Wind and light coordinated control method, device and electronic equipment

Abstract

The application provides a wind and light same field power generation coordination control method and device and electronic equipment, relates to the technical field of power system control, and the method comprises the following steps: determining a target wind turbine operating state and a target photovoltaic control strategy based on environment parameters and equipment state parameters; based on the target wind turbine operating state and the target photovoltaic control strategy, a multi-objective optimization model is constructed and solved to obtain target output distribution values corresponding to wind power and photovoltaic, respectively, and control instructions corresponding to a wind turbine controller and a photovoltaic array controller are generated to realize wind and light same field power generation collaborative control, solve the technical problems of low output matching precision and poor adaptability of the existing control method in a power limiting scene, and achieve the technical effects of improving output matching precision and maximizing power limiting benefits.

Description

Technical Field

[0001] This invention relates to the technical field of power system control, and more specifically, to a method, apparatus, and electronic equipment for coordinated control of wind and solar power generation in the same field. Background Technology

[0002] As the global energy structure accelerates its transition to cleaner energy, wind and solar power, as the most promising renewable energy sources, have become a key focus of national energy strategies for large-scale development and utilization. Against this backdrop, wind-solar hybrid power generation systems are gaining widespread application globally due to their significant advantages, including high resource utilization efficiency, complementary output, and stable power supply. However, the coordinated operation of current wind-solar power generation systems still faces a series of key technological challenges, severely impacting the overall power generation efficiency and economic benefits of the system.

[0003] Current coordinated control of wind-solar hybrid systems suffers from the following technical shortcomings: Existing coordinated control methods often employ simple output superposition or independent dispatch modes, lacking effective multi-energy coordination mechanisms and making it difficult to achieve optimal matching of wind and solar power output; the control strategies for solar arrays are too simplistic, failing to comprehensively consider various influencing factors; in power curtailment scenarios such as grid load constraints and limited transmission and distribution channels, uniform or random curtailment modes are frequently adopted, lacking economically based optimization decision-making capabilities and failing to maximize power generation revenue under curtailment constraints; existing control models struggle to accurately characterize operational characteristics under complex conditions, resulting in insufficient control precision and adaptability; and the system exhibits significant deficiencies in data storage, computing power, and intelligent decision-making. These technical bottlenecks directly restrict the operational efficiency and economic benefits of wind-solar hybrid systems. Summary of the Invention

[0004] The purpose of this invention is to provide a method, device, and electronic equipment for coordinated control of wind and solar power generation, so as to alleviate the technical problems of low output matching accuracy and poor adaptability to power curtailment scenarios in the prior art.

[0005] In a first aspect, embodiments of the present invention provide a method for coordinated control of wind and solar power generation in the same field. The method includes: determining the target wind turbine operating state and the target photovoltaic control strategy based on the environmental parameters and equipment status parameters of the wind and solar power generation system; constructing a multi-objective optimization model based on the target wind turbine operating state and the target photovoltaic control strategy, combined with pre-set constraints; solving the multi-objective optimization model to obtain the target output allocation values ​​corresponding to wind power and photovoltaic power respectively, and generating corresponding control commands to send to the wind turbine controller and the photovoltaic array controller, so as to realize coordinated control of wind and solar power generation in the same field.

[0006] In some optional implementations, the aforementioned wind turbine operating states include: the rotation angle and speed of the wind turbine nacelle, and the blade pitch angle; the aforementioned target photovoltaic control strategy includes: the target tracking angle and target tracking speed of the photovoltaic array.

[0007] In some optional implementations, the environmental parameters include: solar position parameters and irradiance parameters; the equipment status parameters include: photovoltaic array installation parameters, wind power shading parameters, and grid status parameters; the determination method of the above-mentioned target photovoltaic control strategy includes: calculating the reference tracking angle of the photovoltaic array based on the above-mentioned solar position parameters and photovoltaic array installation parameters; dynamically correcting the above-mentioned reference tracking angle based on wind power shading parameters to generate the target tracking angle; and determining the target tracking speed based on irradiance parameters and grid status parameters.

[0008] In some optional implementations, the objective function of the above multi-objective optimization model includes a first objective function and a second objective function; the first objective function is used to minimize the deviation between the total wind and solar power output and the grid load demand; the second objective function is determined based on the real-time market electricity price and the unit time operation and maintenance costs corresponding to wind power and photovoltaic respectively, and is used to maximize the power generation revenue per unit time; the constraints of the above multi-objective optimization model include: equipment output constraints, ramp rate constraints and grid access constraints.

[0009] In some optional implementations, the above multi-objective optimization model is solved to obtain the target output allocation values ​​for wind power and photovoltaic power, respectively. This includes: initializing a particle swarm based on the above objective function and constraints; each particle in the particle swarm is a two-dimensional variable containing both wind power output and photovoltaic output; calculating the fitness function value of each particle according to the above objective function, and selecting feasible particles based on the above constraints; updating the velocity and position of the particles based on the fitness function value and feasible particles, so that the particle swarm converges towards the target region; and selecting the optimal solution from the particles that satisfy the constraints, based on the convergence state of the particle swarm and the preset iteration termination condition, as the target output allocation values ​​for wind power and photovoltaic power.

[0010] In some optional implementations, the method further includes: determining the wind power cost per kilowatt-hour and the photovoltaic cost per kilowatt-hour under the curtailment scenario based on the wind power target output allocation value and the photovoltaic target output allocation value; determining the target power generation entity and allocating curtailment quota according to the wind power cost per kilowatt-hour and the photovoltaic cost per kilowatt-hour, and generating optimized wind power target output allocation value and photovoltaic target output allocation value.

[0011] In some optional implementations, the above method also includes: real-time monitoring of the deviation between the actual output and the target output of wind power and photovoltaic power, and triggering iterative optimization and updating the corresponding control commands when the deviation exceeds a preset threshold.

[0012] Secondly, embodiments of the present invention provide a wind-solar co-generation coordinated control device, which includes: a control decision generation module, used to determine the target wind turbine operating state and the target photovoltaic control strategy based on the environmental parameters and equipment status parameters of the wind-solar co-generation system; a model building module, used to construct a multi-objective optimization model based on the target wind turbine operating state and the target photovoltaic control strategy, combined with pre-set constraints; and a coordinated control module, used to solve the multi-objective optimization model to obtain the target output allocation values ​​corresponding to wind power and photovoltaic respectively, and generate corresponding control commands to send to the wind turbine controller and the photovoltaic array controller, so as to realize the coordinated control of wind-solar co-generation.

[0013] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the steps of the method described in any of the first aspects above.

[0014] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to perform the method described in any of the first aspects above.

