Energy-storage-free photovoltaic adaptive grid-forming control method and device based on elastic energy

Abstract

The application discloses a kind of energy storage-free photovoltaic self-adaptive network construction control method and device based on elastic energy. First, the voltage and current of photovoltaic DC bus capacitor and the voltage of AC side common connection point are collected, and the dynamic safety threshold of capacitor voltage is set in combination with capacitor voltage safety constraint. Then, the adaptive optimization control architecture is constructed to adjust the AC voltage optimization item, and the transient dissipation item is activated to implement rapid voltage reduction when the DC voltage drops below the safety threshold through adaptive variable gain control law. Finally, the droop control logic of adaptive resistance microgrid is embedded to determine the control target. The application realizes full-condition adaptive operation from voltage self-construction to steady-state network operation, effectively solving the technical problems of pure photovoltaic system in resistance network environment, such as easy collapse and poor disturbance resistance.

Description

Technical Field

[0001] This invention belongs to the field of new energy power system control technology, and particularly relates to a method and device for adaptive grid control of photovoltaic systems without energy storage based on elastic energy. Background Technology

[0002] Single-stage photovoltaic (PV) systems, with their simple structure, high conversion efficiency, and low construction cost, exhibit strong environmental adaptability, making them particularly suitable for extreme application scenarios such as high-altitude, remote, and unpowered areas where maintenance is difficult and cost is critical. With further development of grid construction technology, it has become possible for PV systems to possess autonomous voltage generation capabilities. However, in pure PV scenarios without energy storage, the system faces severe stability challenges when encountering transient shocks such as large-scale load switching or fluctuations in sunlight.

[0003] In microgrid operation, single-stage photovoltaic (PV) systems rely solely on DC bus capacitance for energy buffering. Traditional control schemes typically decouple DC-side control from AC grid control. This architecture suffers from severe stability defects under heavy load conditions. When the load power exceeds the instantaneous output capacity of the PV system, the system falls into a positive feedback loop of rapid DC voltage drop and further reduction in PV output. Current PV-based grid control schemes mostly rely on open-loop or quasi-closed-loop designs with large-scale energy storage devices. PV equipment alone cannot provide sufficient disturbance rejection during steady-state operation. Furthermore, existing control architectures often separate transient protection logic from steady-state MPPT (Multi-Level Photovoltaic Test) logic. The system must perform hard switching between different control algorithms, which easily generates circulating currents and transient impacts. In addition, most mainstream grid models are based on traditional inductive network designs, which are difficult to adapt to the highly resistive physical characteristics of short-circuit lines in microgrids, leading to frequency and voltage oscillations during steady-state optimization.

[0004] Therefore, how to endow pure photovoltaic systems with strong transient energy elasticity under extreme conditions of no energy storage and low inertia, and achieve stable grid construction under power impacts such as changes in external illumination and load switching, is currently a key bottleneck in improving the resilience of new energy microgrids. Summary of the Invention

[0005] To address the aforementioned problems, this invention proposes a photovoltaic adaptive grid control method and device based on elastic energy without energy storage. The controlled object of this method and device is a photovoltaic inverter connecting a single-stage photovoltaic array and a resistive microgrid. Under the condition that the microgrid does not rely on external energy storage devices or voltage support from the grid, the AC voltage is directly controlled by dynamically adjusting the DC-side energy safety threshold and combining it with the droop control logic adapted to the resistive network. This autonomously constructs a stable AC voltage and frequency, realizing the adaptive grid operation of the single-stage photovoltaic microgrid and multi-machine collaborative control.

