A distributed photovoltaic power station micro-grid control method

Abstract

The application provides a kind of distributed photovoltaic power station microgrid control method, including each photovoltaic control unit collects local operating parameter, calculates photovoltaic output rate of change and divides smooth and violent fluctuation working condition;Self-adaptive adjustment consistency algorithm iteration step and damping coefficient, realize power collaborative distribution;Real-time monitoring communication link time delay and packet loss rate, realize the smooth switching of distributed control and local control, after communication recovery, gradually re-synchronization algorithm is accessed without impact;Based on local and adjacent unit voltage deviation self-regulation virtual impedance parameter, compensate the voltage deviation caused by line impedance unevenness;When photovoltaic unit is accessed or exited, automatically update communication topology and reconstruct control parameters, support plug and play, relate microgrid operation control technical field, wherein, can improve the dynamic response capability of photovoltaic microgrid under different working conditions, and voltage quality, and have good scalability and reliability.

Description

Technical Field

[0001] This invention relates to the field of microgrid operation and control technology, and in particular to a microgrid control method for distributed photovoltaic power plants. Background Technology

[0002] As the penetration rate of distributed photovoltaic (PV) power stations in microgrid systems continues to increase, the intermittent and random nature of PV output, along with the dispersed deployment of multiple PV units, poses severe challenges to power distribution, voltage and frequency stability, and safe operation of microgrids. Traditional centralized microgrid control methods rely on a central control unit, which suffers from drawbacks such as high risk of single-point failures, strong communication dependence, and poor system scalability, making them unsuitable for the flexible networking requirements of large-scale distributed PV power stations. Traditional distributed control only enables independent local regulation of each PV unit, lacking inter-unit collaborative interaction, which easily leads to problems such as uneven power distribution and voltage exceeding limits, making it difficult to ensure the overall optimized operation of the microgrid.

[0003] Distributed control methods have become the mainstream technology for microgrid regulation, achieving coordinated control through local communication among distributed units, thus balancing system reliability and global regulation performance. However, existing distributed photovoltaic microgrid control methods still have many technical drawbacks:

[0004] First, the core multi-agent consensus algorithm mostly uses a fixed iteration step size, which cannot adapt to the dynamic working conditions of drastic fluctuations in photovoltaic power output. It is very easy for the convergence speed to contradict the system stability, and it is easy to cause power oscillation and frequency shift when the illumination changes suddenly.

[0005] Secondly, it has extremely poor adaptability to non-ideal communication scenarios such as communication delays and data packet loss, and lacks effective communication fault tolerance and seamless switching mechanisms. Communication anomalies directly lead to the failure of collaborative control.

[0006] Third, the control objective is singular, focusing only on power distribution, without coupling multiple dimensions such as photovoltaic maximum power point tracking, line impedance difference compensation, voltage stability and operation loss optimization, resulting in weak multi-objective coordinated control capabilities;

[0007] Fourth, in scenarios where photovoltaic units are plug-and-play and system topology changes dynamically, control parameters cannot be adjusted autonomously and require manual intervention, resulting in insufficient engineering practicality and scenario adaptability.

[0008] In summary, existing distributed microgrid control methods are insufficient to meet the high-efficiency, stable, and flexible operation requirements of high-penetration distributed photovoltaic power plants. There is an urgent need for a new type of distributed control method with dynamic self-adaptation and multi-objective collaboration to address the inherent defects of existing technologies. Summary of the Invention

[0009] To overcome existing problems, this application provides a distributed photovoltaic power station microgrid control method. Addressing issues such as poor dynamic adaptability, insufficient multi-objective coordination capability, and lack of topology adaptation capability in the operation of high-penetration distributed photovoltaic power station microgrids, this method constructs a two-layer adaptive distributed cooperative control architecture. It combines a photovoltaic fluctuation-sensing variable step-size consensus algorithm, a communication fault-tolerant switching mechanism, and a distributed virtual impedance adaptive adjustment strategy to achieve efficient cooperative control of multiple photovoltaic units in the microgrid, thereby improving system operational stability and energy utilization efficiency.

