A photovoltaic accommodation coordination control method and system
By establishing a refined evaluation model and a multi-timescale rolling optimization framework, the optimal output coordination of photovoltaic power plants was achieved, solving the problems of regional coordination and grid security in photovoltaic power consumption, and improving consumption efficiency and grid security.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- PINGYIN POWER SUPPLY CO OF STATE GRID SHANDONG ELECTRIC POWER CO
- Filing Date
- 2025-11-28
- Publication Date
- 2026-06-05
Smart Images
Figure CN122159358A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of photovoltaic power consumption coordination control technology, specifically a photovoltaic power consumption coordination control method and system. Background Technology
[0002] As the penetration rate of photovoltaic (PV) power generation in regional power grids continues to increase, how to coordinate and control multiple distributed PV power stations to maximize grid absorption capacity while ensuring grid security has become an urgent technical challenge. In existing technologies, regional PV absorption mainly employs centralized control or fully distributed control. Centralized control relies on a dispatch center to uniformly calculate and issue instructions, while distributed control relies on each power station making decisions based on local information. This leads to the following problems: First, the lack of regional coordination and the independent control of each photovoltaic power station make it impossible to achieve cluster optimization, resulting in low overall grid absorption efficiency. Second, the control response is slow, and existing methods are unable to cope with the rapid fluctuations in photovoltaic output and grid status, leading to delayed control commands. Third, the neglect of grid safety constraints and the disconnect between control strategies and the actual operating status of the grid may cause safety problems such as line overload.
[0003] The reason for the above problem is: First, the model lacks refinement. Existing assessment models fail to effectively couple grid topology, the N-1 safety criterion, and photovoltaic output characteristics, leading to overly optimistic or conservative calculations of grid absorption capacity. Second, the time scale is limited. Control strategies fail to coordinate and continuously optimize across multiple time scales, including day-ahead, intraday, and real-time, resulting in a lack of dynamic adaptability. Finally, sensitivity analysis is missing. The impact of photovoltaic output at different locations on key grid sections cannot be quantitatively analyzed, leading to a lack of precision in control strategies and an inability to effectively identify and resolve grid absorption bottlenecks.
[0004] Therefore, there is an urgent need for a photovoltaic power consumption coordination and control method and system to solve the above problems. Summary of the Invention
[0005] The purpose of this invention is to provide a photovoltaic curtailment coordination control method and system, which realizes the collaborative optimization of regional photovoltaic clusters and precise control under grid security constraints, thereby improving the curtailment capacity while reducing the curtailment rate.
[0006] To achieve the above objectives, the present invention employs the following technical solution: On the one hand, the present invention provides a photovoltaic power consumption coordination control method, comprising the following steps: Step S1: Collect real-time operation data and grid dispatch instructions from multiple photovoltaic power stations within the regional power grid; Step S2: Based on operational data, assess the photovoltaic absorption capacity of the regional power grid and identify absorption bottlenecks; Step S3: Based on the assessment results of photovoltaic absorption capacity, establish a multi-timescale photovoltaic output coordination and control strategy; Step S4: Implement a coordinated control strategy to coordinate and control the output of multiple photovoltaic power plants within the regional power grid; Step S5: Monitor the control effect in real time and dynamically adjust the control parameters according to the power grid operation status.
[0007] Preferably, in step S2, assessing the photovoltaic absorption capacity of the regional power grid includes: Step S21: Establish a photovoltaic absorption capacity assessment model that considers grid topology and operational constraints: ; in, This indicates the photovoltaic absorption capacity of the regional power grid; Indicates the first The transmission capacity limit of each line; Indicates the first The load requirements of each node; Indicates the first The available transmission capacity of each transmission section; Indicates the first The maximum amount of solar power that can be curtailed from a single photovoltaic power station; Indicates the total number of lines. Indicates the total number of transmission sections. Indicates the total number of photovoltaic power plants; Step S22: Using the Newton-Raphson method, with the power grid topology and the injected power at each node as inputs, solve the steady-state operating equations of the power grid to calculate the power grid state variables, which include the voltage amplitude at each node. Voltage phase angle And the meritorious currents of each branch road and the trend of no effort ; Step S23: Based on the calculated power grid state variables, perform an N-1 security analysis. By simulating a single power grid component failure, specifically verify and determine the security limits of key parameters in the evaluation model established in step S21. These key parameters include: , , , ; Step S24: Calculate the sensitivity matrix of photovoltaic power output changes to power flow and node voltage on critical lines. and The sensitivity matrix is used to quickly locate the limiting absorption capacity when solving the evaluation model. .