[0006] One of the objectives of this invention is achieved through the following technical solution: a photovoltaic adaptive grid control method without energy storage based on elastic energy, the method comprising the following steps:

[0007] Step 1: Real-time acquisition of the photovoltaic system output current in the k-th cycle DC bus capacitor voltage And calculate the DC bus voltage change rate. ;

[0008] Step 2: Combining the DC-side capacitor energy storage characteristics with the photovoltaic physical output boundary, obtain the photovoltaic DC capacitor voltage safety constraint under power surge, and then apply it through a dynamic energy buffer. Actively adjusts the capacitor voltage safety threshold upon detecting a power surge. To store energy for a drop;

[0009] Step 3: Based on the data collected in Step 1, obtain the AC voltage optimization term using adaptive optimization control. An adaptive variable gain control based on voltage deviation is introduced to calculate the discrete transient dissipation term. Discretized unconstrained AC voltage reference values ​​are obtained. Governing equations;

[0010] Step 4: Based on adaptive optimization control, the target value of AC side voltage control is compared with the AC voltage reference value. Considering the physical characteristics of the microgrid, discrete PV and Qf droop control logic is constructed to determine the final control target value. and constrained AC voltage reference value To achieve stable operation of the network.

[0011] Furthermore, step 2 specifically includes the following steps:

[0012] Step 2.1: Considering the energy storage characteristics of the DC-side capacitor and the physical output boundary of the photovoltaic system, the safety constraint relationship of the DC capacitor voltage during power surges is obtained:

[0013]

[0014] in, This is the lower limit of the stable operating voltage for photovoltaic systems; this value is typically taken as the voltage at the photovoltaic maximum power point. Considering Due to the significant influence of environmental factors and the high complexity of existing maximum power point voltage measurement techniques, a method based on photovoltaic open-circuit voltage is adopted here. The maximum power point estimation method uses the value marked on the photovoltaic panel at the factory. Data pairs An estimate is made. For a typical crystalline silicon photovoltaic module: ,in To estimate the coefficient, its value is usually between 0.78 and 0.82. This represents the maximum power surge the system can withstand. To control the response cycle number of the system to external changes, and considering the unavoidable delays in digital control systems, this section considers delay factors such as sampling delay, system calculation delay, and PWM activation delay. The value is 5; For DC bus capacitors;

[0015] Step 2.2: Referring to the capacitor voltage safety constraint form in Step 2.1, construct the dynamic safety threshold for the capacitor voltage. :

[0016]

[0017] in, As a dynamic energy buffer, it is instantaneously filled when a power surge is detected to maximize dropout capacity; under steady-state conditions, the buffer is slowly released, allowing the operating point to approach its maximum. To achieve maximum power output and unleash the photovoltaic potential, a lower limit for release is set, with a safety margin reserved. Based on the capacitor voltage safety constraint obtained in step 2.1, the following is... maximum value Set as a safety constraint value. This invention uses the voltage change rate... Exceeding the limit serves as a marker of a power surge event.

[0018] The specific formula obtained is as follows:

[0019]

[0020] in, The decay step size of the dynamic energy buffer. This sets the lower limit for releasing the preset dynamic energy buffer. The preset trigger threshold, .

[0021] Furthermore, to ensure the effectiveness of the invention, a trigger threshold is set. The tuning must meet the following constraints:

[0022] (1) Disturbance immunity constraint: The absolute value of the threshold must be greater than the maximum voltage change rate caused by normal MPPT disturbance. This is to avoid erroneous actions under steady-state conditions.

[0023]

[0024] (2) Sensitivity constraint: The absolute value of the threshold must be significantly less than the rate of voltage change when the system faces the maximum design load impact. This ensures that the voltage drop can be captured instantly at its initial stage.

[0025]

[0026] (3) Response speed constraint: The threshold design should ensure that the algorithm has enough time to complete the logic switching and buffer establishment before the capacitor energy is exhausted.

[0027] Furthermore, the trigger threshold is the ratio of the voltage change value to the voltage change time. In this invention, the voltage change value is the maximum value of the dynamic energy buffer. This means that a voltage buffer should be reserved when the system is facing the maximum potential surge power; the voltage change time is the shortest time window within which the system can tolerate the maximum buffer voltage being completely depleted, and its value is the number of response time windows. With control cycle The product; to ensure safety, a safety weighting factor can be added. .

[0028]

[0029] The system will adaptively adjust the dynamic energy buffer based on the presence of power surges during operation. This allows for adjustment of the dynamic security threshold. It can achieve rapid recovery after power surges and gradually advance the operating point to maximum power output, fully releasing the potential of photovoltaics.

