating status of the distribution network, the adjustment demand data is ensured to be scientific and accurate. Power imbalance is the core cause of distribution network regulation, but relying solely on power imbalance cannot guarantee comprehensive regulation. Therefore, the type and level of power imbalance are first clarified, and then linked to the key operating indicator of DC bus voltage and the core parameters of multiple converters operating in parallel to comprehensively understand the operating status of the distribution network. By setting preset ratios to clarify the compliance standards, ambiguity in judgment is avoided. At the same time, combined with the adjustable capabilities of energy storage, the adjustment targets are prioritized to ensure that the adjustment demand is both in line with the actual problem and feasible, avoiding ineffective or risky regulation due to control measures being out of the equipment's capabilities.

[0037] For example, this embodiment can be applied to a medium-voltage DC distribution network with distributed photovoltaic access, and the specific technical implementation steps are as follows: This embodiment can extract the calculated power deviation data and determine its positive or negative attribute through numerical sign judgment logic. A value greater than 0 indicates a power surplus, and a value less than 0 indicates a power deficit. Based on a preset fixed deviation threshold range, it is divided into three levels: 0-50kW for level one, 50-100kW for level two, and greater than 100kW for level three. The larger the absolute value, the higher the level, thus determining the power imbalance level. High-precision voltage divider sensors deployed within the distribution network are used to collect DC bus voltage data in real time, with a sampling frequency strictly set to once every 0.5 seconds. The data is transmitted via fiber optic Ethernet. The internal control board of the converter integrates sampling functions to collect parallel operation parameters of each converter, including output current, output voltage, output power, firing angle, and modulation ratio. It can also extract energy storage system status parameters, including remaining capacity, rated charging and discharging power, charging and discharging cutoff voltage, and health status. All data is uploaded to a data processing center composed of local edge computing nodes in the distribution network via a unified industrial bus protocol.

[0038] In addition, the collected DC bus voltage data can be compared with the preset rated voltage of the medium-voltage DC distribution network, such as 35kV, and the result can be rounded to three decimal places to obtain the DC bus voltage deviation data. Based on the determined power imbalance level, voltage fluctuation risk levels are classified according to preset fixed rules: voltage deviation ≤ ±2% and power imbalance level 1 is classified as low risk; voltage deviation ±2%-±5% and power imbalance level 2 is classified as medium risk; and voltage deviation > ±5% or power imbalance level 3 is classified as high risk.

[0039] This embodiment determines the theoretical power allocation based on the collected parallel operation parameters of each converter and the rated power ratio of each converter. The difference between the actual power allocation output of each converter and the theoretical power allocation is calculated to obtain the power allocation deviation of each converter. A Rogowski coil-type DC current sensor connected in series on the parallel busbar of the converters collects the DC current between the converters, obtaining circulating current values ​​with an accuracy of 0.2%. By calling preset ratios (e.g., 5%) and (e.g., 4%), the ratio of each converter's power allocation deviation to the rated power of the DC distribution network and the ratio of the circulating current value to the converter's rated current are calculated. A dual-condition verification is performed on each converter. If all converters meet the condition that the ratio does not exceed the corresponding preset ratio, the power allocation is deemed compliant. If any converter fails to meet one or both conditions, the power allocation is deemed non-compliant.

[0040] Furthermore, this embodiment can also employ multi-factor weighted integration logic to integrate power imbalance type, power imbalance level, voltage fluctuation risk level, and power allocation compliance status of multiple converters, clarifying the core control direction. For example, when high voltage fluctuation risk is superimposed on a power gap, the adjustment target is set to quickly replenish the power gap and pull the DC bus voltage back to the rated range. By using a quantitative assignment and ranking method, values ​​are assigned to the power imbalance level, voltage fluctuation risk level, and power allocation deviation of each converter, and the execution order of the adjustment targets is set according to the total score from high to low, with higher-scoring targets having higher priority.

[0041] For example, the power imbalance level can be 3 points for level 3, 2 points for level 2, and 1 point for level 1. The voltage fluctuation risk level can be 3 points for high risk, 2 points for medium risk, and 1 point for low risk. The power distribution deviation of each converter can be 3 points for a ratio >5%, 2 points for 2%-5%, and 1 point for <2%.

[0042] This embodiment can also dynamically calculate the adjustment range based on the energy storage system's state parameters and the remaining capacity range. When the remaining capacity is between 20% and 80%, the full rated charging and discharging power is included in the adjustable capacity. When the remaining capacity is below 20%, the discharging power is calculated with linear decay. When the remaining capacity is above 80%, the charging power is calculated with linear decay, ultimately obtaining the energy storage adjustable capacity data. By integrating the energy storage adjustable capacity data, adjustment targets, and adjustment priorities of the targets, adjustment resources are allocated sequentially according to priority, prioritizing the adjustment power requirements of high-priority targets. Simultaneously, the adjustment direction and response time limit of each target are clearly defined, and comprehensive and feasible adjustment demand data are generated through comprehensive calculation.

[0043] For example, the adjustment direction can be to charge the energy storage to absorb the surplus and discharge the energy storage to make up for the shortfall, and the response time can be ≤2 seconds for high priority and ≤5 seconds for medium priority.

[0044] This embodiment integrates distribution network operation data from multiple dimensions, comprehensively covering key factors such as power imbalance, voltage conditions, and converter operation. It accurately determines regulation targets and priorities, and combines this with the adjustable capabilities of energy storage to ensure the scientific feasibility of regulation demand data. This embodiment effectively avoids the one-sidedness caused by regulation dominated by a single factor, reduces problems such as excessive voltage fluctuations and converter power distribution imbalances, improves the pertinence and effectiveness of regulation measures, provides a reliable basis for subsequent precise regulation, and ensures the stable and efficient operation of the DC distribution network.