[0008] Preferably, in step S2, identifying the bottleneck includes: Calculate the photovoltaic absorption sensitivity coefficient for each node: ; in, Indicates the first The photovoltaic absorption sensitivity coefficient of each node is dimensionless. The larger its absolute value, the greater the impact of the node on the overall absorption capacity. Indicates the first Photovoltaic injection power of each node; Indicates the absorption capacity for the first The partial derivative of the transmission capacity of the line; Indicates the first Line power to the first Partial derivatives of the photovoltaic injection power at each node; when When this happens, the node is determined to be a bottleneck node for absorption, where This is the preset sensitivity threshold.
[0009] Preferably, in step S3, establishing a multi-timescale photovoltaic output coordinated control strategy includes: Construct a rolling optimization model with three time scales: day-to-day, intraday, and real-time. Current scale optimization objectives: ; in, Indicates the first A photovoltaic power station in Planned output during the specified time period; Indicates the first A photovoltaic power station in Predicted output for a given time period; Indicates the first A photovoltaic power station in Cost of wasting light during certain time periods; , Let be the weighting coefficient, satisfying ; Indicates the total number of optimization periods; The rolling optimization model considers both grid security constraints and photovoltaic power plant operation constraints.
[0010] Preferably, the optimization at the intraday and real-time scales employs a model predictive control method: Establish an optimization problem with time-domain constraints: ; in, This represents the objective function to be optimized. Indicates the length of the prediction time domain; express Photovoltaic output vector at time; express The reference output vector at any given time; express The rate of change of output at any given time; , This is the weight matrix, used to balance tracking accuracy and adjust stability; The optimization problem is solved in a rolling manner during each control cycle, and the optimal control sequence is output.
[0011] Preferably, in step S4, executing the coordination control strategy includes: The optimal output command for each photovoltaic power station is calculated using a distributed optimization algorithm: Establishing a consistency optimization problem: ; in, Indicates the first Local cost function of a photovoltaic power station; Indicates the total regional output target; Through distributed iterative computation: ; in, Step size factor Indicates the relationship with the first A set of neighbors for communication connections of a photovoltaic power station. Indicates the first The power station in the first The dual variable of the next iteration.
[0012] On the other hand, the present invention also provides a photovoltaic grid connection coordination control system for implementing the photovoltaic grid connection coordination control method described above, comprising: The data acquisition module is used to collect real-time operating data and grid dispatch instructions from multiple photovoltaic power stations within the regional power grid. The photovoltaic grid absorption assessment module is used to assess the photovoltaic absorption capacity of the regional power grid based on the operational data and identify absorption bottlenecks. The strategy generation module is used to establish a multi-timescale photovoltaic output coordination control strategy based on the photovoltaic absorption capacity assessment results. An execution control module is used to execute the coordination control strategy and coordinate the output of multiple photovoltaic power plants within the regional power grid. The dynamic adjustment module is used to monitor the control effect in real time and dynamically adjust the control parameters according to the power grid operating status.
[0013] Preferably, the data acquisition module includes: Wide-area measurement unit, used to collect real-time power grid operation data through synchronous phasor measurement devices; A communication processing unit is used to transmit collected data through a dedicated power communication network. The data verification unit is used to perform quality checks on the collected data and identify abnormal data. The data acquisition module supports the IEEE C37.118 standard communication protocol, with a sampling rate of no less than 100 frames / second.
[0014] Preferably, the strategy generation module includes: Multi-timescale coordination units are used to generate day-ahead, intraday, and real-time coordination control strategies; The security verification unit is used to perform N-1 security verification on the generated policy. The priority sorting unit is used to determine the control priority based on the capacity and location of the photovoltaic power station; The strategy generation module also includes a strategy simulation unit, which is used to verify the effect before the strategy is executed.