[0030] Furthermore, step 3 specifically includes the following steps:

[0031] Step 3.1: Initialization =0, , ,in This is the preset AC voltage value. Then... , Multiply to get the output power The voltage difference between the two cycles was further obtained. and power difference

[0032] Step 3.2: Set the control values ​​for adaptive optimization control ;Will , and Input to the adaptive optimization controller:

[0033] when ,like ,but ;like and ,like and .when ,like ,but like ,but .

[0034] Step 3.3: To prevent the DC capacitor voltage from collapsing due to power imbalance, a voltage deviation-based approach is introduced. Adaptive variable gain transient dissipation term :

[0035]

[0036]

[0037] in, To achieve an adaptive variable gain function based on voltage deviation, a piecewise adjustment strategy is adopted to rapidly reduce the AC reference voltage in large steps when the voltage deviation is large; as the DC voltage gradually recovers and approaches the threshold... ,deviation As the system decreases, it enters the final stage of recovery. At this point, fine adjustments are needed to avoid overshoot and oscillations, i.e., a smooth transition using small step sizes is adopted. The preset high gain value, The preset small gain value is generally set as follows: Set as 5-10 times.

[0038] Step 3.4: Calculate the AC voltage optimization term obtained from the adaptive optimization control in this cycle. Transient dissipation term and the reference value of the constrained AC voltage in the previous cycle By integrating the data, the unconstrained AC voltage reference value for this cycle is obtained. :

[0039]

[0040] Furthermore, the droop control logic described in step 4 is as follows:

[0041] (1) Set the control objective of adaptive optimization control as:

[0042]

[0043] in, This is the reference value for the AC side voltage. This is the reference value for the active power output of the converter. This is the measured value of the converter's output active power for this cycle. This is the voltage-active droop factor. , The target value for AC side voltage control in adaptive optimization control. Final output value. Unconstrained AC voltage reference value Control target value The result after constraints is:

[0044]

[0045] (2) AC side frequency control anti-droop control method:

[0046]

[0047] in, This is the frequency control value. This is the AC side frequency reference value. This is the reference value for the reactive power output of the converter. This is the measured value of the reactive power output by the converter in this cycle. This is the frequency-reactive droop factor. .

[0048] Secondly, the present invention also provides a photovoltaic adaptive optimization grid construction control device based on transient energy elasticity without energy storage, including a memory and one or more processors. The memory stores executable code, and when the processor executes the executable code, it implements the aforementioned photovoltaic adaptive grid construction control method based on elastic energy without energy storage.

[0049] Thirdly, the present invention also provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the aforementioned method for adaptive grid control of photovoltaic systems without energy storage based on elastic energy.

[0050] Fourthly, the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the aforementioned method for adaptive grid control of photovoltaic systems without energy storage based on elastic energy.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] (1) By embedding the droop characteristic into the adaptive optimization control, the present invention achieves a seamless transition from zero-voltage start-up to steady-state network operation without any control mode switching. At the same time, thanks to the synergy between the AC voltage optimization term and the transient dissipation term, the system can achieve millisecond-level closed-loop adaptive adjustment when encountering power surges, which significantly improves the robustness against disturbances in steady state.

[0053] (2) This invention gets rid of the strong dependence of traditional photovoltaic grids on energy storage equipment for stable operation, and uses the adaptability of the algorithm to compensate for the lack of hardware inertia, which greatly reduces the construction and maintenance costs of the system in extreme environments.

[0054] (3) The present invention adopts PV droop and Qf anti-droop control logic for resistive network characteristics, which makes full use of the photovoltaic PV characteristics and the coupling law of resistive network, realizes autonomous current sharing of multi-machine system under the condition of no central controller, and significantly enhances the system's operational stability in weak network environment. Attached Figure Description

[0055] Figure 1 This is a diagram illustrating the overall control method of an energy storage-free photovoltaic adaptive grid control method based on elastic energy, as described in Embodiment 1.