[0045] S130. Based on the communication status data of the DC distribution network to be controlled, determine the synchronization time difference of the instructions issued by each control device.

[0046] In some embodiments, the delay datasets of each control device can be determined first based on the communication status data of the DC distribution network to be controlled. Then, based on the delay datasets of each control device, a reference control device and a reference total delay value are determined. Finally, the synchronization time difference of the commands issued by each control device is determined based on the delay datasets of each control device and the reference total delay value.

[0047] The delay dataset can include communication link transmission delay, controller processing delay, and power unit execution delay.

[0048] In this embodiment, the total delay value of each control device can be determined based on the delay dataset of each control device. The control device with the smallest total delay value is identified as the benchmark control device, and the total delay value corresponding to the benchmark control device is identified as the benchmark total delay value. Then, the synchronization time difference for issuing commands by each control device is determined based on the difference between the total delay value of each control device and the benchmark total delay value. A positive difference indicates that the command is issued earlier, while a negative difference indicates that the command is issued later.

[0049] Specifically, three delay components are collected from the DC distribution network communication status data for each control device: communication link transmission delay, controller processing delay, and power unit execution delay. The total delay of each control device is obtained by summing these three delay components and generating a multi-device dynamic delay ledger based on device number. The device with the smallest total delay is selected as the benchmark control device, and its total delay is recorded as the benchmark total delay value. For each non-benchmark control device, its total delay is subtracted from the benchmark total delay value to obtain the device-level total delay difference. This total delay difference is directly set as the synchronization time difference for the device's command issuance. A positive total delay difference indicates that the command is issued earlier by a corresponding amount of time; a negative total delay difference indicates that the command is issued later by a corresponding amount of time. The command issuance synchronization time difference is used to force alignment of the command execution times of all control devices, rather than compensating only for the delay of a single device.

[0050] For example, if device A has a delay of 20ms and device B has a delay of 50ms, with A as the baseline, then the instruction to B will be sent 30ms in advance.

[0051] In this embodiment, communication status data refers to parameters that reflect the communication transmission status between distribution network equipment, such as transmission delay and signal strength.

[0052] This embodiment addresses the issue of asynchronous device control caused by differences in communication transmission delays. Since different control devices have different communication links and installation locations, their transmission delays vary. Directly issuing synchronous commands can lead to delayed execution by some devices, causing power distribution imbalances and voltage fluctuations. Therefore, by collecting communication status data to establish a delay ledger, selecting a benchmark device to calculate the delay difference, and determining the synchronization time difference, this ensures that although each device receives commands at different times, they can execute control actions synchronously, guaranteeing control coordination.

[0053] For example, this embodiment can collect communication status data of each control device through the link monitoring unit in the distribution network communication network. Using ping testing combined with link-layer packet capture technology, the communication transmission status between control devices such as converters and energy storage systems and the control platform is monitored in real time. The sampling frequency is set to once every 5 seconds, recording data such as transmission delay and packet loss rate. Abnormal data with a packet loss rate higher than 1% is eliminated to ensure data reliability. Delay ledger data is established based on the collected communication status data, categorized and organized by device number. The transmission delay value of each control device is extracted, and the average of 10 consecutive samples is taken as the final transmission delay value of that device. For example, the average delay of converter A is 18 milliseconds, the average delay of energy storage system B is 32 milliseconds, and the average delay of converter C is 15 milliseconds. Then, the device with the smallest transmission delay value is selected as the benchmark control device, and its corresponding transmission delay value is the benchmark total delay value. For example, the 15 milliseconds of converter C is the benchmark transmission delay value. By calculating the transmission delay difference of each control device, the transmission delay value of each device is subtracted from the reference total delay value. If the result is positive, it means that the delay of the device is higher than the reference, and if it is negative, it is lower than the reference. For example, the difference of converter A is 3 milliseconds and the difference of energy storage system B is 17 milliseconds.

[0054] Finally, the synchronization time difference for issuing commands to each control device is determined based on the transmission delay difference. For devices with a delay higher than the benchmark, the synchronization time difference is set to the transmission delay difference, which is the time for issuing commands in advance. For devices with a delay lower than the benchmark, the synchronization time difference is set to a negative value, which is the time for issuing commands in advance, to ensure that all devices execute commands synchronously.

[0055] In addition, this embodiment can also verify the calculated synchronization time difference of the issued command. The above calculation is repeated by continuously collecting 5 sets of new communication status data. If the deviation of the results does not exceed 1 millisecond, the synchronization time difference is confirmed to be valid. If the deviation is too large, the data is collected again for calculation to ensure synchronization accuracy.

[0056] This embodiment establishes a delay ledger by collecting communication status data, accurately calculates the transmission delay difference of each device, and determines the synchronization time difference, effectively solving the problem of asynchronous control caused by communication delay differences. This enables all control devices to execute adjustment commands synchronously, avoiding power distribution imbalances, increased circulating current, and other issues, thus improving the coordination and accuracy of distribution network control and ensuring the stable and efficient operation of the DC distribution network.

[0057] S140. Based on the regulation demand data and the energy storage charging and discharging preparation parameters, generate control commands and send the control commands to each regulation device based on the synchronization time difference of the commands issued by each regulation device.

[0058] The energy storage charging and discharging preparatory parameters are determined based on photovoltaic forecast data and equipment operation data.

