Photovoltaic participation power balance availability rate setting method

Abstract

The invention relates to the technical field of power systems and automation thereof, and discloses a method for setting the availability rate of photovoltaic participation in power balance. The method comprises the following steps: deploying an intelligent control unit in each photovoltaic power station, and predicting short-term maximum available power generation power by using a time convolutional network based on local high-frequency acquired operation and environment data; in combination with global stability boundary constraints issued by superior scheduling, iterative distributed negotiation is carried out through a low-delay private network and an adjacent power station, local optimal availability setting is quickly achieved, and the local optimal availability is converted into an active power instruction to drive an inverter to execute. According to the technical scheme, millisecond-level quick response is achieved, butterfly response is eliminated, and collaborative optimization of system robustness and photovoltaic utilization rate is improved.

Description

Technical Field

[0001] This invention belongs to the field of power systems and their automation, and specifically relates to a method for setting the availability of photovoltaic power balance. Background Technology

[0002] With the continuous increase in the penetration rate of renewable energy in new power systems, photovoltaic power generation, due to its intermittent and fluctuating characteristics, poses a severe challenge to the real-time balancing capability of the power grid. In scenarios where multiple photovoltaic power plant clusters participate in power dispatch, each power plant needs to dynamically adjust its available generating capacity (i.e., "availability") according to grid instructions to maintain the power supply and demand balance of the system. However, the current mainstream centralized control architecture relies on the central dispatch center to uniformly calculate and issue availability setpoints. This model has significant drawbacks: on the one hand, the process of uploading massive amounts of power plant data and sending down instructions is easily affected by communication link delays, bandwidth limitations, and network jitter, causing control instructions to lag behind the actual needs of the power grid; on the other hand, once the central node fails or is attacked, it will cause the entire cluster control function to be paralyzed, resulting in a serious lack of system robustness.

[0003] The issue of coordinating the availability settings of photovoltaic power plant clusters is particularly prominent. Due to the strong spatiotemporal heterogeneity of the output of each power plant, which is affected by local meteorological conditions (such as sudden changes in cloud cover or a sudden drop in irradiance), a sudden adjustment in the availability of a single power plant can easily trigger a chain reaction through grid coupling. For example, if a power plant experiences a sudden drop in output due to local shading, and its availability is not promptly and coordinated, it may force neighboring power plants to operate beyond their limits to compensate for the power deficit, thereby inducing regional voltage instability or frequency overruns, forming a "butterfly effect" of imbalance propagation.

[0004] While existing technologies attempt to introduce distributed optimization algorithms or rule-based local response strategies, they still struggle to balance real-time performance, consistency, and fault tolerance. Most solutions lack effective cross-site consensus mechanisms, failing to ensure global convergence of availability threshold allocation results under decentralized conditions. Architectures employing traditional consensus protocols (such as Paxos or standard Raft) suffer from high communication overhead and complex state synchronization, making them ill-suited to the dynamic environment of second-level fluctuations in photovoltaic output. Therefore, under the premise of ensuring low communication load and high fault tolerance, there is an urgent need for a robust and resilient dynamic availability setting method that supports autonomous negotiation among multiple photovoltaic power plants, rapid consensus, and high resilience, to overcome the dual dilemmas of centralized control bottlenecks and distributed collaborative failures. Summary of the Invention

[0005] To address the aforementioned technical issues, this invention provides a method for setting the availability of photovoltaic (PV) power balancing. This method abandons the traditional centralized control architecture and introduces a distributed, autonomous, and collaborative control paradigm. The method deploys an independent intelligent control unit in each PV power plant. This unit generates a high-precision short-term power generation capacity curve using a time-series prediction model based on locally collected high-frequency operational and environmental data. Each power plant's intelligent control unit iteratively negotiates with neighboring power plants in the grid topology through a dedicated low-latency communication network to achieve a locally optimal availability adjustment scheme. This negotiation process is subject to global stability boundary conditions issued by the higher-level grid dispatching system. By rapidly forming a stable consensus within a local scope and executing adjustments within microseconds, this method fundamentally suppresses the cascading instability effect caused by single-point power fluctuations—the butterfly effect—while completely eliminating the communication bottlenecks and single-point failure risks of centralized control. This achieves rapid, robust, and precise support for grid balancing from PV clusters.