[0056] Figure 2 This is a waveform diagram of the AC voltage optimization term and discrete transient dissipation term under typical operating conditions in Example 1;

[0057] Figure 3 This is a control block diagram of a photovoltaic converter using the present invention in Embodiment 1;

[0058] Figure 4 This is the main topology diagram of the photovoltaic microgrid in Example 2;

[0059] Figure 5 These are the AC voltage test results of the algorithm of the present invention and the conventional algorithm in Example 2;

[0060] Figure 6 This is a waveform diagram of the active power output of the photovoltaic converter using the algorithm of the present invention and the conventional algorithm in Embodiment 2;

[0061] Figure 7 This is a waveform diagram of the reactive power output of the photovoltaic converter using the algorithm of the present invention and the conventional algorithm in Example 2;

[0062] Figure 8 This is a waveform diagram of the output voltage of the photovoltaic converter according to the algorithm of the present invention in Embodiment 2;

[0063] Figure 9 This is the output voltage waveform of the photovoltaic converter using the conventional algorithm in Example 2;

[0064] Figure 10 This is a structural diagram of a photovoltaic adaptive optimization grid control device based on transient energy elasticity without energy storage, provided by the present invention in Embodiment 3. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be explained and described below with reference to the accompanying drawings. However, the following embodiments are only preferred embodiments of the present invention and not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments in the implementation methods without creative effort are all within the protection scope of the present invention.

[0066] Example 1:

[0067] This embodiment provides a photovoltaic adaptive grid control method without energy storage based on elastic energy, such as... Figure 1 As shown, the method includes the following steps:

[0068] Step 1: Real-time acquisition of the photovoltaic system output current in the k-th cycle DC bus capacitor voltage The DC bus voltage variation rate was calculated using the backward differential method. :

[0069]

[0070] Where T is the control period; using It serves as an impact criterion for identifying transient power imbalances.

[0071] Step 2: Combining the DC-side capacitor energy storage characteristics with the photovoltaic physical output boundary, obtain the photovoltaic DC capacitor voltage safety constraint under power surge, and then apply it through a dynamic energy buffer. Actively adjusts the capacitor voltage safety threshold upon detecting a power surge. To store energy for the drop space.

[0072] Step 2.1: Considering the energy storage characteristics of the DC-side capacitor and the physical output boundary of the photovoltaic system, the safety constraint relationship of the DC capacitor voltage during power surges is obtained:

[0073]

[0074] in, This is the lower limit of the stable operating voltage for photovoltaic systems; this value is typically taken as the voltage at the photovoltaic maximum power point. Considering Due to the significant influence of environmental factors and the high complexity of existing maximum power point voltage measurement techniques, a method based on photovoltaic open-circuit voltage is adopted here. The maximum power point estimation method uses the value marked on the photovoltaic panel at the factory. Data pairs An estimate is made. For a typical crystalline silicon photovoltaic module: ,in To estimate the coefficient, its value is usually between 0.78 and 0.82. This represents the maximum power surge the system can withstand. To control the response cycle number of the system to external changes, and considering the unavoidable delays in digital control systems, this section considers delay factors such as sampling delay, system calculation delay, and PWM activation delay. The value is 5; For DC bus capacitors;

[0075] Step 2.2: Referring to the capacitor voltage safety constraint form in Step 2.1, construct the dynamic safety threshold for the capacitor voltage. :

[0076]

[0077] in, As a dynamic energy buffer, it is instantaneously filled when a power surge is detected to maximize dropout capacity; under steady-state conditions, the buffer is slowly released, allowing the operating point to approach its maximum. To achieve maximum power output and unleash the photovoltaic potential, a lower limit for release is set, with a safety margin reserved. Based on the capacitor voltage safety constraint obtained in step 2.1, the following is... maximum value Set as a safety constraint value. This invention uses the voltage change rate... Exceeding the limit serves as a marker of a power surge event.

[0078] The specific formula obtained is as follows:

[0079]

[0080] in, The decay step size of the dynamic energy buffer. This sets the lower limit for releasing the preset dynamic energy buffer. The preset trigger threshold, .