[0010] The technical solution adopted by the embodiments of this application to solve its technical problem is:

[0011] A microgrid control method for distributed photovoltaic power plants includes the following steps:

[0012] Step 1: Each distributed photovoltaic control unit independently collects local operating parameters, calculates the rate of change of photovoltaic output per unit time, determines the intensity level of photovoltaic fluctuations, and classifies stable operating conditions and severe fluctuation conditions.

[0013] Step 2: Adaptively adjust the iteration step size and damping coefficient of the consensus algorithm according to the intensity of photovoltaic fluctuations. Under stable conditions, a large step size undamped iteration is used to achieve rapid power distribution. Under severe fluctuation conditions, switch to a small step size damped iteration to suppress system oscillations. Each photovoltaic control unit only interacts with adjacent units through local communication to complete the distributed power collaborative allocation.

[0014] Step 3: Monitor local communication link latency and packet loss rate in real time, and achieve smooth switching between distributed collaborative control mode and local control mode based on preset communication quality thresholds. After communication is restored, achieve shockless collaborative access through a gradual resynchronization algorithm.

[0015] Step 4: Each photovoltaic control unit autonomously adjusts the virtual impedance parameters based on the local voltage deviation and the voltage data exchanged with adjacent units to compensate for the voltage deviation caused by uneven line impedance and optimize line transmission loss.

[0016] Step 5: When a photovoltaic unit is connected or disconnected, each control unit automatically updates the neighbor communication topology and adaptively reconstructs the control parameters online to achieve plug-and-play functionality for the photovoltaic unit.

[0017] Preferably, the local operating parameters in step one include real-time photovoltaic power output, grid connection point voltage amplitude, system frequency, energy storage charging and discharging power, and communication status data of adjacent units. The formula for judging the photovoltaic fluctuation intensity level is as follows:

[0018] = ;

[0019] in, For the rate of change of photovoltaic power output, for Photovoltaic power output at all times for Photovoltaic power output at all times This represents the sampling time interval.

[0020] Preferably, when the rate of change of photovoltaic output is less than or equal to a preset photovoltaic fluctuation intensity threshold, it is determined to be a stable operating condition; when the rate of change of photovoltaic output is greater than the photovoltaic fluctuation intensity threshold, it is determined to be a drastic fluctuation operating condition.

[0021] Preferably, the distributed power collaborative allocation in step two is used to allocate active and reactive power among multiple photovoltaic units according to their rated capacity ratio.

[0022] Preferably, the communication quality thresholds in step three include: a communication link delay threshold and a packet loss rate threshold; when the communication link delay is less than or equal to the communication link delay threshold and the packet loss rate is less than or equal to the packet loss rate threshold, the distributed collaborative control mode is maintained; when the communication link delay is greater than the communication link delay threshold or the packet loss rate is greater than the packet loss rate threshold, the mode is switched to local control mode.

[0023] Preferably, in the local control mode, independent voltage and frequency stabilization control is achieved based on local photovoltaic output and voltage frequency parameters, data interaction between adjacent units is stopped, and the control output is kept continuous without sudden changes.

[0024] Preferably, in step three, the progressive resynchronization algorithm gradually corrects the consistency iteration deviation value to achieve a shockless switching of the cooperative control mode after communication is restored.

[0025] Preferably, the virtual impedance parameter adjustment in step four is achieved based on voltage deviation closed-loop control, and the calculation formula for the virtual impedance parameter is as follows:

[0026] = · ;

[0027] in, This is the voltage regulation coefficient. This represents the deviation between the local voltage and the rated voltage.

[0028] Preferably, in step four, each photovoltaic control unit interacts with voltage deviation data through local communication to achieve coordinated adjustment of virtual impedance across the entire network, thereby controlling the voltage deviation at the grid connection point within ±3% of the rated voltage.