[0059] In some embodiments, energy storage charge / discharge preparation parameters can be determined using photovoltaic (PV) forecast data and equipment operation data. First, from the PV forecast data, the PV output fluctuation trend characteristics, fluctuation amplitude, predicted fluctuation time points, and predicted fluctuation duration are extracted. From the equipment operation data, the current remaining capacity of the energy storage system, the rated power limit for energy storage charge / discharge, the energy storage charge / discharge response rate, and the DC bus voltage data are extracted. Next, based on the PV output fluctuation trend characteristics and fluctuation amplitude, the charge / discharge preparation action type of the energy storage system is determined. Then, based on the current remaining capacity and the rated power limit for energy storage charge / discharge, the safe adjustable range of the energy storage system is determined, and the actual rechargeable power and actual dischargeable power of the energy storage system are determined based on the energy storage charge / discharge response rate. Next, based on the predicted fluctuation time points and predicted fluctuation durations, the pre-response start-up time and the duration of the preparation state of the energy storage system are determined, respectively. Finally, based on the charge / discharge preparation action type, actual rechargeable power, actual dischargeable power, pre-response start-up time, duration of the preparation state, and DC bus voltage data, the energy storage charge / discharge preparation parameters are determined.

[0060] Among them, the types of charge / discharge preparation actions include charging preparation, discharging preparation, or standby preparation.

[0061] In this embodiment, the DC bus voltage deviation can be obtained based on the DC bus voltage data and the DC bus rated voltage. If the charge / discharge preparation action type is charging preparation, the actual rechargeable power is updated based on the voltage deviation to obtain the energy storage charging preparation power. If the charge / discharge preparation action type is discharging preparation, the actual discharging power is updated based on the voltage deviation to obtain the energy storage discharging preparation power. If the charge / discharge preparation action type is standby preparation, the energy storage charge / discharge preparation power is set to 0.

[0062] Energy storage charge / discharge preparation parameters refer to the preset parameters related to the charge / discharge of the energy storage system, which are set in advance to cope with fluctuations in photovoltaic output. These parameters include the type of charge / discharge preparation action and the actual charge / discharge power. The safe adjustable range refers to the power range within which the energy storage system can adjust its charge / discharge under safe operating conditions. For example, when the remaining capacity is 30%, the charging power is 0-80kW and the discharging power is 0-60kW. The pre-response start-up time refers to the moment when the energy storage enters the preparation state in advance. For example, if the fluctuation is predicted to start at 10:00, the start-up time is set to 9:50. The preparation state duration refers to the time the energy storage maintains the preparation state. For example, if the fluctuation lasts for 1 hour, it is set to 65 minutes. The energy storage charge / discharge preparation power refers to the target power value of the energy storage in the preparation state, such as 50kW during charge preparation. Voltage deviation includes voltage too high, voltage within the rated range, or voltage too low. Voltage too high means the DC bus voltage is higher than the rated voltage, such as 36.8kV when the rated voltage is 35kV; voltage too low means the voltage is lower than the rated voltage, such as 33.2kV ​​when the measured voltage is 33.2kV. Actual rechargeable power refers to the maximum charging power that the energy storage can actually withstand, while actual dischargeable power refers to the maximum discharge power that can actually be output.

[0063] By adapting to photovoltaic (PV) output fluctuations in advance, control lags can be avoided, while ensuring the safety of the energy storage system and the stability of the distribution network voltage. Considering the intermittent fluctuations in PV output, real-time responses alone can easily lead to untimely adjustments. Therefore, it is necessary to extract key fluctuation information from PV forecast data to determine energy storage preparatory actions in advance. The operation of the energy storage system is limited by its remaining capacity and rated power. Clearly defining the safe adjustable range and actual charge / discharge power beforehand can prevent overcharging and over-discharging damage to the equipment. This embodiment combines DC bus voltage deviation adjustment with reserve power, ensuring voltage stability and guaranteeing that reserve parameters both meet fluctuation requirements and comply with equipment and distribution network operating constraints, thus improving the targeting and safety of control.

[0064] For example, this embodiment can access photovoltaic (PV) forecast data through a distribution network control platform and extract key information using time-series data analysis methods. Specifically, the PV output fluctuation trend is obtained by linear fitting and slope analysis of the forecast data for the next 24 hours. A positive slope with an absolute value greater than 0.5 kW / min indicates an upward trend. The fluctuation amplitude is calculated by the difference between the maximum and minimum predicted power within the future fluctuation period; for example, a maximum of 120 kW and a minimum of 40 kW results in a fluctuation amplitude of 80 kW. The predicted fluctuation time point is determined by finding the moment of abrupt change in the trend slope, accurate to the minute, such as 9:30 AM. The predicted fluctuation duration is obtained by calculating the time interval during which the trend slope maintains its abrupt change state; for example, from 9:30 AM to 10:30 AM, the duration is 60 minutes. The current remaining capacity of the energy storage system is collected and measured in real-time using coulomb counting, achieving a data accuracy of ±1%. The rated power limits for energy storage charging and discharging are collected through the hardware sampling unit of the energy storage converter, including a maximum charging power of 100 kW and a maximum discharging power of 100 kW. The energy storage charging and discharging response rate is statistically analyzed using historical regulation response data, such as the 2-second time required to reach rated power from startup. DC bus voltage data is collected using voltage divider sensors within the distribution network, with a sampling frequency of once every 0.2 seconds. The data is transmitted to the data processing node via optical fiber.