[0081] To ensure the effectiveness of the invention, a trigger threshold is set. The tuning must meet the following constraints:

[0082] (1) Disturbance immunity constraint: The absolute value of the threshold must be greater than the maximum voltage change rate caused by normal MPPT disturbance. This is to avoid erroneous actions under steady-state conditions.

[0083]

[0084] (2) Sensitivity constraint: The absolute value of the threshold must be significantly less than the rate of voltage change when the system faces the maximum design load impact. This ensures that the voltage drop can be captured instantly at its initial stage.

[0085]

[0086] (3) Response speed constraint: The threshold design should ensure that the algorithm has enough time to complete the logic switching and buffer establishment before the capacitor energy is exhausted.

[0087] The trigger threshold is the ratio of the voltage change value to the voltage change time. In this invention, the voltage change value is the maximum value of the dynamic energy buffer. This means that a voltage buffer should be reserved when the system is facing the maximum potential surge power; the voltage change time is the shortest time window within which the system can tolerate the maximum buffer voltage being completely depleted, and its value is the number of response time windows. With control cycle The product; to ensure safety, a safety weighting factor can be added. .

[0088]

[0089] The system will adaptively adjust the dynamic energy buffer based on the presence of power surges during operation. This allows for adjustment of the dynamic security threshold. It can achieve rapid recovery after power surges and gradually advance the operating point to maximum power output, fully releasing the potential of photovoltaics.

[0090] Step 3: Based on the data collected in Step 1, use adaptive optimization control to optimize the output AC voltage. An adaptive variable gain control based on voltage deviation is introduced to calculate the discrete transient dissipation term. Its variation under typical operating conditions is as follows: Figure 2 As shown.

[0091] Step 3.1: Initialization , , ,in This sets the initial value for the preset AC voltage. Then... , Multiply to get the output power The voltage difference between the two cycles was further obtained. and power difference ;

[0092] Step 3.2: Set the control values ​​for adaptive optimization control ;Will , and Input to the adaptive optimization controller:

[0093] when ,like ,but ;like and ,like and .when ,like ,but like ,but .

[0094] Step 3.3: To prevent the DC capacitor voltage from collapsing due to power imbalance, a voltage deviation-based approach is introduced. Adaptive variable gain transient dissipation term :

[0095]

[0096]

[0097] in, To achieve an adaptive variable gain function based on voltage deviation, a piecewise adjustment strategy is adopted to rapidly reduce the AC reference voltage in large steps when the voltage deviation is large; as the DC voltage gradually recovers and approaches the threshold... ,deviation As the system decreases, it enters the final stage of recovery. At this point, fine adjustments are needed to avoid overshoot and oscillations, i.e., a smooth transition using small step sizes is adopted. The preset high gain value, The preset small gain value is generally set as follows: Set as 5-10 times.

[0098] Step 3.4: Calculate the AC voltage optimization term obtained from the adaptive optimization control in this cycle. Transient dissipation term and the reference value of the constrained AC voltage in the previous cycle By integrating the data, the unconstrained AC voltage reference value for this cycle is obtained. :

[0099]

[0100] in, This represents the reference value of the constrained AC voltage in the (k-1)th cycle.

[0101] Step 4: Based on the physical characteristics of the microgrid, such as... Figure 3 As shown, discrete PV and Qf droop control logic is constructed to determine the control target value. and constrained AC voltage reference value To achieve stable operation of the network.

[0102] Step 4.1: Set the control objective of adaptive optimization control as follows:

[0103]

[0104] in, This is the reference value for the AC side voltage. This is the reference value for the active power output of the converter. This is the measured value of the converter's output active power for this cycle. This is the voltage-active droop factor. , The target value for AC side voltage control in adaptive optimization control. Final output value. Unconstrained AC voltage reference value Control target value The result after constraints is:

[0105] Step 4.2: AC side frequency control method for anti-droop:

[0106]

[0107] in, This is the frequency control value. This is the AC side frequency reference value. This is the reference value for the reactive power output of the converter. This is the measured value of the reactive power output by the converter in this cycle. This is the frequency-reactive droop factor. .