[0029] Preferably, in step five, the neighbor communication topology update adopts a neighbor node automatic identification mechanism; when a photovoltaic unit is added or removed, the adjacent control unit senses the topology change in real time and completes the adaptive reconstruction of the control gain and virtual impedance parameters within 100ms.

[0030] The advantages of the embodiments of this application are:

[0031] By judging the fluctuation intensity in real time by the photovoltaic power output change rate, and adaptively adjusting the iteration step size and damping coefficient of the consensus algorithm accordingly, it can achieve rapid power distribution under stable operating conditions and effectively suppress system oscillation under drastic fluctuation conditions, thus balancing dynamic response speed and system stability.

[0032] Real-time monitoring of communication link latency and packet loss rate enables seamless switching between distributed collaborative control and local control based on thresholds. After communication is restored, a gradual resynchronization algorithm is used to gradually correct consistency deviations and avoid inrush current or power surges.

[0033] Each photovoltaic control unit autonomously adjusts its virtual impedance parameters based on local voltage deviation and data exchanged with adjacent units, achieving coordinated regulation across the entire network. This keeps the grid connection point voltage deviation within ±3% of the rated voltage, effectively compensating for voltage differences caused by uneven line impedance and optimizing power transmission efficiency.

[0034] When a photovoltaic unit is connected or disconnected, the system automatically updates the neighbor communication topology and reconstructs the control parameters online. It completes adaptive adjustment within 100ms without manual intervention, making it suitable for dynamic operation scenarios of photovoltaic power plants. It only needs to interact with adjacent units to exchange data, eliminating the need for a central controller and avoiding the risk of single point of failure. At the same time, it can seamlessly switch to local independent control mode when communication deteriorates, ensuring the continuous and stable operation of the system. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention. In addition, for the sake of convenience, the terms "upper," "lower," "left," and "right" are equivalent to the upper, lower, left, and right directions of the accompanying drawings themselves, and the terms "first," "second," etc., are used for descriptive purposes and have no other special meaning.

[0037] This application provides a microgrid control method for distributed photovoltaic power plants, which solves the problems in the prior art. By judging the fluctuation intensity in real time through the photovoltaic power output change rate, the method adaptively adjusts the iteration step size and damping coefficient of the consensus algorithm accordingly. This enables rapid power distribution under stable operating conditions and effectively suppresses system oscillations under drastic fluctuation conditions, thus balancing dynamic response speed and system stability.

[0038] Real-time monitoring of communication link latency and packet loss rate enables seamless switching between distributed collaborative control and local control based on thresholds. After communication is restored, a gradual resynchronization algorithm is used to gradually correct consistency deviations and avoid inrush current or power surges.

[0039] Each photovoltaic control unit autonomously adjusts its virtual impedance parameters based on local voltage deviation and data exchanged with adjacent units, achieving coordinated regulation across the entire network. This keeps the grid connection point voltage deviation within ±3% of the rated voltage, effectively compensating for voltage differences caused by uneven line impedance and optimizing power transmission efficiency.

[0040] When a photovoltaic unit is connected or disconnected, the system automatically updates the neighbor communication topology and reconstructs the control parameters online. It completes adaptive adjustment within 100ms without manual intervention, making it suitable for dynamic operation scenarios of photovoltaic power plants. It only needs to interact with adjacent units to exchange data, eliminating the need for a central controller and avoiding the risk of single point of failure. At the same time, it can seamlessly switch to local independent control mode when communication deteriorates, ensuring the continuous and stable operation of the system.

[0041] The technical solution in this application is to solve the above problems, and the overall approach is as follows:

[0042] Example

[0043] This embodiment presents a microgrid control method for distributed photovoltaic power stations, such as... Figure 1 As shown, it includes the following steps:

[0044] Step 1: Each distributed photovoltaic control unit independently collects local operating parameters, calculates the rate of change of photovoltaic output per unit time, determines the intensity level of photovoltaic fluctuations, and classifies stable operating conditions and severe fluctuation conditions.