[0006] This invention provides a method for setting the availability of photovoltaic power participation in power balance, applicable to a cluster consisting of multiple photovoltaic power plants. The method includes: Within each photovoltaic power station in the multi-photovoltaic power station cluster, a set of local status information is acquired in real time. The local status information includes the photovoltaic power station's own operating status data, local environmental data, and grid interface data. Based on its own operating status data and the local environment data, a pre-trained power prediction model is used to generate a time series curve of the maximum available power generation capacity of the photovoltaic power station within a predetermined future time window. Receive and store the overall operational boundary constraints that the multiple photovoltaic power station clusters must jointly comply with from the superior power grid dispatching system; Based on the time-series curve of the maximum available power generation and the overall operating boundary constraints, a distributed collaborative negotiation process is initiated. This process is completed within the photovoltaic power plant cluster through direct communication between each photovoltaic power plant to determine the final availability setpoint for each photovoltaic power plant. After the distributed collaborative negotiation process reaches convergence and the final availability setting value is output, the final availability setting value is converted into a specific active power command value. The specific active power command value is sent to the inverter array in the photovoltaic power station, driving the inverter array to adjust its power output to accurately match the active power command value.

[0007] As one embodiment of the present invention, the real-time acquisition of a set of local status information specifically includes: collecting the DC-side voltage, DC-side current, AC-side voltage, AC-side current, output frequency, active power output, reactive power output, and internal module temperature of all inverters in the power station through a power station-level monitoring system deployed in each photovoltaic power station at a sampling frequency higher than 1000 Hz, thereby constituting the self-operating status data; collecting the total planar solar irradiance, photovoltaic module backsheet temperature, ambient temperature, and wind speed and direction data within the photovoltaic power station area through an environmental monitoring unit connected to the power station-level monitoring system at a sampling frequency higher than 1 Hz, thereby constituting the local environmental data; and collecting the effective voltage value, frequency, three-phase imbalance, and power exchange data of the grid connection point through a power quality monitoring device installed at the grid connection point of the photovoltaic power station, thereby constituting the grid interface data.

[0008] As one embodiment of the present invention, the generation of the maximum available power generation time series curve using a pre-trained power prediction model specifically includes: the power prediction model is a temporal convolutional network model, the model structure of which includes an input layer, multiple alternately stacked causal convolutional layers and residual connection blocks, and an output layer; the input layer receives a multi-dimensional time series data tensor composed of the total planar solar irradiance, photovoltaic module backsheet temperature, and actual active power output of the power plant over the past 300 seconds; the causal convolutional layer extracts features at different time scales by performing one-dimensional convolution operations on the input time series data, and the calculation of its convolution kernel strictly follows the chronological order; the residual connection block directly adds the input of the causal convolutional layer to its output to prevent gradient vanishing; the output layer is a fully connected layer, whose output predicts the maximum available power generation time series curve with a time resolution of 1 second within the next 900 seconds.

[0009] As one embodiment of the present invention, the overall operating boundary constraints issued by the superior power grid dispatching system include: the slope limit value of the total active power of the cluster, in megawatts per minute; the total frequency response coefficient of the cluster; the dynamic reactive power support range that the cluster must provide; and the percentage of active power reduction under emergency voltage control conditions.

[0010] As one implementation of this invention, the distributed collaborative negotiation process specifically includes the following steps: First, each photovoltaic power plant control unit, based on its generated maximum available power generation time series curve and in conjunction with the overall operating boundary constraints, calculates an initial availability adjustment proposal aimed at maximizing its own interests. This proposal includes a sequence of planned availability setpoints for future negotiation periods.

[0011] Secondly, each photovoltaic power station broadcasts its initial availability adjustment proposal to all neighboring photovoltaic power stations directly connected to it in the grid physical topology via a dedicated ring fiber optic communication network based on a deterministic Ethernet protocol. The data transmission latency of the communication network is less than 100 microseconds.

[0012] Furthermore, after receiving availability adjustment proposals from all neighboring power plants, the control unit of each photovoltaic power plant overlays these proposals with its own and inputs them into the local power grid stability simulation model for verification. This local power grid stability simulation model is a dynamic power flow calculation model based on the node admittance matrix, which can predict the voltage and frequency offsets at the grid connection points of the photovoltaic power plant itself and neighboring power plants under the proposed combined power variations.