[0108] Example 2:

[0109] Example 2 is an experimental simulation performed according to the method described in Example 1.

[0110] In this embodiment, to fully demonstrate the practical effect of adaptive optimization grid control of energy storage-free photovoltaic systems based on transient energy elasticity, the experimental subject designed using the MATLAB / Simulink simulation platform is as follows: Figure 4 As shown, the system consists of a photovoltaic array. The output of the photovoltaic array is connected to the converter and the line impedance, and then connected to the AC bus. The line resistance is... Line inductance Three additional AC loads are also connected to the AC bus, with AC load 1 being 3Ω, AC load 2 being 10Ω, and AC load 3 being 3Ω. The photovoltaic array consists of 12 parallel * 25 series photovoltaic panels. The open-circuit voltage of a single photovoltaic panel is... Short circuit current 1000W / m 2 Under light intensity and at 25°C, the maximum power point voltage of the photovoltaic array Maximum output power The adaptive optimization control value in this invention is set to 0.2V; the PV droop setting is... , , ;Qf droop settings , , The system starts running at t=0.5s and the temperature remains constant at 25℃.

[0111] In this embodiment, the photovoltaic array 1 is configured to drive the AC load 1 to operate stably. After stabilization, the AC load 2 is connected to the microgrid in 1 second and disconnected from the microgrid in 3 seconds; the AC load 3 is connected to the microgrid in 2 seconds and disconnected from the microgrid in 4 seconds. The light intensity received by the photovoltaic array fluctuates, and in the period from 0 seconds to 2 seconds, the light intensity is maintained at 1000 W / m². 2 Between 2 and 4 seconds, the light intensity suddenly drops to 800 W / m. 2 Between 4 and 5 seconds, the illumination continued to rise to 900 W / m. 2 Furthermore, to highlight the superiority of this invention, a comparative experiment was conducted: only the converter using the traditional algorithm was employed, while all other settings remained the same. The specific experimental results are as follows:

[0112] Experimental results are as follows Figures 5-9 As shown, both the algorithm of this invention and the traditional algorithm can control the AC voltage to slowly rise to a stable value. The traditional algorithm controls the bus voltage to the rated value of 311V, while the algorithm of this invention, under the droop control kernel, controls the bus voltage to 307V. At 1 second, AC load 2 is connected to the system. At this time, the power surge is small and does not reach the dynamic safety threshold of the capacitor voltage. Under the droop control effect, the voltage of the algorithm of this invention naturally decreases to 306V, while the bus voltage remains at the rated value of 311V under the traditional algorithm. At 2 seconds, AC load 3 is connected to the system, and simultaneously, the illumination drops sharply to 800W / m. 2 The system experiences a momentary high-power surge. The AC voltage of the algorithm in this invention rapidly decreases to 218V under the influence of transient dissipation terms, maintaining system stability. In contrast, the traditional algorithm fails to maintain system stability, causing the controller to crash and the output voltage to continuously decrease. The converter output voltage waveforms of the algorithm in this invention and the traditional algorithm are as follows: Figure 8 and Figure 9 As shown, under the dual power surges of a sudden drop in sunlight and the addition of a load, the algorithm of this invention can rapidly reduce the AC voltage reference value to maintain system stability, while the traditional algorithm experiences a gradual voltage decrease, resulting in unstable system control and severe output voltage distortion. At 3 seconds, AC load 2 is disconnected, but the system is still overloaded. To maintain normal system operation, the algorithm of this invention, under the action of the AC optimization term, gradually increases the voltage to 233V to fully utilize the photovoltaic potential and maintain stability. At 4 seconds, AC load 3 is disconnected and the sunlight intensity rises to 900W / m. 2The system is no longer overloaded, and under the action of the AC optimization term, the AC voltage reference value gradually rises to the droop control target value of 307V. For example... Figure 6 and Figure 7 As shown, observing the changes in active and reactive power during the experiment reveals that the algorithm of this invention can adaptively change the output power according to the droop control law, while the traditional algorithm is too fixed and has poor stability. In summary, under the complex experimental scenarios of load switching and illumination changes, the algorithm of this invention can ensure the normal operation of the system and can react quickly to external power surges, maintaining system stability, which is significantly superior to the traditional algorithm.