[0065] This embodiment can also determine the charging / discharging preparation action type based on the extracted photovoltaic power output fluctuation trend characteristics and fluctuation amplitude. If the fluctuation trend is upward and the fluctuation amplitude exceeds 5% of the rated power of the distribution network, it is determined to be charging preparation; if the fluctuation trend is downward and the fluctuation amplitude exceeds 5%, it is determined to be discharging preparation; if the fluctuation trend is flat and the fluctuation amplitude is less than 3%, it is determined to be standby preparation. The safe adjustable range is determined by combining the current remaining capacity of the energy storage system and the rated charging / discharging power limits. When the remaining capacity is below 20%, the safe adjustable range only includes the charging interval, and the upper limit of charging power increases linearly with the remaining capacity. When the remaining capacity is between 20% and 80%, the safe adjustable range covers the entire charging / discharging interval, and the upper limit of power is the rated limit. When the remaining capacity is above 80%, the safe adjustable range only includes the discharging interval, and the upper limit of discharging power increases linearly with the remaining capacity. Based on the energy storage charging / discharging response rate, the actual rechargeable power and actual discharging power are determined through a dynamic power allocation algorithm. For example, a response rate of 2 seconds corresponds to an actual rechargeable power that gradually increases from 50% of the rated power to the full rated power; the same applies to the actual discharging power.

[0066] This embodiment determines the pre-response start time based on the predicted fluctuation time node, setting it according to a rule of 10 minutes in advance. For example, if the predicted fluctuation starts at 9:30, the start time is set to 9:20. The duration of the preparatory state is determined based on the predicted fluctuation duration, setting it according to a rule of exceeding the fluctuation duration by 10%. For example, if the fluctuation lasts for 60 minutes, the duration is set to 66 minutes to ensure complete coverage of the fluctuation cycle. The DC bus voltage deviation can also be calculated by taking the difference between the collected real-time DC bus voltage and the preset rated voltage (e.g., 35kV), then dividing by the rated voltage to obtain the voltage deviation ratio. A deviation ratio higher than 2% is considered voltage too high, lower than -2% is considered voltage too low, and between -2% and 2% is considered voltage within the rated range. The energy storage charging / discharging preparation power is determined based on the charging / discharging preparation action type and the voltage deviation. If it is for charging preparation, the actual rechargeable power is increased by 10% when the voltage is too high, the actual rechargeable power remains unchanged when the voltage is within the rated range, and the actual rechargeable power is reduced by 10% when the voltage is too low, thus obtaining the energy storage charging preparation power. If it is for discharging preparation, the actual discharging power is increased by 10% when the voltage is too low, the actual discharging power remains unchanged when the voltage is within the rated range, and the actual discharging power is reduced by 10% when the voltage is too high, thus obtaining the energy storage discharging preparation power. If it is for standby preparation, the energy storage charging and discharging preparation power is directly set to 0.

[0067] This embodiment integrates charge / discharge preparation action types, actual chargeable power, actual dischargeable power, pre-response start-up time, preparation state duration, and energy storage charge / discharge preparation power to form complete energy storage charge / discharge preparation parameters, which are simultaneously uploaded to the distribution network control platform for backup. By extracting key features from photovoltaic forecast data to plan energy storage preparation actions in advance, the control lag problem caused by photovoltaic output fluctuations is effectively avoided. By combining the energy storage system state parameters to determine the safe adjustable range and actual charge / dischargeable power, damage to the energy storage equipment due to overcharging and over-discharging is avoided, ensuring the safe operation of the equipment. The preparation power is dynamically adjusted based on the DC bus voltage deviation, taking into account the voltage stability of the distribution network. The final generated energy storage charge / discharge preparation parameters accurately adapt to fluctuation requirements and operational constraints, improving the distribution network's ability to cope with photovoltaic fluctuations and ensuring the stable and efficient operation of the distribution network.

[0068] In some embodiments, control commands refer to standardized commands used to instruct distribution network control equipment to perform specific control actions, such as commands to adjust the charging and discharging power of energy storage systems and commands to adjust the power distribution of converters. Equipment update operating parameters refer to the operating parameters that the control equipment needs to update after receiving commands, used to achieve control objectives, such as the target charging and discharging power of the energy storage system, the output power setpoint of the converter, and the response start-up time limit, which directly determine the control execution status of the equipment.

[0069] To ensure that control commands are accurate, feasible, and executed synchronously, this application uses demand data to clearly define control objectives and requirements, and provides energy storage charging and discharging preparatory parameters to form the basis for equipment execution. Combining these two factors to generate commands ensures that the commands are adapted to actual needs and equipment capabilities. Issuing commands according to the synchronization time difference can offset the impact of communication delays, enabling all equipment to execute synchronously and avoiding operational problems caused by misaligned control actions.

[0070] For example, this embodiment can first acquire regulation demand data and energy storage charging and discharging preparation parameters, and then integrate them in a structured manner using an industrial standard data exchange format. A multi-dimensional consistency verification mechanism is used to identify conflicting information. For instance, it verifies whether the total regulation power in the regulation demand is within the adjustable capacity of the energy storage, and whether the regulation response time is shorter than the energy storage charging and discharging response rate. If parameter conflicts exist, a data review process is triggered, and the control platform retrieves the original data again for verification and correction, ensuring that the two types of data are logically consistent and parameter compatible, providing a reliable data foundation for command generation. Based on the integrated valid data, the core command elements of each control device are broken down. For the energy storage system, the energy storage regulation power and regulation direction in the regulation demand are extracted, along with the preparation power and pre-response start time in the energy storage charging and discharging preparation parameters. This clarifies the target charging and discharging power of the energy storage system, the command effective time, and the duration of the preparation state. Simultaneously, the DC bus voltage compensation requirements are correlated to determine the voltage linkage adjustment threshold. For parallel converters, the power allocation ratio and circulating current suppression threshold are extracted from the regulation requirements. Combined with the rated capacity of each converter, the output power setpoint and regulation rate upper limit for each converter are determined to ensure that all command elements comprehensively cover the regulation objectives. By standardizing the command content and adopting the IEC61850 communication standard format commonly used in power distribution networks, information such as the unique equipment identifier, target operating parameters, execution time limit, and verification rules are converted into a binary command stream that the equipment can directly parse. A cyclic redundancy check (CRC) code is added during the encoding process. A check field is generated by performing polynomial operations on the command data to verify data integrity after the equipment receives the command, preventing parameter distortion due to electromagnetic interference during transmission.