[0015] This invention provides a method, device, and electronic equipment for coordinated control of wind and solar power generation. The method includes: determining the target wind turbine operating state and target photovoltaic control strategy based on environmental parameters and equipment status parameters; constructing and solving a multi-objective optimization model based on the target wind turbine operating state and target photovoltaic control strategy to obtain the target output allocation values ​​corresponding to wind power and photovoltaic power respectively; and generating control commands corresponding to the wind turbine controller and photovoltaic array controller to achieve coordinated control of wind and solar power generation. This invention solves the technical problems of low output matching accuracy and poor adaptability to power curtailment scenarios in existing control methods, and achieves the technical effects of improving output matching accuracy and maximizing power curtailment benefits. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a coordinated control method for wind and solar power generation in the same location, provided in an embodiment of the present invention; Figure 2A flowchart illustrating another wind-solar co-generation coordinated control method provided in an embodiment of the present invention; Figure 3 This is a schematic flowchart of a method for calculating and correcting the tracking angle of a photovoltaic array on a tracking bracket, provided by an embodiment of the present invention. Figure 4 A flowchart for solving a multi-objective optimization model is provided in an embodiment of the present invention; Figure 5 A flowchart of a power-limited scenario selective control provided by an embodiment of the present invention; Figure 6 A schematic diagram of the structure of a dedicated storage device for coordinated control of wind and solar power generation provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Against the backdrop of the rapid development of the new energy power generation industry, wind and solar energy, as the most promising renewable energy sources, have become the core direction of global energy structure transformation through large-scale development and grid connection. The wind-solar co-generation model has been widely used due to its advantages such as fully utilizing land resources, mitigating fluctuations in output from a single energy source, and improving energy supply stability. However, existing coordinated control methods for wind-solar co-generation systems still face numerous technical bottlenecks, severely restricting the maximization of power generation benefits. 1. Insufficient power output coordination: Wind power output depends on the dynamic changes in wind speed and direction, exhibiting strong randomness and volatility; photovoltaic power output depends on factors such as solar irradiance and module attitude, and is also significantly affected by weather conditions. Existing coordination control methods mostly adopt simple output superposition or independent dispatch modes, failing to establish a deep coordination mechanism between wind and photovoltaic output. This makes it impossible to achieve optimal overall output at any given time, easily leading to "wind and solar curtailment" and making it difficult to match grid load demand.

[0020] 2. Limitations of photovoltaic control strategies: Most existing photovoltaic arrays in wind and solar co-location sites are installed at a fixed tilt angle, which makes it impossible to implement richer control strategies through photovoltaic arrays; when using tracking brackets, the control strategy only considers the solar position factor and does not take into account the wind power operation status (such as wind turbine position and shading), grid dispatch requirements, market electricity prices and other multi-dimensional factors for optimization.

[0021] 3. Lack of optimization in power curtailment scenarios: In power curtailment scenarios such as grid load shortage and limited transmission and distribution channels, existing control methods mostly adopt uniform or random power curtailment modes, without considering the difference in the levelized cost of electricity between wind power and photovoltaic power, resulting in loss of power generation revenue; there is a lack of control strategies to select the best power generation subject based on the levelized cost of electricity, making it impossible to maximize power generation revenue under power curtailment constraints.

[0022] 4. Single and fixed control model: Existing coordinated control models are mostly linear models or simple nonlinear models, which are difficult to accurately describe the complex characteristics of wind power and photovoltaic output and the coupling relationship of multiple factors; the models do not fully integrate multi-source information such as environmental parameters, equipment status, market factors, and grid constraints, resulting in insufficient control accuracy and adaptability.

[0023] 5. Lack of efficient storage and computing support: The massive amounts of operational data generated by wind and solar co-generation systems (such as environmental parameters, equipment status, and output data) require efficient storage devices for management; at the same time, complex collaborative control algorithms require powerful computing capabilities to support real-time decision-making. Existing storage devices are mostly traditional databases, which have low data storage efficiency, slow query speeds, and lack optimized designs for control algorithms, thus failing to meet the real-time coordinated control requirements of large-scale wind and solar co-generation systems.

[0024] 6. Low level of intelligent decision-making: Existing control methods mostly rely on preset rules or experience models, lacking the ability to make autonomous decisions based on big data analysis and intelligent optimization algorithms; they cannot adaptively optimize control strategies according to dynamic factors such as real-time environmental changes, equipment status fluctuations, and power grid dispatch instructions, resulting in strong subjectivity and poor flexibility in decision-making.

[0025] As the power grid's requirements for absorbing new energy power generation continue to increase, and market competition pursues the maximization of power generation revenue, wind and solar power co-generation coordination control methods and storage devices that combine excellent output synergy, refined photovoltaic control, optimized power curtailment scenarios, intelligent decision-making, and efficient storage and computing have become an urgent need and a direction for technological breakthroughs in the industry.

[0026] Based on this, the present invention provides a method, device and electronic equipment for coordinated control of wind and solar power generation in the same field, so as to solve the technical problems of low output matching accuracy and poor adaptability to power curtailment scenarios in the prior art.

[0027] To facilitate understanding of this embodiment, a detailed description of the wind-solar co-generation coordinated control method disclosed in this embodiment of the invention will be provided first. (See [link to relevant documentation]). Figure 1 The diagram shows a flow chart of a coordinated control method for wind and solar power generation. The method mainly includes the following steps S102 to S106: Step S102: Based on the environmental parameters and equipment status parameters of the wind-solar co-generation system, determine the target wind turbine operating status and the target photovoltaic control strategy; The environmental parameters of the wind-solar co-generation system can be obtained through the following sensors: wind direction and speed sensors, which collect real-time prevailing wind direction and speed data with a measurement accuracy of ±0.1m / s and ±1°; solar position sensors, which collect solar altitude angle and azimuth angle with a measurement accuracy of ±0.5°; irradiance sensors, which collect real-time irradiance intensity on the surface of photovoltaic modules with a measurement accuracy of ±1%; ambient temperature sensors, which collect atmospheric temperature data to correct the power generation efficiency of photovoltaic modules; and precipitation sensors, which collect precipitation data to determine whether to activate photovoltaic array protection measures.

[0028] The equipment status of a wind and solar co-generation system can include: wind power system status (wind turbine nacelle rotation angle, blade pitch angle, current wind power output, wind turbine operating status); photovoltaic system status (current tracking angle of single-axis photovoltaic array, photovoltaic module temperature, current photovoltaic output, array operating status); and grid status (grid frequency, voltage, current grid capacity, power curtailment instructions).

[0029] In one embodiment, the wind turbine operating state includes: the wind turbine nacelle rotation angle and speed, and the blade pitch angle; the target photovoltaic control strategy includes: the target tracking angle and target tracking speed of the photovoltaic array.