[0113] Example 3:

[0114] Corresponding to the aforementioned embodiment of an adaptive grid control method for photovoltaic systems without energy storage based on elastic energy, the present invention also provides an embodiment of an adaptive optimization grid control device for photovoltaic systems without energy storage based on transient energy elasticity.

[0115] See Figure 10 The present invention provides a photovoltaic adaptive optimization grid construction control device based on transient energy elasticity without energy storage, comprising a memory and one or more processors. The memory stores executable code, and when the processor executes the executable code, it is used to implement a photovoltaic adaptive grid construction control method based on elastic energy without energy storage in the above embodiment.

[0116] The embodiment of the energy storage-free photovoltaic adaptive optimization grid control device based on transient energy elasticity provided by this invention can be applied to any device with data processing capabilities, such as a computer. The device embodiment can be implemented through software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of any data processing device loading the corresponding computer program instructions from non-volatile memory into memory for execution. From a hardware perspective, such as... Figure 10 The diagram shown is a hardware structure diagram of any data processing-capable device, including the energy storage-free photovoltaic adaptive optimization grid control device based on transient energy elasticity provided by this invention. (Except for...) Figure 10 In addition to the processor, memory, network interface, and non-volatile memory shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0117] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0118] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0119] This invention also provides a computer-readable storage medium storing a program thereon, which, when executed by a processor, implements a method for adaptive grid control of photovoltaic systems without energy storage based on elastic energy, as described in the above embodiments.

[0120] The computer-readable storage medium can be an internal storage unit of any data processing device described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device of any data processing device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units and external storage devices of any data processing device. The computer-readable storage medium is used to store the computer program and other programs and data required by the data processing device, and can also be used to temporarily store data that has been output or will be output.

[0121] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the aforementioned adaptive grid control method for photovoltaic systems without energy storage based on elastic energy.

[0122] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A photovoltaic adaptive grid control method without energy storage based on elastic energy, characterized in that, The method includes the following steps: Step 1: Real-time acquisition of the photovoltaic system output current in the k-th cycle DC bus capacitor voltage And calculate the DC bus voltage change rate. ; Step 2: Combining the DC-side capacitor energy storage characteristics with the photovoltaic physical output boundary, obtain the photovoltaic DC capacitor voltage safety constraint under power surge, and then apply it through a dynamic energy buffer. Actively adjusts the capacitor voltage safety threshold upon detecting a power surge. To store energy drop space; dynamic energy buffer zone The specific formula is as follows: in, The decay step size of the dynamic energy buffer. The lower limit for releasing the preset dynamic energy buffer; The preset trigger threshold, ; The rate of change of DC bus voltage. The maximum power surge that the system can tolerate. Let T be the number of response cycles of the control system to changes in the external environment, and T be the control period. For DC bus capacitors; Capacitor voltage safety threshold for: This is the lower bound of the stable operating voltage for photovoltaic systems; based on the safety constraint of the photovoltaic DC capacitor voltage, it will... maximum value Set as a safety constraint value; Step 3: Based on the data collected in Step 1, obtain the AC voltage optimization term using adaptive optimization control. An adaptive variable gain control based on voltage deviation is introduced to calculate the discrete transient dissipation term. Discretized unconstrained AC voltage reference values ​​are obtained. The governing equations are as follows: Step 3.1: Initialization =0, , ,in The preset AC voltage value will then be used; , Multiply to get the output power The voltage difference between the two cycles was further obtained. and power difference ; Step 3.2: Set the control values ​​for adaptive optimization control ;Will , and Input into the adaptive optimization controller to determine yes still ; Step 3.3: To prevent the DC capacitor voltage from collapsing due to power imbalance, a voltage deviation-based approach is introduced. Obtain the transient dissipation term of adaptive variable gain Voltage deviation is the difference between the dynamic safety threshold and the voltage difference between the two consecutive cycles; transient dissipation term. The specific formula is as follows: in, To achieve an adaptive variable gain function based on voltage deviation, a piecewise adjustment strategy is adopted to rapidly reduce the AC reference voltage in large steps when the voltage deviation is large; as the DC voltage gradually recovers and approaches the threshold... ,deviation As the voltage decreases, the system enters the final recovery phase. At this point, fine-tuning is needed to avoid overshoot and oscillation, i.e., a smooth transition using small steps is adopted; Step 3.4: The AC voltage optimization term obtained from the adaptive optimization control of this cycle is... Transient dissipation term and the reference value of the constrained AC voltage in the previous cycle By integrating the data, the unconstrained AC voltage reference value for this cycle is obtained. : Step 4: Compare the AC side voltage control target value based on adaptive optimization control with the unconstrained AC voltage reference value. Considering the physical characteristics of the microgrid, construct discrete PV and Qf droop control logic to determine the final control target value. and constrained AC voltage reference value To achieve stable operation of the network.