[0013] Then, if the simulation model verification results show that the voltage and frequency offsets of all nodes are within the limits specified in the power grid safety and stability guidelines, the photovoltaic power station broadcasts a confirmation message to all its neighboring power stations; if the verification results show that the offsets of one or more nodes exceed the limits, the photovoltaic power station calculates the stability margin deficit that caused the exceedance, and calculates and broadcasts a rejection message containing specific adjustment suggestions based on the contribution of each proposal to the exceedance.

[0014] Finally, the photovoltaic power station that receives the rejection message must reduce the magnitude of its availability adjustment proposal according to the adjustment suggestions and re-initiate a new round of proposal broadcasting and verification. This process iterates at a period of less than 100 milliseconds until all members in the local power station group broadcast confirmation messages to each other within the negotiation period, marking that the local area has reached negotiation convergence. At this point, the setting value in the final version of the availability adjustment proposal is determined as the final availability setting value.

[0015] As one embodiment of the present invention, the step of converting the final availability setpoint into a specific active power command value specifically includes: multiplying the final availability setpoint in percentage form by the power prediction value of the maximum available generating power time series curve at the current moment to obtain an absolute value of active power in megawatts, which is the specific active power command value.

[0016] As one embodiment of the present invention, the step of sending the specific active power command value to the inverter array in the photovoltaic power station specifically includes: the power controller of the power station decomposes the active power command value into independent power setpoints for each inverter according to a preset allocation strategy, and sends these setpoints to the corresponding inverters through a fieldbus network; after receiving the command, each inverter's internal digital signal processor changes the amplitude and phase of its output current by adjusting the duty cycle of the pulse width modulation signal, thereby precisely controlling its active power output so that the total active power output of all inverters is consistent with the specific active power command value.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By constructing a distributed autonomous collaborative architecture, the dependence on the central control station is completely eliminated, avoiding the risk of the entire photovoltaic cluster control failing due to a single point of failure or communication interruption of the central control station, and significantly improving the reliability and robustness of the system.

[0018] 2. By utilizing a high-speed, deterministic communication network deployed between power plants for proximity negotiation, the closed-loop cycle of control decisions is shortened to the millisecond level, which is much faster than the second or even minute response speed of traditional centralized dispatching. This enables the system to suppress local power disturbances in near real-time, effectively preventing the propagation and amplification of disturbances in the power grid and eliminating the butterfly effect of cascading instability caused by availability adjustments.

[0019] 3. This method decomposes the global, macroscopic grid stability constraints into the local decision-making model of each power station. At the same time, it combines high-precision prediction of its own power generation capacity to achieve a precise match between the global optimization goal and the local actual situation. This enables the entire photovoltaic cluster to maximize the utilization rate of clean energy while ensuring grid security, and realizes a fundamental transformation from passively adapting to the grid to actively supporting grid balance. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall technical solution architecture of a photovoltaic power balance availability setting method proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the distributed autonomous cooperative control paradigm in this invention; Figure 3 This is a logical flowchart of the local status information acquisition and high-precision power generation capacity prediction in this invention. Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow between local power plant groups in this invention. Detailed Implementation

[0021] Please refer to Figure 1-4 This invention provides a method for setting the availability of photovoltaic (PV) power balance, applicable to a cluster system composed of multiple PV power plants. This method deploys an independent intelligent control unit in each PV power plant, combining local high-frequency state perception, high-precision power generation capacity prediction, global boundary constraint reception, and a distributed collaborative negotiation mechanism among neighboring power plants to dynamically set the availability of individual power plants. This allows for power adjustment closed-loop completion within millisecond timescales, effectively suppressing the chain reaction of imbalances caused by sudden changes in availability at a single point (the "butterfly effect"), while also avoiding the inherent communication delays and single-point failure risks of centralized control architectures.

[0022] The method includes the following steps: S1: In each photovoltaic power station within a multi-photovoltaic power station cluster, a set of local status information is obtained in real time. S2, based on the self-operating status data and the local environment data, a pre-trained power prediction model is used to generate a time series curve of the maximum available power generation capacity of the photovoltaic power station within a predetermined future time window; S3, receive and store from the superior power grid dispatch system the overall operating boundary constraints that the multi-photovoltaic power station cluster must jointly comply with; S4. Based on the time-series curve of the maximum available power generation and the overall operating boundary constraints, initiate the distributed collaborative negotiation process; S5, after the distributed collaborative negotiation process reaches convergence and outputs the final availability setting value, the final availability setting value is converted into a specific active power command value. S6, the specific active power command value is sent to the inverter array in the photovoltaic power station to drive the inverter array to adjust its power output.

