Operation optimization method and system for mountain photoelectric unit

Abstract

The invention relates to the technical field of photovoltaic power generation, and discloses an operation optimization method and system for a mountain photoelectric unit, and the method comprises the steps: obtaining a high-precision digital terrain model, real-time meteorological data and assembly operation data; dynamically predicting a shadow distribution sequence and theoretical irradiance of each photovoltaic string in a future time period based on a high-precision digital terrain model and sun position data; inputting the shadow distribution sequence and the theoretical irradiance into an optimization model, and generating a power set value of each photovoltaic string optimizer and a charging and discharging instruction of an energy storage converter; when it is predicted that the power loss of the specific photovoltaic string is caused by the shadow, synchronously adjusting the power of the photovoltaic string which is not influenced by the shadow and the charging and discharging power of the energy storage converter so as to compensate the total power vacancy; the power set value and the charging and discharging instruction are issued to the corresponding photovoltaic string optimizer and the energy storage converter, cooperative control is executed, active smooth control over the total output power of the power station is achieved, and the power generation benefit is improved.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, and more specifically, to a method and system for optimizing the operation of mountain photovoltaic power units. Background Technology

[0002] With the transformation of the global energy structure and the advancement of the "dual carbon" goal, photovoltaic power generation, as an important component of clean energy, has seen its installed capacity continue to grow. In mountainous areas with complex terrain, the undulating terrain and varying slopes significantly affect sunlight conditions due to topographic shading, posing unique challenges to the construction and operation of mountain photovoltaic power stations.

[0003] Currently, traditional photovoltaic (PV) power plant operation optimization methods are mainly designed for flat terrain, with the core objective typically being to maximize the output power of a single PV module, commonly employing maximum power point tracking (MPPT) technology. However, in mountainous environments, dynamic and irregular shading caused by terrain undulations and vegetation can lead to severe uneven irradiance and power mismatch between different PV strings. This not only reduces the overall power generation efficiency of the power plant but also causes drastic fluctuations in the total output power. These frequent power spikes and drops threaten the power quality and frequency stability of the grid, while also exacerbating the thermal stress and aging rate of PV modules and inverters. Summary of the Invention

[0004] This application provides a method and system for optimizing the operation of mountain photovoltaic power units, effectively solving the technical problem of severe power mismatch between strings and drastic fluctuations in total power output caused by complex terrain shadows in mountain photovoltaic power plants. By integrating a high-precision digital terrain model with real-time meteorological data, accurate prediction of shadow distribution is achieved. Based on the model predictive control (MPC) algorithm, string power and energy storage charging and discharging strategies are collaboratively optimized, thus smoothing power fluctuations in the grid-connected power plant, improving power generation efficiency, and effectively extending the service life of energy storage equipment by smoothing energy storage charging and discharging operations.

[0005] To achieve the above objectives, the present invention provides an operation optimization method for mountain photovoltaic power units, comprising:

[0006] Acquire high-precision digital terrain models, real-time meteorological data, and component operation data for the photovoltaic power station area;

[0007] Based on the high-precision digital terrain model and solar position data, the shadow distribution sequence and theoretical irradiance of each photovoltaic string in the future period are dynamically predicted.

[0008] The shadow distribution sequence and theoretical irradiance are input into the optimization model. With the goal of minimizing the variance of the total output power of the photovoltaic power station, the power setpoints of each photovoltaic string optimizer and the charging and discharging commands of the energy storage converter are generated.

[0009] When a power loss is predicted due to shading in a specific photovoltaic string, the power of the photovoltaic strings not affected by shading and the charging and discharging power of the energy storage converter are adjusted synchronously to compensate for the total power deficit.

[0010] The power setting value and charge / discharge command are sent to the corresponding photovoltaic string optimizer and energy storage converter to perform coordinated control.

[0011] Furthermore, high-precision digital terrain models, real-time meteorological data, and component operation data of the photovoltaic power station area are acquired, specifically including:

[0012] The high-precision digital terrain model is generated by acquiring topographic point cloud data of the photovoltaic power station area through remote sensing equipment mounted on drones or satellites and then processing it.

[0013] The real-time meteorological data is obtained by meteorological monitoring stations and irradiance sensor networks deployed in the photovoltaic power station area. The real-time meteorological data includes total irradiance, diffuse irradiance, ambient temperature, and wind speed.

[0014] The sensor monitoring system deployed in the photovoltaic power plant area collects the component operation data in real time, including the output voltage, output current and operating temperature of each photovoltaic string.