[0071] Then, the calculated synchronization time difference data for issuing control commands is retrieved, and a command issuance timing schedule is established according to the device number. For control devices with transmission delays higher than the reference device, commands are issued in advance according to their corresponding synchronization time difference. For example, if the synchronization time difference of a converter is 20 milliseconds, the command is sent 20 milliseconds earlier than the reference device's command issuance time. For devices with transmission delays lower than the reference device, commands are issued later according to the synchronization time difference, ensuring that all devices start control actions at the same time and eliminating execution time differences caused by communication delays. A dual-mode redundant transmission method using fiber optic Ethernet and industrial bus is adopted for command issuance. Core control devices such as energy storage converters and main converters are transmitted via fiber optic Ethernet, utilizing the low-loss and anti-interference characteristics of fiber optics to ensure transmission rate and stability, with a transmission bandwidth set at 100Mbps. Auxiliary control devices are transmitted via industrial bus to reduce the load on the communication link. A timeout retransmission mechanism is enabled during transmission. If no reception confirmation signal is received from the device within 50 milliseconds after the command is issued, the retransmission process is automatically initiated, with a maximum of 3 retransmissions. If unsuccessful, a fault alarm is triggered. The control platform synchronizes time with each device using a network time protocol, maintaining synchronization accuracy within 1 millisecond to ensure consistent calculation of synchronization time differences. Simultaneously, the control platform receives real-time command reception status data from each device, dynamically displaying command reception results and parameter update progress for each device through a status monitoring interface. If command reception fails or parameter update anomalies occur, the faulty device is immediately flagged, and maintenance personnel are alerted to intervene.

[0072] Finally, after receiving the command, the equipment decodes it according to the encoding rules through its built-in command parsing unit, extracts the target operating parameters, and the internal control module updates the operating parameters using a smooth transition algorithm. For example, the converter output power gradually approaches the target value according to the set adjustment rate, and the energy storage system starts charging and discharging actions according to the pre-response start-up time to avoid voltage and current surges in the distribution network caused by parameter abrupt changes, ensuring a smooth transition in operation. After the command is executed, the control platform continuously collects the actual operating parameters of each device and compares them with the command target parameters in real time to calculate the deviation value. If the deviation exceeds the allowable range of ±1%, a secondary command calibration process is triggered. The command parameters are fine-tuned according to the magnitude of the deviation and reissued until the actual operating parameters of the equipment are consistent with the target parameters, ensuring that the control effect meets the expected requirements.

[0073] As can be seen from the above, this embodiment solves the problems of lagging regulation and asynchronous regulation actions of various devices in the prior art, effectively improving the operational stability and power supply reliability of the DC distribution network after photovoltaic power is connected, and adapting to the access and use requirements of photovoltaic distributed power sources. Addressing the lag problem of existing post-event regulation, this embodiment no longer relies solely on real-time monitoring data for passive regulation, but corrects power deviations and determines the energy storage charging and discharging preparation parameters through photovoltaic prediction data. It anticipates the fluctuation trend of photovoltaic output in advance, allowing the energy storage system to enter the corresponding preparation state in advance, achieving proactive regulation of photovoltaic output changes. This avoids the problem of excessive DC bus voltage fluctuations caused by sudden changes in photovoltaic output from the source, improving the operational stability of the distribution network. Addressing the problem of asynchronous regulation actions of existing devices, this embodiment obtains communication delay log data of each device, thereby determining the synchronization time difference for issuing regulation commands. This ensures that the issuance of regulation commands matches the communication delay characteristics of each device, allowing the regulation actions of each distribution device to precisely match in time. This solves the problem of exacerbated power distribution deviations caused by communication delays, significantly improving the overall effect of power balance regulation. To facilitate understanding of the DC distribution network operation control method provided by this invention, MATLAB / Simulink simulation was used for verification. The simulation model was configured according to the actual configuration of a low-voltage DC distribution network project: 100kW distributed photovoltaic + 50kWh energy storage system + 3 parallel bidirectional converters, with a rated DC bus voltage of 380V. The test scenarios covered typical operating conditions such as sudden increases / decreases in photovoltaic output and sudden increases / decreases in load. All data are the average values ​​of multiple simulations under typical operating conditions. The performance indicators are consistent with the engineering limits and technical levels of actual DC distribution network operation. A quantitative comparison with existing post-event regulation control technologies is as follows: From the perspective of DC bus voltage fluctuation range: Simulation results of existing post-regulation technologies show that when photovoltaic output fluctuates by ±60kW (a common fluctuation range in engineering projects) and there are sudden load changes, the DC bus voltage fluctuation range reaches ±3.5% of the rated voltage. For a 380V system, the corresponding fluctuation is 13.3V, which is in the upper-middle range of the allowable voltage fluctuation range of the distribution network. By adopting the method provided in this invention—using photovoltaic predictive forward-looking regulation + energy storage for advance preparation—the voltage fluctuation range is reduced to ±1.8% of the rated voltage. For a 380V system, the corresponding fluctuation is 6.84V, which is at the lower end of the allowable voltage fluctuation range. It can be seen that the voltage fluctuation range is reduced by 48.6%, effectively avoiding the risk of voltage approaching the limit and improving the voltage stability of the distribution network.