[0030] In one embodiment, the environmental parameters include: solar position parameters and irradiance parameters; the equipment status parameters include: photovoltaic array installation parameters, wind power shading parameters, and grid status parameters. Accordingly, the determination of the target photovoltaic control strategy may include: calculating the reference tracking angle of the photovoltaic array based on the solar position parameters and the photovoltaic array installation parameters; dynamically correcting the reference tracking angle based on the wind power shading parameters to generate the target tracking angle; and determining the target tracking speed based on the irradiance parameters and grid status parameters.

[0031] Specifically, solar position parameters include solar altitude angle and azimuth angle; photovoltaic array installation parameters include the installation latitude and longitude of the photovoltaic array. The formula for calculating the reference tracking angle of the photovoltaic array is: θ_ref=arctan[(sinh×cosA) / (cosh×sinφ-sinh×sinA×cosφ)]; Where h represents the solar altitude angle, A represents the solar azimuth angle, φ represents the installation latitude of the photovoltaic array, λ represents the installation longitude of the photovoltaic array, and θ_ref is the theoretically optimal tracking angle of the photovoltaic array, which is used as its reference tracking angle.

[0032] Preferably, the wind power shading parameters may include the wind turbine nacelle rotation angle and the relative distance between the wind turbine position and the photovoltaic array. Based on the above wind power shading parameters, the shading range and shading time of the wind turbine blades on the photovoltaic array can be calculated, thereby dynamically correcting the reference tracking angle and generating a target tracking angle. For example, when shading is predicted to occur based on the shading range and shading time, the tracking angle of the photovoltaic array can be adjusted in advance (executing the corresponding target tracking angle at this time) to avoid the shading area and reduce shading losses.

[0033] The relative distance between the wind turbine and the photovoltaic array can be a parameter of the equipment status of the wind-solar co-generation system, which is usually a fixed geographic spatial parameter known during the initial deployment of the system. The shading range of the wind turbine blades on the photovoltaic array represents the physical area covered by the shadow that may be cast onto the surface of the photovoltaic array during the rotation of the blades under the current wind turbine operating state. The result can be directly used to determine the azimuth offset direction that needs to be avoided from shading. The shading time represents the time window during which the shaded area continuously affects the photovoltaic output. Its length can determine the lead time and urgency of the correction action.

[0034] Preferably, the irradiance parameter is the real-time irradiance intensity on the surface of the photovoltaic module collected by the irradiance sensor; the grid status parameter may include photovoltaic output limiting commands from the grid dispatching system.

[0035] Furthermore, based on the aforementioned irradiance parameters and grid state parameters, determining the target tracking speed can specifically include: based on the aforementioned shading range and shading time, combined with the aforementioned benchmark tracking angle and real-time irradiance parameters (used to determine whether shading occurs during high irradiance periods, thereby weighting and correcting priorities), a dynamic correction amount is collaboratively generated; this correction amount is superimposed on the benchmark tracking angle, and the output is the target tracking angle, which serves as one of the key input variables for predicting photovoltaic output when constructing a multi-objective optimization model in subsequent steps. That is, the target tracking angle directly affects the effective area of ​​the photovoltaic array receiving irradiance, thereby determining the theoretical maximum value constraint boundary of photovoltaic output, and finally participating in the joint solution of the first objective function and the second objective function.

[0036] Step S104: Based on the target wind turbine operating status and the target photovoltaic control strategy, a multi-objective optimization model is constructed in combination with pre-set constraints; First, based on the target wind turbine operating status and the target photovoltaic control strategy, the target optimization function and constraints can be determined; then, a multi-objective optimization model can be constructed based on the target optimization function and the pre-set constraints.

[0037] In one embodiment, the objective function of the multi-objective optimization model may include a first objective function and a second objective function; the first objective function is used to minimize the deviation between the total output of wind and solar power and the grid load demand; the second objective function is determined based on the real-time market electricity price and the unit time operation and maintenance costs corresponding to wind power and photovoltaic power respectively, and is used to maximize the power generation revenue per unit time.

[0038] As a preferred example, the objective optimization function may include: The first objective function (optimal output matching) is to minimize the deviation between the total wind and solar power output and the grid load demand; the expression is: MinF1=|P_total-P_load|; Where P_total is the total output of wind and solar power (P_total=P_w+P_p, P_w is the wind power output, P_p is the photovoltaic power output), and P_load is the real-time load demand of the power grid.

[0039] The second objective function (maximizing power generation revenue): maximizes the power generation revenue per unit time; the expression is: MaxF2=P_w×C_e+P_p×C_e-C_w-C_p; Where C_e is the real-time market electricity price, C_w is the wind power unit time operation and maintenance cost, and C_p is the photovoltaic unit time operation and maintenance cost (including the energy consumption cost of the tracking agency).

[0040] As a preferred example, the above constraints may include the respective equipment output constraints, ramp rate constraints, and grid connection constraints for wind power and photovoltaics.

[0041] Specifically, the equipment output constraints include: P_w_min≤P_w≤P_w_max, P_p_min≤P_p≤P_p_max, where P_w_min and P_w_max are the minimum and maximum output of the wind turbine, respectively, and P_p_min and P_p_max are the minimum and maximum output of the photovoltaic array, respectively.

[0042] The ramp rate constraints include: |P_w(t)-P_w(t-1)|≤R_w, |P_p(t)-P_p(t-1)|≤R_p, where R_w is the maximum ramp rate of the wind turbine (e.g., 10%P_w_max / min), R_p is the maximum ramp rate of the photovoltaic array (e.g., 15%P_p_max / min), t is the current time, and t-1 is the previous time.

[0043] The grid access constraints include: P_total ≤ P_grid_max, where P_grid_max is the maximum allowable grid access capacity.

[0044] Step S106: Solve the multi-objective optimization model to obtain the target output allocation values ​​for wind power and photovoltaic power respectively, and generate corresponding control commands to send to the wind turbine controller and photovoltaic array controller to achieve coordinated control of wind and solar power generation in the same field.

[0045] The target output allocation value for wind power and photovoltaic power is the optimal wind power and photovoltaic output allocation scheme, which includes the optimal wind power output allocation value and the optimal photovoltaic output allocation value. Control commands may include: pitch angle adjustment commands sent to the wind turbine controller and tracking angle adjustment commands sent to the photovoltaic array controller.

[0046] In one embodiment, solving the multi-objective optimization model to obtain the target output allocation values ​​for wind power and photovoltaic power respectively can include: solving the multi-objective optimization model by improving the particle swarm optimization algorithm to generate the target output allocation values ​​for wind power and photovoltaic power.