[0015] Furthermore, based on the aforementioned high-precision digital terrain model and solar position data, the shadow distribution sequence and theoretical irradiance of each photovoltaic string are dynamically predicted for future periods, specifically including:

[0016] The real-time altitude and azimuth angle sequence of the sun relative to the photovoltaic power station area within a future set time period is calculated based on astronomical calendar.

[0017] The solar altitude angle and azimuth angle sequence, the high-precision digital terrain model and the three-dimensional layout model of the photovoltaic power station are spatially superimposed, and the shading relationship between terrain obstacles and arrays is simulated by the light projection algorithm to generate a minute-level shadow distribution time series of each photovoltaic string.

[0018] Based on the shadow distribution time series and the real-time collected direct and diffuse irradiance data, the theoretical irradiance change curve of each photovoltaic string under the influence of shadow in the future period is calculated using the irradiance transfer model.

[0019] Furthermore, the theoretical irradiance variation curves of each photovoltaic string under the influence of shading in the future period are calculated using the irradiance transport model. The irradiance transport model specifically includes:

[0020] An irradiance calculation model based on the Hay model is constructed to separate and process the real-time collected direct irradiance and diffuse irradiance.

[0021] For any photovoltaic string, a judgment is made based on the corresponding shadow distribution time series: if the photovoltaic string has no shadow at the current time, the direct irradiance is fully included in the calculation; if there is shadow, the direct irradiance is converted into diffuse irradiance according to a preset ratio.

[0022] The processed direct and diffuse irradiance components are combined with the installation tilt and azimuth parameters of the photovoltaic string and synthesized using the irradiance calculation model to finally output the theoretical irradiance variation curve of the photovoltaic string with minute-level resolution over a future period.

[0023] Furthermore, the shadow distribution sequence and theoretical irradiance are input into the optimization model. With the objective of minimizing the variance of the total output power of the photovoltaic power station, power setpoints for each photovoltaic string optimizer and charging / discharging commands for the energy storage converter are generated, specifically including:

[0024] Construct an optimization objective function with the goal of minimizing the variance of the total output power of the photovoltaic power plant;

[0025] Establish optimization model constraints including power constraints of photovoltaic string optimizers, charging and discharging power and capacity constraints of energy storage converters;

[0026] The Model Predictive Control (MPC) algorithm is used to solve the optimization objective function in a rolling manner, generating the power setpoint sequence of each photovoltaic string optimizer and the charging and discharging command sequence of the energy storage converter for future time periods.

[0027] Furthermore, an optimization objective function is constructed with the goal of minimizing the variance of the total output power of the photovoltaic power plant. The optimization objective function specifically includes:

[0028] Set the optimization objective function ;

[0029] Where t is the current time;

[0030] For the prediction time domain of model predictive control;

[0031] For a moment The total power output from the internal photovoltaic power station to the grid is obtained by the algebraic superposition of the output power of each photovoltaic string and the discharge power of the energy storage system.

[0032] The power smoothing reference value is the moving average of the theoretical total output power within the prediction time domain;

[0033] This represents the power change of the energy storage system at adjacent sampling times;

[0034] This is a weighting coefficient used to adjust the balance between power smoothing targets and energy storage device wear.

[0035] Furthermore, in employing the Model Predictive Control (MPC) algorithm to solve the optimization objective function in a rolling manner, the MPC algorithm specifically includes:

[0036] At the current sampling time t, based on the latest acquired shadow distribution sequence, theoretical irradiance, and system state, in the prediction time domain... The optimization objective function is solved internally to obtain the optimal control command sequence, which includes the power setpoint of each photovoltaic string optimizer and the charging and discharging commands of the energy storage converter.

[0037] The first control vector in the optimal control command sequence is used as the actual control command and sent to the corresponding photovoltaic string optimizer and energy storage converter for execution.

[0038] At the next sampling time t+1, the prediction time domain is rolled forward one step, and the above two steps are repeated to achieve closed-loop control based on the latest system state.

[0039] Furthermore, in the prediction time domain Solving the objective function internally yields the optimal control command sequence, which specifically includes:

[0040] The optimal control command sequence is represented as follows:

[0041] ;

[0042] Each control vector Include:

[0043] Power setpoint of each photovoltaic string optimizer at time k This is used to control the actual output power of the corresponding string;

[0044] The charging and discharging commands of the energy storage converter at time k Used to regulate the charging and discharging power of the energy storage system;

[0045] This sequence is obtained through the prediction time domain. Within this framework, by solving an optimization problem that satisfies all constraints and minimizes the objective function value, a complete plan for a set of future control commands is obtained.