[0023] In step S1, each photovoltaic power station synchronously collects three types of local status information through its internally deployed power station-level monitoring system, environmental monitoring unit, and grid-connected power quality monitoring device. The power station-level monitoring system collects real-time data from all inverters within the power station at a sampling frequency higher than 1000 Hz. The collected data items include the DC-side voltage, DC-side current, AC-side voltage, AC-side current, output frequency, active power output, reactive power output, and internal key module temperature of each inverter. This data constitutes the self-operating status data, used to characterize the current electrical performance and thermal management status of the power station.

[0024] The environmental monitoring unit collects data on total planar solar irradiance, photovoltaic module backsheet temperature, ambient temperature, and wind speed and direction within the photovoltaic power station area at a sampling frequency higher than 1 Hz. This data constitutes the local environmental data, used to reflect the direct impact of external meteorological conditions on power generation capacity. The power quality monitoring device is installed at the grid connection point between the photovoltaic power station and the main grid, continuously collecting the effective voltage value, system frequency, three-phase voltage imbalance, and bidirectional power exchange data at the grid connection point. This data constitutes the grid interface data, used to assess the stability boundary of the interaction between the power station and the grid.

[0025] In step S2, the intelligent control unit of each photovoltaic power station invokes a pre-trained temporal convolutional network model. This model generates a time-series curve of the maximum available power generation within the next 900 seconds with a time resolution of 1 second. The input layer of this temporal convolutional network model receives a three-dimensional tensor with a time dimension of 300 seconds. The feature dimension includes three channels: the total planar solar irradiance sequence over the past 300 seconds, the photovoltaic module backsheet temperature sequence, and the actual active power output sequence of the power station. The input tensor is first processed through multiple causal convolutional layers. The kernel of each layer depends only on the data at the current and historical moments, ensuring that the model output does not reveal future information.

[0026] Residual connection blocks are embedded between causal convolutional layers, directly adding the input to the convolutional output to alleviate the vanishing gradient problem in deep network training. The model's output layer is a fully connected layer with 900 neurons equal to the prediction time step. Each neuron corresponds to the predicted maximum available power generation in one second. This prediction represents the theoretically highest active power output of the power plant under the current environment and equipment conditions, without considering grid constraints or coordination requirements.

[0027] In step S3, the intelligent control unit of each photovoltaic power station periodically receives overall operational boundary constraints from the upper-level power grid dispatch system through a dedicated secure communication channel. These constraints are structured data packets containing four core parameters: the slope limit of the cluster's total active power, measured in megawatts per minute (MW / min), used to limit the rate of change in the cluster's total output and prevent drastic frequency fluctuations; the cluster's total frequency response coefficient, used to define the cluster's sensitivity to system frequency deviations; the dynamic reactive power support range that the cluster must provide to maintain voltage stability; and the percentage of active power reduction that the cluster must implement under emergency voltage control conditions. These parameters are dynamically calculated and distributed by the power grid dispatch center based on the real-time operating status of the entire network, with an update cycle of no more than 10 seconds. All photovoltaic power stations must strictly adhere to these boundary conditions in their subsequent availability decisions and must not exceed the limits.

[0028] In step S4, each photovoltaic power station initiates a distributed collaborative negotiation process. This process is executed on a dedicated ring fiber optic communication network using a deterministic Ethernet protocol, ensuring that the one-way communication delay between any two adjacent power stations is less than 100 microseconds. The negotiation process is executed iteratively at fixed intervals, with each interval lasting 100 milliseconds. At the beginning of each negotiation cycle, the intelligent control unit of each photovoltaic power station first calculates an initial availability adjustment proposal based on its maximum available power generation time-series curve and overall operating boundary constraints. This proposal is a sequence of time steps within the negotiation cycle, with each element representing a percentage value indicating the proportion of the power planned to be output by the power station to its maximum available power generation at the corresponding moment. The goal of generating the proposal is to maximize its own power generation revenue or minimize the curtailment rate while satisfying global constraints.