[0074] From the perspective of power distribution deviation in parallel converters: Simulation results of existing post-processing techniques show that due to control lag and equipment communication delays leading to asynchronous actions, the maximum deviation between the actual and theoretical power distribution of parallel converters reaches 5.0% of the rated power of the DC distribution network, exceeding the ideal deviation range for engineering applications. By adopting the method provided in this invention, which matches the synchronization time difference of command issuance with equipment communication delays, the power distribution deviation is controlled within 2.2% of the rated power of the DC distribution network, meeting the ideal deviation requirements for engineering applications. Quantitative improvement: The power distribution deviation is reduced by 56%, significantly improving the power distribution balance of multiple converters in parallel.

[0075] From the perspective of circulating current values ​​between converters: existing post-processing control technology simulation results show that due to asynchronous equipment control actions, the maximum no-load circulating current formed between converters reaches 4.0% of the converter's rated current, increasing the converter's reactive power loss and affecting equipment operating efficiency. By adopting the method provided by this invention, which achieves precise matching of the control actions of each converter based on the synchronization time difference, the circulating current value is reduced to within 1.5% of the converter's rated current, significantly reducing no-load losses. Quantitative improvement: The circulating current value is reduced by 62.5%, reducing converter equipment losses and improving overall operating efficiency.

[0076] From the perspective of energy storage response speed: Simulation results of existing post-event regulation technologies show that the energy storage system operates in a passive response mode. Due to delays in real-time data acquisition and command generation and issuance, the actual response time from receiving the regulation command to reaching the target charging / discharging power is approximately 200ms. By adopting the method provided by this invention, the energy storage system enters a charging / discharging / standby preparatory state in advance. After the pre-response, only fine-tuning of the power is required, reducing the actual response time from receiving the formal command to reaching the target power to 80ms. Quantitative improvement: The energy storage response speed is improved by 60%, achieving rapid power compensation for photovoltaic output fluctuations, which meets the actual needs of millisecond-level regulation in engineering projects.

[0077] From the perspective of overall power balance regulation response time: existing post-regulation technology simulation results show that the overall regulation time from the occurrence of power imbalance, data acquisition to the completion of regulation and restoration of power balance is approximately 350ms. By adopting the method provided by this invention, which combines photovoltaic forecasting and forward-looking prediction with synchronous control of equipment actions, the effective time for data processing and command execution is significantly shortened, reducing the overall regulation time to 120ms. Quantitative improvement: The overall power balance regulation time is reduced by 65.7%, achieving rapid elimination of power imbalance and avoiding cascading problems caused by the continuous expansion of deviations.

[0078] The above data, from a simulation perspective, verifies the practical engineering effectiveness of this method in solving the problems of lagging regulation and asynchronous equipment operation in existing technologies, and can effectively improve the operational stability of DC distribution networks after photovoltaic power is connected.

[0079] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0080] This invention addresses the issue of regulation lag by moving beyond passive adjustment based solely on real-time monitoring data. Instead, it corrects power deviations using photovoltaic (PV) forecast data and determines energy storage charging and discharging preparation parameters. This allows for advance prediction of PV output fluctuations, enabling the energy storage system to enter the corresponding preparation state ahead of time. This proactive control of PV output changes avoids excessive DC bus voltage fluctuations caused by sudden changes in PV output, thus improving the stability of the distribution network. Furthermore, by acquiring communication delay logs from each device, the synchronization time difference for issuing regulation commands is determined. This ensures that the regulation commands are matched to the communication delay characteristics of each device, allowing for precise timing of the regulation actions of each distribution device. This solves the problem of exacerbated power distribution deviations caused by communication delays, significantly improving the overall effectiveness of power balance regulation.

[0081] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0082] Figure 2 A schematic diagram of the operation control device for a DC distribution network provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 2 As shown, the operation control device for a DC distribution network includes: The correction module 210 is used to correct the initial power deviation data based on the photovoltaic prediction data of the DC distribution network to be controlled, so as to obtain the power deviation data. The first determining module 220 is used to determine the adjustment demand data based on the power deviation data, the photovoltaic prediction data, and the energy storage system state parameters; The second determining module 230 is used to determine the synchronization time difference of the instructions issued by each control device based on the communication status data of the DC distribution network to be controlled. The adjustment module 240 is used to generate control commands based on the adjustment demand data and energy storage charging and discharging preparation parameters, and to send the control commands to each control device based on the synchronization time difference of the commands issued by each control device; wherein, the energy storage charging and discharging preparation parameters are determined based on the photovoltaic prediction data and the equipment operation data.

[0083] In one possible implementation, the first determining module 220 is used to acquire DC bus voltage data and parallel operation parameters of each converter; Based on the DC bus voltage data and the parallel operation parameters of each converter, determine the voltage fluctuation risk level, the power allocation compliance status of each converter, and the power allocation deviation of each converter. Based on the power deviation data, determine the power imbalance type and power imbalance level corresponding to the power deviation data; Based on the power imbalance type, the power imbalance level, the voltage fluctuation risk level, the power allocation compliance status of each converter, the power allocation deviation of each converter, and the energy storage system status parameters, the adjustment demand data is determined.

[0084] In one possible implementation, the first determining module 220 is used to determine the DC bus voltage deviation data based on the DC bus voltage data and the DC bus rated voltage; Based on the DC bus voltage deviation data and the power imbalance level, the voltage fluctuation risk level is determined; Based on the parallel operation parameters of each converter, the power distribution deviation and circulating current value of each converter are determined, and based on the power distribution deviation and circulating current value of each converter, the power distribution compliance status of each converter is determined; wherein, the power distribution compliance status of each converter includes whether the power distribution of each converter meets the standard or whether the power distribution of each converter does not meet the standard.