[0045] The local operating parameters include real-time photovoltaic power output, grid connection point voltage amplitude, system frequency, energy storage charging and discharging power, and communication status data of adjacent units. The formula for judging the intensity level of photovoltaic fluctuations is as follows:

[0046] = ;

[0047] in, For the rate of change of photovoltaic power output, for Photovoltaic power output at all times for Photovoltaic power output at all times The sampling time interval;

[0048] When the rate of change of photovoltaic power output is less than or equal to the preset photovoltaic fluctuation intensity threshold, it is determined to be a stable operating condition; when the rate of change of photovoltaic power output is greater than the photovoltaic fluctuation intensity threshold, it is determined to be a drastic fluctuation operating condition.

[0049] Furthermore, the system deploys four groups of distributed photovoltaic (PV) units, each equipped with an independent control terminal. Each terminal samples data with a 5ms sampling period, collecting local PV output power, grid connection voltage, system frequency, and communication data between adjacent units. This data is then processed using a formula... = Calculate the rate of change of photovoltaic output, and set the fluctuation intensity threshold to 50kW / s. When the calculated value is ≤50kW / s, it is a stable operating condition, and when it is greater than 50kW / s, it is a drastic fluctuation operating condition.

[0050] Step 2: Adaptively adjust the iteration step size and damping coefficient of the consensus algorithm according to the intensity of photovoltaic fluctuations. Under stable operating conditions, a large step size undamped iteration is used to achieve rapid power distribution. Under drastic fluctuation conditions, switch to a small step size damped iteration to suppress system oscillations. Each photovoltaic control unit only interacts with adjacent units through local communication to complete the distributed power collaborative allocation, which is used to realize the allocation of active and reactive power of multiple photovoltaic units according to the rated capacity ratio.

[0051] Furthermore, under stable operating conditions, a large-step and undamped consensus iterative algorithm is adopted to achieve rapid and equal distribution of active and reactive power among photovoltaic units according to the rated capacity ratio. The convergence is usually completed within 5 iterations.

[0052] Under conditions of severe fluctuations, a small-step, damped consensus algorithm is switched to suppress system oscillations. Each unit communicates only with its neighbors, and the interaction includes: power allocation coefficient, voltage deviation, and virtual impedance value. This step ensures that the system can maintain stable operation even under dynamic changes.

[0053] Each control terminal only interacts with 1-2 adjacent terminals to exchange power deviation consistency data, without the need for global communication, to achieve distributed collaborative power allocation, ensuring that each photovoltaic unit allocates power according to its capacity ratio, with an allocation error of ≤2%.

[0054] Step 3: Monitor local communication link latency and packet loss rate in real time, and achieve smooth switching between distributed collaborative control mode and local control mode based on preset communication quality thresholds. After communication is restored, a progressive resynchronization algorithm is used to achieve shockless collaborative access. The progressive resynchronization algorithm gradually corrects the consistency iteration deviation value to achieve shockless switching of collaborative control mode after communication is restored.

[0055] In the local control mode, independent voltage and frequency stabilization control is achieved based on local photovoltaic output and voltage frequency parameters, stopping data interaction between adjacent units and maintaining continuous control output without sudden changes.

[0056] Communication quality thresholds include: communication link delay threshold and packet loss rate threshold; when the communication link delay is less than or equal to the communication link delay threshold and the packet loss rate is less than or equal to the packet loss rate threshold, the distributed collaborative control mode is maintained; when the communication link delay is greater than the communication link delay threshold or the packet loss rate is greater than the packet loss rate threshold, the mode is switched to local control mode.

[0057] Furthermore, the preset communication link latency threshold is 50ms, and the packet loss rate threshold is 5%. Each control unit monitors the communication quality with its neighbors in real time.

[0058] When the latency is less than or equal to 50ms and the packet loss rate is ≤5%, maintain the distributed collaborative control mode;

[0059] If the latency is greater than 50ms or the packet loss rate is greater than 5%, switch to local control mode.