[0046] Furthermore, when a power loss due to shading is predicted for a specific photovoltaic string, the power of the photovoltaic strings not affected by shading and the charging and discharging power of the energy storage converter are simultaneously adjusted to compensate for the total power deficit, specifically including:

[0047] Based on the predicted power deficit size and duration, real-time adjustment commands are generated according to the preset power allocation strategy.

[0048] The power allocation strategy prioritizes allocating unshaded photovoltaic strings to increase power within the maximum power output range, while simultaneously calculating the required compensation power difference.

[0049] The compensated power difference is converted into real-time charging and discharging commands for the energy storage converter, wherein:

[0050] When the total increase in power of the photovoltaic strings not affected by shading is less than the power deficit, the energy storage converter supplements it with differential power discharge;

[0051] When the total increase in power of the photovoltaic strings not affected by shading exceeds the power deficit, the energy storage converter absorbs the excess power by charging.

[0052] To achieve the above objectives, the present invention also provides an operation optimization system for mountain photovoltaic power units, comprising:

[0053] The data acquisition module acquires high-precision digital terrain models, real-time meteorological data, and component operation data of the photovoltaic power station area;

[0054] The dynamic prediction module, based on the high-precision digital terrain model and solar position data, dynamically predicts the shadow distribution sequence and theoretical irradiance of each photovoltaic string in the future period.

[0055] The collaborative optimization module inputs the shadow distribution sequence and theoretical irradiance into the optimization model, and generates power setpoints for each photovoltaic string optimizer and charging and discharging commands for the energy storage converter with the goal of minimizing the variance of the total output power of the photovoltaic power station.

[0056] The dynamic compensation module, when predicting that a specific photovoltaic string will suffer power loss due to shading, synchronously adjusts the power of the photovoltaic strings not affected by shading and the charging and discharging power of the energy storage converter to compensate for the total power deficit.

[0057] The instruction execution module sends the power setting value and the charging and discharging command to the corresponding photovoltaic string optimizer and the energy storage converter to perform coordinated control.

[0058] Compared with existing technologies, the beneficial effects of this invention are as follows: by integrating high-precision digital terrain models and real-time meteorological data, the shading distribution of mountain photovoltaic power stations is predicted; a collaborative optimization mechanism for photovoltaic strings and energy storage systems is constructed using the Model Predictive Control (MPC) algorithm, which can actively adjust the output of unshaded strings and optimize the energy storage charging and discharging strategy when shading occurs, effectively solving the problem of drastic power fluctuations caused by terrain shading in traditional methods, significantly improving the stability of power station operation, power generation efficiency and equipment lifespan, and enhancing the overall benefits of mountain photovoltaic power stations. Attached Figure Description

[0059] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0060] Figure 1 A flowchart illustrating an operation optimization method for a mountain photovoltaic power unit according to an embodiment of the present invention is shown;

[0061] Figure 2 A schematic diagram of the operation optimization system for a mountain photovoltaic power unit is shown in an embodiment of the present invention. Detailed Implementation

[0062] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0063] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0064] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0065] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0066] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0067] S110: Acquire high-precision digital terrain models, real-time meteorological data, and component operation data of the photovoltaic power station area;

[0068] In some embodiments of the present invention, acquiring high-precision digital terrain models, real-time meteorological data, and component operation data of the photovoltaic power station area specifically includes:

[0069] The high-precision digital terrain model is generated by acquiring topographic point cloud data of the photovoltaic power station area through remote sensing equipment mounted on drones or satellites and then processing it.

[0070] The real-time meteorological data is obtained by meteorological monitoring stations and irradiance sensor networks deployed in the photovoltaic power station area. The real-time meteorological data includes total irradiance, diffuse irradiance, ambient temperature, and wind speed.

[0071] The sensor monitoring system deployed in the photovoltaic power plant area collects the component operation data in real time, including the output voltage, output current and operating temperature of each photovoltaic string.

[0072] The beneficial effects of the above scheme are as follows: by integrating UAV / satellite remote sensing terrain data, meteorological monitoring network and component operation sensors, a multi-dimensional data perception system is constructed, which realizes accurate perception of the terrain features, meteorological conditions and equipment operation status of mountain photovoltaic power stations, provides a reliable data foundation for subsequent shadow prediction and operation optimization, and effectively solves the control lag problem caused by data missing or inaccurate in traditional methods.