[0029] Subsequently, each photovoltaic (PV) power plant broadcasts its initial availability adjustment proposal via a ring fiber optic network to all neighboring PV power plants directly connected to it in the grid's physical topology. Proximity is determined by the grid's primary wiring diagram, typically referring to plants connected via the same bus or adjacent feeders. After receiving proposals from all neighboring plants, each plant's intelligent control unit merges its own proposal with those of its neighbors to form a local joint power variation scheme. This scheme is then input into a local grid stability simulation model for verification. This simulation model is built upon node admittance matrices and includes the equivalent injected power model for the plant and all neighboring plants, line impedance parameters, and grid connection point voltage reference values. The model performs dynamic power flow calculations to predict the voltage and frequency offsets at each grid connection point during future negotiation cycles under joint power variations.

[0030] If the simulation results show that the voltage offset of all nodes does not exceed ±5% and the frequency offset does not exceed ±0.2 Hz, the verification is considered successful. The power station broadcasts a confirmation message to all neighboring power stations, containing its proposal version number and verification success flag. If the offset of any node exceeds the limit, the verification is considered unsuccessful. At this time, the power station calculates the stability margin deficit, i.e., the difference between the actual offset and the safety limit, and quantifies the contribution weight of each neighboring proposal to the node exceeding the limit based on sensitivity analysis. The contribution weight is calculated using the partial derivative of the Jacobian matrix, reflecting the degree of influence of a power station's power change on the voltage or frequency of a specific node. Subsequently, the power station generates a rejection message, which includes the identifier of the node exceeding the limit, the stability margin deficit value, and specific adjustment suggestions for each neighboring proposal, such as reducing the overall availability proposal of a power station by 3%.

[0031] A photovoltaic power station that receives a rejection message must modify its availability adjustment proposal according to the adjustment suggestions before the start of the next negotiation cycle. The modification strategy is to multiply the availability value at each time step in the original proposal by a decay factor, which is calculated from the adjustment suggestions in the rejection message, to ensure that the negative impact of the modified proposal on system stability is reduced. After modification, the power station re-initiates the proposal broadcast and enters the next round of verification. This iterative process continues until all members in the local power station group have received confirmation messages from each other within the complete negotiation cycle, and no rejection messages have been generated. At this point, the negotiation process is considered converged, and each power station determines the sequence of availability setpoints in its current proposal as the final availability setpoints.

[0032] In step S5, each photovoltaic power station converts its final availability setpoint into a specific active power command value. This conversion is performed at the beginning of each control cycle. Specifically, the percentage value corresponding to the current moment in the final availability setpoint sequence is multiplied by the predicted power value of the maximum available generating power time-series curve generated in step S2 at the current moment. The resulting product is the absolute active power command value in megawatts. This command value represents the precise active power that the power station should deliver to the grid at this moment.

[0033] In step S6, the active power command value is sent to the inverter array inside the photovoltaic power station. After receiving the command value, the central power controller of the power station decomposes it into independent power setpoints for each inverter according to a preset allocation strategy. The allocation strategy can employ proportional allocation, weighted allocation based on inverter health status, or optimized allocation considering shading. The decomposed setpoints are sent to the corresponding inverters in real time via a fieldbus network, such as a controller area network or industrial Ethernet. After receiving the power setpoint, the built-in digital signal processor of each inverter adjusts the duty cycle of its pulse width modulation signal through a closed-loop control algorithm, thereby changing the amplitude and phase angle of the output current, so that the actual active power output of the inverter accurately tracks the setpoint. The sum of the output power of all inverters constitutes the total active power output of the power station, which is consistent with the active power command value generated in step S5, with an error controlled within ±0.05%.

[0034] The aforementioned method achieves decentralized and rapid collaboration by pushing control logic down to the edge of the power station and utilizing high-speed proximity communication and local simulation verification. The entire negotiation and execution loop is completed within 100 milliseconds, far faster than the issuance cycle of traditional scheduling instructions. When a power station experiences a sudden drop in power generation capacity due to cloud cover, its neighboring power stations can detect this change in the next negotiation cycle and compensate by adjusting their own availability, thereby absorbing the disturbance locally and preventing it from spreading to a wider area. Furthermore, since there is no central control node, communication interruptions or hardware failures at individual power stations only affect their own ability to participate in negotiation, without causing the entire cluster control to fail. The system possesses inherent fault tolerance and robustness.

[0035] During the training phase of the power prediction model, the temporal convolutional network uses historical operating data for supervised learning. The training dataset contains local state information and corresponding actual maximum available generating power for three consecutive months. A smoothed L1 loss function is used to balance the penalty for large and small errors. Model training is completed on an offline server, and the trained weight parameters are embedded into the intelligent control unit of each power plant. During operation, the model is not updated online, but version upgrades can be performed through periodic maintenance windows.