[0015] Preferably, the execution control module includes: The command distribution unit is used to send output control commands to each photovoltaic power station; A closed-loop control unit is used to achieve precise tracking control of photovoltaic power output; Safety protection unit, used to execute emergency control strategies in the event of a power grid failure; The execution control module supports multiple communication methods, including 4G / 5G wireless communication and fiber optic communication.
[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention establishes a refined evaluation model that couples the power grid topology, the N-1 safety criterion, and operational constraints. By using a distributed optimization algorithm, it calculates and allocates the optimal output of each photovoltaic power station while satisfying all safety constraints, thereby improving the overall photovoltaic absorption capacity of the regional power grid. 2. This invention constructs a rolling optimization framework with multiple time scales of day-day, intraday, and real-time, and adopts a model predictive control method. In each control cycle, the optimization problem is resolved based on the latest power grid state and prediction information, and control commands are generated, thereby realizing advanced control and rolling adjustment, and ensuring the real-time performance and adaptability of the control strategy. 3. This invention uses power flow calculation and N-1 safety verification as necessary sub-steps in the evaluation model, ensuring that any control strategy is generated within strict safety boundaries. At the same time, the sensitivity analysis set by this invention can accurately locate the absorption bottleneck, fundamentally preventing safety risks such as line overload and voltage over-limit, and realizing precise control under the premise of safety. Attached Figure Description
[0017] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation
[0018] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined in this application.
[0019] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.
[0020] Example: like Figure 1 As shown, this embodiment provides a photovoltaic power consumption coordination control method, including the following steps: Step S1: Collect real-time operation data and grid dispatch instructions from multiple photovoltaic power stations within the regional power grid; Step S2: Based on operational data, assess the photovoltaic absorption capacity of the regional power grid and identify absorption bottlenecks; Step S3: Based on the assessment results of photovoltaic absorption capacity, establish a multi-timescale photovoltaic output coordination and control strategy; Step S4: Implement a coordinated control strategy to coordinate and control the output of multiple photovoltaic power plants within the regional power grid; Step S5: Monitor the control effect in real time and dynamically adjust the control parameters according to the power grid operation status.
[0021] like Figure 2 As shown, this embodiment also provides a photovoltaic power consumption coordination control system, including: The data acquisition module is used to collect real-time operating data and grid dispatch instructions from multiple photovoltaic power stations within the regional power grid. The photovoltaic grid absorption assessment module is used to assess the photovoltaic absorption capacity of the regional power grid based on the operational data and identify absorption bottlenecks. The strategy generation module is used to establish a multi-timescale photovoltaic output coordination control strategy based on the photovoltaic absorption capacity assessment results. An execution control module is used to execute the coordination control strategy and coordinate the output of multiple photovoltaic power plants within the regional power grid. The dynamic adjustment module is used to monitor the control effect in real time and dynamically adjust the control parameters according to the power grid operating status. The data acquisition module includes: Wide-area measurement unit, used to collect real-time power grid operation data through synchronous phasor measurement devices; A communication processing unit is used to transmit collected data through a dedicated power communication network. The data verification unit is used to perform quality checks on the collected data and identify abnormal data. The data acquisition module supports the IEEE C37.118 standard communication protocol, with a sampling rate of no less than 100 frames / second; The strategy generation module includes: Multi-timescale coordination units are used to generate day-ahead, intraday, and real-time coordination control strategies; The security verification unit is used to perform N-1 security verification on the generated policy. The priority sorting unit is used to determine the control priority based on the capacity and location of the photovoltaic power station; The strategy generation module also includes a strategy simulation unit, which is used to verify the effect before the strategy is executed; The execution control module includes: The command distribution unit is used to send output control commands to each photovoltaic power station; A closed-loop control unit is used to achieve precise tracking control of photovoltaic power output; Safety protection unit, used to execute emergency control strategies in the event of a power grid failure; The execution control module supports multiple communication methods, including 4G / 5G wireless communication and fiber optic communication.
[0022] This embodiment takes the power grid of area A (hereinafter referred to as the A area power grid) as an example to explain in detail the implementation of the above method and system.