[0047] Preferably, the above-mentioned method for solving the model to generate wind power target output allocation values ​​and photovoltaic target output allocation values ​​may include: first, initializing a particle swarm based on the objective function and constraints, where each particle in the swarm is a two-dimensional variable (P_w, P_p) containing wind power output and photovoltaic output; then, calculating the fitness function value of each particle according to the objective function, and selecting feasible particles that satisfy all constraints based on the constraints; further, updating the velocity and position of the particles based on the fitness function value and feasible particles, so that the particle swarm converges towards the target region; finally, combining the convergence state of the particle swarm and the preset iteration termination condition, selecting the optimal solution from the particles that satisfy the constraints as the wind power target output allocation value and photovoltaic target output allocation value.

[0048] The target region for particle swarm convergence can be the optimal solution region that balances output matching accuracy and power generation revenue. Specifically, this target region can be the mapping region of the Pareto front defined by the first objective function and the second objective function in the two-dimensional decision space (P_w, P_p). This region is a feasible solution dense band with a reasonable width. Its upper bound is jointly limited by the grid access constraint P_total ≤ P_grid_max and the upper limit of equipment output, and its lower bound is guaranteed by the ramp rate constraint and the minimum technical output requirement, thereby ensuring that the converged solution satisfies both physical executability and achieves a balance between economy and matching.

[0049] Combining the convergence state of the particle swarm with the preset iteration termination condition, the optimal solution is selected from the particles that satisfy the constraints. Specifically, the selection process for the optimal solution may include the following two levels of judgment: The first level determines the convergence state: when the fluctuation range of the global optimal fitness value of the feasible particle swarm is less than the threshold ε (e.g., ε = 0.5%, N = 5) in N consecutive iterations, and the proportion of feasible particles is consistently higher than 80%, the particle swarm is determined to have entered a stable convergence state. The second level of optimal solution selection: Among the feasible particle set that satisfies all constraints, priority is given to selecting the one that makes the weighted comprehensive objective function F = α·F1′ + (1 The particle that minimizes α)·F2 (where F1′ is the equivalent maximization form of the normalized first objective function F1, F2 is the second objective function, and α is the dynamic weight coefficient, ranging from 0.3 to 0.7, the specific value of which can be determined by the type of grid dispatch command received: if the command contains strict load tracking requirements, then α takes a high value; if the command focuses on economic dispatch, then α takes a low value); the wind power output value and photovoltaic power output value corresponding to this particle are the final output wind power target output allocation value and photovoltaic target output allocation value.

[0050] In another embodiment, the above method may further include: determining the wind power cost per kilowatt-hour and the photovoltaic cost per kilowatt-hour under the power curtailment scenario based on the wind power target output allocation value and the photovoltaic target output allocation value; determining the target power generation entity and allocating the power curtailment quota according to the wind power cost per kilowatt-hour and the photovoltaic cost per kilowatt-hour, and generating the optimized wind power target output allocation value and the photovoltaic target output allocation value.

[0051] Preferably, the levelized cost of wind power in power curtailment scenarios can be calculated using the life-cycle cost method, and the calculation formula is as follows:

[0052] Where LCOE_w is the levelized cost of wind power, C_inv_w is the investment cost of the wind turbine, Co_om_w is the operation and maintenance cost of the wind turbine, S_w is the residual value of the wind turbine, n is the life cycle of the wind turbine (usually 20 years), H_w is the annual effective power generation hours of the wind turbine, i is the discount rate (usually 8%), and P_w_rated is the rated power of the wind turbine.

[0053] The preferred formula for calculating the cost per kilowatt-hour of photovoltaic power under power curtailment scenarios is as follows:

[0054] Where LCOE_p is the photovoltaic cost per kilowatt-hour, C_inv_p is the photovoltaic array investment cost, C_om_p is the photovoltaic operation and maintenance cost, S_p is the residual value of the photovoltaic, n is the photovoltaic life cycle (usually 25 years), and P_p_rated is the rated power of the photovoltaic array.

[0055] In another embodiment, the method may further include: real-time monitoring of the deviation between the actual output and the target output of wind power and photovoltaic power, and when the deviation exceeds a preset threshold, triggering iterative optimization and updating the corresponding control commands.

[0056] Specifically, the actual output data of wind power and photovoltaic power can be obtained through the equipment status parameter acquisition stage, acquiring the actual wind power output (P_w_actual) fed back by the wind turbine controller and the actual photovoltaic output (P_p_actual) fed back by the photovoltaic array controller; the target output corresponding to wind power and photovoltaic power are respectively the optimized wind power target output allocation value (P_w_opt') and the optimized photovoltaic target output allocation value (P_p_opt') obtained through the above steps; the deviation rate can be calculated from the two. δ = |(P_w_actual+P_p_actual)-(P_w_opt'+P_p_opt')| / (P_w_opt'+P_p_opt')×100%, and compare it with a preset threshold (e.g., 5%). When δ is greater than the threshold, immediately invoke the full-link control logic involved in the above steps, and re-execute the entire process of target state determination, model construction, optimization solution, power limiting selection, and instruction generation with the latest measured environmental parameters, equipment status parameters, and power grid status parameters as input, to generate a new round of control instructions.

[0057] The core of the coordinated control method of this invention lies in establishing a full-process control system of "photovoltaic refined control - wind power optimization control - wind and solar synergistic optimization - curtailment selective control". It comprehensively considers multiple factors such as environmental parameters, equipment status, grid constraints, and market electricity prices, and achieves the overall optimal output and maximized power generation revenue of the wind and solar co-generation system through intelligent optimization algorithms.

[0058] This invention also provides an application example of a coordinated control method for wind and solar power generation in the same field, see [link to relevant documentation]. Figure 2 The flowchart shown is another method for coordinated control of wind and solar power generation. This method mainly includes the following steps S201 to S206: Step S201: Multi-source data acquisition and preprocessing; Environmental parameter acquisition: Deploy a multi-dimensional sensor group, including wind direction and speed sensors (collecting real-time prevailing wind direction and speed data, with a measurement accuracy of ±0.1m / s and ±1°), solar position sensors (collecting solar altitude angle and azimuth angle, with a measurement accuracy of ±0.5°), irradiance sensors (collecting real-time irradiance on the surface of photovoltaic modules, with a measurement accuracy of ±1%), ambient temperature sensors (collecting atmospheric temperature to correct the power generation efficiency of photovoltaic modules), and precipitation sensors (collecting precipitation data to determine whether to activate photovoltaic array protection measures).