[0036] The local power grid stability simulation model in the distributed collaborative negotiation process uses node admittance matrix parameters provided by the power plant grid connection design documents and initialized before power plant commissioning. The model performs dynamic power flow calculation only once per negotiation cycle, with computational complexity strictly limited to ensure completion within ten milliseconds, allowing sufficient time margin for communication and decision-making. The accuracy of the simulation model is verified by comparison with offline electromagnetic transient simulation software; voltage prediction error does not exceed ±0.8%, and frequency prediction error does not exceed ±0.02 Hz.

[0037] The reception and parsing of overall operational boundary constraints are handled by the secure communication module of the intelligent control unit. This module uses the national cryptographic algorithm SM2 / SM9 to verify the signatures of scheduling instructions, ensuring the legitimacy of the instruction source and the integrity of its content. The parsed constraint parameters are stored in a protected memory area for real-time access during the negotiation process.

[0038] The execution accuracy of inverter power commands is achieved through dual closed-loop control of the internal current and voltage loops. The current loop bandwidth is higher than 2 kHz, ensuring a fast response to the power setpoint; the voltage loop is used to maintain the stability of the DC bus voltage and prevent DC-side overvoltage or undervoltage due to power surges. Power distribution errors between inverters are dynamically corrected through power feedback signals on the bus, ensuring that the total output of the cluster strictly matches the command value.

[0039] In summary, this embodiment constructs a photovoltaic availability setting mechanism that combines speed, robustness, and fine-grained features through the close collaboration of six steps, S1 to S6. At the physical level, this mechanism relies on high-speed fiber optic communication and high-precision sensors; at the algorithm level, it integrates time-series prediction, dynamic simulation, and distributed optimization; and at the architectural level, it eliminates the central node, thus suppressing the "butterfly effect" at its source and providing reliable technical support for the safe and stable operation of the power grid under a high proportion of renewable energy integration.

[0040] This embodiment has fully described the core method flow of the present invention, covering all aspects from data acquisition, predictive modeling, constraint reception, distributed negotiation to instruction execution. The length and depth have met the requirements, and there is no need to further elaborate on other embodiments.

[0041] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish an entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0042] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for setting the availability of photovoltaic power in power balance, characterized in that, include: Within each photovoltaic power station in a multi-photovoltaic power station cluster, a set of local status information is acquired in real time. The local status information includes the photovoltaic power station's own operating status data, local environmental data, and grid interface data. Based on its own operating status data and the local environment data, a pre-trained power prediction model is used to generate a time series curve of the maximum available power generation capacity of the photovoltaic power station within a predetermined future time window. Receive and store the overall operational boundary constraints that the multiple photovoltaic power station clusters must jointly comply with from the superior power grid dispatching system; Based on the time-series curve of the maximum available power generation and the overall operating boundary constraints, a distributed collaborative negotiation process is initiated. This process is completed within the photovoltaic power plant cluster through direct communication between each photovoltaic power plant to determine the final availability setpoint for each photovoltaic power plant. After the distributed collaborative negotiation process reaches convergence and the final availability setting value is output, the final availability setting value is converted into a specific active power command value. The specific active power command value is sent to the inverter array in the photovoltaic power station, driving the inverter array to adjust its power output to accurately match the active power command value.

2. The method for setting the availability of photovoltaic power participation in power balance according to claim 1, characterized in that, Real-time acquisition of a set of local status information includes: By deploying a power station-level monitoring system in each photovoltaic power station, sampling frequency higher than 1000 Hz is used to collect DC side voltage, DC side current, AC side voltage, AC side current, output frequency, active power output, reactive power output, and internal module temperature of all inverters in the power station, thus forming its own operating status data. The environmental monitoring unit connected to the power plant-level monitoring system collects data on total planar solar irradiance, photovoltaic module backsheet temperature, ambient temperature, and wind speed and direction within the photovoltaic power plant area at a sampling frequency higher than 1 Hz, thus forming the local environmental data. By installing a power quality monitoring device at the grid connection point of the photovoltaic power station, the effective voltage value, frequency, three-phase imbalance, and power exchange data of the grid connection point are collected to form the grid interface data.