[0023] First, deploy the aforementioned control system in the power grid of Area A: The wide-area measurement unit of the data acquisition module is equipped with synchronous phasor measurement devices at the grid connection points of power plants A1, A2, and A3, the 220kV hub substation, and key 110kV nodes. These devices collect real-time operating data of the power grid at a rate of 100 frames per second, in accordance with the IEEE C37.118 standard, including voltage phasors, current phasors, and frequency. The communication processing unit transmits these real-time data, as well as power grid dispatch instructions (such as cross-sectional limits) from the superior dispatch center, to the central server through a dedicated power fiber optic network. The data verification unit performs quality checks on the data, such as identifying abnormal data by comparing the power differences between adjacent PMU nodes, and interpolating short-term data loss caused by communication interruptions to ensure that the data input to subsequent modules is true and reliable.
[0024] After the power grid in Area A completes the deployment of the control system, the power consumption assessment module initiates an assessment process every 5 minutes, including: The module first establishes a photovoltaic (PV) grid integration capacity assessment model for the power grid in Area A, taking into account the grid topology and operational constraints: ; in, This indicates the maximum transmission capacity of a 220kV trunk line, which is 280MW. This indicates the total load of the region, which changes in real time. This indicates the available transmission capacity of the 220kV section, given by the scheduling command; This represents the sum of the maximum curtailable solar power from the three photovoltaic power plants, set at 20% of the installed capacity, i.e., 60MW. The photovoltaic grid integration capacity assessment model represents the grid integration capacity of a region. Constrained by both the physical limits of power grid transmission and the adjustable range of the power station itself, the smaller value between the two is taken. After establishing the photovoltaic (PV) grid integration capacity assessment model, the model was solved through power flow calculations, security verification, and sensitivity analysis. Specifically: The power flow calculation uses the Newton-Raphson method, based on the grid topology (node-branch model) and the injected power at each node (real-time output of three photovoltaic power plants). , , and regional total load Using as input, solve the nonlinear power flow equations to calculate the voltage magnitude (e.g., required to be maintained between 0.95 and 1.05 pu) and voltage phase angle at each node. And the meritorious currents of each branch road and the trend of no effort ; Safety verification is used to quantitatively evaluate the operational constraint boundaries in the model and ensure its absorption capacity. It is safe: Perform an N-1 safety analysis, for example, simulating a fault disconnection of the 220kV line connecting the A-area power grid to the main grid, and then, based on the power flow calculation results, verify whether the remaining 110kV network can safely carry the transfer of photovoltaic power, and check whether there are any line or transformer overloads. And whether the node voltage exceeds the limit, through repeated simulation calculations, the actual safe transmission limit of the 220kV line under the N-1 criterion was finally determined. It may only be 260MW (below the physical limit of 280MW), a value that has been verified for safety. It will be substituted into the evaluation model, replacing the original. ; Sensitivity analysis aims to identify bottlenecks in photovoltaic grid integration and provide direction for optimization: calculating the photovoltaic grid integration sensitivity coefficient. Specifically, calculate the impact of changes in the output of each photovoltaic power station on key constraints (such as the power output of the 220kV line). The impact of ) was found through calculation. , , This indicates that the output of power station A2 has the greatest impact on the power flow of the main line, followed by A1, and A3 has the least impact. When the model calculates the absorption capacity When constraints exist, sensitivity analysis can immediately identify which power station and which transmission line is the primary limiting factor. For example, if the 220kV line is the bottleneck, then reducing... The high output of the A2 power plant can most effectively eliminate over-limit conditions with minimal cost of curtailment.
[0025] Based on the above evaluation results, the strategy generation module generates a hierarchical coordination and control strategy, including: Scale optimization (formulating an economic scheduling plan for the next 24 hours): Construct an optimization objective function with a 15-minute time interval: ; in, Indicates power station based on weather forecast exist Predicted output for a given time period; This represents the cost of curtailing solar power, which is related to the amount of solar power curtailed and the electricity price. During optimization, strictly substitute the values determined by the safety verification sub-step. Under the same constraints, the day-ahead planning curves of the three power plants are generated. The priority ranking unit will prioritize power generation of power plant A3 (low sensitivity) under the same conditions, based on the sensitivity analysis results, and regard power plant A2 (high sensitivity) as the main regulation resource. Intraday and real-time scale optimization (eliminating forecast errors and addressing real-time fluctuations): Model predictive control is employed. During the real-time control cycle (e.g., 5 minutes), the module uses the current grid state as the initial value and predicts the future time domain. (e.g., 30 minutes) Perform rolling optimization: ; in, This indicates a reference value derived from the current plan or after minor adjustments based on real-time absorption capacity. This term (weight) represents the rate of change in output. This is to avoid drastic fluctuations in photovoltaic power output and ensure grid frequency stability; In each cycle, the optimization problem is solved, only the first control instruction is executed, and then the optimization is rolled over again in the next cycle, thereby achieving closed-loop feedback and adaptive adjustment.