[0059] Equipment status data acquisition: Real-time acquisition of parameters such as wind power system status (wind turbine nacelle rotation angle, blade pitch angle, current wind power output, wind turbine operating status), photovoltaic system status (current tracking angle of single-axis photovoltaic array, photovoltaic module temperature, current photovoltaic output, array operating status), and grid status (grid frequency, voltage, current connected capacity, power curtailment command) through equipment controllers, monitoring terminals, etc.

[0060] Market and dispatch data acquisition: Real-time market electricity price data (acquisition frequency 15 minutes / time) and power grid dispatch instructions (including total output limits, ramp rate limits, etc.) are accessed through the communication module.

[0061] Data preprocessing: The collected multi-source data is filtered, denoised, and normalized to remove abnormal data (such as outlier values ​​caused by sensor failure or missing values ​​caused by communication interference); missing data is filled in using linear interpolation to ensure data integrity; a real-time database is established to store the preprocessed data by category to provide data support for the control algorithm.

[0062] Step S202, wind power system optimization control decision; Based on environmental parameters and grid conditions, the wind turbine operating status is optimized to achieve optimal wind power output. The specific control logic is as follows: Nacelle rotation control: Based on the prevailing wind direction data collected by the wind direction sensor, the nacelle rotation drive mechanism is controlled to make the blades face the prevailing wind direction (with a deviation of no more than ±5°), ensuring that the windward surface of the blades is maximized and improving wind energy capture efficiency; when the wind direction change rate exceeds 10° / min, the nacelle rotation speed is accelerated to quickly adapt to the wind direction change.

[0063] Blade pitch angle control: Dynamically adjust the blade pitch angle based on real-time wind speed data. When the wind speed is lower than the rated wind speed (e.g., 12 m / s), the pitch angle is kept at the minimum angle (e.g., 0°~5°) to maximize the windward area and improve wind power output; When the wind speed is between the rated wind speed and the cut-out wind speed (e.g., 25 m / s), the wind turbine speed is limited by increasing the pitch angle (5°~30°) so that the wind power output is stabilized near the rated value. When the wind speed exceeds the cut-out wind speed, quickly adjust the blade pitch angle to 90° to achieve shutdown protection and avoid damage to the wind turbine.

[0064] Wind power output constraint control: If a total output limit instruction is received from the grid dispatch, the blade pitch angle is adjusted according to the wind-solar synergy optimization results to control the wind power output to not exceed the allocated output limit and ensure that grid constraints are met.

[0065] Step S203: Make refined control decisions for the photovoltaic array tracking bracket; This paper proposes an optimized control strategy for photovoltaic (PV) arrays while ensuring optimal wind power output. Preferably, taking a single-axis tracking bracket PV array as an example, a multi-factor optimized tracking control strategy is constructed to maximize PV output. (See also...) Figure 3 The diagram shown illustrates a method for calculating and correcting the tracking angle of a photovoltaic array on a tracking bracket. The specific control logic is as follows: S301, Solar position acquisition; S302, Calculation of the theoretical optimal angle; Tracking angle reference calculation: Based on the solar altitude angle (h) and azimuth angle (A) collected by the solar position sensor, combined with the installation latitude (φ) and longitude (λ) of the single-axis photovoltaic array, the theoretical optimal tracking angle (θ_ref) of the array is calculated. The calculation formula is as follows: θ_ref=arctan[(sinh×cosA) / (cosh×sinφ-sinh×sinA×cosφ)]; This formula ensures that the array panel is always as perpendicular as possible to the incident sunlight, maximizing the amount of irradiance received.

[0066] Next, dynamic correction of the tracking angle will be performed, which may include the following aspects of correction: S303, Fan obstruction correction; Based on the rotation angle of the wind turbine nacelle and the relative distance between the wind turbine and the photovoltaic array, the shading range and duration of the wind turbine blades on the photovoltaic array are calculated. When shading is predicted to occur, the tracking angle of the photovoltaic array is adjusted in advance (the correction amount Δθ1 is 5°~10°) to avoid the shading area and reduce shading losses.

[0067] S304, irradiation intensity correction; When the irradiance sensor detects that the irradiance is below the threshold (e.g., 200W / ㎡), the tracking speed is appropriately reduced (from the default 0.5° / s to 0.1° / s) to reduce the energy consumption of the tracking mechanism; when the irradiance changes abruptly (the rate of change is ≥100W / ㎡), the tracking response speed is accelerated (adjusted to 1° / s) to quickly adapt to the irradiance change.

[0068] S305, Power grid constraint correction; If a photovoltaic output restriction instruction is received from the grid dispatch, the tracking angle is adjusted (the correction amount Δθ2 is -10° to -5°) according to the difference between the output restriction value and the current photovoltaic output, and the photovoltaic output is appropriately reduced to ensure that the grid constraints are met.

[0069] S306, final tracking angle output; S307, Tracking speed adjustment; The array tracking speed is adaptively adjusted (0.1° / s to 1° / s) based on the rate of change of solar azimuth angle and the changes in irradiance. When the rate of change of solar azimuth angle is large (such as around noon) or the irradiance is high, the tracking speed is increased; when the rate of change of solar azimuth angle is small (such as in the early morning or evening) or the irradiance is low, the tracking speed is decreased to balance power generation efficiency and equipment energy consumption.

[0070] Safety protection control: When the precipitation sensor detects severe weather such as rain or snow, or when the wind speed exceeds the safety threshold of the photovoltaic array (e.g., 15m / s), the control system adjusts the single-axis photovoltaic array to a horizontal, windproof position to avoid equipment damage; when the temperature of the photovoltaic module exceeds the threshold (e.g., 65℃), the tracking angle is adjusted appropriately to reduce the amount of irradiance received by the module, lower the module temperature, and avoid efficiency degradation.

[0071] Step S204, wind-solar collaborative optimization control decision; A multi-objective optimization model is constructed to achieve the optimal overall output of wind power and photovoltaic power and the maximum power generation revenue. The specific control logic is as follows: Optimize the objective function establishment: Objective 1 (Optimal Power Output Matching): Minimize the deviation between the total wind and solar power output and the grid load demand, expressed as: MinF1=|P_total-P_load|; Where P_total is the total output of wind and solar power (P_total=P_w+P_p, P_w is the wind power output, P_p is the photovoltaic power output), and P_load is the real-time load demand of the power grid.

[0072] Objective 2 (Maximize Power Generation Revenue): Maximize the revenue generated per unit of time, expressed as: MaxF2=P_w×C_e+P_p×C_e-C_w-C_p; Where C_e is the real-time market electricity price, C_w is the wind power unit time operation and maintenance cost, and C_p is the photovoltaic unit time operation and maintenance cost (including the energy consumption cost of the tracking agency).