[0073] S120: Based on the high-precision digital terrain model and solar position data, dynamically predict the shadow distribution sequence and theoretical irradiance of each photovoltaic string in the future period;

[0074] In some embodiments of the present invention, based on the high-precision digital terrain model and solar position data, the shadow distribution sequence and theoretical irradiance of each photovoltaic string in a future period are dynamically predicted, specifically including:

[0075] The real-time altitude and azimuth angle sequence of the sun relative to the photovoltaic power station area within a future set time period is calculated based on astronomical calendar.

[0076] The solar altitude angle and azimuth angle sequence, the high-precision digital terrain model and the three-dimensional layout model of the photovoltaic power station are spatially superimposed, and the shading relationship between terrain obstacles and arrays is simulated by the light projection algorithm to generate a minute-level shadow distribution time series of each photovoltaic string.

[0077] Based on the shadow distribution time series and the real-time collected direct and diffuse irradiance data, the theoretical irradiance change curve of each photovoltaic string under the influence of shadow in the future period is calculated using the irradiance transfer model.

[0078] In this embodiment, the irradiance transport model specifically includes:

[0079] An irradiance calculation model based on the Hay model is constructed to separate and process the real-time collected direct irradiance and diffuse irradiance.

[0080] For any photovoltaic string, a judgment is made based on the corresponding shadow distribution time series: if the photovoltaic string has no shadow at the current time, the direct irradiance is fully included in the calculation; if there is shadow, the direct irradiance is converted into diffuse irradiance according to a preset ratio.

[0081] The processed direct and diffuse irradiance components are combined with the installation tilt and azimuth parameters of the photovoltaic string and synthesized using the irradiance calculation model to finally output the theoretical irradiance variation curve of the photovoltaic string with minute-level resolution over a future period.

[0082] In this embodiment, astronomical calendar calculation refers to the calculation of the precise altitude and azimuth angles of the sun relative to the power station area in real time based on a precise mathematical model of the laws governing celestial motion, according to the latitude and longitude coordinates of the photovoltaic power station location and specific time information, so as to provide high-precision spatial location input for subsequent shadow simulation.

[0083] In this embodiment, the light projection algorithm refers to using computer graphics technology to emit virtual light from the sun's position and perform spatial intersection calculations with a high-precision digital terrain model and the three-dimensional layout of the photovoltaic array. This accurately simulates the dynamic shading relationship of terrain obstacles on the photovoltaic strings, enabling quantitative prediction of the minute-level shadow distribution sequence of each photovoltaic string.

[0084] The beneficial effects of the above scheme are as follows: accurate solar trajectories are obtained through astronomical calendar calculations, and precise simulations of the shading relationship between terrain and components are achieved by combining light projection algorithms. Furthermore, the Hay irradiance transmission model is used to intelligently convert and synthesize direct and diffuse irradiance, ultimately generating theoretical irradiance variation curves with minute-level accuracy. This forms a complete technology chain from solar positioning and shadow prediction to irradiance calculation, providing reliable data support for the operation optimization of mountain photovoltaic power stations.

[0085] S130: Input the shadow distribution sequence and theoretical irradiance into the optimization model, and generate the power setpoint of each photovoltaic string optimizer and the charging and discharging command of the energy storage converter with the goal of minimizing the variance of the total output power of the photovoltaic power station.

[0086] In some embodiments of the present invention, the shadow distribution sequence and theoretical irradiance are input into the optimization model, with the goal of minimizing the variance of the total output power of the photovoltaic power station, to generate power setpoints for each photovoltaic string optimizer and charging / discharging commands for the energy storage converter, specifically including:

[0087] Construct an optimization objective function with the goal of minimizing the variance of the total output power of the photovoltaic power plant;

[0088] Establish optimization model constraints including power constraints of photovoltaic string optimizers, charging and discharging power and capacity constraints of energy storage converters;

[0089] The Model Predictive Control (MPC) algorithm is used to solve the optimization objective function in a rolling manner, generating the power setpoint sequence of each photovoltaic string optimizer and the charging and discharging command sequence of the energy storage converter for future time periods.

[0090] In this embodiment, the optimization model is a mathematical decision-making model constructed to achieve smooth power output of the photovoltaic power station. The model consists of two core parts: the optimization objective function and the system constraints. The optimization objective function focuses on minimizing the variance of the total output power of the photovoltaic power station, while also taking into account the losses of the energy storage equipment. The constraints include the power adjustment range of the photovoltaic string, the charging and discharging limits of the energy storage converter, and the battery capacity limit. The optimization objective function is solved in a rolling manner through the model predictive control (MPC) algorithm to finally generate the optimal coordinated control command.