[0026] The execution control module is responsible for translating the optimization strategy into actions for each power station: For example, when real-time monitoring detects that the total regional output needs to be reduced by 10MW to resolve the line over-limit issue: Distributed computing using consensus algorithms: Each power station Locally solve its cost function The minimization problem; Exchange dual variables with neighboring power plants via communication networks information; Iteration ; After several iterations, the three power stations When the values tend to be consistent, their respective outputs will change. This constitutes the optimal solution that meets the requirement of a total reduction of 10MW and has the lowest overall cost. This method is highly reliable, and even if communication with the central network is interrupted, the local network can still coordinate. The command distribution unit sends the calculated output command to the inverter control system of each photovoltaic power station; The closed-loop control unit compares the actual output with the command value in real time on the power station side, and achieves precise tracking of the output by adjusting the inverter trigger pulse; The dynamic adjustment module monitors the control effect in real time. If it detects a significant deviation between the actual voltage or power flow and the expected value, it will automatically fine-tune the weighting coefficients in the optimization model (e.g., ...). , (or constraint boundaries) to achieve adaptive optimization of the system; Safety protection unit: When a grid fault is detected (such as an abnormal frequency drop), an emergency control strategy will be immediately triggered, which will exceed the optimization algorithm and force the photovoltaic power station to quickly reduce its output or disconnect from the grid in order to support grid recovery.
[0027] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.
Claims
1. A photovoltaic power consumption coordination control method, characterized in that, Includes the following steps: Step S1: Collect real-time operation data and grid dispatch instructions from multiple photovoltaic power stations within the regional power grid; Step S2: Based on operational data, assess the photovoltaic absorption capacity of the regional power grid and identify absorption bottlenecks; Step S3: Based on the assessment results of photovoltaic absorption capacity, establish a multi-timescale photovoltaic output coordination and control strategy; Step S4: Implement a coordinated control strategy to coordinate and control the output of multiple photovoltaic power plants within the regional power grid; Step S5: Monitor the control effect in real time and dynamically adjust the control parameters according to the power grid operation status.
2. The photovoltaic power consumption coordination control method according to claim 1, characterized in that, In step S2, assessing the photovoltaic absorption capacity of the regional power grid includes: Step S21: Establish a photovoltaic absorption capacity assessment model that considers grid topology and operational constraints: ； in, This indicates the photovoltaic absorption capacity of the regional power grid; Indicates the first The transmission capacity limit of each line; Indicates the first The load requirements of each node; Indicates the first The available transmission capacity of each transmission section; Indicates the first The maximum amount of solar power that can be curtailed from a single photovoltaic power station; Indicates the total number of lines. Indicates the total number of transmission sections. Indicates the total number of photovoltaic power plants; Step S22: Using the Newton-Raphson method, with the power grid topology and the injected power at each node as inputs, solve the steady-state operating equations of the power grid to calculate the power grid state variables, which include the voltage amplitude at each node. Voltage phase angle And the meritorious currents of each branch road and the trend of no effort ; Step S23: Based on the calculated power grid state variables, perform an N-1 security analysis. By simulating a single power grid component failure, specifically verify and determine the security limits of key parameters in the evaluation model established in step S21. These key parameters include: , , , ; Step S24: Calculate the sensitivity matrix of photovoltaic power output changes to power flow and node voltage on critical lines. and The sensitivity matrix is used to quickly locate the limiting absorption capacity when solving the evaluation model. .