2. The method for adaptive grid control of photovoltaic systems without energy storage based on elastic energy according to claim 1, characterized in that, Step 2 specifically includes the following steps: Step 2.1: Considering the energy storage characteristics of the DC-side capacitor and the physical output boundary of the photovoltaic system, obtain the safety constraint relationship of the DC capacitor voltage during power surge; Step 2.2: Based on the DC capacitor voltage safety constraint relationship, construct the dynamic safety threshold of the capacitor voltage. .

3. The method for adaptive grid control of photovoltaic systems without energy storage based on elastic energy according to claim 1, characterized in that, voltage change rate Has the predicted threshold been exceeded? As a marker of a power surge event; trigger threshold The tuning must meet the following constraints: (1) Disturbance immunity constraint: The absolute value of the threshold must be greater than the maximum voltage change rate caused by normal MPPT disturbance. To avoid erroneous actions under steady-state conditions; (2) Sensitivity constraint: The absolute value of the threshold must be significantly less than the rate of voltage change when the system faces the maximum design load impact. This ensures that the voltage drop can be captured instantly at its initial stage. (3) Response speed constraint: The threshold design should ensure that the algorithm has enough time to complete the logic switching and buffer establishment before the capacitor energy is exhausted.

4. The adaptive grid control method for photovoltaic systems without energy storage based on elastic energy according to claim 3, characterized in that, The trigger threshold is the ratio of the voltage change value to the voltage change time multiplied by a safety weighting factor. The voltage change value uses the maximum value of the dynamic energy buffer, i.e., a voltage buffer should be reserved when facing the maximum potential surge power. The voltage change time is the shortest time window within which the system can tolerate the maximum buffer voltage being completely depleted, and its value is the number of response time windows. With control cycle The product; the system will adaptively adjust the dynamic energy buffer based on the presence of power surges during operation. This allows for adjustment of the dynamic security threshold. .

5. The adaptive grid control method for photovoltaic systems without energy storage based on elastic energy according to claim 1, characterized in that, The droop control logic described in step 4 is as follows: (1) The control objective of the algorithm is obtained based on the AC side voltage reference value and the photovoltaic output power reference value. The final output value of the algorithm Unconstrained AC voltage reference value Control target value The result after constraints; (2) The frequency control value for AC side frequency control anti-droop control is obtained based on the AC side frequency reference value, the converter output reactive power reference value and the measured value of the converter output reactive power.

6. A photovoltaic adaptive optimization grid control device based on transient energy elasticity, comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that, When the processor executes the executable code, it implements a photovoltaic adaptive grid control method based on elastic energy without energy storage, as described in any one of claims 1-5.

7. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements a photovoltaic adaptive grid control method based on elastic energy without energy storage, as described in any one of claims 1-5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the adaptive grid control method for photovoltaic systems without energy storage based on elastic energy as described in any one of claims 1-5.

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