[0073] Constraint settings: Equipment output constraints: P_w_min≤P_w≤P_w_max, P_p_min≤P_p≤P_p_max, where P_w_min and P_w_max are the minimum and maximum output of the wind turbine, respectively, and P_p_min and P_p_max are the minimum and maximum output of the photovoltaic array, respectively.

[0074] Ramp-up constraint: |P_w(t)-P_w(t-1)|≤R_w, |P_p(t)-P_p(t-1)|≤R_p, where R_w is the maximum ramp-up rate of the wind turbine (e.g., 10%P_w_max / min), R_p is the maximum ramp-up rate of the photovoltaic array (e.g., 15%P_p_max / min), t is the current time, and t-1 is the previous time.

[0075] Grid access constraint: P_total≤P_grid_max, where P_grid_max is the maximum allowable grid access capacity.

[0076] The optimization algorithm solution includes: using an improved particle swarm optimization (PSO) algorithm to solve the multi-objective optimization model, combined with... Figure 4 The flowchart for solving a multi-objective optimization model is shown below. The specific steps are as follows: S401, Initialize the particle swarm; Each particle represents a combination of wind power output (P_w) and photovoltaic power output (P_p). The particle swarm size is set to 50, and the number of iterations is set to 100.

[0077] S402, Calculate the fitness function; The multi-objective function is transformed into a single-objective fitness function by using a weighted summation method: F = α × F1' + (1 - α) × F2, where F1' is the normalized value of F1 (converted to the maximization objective), and α is the weight coefficient (0.3~0.7, which can be dynamically adjusted according to the power grid demand).

[0078] S403, update particle velocity and position; S404, determine if the iteration count has been reached; The algorithm searches for the optimal solution by updating the velocity and position of the particles; an adaptive adjustment mechanism for inertia weight is introduced (the inertia weight is 0.9 in the early stage of iteration and linearly reduced to 0.4 in the later stage of iteration) to improve the convergence speed and optimization accuracy of the algorithm.

[0079] S405 outputs the optimal power distribution scheme; After the iteration is completed, the optimal wind power output allocation value (P_w_opt) and photovoltaic power output allocation value (P_p_opt) are output.

[0080] Control command issuance: Based on the obtained P_w_opt and P_p_opt, the pitch angle adjustment command is issued to the wind turbine controller and the tracking angle adjustment command is issued to the photovoltaic array controller to achieve coordinated optimization of wind and solar power output.

[0081] Step S205: Optimal control decision under power rationing scenario; When receiving a grid power rationing instruction (P_total ≤ P_limit, where P_limit is the maximum allowable total output after power rationing), based on the comparison of the levelized cost of electricity (LCOE) between wind power and photovoltaic power, the power generation entity is preferentially selected to maximize the revenue under the power rationing constraint. The specific control logic is as follows: Calculation of the levelized cost of electricity: Levelized cost of electricity for wind power (LCOE_w): Calculated using the life cycle cost method, considering the investment cost of the wind turbine (C_inv_w), operation and maintenance cost (C_om_w), salvage value (S_w), life cycle (n, usually 20 years), and annual effective power generation hours (H_w). The calculation formula is as follows: LCOE_w = [C_inv_w × (i × (1 + i)^n) / ((1 + i)^n - 1) + C_om_w - S_w / (1 + i)^n] / (H_w × P_w_rated); Where, i is the discount rate (usually taken as 8%), and P_w_rated is the rated power of the wind turbine.

[0082] Levelized cost of electricity for photovoltaic power (LCOE_p): Similarly, considering the investment cost of the photovoltaic array (C_inv_p), operation and maintenance cost (C_om_p), salvage value (S_p), life cycle (n = 25 years), and annual effective power generation hours (H_p). The calculation formula is as follows: LCOE_p = [C_inv_p × (i × (1 + i)^n) / ((1 + i)^n - 1) + C_om_p - S_p / (1 + i)^n] / (H_p × P_p_rated); Where, P_p_rated is the rated power of the photovoltaic array.

[0083] Optimal power generation strategy: When LCOE_w < LCOE_p: Give priority to ensuring the wind power output, adjust the wind power output to the maximum allowable value (P_w_limit = min(P_w_max, P_limit)), and allocate the remaining output quota (P_p_limit = P_limit - P_w_limit) to the photovoltaic array; if the maximum wind power output has exceeded the total power rationing quota, only retain the wind power output up to the power rationing quota and suspend the photovoltaic power generation.

[0084] When LCOE_p < LCOE_w: Give priority to ensuring the photovoltaic output, adjust the photovoltaic output to the maximum allowable value (P_p_limit = min(P_p_max, P_limit)), and allocate the remaining output quota (P_w_limit = P_limit - P_p_limit) to the wind turbine; if the maximum photovoltaic output has exceeded the total power rationing quota, only retain the photovoltaic output up to the power rationing quota and suspend the wind power generation.

[0085] When |LCOE_w - LCOE_p| ≤ 5%: a strategy of proportionally allocating curtailment quotas is adopted, with wind power and solar power output allocated curtailment quotas according to their respective rated power proportions, i.e.: P_w_limit=P_limit×(P_w_rated / (P_w_rated+P_p_rated)), P_p_limit = P_limit × (P_p_rated / (P_w_rated + P_p_rated)) ensures balanced power distribution.

[0086] Dynamic adjustment: Real-time monitoring of changes in the levelized cost of electricity (LCOE) of wind power and photovoltaic power (such as changes in wind speed leading to a decrease in LCOE of wind power, and changes in irradiance leading to a decrease in LCOE of photovoltaic power). The LCOE is recalculated every 15 minutes, and the optimal selection strategy is dynamically adjusted based on the calculation results to ensure that revenue is maximized under power curtailment constraints.

[0087] As a concrete example, combined Figure 5 As shown, the optimal control in the power curtailment scenario includes: S501 receiving power curtailment command; S502 calculating the cost per kilowatt-hour; S503 comparing the cost per kilowatt-hour; S504 prioritizing low-cost power generation entities; S505 allocating power curtailment quotas; and S506 output control.

[0088] Step S206: Real-time feedback and iterative optimization; Output Deviation Monitoring: Continuously collect actual output data (P_w_actual, P_p_actual) of wind power and photovoltaic power after adjustment, and calculate the deviation rate between the actual total output and the optimized target output. δ=|(P_w_actual+P_p_actual)-(P_w_opt+P_p_opt)| / (P_w_opt+P_p_opt)×100%.