3. The photovoltaic power consumption coordination control method according to claim 2, characterized in that, In step S2, identifying the bottlenecks in wastewater treatment includes: Calculate the photovoltaic absorption sensitivity coefficient for each node: ； in, Indicates the first The photovoltaic absorption sensitivity coefficient of each node is dimensionless. The larger its absolute value, the greater the impact of the node on the overall absorption capacity. Indicates the first Photovoltaic injection power of each node; Indicates the absorption capacity for the first The partial derivative of the transmission capacity of the line; Indicates the first Line power to the first Partial derivatives of the photovoltaic injection power at each node; when When this happens, the node is determined to be a bottleneck node for absorption, where This is the preset sensitivity threshold.
4. The photovoltaic power consumption coordination control method according to claim 1, characterized in that, In step S3, establishing a multi-timescale photovoltaic output coordinated control strategy includes: Construct a rolling optimization model with three time scales: day-to-day, intraday, and real-time. Current scale optimization objectives: ； in, Indicates the first A photovoltaic power station in Planned output during the specified time period; Indicates the first A photovoltaic power station in Predicted output for a given time period; Indicates the first A photovoltaic power station in Cost of wasting light during certain time periods; , Let be the weighting coefficient, satisfying ; Indicates the total number of optimization periods; The rolling optimization model considers both grid security constraints and photovoltaic power plant operation constraints.
5. The photovoltaic power consumption coordination control method according to claim 4, characterized in that, The optimization at the intraday and real-time scales employs a model predictive control method: Establish an optimization problem with time-domain constraints: ； in, This represents the objective function to be optimized. Indicates the length of the prediction time domain; express Photovoltaic output vector at time; express The reference output vector at any given time; express The rate of change of output at any given time; , This is the weight matrix, used to balance tracking accuracy and adjust stability; The optimization problem is solved in a rolling manner during each control cycle, and the optimal control sequence is output.
6. The photovoltaic power consumption coordination control method according to claim 1, characterized in that, In step S4, the execution of the coordination control strategy includes: The optimal output command for each photovoltaic power station is calculated using a distributed optimization algorithm: Establishing a consistent optimization problem: ； in, Indicates the first Local cost function of a photovoltaic power station; Indicates the total regional output target; Through distributed iterative computation: ； in, Step size factor Indicates the relationship with the first A set of neighbors for communication connections of a photovoltaic power station. Indicates the first The power station in the first The dual variable of the next iteration.
7. A photovoltaic grid connection coordination control system, used to implement the photovoltaic grid connection coordination control method as described in any one of claims 1-6, characterized in that, include: The data acquisition module is used to collect real-time operating data and grid dispatch instructions from multiple photovoltaic power stations within the regional power grid. The photovoltaic grid absorption assessment module is used to assess the photovoltaic absorption capacity of the regional power grid based on the operational data and identify absorption bottlenecks. The strategy generation module is used to establish a multi-timescale photovoltaic output coordination control strategy based on the photovoltaic absorption capacity assessment results. An execution control module is used to execute the coordination control strategy and coordinate the output of multiple photovoltaic power plants within the regional power grid. The dynamic adjustment module is used to monitor the control effect in real time and dynamically adjust the control parameters according to the power grid operating status.
8. A photovoltaic power consumption coordination control system according to claim 7, characterized in that, The data acquisition module includes: Wide-area measurement unit, used to collect real-time power grid operation data through synchronous phasor measurement devices; A communication processing unit is used to transmit collected data through a dedicated power communication network. The data verification unit is used to perform quality checks on the collected data and identify abnormal data. The data acquisition module supports the IEEE C37.118 standard communication protocol, with a sampling rate of no less than 100 frames / second.
9. A photovoltaic power consumption coordination control system according to claim 7, characterized in that, The strategy generation module includes: Multi-timescale coordination units are used to generate day-ahead, intraday, and real-time coordination control strategies; The security verification unit is used to perform N-1 security verification on the generated policy. The priority sorting unit is used to determine the control priority based on the capacity and location of the photovoltaic power station; The strategy generation module also includes a strategy simulation unit, which is used to verify the effect before the strategy is executed.
10. A photovoltaic power consumption coordination control system according to claim 7, characterized in that, The execution control module includes: The command distribution unit is used to send output control commands to each photovoltaic power station; A closed-loop control unit is used to achieve precise tracking control of photovoltaic power output; Safety protection unit, used to execute emergency control strategies in the event of a power grid failure; The execution control module supports multiple communication methods, including 4G / 5G wireless communication and fiber optic communication.