[0091] In this embodiment, the purpose of setting optimization model constraints is to ensure that the system operates safely and stably within the physically feasible range: by setting power constraints on the photovoltaic string optimizer to prevent equipment overload operation, limiting the charging and discharging power of the energy storage converter to avoid damage to power devices, and setting battery capacity constraints to maintain the energy storage system operating in a safe state of charge, the physical feasibility of the control strategy is guaranteed, as well as the operational safety and service life of the power equipment.

[0092] In this embodiment, optimizing the objective function specifically includes:

[0093] Set the optimization objective function ;

[0094] Where t is the current time;

[0095] For the prediction time domain of model predictive control;

[0096] For a moment The total power output from the internal photovoltaic power station to the grid is obtained by the algebraic superposition of the output power of each photovoltaic string and the discharge power of the energy storage system.

[0097] The power smoothing reference value is the moving average of the theoretical total output power within the prediction time domain;

[0098] This represents the power change of the energy storage system at adjacent sampling times;

[0099] This is a weighting coefficient used to adjust the balance between power smoothing targets and energy storage device wear.

[0100] In this embodiment, the Model Predictive Control (MPC) algorithm specifically includes:

[0101] At the current sampling time t, based on the latest acquired shadow distribution sequence, theoretical irradiance, and system state, in the prediction time domain... The optimization objective function is solved internally to obtain the optimal control command sequence, which includes the power setpoint of each photovoltaic string optimizer and the charging and discharging commands of the energy storage converter.

[0102] The first control vector in the optimal control command sequence is used as the actual control command and sent to the corresponding photovoltaic string optimizer and energy storage converter for execution.

[0103] At the next sampling time t+1, the prediction time domain is rolled forward one step, and the above two steps are repeated to achieve closed-loop control based on the latest system state.

[0104] In this embodiment, the optimal control command sequence specifically includes:

[0105] The optimal control command sequence is represented as follows:

[0106] ;

[0107] Each control vector Include:

[0108] Power setpoint of each photovoltaic string optimizer at time k This is used to control the actual output power of the corresponding string;

[0109] The charging and discharging commands of the energy storage converter at time k Used to regulate the charging and discharging power of the energy storage system;

[0110] This sequence is obtained through the prediction time domain. Within this framework, by solving an optimization problem that satisfies all constraints and minimizes the objective function value, a complete plan for a set of future control commands is obtained.

[0111] In this embodiment, the power setpoint of each photovoltaic string optimizer and the charge / discharge command of the energy storage converter refer to the collaborative control strategy solved by the Model Predictive Control (MPC) algorithm. The power setpoint is used to dynamically adjust the real-time output power of each photovoltaic string to achieve the system-level optimization goal, while the charge / discharge command is used to control the energy storage system to perform precise power compensation. Together, they constitute the core control signal for maintaining the stable operation of the power plant.

[0112] In this embodiment, minimizing the variance of the total output power of the photovoltaic power station is selected as the core objective. Mathematical methods are used to quantify and suppress the drastic power fluctuations caused by dynamic shading in mountainous environments. Specifically, high-precision shading prediction is used to detect power fluctuation trends in advance. The Model Predictive Control (MPC) algorithm is used to calculate the optimal coordination strategy between the photovoltaic strings and the energy storage system before the fluctuations occur. Dynamic compensation is formed by adjusting the output power of the photovoltaic strings that are not affected by shading and the charging and discharging of the energy storage in real time, so that the output power of the power station is always kept in a stable range.

[0113] In this embodiment, the prediction time domain of model predictive control is an optimized parameter determined by comprehensively considering factors such as the shadow change characteristics of the mountain photovoltaic power station, the system response speed and control cycle. Its specific value is based on the actual operation requirements of the power station. By analyzing the historical shadow movement speed and power fluctuation cycle, and combining the computing power of the control system, an appropriate time length that can cover the main fluctuation process is selected. This time length is usually 15-60 minutes, so that the system can achieve the best balance between control accuracy and computing load.

[0114] The beneficial effects of the above scheme are as follows: by constructing an optimization model that includes an objective function and multi-dimensional constraints, and by using the Model Predictive Control (MPC) algorithm, within the prediction time domain covering the main fluctuation process, with the goal of minimizing the variance of the total output power of the photovoltaic power station, the scheme coordinates the optimization of the photovoltaic string power setpoint and the energy storage charging and discharging commands. This achieves advanced suppression and dynamic compensation for the output power fluctuations of the photovoltaic string caused by mountain shadows, thereby significantly improving the grid-connected power quality, enhancing the stability of grid operation, and effectively reducing the impact losses of power fluctuations on the power generation system and grid equipment.