[0089] Iterative optimization trigger: If the deviation rate δ≤5%, maintain the current control command; if δ>5%, re-execute steps S201 to S205 to dynamically correct the control parameters; when environmental parameters (such as wind speed, irradiance) change abruptly, the power grid power restriction command is adjusted, or the market electricity price fluctuates significantly, the emergency optimization mechanism is triggered to quickly recalculate the optimal control parameters to ensure the real-time performance and accuracy of the control strategy.

[0090] Fault warning and protection: Real-time monitoring of the operating status of wind turbines and photovoltaic arrays. If a fault is detected (such as wind turbine pitch angle jamming, photovoltaic tracking mechanism failure, sensor failure), an early warning signal is immediately triggered. At the same time, the control strategy is adjusted to transfer the output of the faulty equipment to the normal equipment (such as increasing photovoltaic output compensation when the wind turbine fails) to ensure stable system operation. The early warning information is uploaded to the control terminal in real time through the communication module to notify the operation and maintenance personnel to handle it in a timely manner.

[0091] The control modes include the following options: Automatic control mode: The system defaults to automatic control mode, which requires no manual intervention. Based on multi-source data collected by sensors and preset control logic, it automatically completes the control decisions and command issuance for each step. It is suitable for large-scale wind and solar power plants, unattended power plants and other scenarios.

[0092] Manual control mode: Maintenance personnel can manually input control commands through local monitoring terminals or remote mobile terminals to adjust parameters such as wind power output and photovoltaic array tracking angle. This mode is suitable for scenarios such as equipment commissioning, fault diagnosis, and emergency handling of special weather conditions. Manual control commands have higher priority than automatic control commands.

[0093] Custom control mode: Users can customize and optimize parameters such as target weight coefficient (α), cost per kilowatt-hour comparison threshold under power rationing scenarios, tracking speed range, and output deviation rate threshold according to the actual needs of the project, so as to realize personalized control strategies.

[0094] Based on the same inventive concept, this invention also provides a wind and solar power generation coordination control device, which mainly includes the following parts: The control decision generation module is used to determine the target wind turbine operating status and target photovoltaic control strategy based on the environmental parameters and equipment status parameters of the wind and solar co-generation system. The model building module is used to construct a multi-objective optimization model based on the target wind turbine operating status and the target photovoltaic control strategy; The collaborative control module is used to solve the multi-objective optimization model, obtain the target output allocation values ​​for wind power and photovoltaic power respectively, and generate corresponding control commands to send to the wind turbine controller and photovoltaic array controller to realize the collaborative control of wind and solar power generation in the same field.

[0095] The wind-solar co-generation coordination control device provided in this embodiment of the invention can be specific hardware on the equipment or software or firmware installed on the equipment. The implementation principle and technical effects of the device provided in this embodiment of the invention are the same as those in the foregoing method embodiments. For the sake of brevity, any parts not mentioned in the device embodiments can be referred to the corresponding content in the foregoing method embodiments. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.

[0096] To support the real-time operation of the aforementioned coordinated control method, a dedicated storage device for coordinated control of wind and solar power generation is designed. This device features high data storage efficiency, fast read / write speeds, high reliability, and compatibility with control algorithms. The specific design is as follows: Storage device hardware structure (see) Figure 6 (as shown) Main control module 601: It adopts a multi-core ARM processor (such as STM32H7 series) with a main frequency of not less than 400MHz, has powerful computing capabilities, supports multi-threaded processing, and can run data storage management program and control algorithm preprocessing program at the same time.

[0097] Storage module 602: adopts a hybrid storage architecture of "SSD solid-state drive + high-speed DDR4 memory"; the SSD solid-state drive has a capacity of not less than 1TB and is used for persistent storage of historical data (such as environmental parameters, equipment status, output data, and control command records), with a read and write speed of not less than 500MB / s; the DDR4 memory has a capacity of not less than 8GB and is used to cache real-time data and intermediate data during the operation of the control algorithm, with a read and write speed of not less than 2133MT / s to ensure the control algorithm's fast access to data.

[0098] Interface Module 603: Equipped with multiple high-speed communication interfaces, including an Ethernet interface (Gigabit Ethernet, supporting TCP / IP protocol), an RS485 interface (supporting Modbus protocol), and a CAN bus interface (supporting CANopen protocol), for data interaction with sensors, device controllers, and control terminals; it is also equipped with a USB 3.0 interface for data export and device debugging.

[0099] Power module 604: It adopts dual redundant power supply, with an input voltage range of AC220V±10% and an output voltage of DC12V and 5V. It has overvoltage, overcurrent and short circuit protection functions to ensure that the storage device can still operate stably when the mains voltage fluctuates or a single power supply fails.

[0100] Heat dissipation module 605: It adopts a heat dissipation method that combines passive and active heat dissipation. The equipment shell is made of aluminum alloy, which has good heat dissipation performance. An internal silent fan is set up to automatically start and stop according to the equipment temperature (start when the temperature is higher than 60℃ and shut down when the temperature is lower than 40℃) to ensure stable operation of the equipment in high-temperature environments.

[0101] Furthermore, the storage device also includes a sensor 606 and a control terminal 607.

[0102] Storage device software functions: Data classification and storage: A partitioned storage strategy is adopted, dividing the data into four categories: real-time data area, historical data area, control command area, and fault record area, storing different types of data respectively; the real-time data area uses cyclic overwrite storage (storage period is 24 hours), the historical data area uses compressed storage (compression algorithm is LZ77, compression ratio can reach 3:1), and the control command area and fault record area use permanent storage (automatically backed up to external hard drive after full storage).

[0103] Fast data retrieval: A multi-level indexing mechanism is established to create index tables for different types of data (such as indexes by time, by device number, and by data type). It supports retrieval by multiple conditions such as time range, device name, and data type, with a retrieval response time of no more than 100ms, meeting the control algorithm's requirements for fast querying of historical data.

[0104] Data backup and recovery: Supports both automatic and manual backup modes. In automatic backup mode, historical data is backed up to the backup partition of the SSD solid-state drive at 2:00 AM every day. In manual backup mode, users can trigger backup commands through the control terminal to back up data for a specified period of time to an external USB device. It also has a data recovery function, which can quickly restore data when stored data is damaged or lost by backing up the data.

[0105] Data communication protocols: Supports multiple industrial communication protocols such as Modbus, TCP / IP, and CANopen, and is compatible with sensors, equipment controllers, and control terminals from different manufacturers; Employs a data encryption transmission mechanism (AES-128 encryption algorithm) to ensure data security during transmission and prevent data from being tampered with or stolen.

[0106] Status monitoring and alarms: Real-time monitoring of the operating status of storage devices (such as hard disk utilization, memory usage, device temperature, and power status). When abnormal conditions such as hard disk utilization exceeding 90%, memory usage exceeding 80%, device temperature exceeding 70°C, or power failure are detected, alarm signals are immediately triggered (including local audible and visual alarms and remote communication alarms) to notify maintenance personnel for timely handling.