[0085] In one possible implementation, the first determining module 220 is used to determine the adjustment target based on the power imbalance type, the power imbalance level, the voltage fluctuation risk level, and the power allocation compliance status of each converter; Based on the power imbalance level, the voltage fluctuation risk level, and the power allocation deviation of each converter, the adjustment priority of the adjustment target is determined; Based on the state parameters of the energy storage system, determine the energy storage adjustability; The regulation demand data is determined based on the energy storage adjustability, the regulation target, and the regulation priority of the regulation target.

[0086] In one possible implementation, the correction module 210 is used to extract photovoltaic output fluctuation trend characteristics, fluctuation amplitude, and predicted fluctuation time nodes based on the photovoltaic prediction data of the DC distribution network to be controlled. Based on the photovoltaic power output fluctuation trend characteristics, the fluctuation amplitude, and the predicted fluctuation time nodes, the photovoltaic power output change is obtained; Based on the change in photovoltaic output, a power deviation correction amount is determined; wherein, the magnitudes of the change in photovoltaic output and the power deviation correction amount are positively correlated. The total power supply is determined based on the photovoltaic output power, the total output power of each parallel converter, and the discharge power of the energy storage system. The total power demand is determined based on the total load demand and the charging power of the energy storage system. The initial power deviation data is determined based on the total power supplied and the total power demand. The initial power deviation data is corrected based on the power deviation correction amount to obtain the power deviation data.

[0087] In one possible implementation, the second determining module 230 is used to determine the delay dataset of each control device based on the communication status data of the DC distribution network to be controlled. Based on the delay dataset of each control device, a benchmark control device and a benchmark total delay value are determined. Based on the delay dataset of each control device and the baseline total delay value, the synchronization time difference of the instructions issued by each control device is determined.

[0088] In one possible implementation, the delay dataset includes communication link transmission delay, controller processing delay, and power unit execution delay; The second determining module 230 is used to determine the total delay value of each control device based on the delay dataset of each control device, determine the control device with the smallest total delay value as the benchmark control device, and determine the total delay value corresponding to the benchmark control device as the benchmark total delay value. Based on the difference between the total delay value of each control device and the benchmark total delay value, the synchronization time difference of the instructions issued by each control device is determined; wherein, if the difference is positive, the instructions are issued earlier, and if the difference is negative, the instructions are issued later.

[0089] In one possible implementation, the adjustment module 240 is used to extract photovoltaic power output fluctuation trend characteristics, fluctuation amplitude, predicted fluctuation time nodes and predicted fluctuation duration from the photovoltaic prediction data; Extract the current remaining capacity of the energy storage system, the rated power limit of energy storage charging and discharging, the energy storage charging and discharging response rate, and the DC bus voltage data from the equipment operation data. Based on the characteristics of the photovoltaic power output fluctuation trend and the fluctuation amplitude, the charging and discharging preparation action type of the energy storage system is determined; Based on the current remaining capacity of the energy storage system and the rated power limit of the energy storage charging and discharging, the safe adjustable range of the energy storage system is determined, and based on the energy storage charging and discharging response rate, the actual rechargeable power and actual dischargeable power of the energy storage system are determined. Based on the predicted fluctuation time point and the predicted fluctuation duration, the pre-response start-up time and the preparatory state duration of the energy storage system are determined respectively. Based on the charge / discharge preparation action type, the actual rechargeable power, the actual dischargeable power, the pre-response start time, the duration of the preparation state, and the DC bus voltage data, the energy storage charge / discharge preparation parameters are determined.

[0090] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 3 As shown, the electronic device 3 of this embodiment includes a processor 30 and a memory 31. The memory 31 stores a computer program 32. When the processor 30 executes the computer program 32, it implements the steps in the various method embodiments described above. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the various device embodiments described above.

[0091] For example, computer program 32 may be divided into one or more modules / units, which are stored in memory 31 and executed by processor 30 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 32 in electronic device 3.

[0092] Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3 This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 3 may also include input / output devices, network access devices, buses, etc.

[0093] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0094] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0095] The above-described 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for operation control of a DC distribution network, characterized in that, include: The initial power deviation data is corrected based on the photovoltaic prediction data of the DC distribution network to be controlled, and the power deviation data is obtained. Based on the power deviation data, the photovoltaic forecast data, and the energy storage system status parameters, the adjustment demand data is determined; Based on the communication status data of the DC distribution network to be controlled, the synchronization time difference of the instructions issued by each control device is determined; Based on the regulation demand data and energy storage charging and discharging preparation parameters, control commands are generated, and based on the synchronization time difference of the commands issued by each regulation device, the control commands are issued to each regulation device; wherein, the energy storage charging and discharging preparation parameters are determined based on the photovoltaic forecast data and the equipment operation data.

2. The operation control method for a DC distribution network according to claim 1, characterized in that, The step of determining the adjustment demand data based on the power deviation data, the photovoltaic forecast data, and the energy storage system state parameters includes: Acquire DC bus voltage data and parallel operation parameters of each converter; Based on the DC bus voltage data and the parallel operation parameters of each converter, determine the voltage fluctuation risk level, the power allocation compliance status of each converter, and the power allocation deviation of each converter. Based on the power deviation data, determine the power imbalance type and power imbalance level corresponding to the power deviation data; Based on the power imbalance type, the power imbalance level, the voltage fluctuation risk level, the power allocation compliance status of each converter, the power allocation deviation of each converter, and the energy storage system status parameters, the adjustment demand data is determined.