[0115] S140: When it is predicted that a specific photovoltaic string will suffer power loss due to shading, the power of the photovoltaic string that is not affected by shading and the charging and discharging power of the energy storage converter are adjusted synchronously to compensate for the total power deficit.

[0116] In some embodiments of the present invention, when a power loss due to shading is predicted for a specific photovoltaic string, the power of the photovoltaic strings not affected by shading and the charging and discharging power of the energy storage converter are simultaneously adjusted to compensate for the total power deficit, specifically including:

[0117] Based on the predicted power deficit size and duration, real-time adjustment commands are generated according to the preset power allocation strategy.

[0118] The power allocation strategy prioritizes allocating unshaded photovoltaic strings to increase power within the maximum power output range, while simultaneously calculating the required compensation power difference.

[0119] The compensated power difference is converted into real-time charging and discharging commands for the energy storage converter, wherein:

[0120] When the total increase in power of the photovoltaic strings not affected by shading is less than the power deficit, the energy storage converter supplements it with differential power discharge;

[0121] When the total increase in power of the photovoltaic strings not affected by shading exceeds the power deficit, the energy storage converter absorbs the excess power by charging.

[0122] In this embodiment, the power allocation strategy is a decision-making mechanism that dynamically coordinates photovoltaic and energy storage resources based on power deficit. This strategy follows the principle of prioritizing photovoltaic string adjustment and supplementing with energy storage converters. First, it makes full use of photovoltaic strings that are not affected by shading for power compensation. When the adjustment capacity of the photovoltaic strings is insufficient or excessive, the energy storage system is then activated for precise differential adjustment, thereby achieving optimized resource allocation and stable system operation.

[0123] In this embodiment, the real-time adjustment command refers to the collaborative control signal dynamically generated by the system based on the predicted power deficit. This command includes a power boost command for photovoltaic strings that are not affected by shading and a charge / discharge command for the energy storage system. Through the coordination of the two, the power deficit is quickly and accurately compensated, ensuring that the total output power of the power station remains stable.

[0124] The beneficial effects of the above scheme are: by dynamically coordinating photovoltaic and energy storage resources through a preset power allocation strategy, when a power deficit caused by shading is detected, the photovoltaic strings that are not affected by shading are prioritized to increase their output power for rapid compensation, and the energy storage system is used for precise differential adjustment, forming a multi-resource collaborative power balance mechanism, thereby achieving rapid smoothing of the output power fluctuations of the mountain photovoltaic power station and significantly improving the stability of system operation and energy utilization efficiency.

[0125] S150: The power setting value and the charge / discharge command are sent to the corresponding photovoltaic string optimizer and the energy storage converter to perform coordinated control.

[0126] In this embodiment, the power setpoint and charge / discharge command are sent to the corresponding photovoltaic string optimizer and energy storage converter to perform coordinated control, specifically including:

[0127] By sending the power setpoints and charge / discharge commands generated by the optimization model to the corresponding photovoltaic string optimizers and energy storage converters, the photovoltaic system and energy storage devices are coordinated and controlled. The photovoltaic string optimizers dynamically adjust the output power of each component according to the power setpoints, and the energy storage converters perform rapid power compensation according to the charge / discharge commands. The two work together to maintain the stability of the total output power of the power station, forming a complete closed-loop control system.

[0128] The beneficial effects of the above scheme are: by accurately sending optimization instructions to the photovoltaic string optimizer and energy storage converter, the coordinated control of the photovoltaic system and energy storage is realized, forming a closed-loop control loop from prediction optimization to execution feedback, which effectively ensures the power output stability of the power station under complex mountain lighting conditions, significantly improves the power quality of the grid, and extends the service life of the equipment by optimizing resource allocation.

[0129] To further illustrate the technical concept of this invention, the technical solution of this invention will now be described in conjunction with specific application scenarios.

[0130] Correspondingly, such as Figure 2 As shown, this application also provides an operation optimization system for mountain photovoltaic power units, including:

[0131] The data acquisition module acquires high-precision digital terrain models, real-time meteorological data, and component operation data of the photovoltaic power station area;

[0132] The dynamic prediction module, based on the high-precision digital terrain model and solar position data, dynamically predicts the shadow distribution sequence and theoretical irradiance of each photovoltaic string in the future period.