[0060] In local control mode, the unit relies solely on local photovoltaic output, voltage, and frequency data to perform independent voltage and frequency stabilization control, ceasing data interaction with neighbors, but maintaining continuous control output without sudden changes to avoid impacts.

[0061] Once communication is restored, a progressive resynchronization algorithm is initiated: the consistency iteration deviation caused by the communication interruption is gradually corrected, and collaborative control is restored at a rate of no more than 5% per step, achieving shockless access.

[0062] Step 4: Each photovoltaic control unit autonomously adjusts the virtual impedance parameters based on the local voltage deviation and the voltage data exchanged with adjacent units to compensate for the voltage deviation caused by uneven line impedance and optimize line transmission loss. At the same time, each photovoltaic control unit exchanges voltage deviation data through local communication to achieve coordinated adjustment of virtual impedance across the entire network, controlling the voltage deviation at the grid connection point within ±3% of the rated voltage.

[0063] The virtual impedance parameter adjustment is achieved based on voltage deviation closed-loop control, and the calculation formula for the virtual impedance parameter is as follows:

[0064] = · ;

[0065] in, This is the voltage regulation coefficient. This represents the deviation between the local voltage and the rated voltage.

[0066] Furthermore, each control terminal calculates the local voltage deviation in real time, according to the formula. = · The virtual impedance value is calculated, and the virtual impedance is adjusted collaboratively through local communication and voltage data exchange to compensate for line impedance differences, thereby controlling the voltage deviation at the grid connection point of the entire network within ±3% and reducing line transmission loss.

[0067] formula = · The voltage regulation coefficient is 0.5Ω / V. If the deviation between the local voltage and the rated voltage is 4V, the virtual impedance parameter is 2Ω. The virtual impedance is increased to suppress the output current, thereby increasing the voltage.

[0068] Each unit exchanges voltage deviation data through local communication and coordinates the adjustment of the virtual impedance of the entire network, with the goal of controlling the voltage deviation at the grid connection point within ±3% of the rated voltage.

[0069] Step 5: When a photovoltaic unit is connected or disconnected, each control unit automatically updates the neighbor communication topology and adaptively reconstructs the control parameters online to achieve plug-and-play functionality for the photovoltaic unit.

[0070] Among them, the neighbor communication topology update adopts the automatic identification mechanism of neighbor nodes; when photovoltaic units are added or removed, the adjacent control unit senses the topology change in real time and completes the adaptive reconstruction of control gain and virtual impedance parameters within 100ms.

[0071] Furthermore, when photovoltaic units are added or removed, the neighbor control unit senses the topology changes in real time through the automatic neighbor node identification mechanism and updates the local neighbor communication table.

[0072] Within 100ms, control parameters are automatically reconfigured, including the gain coefficient of the consensus algorithm, the virtual impedance adjustment coefficient, and the power allocation weight, ensuring that the system can quickly recover stable operation after topology changes without manual intervention or global reconfiguration.

[0073] When a new set of photovoltaic units is added, the adjacent control terminal automatically identifies the new node, updates the communication topology within 100ms, and adaptively adjusts the consistency control gain and virtual impedance parameters. No manual parameter tuning is required, enabling seamless access of photovoltaic units and continuous and stable system operation.