[0107] Adaptation design with control algorithm: Data prefetching function: Based on the running cycle of the control algorithm (e.g., 100ms / time), the real-time data and historical data (e.g., wind speed and irradiance data in the last 10 minutes) required by the algorithm are prefetched into DDR4 memory in advance, reducing the data reading latency during the algorithm's operation and ensuring the real-time nature of control decisions.

[0108] Algorithm intermediate data cache: Provides a dedicated intermediate data cache area for coordination control algorithms (such as the improved PSO algorithm), caches intermediate data such as particle positions, fitness values, and optimal solutions during the algorithm's operation, avoids redundant calculations, and improves the algorithm's running efficiency.

[0109] Synchronous storage of control commands: The issued control commands and the execution results fed back by the equipment are stored synchronously to establish a complete data chain of "command-execution-feedback", which makes it easier for maintenance personnel to trace the control process and analyze the control effect.

[0110] This invention primarily addresses the following problems existing in the prior art: insufficient optimization of wind and solar power output coordination leading to energy waste; simplistic photovoltaic control strategies; lack of economic optimization under power curtailment scenarios; insufficient adaptability of control models; bottlenecks in data storage and computing capabilities; and low level of intelligent system decision-making. The wind-solar co-generation coordinated control method, device, and electronic equipment provided by the embodiments of this invention significantly improve the overall operational performance and economic benefits of wind-solar hybrid systems.

[0111] Based on the same inventive concept, embodiments of the present invention also provide an electronic device, specifically, the electronic device includes a processor and a storage device; the storage device stores a computer program, and the computer program, when run by the processor, executes the method described in any of the above embodiments.

[0112] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 400 includes: a processor 410, a memory 420, a communication interface 430, and a bus 440. The memory 420 stores machine-readable instructions that can be executed by the processor 410. When the electronic device is running, the processor 410 communicates with the memory 420 through the bus 440. The processor 410 executes the machine-readable instructions to perform the steps of the method described above.

[0113] Specifically, the memory 420 and processor 410 can be general-purpose memory and processor, without any specific limitations. When the processor 410 runs the computer program stored in the memory 420, it can execute the above method.

[0114] Processor 410 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 410 or by instructions in software form. The processor 410 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 420, and processor 410 reads the information from memory 420 and, in conjunction with its hardware, completes the steps of the above method.

[0115] Corresponding to the above method, this embodiment of the invention also provides a computer-readable storage medium storing machine-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to perform the steps of the above method.

[0116] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0117] Furthermore, the units described as separate components may or may not be physically separate. 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0118] Furthermore, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0119] It should be noted that if the functionality is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0121] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for coordinated control of wind and solar power generation, characterized in that, The method includes: Based on the environmental parameters and equipment status parameters of the wind and solar co-generation system, the target wind turbine operating status and target photovoltaic control strategy are determined. Based on the target wind turbine operating status and the target photovoltaic control strategy, a multi-objective optimization model is constructed in combination with pre-set constraints. The multi-objective optimization model is solved to obtain the target output allocation values ​​for wind power and photovoltaic power respectively, and corresponding control commands are generated and sent to the wind turbine controller and photovoltaic array controller to achieve coordinated control of wind and solar power generation in the same field.

2. The method according to claim 1, characterized in that, The wind turbine operating status includes: the rotation angle and speed of the wind turbine nacelle, and the blade pitch angle; the target photovoltaic control strategy includes: the target tracking angle and target tracking speed of the photovoltaic array.

3. The method according to claim 2, characterized in that, The environmental parameters include: solar position parameters and irradiance parameters; the equipment status parameters include: photovoltaic array installation parameters, wind power shading parameters, and grid status parameters; the method for determining the target photovoltaic control strategy includes: Based on the solar position parameters and the installation parameters of the photovoltaic array, the reference tracking angle of the photovoltaic array is calculated; The reference tracking angle is dynamically corrected based on the wind power obstruction parameters to generate the target tracking angle. The target tracking speed is determined based on the irradiance parameters and the power grid state parameters.

4. The method according to claim 1, characterized in that, The objective function of the multi-objective optimization model includes a first objective function and a second objective function; the first objective function is used to minimize the deviation between the total wind and solar power output and the grid load demand; the second objective function is determined based on the real-time market electricity price and the unit time operation and maintenance costs corresponding to wind power and photovoltaic power respectively, and is used to maximize the power generation revenue per unit time. The constraints of the multi-objective optimization model include: equipment output constraints, ramp rate constraints, and grid connection constraints.

5. The method according to claim 4, characterized in that, Solving the multi-objective optimization model yields the target output allocation values ​​for wind power and photovoltaic power, respectively, including: The particle swarm is initialized based on the objective function and the constraints; each particle in the particle swarm is a two-dimensional variable containing wind power output and photovoltaic power output. Based on the objective function, calculate the fitness function value of each particle, and select feasible particles based on the constraints. Based on the fitness function value and feasible particles, update the velocity and position of the particles so that the particle swarm converges towards the target region. Combining the convergence state of the particle swarm with the preset iteration termination condition, the optimal solution is selected from the particles that satisfy the constraints, and used as the target output allocation value for wind power and photovoltaic power.

6. The method according to claim 5, characterized in that, The method further includes: Based on the wind power target output allocation value and the photovoltaic target output allocation value, the wind power cost per kilowatt-hour and the photovoltaic cost per kilowatt-hour under the power curtailment scenario are determined respectively. Based on the wind power cost per kilowatt-hour and the photovoltaic cost per kilowatt-hour, the target power generation entities are determined and power curtailment quotas are allocated, generating optimized wind power target output allocation values ​​and photovoltaic target output allocation values.

7. The method according to claim 1, characterized in that, The method further includes: The system monitors the deviation between the actual output and the target output of wind and solar power in real time. When the deviation exceeds the preset threshold, iterative optimization is triggered and the corresponding control commands are updated.

8. A wind-solar co-generation coordinated control device, characterized in that, The device includes: The control decision generation module is used to determine the target wind turbine operating status and target photovoltaic control strategy based on the environmental parameters and equipment status parameters of the wind and solar co-generation system. The model building module is used to build a multi-objective optimization model based on the target wind turbine operating status and the target photovoltaic control strategy, combined with pre-set constraints. The collaborative control module is used to solve the multi-objective optimization model, obtain the target output allocation values ​​corresponding to wind power and photovoltaic power respectively, and generate corresponding control commands to send to the wind turbine controller and photovoltaic array controller to realize the collaborative control of wind and solar power generation in the same field.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.

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