[0133] The collaborative optimization module inputs the shadow distribution sequence and theoretical irradiance into the optimization model, and generates power setpoints for each photovoltaic string optimizer and charging and discharging commands for the energy storage converter with the goal of minimizing the variance of the total output power of the photovoltaic power station.

[0134] The dynamic compensation module, when predicting that a specific photovoltaic string will suffer power loss due to shading, synchronously adjusts the power of the photovoltaic strings not affected by shading and the charging and discharging power of the energy storage converter to compensate for the total power deficit.

[0135] The instruction execution module sends the power setting value and the charging and discharging command to the corresponding photovoltaic string optimizer and the energy storage converter to perform coordinated control.

[0136] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0137] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in this invention can be combined with each other in any way. The fact that not all of these combinations are described in this specification is merely for the sake of brevity and resource conservation.

[0138] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for optimizing the operation of a mountain photovoltaic power unit, characterized in that, include: Acquire high-precision digital terrain models, real-time meteorological data, and component operation data for the photovoltaic power station area; Based on the high-precision digital terrain model and solar position data, the shadow distribution sequence and theoretical irradiance of each photovoltaic string in the future period are dynamically predicted. The shadow distribution sequence and theoretical irradiance are input into the optimization model. With the goal of minimizing the variance of the total output power of the photovoltaic power station, the power setpoints of each photovoltaic string optimizer and the charging and discharging commands of the energy storage converter are generated. When a power loss is predicted due to shading in a specific photovoltaic string, the power of the photovoltaic strings not affected by shading and the charging and discharging power of the energy storage converter are adjusted synchronously to compensate for the total power deficit. The power setting value and charge / discharge command are sent to the corresponding photovoltaic string optimizer and energy storage converter to perform coordinated control.

2. The method for optimizing the operation of a mountain photovoltaic power unit according to claim 1, characterized in that, Acquire high-precision digital terrain models, real-time meteorological data, and component operation data for the photovoltaic power station area, specifically including: The high-precision digital terrain model is generated by acquiring topographic point cloud data of the photovoltaic power station area through remote sensing equipment mounted on drones or satellites and then processing it. The real-time meteorological data is obtained by meteorological monitoring stations and irradiance sensor networks deployed in the photovoltaic power station area. The real-time meteorological data includes total irradiance, diffuse irradiance, ambient temperature, and wind speed. The sensor monitoring system deployed in the photovoltaic power plant area collects the component operation data in real time, including the output voltage, output current and operating temperature of each photovoltaic string.

3. The method for optimizing the operation of a mountain photovoltaic power unit according to claim 1, characterized in that, Based on the high-precision digital terrain model and solar position data, the shadow distribution sequence and theoretical irradiance of each photovoltaic string are dynamically predicted for future periods, specifically including: The real-time altitude and azimuth angle sequence of the sun relative to the photovoltaic power station area within a future set time period is calculated based on astronomical calendar. The solar altitude angle and azimuth angle sequence, the high-precision digital terrain model and the three-dimensional layout model of the photovoltaic power station are spatially superimposed, and the shading relationship between terrain obstacles and arrays is simulated by the light projection algorithm to generate a minute-level shadow distribution time series of each photovoltaic string. Based on the shadow distribution time series and the real-time collected direct and diffuse irradiance data, the theoretical irradiance change curve of each photovoltaic string under the influence of shadow in the future period is calculated using the irradiance transfer model.

4. The method for optimizing the operation of a mountain photovoltaic power unit according to claim 3, characterized in that, The theoretical irradiance variation curves of each photovoltaic string under shading influence in the future period were calculated using the irradiance transport model. The irradiance transport model specifically includes: An irradiance calculation model based on the Hay model is constructed to separate and process the real-time collected direct irradiance and diffuse irradiance. For any photovoltaic string, a judgment is made based on the corresponding shadow distribution time series: if the photovoltaic string has no shadow at the current time, the direct irradiance is fully included in the calculation; if there is shadow, the direct irradiance is converted into diffuse irradiance according to a preset ratio. The processed direct and diffuse irradiance components are combined with the installation tilt and azimuth parameters of the photovoltaic string and synthesized using the irradiance calculation model to finally output the theoretical irradiance variation curve of the photovoltaic string with minute-level resolution over a future period.