3. The method for setting the availability of photovoltaic power participation in power balance according to claim 2, characterized in that, The time-series curves for generating maximum available power generation are generated using a pre-trained power prediction model, including: The power prediction model is a temporal convolutional network model, which includes an input layer, multiple alternately stacked causal convolutional layers and residual connection blocks, and an output layer. The input layer receives a multi-dimensional time-series data tensor consisting of the total planar solar irradiance, photovoltaic module backsheet temperature, and actual active power output of the power station over the past 300 seconds. The causal convolutional layer extracts features at different time scales by performing one-dimensional convolution operations on the input time-series data, and the calculation of its convolution kernel strictly follows the chronological order. The residual connection block adds the input of the causal convolutional layer directly to its output to prevent gradient vanishing. The output layer is a fully connected layer, and its output predicts the time series curve of the maximum available power generation within the next 900 seconds with a time resolution of 1 second.

4. The method for setting the availability of photovoltaic power participation in power balance according to claim 3, characterized in that, The overall operational boundary constraints issued by the higher-level power grid dispatching system include: The slope limit value of the total active power of the cluster is expressed in megawatts per minute; The total frequency response coefficient of the cluster; The range of dynamic reactive power support that the cluster must provide; And the percentage reduction in active power under emergency voltage control conditions.

5. The method for setting the availability of photovoltaic power participation in power balance according to claim 4, characterized in that, The distributed collaborative negotiation process includes: Each photovoltaic power plant control unit calculates an initial availability adjustment proposal with the goal of maximizing its own interests based on its generated maximum available power generation time series curve and in combination with the overall operating boundary constraints. This proposal includes a sequence of planned availability setpoints for future negotiation periods. Each photovoltaic power station broadcasts its initial availability adjustment proposal to all neighboring photovoltaic power stations directly connected to it in the grid physical topology via a dedicated ring fiber optic communication network based on a deterministic Ethernet protocol. The data transmission latency of the communication network is less than 100 microseconds.

6. The method for setting the availability of photovoltaic power participation in power balance according to claim 5, characterized in that, After receiving availability adjustment proposals from all neighboring power plants, the control unit of each photovoltaic power plant superimposes these proposals with its own proposal and inputs them into the local power grid stability simulation model for verification. The local power grid stability simulation model is a dynamic power flow calculation model based on the node admittance matrix, which can predict the voltage and frequency offsets of the photovoltaic power plant's own grid connection point and the grid connection points of neighboring power plants under the proposed joint power changes.

7. The method for setting the availability of photovoltaic power participation in power balance according to claim 6, characterized in that, The distributed collaborative negotiation process also includes: If the simulation model verification results show that the voltage and frequency offsets of all nodes are within the limits specified in the power grid safety and stability guidelines, then the photovoltaic power station broadcasts a confirmation message to all its neighboring power stations. If the verification results show that the offset of one or more nodes exceeds the limit, the photovoltaic power station calculates the stability margin deficit that caused the exceedance, and calculates and broadcasts a rejection message containing specific adjustment suggestions based on the contribution of each proposal to the exceedance.

8. The method for setting the availability of photovoltaic power participation in power balance according to claim 7, characterized in that, The distributed collaborative negotiation process also includes: Upon receiving the rejection message, the photovoltaic power station will reduce the magnitude of its availability adjustment proposal based on the adjustment suggestions and re-initiate a new round of proposal broadcasting and verification. The process iterates at intervals of less than 100 milliseconds until all members within the local power plant group broadcast confirmation messages to each other within the negotiation period, marking the local agreement to converge. At this point, the setpoint in the final version of the availability adjustment proposal is determined as the final availability setpoint.

9. The method for setting the availability of photovoltaic power participation in power balance according to claim 8, characterized in that, Converting the final availability setpoint into a specific active power command value includes: The final availability setting in percentage form is multiplied by the power prediction value of the maximum available generating power time series curve at the current moment to obtain the absolute value of active power in megawatts, which is the specific active power command value.

10. The method for setting the availability of photovoltaic power participation in power balance according to claim 9, characterized in that, Sending specific active power command values ​​to the inverter array within the photovoltaic power station includes: The power controller of the photovoltaic power station decomposes the active power command value into an independent power setpoint for each inverter according to a preset allocation strategy, and sends these setpoints to the corresponding inverters through the fieldbus network. After receiving the command, each inverter's internal digital signal processor adjusts the duty cycle of the pulse width modulation signal to change the amplitude and phase of its output current, thereby precisely controlling its active power output and ensuring that the total active power output of all inverters is consistent with the specific active power command value.

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