3. The operation control method for a DC distribution network according to claim 2, characterized in that, The process of determining the voltage fluctuation risk level, the power allocation compliance status of each converter, and the power allocation deviation of each converter based on the DC bus voltage data and the parallel operation parameters of each converter includes: Based on the DC bus voltage data and the DC bus rated voltage, determine the DC bus voltage deviation data; Based on the DC bus voltage deviation data and the power imbalance level, the voltage fluctuation risk level is determined; Based on the parallel operation parameters of each converter, the power distribution deviation and circulating current value of each converter are determined, and based on the power distribution deviation and circulating current value of each converter, the power distribution compliance status of each converter is determined; wherein, the power distribution compliance status of each converter includes whether the power distribution of each converter meets the standard or whether the power distribution of each converter does not meet the standard.

4. The operation control method for a DC distribution network according to claim 2, characterized in that, The determination of regulation demand data based on the power imbalance type, power imbalance level, voltage fluctuation risk level, power allocation compliance status of each converter, power allocation deviation of each converter, and energy storage system status parameters includes: Based on the power imbalance type, the power imbalance level, the voltage fluctuation risk level, and the power allocation compliance status of each converter, the adjustment target is determined; Based on the power imbalance level, the voltage fluctuation risk level, and the power allocation deviation of each converter, the adjustment priority of the adjustment target is determined; Based on the state parameters of the energy storage system, determine the energy storage adjustability; The regulation demand data is determined based on the energy storage adjustability, the regulation target, and the regulation priority of the regulation target.

5. The operation control method for a DC distribution network according to claim 1, characterized in that, The initial power deviation data is corrected based on the photovoltaic prediction data of the DC distribution network to be controlled, resulting in power deviation data, including: Based on the photovoltaic forecast data of the DC distribution network to be controlled, the photovoltaic output fluctuation trend characteristics, fluctuation amplitude and predicted fluctuation time nodes are extracted. Based on the photovoltaic power output fluctuation trend characteristics, the fluctuation amplitude, and the predicted fluctuation time nodes, the photovoltaic power output change is obtained; Based on the change in photovoltaic output, a power deviation correction amount is determined; wherein, the magnitudes of the change in photovoltaic output and the power deviation correction amount are positively correlated. The total power supply is determined based on the photovoltaic output power, the total output power of each parallel converter, and the discharge power of the energy storage system. The total power demand is determined based on the total load demand and the charging power of the energy storage system. The initial power deviation data is determined based on the total power supplied and the total power demand. The initial power deviation data is corrected based on the power deviation correction amount to obtain the power deviation data.

6. The operation control method for a DC distribution network according to claim 1, characterized in that, The determination of the synchronization time difference of the instructions issued by each control device based on the communication status data of the DC distribution network to be controlled includes: Based on the communication status data of the DC distribution network to be controlled, the delay data set of each control device is determined; Based on the delay dataset of each control device, a benchmark control device and a benchmark total delay value are determined. Based on the delay dataset of each control device and the baseline total delay value, the synchronization time difference of the instructions issued by each control device is determined.

7. The operation control method for a DC distribution network according to claim 6, characterized in that, The delay dataset includes communication link transmission delay, controller processing delay, and power unit execution delay; The determination of the benchmark control device and the benchmark total delay value based on the delay dataset of each control device includes: Based on the delay dataset of each control device, the total delay value of each control device is determined, the control device with the smallest total delay value is determined as the benchmark control device, and the total delay value corresponding to the benchmark control device is determined as the benchmark total delay value; The determination of the command synchronization time difference for each control device based on the delay dataset of each control device and the baseline total delay value includes: Based on the difference between the total delay value of each control device and the benchmark total delay value, the synchronization time difference of the instructions issued by each control device is determined; wherein, if the difference is positive, the instructions are issued earlier, and if the difference is negative, the instructions are issued later.

8. The operation control method for a DC distribution network according to claim 1, characterized in that, The step of generating control commands based on the adjustment demand data and energy storage charging and discharging preparation parameters includes: From the photovoltaic forecast data, extract the photovoltaic output fluctuation trend characteristics, fluctuation amplitude, predicted fluctuation time nodes, and predicted fluctuation duration; Extract the current remaining capacity of the energy storage system, the rated power limit of energy storage charging and discharging, the energy storage charging and discharging response rate, and the DC bus voltage data from the equipment operation data. Based on the characteristics of the photovoltaic power output fluctuation trend and the fluctuation amplitude, the charging and discharging preparation action type of the energy storage system is determined; Based on the current remaining capacity of the energy storage system and the rated power limit of the energy storage charging and discharging, the safe adjustable range of the energy storage system is determined, and based on the energy storage charging and discharging response rate, the actual rechargeable power and actual dischargeable power of the energy storage system are determined. Based on the predicted fluctuation time point and the predicted fluctuation duration, the pre-response start-up time and the preparatory state duration of the energy storage system are determined respectively. Based on the charge / discharge preparation action type, the actual rechargeable power, the actual dischargeable power, the pre-response start time, the duration of the preparation state, and the DC bus voltage data, the energy storage charge / discharge preparation parameters are determined.

9. An operation control device for a DC distribution network, characterized in that, include: The correction module is used to correct the initial power deviation data based on the photovoltaic prediction data of the DC distribution network to be controlled, so as to obtain the power deviation data. The first determining module is used to determine the adjustment demand data based on the power deviation data, the photovoltaic prediction data, and the energy storage system state parameters; The second determining module is used to determine the synchronization time difference of the instructions issued by each control device based on the communication status data of the DC distribution network to be controlled. The adjustment module is used to generate control commands based on the adjustment demand data and energy storage charging and discharging preparation parameters, and to send the control commands to each control device based on the synchronization time difference of the commands issued by each control device; wherein, the energy storage charging and discharging preparation parameters are determined based on the photovoltaic forecast data and the equipment operation data.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 8.

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