5. The method for optimizing the operation of a mountain photovoltaic power unit according to claim 1, characterized in that, The shadow distribution sequence and theoretical irradiance are input into the optimization model. With the objective of minimizing the variance of the total output power of the photovoltaic power station, power setpoints for each photovoltaic string optimizer and charging / discharging commands for the energy storage converter are generated. Specifically, this includes: Construct an optimization objective function with the goal of minimizing the variance of the total output power of the photovoltaic power plant; Establish optimization model constraints including power constraints of photovoltaic string optimizers, charging and discharging power and capacity constraints of energy storage converters; The Model Predictive Control (MPC) algorithm is used to solve the optimization objective function in a rolling manner, generating the power setpoint sequence of each photovoltaic string optimizer and the charging and discharging command sequence of the energy storage converter for future time periods.

6. The method for optimizing the operation of a mountain photovoltaic power unit according to claim 5, characterized in that, An optimization objective function is constructed with the goal of minimizing the variance of the total output power of the photovoltaic power plant. The optimization objective function specifically includes: Set the optimization objective function ; Where t is the current time; For the prediction time domain of model predictive control; For a moment The total power output from the internal photovoltaic power station to the grid is obtained by the algebraic superposition of the output power of each photovoltaic string and the discharge power of the energy storage system. The power smoothing reference value is the moving average of the theoretical total output power within the prediction time domain; This represents the power change of the energy storage system at adjacent sampling times; This is a weighting coefficient used to adjust the balance between power smoothing targets and energy storage device wear.

7. The method for optimizing the operation of a mountain photovoltaic power unit according to claim 5, characterized in that, Model predictive control (MPC) algorithm, specifically including: At the current sampling time t, based on the latest acquired shadow distribution sequence, theoretical irradiance, and system state, in the prediction time domain... The optimization objective function is solved internally to obtain the optimal control command sequence, which includes the power setpoint of each photovoltaic string optimizer and the charging and discharging commands of the energy storage converter. The first control vector in the optimal control command sequence is used as the actual control command and sent to the corresponding photovoltaic string optimizer and energy storage converter for execution. At the next sampling time t+1, the prediction time domain is rolled forward one step, and the above two steps are repeated to achieve closed-loop control based on the latest system state.

8. The method for optimizing the operation of a mountain photovoltaic power unit according to claim 7, characterized in that, The optimal control command sequence specifically includes: The optimal control command sequence is represented as follows: ； Each control vector Include: Power setpoint of each photovoltaic string optimizer at time k This is used to control the actual output power of the corresponding string; The charging and discharging commands of the energy storage converter at time k Used to regulate the charging and discharging power of the energy storage system; This sequence is obtained through the prediction time domain. Within this framework, by solving an optimization problem that satisfies all constraints and minimizes the objective function value, a complete plan for a set of future control commands is obtained.

9. The method for optimizing the operation of a mountain photovoltaic power unit according to claim 1, characterized in that, When a power loss due to shading is predicted for a specific photovoltaic string, the power of the photovoltaic strings not affected by shading and the charging and discharging power of the energy storage converter are simultaneously adjusted to compensate for the total power deficit. Specifically, this includes: Based on the predicted power deficit size and duration, real-time adjustment commands are generated according to the preset power allocation strategy. The power allocation strategy prioritizes allocating unshaded photovoltaic strings to increase power within the maximum power output range, while simultaneously calculating the required compensation power difference. The compensated power difference is converted into real-time charging and discharging commands for the energy storage converter, wherein: When the total increase in power of the photovoltaic strings not affected by shading is less than the power deficit, the energy storage converter supplements it with differential power discharge; When the total increase in power of the photovoltaic strings not affected by shading exceeds the power deficit, the energy storage converter absorbs the excess power by charging.

10. An operation optimization system for mountain photovoltaic power units, applied to the operation optimization method for mountain photovoltaic power units as described in any one of claims 1-9, characterized in that, include: The data acquisition module acquires high-precision digital terrain models, real-time meteorological data, and component operation data of the photovoltaic power station area; The dynamic prediction module, based on the high-precision digital terrain model and solar position data, dynamically predicts the shadow distribution sequence and theoretical irradiance of each photovoltaic string in the future period. The collaborative optimization module inputs the shadow distribution sequence and theoretical irradiance into the optimization model, and generates power setpoints for each photovoltaic string optimizer and charging and discharging commands for the energy storage converter with the goal of minimizing the variance of the total output power of the photovoltaic power station. The dynamic compensation module, when predicting that a specific photovoltaic string will suffer power loss due to shading, synchronously adjusts the power of the photovoltaic strings not affected by shading and the charging and discharging power of the energy storage converter to compensate for the total power deficit. The instruction execution module sends the power setting value and the charging and discharging command to the corresponding photovoltaic string optimizer and the energy storage converter to perform coordinated control.

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