[0074] Finally, it should be noted that the above embodiments are merely examples for clearly illustrating the present invention and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A microgrid control method for distributed photovoltaic power stations, characterized in that, Includes the following steps: Step 1: Each distributed photovoltaic control unit independently collects local operating parameters, calculates the rate of change of photovoltaic output per unit time, determines the intensity level of photovoltaic fluctuations, and classifies stable operating conditions and severe fluctuation conditions. Step 2: Adaptively adjust the iteration step size and damping coefficient of the consensus algorithm according to the intensity of photovoltaic fluctuations. Under stable conditions, a large step size undamped iteration is used to achieve rapid power distribution. Under severe fluctuation conditions, switch to a small step size damped iteration to suppress system oscillations. Each photovoltaic control unit only interacts with adjacent units through local communication to complete the distributed power collaborative allocation. Step 3: Monitor local communication link latency and packet loss rate in real time, and achieve smooth switching between distributed collaborative control mode and local control mode based on preset communication quality thresholds. After communication is restored, achieve shockless collaborative access through a gradual resynchronization algorithm. Step 4: Each photovoltaic control unit autonomously adjusts the virtual impedance parameters based on the local voltage deviation and the voltage data exchanged with adjacent units to compensate for the voltage deviation caused by uneven line impedance and optimize line transmission loss. Step 5: When a photovoltaic unit is connected or disconnected, each control unit automatically updates the neighbor communication topology and adaptively reconstructs the control parameters online to achieve plug-and-play functionality for the photovoltaic unit.

2. The microgrid control method for a distributed photovoltaic power station according to claim 1, characterized in that, The local operating parameters in step one include real-time photovoltaic power output, grid connection point voltage amplitude, system frequency, energy storage charging and discharging power, and communication status data of adjacent units. The formula for judging the intensity level of photovoltaic fluctuations is as follows: = ； in, For the rate of change of photovoltaic power output, for Photovoltaic power output at all times for Photovoltaic power output at all times This represents the sampling time interval.

3. The microgrid control method for a distributed photovoltaic power station according to claim 1, characterized in that, When the rate of change of photovoltaic output is less than or equal to the preset photovoltaic fluctuation intensity threshold, it is determined to be a stable operating condition; when the rate of change of photovoltaic output is greater than the photovoltaic fluctuation intensity threshold, it is determined to be a violently fluctuating operating condition.

4. The microgrid control method for a distributed photovoltaic power station according to claim 1, characterized in that, The distributed power collaborative allocation in step two is used to allocate active and reactive power among multiple photovoltaic units according to their rated capacity ratio.

5. The microgrid control method for a distributed photovoltaic power station according to claim 1, characterized in that, The communication quality thresholds in step three include: a communication link delay threshold and a packet loss rate threshold. When the communication link delay is less than or equal to the communication link delay threshold and the packet loss rate is less than or equal to the packet loss rate threshold, the distributed collaborative control mode is maintained. When the communication link delay is greater than the communication link delay threshold or the packet loss rate is greater than the packet loss rate threshold, the mode is switched to local control mode.

6. The microgrid control method for a distributed photovoltaic power station according to claim 5, characterized in that, In the local control mode, independent voltage and frequency stabilization control is achieved based on local photovoltaic output and voltage frequency parameters, data interaction between adjacent units is stopped, and the control output remains continuous without sudden changes.

7. The microgrid control method for a distributed photovoltaic power station according to claim 1, characterized in that, In step three, the progressive resynchronization algorithm gradually corrects the consistency iteration deviation value to achieve a shockless switching of the cooperative control mode after communication is restored.

8. The microgrid control method for a distributed photovoltaic power station according to claim 1, characterized in that, In step four, the virtual impedance parameter adjustment is achieved based on voltage deviation closed-loop control. The calculation formula for the virtual impedance parameter is as follows: = · ； in, This is the voltage regulation coefficient. This represents the deviation between the local voltage and the rated voltage.

9. A microgrid control method for a distributed photovoltaic power station according to claim 8, characterized in that, In step four, each photovoltaic control unit interacts with voltage deviation data through local communication to achieve coordinated adjustment of virtual impedance across the entire network, controlling the voltage deviation at the grid connection point within ±3% of the rated voltage.

10. A microgrid control method for a distributed photovoltaic power station according to claim 1, characterized in that, In step five, the neighbor communication topology update adopts the automatic identification mechanism of neighbor nodes; when a photovoltaic unit is added or removed, the adjacent control unit senses the topology change in real time and completes the adaptive reconstruction of control gain and virtual impedance parameters within 100ms.

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